Stanford University
Showing 31-40 of 96 Results
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J. Christian Gerdes
Professor of Mechanical Engineering, Emeritus
BioChris Gerdes is a Professor of Mechanical Engineering at Stanford University and Co-Director of the Center for Automotive Research at Stanford (CARS). His laboratory studies how cars move, how humans drive cars and how to design future cars that work cooperatively with the driver or drive themselves. When not teaching on campus, he can often be found at the racetrack with students, trying out their latest prototypes for the future. Vehicles in the lab include X1, an entirely student-built test vehicle; Niki, a Volkswagen GTI capable of turning a competitive lap time around the track without a human driver; and Marty, our electrified, automated, drifting DeLorean. Chris' interests in vehicle safety extend to ethics and government policy, having helped to develop the US Federal Automated Vehicle Policy while serving as the first Chief Innovation Officer of the US Department of Transportation.
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Siegfried Glenzer
Professor of Photon Science and, by courtesy, of Mechanical Engineering
On Leave from 09/15/2023 To 09/14/2024Current Research and Scholarly InterestsPlease see our website for detailed information: https://heds.slac.stanford.edu
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Kenneth Goodson
Senior Associate Dean for Faculty and Academic Affairs, Davies Family Provostial Professor, and Professor, by courtesy, of Materials Science and Engineering
Current Research and Scholarly InterestsProf. Goodson’s Nanoheat Lab studies heat transfer in electronic nanostructures, microfluidic heat sinks, and packaging, focussing on basic transport physics and practical impact for industry. We work closely with companies on novel cooling and packaging strategies for power devices, portables, ASICs, & data centers. At present, sponsors and collaborators include ARPA-E, the NSF POETS Center, SRC ASCENT, Google, Intel, Toyota, Ford, among others.
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Catherine Gorle
Associate Professor of Civil and Environmental Engineering and, by courtesy, of Mechanical Engineering
Current Research and Scholarly InterestsGorle's research focuses on the development of predictive flow simulations to support the design of sustainable buildings and cities. Specific topics of interest are the coupling of large- and small-scale models and experiments to quantify uncertainties related to the variability of boundary conditions, the development of uncertainty quantification methods for low-fidelity models using high-fidelity data, and the use of field measurements to validate and improve computational predictions.
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Wendy Gu
Assistant Professor of Mechanical Engineering and, by courtesy, of Materials Science and Engineering
BioThe Gu Group studies the mechanical behavior of nanomaterials. We work at the intersection of solid mechanics, materials science and nano-chemistry. We research the unique properties of nanoscale metals, ceramics and nano-architected composites in order to design strong, tough and lightweight structural materials, materials for extreme environments, and mechanically-actuated sensors. Our experimental tools include nanoindentation, electron microscopy, and colloidal synthesis.
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Ronald Hanson
Clarence J. and Patricia R. Woodard Professor of Mechanical Engineering
Current Research and Scholarly InterestsProfessor Hanson has been an international leader in the development of laser-based diagnostic methods for combustion and propulsion, and in the development of modern shock tube methods for accurate determination of chemical reaction rate parameters needed for modeling combustion and propulsion systems. He and his students have made several pioneering contributions that have impacted the pace of propulsion research and development worldwide.
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Gianluca Iaccarino
Professor of Mechanical Engineering
Current Research and Scholarly InterestsComputing and data for energy, health and engineering
Challenges in energy sciences, green technology, transportation, and in general, engineering design and prototyping are routinely tackled using numerical simulations and physical testing. Computations barely feasible two decades ago on the largest available supercomputers, have now become routine using turnkey commercial software running on a laptop. Demands on the analysis of new engineering systems are becoming more complex and multidisciplinary in nature, but exascale-ready computers are on the horizon. What will be the next frontier? Can we channel this enormous power into an increased ability to simulate and, ultimately, to predict, design and control? In my opinion two roadblocks loom ahead: the development of credible models for increasingly complex multi-disciplinary engineering applications and the design of algorithms and computational strategies to cope with real-world uncertainty.
My research objective is to pursue concerted innovations in physical modeling, numerical analysis, data fusion, probabilistic methods, optimization and scientific computing to fundamentally change our present approach to engineering simulations relevant to broad areas of fluid mechanics, transport phenomena and energy systems. The key realization is that computational engineering has largely ignored natural variability, lack of knowledge and randomness, targeting an idealized deterministic world. Embracing stochastic scientific computing and data/algorithms fusion will enable us to minimize the impact of uncertainties by designing control and optimization strategies that are robust and adaptive. This goal can only be accomplished by developing innovative computational algorithms and new, physics-based models that explicitly represent the effect of limited knowledge on the quantity of interest.
Multidisciplinary Teaching
I consider the classical boundaries between disciplines outdated and counterproductive in seeking innovative solutions to real-world problems. The design of wind turbines, biomedical devices, jet engines, electronic units, and almost every other engineering system requires the analysis of their flow, thermal, and structural characteristics to ensure optimal performance and safety. The continuing growth of computer power and the emergence of general-purpose engineering software has fostered the use of computational analysis as a complement to experimental testing in multiphysics settings. Virtual prototyping is a staple of modern engineering practice! I have designed a new undergraduate course as an introduction to Computational Engineering, covering theory and practice across multidisciplanary applications. The emphasis is on geometry modeling, mesh generation, solution strategy and post-processing for diverse applications. Using classical flow/thermal/structural problems, the course develops the essential concepts of Verification and Validation for engineering simulations, providing the basis for assessing the accuracy of the results.
