“The moment you push performance limits, heat becomes the enemy that never sleeps.”

Alexander Fischer

Co-founder & Product Manager, SimScale

Electric vehicles depend on compact thermal architectures that keep batteries and power electronics within a narrow operating windows. AI accelerators concentrate extraordinary wattage into small footprints. Industrial automation, renewable energy hardware and medical technology all follow the same pattern.

They raise performance expectations while shrinking available space. This creates a new reality in which cold plate design becomes a strategic engineering function rather than a late stage add on. Teams that recognize this shift early gain more performance, more reliability and more control over how their products evolve.

The Practical Challenges Facing Design Teams

Engineering teams face real constraints. They must balance:

• manufacturability,
• pressure drop,
• integration,
• weight targets,
• and routing!

You often work within tight envelopes while trying to handle rising heat flux. Parametric CAD can slow the process because feature trees resist change and complex channels break easily when edited. Conservative geometry becomes the default. This is risky as thermal loads continue to rise across industries. Cold plate cooling demands broader concept exploration, faster iteration and clearer structure throughout the development process.

A High Level View of the Cold Plate Design Workflow Step by Step

A typical cold plate project moves through several major steps from concept to validated geometry.

• It begins with requirement gathering where engineers define heat flux levels, target temperatures, available space, allowable pressure drop, material constraints and manufacturing options.
• Next comes the architectural exploration where macro level decisions such as cooling method, channel layout, inlet and outlet placement and flow balance strategies are evaluated.
• Concept modeling follows with early geometry that tests feasibility and identifies potential performance issues.
• Detailed design development then refines internal channels, surface area enhancements, flow paths and structural supports.
• In parallel, system level integration ensures correct fit and interaction with electronics, enclosures and the larger cooling loop.
• The final stages focus on simulation driven optimization, design for manufacturability and preparation for prototyping.

High performance applications cycle through these steps rapidly as iteration speed becomes a core advantage.

How Implicit Modeling Transforms the Design Phase

Implicit modeling fits directly into this workflow and accelerates it significantly. Traditional parametric CAD relies on sketches, constraints and feature trees. Implicit modeling uses continuous mathematical fields to define form.

Complex shapes become easy to create and sturdy during modification. Families of designs can be generated quickly without model failures. Smooth blends are inherent. Microchannels, graded thicknesses, TPMS surfaces or lattice supported walls appear without manual surfacing.

This matters because cold plate cooling often benefits from organic or highly detailed internal geometry that explicit modeling tools struggle to express.

Why Advanced Cooling Geometry Matters Now

This shift aligns perfectly with the pressure placed on modern hardware. EV power electronics keep increasing in output while packaging shrinks. AI hardware demands targeted thermal strategies that match component level heat flux. Data centers monitor every watt because cooling efficiency now affects operating cost directly. Aerospace, hydrogen systems and compact industrial machinery all follow similar trends. They require high performance cooling solutions that combine low weight, high efficiency and manufacturable complexity.

Cold plate design sits at this intersection because it enables direct heat removal and supports structurally complex yet lightweight geometries.

The Impact of Simulation and AI Assisted Optimization

When advanced modeling is paired with CAE simulation or AI driven physics prediction, the later stages of the workflow become dramatically more effective. Engineers can apply cold plate topology optimization to reshape channels for uniform thermal behavior. Microchannel networks can align with localized heat flux. TPMS or lattice structures can increase surface area while keeping weight low. Iteration becomes flexible and exploration becomes normal rather than exceptional. Cold plates evolve into highly tuned components tailored to the exact demands of each device.

Key Insights

1. Microchannel cold plates deliver high surface area for extreme heat flux handling
2. TPMS and lattice structures enable lightweight internal geometries with strong manufacturability profiles
3. Implicit modeling and topology optimization accelerates every design stage and supports shapes that parametric tools struggle to represent
4. Simulation driven workflows improve accuracy and bridge the gap between concept and validated performance
5. Cold plate design has become a strategic differentiator for any product facing rising thermal loads

Cold plates are no longer secondary components. They enable the future of mobility, computing and energy systems and they reward engineering teams that prioritize them early in development.

The post Cold Plate Cooling Design appeared first on SimScale.

For decades, the language of design has been based on surfaces, sketches, and constraints.

Engineers have grown used to constructing geometry one step at a time – extruding, sweeping, or filleting to build models layer by layer.

But what happens when geometry is no longer described by a sequence of operations, but instead by mathematical fields and equations?

That’s the shift implicit geometry modeling brings. It’s not a new CAD feature or another incremental tool. It’s a fundamentally different way of thinking about how objects are created, changed, and optimized.

Even better, implicit modeling unlocks powerful opportunities to integrate with AI algorithms, enabling advanced shape and topology optimization.

The changing landscape of design

Every product engineer knows the trade-off between creativity and control. Traditional parametric CAD excels at precision, repeatability, and manufacturability—but struggles with complexity and adaptability. The moment a design needs to evolve beyond its original constraints, the model often breaks. Surfaces fail to regenerate. Feature trees become tangled. Performance and hardware requirements add to the challenge. The geometry, instead of serving creativity, starts limiting it.

At the same time, AI simulation-driven design and optimization are moving to the center of product development. Engineers want to explore hundreds of design iterations, automatically test performance, and converge on the best possible shape.

Traditional CAD, built around static geometry, simply can’t keep up. Implicit modeling offers an answer.

What is implicit modeling?

Implicit geometry modeling represents 3D shapes using mathematical functions rather than explicit surface definitions.

Instead of describing a solid by its boundaries (as in B-Rep or mesh-based systems), an implicit model defines a region of space where a function equals zero—the so-called implicit surface. This allows for smooth, continuous transitions, blending, and deformation at any scale without worrying about topology or feature dependencies.

In practice, this means you can modify or combine complex geometries – like lattices, organic forms, or porous structures – using simple operations. Shapes can be added, subtracted, or morphed together using equations instead of manual CAD features. The result is a workflow that is more robust, more flexible, and dramatically faster when exploring non-traditional geometries.

Implicit vs. traditional modeling

To understand the impact, consider a typical CAD-based workflow.

1. You start with sketches
2. define constraints
3. extrude features
4. and trim surfaces.

Every change requires the system to recalculate dependencies. It’s precise, but fragile.

Now imagine instead defining the same geometry as a mathematical field. You can modify it globally – smooth transitions, blend regions, or adjust material density – without breaking any relationships.

This difference has huge implications for design automation and optimization. Implicit models can directly interface with algorithms that search, test, and evolve geometry automatically. They’re also inherently compatible with lattice generation, topology optimization, and generative design tools. Instead of trying to force simulation-ready meshes out of rigid CAD structures, implicit models create analysis-ready geometries by default.

Why now?

Several trends are converging to make implicit modeling more relevant than ever.

First, manufacturing is changing. Additive processes – such as metal 3D printing or high-resolution polymer fabrication—allow the production of complex, non-linear geometries that traditional CAD was never built to handle.

Second, computational power has caught up. With cloud-based platforms and GPU acceleration, implicit models can be calculated, visualized, and simulated in real time.

And third, simulation-driven design and AI-based optimization are entering everyday workflows. Engineers no longer design once and simulate later; they design through simulation. Implicit geometry provides the missing foundation for this level of integration.

Real engineering impact

Implicit modeling isn’t just about generating futuristic shapes—it’s about solving real engineering challenges. Lightweighting, for instance, becomes more than removing material; it becomes a question of continuously varying density to match structural or thermal demands. Fluid flow optimization can be achieved by smoothly adjusting surfaces for better aerodynamics or cooling. Complex lattices can be embedded into structural components without manual feature management.

In fields like aerospace, medical devices, or consumer products, this approach means faster iteration, fewer redesigns, and products that are both lighter and stronger. The design space expands, while the time-to-simulation and time-to-market shrink.

The power of integration: Implicit + Simulation + AI

The real magic happens when implicit modeling connects directly with CAE simulation or AI-driven physics prediction.

Optimizing within the implicit space opens up massive opportunities. The feedback loop tightens. Designers no longer need to spend cumbersome work to rebuild and simplify models for each test or optimization cycle – changes are immediately updated in the field representation.

Powerful simulation technologies using meshless or quasi-meshless Cartesian techniques allow to directly evaluate the design without manual user input.

This synergy unlocks new frontiers for generative design, topology optimization, and AI-assisted shape exploration. Imagine defining not just a component, but an entire system, where materials, structures, and flows are optimized together, automatically, based on real physics.

The essential takeaways

Here’s what makes implicit modeling a quiet revolution in design engineering:

• Continuous geometry control – Modify shapes smoothly without constraint rebuilds or topology breaks
• Seamless integration with simulation – Connects directly with CAE and generative optimization workflows
• Ready for AI and automation – Enables algorithmic exploration and machine learning in design space
• Scalable complexity – Handle intricate lattices and organic structures efficiently
• Accelerated innovation – Iterate faster with fewer modeling bottlenecks and simulation-ready output

Looking ahead

Implicit modeling challenges a long-held belief in engineering: that geometry must be built piece by piece. As more tools adopt implicit representations, designers and engineers will find themselves working less with constraints and more with possibilities. Instead of fighting the model, they’ll collaborate with it—shaping, simulating, and refining in one continuous loop.

For design engineers, this is not just an efficiency gain; it’s a creative shift. It’s the ability to think in systems, not sketches. To design performance into geometry, rather than fitting geometry to performance. And as implicit modeling merges with AI-driven design, we’re seeing the emergence of a new era of computational creativity.

Ready to see how implicit modeling connects with advanced simulation? Explore the partnership here.

The post Implicit Modeling appeared first on SimScale.

As power densities increase and form factors shrink, the choice between a passive or active cooling strategy becomes one of the most critical decisions in the design cycle. Making the wrong call leads to costly redesigns and field failures.

This is where simulation provides a decisive advantage. Cloud-based analysis allows engineers to test, validate, and optimize thermal solutions before committing to physical prototypes, transforming a high-stakes gamble into a predictable science. This article will dissect the two primary cooling methodologies – passive and active cooling methods – and provide a comprehensive framework for selecting and simulating the optimal approach to provide cooling for your project.

Passive Cooling: The Silent Guardian

Passive cooling represents engineering elegance – achieving thermal management without active cooling components consuming additional energy. It is a reliable, silent, and cost-effective solution for dissipating low-to-moderate heat loads, making it a cornerstone of modern electronics design.

What is Passive Cooling?

Passive cooling leverages the fundamental laws of physics to transport thermal energy. It relies on conduction, natural convection, and radiation to move heat from a source to the surrounding environment. Because these systems have no moving parts, the cooling systems are inherently fail-proof from a mechanical standpoint. This is a key principle behind passive cooling strategies offering unparalleled long-term reliability.

The process begins with conduction, governed by Fourier’s Law (q=−k∇T), where heat moves through a solid material like aluminum or copper. The heat then transfers to the surroundings via natural convection and radiation. Radiative cooling, described by the Stefan-Boltzmann Law (P=ϵσA(Thot⁴​−Tcold⁴​)), is why heat sinks are often anodized or painted black—to increase their emissivity (ϵ) and maximize heat dissipation.

How Does Passive Cooling Work?

The primary workhorse of passive cooling is the heat sink, which uses a large surface area to efficiently transfer heat. The process is straightforward:

1. Conduction: Heat is generated by the electronic component and conducted into the heat sink base, often through a Thermal Interface Material (TIM) that minimizes thermal resistance on the interface.
2. Dissipation: The heat spreads through the heat sink to its fins, which dramatically increase the surface area for dissipation into the ambient via natural convection and radiation.

More advanced passive systems like heat pipes and vapor chambers use two-phase heat transfer. A sealed working fluid evaporates at the hot interface (absorbing latent heat) and condenses at the cold interface (releasing heat), achieving an effective thermal conductivity that is orders of magnitude higher than solid copper.

Benefits of Passive Cooling

• Extreme Reliability: With no moving parts, there are zero mechanical failure points, which is essential for systems in inaccessible locations like satellites or remote telecom towers. Systems without openings offer a huge advantage in terms of preventing dirt and dust to negatively affect the cooling system or require regular maintenance.
• Zero Operational Cost: These solutions add nothing to a product’s energy consumption or a facility’s utility bill.
• Silent Operation: The absence of fans is a critical requirement for noise-sensitive applications like high-fidelity audio equipment or medical devices.
• Lower Cost: Passive solutions are typically cheaper to manufacture than their active counterparts.

Passive Cooling Systems Examples

• Extruded Aluminum Heat Sinks: The most common type, found in routers, set-top boxes, and solid-state drives (SSDs).
• Heat Pipes & Vapor Chambers: Used in high-performance laptops and compact, fanless PCs.
• Strategically Vented Enclosures: Designing a product’s housing to maximize the natural “chimney effect” of rising hot air.
• Phase Change Materials (PCMs): Materials that absorb thermal spikes by melting and re-solidify when the load decreases.

Real world passive cooling example

Cobalt Design used SimScale to reduce their passive heat sink temperature by 11% through the analysis of existing designs which highlighted localized peak temperatures inside the unit without an adequate exfiltration path.

Read the case study

Active Cooling: The Power Play

When the heat load generated by a system surpasses the capacity of passive methods, engineers must turn to active cooling. This approach uses forced convection components  to dramatically accelerate heat removal, making active cooling solutions essential for enabling performance levels that would be otherwise impossible.

What is Active Cooling?

Active cooling is any thermal management system that consumes energy to enhance heat transfer. By introducing a mechanical component like a fan or pump, these systems overcome the limitations of natural convection, allowing them to manage much higher heat fluxes within a compact form factor.

How Does Active Cooling Work?

The most common form of active cooling is forced convection. A fan or blower moves air across a heat sink at high velocity. This turbulent flow dramatically increases the heat transfer coefficient (h), enhancing the cooling performance and meaning more thermal energy is transferred away from the component.

For more demanding applications, active liquid cooling is used. A pump circulates a coolant through a cold plate mounted on the heat source. The heated liquid then flows to a radiator, where a fan dissipates the heat into the air, improving overall energy efficiency. A case study on high-power electronics demonstrated that a direct liquid cooling solution could maintain a component’s temperature at 55°C, while an air-cooled solution could only manage 77°C under the same heat load—a crucial 22°C difference.

Benefits of Active Cooling

• Superior Thermal Performance: The ability to dissipate immense heat loads enables high-performance CPUs and GPUs to operate at peak potential without throttling.
• Precise Thermal Control: Fan speeds can be dynamically adjusted using Pulse Width Modulation (PWM) based on sensor data, optimizing cooling while minimizing noise and power use.
• Design Compactness: Active cooling achieves high performance in tight spaces, like blade servers, where a comparable passive solution would be too large.

Active Cooling Systems Examples

• Axial Fans & Centrifugal Blowers: Found in virtually all desktop computers, servers, and industrial cabinets.
• Closed-Loop Liquid Coolers: Standard for enthusiast PCs, workstations, and increasingly, direct-to-chip data center cooling.
• Thermoelectric Coolers (TECs): Solid-state Peltier devices that “pump” heat electrically, used for spot cooling in lab equipment and portable refrigerators.

Real world active cooling example

Rimac Automobili used SimScale to improve the thermal management of their EV batteries which lead to a 96% time saving for simulations as well as improved overall performance.

Read the case study

Choosing Active vs. Passive Cooling: A Design Framework

The decision between active and passive cooling is a trade-off analysis based on key design constraints. There is no single “best” solution, only the most appropriate one.

• Thermal Design Power (TDP) & Heat Flux: This is the starting point. Below ~15W, passive solutions usually suffice. Above 100W, active cooling is almost always necessary. The region between is a complex trade-off zone.
• Environment & Form Factor: High ambient temperatures reduce the effectiveness of all cooling but can render passive solutions inadequate. The available volume will also dictate if a large passive heat sink is even a viable option.
• Acoustics & Vibration: If silent operation is a primary requirement (e.g., medical devices), passive cooling is the clear choice. Fans introduce noise and micro-vibrations that can be problematic for sensitive equipment.
• Reliability & Maintenance (MTBF): Compare the Mean Time Between Failures of a fan (30k-70k hours) against the near-infinite lifespan of a solid heat sink. For products designed to last a decade, a fan is a potential point of failure.
• Total Cost of Ownership (TCO): An active solution has ongoing operational costs due to its power consumption. A slightly more expensive passive solution may have a lower TCO over the product’s lifetime.

Often, a hybrid approach is optimal, using a passive heat sink for normal operation and a fan that activates only under peak thermal load.

Simulate Your Active and Passive Cooling Solution with SimScale

Guesswork and over-engineering are not effective design strategies, especially when it comes to implementing hybrid cooling systems . Before committing to expensive tooling, you must validate your design. Cloud-native simulation with SimScale provides the quantitative proof needed to make data-driven decisions.

• De-Risk Your Design: Identify thermal failures in the digital domain to save weeks of time and thousands in wasted prototypes. Integrating CFD simulation early can reduce the number of physical prototypes required to one or a few at max and transform the physical testing into a pure validation step at the end of the design phase.
• Optimize for Performance: Run parametric studies on heat sink fin geometry or fan placement in parallel on the cloud. This allows you to find the configuration that offers the lowest thermal resistance (Rth​) for the lowest mass.
• Visualize the Invisible: Use CFD analysis to get a complete picture of airflow and heat distribution. You can visualize recirculation zones, identify thermal bottlenecks, and ensure your cooling solution performs as intended.
• Quantify with Precision: Move from estimation to prediction. A SimScale thermal simulation provides precise temperature calculations, confirming that a critical processor will be cooled from a dangerous 95°C to a safe 78°C, ensuring you meet reliability targets before manufacturing begins.

Stop gambling with your product’s thermal performance. Start your free trial of SimScale today and discover how cloud-based simulation can help you build more reliable, efficient, and powerful products with confidence.

The post Active vs Passive Cooling appeared first on SimScale.

Recent advancements have profoundly shifted the legacy product development workflow. Cloud computing has allowed engineers to collaborate in real-time, access advanced simulation capabilities earlier in the design process, obviate costly physical prototyping, and avoid expensive hardware costs. The virtually unlimited computing power and scalability of the cloud mean that deploying these capabilities across an entire distributed engineering organization is now considered a preferred strategy. 

Additional benefits of cloud-native simulation include access to 3D parametric scenarios analyses without any impedance to time and computing resources. The value added is the ability to fully explore design space and disqualify poor design candidates earlier in the development process. Application programming interfaces (APIs) further amplify the toolkit available to electronics designers and enable third-party CAD, analysis, optimization, and parametric design tools to talk to each other.

However, common bottlenecks to simulation have been CAD preparation and the numerical discretization of that model (meshing). Both consume time and manual intervention. The advent of advanced physics solvers and novel meshing techniques, such as the immersed boundary method, means that engineers spend less time making their CAD models simulation-ready and more time on insight-driven design. Skipping the time-intensive CAD preparation also opens up the possibility of doing simulations very early when some components are still in the draft stage and comparing many variants that otherwise would have required repeated CAD simplification efforts.

Immersed Boundary Method in SimScale

The Immersed boundary method is based on a cartesian grid, in which the geometry gets immersed. It is resilient to geometrical details and does not require CAD simplification, even for very complex models. Salient features of this approach include:

• Automatic defeaturing of small geometrical details
• Mesh refinements are physics-based rather than geometry-based
• Perfect hexahedral meshes
• Highly flexible mesh sizing from very coarse to very fine for all levels of CAD complexity

The Challenge with Complex CAD Models

Engineers who want to improve their thermal design by running 3D simulations with conventional tools based on so-called body-fitted meshing are forced to pick the lesser evil of simplifying their CAD model heavily or requiring huge computational resources to simulate.

In any case, they will waste valuable time. Either they spend hours of their limited working time on tedious CAD cleanup and simplification to reduce the model complexity and required computing resources, or they need to put an unreasonable amount thereof to solve the model. While the second approach allows the engineer to continue working on other topics during the calculation, it likely still requires some level of initial CAD cleanup even to get a successful mesh. Additionally, it blocks any advances on their current design for the time the simulation is running, not even speaking about the required hardware costs.

Immersed Boundary Method to the Rescue

The Immersed Boundary method addresses the core of this dilemma. It completely removes the CAD preparation or reduces it to a few minutes at most. At the same time, the physics-driven meshing avoids high mesh resolutions on detailed CAD features that are insignificant to the system’s thermal behavior. Yet, it resolves physically relevant regions like power sources or flow channels to the level the user requires. This level might differ significantly based on the current simulation intent.

Early in the design process, the engineer might be more interested in qualitative insights into the thermal management concepts he is experimenting with. Those simulations often only require coarse mesh resolutions. Body-fitted approaches do not allow this design space as the geometric details always lead to large mesh sizes (see Figure 2 above).

Later in the process, the focus shifts towards quantitative results, such as maximum temperatures on critical components. In order to provide the required accuracy, the mesh resolution required by body-fitted and IBM meshing will be closer. The CAD preparation time, of course, remains as saved time, and the engineer benefits from the underlying solving based on the same high-fidelity finite volume implementation in both cases.

The Main Benefits for Engineers

Summarizing the main benefits for the engineer, immersed boundary method enables you to:

• Simulate early in the design phase when parts of the system are in the concept phase
• Simulate the detailed original model without the need for CAD preparation
• Run extensive design of experiment studies
• Derive accurate critical temperatures during the validation phase

The graphic below shows how an important result quantity e.g. the junction temperature of a chip changes with higher mesh resolutions i.e. higher computational costs for body-fitted and IBM-based simulations.

So what’s the catch? Well, there is no catch. The Immersed boundary method is a perfect fit for thermal engineers dealing by default with complex systems and looking for optimization insights throughout the design process.

Suppose an engineer is looking for a very accurate representation of the flow boundary layer, for example, when designing fan blades or something similar. In that case, it usually makes more sense to capture the physical effect with mesh boundary layers and prepare the CAD for a conventional body-fitted simulation.

A Case Study of an Electric Vehicle Battery Pack

We have shown a case of an electric vehicle battery pack. The design is an example of an air-cooled lithium-ion battery model for an FSAE electric race car, which is utilized as the accumulator to power the car. Effective thermal management of the battery pack is essential to ensure the reliable and safe operation of the battery cells and hence the car.

When the rest of the vehicle design is constantly changing to meet performance objectives, a continuous update in the battery pack is also required to align with these evolving design parameters. Throughout each design iteration, key questions arise regarding the amount of heat generated within the batteries for relevant duty cycles and the necessary air flow rate to maintain the battery pack within its designated temperature range.

Finding accurate answers to these questions using a robust electronics cooling simulation not only increases the confidence in the design but also makes it possible to get those answers faster, even when the model is geometrically very detailed. The provided example demonstrates the significant advantage of the immersed boundary method in handling geometrical details without the need for CAD simplification. As a result, valuable insights can be swiftly obtained from each design iteration, enabling a more efficient and thorough evaluation of the design without compromising accuracy or intricate geometric details.

Comparing Meshing Methods

In this battery pack example, we attempted to simulate both the original and simplified versions of the CAD model using Conjugate Heat Transfer analyses with body-fitted and Immersed Boundary Method solvers. The main advantage of using the cartesian mesh-based Immersed Boundary Method is that it allows us to overcome the inability to mesh the original model using a body-fitted mesh. By immersing the geometry into the cartesian grid, the cartesian mesh automatically defeatures small geometrical details.

Whereas to prepare the model for CHT analysis, we need to invest time in CAD cleaning. This involves removing small details that do not directly impact the problem’s physics and achieving an efficient mesh size without unnecessary refinements. The time devoted to cleaning the small geometric details for the body-fitted mesh, in this case, ~ 2.5 hours, can be instead well-spent on design iterations when using the immersed boundary method.

Benefits of Using the Immersed Boundary Analysis in SimScale

Based on the results of the two different methods, the benefits of using IBM can be summarized as follows:

• Automatic handling of small details: With IBM, the geometry is ready for simulation without the need for CAD cleaning, even when the original model contains numerous small details. In contrast, CHT relies on a body-fitted mesh, and if the unsimplified original model is not simulation-ready, additional effort must be spent on CAD cleaning.
• Computational efficiency: By manually cleaning the small details in the geometry, the model becomes ready for CHT. Even when this is the case, IBM requires significantly fewer computational resources than CHT. With IBM, the mesh size is reduced by 72%, computational resources are reduced by 61%, and the runtime is reduced by 62%. Moreover, the results of the two analysis methods are almost identical (0.002% difference), demonstrating the accuracy of using IBM for design iterations while efficiently utilizing resources.
• Effective design exploration: Even when the original model is simulated using IBM, the computational resources required are still much less compared to the simplified model simulated with CHT. The mesh size of the original model with IBM is reduced by 70% compared to the simplified model with CHT, while both the computational resources and the runtime are reduced by 44%. This highlights that without wasting time cleaning the model, multiple design iterations can be tested without the overhead of fine mesh regions around insignificant details. Consequently, the time required for users to discover the best design is significantly shortened.

The post Simplify Your Thermal Simulation With Immersed Boundary Method appeared first on SimScale.

Joule heating is an important phenomenon to capture in the design of many power electronics products and components. It is the physical effect of current passing through an electrical conductor and converting to thermal energy, causing heating. Increasing temperatures in the conductor material can impact the overall efficiency of the components or even be harnessed.

SimScale has launched new features that enable engineers to perform Joule heating simulations on the platform using an easy-to-use interface with powerful and automated post-processing features.

Simulating Joule heating is necessary for numerous industry applications where resistive heating is a common artifact, whether that is intentional or unintentional. For intentional usages such as electric heaters and soldering irons, Joule heating analysis is necessary to optimize the heat output of the device.

More commonly, however, the increase in temperature from converting electrical energy into thermal is an unwanted effect that could decrease the overall efficiency of components. Examples include busbars and wiring in power electronics, where the efficiency drops with the inverse of increasing temperature.

A similar effect is observed in batteries that have an ideal operating temperature range. Above this, the battery performance and lifetime begin to degrade. Other common components like fuse blocks and resistors are also impacted by Joule heating.

Joule Heating Analysis in SimScale

Simplified approaches to Joule heating analysis included adding dissipated power as a power source on the electronic components. The dissipated power was based on hand calculations, approximated, and could not robustly handle situations where the current density was not uniformly distributed, including:

• Varying electrical resistivities in parallel
• Different cross-section-sized components in serial alignment 
• Contact resistances, for example, soldering connections

With the new features introduced in SimScale, users can now explicitly define the key Joule heating parameters, variables and output key metrics to base design decisions on. 

• Analysis type: users can toggle on Joule Heating when setting up a Conjugate heat transfer (CHTv2 or IBM) analysis type in SimScale.
• Materials: when defining material properties, choose the isotropic or orthotropic conductor option. The materials can be imported from the library or added to the database and can be shared among projects and teams. 
• Boundary conditions: in the boundary conditions dialog box, users can specify the current flow direction and electric potential (see images below).
• Outputs: include current density, electric potential, and Joule heat generation.

Joule Heating Simulation Setup

A case of an electrical inverter used in race cars is used to demonstrate the new Joule heating features in SimScale. The image below shows the 3D geometry of an inverter that is liquid-cooled using a water and glycol mix with a flow of 3 L/Min.

The model contains various MOSFETS and capacitors with electrical load and current of up to 70 Amps RMS continuous load. The twelve MOSFETS for 6-phase AC current supply are each modeled with 18.5 W applied to them.

We have used the materials database in SimScale to apply conducting materials and coolants that can be parameterized to evaluate material properties if needed.

New options in the simulation setup dialog boxes are used to specify Joule heating simulation particulars:

1. Specifying Joule heating analysis

2. Material properties for Joule Heating simulation

3. Adding boundary conditions for Joule Heating simulation

Visualising Joule Heating Simulation Results

The Electric Potential (voltage) gives insights into the voltage drop across electrical components and wires within the electric circuit and the expected voltage drop overall in case of a current drain-driven simulation. Derived from the electric potential field, the electric current density (A/m²) provides essential information about the electric current flow in the whole circuit and if current density spikes occur, e.g., in small cross sections or at sharp corners.

Those often lead to high thermal losses as the dissipated power depends on the electric current by the power of two. This information can be used to thicken the cross sections at critical spots or change the overall current path by rounding off unfavorable edges.

The Joule Heat Generation (W/m³) result field comes in handy when judging the heat flux that needs to be considered when designing the thermal management solution for the system. Even if the Joule heating contribution is not the main thermal load in the system, it can harm the overall performance or reliability of the product if local heat flux spikes happen distant or shielded from the main cooling solution, e.g., a liquid cooling plate or a fan.

Using the statistical tools in the Post-Processor, one can extract both the distributed heat load as well as the integrated total power loss on a part of the model.

Power Electronics Simulation in SimScale

Joule heating is essential or relevant to the design of a variety of applications. Next to the inverter use case, those include other components of the electric powertrain in electric vehicles such as the battery pack or electric motors. It is also the most important aspect of electric resistor thermal considerations. In the following, we present industry-relevant examples.

Resistors

Resistors are used to protect other components in an electric circuit with high voltages or current pulses using highly resistive materials, ideally in a compact structure. While the potential drop and the resulting heat conversion are therefore intended, the heat can still be damaging to the resistor or the surrounding system. Cooling solutions include mostly mounted heat sinks but can also involve active air or liquid cooling.

The power resistor model has four separate resistive conductor circuits and is a device that has been used in the automotive industry in the past extensively. This particular model was used in Jaguar oldtimers and ensures the electronic control unit (ECU) for fuel injection does not overload from high current spikes.

In order to open the injectors, the full 12V potential is connected and provides the required high opening current of 22.6 A. After that, the required current is much lower and the components can not withstand the high current load for long. Hence the ~6 Ohm resistor circuits are added to the circuit with a switch and reduce the current below 2A. The component is mounted at a sheet metal component next to the engine and therefore must only rely on natural convection cooling.

Electric Vehicle Battery

The battery module case is from an electric vehicle from the Formula SAE (Society of Automotive Engineers) race cars. Formula SAE is a series of international competitions in which university teams compete to design and manufacture the best-performing race cars and simulation is extensively used by academic teams to optimize their race car designs and components.

The battery module uses forced convection air cooling from fans for thermal management and has aluminum busbars. It has 100 lithium-ion cells in a 10S10P arrangement (10 cells in series, 10 cells in parallel).

The Joule heating analysis is needed to predict heat gain from current flowing through the battery components and test optimal cooling strategies. Analyzing the electric potential drop and current density is additionally helpful in order to avoid current spikes at sharp corners or thin sections and aim for a uniform load across the pack. In this case, a 1C scenario is simulated with 40 Amps drained from the module.

Fuse Blocks

The following fuse block case is widely used by automotive Original Equipment Manufacturers (OEMs). Fuses operate under a small potential difference, allowing current to flow within the fuse. As long as the current remains within safe limits, the fuse functions normally.

However, fuses are designed to serve as intentional weak links in electrical circuits, such that they sacrifice themselves by failing at their weakest points to protect expensive or sensitive equipment from high current values. The failure of a fuse occurs due to heat generated by current flow at its weakest points, which may result in local melting around those regions.

To simulate the operation of a fuse and be able to observe the temperature rise around the weak regions, a transient conjugate heat transfer analysis is used with the potential difference between the two ends of the fuse being set. In this scenario, a potential drop of 0.2V was applied to the fuse with a resistance of only a few milliOhm resulting in a huge overload current with the highest current density around the weak point.

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Cloud-Native Engineering Simulation for Thermal Analysis

SimScale offers multiple analysis types for engineering simulation including conjugate heat transfer (CHT) capabilities coupling solid and fluid domains and also leverages automated parallel computation capabilities in the cloud. These capabilities give engineers and designers the benefits of:

• Faster design cycles and electronics performance insights
• A full exploration of multiple design iterations in parallel
• Reduced costs and time investment in the costly prototyping phases
• In-platform CAD editing for streamlined simulation workflows

All electronics devices generate heat that must be managed to avoid overheating and component failure. The SimScale platform can be used to simulate and optimize multiple electronics enclosure cooling strategies and common features, including:

• Natural and forced convection
• Air and liquid cooling 
• Fan modeling
• Anisotropic materials (PCB)
• Thin layer resistances
• Power networks

The conjugate heat transfer (CHT) v2.0 analysis type in SimScale enables heat transfer analysis between solid and fluid domains by transferring energy (thermal) at the interfaces (contacts) between them. This means that the model must contain at least one fluid and solid region. In most cases, a CAD geometry will not have a fluid domain assigned as default. The SimScale CAD mode provides a flow volume extraction tool to do this. Typical applications of CHT analysis type include analysis of heat exchangers, cooling of electronic equipment and electronics enclosures, LED luminaire design, and similar cooling and heating systems. The upgraded version of the CHT analysis type in SimScale (CHT v2.0) is more stable and provides faster convergence as the energy equation is strongly coupled between the solid and fluid regions. With the upgrades, both incompressible and compressible flows can now be modeled including the impact of radiation heat transfer. Both fluid and solid domain mesh are required for a CHT simulation with clear definitions of the fluid and solid interfaces or contacts. With these interfaces properly defined, the mesh is automatically taken care of in SimScale.

Avoid Electronics Enclosure Overheating with Active Cooling

In the example of the forced convection cooling case, the key design considerations that are explored include the velocity and temperature distributions across electronic components and their dependence on fan (forced convection) inlet speeds. Multiple heat sink designs can also be simulated in parallel for comparison.

The case in question is the heat transfer analysis (cooling) of densely packaged electronics inside an enclosure. The simulation setup is as follows:

• A conjugate heat transfer (CHT) simulation is used to model conduction in solids and forced convection (air).
• There are two inlet fans (simulated) providing forced convection into the electronics enclosure. Two velocity inlet boundary conditions where the fans would be on one end, give a ventilation rate of 0.014 m³/s per fan at ambient conditions (20℃). The other end of the enclosure has openings using a pressure outlet boundary condition. 
• The fan flow rate is constant. For more detailed analyses, a custom fan performance curve can also be uploaded into the SimScale platform.  
• Materials including silicon, tin, copper, aluminum, and polylactic acid (PLA) have been assigned to the electronics enclosure, boards, and components using the extensive material library. Custom materials can also be defined.  
• Heat transfer coefficients (HTC) are applied to the walls.
• Electronics components receive electrical power and generate heat (e.g. resistive losses). The heat load from each component can be defined by assigning an absolute power source in watts. Various thermal loads and power sources from IC chips, resistors, etc. have been defined totaling 100 Watts with the single largest load being the main CPU at 40 Watts.

Simulating Heat Transfer for Better Electronics Design

The simulation takes approximately 30 minutes to run. Because of the high-density power electronics in a confined space, we observe high temperatures on some of the components because not enough airflow is reaching those parts. The SimScale platform allows users to easily alter the flow rate, reposition the supply fans, or employ a combination of the two.

A design study on the heat sink, using various materials, or altering the number and spacing of fins, can also be undertaken at this stage. Importantly, when users set up and run these alternative designs, running all of them together simultaneously would still only take 40 minutes, saving considerable time when compared to sequential runs. Simulating heat transfer analysis at the early stages of design can give designers more options to finalize a design before spending money on costly prototyping. 

Evaluate the electronics enclosure cooling design and learn more about simulating forced convection cooling from a  web browser, in our on-demand webinar, Thermal Analysis of an Electronics Enclosure: Forced Convection Simulation.

The post Electronics Enclosure Cooling: Forced Convection Simulation appeared first on SimScale.

Cloud-based simulation equips engineers with the ability to test multiple scenarios and validate designs without the time-consuming and costly practice of prototyping for each iteration. This article shows you how and presents a case study from Forwiz System, who used thermal simulation to further optimize a CPU cooling system, winning their client improved product performance at less cost.

What is a CPU Cooling System?

A CPU cooling system effectively moves heat away from sensitive (and expensive) component parts through the use of heat pipes and heat sinks. Heat pipes are rapid heat-transferring devices with high thermal conductivity, sometimes up to 100 times more effective at conducting heat than typical metals.

Heat pipes transfer heat generated by the CPU towards the heat sink. The functionality of the heat sink is to increase the contact area with air to accelerate the final dissipation of heat via natural or forced convection to the surrounding environment. For the effective performance of the CPU cooling, each component needs to be optimized.

How to Improve CPU Cooling with Simulation 

By nature, the design process for electronics cooling is very iterative. CPU cooling systems entail many parameters, and each plays a critical role. In their journey towards optimization, engineers are presented with a variety of strategies to experiment with including adjusting the number of heat pipes or changing their diameter, increasing the number of heat sink fins or adjusting their thicknesses, use of surface treatment, radiative paint for optimal heat loss via thermal radiation or testing out different materials. 

As engineers are striving for the optimal solution across these multiple parameters, virtual testing before prototyping represents a huge cost and time-saving opportunity. A traditional design workflow for a CPU cooling system involves testing designs against their expected results, often a target temperature.

Electronics design that does not achieve the required cooling performance during thermal analysis requires a second or third iteration which means manufacturing more than one prototype. Manufacturing, shipping, and prepping the prototype for testing all contribute to time delays and increase the number of steps in the process, and thus, the number of possibilities for issues. With simulation, engineers have access to testing many scenarios in a simpler workflow with shorter turnaround times. A design tested and optimized for simulation can then be moved along to the final stage of the process, i.e., prototype testing, and the cost and time investment associated with it need only occur once. 

Cloud-based simulation takes this even further. With computing power off-loaded to servers, high-fidelity engineering simulation is made accessible, regardless of an engineer’s hardware capacity. Cloud-based simulation platforms, like SimScale, make it possible to run all simulations in parallel, driving down the design process from weeks to hours. Rapid iterations in-house eliminate the need for external simulation consultants, which provides cost savings, as EUROpack A/S. found when integrating SimScale into their workflow.

Case Study: Forwiz System

Forwiz System, an IT services company, received a request from a client to improve the cooling of the CPU inside their 2U servers. The cooling needs of their CPU chip, which had many cores, were not being met by the CPU coolers readily available on the market. In fact, when the chips were fully operational the CPU temperature was easily exceeding 90 degrees Celsius. This restricted their CPU from being fully operational. 

As the position and arrangement of different components installed inside their server were fixed, Forwiz had to work within the existing system to take on the challenge. 

To achieve better cooling performance they first increased the width of the upper part of the heat sink, taking advantage of previously unused, surrounding space. Then, they added more heat pipes to the newly increased size and lastly, they applied a special paint that has high-emissivity for additional thermal radiation.

Results from their initial experiment show that the structural change significantly increased performance when compared to the existing “benchmark” cooler design. The effect of the radiation paint also contributed to a drop in temperature, but not significantly which is important to note because that requires extra cost and manufacturing process.

The changes to the CPU cooling system made by Forwiz dropped the operating temperature from 90 degrees C down to 80 degrees C, within five degrees from the target temperature at which the client’s CPU chips could be fully operational. With cloud-based simulation, the engineering team was able to further optimize and deliver the target temperature for their client.

Forwiz used SimScale’s CHT solver to test the geometry of heat pipes and the number of fins within the CPU cooler and to validate the effectiveness of their previous cooling strategies.

With more than 100 simulations run, the results not only showed that their original structural changes contributed to a 15-degree C decrease in temperature but revealed that the high-emissivity paint could account for another 4 degrees C. The new heat sink geometry, optimized via simulation, reduced the temperature by another 5 degrees C. With the newly proposed CPU cooling system, the final temperature reached was around 77-76 degrees C, achieving the target temperature set by their client. 

For Forwiz, simulation enabled the engineering team to reach optimal cooling performance and facilitate the full operating capacity of their client’s CPUs. Simulation in the cloud is a powerful tool for designers seeking optimal thermal management in a quick and highly iterative manner.

To learn more about electronics cooling and how engineers can optimize the thermal management of their designs with SimScale, check out more resources here: 

• The Evolution of Evaluating Thermal Management: SimScale Customer Spotlight
• Thermal Management: Lighting, Battery, and Enclosure Cooling Projects
• Conjugate Heat Transfer Simulation: Best Practices in SimScale

The post How to Optimize a CPU Cooling System with Simulation appeared first on SimScale.

The first step in optimizing LED performance for LED lighting is to understand the key components of a typical LED fixture. LED manufacturers usually produce a complete LED package, which includes 4 main components — a die, a phosphor, a substrate, and a lens. The die is the semiconductor that emits blue lights when an electric current runs through it. Multiple dies can be mounted on an aluminum or ceramic substrate. In most applications, white or yellowish light is required, therefore phosphor is applied, either directly onto the die or mixed in the lens. The phosphor particles emit white light when the blue light reaches them. The light emitted is extracted and directed by the lens.

The LED package is typically mounted on a printed circuit board (PCB) with thermal interface material (TIM) paste. Lastly, a heatsink is attached to the PCB to dissipate the heat away from the LED package and toward the ambient air through conduction, convection, and radiation.

Although LEDs are, on average, six times more efficient than traditional incandescent light bulbs, there is still a considerable amount of heat converted from the electrical energy applied to the device. Unlike Edison light bulbs, where heat waste is radiated, the heat generated by LEDs is conducted. This heat must then be moved away from the device to preserve its LED performance.

LED Junction Temperature: What Is It and Why Does It Matter?

The temperature at the junction between the LED die and the substrate it is on is referred to as junction temperature. This junction is usually the highest temperature in the device making it a suitable value to indicate heat dissipation performance. Today’s LED packages are designed with conductive heat paths to move heat away from the junction point to the solder point. The solder point is located at the interface between the LED package and the PCB and/or a standalone heatsink.

The effectiveness of internal heat paths is indicated by the LED’s internal thermal resistance. The lower the internal temperature the higher the quality of the LED, thermally speaking. When designing a LED fixture from a thermal management perspective, it is essential for the design engineer to access the value of thermal resistance. This value will be used by CFD solvers to accurately determine the temperature of the LED and verify if the device has exceeded the maximum limit recommended by the manufacturer. In modern LEDs, it is standard that junction temperatures reach up to 100°C and beyond. The ambient temperature, thermal resistance between the LED junction and its surroundings, and the power dissipated by the chip all impact its value.

What Are the Effects of Poor LED Heat Dissipation?

As a general principle, when LEDs heat up, their efficiency drops. There are two primary reasons why maintaining a low temperature is critical to LED performance.

The first reason is that the light output of the LED degrades significantly with higher temperatures. This phenomenon is particularly noticeable with red and amber wavelengths. The graph below illustrates the typical decline in relative light output over temperatures at different colors.

The second reason is that the light output falls gradually over time. This gradual decrease is accelerated by higher temperatures. Below is a typical example of light output versus the working hours of an LED at different temperatures. We can see that a drop of 11°C  translates to an extension of 25K hours of working life!

As LED systems can see their life shorten significantly when exposed to prolonged heat, testing of thermal aspects is crucial. Physical testing, which includes setting up test benches, laying out thermocouples, and following standardized testing procedures is expensive and time-consuming. Thermal CFD simulation offers a solution for those wanting to optimize their LED lighting design quickly and efficiently, without sacrificing results accuracy, and reliability.

Thermal Design Considerations

Any LED fixture must be designed to keep LEDs cool by reducing heat resistance from the LED to the ambient air. This is done by considering and optimizing all three modes of heat dissipation — conduction, convection, and thermal radiation, in any part of the fixture design.

1. LED Spacing and Arrangement

Designers often want to reduce the spacing between LEDs on a PCB to produce compact LED fixture designs. However, this means an increase in thermal power density and, therefore, a rise in the temperature of the LEDs, as seen below.

LED manufacturers often provide a recommended spacing between LEDs and indicate the rise in temperature one can expect to see when spacing is reduced by a certain amount.

In terms of LED arrangement on the board, studies have shown that uniform and symmetrical chip arrangement provides a similar heat load whether it is rectangular, hexagonal, or circular.

2. LED Module Choice

LEDs come in a wide range of varieties, from the direct in-line package (DIP) to the most recent multiple chips on board (MCOB) LEDs.

DIP LEDs, characterized by their bullet-shaped design, are used mainly for signage and display on household electronics.

Surface Mounted Chips (SMD) LEDs are square diodes that can emit light in the full RGB spectrum. These blocks are mounted on a PCB surface. Instead of wire connections or legs these blocks are mounted and soldered directly onto a circuit board.

Chips on Board (COB) LEDs have nine or more chips sharing the same substrate/base. Thanks to this common substrate/base, COB LEDs are typically mounted directly to a heat sink using a screw-type connector. COB LEDs are more compact and energy-efficient than SMD LEDs, delivering a higher number of lumen per watt in a simpler design of only one circuit and two contacts. However, they are not well suited for color-changing bulbs. COB LEDs are typically mounted to a heat sink using a connector that attaches to the heat sink with screws.

The MCOB types of LEDs that have been recently introduced to the market combine multiple COB LEDs onto a single aluminum plate, generating more than 130 lumen/W.

Another variety, the COB LED flip-chip, is known to have a 70% better heat transmission rate and 30% higher lumen output compared to SMD chips.

3. Printed Circuit Board Core Material

Printed circuit boards host the LEDs (and other electronic components) in a specific arrangement on a flat surface. The role of a PCB’s core material is to redirect the heat away from the components. The most commonly used material is made out of FR-4, a glass-reinforced exposed laminate. Adding thermal vias that penetrate the PCB can significantly improve the thermal resistance.

The second most common core material is ceramic, used mainly for harsh environments characterized by high pressure, temperature, and frequency. Though not as economical as the standard FR-4 type PCBs, they are the better choice in terms of the conductivity of the material used (copper or silver palladium). 

The last type of PCB is the metal core printed circuit board (MCPCB), which has either aluminum, copper, or steel alloy-based core material. Copper and aluminum provide superior thermal conductivity and are 6-10 times as thermally conductive as FR4, as illustrated below.

PCBs are one of the most sensitive elements of thermal design given their prime location in the heat pat. Designers must, therefore, be thoughtful when selecting their components. MCPCB may seem like an obvious choice for better heat transfer, however, an FR-4 core PCB with adequately installed thermal vias offers a strong alternative. More research (1) (2) exists on the different types of PCBs and their thermal performances. A complete list of the available PCB technology for LED applications can be found here.

4. Contact Surfaces and Thermal Interface

Heat is transferred from one component to another via surface contact meaning these points of contact must be considered in the overall thermal design process.

Regardless of the means of fabrication, any manufactured part can contain imperfect surfaces, with wavy and rough profiles. When two surfaces are placed in contact, there is normally only a small area of direct physical contact at the interface. And, heat transfer occurs only at these small areas of surface contact.

Reduction in the points of physical contact reduces the heat conduction across the interface, as there will be gaps filled with air, which has low thermal conductivity. Such irregularities and gaps create thermal contact resistance. It is therefore essential to produce contact surfaces as planar, smooth, and clean as possible.

There are two main steps to reduce this thermal contact resistance. The first is to eliminate air by introducing a fluidic material, such as thermal grease, adhesives, phase-change materials (PCM), and filled polymers, into the voids created by uneven surfaces. Thermal grease, for example, can lower the thermal resistance of the interface by a factor of around five (depending on the pressure applied) (3). If a smaller heat resistance is required, the third-party material can be a thermal interface material (TIM) containing fillers that enhance the conduction process. The layer thickness of a thermal interface compound has a significant effect on thermal resistance.

5. Heat Sink Design

Heat sinks play an essential part in managing LED heat dissipation. They are designed to conduct the heat away from the LED and PCB and convect and radiate heat to the surrounding environment. The ambient air circulates through and around the heatsink to help cool it. 

The heat sink must transfer the heat away from the PCB so that no thermal buildup occurs within the LED packages. This happens when heat transfer rates of the heat sink to the air outpace the heat transfer rates from the LED to the PCB. The material used to maximize the heat transfer between the PCB and exposed convective surface must be highly conductive, like aluminum alloy which boasts a thermal conductivity of around 190 W/mk.

Radiation does not typically play a major role in the heat dissipation of low-to-mid power LED fixtures, as it pertains mostly to large surface areas and temperatures of over 100 degrees Celsius.  However, for high-power LED fixtures (e.g. a high bay light), radiative heat dissipation can become a solution to improve thermal performance. Indeed, such luminaries employ large heat sinks and operate at a high case temperature. Such devices are often equipped with heat sinks that have received some surface treatment like chemical blacking or oxidizing in order to increase the emissivity.

Recent research and projects for natural design heat sinks use computational fluids dynamics and additive manufacturing to produce topology-optimized heat sink geometries.

6. Active Cooling: Small Fans or Liquid Cooling

The air circulating around a heat sink can be either pushed or pulled through mechanical means or with ventilation.

When developing heat sink designs, engineers must consider many constraints such as space, fixture shape, manufacturability, cost, and air conditions. As previously mentioned, convection is a very important factor for the heat management of the whole LED fixture. A passive cooling approach, in which no forces to enhance air circulation are artificially induced, is always favored as it translates to cheaper, quieter, and more reliable designs. Generally, passive cooling designs that rely on natural convection are effective in their role of transferring heat to the air. For some industrial, heavy-duty applications using large power ratings, the heat transfer coefficient must be increased by using a fan or liquid cooling methods.

As an example, a heat sink operating with natural convection would transfer heat to the air at a rate of 5 to 20 W/m2K whereas the use of a fan can increase these values to 25-250W/m2K. When LED fixtures are cooled by liquids, the convection coefficient reaches up to 100-20K W/m2K.

To evaluate these convective heat transfer coefficients at the design stage of the LED fixture, it is very helpful to perform CFD thermal simulations which take into account the 3D geometry, the material, and the environmental conditions and considers all the heat transfer mechanisms to highlight problematic areas in the design. Evaluating the results, engineers can easily discover hot spots and address the issues, using an iterative design process to find a satisfactory solution.

With such an inexpensive, fast, and easy-to-use solution, it is possible to drastically reduce the number of workflow steps over a physical prototype by having already reduced the number of design options through numerical simulations. Furthermore, the capacity of parallel computation, data storage, and reliability that cloud computing offers, in turn, unfolds possibilities for optimizing designs to an extent that has never been seen before.

Conclusion

The heat dissipation performance of LED fixtures depends on a number of factors, materials, interfaces, geometries, and environmental conditions. Taking into account such factors is essential to keep the LEDs cool under working conditions which will therefore prolong their lifetime and satisfy performance requirements. Using CFD thermal simulation numerically replicates working conditions, materials, and heat thermal transfer on a 3D model so that the design engineers can make informed decisions. The time and effort required for many iterative physical tests can be significantly decreased by harvesting the power of CFD thermal simulation in the cloud. Multiple scenarios and designs can be tested simultaneously to rapidly find a thermally effective LED fixture solution. Designing optimized LED lighting solutions can mean a more accessible market price, making energy-efficient LED lighting systems the go-to standard.

Additional Resources on LED Lighting From SimScale:

• On-Demand Webinar: This Might Get Hot! Thermal Simulation of High-Power Density Electronics
• Assessing LED Performance and Eco-Friendly Lighting Options
• LED Cooling: Liquid Cooling Thermal Management
• Electronics Cooling Project: LED Spotlight

The post LED Heat Dissipation: An Optimization Guide for Engineers appeared first on SimScale.

So, what do consumers consider when making the switch to eco-friendly lighting? In this article, we will explore this, as well as where LED performance compares to other key environmentally friendly lighting options.

Consumer Considerations for Installing Lighting (Eco-Friendly)

Consumers first consider the utility of the space in which they are looking to install new lighting systems or alter existing lights. Second to energy-efficient light bulbs, lighting control technology creates a meaningful impact on energy consumption. Lighting control refers to the mechanisms used to power light sources on or off. Eco-friendly lighting asks consumers to think beyond the conventional switch, considering motion-sensor lighting control or a photosensor switch, which would power on light bulbs only as natural light diminishes.

The fixtures used in lighting systems can also have a direct effect on energy demands. Lighting fixtures, sometimes referred to as luminaries, play a role in directing or diffusing the illumination provided by light bulbs. Therefore, selecting the appropriate fixture for a space — one that offers the best light output ratio —reduces the need for additional light sources. 

The Complete Lifecycle of Eco-Friendly Lighting

The final stage of a light bulb’s life is often an afterthought when selecting lighting. However, for those wishing to center environmental consciousness in their design, it is a crucial aspect to consider, closely following LED performance. This involves the disposal and/or recycling options available in the consumer’s given area. Safe and proper disposal is a large part of mitigating the overarching ecological effect of lighting demand and production.

LEDs, for example, are constructed from certain metals and glass that are, by some accounts, up to 95% recyclable. Important here is that burnt-out or defective eco-friendly light bulbs be handed over to the proper organization to be disposed of or recycled safely—particularly when handling CFLs, which contain mercury. Consumers must check with local government authorities, hardware stores or online send-in programs that offer recycling or safe disposal programs. 

How Does LED Performance Compare in Eco-Friendly Lighting?

Not only do LEDs fit the bill for the aforementioned consumer considerations, but LED performance is also top-notch when compared to its more environmentally-friendly predecessors, CFLs. However, lighting solutions powered by solar energy and schemes that incorporate the use of natural light are also eco-friendly options to consider. Let’s take a closer look at the four lighting options consumers consider:  

• LEDs – An abbreviation for Light Emitting Diodes, these lightbulbs are the gold standard of energy efficiency in both private and commercial spaces. They not only consume up to 80% less electricity than traditional incandescent light bulbs but offer one of the longest operating lives on the market based on their LED performance, sometimes exceeding 50K hours.  

• CFLs – While Compact Fluorescent bulbs are considered to be 4 times more efficient than incandescent lighting, LED performance still offers a higher energy-saving rate.

• Solar Power – Lighting that is powered by renewable sources of energy, like solar, can encompass indoor lighting which draws from grid-tied solar power systems or outdoor solar lights. The latter stores solar energy in rechargeable cell batteries.

• Natural Lighting – Reducing the need for artificial lighting overall is one of the best steps to reducing energy consumption. Removing obstructions or installing windows and skylights can be incorporated into efforts to create a more eco-friendly lighting solution for a dedicated room or space.

Why LED Performance Makes the Difference 

Though the upfront purchasing price is slightly higher with LEDs, the cost savings over time offered by these energy-efficient light bulbs is unparalleled considering their LED performance. However, economic considerations are not the only draw for consumers. LEDs break apart from the eco-friendly lighting pack in a few other key areas: 

• Longevity – LEDs offer a longer burn time compared to CFLs and certainly when compared to old-guard incandescent bulbs. Whereas typical LEDs boast a lifespan of over 13 years, an incandescent bulb would have to be replaced at least 20 times in this timeframe. This allows for a convenient “set it and forget it” mentality and maximizes time savings for consumers. 

• Thermal considerations – LEDs (with some exceptions) do not produce heat in the form of infrared radiation, leaving them cool to the touch. This makes them a go-to option for lighting needs that sets light sources within reach of humans or animals or near temperature-sensitive objects like foods, textiles, etc. A major caveat to keep in mind, however, is that despite feeling cool LEDs do still generate heat, as with any light source. As such, engineers must incorporate an effective heat sink design for their LED designs, since the temperature at which an LED chip is maintained directly affects its rate of light output over time.

• Additional functionalities – Whereas CFLs can be sensitive to cold temperatures and take time to reach full brightness, LEDs power completely and straightaway and can even be used with dimmer settings. Newer LEDs even offer a range of ‘color temperatures’ to more closely recreate the warm tone produced by incandescent light bulbs while maintaining a high level of LED performance, making them the most versatile in the range of eco-friendly lighting options. 

Making the Switch to Optimum LED Performance Lighting  

Thoughtfully considering the best environmentally friendly lighting solutions for a space can not only lessen the strain on the planet’s finite resources but also result in a lower energy bill. To best capitalize on this win-win, consumers can assess their lighting needs based on utility, budget, and energy demand.

In each case, LEDs continue to offer a leading-edge eco-friendly solution which lasts longer, costs less overtime, and consumes less energy due to their unmatched LED performance. Market prices for LEDs continue to drop making opting for a more sustainable lighting solution more accessible than ever to consumers. 

To learn more about LEDs and how engineers can optimize the LED performance of their lighting designs with SimScale, check out more resources here: 

• LED Cooling: Liquid Cooling Thermal Management
• Thermal Performance of Lighting Solutions: LM-80 and TM-21 Lighting Standards
• Thermal Simulation of Lighting Designs to Achieve Higher Performance

Additional input provided by:

Jennifer Bell, a freelance writer for Prime Electrical Services.

The post Assessing LED Performance and Eco-Friendly Lighting Options appeared first on SimScale.