Electromagnetic & System Simulation Engineer
Dr. Daniel Smallwood is an engineering physicist and a computational scientist, originally from Cocoa Beach, FL. He has a B.Sc. in Chemical Engineering from The University of Alabama in Huntsville, a Ph.D. in Engineering Science from Tyndall National Institute, Ireland, and 10+ years’ experience working on diverse projects in both research and industry. He has expertise in computational physics, materials science, electromagnetism, electrochemistry, condensed matter physics, quantum physics and device/system design. His research has been published in several scientific journals, and he is an inventor on several patent applications. Following his Ph.D., Dr. Smallwood served as the Director of Materials Research and Development at Atlas Magnetics, where he developed innovative solutions for next-generation integrated magnetic materials. He then worked as a Postdoctoral Researcher at Dartmouth College, with a focus on quantum magnetometry, quantum computing and bosonic systems simulation. He currently works as an Electromagnetic & System Simulation Presales Engineer at Maya HTT, an industry-leading developer of advanced simulation tools with applications in CAD, CAM, CAE, PLM, IIoT, AI and ML. In his current role, Dr. Smallwood provides expert consultation and support to clients and partners on Siemens software portfolio tools in the areas of low and high frequency electromagnetics and complex systems simulation. He leverages a variety of solution methods ranging from discrete, modular simulation to co-simulation and fully-coupled multiphysics to address computational physics needs in application domains ranging from automotive to aerospace, energy, medical, biotechnology and micro-nano systems.
System Level Optimization and Validation of High-Fidelity Electric Vehicle Motor Designs
In today’s automotive market, end users expect quality electric vehicles (EV) that meet or exceed the performance metrics of standard petroleum cars. To meet this expectation, EV designs are becoming increasingly complex with many system topology options. This presents both a challenge and a significant opportunity for EV designers. To gain a competitive edge in the market, innovative EV system designs require rapid validation and optimization with realistic component-level simulations. In this presentation, we focus on integrating fast and accurate motor simulations with system level EV designs. This methodology provides EV designers with solutions for rapid validation and optimization of motor designs that satisfy cutting-edge EV system specifications and corresponding design topologies. Furthermore, integrating system and component-level simulations is essential to enable a digital twin of a physical EV that can provide valuable information on motor performance within the EV operating environment. We expect that this integrated simulation approach will empower EV designers to rapidly deliver innovative and impactful EV system designs.