Changing Electronics Cooling


It has been a while since we have put out a Blog on electronics cooling and there is a very good reason for that – not much has changed, until now.


Progressive companies manufacturing electronic components and circuits consistently challenge the limits of component performance by offering increased functionality in a decreased product size. The associated increase in power density develops significant thermal energy that must be dissipated to maintain accurate long term performance. For components and circuits used in critical applications required to maintain operation such as continuous manufacturing operations and emergency communication systems, passive approaches for dissipating thermal energy are preferred.


But as we have demonstrated in previous blogs, traditional approaches for evaluating the thermal margin of safety are inherently conservative due to the significant assumptions made in calculating dissipation of thermal energy.  Consequently, assessing the thermal response of a new device is generally left until late in the design process – following form and function. From the designer’s standpoint, getting traction on thermal challenges early in the design process is difficult for a few reasons:


  • Estimating heat transfer rates before prototypes are available is not easy
  • Allowable thermal margins may be masked by inherent limitations even after prototypes are made
  • Waiting for sufficient testing can be a time-consuming and expensive process


If accuracy is required, predictive physics based computational analysis can be used but this requires access to skilled personnel, and sophisticated hardware and software. Any one of these could be a significant hindrance, but when all three are combined the resulting obstacle may become insurmountable for all but the largest companies.


A solution we developed following discussions with many of our customers, ranging from large multinational organizations to small individual developers, uses a computational simulation application (CApp) to explore the thermal behavior of power electronic devices. The CApp is HeatSinkSim which provides the accuracy of a physics based computational analysis with the ease of use of a spreadsheet – the first of a series of CApps to provide designers with the capability to examine the effect of heat sink design on thermal dissipation in power electronic components.





HeatSinkSim solves the conjugate heat transfer problem for a vertically oriented plate fin heat sink operating under natural convection. Heat transfer is analyzed as a combination of conduction, convection and radiation with a full solution to the associated thermal and fluid flow problem. Two levels of analysis are available: first, a parametric study of heat sink design, and secondly, an optional detailed analysis that provides highly accurate temperature distributions for the optimum design of heat sink. The second level of analysis is recommended when device specific limits on casing temperature and/or junction temperature are approached. The model was developed and validated in conjunction with detailed experimental measurements that have allowed inclusion of these automated warnings based on the level of accuracy expected from the analysis.


The user inputs the heat sink geometry, materials of construction and operating conditions.




Once the desired geometry, materials and operating conditions are established the associated computational analysis file, including geometry development, meshing, physics set up and solver settings, is automatically generated and submitted for execution. The complexity of the conjugate heat transfer analysis requires significant computational resources to provide an accurate solution and thus HeatSinkSim has been configured to run on cluster computing hardware. The App automatically identifies the computational resources required to complete the analysis and distributes the analysis over the available nodes/cores. On completion of the analysis the user is automatically prompted to review the results and download a standardized report. To allow general access, AltaSim is making HeatSinkSim available for use on personal clusters as well as through secure connection to independent parallel computing resources to ensure confidentiality; further customization for individual users can be performed if needed.






Access to the app and the hardware required to run the simulations is available through AweSim using a variety of payment options ranging from an annual license with unlimited use to pay-per-use options.


For more information on HeatSinkSim, contact Jeff Crompton at AltaSim Technologies (jeff at altasimtechnologies.com).



Modeling and Simulation: Opportunity

Modeling and Simulation: Opportunity


“It’s not just what you do it’s also why you do it” – Part 2

With all these advantages of modeling and simulation that were documented in Part 1 of this blog where is computational analysis and virtual prototyping being used and what is the opportunity for future use? A 2015 study (1) (Figure 1), suggested that in leading companies computational analysis has made significant inroads into general use but there are many areas where it is not being applied.


Although the data show a reasonably consistent use of modeling and simulation across all company sizes with “dedicated” and “frequent and consistent” use, by far the largest percentage in the study shows “infrequent and inconsistent”. Although it is recognized that modeling and simulation provides value to an organization there are many functional areas where it is not being applied and instead organizations remain reliant on traditional approaches such as “rules of thumb”, experience or spreadsheet based calculations. One reason for this is that computational analysis is viewed as the domain of an expert and in many cases expert knowledge is required to gain access to the appropriate software. Thus a major growth area for the use of modeling and simulation requires that the expertise embedded in computational models be made more readily available for use by personnel with limited expertise in computational modeling. By bridging this gap, design and process engineers can take advantage of predictive physics-based results earlier in the design process, make more accurate decisions about the developments and thereby reduce the extent of prototype testing and evaluation that is required.


To accomplish this objective two primary components of the problem need to be addressed: first, approaches that enable computational analyses developed by experts to be used by scientists and engineers who may have limited experience with computational analysis; and secondly, mechanisms by which computational analyses can be widely distributed without the need to invest in the hardware, software and personnel required to effectively operate them. For the remainder of this article we focus on the first of these areas, a future article will center on the topic of packaging the product for use by a wider audience.


It is estimated that globally there are~750,000 computational simulation experts but there are ~80 million scientists and engineers who can make use of computational analysis. How can computational simulation based tools be made available for use by this large group? One method to facilitate the spread of computational analysis is to package the expert’s knowledge into easy to use computational analysis files that use simplified interfaces to set up analyses of selected problems. This allows design and process engineers to run a series of analyses easily and use the results to aid decisions on developments without having to make direct use of computational analysis domain experts. This approach has recently become a viable option through the release of a number of platforms that allow the development and distribution of packaged Computational Simulation Applications (CApps) that can fall into two categories; first those that are maintained as proprietary within an organization, and secondly, general ones that seek to provide results across a generic industry problem.


Recently, AltaSim has developed a range of CApps to address technology associated with:

  1. Heat sink design
  2. Quenching on metal components
  3. CMC RMI processing
  4. Mass transport through barrier layers
  5. Additive manufacturing
  6. Plasma devices


These CApps are based on computational analyses developed using COMSOL Multiphysics that are then adapted using the COMSOL Application Builder to produce CApps that can be run using a COMSOL Multiphysics or COMSOL Server license. A simplified interface, eg Figure 2, allows the user to quickly and easily define the input parameters and conditions for an analysis to examine the effect of heat sink design on dissipation of thermal energy from electronic components using HeatSinkSim.




Once the problem set up is confirmed, analysis is automatically performed using verified conditions defined by the computational analysis expert. In this case the analysis solves the natural convection problem and incorporates thermal dissipation due to conduction, convection and radiation to the surrounding environment to allow the effect of the design of a heat sink on the thermal distribution to be defined. Previously these calculations incorporated gross assumptions on heat transfer coefficients, extrapolated form 1-D solutions and neglected critical factors such as radiation. Use of HeatSinkSim has enabled designers to identify options and limitations earlier in the design process, and safely operate under conditions that approach component limits thus allowing more functionality and smaller product forms to be utilized.


In summary, the motivation for using computational analysis is becoming clearer and more quantified: integration into the development cycle provides advantages in the critical areas of product launch date, cost of development and product quality. This advantage is being used by leading companies to establish, gain and protect market share at the expense of those companies who ignore the benefits of modeling and simulation. In companies where modeling and simulation is established there remains a significant opportunity to extend its reach by replacing traditional engineering based approximations that may have been codified in company guidelines, industry codes of practice or individual spreadsheets by predictive physics based computational analysis. Computational Applications can capture expert knowledge and present it in a way that it is easily and readily accessible for use by a wider group of scientists and engineers who can then make informed decisions during the development and implementation of new technology.


  1. Hardware design engineering study, Lifecycle Insights, August, 2015

Modeling and Simulation: Motive

Modeling and Simulation: Motive


“It’s not just what you do it’s also why you do it” – Part 1


As scientists and engineers involved with modeling and simulation it is natural for us to focus on the intricacies of the tools that we use and to instinctively value the use of computational analysis. Consequently we often hear that modeling and simulation can enable a greater understanding, provide insight, identify solutions and isolate critical factors that affect performance. But increasingly we are asked to justify the use of computational analysis to individuals who don’t have the same intuitive relationship to the work. So what is the motivation for increasing the use of modeling and simulation, and as importantly where is the opportunity? In this blog we will address the issue of motive; a subsequent one will address our view of the future opportunity for modeling and simulation.


In the past companies and individuals have attempted to develop a more quantitative value proposition for modeling and simulation with statements such as “$7 return for every $1 spent on modeling and simulation”, “Expenditures on testing dropped from 40% to 15%” and “Design cycle reduced from 2 years to 8 months”. Our own evaluations performed for DARPA suggested 90% reduction in time and 50% reduction in cost for a specific product development. But how encompassing are these statements or are they only specific to isolated operations that cannot be generalized? Recently there have been a number of surveys that have looked more widely at the benefits of modeling and simulations, here we provide a brief summary of those findings in the hope that it will allow you to see the motive behind using modeling and simulation as well as see potential future opportunities to increase the role that it can play. Let’s start by looking at information that has tried to quantify the benefit of modeling and simulation.


In 2014 an estimated 1/3rd of a company’s annual revenue came from new products, meaning that continued innovation is now required to establish, maintain or grow market share. The Aberdeen Group recently surveyed (1) over 550 companies to identify how well they performed in the critical areas of cost for new product development, timeliness of delivery and quality of new products (Figure 1).




The top 20% were deemed “Best in Class” and the data suggest that this group of companies outperform the average by up to 20 percentage points in the critical areas of launch date and cost. The companies in the bottom 30% of performers, termed “Laggards”, are so far behind the best in class performers in launch date and cost that you have to wonder if they will ever catch up. Interestingly, the scores in the quality metric are closer for the three groups suggesting that activity over the last few decades to improve quality and consistency is now firmly embedded in the new product development cycle, and that broadly speaking most new products are of high quality.


But the questions that immediately follow are: “How are the best-in-class achieving their targets?” and “What are they doing that others are not?” The consistent answer is that these companies have embraced the use of computational analysis and virtual prototyping over the traditional testing and evaluation approaches. The ability to simulate real world problems coupled with easier access to the required hardware and software has enabled the forward thinking companies to integrate computational analysis into their product development cycle. In this way they have been able to differentiate themselves from the competition and outperform the market by reducing the number of failures during the development cycle and being able to hit target launch dates. The reported benefits of an approach that integrates computational analysis compared to one that relies on traditional prototype build and test approaches are quantified in Figure 2.




Integrating computational analysis was seen to provide decreases in the number of prototypes used during development, the cost of development and the time required thus allowing products to be launched on time. In contrast, developments relying on physical prototypes showed increases in all of these categories. These data are supported by another survey (2) that suggested that reducing the number of prototype failures during development significantly increases the likelihood of meeting release dates.


In conclusion, the value of using computational simulation has been known by practitioners in the art, but more recent studies have developed data that documents the benefits in a broader industry environment. These benefits include fewer prototype builds and modifications required during the prototyping phase, improved ability to hit targeted launch dates for new products and processes, and increased quality of the final product. When combined these attributes are allowing visionary companies who make routine use of modeling and simulation to differentiate themselves from their competitors, increase market share and increase profitability.



  1. The Value of Virtual Simulation Versus Traditional testing, Reid Paquin, The Aberdeen Group, 2014
  2. The PLM Study, Lifecycle Insights, February 2015