His work focuses on the simulation of large scale combustion systems – aircraft engines, stationary power turbines, hypersonic engines – with the goal of advancing computations-aided systems design. This involves large scale computations accounting for detailed behavior of the chaotic turbulent flow in these systems, combined with enabling science in computational chemistry and algorithms. One aspect of my research is the prediction of rare events that lead to catastrophic system failure (as in flight crash, engine failure etc.). This work also involves the understanding of uncertainty in models, and streamlining knowledge in the form of mathematical models.
Prof. Powell’s work focuses on algorithm development for fluid dynamics, aerodynamics and plasmadynamics, and the application of computational methods to problems in aerodynamics, aeroelasticicty, fluid dynamics and space environment/space weather.
His major current project is the creation an new third-order accurate CFD method called the Active Flux method, with many original features, sponsored by NASA under the Revolutionary Computational Aerodynamics program. Linked with this is joint work with Chris Fidkowski on entropy-based mesh adaptation. Another current interest is the design of improved Lagrangian hydrocodes that avoid “mesh imprinting” by emphasis on symmetry properties of the discretization, including the preservation of discrete vorticity.
Prof. Sundararaghavan develops multi-scale computational methods for polycrystalline alloys, polymer composites, and ultra-high temperature ceramic composites to model the effect of microstructure on the overall deformation, fatigue, failure, thermal transport and oxidation response. Recent packages developed include a fully parallel multiscale approach for optimization of polycrystalline alloys during forming processes and a multiscale approach for modeling oxidative degradation in high temperature fiber reinforced ceramic matrix composites. He has made seminal contributions towards the use of multiscale models for accelerated “microstructure-sensitive design” including development of data mining methods for microstructures and reduced order techniques for graphical visualization of microstructure-process-property relationships.
Anthony Waas is the Felix Pawlowski Collegiate Professor Emeritus of Aerospace Engineering and Mechanical Engineering (courtesy). The development of validated analytical and computational methods to understand how a structure (such as an air-vehicle wing, a fuselage, the load bearing structure of a land-vehicle, the wing of an insect, a wind turbine blade) made of multi-materials responds to external environments is the overarching goal of Wass’ research group. Naturally, this involves multi-physics and mechanics based models at different spatial and temporal scales. To achieve this goal, the group performs a combination of experiments, computational modeling and analysis, and theoretical developments when necessary. This work has led to novel algorithms and multi-scale methods that provide a balance between high fidelity and computational efficiency, with particular emphasis on capturing damage and failure mechanics, including interaction between these in a mesh (discretization) objective manner. Publications listed in ISI Web of Science, under the name “Waas, AM” will show the diversity of computational discovery and engineering related research that the group has done and is doing.
Martins’ research is on algorithms for multidisciplinary design optimization (MDO) that can take advantage of high-fidelity simulations and high-performance parallel computing. He has been focusing on applying these algorithms to the design optimization of new aircraft configurations. In the design of an aircraft wing in particular, since it is flexible, it is crucial to consider the coupling of the aerodynamics and the structure. In addition, when performing design optimization, it is important to simultaneously account for the aerodynamic performance and the structural failure constraints. In the MDO Lab, Martins’ team has developed ways to perform design optimization based a Reynolds-averaged Navier-Stokes model for the aerodynamics that is tightly coupled to a detailed structural finite-element model. The optimization of the coupled system is done with a gradient-based algorithm, where the gradients of the coupled system are computed using a two-field adjoint system of equations. This enables the high-fidelity aerostructural design of aircraft configurations with respect to thousands of design variables.
Fidkowski’s research interests lie in the development of robust, scalable, and adaptive solvers for computational fluid dynamics. Target applications include steady and unsteady convection dominated flows, such as those observed in external aerodynamics. Quantitative numerical error estimates for these problems are important for vehicle analysis and design; however they are challenging to obtain, especially for multi-dimensional simulations involving complex physical models running on parallel architectures. Fidkowski’s group is applying adjoint-based error estimation techniques to these problems, with the goal of generating tailored meshes for the prediction of selected outputs of interest. Research topics under investigation include improving effectivity of error estimates, applying error estimation to novel discretizations, combining error estimation with uncertainty quantification and optimization, and diversifying adaptation mechanics, especially for high-order unsteady simulations on deformable domains.
A variety of computational methods, (including CFD, particle, Monte Carlo, and molecular dynamics) are being developed for analysis of nonequilibrium processes in gas and plasma flows. Relevant nonequilibrium phenomena include chemical reactions, relaxation of internal energy modes, ionization, radiation, and surface ablation. In many situations, we are studying multi-physics or multi-scale environments that require simultaneous application of multiple computational methods. For example, we are combining CFD and particle methods for analysis of multi-scale gas flows and for multi-physics plasma flows. Examples of application areas of our research include hypersonic vehicles, spacecraft propulsion systems, rocket plumes, and low pressure chemical reactors.
Prof. Duraisamy is interested in the development of computational models, algorithms and uncertainty quantification approaches with application to fluid flows. This research includes fluid dynamic modeling at a fundamental level as well as in an integrated system-level setting. An overarching theme in his research involves the use of simulation and data-driven methods to answer scientific and engineering questions with an appreciation of the effect of modeling uncertainties on the predicted results. Prof. Duraisamy’s group is also interested in developing numerical algorithms to operate on evolving computational architectures such as GPUs. He is the Director of the Center for Data-Driven Computational Physics.