Computational Simulation for Medical Device Design

Engineers have been using models since the beginning of time. Computational simulations provide a better way for engineers to model.

Computational simulation provides excellent value throughout the product development lifecycle. This engineering tool has significantly improved product quality, and lowered development cost and time-to-market for companies. In addition, engineers have used simulation to create highly innovative products and generate revenue growth for their businesses.

Simulation benefits many industries, and the medical device industry has gained more than most from using simulation. Medical device companies have relied heavily on traditional build-and-test methods for their product development. These methods include in vitro and in vivo testing, and these tests are often extremely expensive.  In vivo testing introduces complexities that many other industries do not experience. Due to the higher than typical costs associated with physical testing of prototypes, the medical device industry continues to expand use of in silico testing through computational simulation.

Designers and engineers have benefited from using simulation at each phase of product lifecycle. From the discovery phase and regulatory decision making, to product launch and monitoring, simulation has demonstrated value to companies that have implemented it.

In the discovery phase, simulation is becoming an enabling technology for product innovation. By using simulation, engineers can conduct hundreds of in silico tests on design permutations. Companies that have used simulation in this way, have been able to evaluate high-risk / high potential ideas that would not have been investigated through in vitro testing. Companies that have taken this approach have grown their top-line revenue through improved new product designs.

Simulation also provides value for medical devices in the field by providing insight and understanding into problems that arise during use. In these cases, simulation can guide remedial action for the existing device design. For example, the safety of medical devices during magnetic resonance imaging (MRI) has been demonstrated through simulation. The electromagnetic fields associated with an MRI may be focused by a metal implant. This focused energy produces tissue heating that may be enough to damage surrounding tissue. Simulations calculate the temperature rise in the tissue to determine the safety of a specific implant within the MRI electromagnetic field. Demonstrating safety during MRI testing enables patients to experience the diagnostic benefits of an MRI.

Computational simulation has reduced development cost and time for medical device companies that have used it during their product development. The use of simulation in medical device design continues to grow and is supported by the FDA. Where does simulation fit into your company’s research and development?

AltaSim Technologies is teaming with Mechanical Engineering and COMSOL to host a webinar to share our experiences with using simulation in medical device design.  The webinar is on April 16, 2020 at 2:00 pm EDT. If you would like to learn more about how simulation fits into your company’s product development cycle, then please register and attend.

Simulation of Antenna Arrays – Part 1

Simulation of Antenna Arrays - Part 1

Asymptotic Waveform Evaluation Method to Speed-Up RF Resonant Simulations

Frequency domain simulation techniques are widely used to analyze the behavior of arbitrarily shaped objects and complex materials. One of the disadvantages of traditional frequency domain techniques, however, is the computational cost involved in obtaining solutions over a range of frequencies. Simulations have to be repeated at each frequency of interest to obtain a complete response over the required frequency range. For frequency dependent systems such as resonant structures, the number of simulations required to capture details of the resonant response can cause noticeable increases in the number of simulations. To adequately resolve narrow peaks in resonant behavior, further fine scale increments in frequencies may be required such that computational resources and time can escalate rapidly.


For simulation of the resonant response of RF structures, the RF Module in COMSOL Multiphysics offers two reduced-order modeling techniques that can help overcome the need for long simulation times and extensive computational resources associated with traditional frequency domain analyses of resonant behavior, these methods are:

  • Frequency-domain modal method
  • Asymptotic waveform evaluation (AWE)

The frequency-domain modal method is best suited for quickly analyzing systems with multiple resonances in a defined frequency band. As a precursor to performing a frequency-domain modal analysis efficiently the results of an initial Eigenfrequency analysis must be refined to remove unwanted low frequency residues. The frequency-domain modal analysis is then applied with fine frequency stepping to define the multiple resonances.


In contrast, the AWE method is best used for analysis of smooth frequency responses containing a single resonance or no resonance. Therefore, it is extremely useful when simulating resonant circuits in which many frequency points need to be analyzed to resolve a single, narrow resonant response accurately. In this article we provide some background information on the AWE approach that will enable practicing simulation engineers to make effective use of AWE approaches for the evaluation of resonant behavior in RF systems.


Most of the frequency domain techniques used in computational electromagnetics result in a matrix equation which is solved at a single frequency. In the AWE technique, the Taylor series expansion around that frequency is applied to the matrix equation. Coefficients of the Taylor’s series are obtained in terms of the frequency derivatives of the matrices evaluated at the expansion frequency and are used to predict the frequency response of the system over a frequency range. AWE basically combines the fast-recursive moment method with the Padé approximation to compute poles and residues of truncated transfer functions. AWE was proposed in 1990 by Lawrence Pileggi from Carnegie Mellon University, its implementation in COMSOL Multiphysics is obtained in a special Adaptive Frequency Sweep study.


To see how it works, let’s consider the Microstrip Patch Antenna model from the COMSOL Application Library:


The study setup for a traditional frequency sweep from  to  in steps of  is shown below:


The resulting frequency response of the S-parameter of the microstrip patch antenna evaluated with a  resolution is shown below:


To improve the resolution of the S-parameter we need to perform a sweep with a  resolution. Conventional frequency domain analysis approaches would require 100 times more CPU; alternatively, we can use the AWE methodology incorporated into the Adaptive Frequency Sweep approach available in the predefined frequency-domain study types:


After adding the Adaptive Frequency Sweep study type, we must specify the same frequency range and include a frequency sampling step of :


During the Adaptive Frequency Sweep study computation, we can observe the solver log which shows the Taylor series expansion performed around a few frequencies over the entire frequency range:


The resulting frequency response of the microstrip patch antenna is  shown below and indicates the  bandwidth wider than observed with the previous  step:


The Table below summarizes the performance of AWE method using a 100 times finer frequency resolution. The AWE methodology uses only ~3 three times more CPU compared to the conventional frequency sweep with a coarse frequency resolution:


Sweep method DOFs Frequency step Number of sampling points CPU
Conventional 366,084 10 MHz 6 2 min
AWE 366,084 100 kHz 501 4.5 min


Through the AWE or frequency-domain modal method, we can observe the system response with fine increments in frequency and resolve resonant system response. These techniques provide a significant reduction in the amount of stored data compared to that generated by traditional frequency domain analyses with the same frequency resolution. Further reductions in the amount of stored data can be obtained by only storing data that is of direct relevance to the field of interest.  In RF systems we are usually only interested in the S-parameters of the system response, and not in the data in the entire simulation domain. The data of direct interest is associated with the boundaries for S-parameter calculation, i.e. the lumped port and port boundaries. These boundary sizes are relatively small and thus we can reduce the file size of the stored data by running modal order reduction techniques and storing the solution only on the relevant port boundaries.


This can be achieved in two steps:


1. Use the Create Selection button for the Lumped Port boundary

  • This creates the Explicit 1 selection for the antenna port boundary under the Component Definitions:



2. Use the above defined Explicit 1 selection to store the solution only on the port boundary:


For the example used here, storing only on the results on the port boundaries reduces the size of the mph file from about 3 GB to 3 MB.


Computationally, frequency-domain modal methods and AWE methodologies may increase the storage and CPU time requirements compared to a single frequency calculation. However, for a range of frequencies required to compute the resonant frequency response of a system, the frequency-domain modal and AWE techniques provides significant overall improvements in performance. When coupled with the use of modal order reduction techniques, the amount of data required to be stored to define the response of a resonant system can be significantly reduced.


AltaSim Training

For many years, instructor-led training was the only available option for students seeking to learn.  In recent years, e-learning has become another method of learning.  Both methods provide value to students.  E-learning educates students on specific, concise topics at a much lower cost than instructor-led training.  However, e-learning does not fit every learning situation.
AltaSim trains our students to solve problems using COMSOL Multiphysics through instructor-led training provided in our classroom or delivered via the web.  In both cases, the training is live and led by an instructor.  We believe that this approach to training provides significant benefits, because COMSOL Multiphysics solves highly complex problems.  For these types of problems, having a live instructor has proven to be extremely valuable to our students.  In our classes, highly skilled instructors answer our student’s difficult questions with detailed answers backed up by 10+ years of experience.  Our instructors use COMSOL Multiphysics every day to solve difficult problems for our clients.  In addition, they have extensive experience translating that learning into their teaching.
We have also observed the power of learning in a classroom.  Students learn from each other through their interactions in the classroom, and by hearing answers to other student’s questions.  In our advanced classes, the students are all working in similar physics (e.g., CFD, RF, acoustics), and the cross pollination of ideas has been powerful.
Our students are typically high-value employees of their companies, and instructor-led training is an excellent investment in these employees.  They feel valued by being able to attend high-quality training, and the training helps them be more efficient and effective in their problem solving.
Click here to reserve time for a free training needs assessment with AltaSim

Saving Time and Money for Rockwell Automation

Problem: As Rockwell Automation looked to improve product functionality, engineers relied on developing physical prototypes to test heat sink designs for its powerful microprocessor chips. This approach left mechanical engineers scrambling at the end of the design cycle to find an electronics cooling solution that would fit the prototypes and not delay new product releases.


Solution: Rockwell leveraged AltaSim’s extensive simulation expertise to quickly deliver accurate simulations through increasingly complex models for a wide range of product design calculations.


Result: AltaSim’s simulations enabled Rockwell to test electronics cooling solutions prior to the costly development of the first physical prototypes. This approach reduced Rockwell’s reliance on physical prototyping, driving down both the duration and cost of the design cycle.

Electronics Cooling Results

Air flow and temperature inside Electronics Cabinet


Interested in learning more about how our S3 process can accelerate your design process?  Please call us at 614-861-7015, or complete the webform provided: