جست‌وجوی استاد و پژوهشگر

• Computational Fluid Dynamics
• Lattice Boltzmann Method
• Turbulence Modeling
• +۲ مورد دیگر

Githin Zachariah is a Research Fellow at the SSPC (Science Foundation Ireland Research Centre for Pharmaceuticals) and the School of Engineering at the University of Limerick. His work focuses on advanced computational fluid dynamics research with applications in pharmaceutical manufacturing processes. His primary research interests include Computational Fluid Dynamics , Lattice Boltzmann Method , and Turbulence Modeling , with specialized expertise in Porous Media Flow and Multicomponent Flow systems. He develops high-fidelity simulation techniques for homogeneous isotropic turbulence and drying phenomena in capillary structures, addressing critical challenges in fluid-particle interactions and scale modeling. Analysis of his 2019-2023 publications reveals consistent methodological innovation in lattice Boltzmann simulations, particularly for turbulence representation and drying processes. His work bridges micro-scale fluid dynamics with macro-scale engineering applications, showing strong impact through citation metrics (up to 44 citations) and reader engagement across fluid dynamics and pharmaceutical engineering domains. Dr. Zachariah maintains active collaborations within the University of Limerick's engineering community and the SSPC research network. His current work involves developing novel simulation frameworks for pharmaceutical drying processes and turbulence modeling, with potential applications in industrial-scale manufacturing optimization.

Dawn C Marshallمشاهده پروفایل

پژوهشگر

• Driving Safety
• Human Factors
• Vehicle Safety Systems
• +۳ مورد دیگر

Dawn Marshall is a Researcher and Director of the SAFER-SIM University Transportation Center at the University of Iowa's College of Engineering, serving as Research Manager for the Driving Safety Research Institute (DSRI) since 2004. With over 25 years of experience in driving safety research, she specializes in human factors analysis of in-vehicle safety systems and driving simulation methodologies. Education: M.S., Industrial Engineering, University of Iowa, 2004 B.S., Industrial Engineering, University of Iowa, 2000 B.A., Finance, University of Iowa, 1988 Her research spans driving simulation, transportation safety, and in-vehicle warning systems, with significant contributions to evaluating adaptive cruise control, lane departure warning, forward collision warning, and intersection violation systems. Current work focuses on auditory and haptic alert characteristics, driver behavior analysis for older adults and teens, and connected vehicle safety applications. Her expertise in simulator validation and multi-site study design has established her as a leader in transportation human factors research. Marshall's 15 most recent publications (2009-2019) reveal consistent focus on driver response to safety-critical alerts, with emerging emphasis on connected/automated vehicles and demographic-specific system effectiveness. Key trends include rigorous evaluation of warning timing/modalities, intersection safety protocols, and distraction mitigation strategies across diverse driver populations through high-fidelity simulation. Scientific Awards: No specific awards documented in source materials As Principal Investigator for NHTSA, Iowa DOT, Rockwell Collins, and Hyundai Motor Group projects, Marshall has secured substantial funding for transportation safety research. Her leadership of SAFER-SIM UTC coordinates multi-institutional efforts to advance simulation-based safety evaluation, with documented impact on vehicle system design standards. Marshall operates within the National Advanced Driving Simulator (NADS) facility at the University of Iowa, collaborating through Daniel V McGehee's Lab and the Driving Safety Research Institute. Her team integrates human factors expertise with engineering analysis to address real-world driving challenges through controlled simulation environments.

Gregory Chiniمشاهده پروفایل

استاد

• Fluid Dynamics
• Applied Mathematics
• Geophysical Fluid Dynamics
• +۱۲ مورد دیگر

Gregory Chini is a Professor in the Department of Mechanical Engineering at the University of New Hampshire (UNH), where he has been a faculty member since 1999. He also serves as the Director of the Integrated Applied Mathematics (IAM) Ph.D. program and holds affiliations with the College of Engineering and Physical Sciences. He has been a visiting researcher at the California Institute of Technology and the University of Nottingham, and is a regular participant in the Woods Hole Summer Program in Geophysical Fluid Dynamics. Ph.D., Aerospace and Aeronautical Engineering, Cornell University M.S., Aerospace and Aeronautical Engineering, Cornell University B.S., Aerospace and Aeronautical Engineering, University of Virginia Prof. Chini's research lies at the intersection of fluid dynamics and applied mathematics, with a focus on modeling geophysical, environmental, biological, and industrial flows. He investigates the stability and dynamics of coherent structures such as vortices, waves, and boundary layers using asymptotic, variational, and spectral methods. His work emphasizes reduced-order modeling to understand complex systems like turbulent convection and porous media flows. The recent publications highlight a strong trend in multiscale modeling, turbulent transport, and mathematical analysis of fluid systems. His articles frequently address Rayleigh-Bénard convection, stratified turbulence, boundary layer dynamics, and optimal transport, often employing quasilinear and asymptotic frameworks to extract physical insights. The research spans from theoretical analysis to computational modeling, with applications in oceanography, geophysics, and soft matter. He has been awarded multiple research grants from the National Science Foundation (NSF) and the U.S. Department of Defense (Navy), supporting projects on high Reynolds number turbulence, wall-bounded flows, and multiscale oceanic modeling. These grants reflect sustained funding and leadership in fundamental fluid mechanics research. National Science Foundation (NSF): Development of Asymptotically-Reduced Multi-Scale Models (2014–2019) National Science Foundation (NSF): Multiscale Modeling of Oceanic Mixed Layer (2009–2015) U.S. DOD, Navy: Predicting Non-Equilibrium Wall-Flow Phenomena (2017–2023) Mentis Sciences Inc: Cooling System for Laser Enclosure (2018) Prof. Chini teaches core courses such as Fluid Dynamics (ME 608), Thermodynamics (ME 503), Viscous Flow (ME 909), and Asymptotic Methods (IAM 940), and supervises doctoral research in applied mathematics and mechanical engineering. He advises Ph.D. students and collaborates widely, particularly with researchers like Christopher White. His lab and research group focus on theoretical and computational fluid dynamics, with an emphasis on model reduction and predictive simulation of complex flows.

Regis Duvigneauمشاهده پروفایل

پژوهشگر

• Scientific Computing
• Numerical Methods
• Isogeometric Analysis
• +۱۰ مورد دیگر

Regis Duvigneau is a senior researcher ( Directeur de Recherche ) at Acumes project-team within Inria Centre at Université Côte d'Azur, and affiliated with the Jean-Alexandre Dieudonné laboratory. His work bridges scientific computing, fluid mechanics, and optimization. Academic Background : INRIA researcher (2016-present), Opale team (2005-2015), post-doctoral fellow at Polytechnic Montreal (2003-2004), PhD at Centrale Nantes (2000-2002) Research focuses on numerical methods for PDE systems , particularly in fluid mechanics. Key areas: isogeometric analysis, machine learning algorithms, sensitivity analysis, Bayesian optimization, and uncertainty quantification. His recent publications demonstrate expertise in: Isogeometric Discontinuous Galerkin schemes for compressible flows Physics-Informed Neural Networks for multiphysics problems Statistical learning for metasurface optimization Shape sensitivity analysis with high-order accuracy Multi-objective optimization frameworks Collaborative projects span aerodynamics, nanophotonics, and marine engineering, including ANR contracts FREEFORM (2025-2029) and FP7 initiatives. Current advisee Nathan Ricard works on Physics-Informed Neural Networks, while past students include experts in isogeometric analysis, Bayesian optimization, and sensitivity analysis (2014-2024).

• Maternal Obesity
• Risk Perception in Childbirth
• Midwifery Education
• +۴ مورد دیگر

Gail Norris is an Associate Professor at Edinburgh Napier University's School of Health and Social Care , where she has worked since 2003. She previously served as Lead Midwife for Education (2014-2020) and currently holds the position of Head of International. Gail is a registered midwife with over a decade of clinical experience and leads the Maternal and Child Health and Wellbeing Research Group . Her expertise spans both research and education domains. Research Interests: Maternal obesity and risk perception, respectful midwifery care, IPA methodology International Work: Adjunct Associate Professor at Edith Cowan University, Australia; Honorary Consultant Midwife for NHS Lothian Scientific Awards: Advanced HE Women's Senior Leadership Programme Above & Beyond Award - Innovation 2022 Shortlisted for RCM Awards 2018 Senior Fellow & Fellow of Higher Education Academy

• Nuclear Reactor Safety
• Reactor Dynamics
• Nuclear Accident Analysis
• +۱۱ مورد دیگر

Dr. Sören Kliem is the Head of the Reactor Safety Department at the Institute of Resource Ecology, Helmholtz-Zentrum Dresden-Rossendorf (HZDR), a leading German research center in nuclear and environmental sciences. He holds a PhD from the Technical University Dresden and has been a key figure in reactor safety research since joining Forschungszentrum Rossendorf in 1994. He leads a department focused on advanced nuclear safety analysis, code development, and experimental validation. Education: PhD, Technical University Dresden, Faculty of Machinery Building, 2010 Diploma, Moscow Power Engineering Institute (MEI), 1992 His research interests include reactor dynamics, nuclear accident analysis, thermal-hydraulics, neutron kinetics, coolant mixing in PWRs, and the development and validation of coupled simulation codes such as DYN3D and ATHLET. He is actively involved in EU-funded projects like Horizon 2020 R2CA, McSAFER, and EURATOM EASI-SMR, focusing on the safety of conventional and innovative reactor designs, particularly small modular reactors (SMRs). The 15 most recent publications reflect a strong focus on reactor safety modeling, including boron dilution transients, severe accident management, code validation using experimental data (e.g., from ROCOM), and the application of high-fidelity simulation tools (CFD, coupled codes) to nuclear safety problems. His work emphasizes both numerical simulation and experimental benchmarking, particularly in the context of PWRs and VVER reactors. Scientific Projects: Horizon 2020 R2CA: Reduction of radiological consequences of design basis and design extension accidents (2019–2023) Horizon 2020 McSAFER: High-Performance Advanced Methods and Experimental Investigations for the Safety Evaluation of Generic Small Modular Reactors (2020–2023) Horizon EURATOM EASI-SMR: Ensuring Assessment of Safety Innovations for SMR (2024–2028) Kliem plays a central role in advising and leading collaborative research efforts, supervising junior researchers, and contributing to international benchmarking initiatives. He has no listed formal students, but his leadership implies significant mentoring. He is involved in multiple research facilities, including the ROCOM and TOPFLOW test facilities, which are critical for experimental validation of safety codes. Laboratories and Facilities: ROCOM (Rossendorf Coolant Mixing) test facility – for studying coolant mixing and boron dilution TOPFLOW facility – for two-phase flow and safety experiments DYN3D/ATHLET code development and validation environment

• Reactor physics
• Nuclear engineering
• Advanced nuclear fuel cycles
• +۲ مورد دیگر

Emil Fridman is a Senior Scientist in the Reactor Safety Division at the Institute of Resource Ecology, Helmholtz-Zentrum Dresden-Rossendorf (HZDR). He has been with HZDR since 2008, progressing from Research Assistant to his current position since 2015, and previously served as Head of the Junior Research Group Reactor Physics from 2012-2014. Dr. Fridman earned his Ph.D. in Nuclear Engineering from Ben-Gurion University between 2004-2008. His academic background forms the foundation for his expertise in nuclear reactor systems and safety analysis. His research focuses on reactor physics and engineering, advanced nuclear fuel cycles, safety analysis of nuclear reactors, and nuclear waste management. Dr. Fridman has led and participated in numerous international projects including Horizon Europe ESFR-SIMPLE (2022-2026), Horizon 2020 McSAFER (2020-2023), and several IAEA Coordinated Research Projects on sodium fast reactor safety and benchmarking. His work bridges theoretical modeling with practical safety applications for next-generation nuclear systems. Analysis of Dr. Fridman's recent publications (2022-2025) reveals a strong emphasis on sodium-cooled fast reactors (SFRs), with particular focus on safety analysis, neutronics modeling, and code development/validation. His research frequently involves international collaborations across Europe and addresses critical challenges in reactor transient analysis, core deformation scenarios, and benchmark validation for next-generation nuclear systems. The consistent publication record demonstrates his leadership in advancing computational methods for nuclear safety analysis. Dr. Fridman has secured significant research funding through major international projects including Horizon Europe ESFR-SIMPLE, Horizon 2020 McSAFER, and multiple IAEA Coordinated Research Projects. His leadership in the Reactor Safety Division at HZDR involves mentoring junior researchers and coordinating international collaborations across European nuclear research institutions. As a key member of the Reactor Safety Division at HZDR, Dr. Fridman contributes to the development and validation of the DYN3D/ATHLET code system for reactor safety analysis. His work is integrated within HZDR's broader mission in resource ecology and nuclear safety research, collaborating with teams across the Institute of Resource Ecology and international partners in the European nuclear research community.

• Nursing Ethics
• Healthcare Education
• Simulation-Based Learning
• +۳ مورد دیگر

Anders Sterner is affiliated with the University of Borås as part of the Faculty of Caring Science, Work Life and Social Welfare, specifically within the Department of Work Life and Social Welfare. His research focuses on nursing ethics, simulation-based education, and interprofessional collaboration in healthcare. He serves as an Assistant Supervisor for doctoral students in ambulance specialist nursing programs. His recent work includes studies on enhancing ethical reasoning through high-fidelity simulations in nursing education and exploring collaborative practices between care managers and physicians in integrated care systems. Sterner contributes to advancements in healthcare education methodologies and clinical ethics, with a particular emphasis on practical applications in ambulance care and community health settings. His affiliations include the PreHospen research group at the University of Borås. While no specific grants or awards are explicitly mentioned, his active publication record reflects ongoing engagement in impactful healthcare research.

Dimitris Drikakisمشاهده پروفایل

استاد

• Fluid Dynamics
• Computational Science
• Acoustics
• +۳ مورد دیگر

Professor Dimitris Drikakis serves as Vice President for Global Partnerships at the University of Nicosia, Cyprus, and holds a joint professorship in the School of Sciences and Engineering. His academic career spans over 24 years, including roles as Executive Dean and Head of Department at UK universities. His research focuses on fluid dynamics, acoustics, computational science, and nanotechnology, emphasizing uncertainty reduction and physics-based modeling. He has co-authored two books, published over 400 papers, and graduated 45 PhD students globally. Research interests include super-resolution fluid flow analysis, machine learning applications in turbulence simulation, and computational methods for aerodynamics and heat transfer. His work on indoor aerosol transmission and epidemiological modeling has addressed pandemic-related challenges. He serves on editorial boards for journals like Computers and Fluids and Physics of Fluids . Recent articles highlight advancements in deep learning for fluid-structure interaction, shock-wave predictions, and AI-driven thermal optimization. His contributions bridge computational science with real-world engineering and public health applications.

Olivier Desjardinsمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Multiphase Flows
• Turbulence
• +۷ مورد دیگر

Olivier Desjardins is a Professor in the Sibley School of Mechanical and Aerospace Engineering at Cornell University, where he has been a faculty member since July 2011. He previously served on the Mechanical Engineering faculty at the University of Colorado at Boulder. His research is centered on high-fidelity computational modeling of turbulent, reacting, and multiphase flows with applications in energy, propulsion, and combustion systems. Ph.D., Mechanical Engineering, Stanford University, 2008 M.Sc., Aeronautics & Astronautics, SUPAERO (ENSAE), Toulouse, 2003 M.Sc., Mechanical Engineering, Stanford University, 2003 Dr. Desjardins’ research focuses on developing and applying advanced numerical methods such as large-eddy simulation (LES) and direct numerical simulation (DNS) to study complex fluid dynamics involving liquid-gas interfaces, atomization, and interfacial instabilities. His work spans fundamental fluid mechanics and practical applications in combustors and biomass reactors. Key areas include turbulence modeling, surface tension effects, and multiscale simulations of spray formation and breakup. His recent publications (2021–2025) demonstrate a strong emphasis on improving the accuracy and efficiency of volume-of-fluid (VOF) methods, including interface reconstruction, subgrid-scale modeling, and machine learning-enhanced simulations. There is a clear trend toward integrating physics-informed models, adjoint-based control, and rigorous experimental validation, especially in air-blast atomization and droplet dynamics. His group also contributes to open-source frameworks like OpenFOAM. NSF CAREER Award (2014) Junior Award, International Conference on Multiphase Flow (2016) Distinguished Paper Award, 33rd International Symposium on Combustion (2010) Research Excellence Award, Cornell College of Engineering (2020) Robert '55 and Vanne '57 Cowie Teaching Award, Cornell (2016) Outstanding Graduate Education Award, University of Colorado (2008) Dr. Desjardins has led multiple federally funded research projects, including those supported by the National Science Foundation. His work often involves collaboration with experimentalists and validation against physical data, including radiography and shadowgraph imaging. He has advised graduate students in mechanical engineering and computational science, contributing to advancements in multiphase flow modeling. His research group develops open, reproducible methodologies for simulating complex interfacial flows. His lab focuses on computational modeling of multiphase systems, particularly through the development of high-fidelity simulation frameworks. The team works on algorithm development for interface tracking, turbulence modeling, and multiscale coupling, with applications in energy systems and aerospace engineering. Projects include microgravity droplet dynamics (ISS-related), spray control, and catalytic biomass conversion.

Jian-Xun Wangمشاهده پروفایل

دانشیار

• Scientific Machine Learning
• Bayesian Data Assimilation
• Differentiable Programming
• +۸ مورد دیگر

Dr. Jian-Xun Wang is an Associate Professor at the Sibley School of Mechanical and Aerospace Engineering at Cornell University (starting 2025). Previously, he held positions at the University of Notre Dame as Robert W. Huether Collegiate Associate Professor. He earned his Ph.D. in Aerospace Engineering from Virginia Tech in 2017, followed by postdoctoral training at UC Berkeley. His research focuses on computational mechanics and scientific AI, integrating advanced machine learning with physics-based models. Key areas include Scientific Machine Learning, Bayesian Data Assimilation, and Uncertainty Quantification, with applications in aerodynamics, biomedical engineering, and advanced manufacturing. He directs the Computational Mechanics & Scientific AI Lab (CoMSAIL), funded by NSF, NIH, and others. Education: M.S. (Ocean Engineering, Virginia Tech, 2016), Ph.D. (Aerospace Engineering, Virginia Tech, 2017), Postdoc (Bioengineering, UC Berkeley, 2018) Research Lab: CoMSAIL (Cornell) Key Awards: ONR YIP (2023), NSF CAREER (2021) Teaching interests span Computational Fluid Dynamics, Numerical Methods, and Bayesian Learning. His work bridges AI and computational physics to address complex multiscale problems in fluid dynamics, solid mechanics, and biomedical systems.

Phaedon-Stelios Koutsourelakisمشاهده پروفایل

استاد

• Uncertainty Quantification
• Multiscale Modeling
• Computational Mechanics
• +۳ مورد دیگر

Prof. Phaedon-Stelios Koutsourelakis is a Professor of Continuum Mechanics at the Technical University of Munich (TUM), affiliated with the TUM School of Engineering and Design. His research focuses on computational strategies for modeling and analyzing physical systems with a strong emphasis on uncertainty quantification, multiscale phenomena, and stochastic systems. He holds a PhD from Princeton University and has held academic positions at institutions including Cornell University, Heriot-Watt University, and the Lawrence Livermore National Laboratory. His educational background includes a degree from the National Technical University of Athens and a PhD from Princeton University (1998). Key research areas include data-driven coarse-graining techniques, Bayesian inference in engineering systems, and physics-aware machine learning models for high-dimensional problems. He has received notable awards such as the Top Teaching Trophy from the Munich School of Engineering (2014–2016) and the Dean’s First Year Merit Prize at Princeton (1998). His work bridges computational mechanics with statistical methods, addressing challenges in materials design, structural health monitoring, and predictive modeling under uncertainty. Recent projects include developing physics-constrained deep learning frameworks for surrogate modeling and quantifying model bias in digital twins of infrastructure systems. Notable contributions include the Predictive Coarse-Graining framework and probabilistic methods for uncertainty quantification in heterogeneous media. His research also extends to optimization under uncertainty and interdisciplinary applications in biomedical engineering and materials science.

Christian Stemmerمشاهده پروفایل

استاد

• Hypersonic Aerodynamics
• Fluid Mechanics
• Thermal and Chemical Nonequilibrium Flows
• +۳ مورد دیگر

Prof. Christian Stemmer is a Professor of High-Speed Aerodynamics at the Technical University of Munich (TUM), affiliated with the TUM School of Engineering and Design and the Department of Aerodynamics and Fluid Mechanics. His research focuses on hypersonic flows, boundary layer transition, thermal and chemical nonequilibrium phenomena, and research data management. Stemmer holds a Dr.-Ing. habil. and has extensive international experience, including postdoctoral work at Stanford University and NASA Ames. Career highlights: Appointment as extraordinary professor in 2019, leadership roles in the Collaborative Research Center TRR40 (SFB/TRR40), and membership in editorial boards such as Advances in Aerodynamics . Awards: 2020 NATO AVT Panel Excellence Award for contributions to hypersonic flow research. Key projects: Investigation of hypersonic boundary layer transition under high-enthalpy conditions, development of numerical models for rocket combustion chambers, and leadership in national and international research consortia. His work bridges fundamental fluid mechanics with aerospace engineering applications, emphasizing high-performance computing and data-driven methodologies. Recent contributions include studies on roughness-induced instabilities, shock-wave interactions, and metadata extraction frameworks for HPC workflows.

Samuel Woodnuttمشاهده پروفایل

دانشیار

• Patient Safety
• Staff Safety
• Organizational Systems
• +۵ مورد دیگر

Samuel Woodnutt serves as a Principal Teaching Fellow (academic rank equivalent to Associate Professor) and Director of Programmes for Nursing and Midwifery at the University of Southampton's School of Health Sciences. A registered mental health nurse and qualified teacher with over a decade of clinical experience, he bridges acute psychiatric practice with academic leadership. His educational trajectory includes a BA in English Literature (Bath, 2005), PGCE in Secondary Education (Southampton, 2008), PGDip in Mental Health Nursing (2012), and MSc with distinction (2017). Currently pursuing a PhD under Professor Peter Griffiths and Dr. Chiara Dall'ora, his scholarly foundation integrates clinical practice with rigorous research methodology. Woodnutt's research centers on quantitative analysis of patient/staff safety systems, epidemiological modeling of staffing configurations, and AI applications in mental healthcare. His work employs advanced statistical techniques in R programming to transform incident reporting data into actionable safety insights, with growing focus on large language models for clinical decision support. This dual expertise in mental health systems and computational methods drives innovation in care delivery. His 2023-2025 publications reveal three converging trends: 1) Systematic analyses of staffing-safety relationships in acute mental health settings, 2) Methodological innovations in epidemiological approaches to incident data, and 3) Pioneering investigations into AI's role in nursing practice and education. This trajectory demonstrates increasing sophistication in data-driven safety science. Key awards include: Dean's Award for Citizenship (2023) RCN Strategic Scholarship Alliance Award (2022) Woodnutt actively supervises undergraduate and postgraduate students in systematic reviews while contributing to national mental health nursing standards through NHS England, Health Education England, and Queen's Nursing Institute steering groups. His external review work spans eight journals including Journal of Advanced Nursing and Human-Computer Interaction, reflecting interdisciplinary impact. Clinical experience in acute psychiatry, crisis intervention, and therapeutic care informs his high-fidelity simulation teaching and research design. Leading the 'StaffedWards' research project and directing nursing/midwifery programs, he operates at the nexus of clinical practice, educational leadership, and national policy development within the UK mental health ecosystem.

• Wave Physics
• Computational Seismology
• Earthquake Seismology
• +۴ مورد دیگر

Dr. Maria Koroni is a senior researcher at the Swiss Seismological Service (SED) within ETH Zürich's Department of Earth Sciences. She specializes in computational seismology, focusing on physics-based numerical modeling of ground motion and hybrid methodologies combining deterministic and stochastic approaches. Her work directly supports seismic hazard assessment for Swiss nuclear facilities through ENSI-funded projects. Education: PhD in Computational Seismology (2018), Utrecht University MSc in Geophysics (2014), Utrecht University BSc in Earth Science (2011), Aristotle University of Thessaloniki Research Interests: Wave physics, earthquake seismology, full-waveform inversion, and high-performance computing. Her current projects involve modular numerical meshes for high-fidelity seismic simulations using 3D velocity models and integrating machine learning into seismic waveform modeling. Publications reflect a strong focus on: Core-mantle boundary studies using adjoint methods Alpine valley ground motion modeling Gender representation in seismology authorship 3D basin structure analysis in Swiss geological contexts Grants include ERC Horizon 2020 funding (2018-2021) and ongoing ENSI support. She teaches Engineering Seismology at ETH Zürich and collaborates with the Institute of Geophysics on advanced seismic instrumentation projects.