جست‌وجوی استاد و پژوهشگر

• Fluid Dynamics
• Turbulence
• Heat Transfer
• +۶ مورد دیگر

Andrea Ianiro is a Full Professor in the Aerospace Engineering Department at Universidad Carlos III de Madrid (UC3M), where he leads research in fluid dynamics, turbulence, and heat transfer. His work bridges experimental techniques and machine learning applications for flow analysis and control. He serves as Associate Editor of the International Journal of Heat and Mass Transfer (2025-2028) and directs the EFM Lab (Experimental Fluid Mechanics Laboratory) at UC3M. Professor Ianiro's research focuses on turbulence characterization, boundary layer flows, and the application of machine learning to fluid mechanics problems. His work spans experimental techniques including Particle Image Velocimetry (PIV), infrared thermography, and advanced data processing methods. Recent research emphasizes data-driven approaches for flow field reconstruction, turbulence control, and heat transfer optimization in wall-bounded flows. His projects often combine theoretical, experimental, and computational approaches to address complex fluid mechanics challenges. The analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional fluid mechanics. His work increasingly focuses on using deep learning techniques (particularly CNNs and GANs) for flow field prediction from limited measurements, developing meshless computational methods for flow analysis, and applying optimization techniques (including genetic algorithms) to heat transfer enhancement. His research maintains a strong experimental foundation while embracing data-driven approaches to tackle turbulence modeling challenges. Associate Editor of the International Journal of Heat and Mass Transfer (2025-2028) Professor Ianiro leads multiple significant research projects including SPANDRELS (SParse AND paRsimonious Event-based fLow Sensing, 2025-2030), HumanIC (Human-Centric Indoor Climate for Healthcare Facilities, 2024-2027), and EXCALIBUR (Extraction of machine learning strategies for turbulent flow control, 2023-2026). His work has attracted funding from the European Commission, Spanish National Research Agency, and industry partners including Airbus. He has supervised numerous theses on topics including AI-based sensing of turbulent flows, convective heat transfer control, and turbulent boundary layers. At UC3M, Professor Ianiro directs the Experimental Fluid Mechanics Laboratory (EFM Lab), which focuses on advanced measurement techniques for fluid flow and heat transfer characterization. The lab specializes in PIV/PTV techniques, infrared thermography, and the development of novel experimental approaches for turbulence research. Current research directions include machine learning applications for flow field reconstruction, plasma-based flow control, and heat transfer optimization in complex flow configurations.

Fernando Camelliمشاهده پروفایل

دانشیار

• Computational Fluid Dynamics (CFD)
• Urban Environmental Modeling
• Contamination Dispersion Modeling
• +۳ مورد دیگر

Fernando Camelli is an Associate Professor in the Physics & Astronomy Department at George Mason University, holding dual roles as Instructional Faculty and Faculty. His research focuses on computational fluid dynamics (CFD), urban environmental modeling, and high-performance computing. He specializes in simulating complex fluid flows in urban environments, subway systems, and industrial applications, with particular emphasis on turbulence modeling, fluid-structure interaction, and GPU-accelerated algorithms. Key research areas include: CFD for urban airflow and contamination dispersion Meshless and immersed boundary methods Integration of geographic information systems (GIS) with CFD Large-scale simulations using parallel computing His work addresses practical challenges such as subway ventilation optimization, emergency contaminant dispersion prediction, and urban infrastructure design. Recent studies emphasize scalability improvements for fluid-structure interaction simulations and GPU-based code modernization.

• Fluid Dynamics
• Biomedical Fluid Mechanics
• Microfluidics
• +۳ مورد دیگر

Dario De Marinis is an Assistant Professor at the Department of Mechanics, Mathematics & Management, Politecnico di Bari, Italy. His research focuses on fluid dynamics with applications in biomedical engineering, aerospace, and computational physics. Research Interests Fluid-structure interaction modeling Microfluidics and particle transport Biomedical applications (blood flow, valve mechanics) Aerospace engineering (hypersonic flows, turbulence) Numerical methods (Lattice Boltzmann, immersed boundary) Publications Trend Dario's recent work (2015–2025) spans computational fluid dynamics, with emphasis on multiphase flows, viscoelastic material behavior, and biomedical microfluidic devices. He has contributed to aerospace applications and turbulent thermal flows.

• Hydraulic Energy
• Hydroelectric Power
• CFD Simulation
• +۶ مورد دیگر

Cécile Münch-Alligné is a Professor in Hydraulic Energy at the University of Applied Sciences and Arts Western Switzerland (HES-SO) in Sion, where she serves as the Head of the Hydroelectricity Research Group and the Renewable Energy Program. She leads the Hydro Alps Lab, which conducts applied research in hydropower combining experimental and numerical approaches. Her work focuses on enhancing the flexibility of both small and large hydropower plants, with particular emphasis on adapting these systems to the evolving energy landscape and integration of renewable energy sources. Her educational background includes a BSc in Energy and Environmental Techniques, an MSc in Engineering, and a BSc in Industrial Systems, all from HES-SO Valais-Wallis. Her research spans multiple domains within hydraulic engineering and renewable energy systems, with particular expertise in CFD simulation, numerical methods, and hydraulic machine design. Münch-Alligné's research interests primarily center around improving hydropower flexibility through innovative approaches such as hydraulic short-circuit operating modes, variable speed operation, and energy recovery systems in water networks. She investigates both large-scale pumped storage power plants and micro-hydropower systems for urban water networks, with a strong focus on practical implementation and commercialization of research findings. Her work bridges theoretical modeling with experimental validation to address real-world challenges in the energy transition. Her research has been published extensively in leading journals, covering topics from Pelton turbine dynamics and Francis turbine vortex analysis to micro-turbine implementations in drinking water networks. The publications reveal a clear trend toward enhancing operational flexibility of hydropower systems to better integrate with intermittent renewable energy sources, with increasing emphasis on practical demonstration projects and commercial applications. As Principal Investigator, she has led multiple significant research projects including the SCCER 4 WP 3.2.0 2017-2020 (Supply of Electricity), Hydrolienne pour canaux artificiels Centrale de Lavey, and SOLUTION DE TRANSFERT D'ENERGIE PAR POMPAGE-TURBINAGE A PETITE ECHELLE. These projects, totaling over 2 million CHF in funding from sources including CTI, OFEN, and industrial partners, demonstrate her ability to secure substantial research funding and collaborate effectively with both academic and industry partners. Münch-Alligné leads the Hydro Alps Lab research team, which includes numerous researchers such as Steiner Amandus, Walpen Olivier, Vaccari Aldo, and others. Her collaborative approach extends to partnerships with institutions like Stahleinbau GmbH and The Ark Energy, facilitating the transfer of knowledge from research to industry application. The lab's work spans from fundamental fluid dynamics research to full-scale demonstration projects, creating a comprehensive pipeline from theory to practical implementation.

James Campbellمشاهده پروفایل

استاد

• Numerical Modelling
• Impact Engineering
• Crashworthiness
• +۴ مورد دیگر

Dr James Campbell is a Reader in Structural Integrity at Brunel University London's Department of Mechanical and Aerospace Engineering within the College of Engineering, Design and Physical Sciences. With a PhD in hypervelocity impact on spacecraft and 20+ years of experience leading multidisciplinary projects, he specializes in non-linear numerical methods (FE and SPH), structural integrity, and impact analysis across aerospace, defense, and automotive sectors. BEng in Aeronautical Engineering, Imperial College London MSc and PhD in Astronautics and Space Engineering, Cranfield University His research focuses on: Transient response of materials/structures (e.g., space debris impact, aircraft crashworthiness) Meshless methods like Smoothed Particle Hydrodynamics (SPH) Constitutive models for isotropic/orthotropic materials Fluid-structure interaction in ditching and extreme wave events Recent work trends include space debris removal tools, composite material development for offshore energy, and advanced SPH algorithms for impact simulations. Awards include the Derek George Astridge Safety in Aerospace Award (2009) and Royal Institute of Naval Architects Medal (2010). He supervises PhD/MSc students and delivers CPD courses for industry (Boeing, Leonardo). Research group: IMM (International Marine and Offshore). Collaborations include Airbus, DLR, and ESA.

• Applied Analysis
• Numerical Analysis
• Data Science
• +۶ مورد دیگر

Leon Bungert is a Professor of Mathematics of Machine Learning at the University of Würzburg, working in applied analysis and numerics with a particular focus on data science and machine learning. His research investigates PDEs and variational models on graphs, adversarial robustness of machine learning, variational regularization, and nonlinear optimization. Dr. Bungert serves as a guest editor for the European Journal of Applied Mathematics, an associate editor for Advances in Continuous and Discrete Models: Theory and Applications, and is a member of the program committee at SSVM 2025. He is also an ELLIS member and actively organizes conferences and workshops, including "MIA'25" at IHP in Paris (January 13-15, 2025), "Synergies of Machine Learning and Numerics" in Osaka (March 11-13, 2025), and "Mathematical Analysis of Adversarial Machine Learning" in Oaxaca (August 17-22, 2025). Research Interests Dr. Bungert's primary research areas include: PDEs on graphs Adversarial robustness in machine learning Inverse problems Optimization Variational problems in L-infinity Nonlinear eigenvalue problems Image reconstruction with structural priors His work bridges theoretical mathematics with practical applications in machine learning, particularly focusing on the mathematical foundations of deep learning and developing robust algorithms that can withstand adversarial attacks. He has made significant contributions to understanding the connections between partial differential equations and machine learning algorithms. Research Trends Analysis of Dr. Bungert's recent publications reveals a strong focus on the intersection of machine learning and mathematical analysis. A key theme is the application of variational methods and partial differential equations to machine learning problems, particularly in understanding and improving the robustness of neural networks against adversarial examples. His work on Lipschitz learning on graphs has established important theoretical foundations for graph-based semi-supervised learning. Additionally, his research on the infinity Laplacian and p-Laplacian equations provides deep insights into the mathematical structure of machine learning algorithms. The development of Bregman learning frameworks for sparse neural networks represents a significant contribution to efficient deep learning model training. Professional Activities Dr. Bungert is actively involved in the academic community through editorial roles and conference organization. His current professional activities include: Guest editor for the European Journal of Applied Mathematics Associate editor for Advances in Continuous and Discrete Models: Theory and Applications Member of the program committee at SSVM 2025 ELLIS member Co-organizer of multiple international conferences and workshops Technical Contributions Dr. Bungert has developed several open-source software packages that implement his theoretical contributions, including: Code for convergence rates of Lipschitz learning on graphs A Bregman training framework for sparse neural networks CLIP: Cheap Lipschitz Training of Neural Networks Nonlinear Power Method for Proximal Operators and Neural Networks Robust Image Reconstruction with Misaligned Structural Information These implementations are primarily in Python and MATLAB, demonstrating his commitment to making theoretical advances accessible for practical applications.

• Cardiac Flow Dynamics
• Echocardiography
• Biomedical Engineering
• +۳ مورد دیگر

Brett Meyers is an Assistant Research Professor at Purdue University's College of Engineering, specializing in biomedical engineering and cardiovascular fluid dynamics. His research focuses on advancing echocardiographic techniques, computational fluid dynamics (CFD), and medical imaging technologies to assess cardiac function in patients with congenital heart defects, sepsis, and other cardiovascular conditions. Key contributions include developing the Doppler Velocity Reconstruction (DoVeR) method and enhancing fetal/neonatal echocardiography for early diagnosis. Education details are not explicitly stated in the provided text. His work spans pediatric cardiology, adult heart failure, and biomaterials, with a strong emphasis on translational research. Meyers has pioneered methods to quantify intracardiac flow patterns using 4D flow MRI and ultrasound-based techniques like EchoPIV (Echocardiographic Particle Image Velocimetry). Research interests include hemodynamic biomarker development for sepsis prognostication, single ventricle heart biomechanics, and fluid mechanics in biological systems (e.g., snake tongue flicking). He has authored over 50 peer-reviewed articles since 2013, with recent work focusing on AI-driven analysis of echocardiograms and novel drug delivery systems using synchrotron imaging. While no specific awards are listed, his innovative methodologies have been applied in clinical settings, such as assessing exercise capacity in repaired Tetralogy of Fallot patients and refining therapeutic regimens for thrombotic disorders. Meyers collaborates with multidisciplinary teams in Purdue's biomedical engineering and mechanical engineering departments, contributing to both academic and industrial partnerships.

• Computational Fluid Dynamics (CFD)
• Hydraulic Turbines
• Cavitation Modeling
• +۴ مورد دیگر

Jean Decaix is a Researcher at the University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis - Haute Ecole d'Ingénierie ) within the Department of Energy and Environmental Techniques . His work focuses on hydropower systems , computational fluid dynamics (CFD) , and cavitation modeling for hydraulic turbines. Decaix contributes to projects like SCCER-SoE (Supply of Electricity center) and XFlex Hydro , aiming to enhance grid stability through advanced turbine operation. Decaix's research spans Francis and Pelton turbines , with a focus on flow topology , unsteady cavitating flows , and hydraulic short-circuit modes . He develops freely distributable CFD tools for building airflow and turbine efficiency, validated through experimental measurements and numerical simulations . Notable projects : SCCER-SoE (2017-2020): Innovation roadmaps for geothermal and hydropower Solution de transfert d'énergie par pompage-turbinage à petite échelle (2015-2017): Economic model development for small-scale hydropower Decaix's 15 most recent publications (2015-2024) cover topics like cavitation suppression , vortex rope dynamics , Pelton turbine efficiency , and CFD validation for building energy systems. His work emphasizes renewable energy integration and mechanical stress reduction in hydropower plants.

Ghigliotti Giovanniمشاهده پروفایل

مدرس

• Multiphase Fluid Dynamics
• Numerical Simulation
• Heat Transfer
• +۳ مورد دیگر

Giovanni Ghigliotti is a Lecturer and researcher at the University of Grenoble Alpes since 2014, affiliated with the LEGI laboratory (UMR 5519). He is part of the MOST team, specializing in Turbulence Modeling and Simulation. His research focuses on multiphase fluid dynamics, particularly phase-change phenomena like boiling and cavitation, with an emphasis on numerical simulation to study hydrodynamic interactions, wetting, and material deformation. He holds a PhD in Fluid Mechanics from Joseph-Fourier University (2010). His work spans cavitation erosion mechanisms, fluid-structure interaction modeling, and heat transfer in multiphase systems. He contributes to the development of the Yales2 code, a parallel numerical simulation tool for complex flows. In teaching, he leads the Energy pathway of the Master's program in Process Engineering at Grenoble Alpes, instructing fluid mechanics, numerical methods, and thermodynamics. His research also involves collaborations with the GIS SUCCESS group, advancing high-performance computing and turbulence modeling. Key research themes include: Boiling crisis dynamics and thermal insulation effects Cavitation bubble collapse and material erosion Centrifugal microencapsulation process analysis Fluid-structure interaction in multiphase flows Unstructured grid simulations for phase change phenomena His recent publications (2024-2019) demonstrate expertise in non-Newtonian fluid simulation, cavitation erosion mechanics, and advanced computational methods for multiphase systems.

Armin Iskeمشاهده پروفایل

استاد

• Numerical Approximation
• Applied Mathematics
• Kernel Methods
• +۴ مورد دیگر

Prof. Dr. Armin Iske is a Full Professor of Numerical Approximation at the University of Hamburg's Department of Mathematics, within the Faculty of Mathematics, Computer Science and Natural Sciences. He holds a PhD from the University of Göttingen (1994) and habilitation from TU Munich (2002). His research focuses on numerical approximation, kernel-based methods, computational fluid dynamics, and medical imaging. He has held academic positions globally, including visiting roles at ANU (Australia) and the University of Leicester (UK). Research interests include scattered data approximation, adaptive particle methods for flow simulation, and high-dimensional data analysis. He has authored 118+ publications, including works on kernel interpolation, medical imaging reconstruction, and machine learning applications. He serves on editorial boards for journals like Advances in Computational Mathematics and Sampling Theory . His contributions span interdisciplinary projects, such as SFB/TRR 181 on energy transfer in atmosphere and ocean, and collaborations in nanotechnology for brain interfaces. His work bridges theoretical mathematics with practical applications in engineering and biosciences.

• Optimization
• Aerodynamics
• Machine Learning
• +۲ مورد دیگر

Andrew Ning is a Professor in the Department of Mechanical Engineering at Brigham Young University, with a joint appointment at the National Renewable Energy Laboratory. He leads the FLOW Lab, focusing on optimization, deep learning, and aerodynamic simulation methods for wind energy systems and aircraft design . Education PhD & MS in Aeronautics and Astronautics from Stanford University BS in Applied Physics from Brigham Young University His research spans optimization , aerodynamics , and machine learning , addressing challenges in wind farm layout, aerostructural design, and urban air mobility vehicle analysis. The FLOW Lab develops advanced computational tools including vortex particle methods, beam theory implementations, and meshless simulation techniques. Recent publications demonstrate expertise in gradient-based optimization , unsteady potential flow , and multirotor aerodynamics . Key application areas include electric propulsion , solar aircraft , and floating offshore wind turbines . His work integrates high-fidelity modeling with computational efficiency through novel algorithm development. Office hours for Winter 2025: Mondays 3:30-4:00pm, Tuesdays 4:00-5:00pm, Wednesdays 11:00am-12:00pm, and Thursdays 1:00-2:00pm.

Jose Manuel Dominguez Alonsoمشاهده پروفایل

پژوهشگر

• Earth Physics
• Computational Fluid Dynamics
• Marine Engineering
• +۴ مورد دیگر

Jose Manuel Dominguez Alonso is a researcher at the University of Vigo , affiliated with the Faculty of Sciences and the Marine Research Center . His work focuses on Earth Physics and Smoothed Particle Hydrodynamics (SPH) applications in marine and renewable energy systems. He is part of the FA9 EphysLab research group at the Ourense campus. Education: PhD in Applied Physics from the University of Vigo (2014) under advisors Dr. Moncho Gómez Gesteira and Dr. Alejandro Jacobo Cabrera Crespo. His research explores fluid dynamics , off-shore wind turbines , wave energy converters , and fluid-structure interaction . He has developed DualSPHPhysics for high-performance computing in marine environments. His recent articles (2024-2025) emphasize teaching numerical modeling to students, 3D hydrodynamic analysis , and coupled simulation techniques . His work bridges computational methods with real-world marine engineering challenges like coastal protection and renewable energy optimization.

Qingwei Maمشاهده پروفایل

استاد

• Hydrodynamics
• Fluid-Structure Interaction
• Offshore Engineering
• +۱۲ مورد دیگر

Professor Qingwei Ma is a full Professor of Hydrodynamics at City, University of London, where he also serves as the Director of the Research Centre for Fluid-Structure Interaction. He holds a PhD in Ocean Engineering & Naval Architecture from University College London and has built a distinguished career in fluid dynamics and offshore engineering. His academic journey includes positions at Ocean University of Qingdao, University College London, Robert Gordon University, and City, University of London, where he has been since 2002. PhD, Ocean Engineering & Naval Architecture, University College London, 1998 MEng, Naval Architecture & Ocean Engineering, Harbin Engineering University, 1984 BEng, Naval Architecture & Ocean Engineering, Harbin Engineering University, 1982 Professor Ma’s research focuses on hydrodynamics of marine structures , nonlinear wave-structure interaction , and advanced numerical methods such as QALE-FEM, MLPG_R, and SPH. His recent work emphasizes multi-scale simulations for offshore wind turbines, wave energy converters, and floating platforms. He has developed and applied hybrid numerical models to simulate extreme wave events, vortex-induced vibrations, and fluid-structure interactions under complex environmental conditions. The 15 most recent publications reflect a strong trend toward hybrid computational methods , machine learning integration (e.g., GNN, CNN), and multi-physics coupling in offshore and renewable energy systems. His work spans journal articles, conference proceedings, and books, with a consistent focus on improving accuracy and efficiency in simulating violent wave interactions. His scientific recognitions include: CH Kim Award, ISOPE (2016) Vice-chancellor’s Award for Excellence in Learning and Teaching (2015) Chang Jiang Scholarship, China Professor Ma has supervised numerous PhD and master’s students, led major research projects, and contributed to editorial boards of journals such as Ocean Systems Engineering (Editor-in-Chief), Computer Modeling in Engineering & Sciences , and Journal of Ocean Engineering and Marine Energy . He is actively involved in international collaborations, particularly with Harbin Engineering University, where he holds a Visiting Chair Professorship. He leads the development of innovative simulation tools for offshore renewable energy systems, with applications in floating wind turbines, wave energy devices, and hybrid platforms. His lab focuses on high-fidelity numerical modelling using particle and finite element methods, with strong ties to experimental validation and industrial applications.

Nima Rabieiمشاهده پروفایل

استادیار

• Computational Mathematics
• Numerical Analysis
• Optimal Control
• +۴ مورد دیگر

Nima Rabiei is an Assistant Professor at the International University of Sarajevo in the Department of Applied Mathematics . His research focuses on computational mathematics, numerical analysis, and their applications to engineering and environmental systems. Education: PhD in Applied Mathematics (2014) from Universitat Politècnica de Catalunya-BarcelonaTech (UPC) His work spans population balance modeling , non-linear optimal control , integro-differential equations , and limit analysis decomposition methods . He has developed numerical schemes for crystallizers and explored statistical methods for hyper-ellipsoids. Recent publications highlight his contributions to adjoint methods , Monte Carlo techniques , and meshless computational approaches .

Darshan Sarojiniمشاهده پروفایل

استادیار

• Multidisciplinary Design, Analysis, and Optimization (MDAO)
• Aircraft Design
• Systems Engineering
• +۵ مورد دیگر

Darshan Sarojini is an Assistant Professor in the Aerospace and Ocean Engineering Department at Virginia Tech (College of Engineering). His research focuses on Multidisciplinary Design, Analysis, and Optimization (MDAO) , Certification-Driven Aircraft Design , and Systems Engineering , with applications in Urban Air Mobility (UAM) and electric vertical takeoff and landing (eVTOL) systems. He leads the Intelligent Design Optimization (IDOpt) Lab , which develops advanced methods for complex engineering systems design. Education: Ph.D. & M.S. in Aerospace Engineering, Georgia Institute of Technology M.S. in Computational Science and Engineering, Georgia Tech B.E. in Mechanical Engineering, B.M.S. College of Engineering, India Research expertise includes integrating Model-Based Systems Engineering (MBSE) with MDAO, high-performance computing (HPC) workflows, and structural optimization under dynamic loads. He has served as Technical Discipline Chair for the AIAA Modeling and Simulation Technologies committee and contributed to NASA ULI projects. Recent work emphasizes large-scale MDO of eVTOL concepts, certification-constrained design, and computational frameworks for aircraft systems. He has mentored students from the ENLACE program and collaborated with industry partners like Archer Aviation and Aurora Flight Sciences. Honors include Best Application Paper Awards at IFToMM 2015 and the 2015 National Conference on Machines and Mechanisms. His lab actively engages in student competitions (e.g., IndySCC) and promotes broader participation in HPC through workshops.