جست‌وجوی استاد و پژوهشگر

Nadine Aubryمشاهده پروفایل

استاد

• Fluid Dynamics
• Turbulence Modeling
• Microfluidics
• +۵ مورد دیگر

Nadine Aubry is Professor and Senior Advisor to the Dean of Engineering at Tufts University School of Engineering, Department of Mechanical Engineering. An internationally recognized scholar, she previously served as Provost and Senior Vice President at Tufts (2019-2021), Dean of Engineering at Northeastern University (2012-2019), and Department Head at Carnegie Mellon University. Her leadership spans multiple institutions with global impact. Education: Ph.D. in Mechanical and Aerospace Engineering, Cornell University (1987) M.S. in Mechanical Engineering, Université Scientifique et Médicale de Grenoble B.S., Grenoble - Institut National Polytechnique Research Focus: Dr. Aubry pioneers computational approaches in fluid dynamics, specializing in turbulence modeling, microfluidics, and electrohydrodynamics. Her work integrates dynamical systems theory with nanoparticle manipulation and biofluid applications. Recent research emphasizes machine learning-enhanced methods for flow prediction, convective heat transfer optimization, and aerodynamic design using physics-informed neural networks. Publication Trends: Recent articles demonstrate strong emphasis on machine learning applications in fluid mechanics and thermal systems. Dominant themes include physics-informed neural networks for flow prediction, deep reinforcement learning for active flow control, convolutional networks for aerodynamic optimization, and hybrid AI methods for multiphysics problems across aerospace, electronics cooling, and biomedical domains. Awards & Honors: Elected Member: U.S. National Academy of Engineering Fellow: American Academy of Arts & Sciences, American Physical Society, ASME, AAAS, AIAA G.I. Taylor Medal for Fluid Mechanics Research National Academy of Inventors Fellow C.C. Mei Distinguished Lecturer Grants & Leadership: Secured USDA funding for aging research (2014-2019). Chaired International Union of Theoretical and Applied Mechanics assemblies globally. Serves on National Academy of Engineering prize committees and governance boards.

Ming Huمشاهده پروفایل

استاد

• Thermal Transport
• Nanotechnology
• Energy Systems
• +۸ مورد دیگر

Ming Hu is a Professor in the Department of Mechanical Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina. His research focuses on computational thermal science with applications in energy systems, nanotechnology, and advanced materials engineering. Education: Dr.-Ing. in Mechanics from the Institute of Mechanics, Chinese Academy of Sciences (2006) B.S. in Mechanical Engineering from the University of Science and Technology of China (2001) Dr. Hu's research centers on micro-/nano-scale thermal transport phenomena, particularly in low-dimensional materials and nanostructures. His work spans energy nanotechnology, interfacial heat transfer for thermal management, and multi-scale multiphysics modeling of complex energy systems. He employs advanced computational techniques including molecular dynamics, first-principles simulations, and machine learning to investigate phonon transport mechanisms and thermal properties of novel materials. Analysis of recent publications reveals a strategic shift toward artificial intelligence applications in thermal science. Dr. Hu has pioneered graph neural network architectures for high-throughput prediction of phonon properties and thermal conductivity, with significant focus on materials for wide bandgap semiconductor cooling, thermoelectrics, and battery systems. His current work emphasizes accelerating materials discovery through machine learning while maintaining physical interpretability. Scientific Awards: No major awards were documented in the provided information. Dr. Hu leads the Nano Energy and Engineering Laboratory, which specializes in computational thermal science research. The laboratory develops and applies advanced simulation methodologies to investigate heat transfer in next-generation materials. No information regarding graduate students or research grants was available in the provided materials.

Yue Lingمشاهده پروفایل

دانشیار

• Multiphase Flows
• Atomization & Sprays
• Computational Fluid Dynamics
• +۳ مورد دیگر

Yue (Stanley) Ling is an Associate Professor in Mechanical and Aerospace Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing. He directs the Computational Thermo-Fluids Laboratory, where his team develops advanced numerical methods for high-fidelity simulation of multiphase flows and heat transfer processes. Research focuses on atomization mechanisms, droplet/bubble dynamics with phase change, and fluid-surface interactions, using adaptive mesh refinement and massively parallel computing. Applications span fuel injection, thermal management, aerodynamics, and environmental processes. Dr. Ling's NSF CAREER Award (2020) supports fundamental research on spray formation mechanisms under turbulent conditions. Education: Ph.D. in Mechanical Engineering, University of Florida (2010) B.S. in Engineering Mechanics, Beihang University (2004) Recent publications develop conservative volume-of-fluid methods for compressible multiphase flows and characterize turbulence-interface interactions. Awards include ASME's Best Video Presentation (2021) and AIAA's Flow Visualization honor (2019). Dr. Ling leads multiple NSF-funded projects and collaborates internationally through the PARIS (Parallel, Robust, Interface Simulator) consortium.

Yi Wangمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Microfluidics
• Reduced Order Modeling
• +۲ مورد دیگر

Dr. Yi Wang is Professor of Mechanical Engineering at University of South Carolina's College of Engineering and Computing. He directs the Integrated Multiphysics & Systems Engineering Laboratory (iMSEL) researching computational and data-enabled science for multiphysics systems including microfluidics, additive manufacturing, and autonomous systems. Research develops physics-based adaptive modeling, reduced order techniques, and data-driven surrogate models for complex engineering systems. Applications include microfluidic device optimization, aeroelastic reduced order models, robotic path planning, and structural health monitoring. Honors include Breakthrough Stars Award, Research Progress Award, and multiple Dean's Excellence Awards. He serves as Associate Editor for Discover Applied Sciences and editorial board member for multiple journals. Current NSF and DoD-funded projects focus on aeroservoelastic reduced order modeling and autonomous systems perception. Research group includes 5 postdoctoral fellows and 12 graduate students working on multidisciplinary computational projects. Teaching includes Flight Dynamics and Control, Engineering Analysis, and senior design courses.

Xingjian Xueمشاهده پروفایل

دانشیار

• Solid Oxide Fuel Cells
• Electrochemical Energy Conversion
• Gas Separation Membranes
• +۴ مورد دیگر

Dr. Xingjian Xue is an Associate Professor in Mechanical Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing. He holds a Ph.D. in Mechanical Engineering from the University of Connecticut with complementary training in mechatronics and materials science. His research advances electrochemical energy technologies, focusing on solid oxide fuel cells (SOFCs), gas separation membranes, and materials for energy conversion. Recent innovations include nanostructured cathodes, functionally graded membranes for oxygen separation, and thin-film SOFCs. His work integrates computational modeling with experimental validation to enhance performance and durability. Recent publications demonstrate progress in scaling membrane technologies for industrial oxygen separation and developing methane-fueled SOFC systems. Research consistently addresses fundamental transport phenomena and kinetic processes in electrochemical systems. Laboratory Leadership: Directs the High Temperature Electrochemical Materials and Energy Lab, focusing on materials synthesis, electrochemical characterization, and system prototyping.

Adam Robertsمشاهده پروفایل

پژوهشگر ارشد

• Offshore Renewable Energy
• Digital Engineering
• Operations & Maintenance
• +۲ مورد دیگر

Adam Roberts is a Research Fellow in Operations & Maintenance at the University of Plymouth's School of Engineering, Computing and Mathematics within the Faculty of Science and Engineering. His expertise bridges renewable energy systems and digital engineering for offshore renewable applications. Research applies analytical, numerical, and practical methods across technology readiness levels. Specializes in developing simulation tools for floating offshore wind farms, including the Ec Simulator for the Cornwall FLOW Accelerator project. Focuses on packaging complex models into user-friendly applications for decision support. Prior to Plymouth, worked at University of Exeter's Offshore Renewable Energy Group and High Value Manufacturing Catapult. At Exeter, supported Marine-I project providing modeling expertise to marine technology companies. At MTC, conducted multiphysics analysis and developed simulation apps for commercial R&D. Research interests include operations optimization for floating offshore wind, digital twin development, and marine renewable energy technology advancement.

Pedro Diezمشاهده پروفایل

استاد

• Reduced Order Models
• Error Estimation and Adaptivity
• Extended Finite Element Methods (X-FEM)
• +۴ مورد دیگر

Professor Pedro Diez is a faculty member at the Universitat Politècnica de Catalunya (UPC), specializing in computational mechanics and reduced-order modeling. He holds a BSc and PhD in Civil Engineering from UPC (1989 and 1996, respectively). His research focuses on error estimation, extended finite element methods (X-FEM), and applications in automotive and geophysical systems. He co-organizes the International Conference in Adaptive Modeling and Simulation since 2003, with the next edition in 2023. Key research areas include: Reduced Order Models (POD/PGD) Goal-oriented adaptivity in numerical simulations Geophysical inversion and thermal modeling Automotive structural dynamics and NVH analysis Sustainable materials for civil engineering His work bridges computational methods with real-world applications, such as earth dam monitoring, hydraulic fracturing modeling, and biomechanical simulations. Recent publications emphasize real-time risk analysis, parametric solutions for nonlinear systems, and inverse problem techniques in geophysics. His contributions to numerical methods have advanced high-fidelity, low-cost simulations across engineering disciplines. Publications span over 30 years, with a focus on: Efficient parametric solutions for transient coupled systems Nonintrusive reduced basis methods Thermo-hydro-mechanical modeling of geological media Advances in error estimation and model order reduction underpin much of his work, enabling applications in structural health monitoring, crashworthiness analysis, and geophysical exploration.

Trevor Kent Howardمشاهده پروفایل

استادیار

• Fluid-structure interactions
• Computational multiphysics
• Nuclear reactor thermal hydraulics
• +۳ مورد دیگر

Trevor Kent Howard serves as Assistant Professor in the Department of Nuclear Science and Engineering within Oregon State University's College of Engineering, where he has been affiliated since 2020. His primary expertise centers on fluid-structure interactions and computational multiphysics with emphasis on high performance research reactors. His academic credentials include: Ph.D. in Nuclear Engineering (Minor: Mechanical Engineering) from Oregon State University (2018) M.S. in Nuclear Engineering from Oregon State University (2015) B.S. in Nuclear Engineering from Oregon State University (2012) Howard's research portfolio spans advanced nuclear reactor thermal hydraulics, novel instrumentation and data analysis techniques, verification/validation/uncertainty quantification for multiphysics systems, and AI applications in nuclear design. His work integrates high-performance computing with experimental approaches to enhance reactor safety and efficiency, particularly through innovative modeling of fluid-structure dynamics in extreme thermal environments. His notable recognitions include: OSU College of Engineering Graduate Research Assistant Award (2018) ORNL Mission Support Team Award (2019) ARCS Scholar designation Howard's advising activities and grant funding details were not specified in available materials. Prior to his current position, he contributed to molten salt reactor development, aeroacoustic instrumentation, and thermal hydraulic analyses during his tenure as Thermal Hydraulics Research and Development Associate in Oak Ridge National Laboratory's Energy Systems Development group.

Sourabh Apteمشاهده پروفایل

استاد

• Multiphase Turbulent Flows
• Numerical Simulations
• Predictive Numerical Techniques
• +۴ مورد دیگر

Sourabh Apte is a Professor in the Department of Mechanical, Industrial, and Manufacturing Engineering at Oregon State University (OSU), part of the College of Engineering. He has been affiliated with OSU since 2005. His research focuses on computational fluid dynamics, particularly numerical simulations and modeling of multiphase turbulent flows, with applications in porous media, environmental flows, and spray systems. He leads the Computational Flow Physics Laboratory. Dr. Apte holds a Ph.D. in Mechanical and Nuclear Engineering from Pennsylvania State University (2000), an M.E. from the Indian Institute of Science (1996), and a B.E. from COEP, University of Pune (1994). He has held adjunct roles in OSU’s Water Resources Engineering and Water Resources Science Graduate Programs and served as a Visiting Professor at Aix-Marseille Université (2019). He was an ASEE Summer Faculty Fellow at Wright Patterson Air Force Base (2010–2011) and an ORISE researcher at NETL Albany (2006–2010). His research group develops advanced algorithms for multiphysics turbulent flow simulations using direct and large-eddy simulation techniques. Notable work includes studies on sediment transport and spray systems. He received the Engelbrecht Young Faculty Award from OSU and serves as an Associate Editor for the International Journal of Rotating Machinery.

• Structural Dynamics
• Composite Structure Design
• Experimental Modal Analysis
• +۵ مورد دیگر

Bekir Bediz is an Associate Professor at Sabanci University , Faculty of Engineering and Natural Sciences, with a focus on structural dynamics and multi-scale manufacturing processes. He holds a Ph.D. in Mechanical Engineering from Carnegie Mellon University (2014) and earlier degrees from Middle East Technical University (B.S. 2006, M.S. 2009). Education: Ph.D., Mechanical Engineering, Carnegie Mellon University (2014) M.Sc., Mechanical Engineering, Middle East Technical University (2009) B.S., Mechanical Engineering, Middle East Technical University (2006) Academic Positions: Associate Professor, Sabanci University (2023–present) Assistant Professor, Sabanci University (2016–2023) Instructor, Middle East Technical University (2015–2016) Post-doctoral Researcher, Carnegie Mellon University (2015) Research Interests include high-fidelity dynamic modeling of complex structures, variable stiffness composite design, experimental modal analysis, mechanical micromachining, and vibration energy harvesting systems. His work integrates spectral Chebyshev methods with lamination parameters to optimize composite panels and develop novel manufacturing techniques for micro-devices. Recent Publications highlight advancements in: Active magnetic bearing spindle design for micro-milling Manufacturable variable stiffness composite laminates Particle damper integration in additive manufacturing Microneedle array fabrication for localized drug delivery Nonlinear vibration analysis of rotating functionally graded beams Scientific Awards include multiple TUBITAK grants for projects on: Multi-objective optimization of stiffened composite panels (TUBITAK 1001, 2023–2026) Ultra-high-speed spindle design (TUBITAK 1002, 2023–2024) Variable stiffness composite manufacturing (TUBITAK 1005, 2020–2022) Research Group includes Ph.D. and M.Sc. students working on composite optimization, microneedle design, and spindle dynamics. His projects often involve collaborations with institutions like Koc University, Istanbul Technical University, and Hacettepe University.

Shun Uchidaمشاهده پروفایل

دانشیار

• Geotechnical Numerical Analysis
• Energy Geotechnics
• Computational Geomechanics
• +۵ مورد دیگر

Shun Uchida is an Associate Professor in the Department of Civil and Environmental Engineering at Rensselaer Polytechnic Institute (RPI), joining the Geotechnical Group in 2014. He specializes in multiphysics problems at the intersection of Energy Geotechnics , Computational Geomechanics , and Environmental Geotechnics . Education: BSc in Civil Engineering, Waseda University (2006) MPhil and PhD in Geotechnical Engineering, University of Cambridge (2008, 2013) Postdoctoral Research, Technion – Israel Institute of Technology His research focuses on coupled thermo-hydro-mechanical (THM) processes in geomaterials, particularly methane hydrate-bearing sediments , seismic-induced submarine landslides , and contaminant barrier systems . His work integrates advanced numerical modeling with experimental validation. Key trends in his publications include gas hydrate extraction optimization , sand migration dynamics , and AI-driven geotechnical analysis . Recent studies emphasize CO2 injection for methane recovery and environmental impacts of hydrate dissociation.

• Nuclear Engineering
• Nuclear Data Measurement
• Thermal Neutron Scattering
• +۳ مورد دیگر

Dominik Fritz is an Assistant Professor in the Department of Mechanical, Aerospace and Nuclear Engineering at Rensselaer Polytechnic Institute (RPI). He holds a B.S. (2018) and Ph.D. (2022) in Nuclear Engineering from RPI, with doctoral research focused on neutron moderator system design and thermal neutron cross-section measurements. After completing his Ph.D., he worked as a Senior Nuclear Engineer at NorthStar Medical Radioisotopes before returning to RPI's faculty in Fall 2024. His research centers on the Gaerttner LINAC facility, a 60 MeV electron linear accelerator, with key focus areas including nuclear data validation, thermal neutron scattering analysis, high-energy photon interactions, medical radioisotope production, and high-heat-flux target design. Student opportunities involve equipment design for radiation environments, experimental execution, multiphysics radiation modeling, and nuclear data validation. Current positions are available for students interested in his research group focused on advanced radiation systems and nuclear applications.

Shankar Narayanمشاهده پروفایل

دانشیار

• Thermal Transport
• Interfacial Science
• Phase-Change Phenomena
• +۴ مورد دیگر

Dr. Shankar Narayan is an Associate Professor in the Department of Mechanical, Aerospace, and Nuclear Engineering at Rensselaer Polytechnic Institute (RPI). He holds a Ph.D. in Mechanical Engineering from Georgia Institute of Technology and conducted postdoctoral research at MIT. His research focuses on interfacial science, thermal transport, phase-change phenomena, and the design of nanoengineered surfaces for solar-thermal energy conversion, thermal energy storage, and electronics cooling. Dr. Narayan has authored over 75 peer-reviewed publications and holds multiple U.S. patents in advanced thermal systems. Education: Ph.D. in Mechanical Engineering, Georgia Institute of Technology Postdoctoral Research, Massachusetts Institute of Technology Research interests include: Thermal energy systems and phase-change materials Material design for solar-thermal applications Advanced cooling solutions for electronics Environmental sustainability through fog harvesting and smart materials His recent publications emphasize: Photonic crystal thermal radiation Optimization of microchannel evaporators Nanofiber-based thermal control in extreme environments Awards: ASME Fellow NSF CAREER Award (2020) NASA Early Career Award (2021) Class of 1951 Teaching Award (2020) Led the Energy Systems Lab ( energysystemslab.com ), focusing on sustainable thermal solutions for aerospace and terrestrial applications.

• Hypersonic Flows
• Computational Physics
• Rarefied Gas Dynamics
• +۴ مورد دیگر

Dr. Ozgur Tumuklu is an Assistant Professor in the Department of Mechanical, Aerospace and Nuclear Engineering at Rensselaer Polytechnic Institute (RPI). His research focuses on computational physics, rarefied non-equilibrium flows, and stability analysis of hypersonic reacting flows. He holds a Ph.D. in Aerospace Engineering from the University of Illinois at Urbana-Champaign (2018), and prior roles include postdoctoral research with NASA's Artemis project at the Jet Propulsion Laboratory and software development at the University of Arizona. Education : Ph.D., Aerospace Engineering, University of Illinois at Urbana-Champaign (2018) M.S., Aerospace Engineering, Middle East Technical University (2013) B.S., Aerospace Engineering & Physics, Middle East Technical University (2010/2009) Research : Dr. Tumuklu's work integrates high-performance computing, open-source software development, and data-driven techniques to study hypersonic flows across continuum-to-rarefied regimes. Key areas include shock-boundary layer interactions, nonequilibrium aero-optics, and adaptive combustion control in ramjets. His lab (Havalab) develops tools like SU2-NEMO for multiphysics simulations. Publications : His recent work emphasizes hypersonic instability dynamics, magnetohydrodynamic effects, and hybrid particle-continuum modeling. Themes include predictive signature analysis, wake turbulence, and flow control strategies in extreme regimes. Advising & Grants : While specific grants are not listed, his research aligns with NASA-funded initiatives and hypersonic propulsion projects. He mentors students in advanced CFD and plasma-kinetic simulations. Labs/Teams : Director of Havalab, a research group advancing computational tools for hypersonic aerothermodynamics and rarefied flows.

Gregor Kovačičمشاهده پروفایل

استاد

• Mathematical Neuroscience
• Wave Dynamics
• Integrable Systems
• +۴ مورد دیگر

Gregor Kovačič serves as Professor and Undergraduate Program Director in the Department of Mathematical Sciences at Rensselaer Polytechnic Institute (RPI), holding a Ph.D. in Applied Mathematics from Caltech and prior postdoctoral experience at Los Alamos National Laboratory. His academic journey began with dual bachelor's degrees in Physics and Mathematics from the University of Ljubljana, Slovenia. His educational background includes: Ph.D. in Applied Mathematics, California Institute of Technology, 1990 Bachelor's degrees in Physics and Mathematics, University of Ljubljana, Slovenia Kovačič's research integrates mathematical theory with physical applications across neuroscience and wave phenomena. Primary interests include mathematical neuroscience for modeling neuronal networks, wave dynamics in dispersive systems, integrable partial differential equations, and nonlinear optics in metamaterials. Recent work examines light propagation through exotic refractive media and neural mechanisms underlying insect olfaction. Analysis of his 2020-2025 publications reveals dominant themes in quantum optics (Maxwell-Bloch systems), computational wave dynamics (high-order numerical schemes), and cortical neuroscience (pyramidal cell coupling). His work consistently bridges abstract mathematical frameworks with experimental physics and biological applications, particularly in nonlinear resonant systems and wave turbulence phenomena. Major recognitions include: Prešeren's Student Prize (Slovenia) Director's Funded Postdoctoral Fellowship (Los Alamos) NSF Career Award Sloan Research Fellowship As Undergraduate Program Director, Kovačič oversees curriculum development while maintaining active research leadership. His NSF Career Award and Sloan Fellowship indicate substantial grant funding, though specific projects aren't detailed. The absence of listed advisees suggests primary focus on undergraduate mentorship rather than graduate supervision. Kovačič collaborates through RPI's Center for Modeling, Optimization and Computational Analysis (MOCA), advancing interdisciplinary work in computational mathematics with applications spanning optical physics and neural modeling.