

ResearchMy research interests lie in computational simulation and optimization of engineering systems, with specific contributions in model reduction of largescale systems and multidisciplinary design optimization.
Model reduction is a mathematical and computational field of study that aims to systematically generate costefficient representations of largescale computational models, such as those resulting from discretization of partial differential equations. Model reduction targets the critical need for lowdimensional, efficient dynamical models that retain predictive fidelity of highresolution simulations. In the case of computational fluid dynamics (CFD), such models are often essential for (1) multidisciplinary applications such as aeroelasticity and active flow control, (2) probabilistic design, and (3) realtime control and optimization of dynamic processes. Despite sustained advances in computing power, computational models for such applications yield large systems that are computationally intractable to solve. For example, probabilistic design requires the physical system to be simulated repeatedly. As another example, a realtime simulation capability requires the development of accurate models that can be solved sufficiently rapidly to permit control decisions in real time. Current Research Projects
The SAGUARO (Scalable Algorithms for Groundwater Uncertainty Analysis and Robust Optimization) Project focuses on the development of scalable numerical algorithms for largescale Bayesian inversion in complex systems. Specifically, we are developing stochastic spectral approximations together with methods to exploit prior information and approximations of the Hessian operator, thereby capitalizing on advances in largescale simulationbased optimization and inversion methods. Our application target is a challenging testbed problem in subsurface flow and transport.
Collaborators on the SAGUARO project include
Prof. G. Biros (GATech), Prof.C. Dawson (UTAustin), Prof.O. Ghattas (UTAustin),and Prof. Y. Marzouk (MIT). Bayesianbased multifidelity multidisciplinary vehicle design optimization This project is developing new methods for multidisciplinary design that are based on estimation theory, with a focus on management of multifidelity models in MDO. The method employs maximum entropy characterizations of model uncertainties that can be established via expert opinion or historical data, and incorporates global sensitivity analysis to rigorously apportion variation in performance parameters associated with critical design constraints to individual disciplines. This provides a means of determining, with confidence, when low, medium, and high fidelity models need to be incorporated into the design process. This project is being conducted in collaboration with Aurora Flight Sciences. Multifidelity methods for aircraft conceptual design Multifidelity optimization approaches seek to bring higherfidelity analyses earlier into the design process by using performance estimates from lowerfidelity models to accelerate convergence towards the optimum of a highfidelity design problem. We have developed multifidelity optimization methods that combine Bayesian calibration and radial basis function approximations with trust region model management, to yield a provablyconvergent algorithm. The gradientfree version of the approach does not require the gradient of the highfidelity functionin this case, convergence is guaranteed using sensitivity information from the calibrated lowfidelity models. We have demonstrated the method for aerodynamic shape optimization, showing an 80% reduction in the number of highfidelity analyses compared with a singlefidelity sequential quadratic programming formulation and a similar number of highfidelity analyses compared with a multifidelity trustregion algorithm that estimates the highfidelity gradient using finite differences. Model reduction for probabilistic analysis and design under uncertainty Effective computational tools to support decisionmaking under uncertainty are becoming essential in the design and operation of aerospace systems. The accurate and efficient propagation of uncertainties in parameters through complex, high fidelity computational models is a significant challenge. Since analytical characterizations of uncertainties in the system outputs are typically not available, numerical methods must be used that require repeated evaluations of models at suitably sampled parameters. Model reduction is a promising technique to substantially reduce the computational cost involved in the propagation of uncertainty. While model reduction has been applied successfully to many different applications in deterministic simulation and optimization settings, the goal of this project is the development, analysis, and application of reducedorder models to support decisionmaking under uncertainty. We are applying our methods to applications in reacting flows and fluidstructure interaction. This project is being conducted in collaboration with Prof. M. Heinkenschloss and Prof. D. Sorensen of Rice University. Multiscale fusion of information for uncertainty quantification and management in largescale simulations This MURI project sponsored by AFOSR aims to develop an integrated methodology for uncertainty quantification. The MURI project includes five research areas: (1) Mathematical analysis of SPDEs and multiscale formulation, (2) Numerical solution of SPDEs, (3) ReducedOrder modeling, (4) Estimation/Inverse problems, and (5) Robust optimization and control. Our work in areas (3) and (5) is combining certified reduced models with robust optimization methods and is developing an integrated method for optimal design subject to uncertainties due to lack of information. More information can be found at the MURI project website. Aviation environmental Portfolio Management Tool (APMT)The increasing concern over environmental impacts of aircraft is reflected in an ongoing commitment by the FAA to develop a comprehensive suite of tools to support aviation environmental policy decisionmaking. Two primary components of this toolkit are the Aviation Environmental Portfolio Management Tool (APMT), which will provide support to the international policy decisionmaking process through assessments of interdependencies among aviationrelated noise and emissions, impacts on health and welfare, and industry and consumer costs, under different policy, technology, operations and market scenarios, and the Environmental Design Space, which will provide an integrated analysis of noise, emissions and economics at the aircraft level for future and current aircraft. The scale and complexity of this problem are immense; for example, simulation of one year involves over two million flights with 350 aircraft types, analyzed with blackbox models spanning airline economics, environmental economics, aircraft operations, aircraft performance and emissions, noise, local air quality, and global climate. Furthermore, while just simulating the system is a daunting task, uncertainty must be characterized, computed and communicated in a way tangible to the domestic and international policy decisionmakers. Together with a large team of researchers from the PARTNER Center of Excellence, we are working to create methods to assess the uncertainties in the APMT tools. Surrogate modelsbuilding on concepts from model reductionare a critical component of addressing this challenge. More information can be found at the APMT PARTNER website. International Design Center, Singapore University of Design and TechnologyThe Singapore University of Design and Technology is a newly established university that focuses on design and technology. Past Research ProjectsReducedorder models for unsteady aerodynamic applications, active flow control, and aeroelasticity My group's contributions in model reduction have focused on development of rigorous methodology suitable for application to largescale systems, with a focus on CFD. Classical reduction techniques are mathematically rigorous, but are limited in application to small systems; conversely, largescale reduction methods have lacked mathematical rigor and robustness. Using dynamical systems concepts we have introduced new methodology to link largescale reduction approaches to more rigorous classical techniques. For example, we proposed a balanced version of POD that includes system outputs for PODbased reduction of largescale systems. Including output information is important for control applications and also leads to more efficient models. In collaboration with Prof. Alexandre Megretski, I introduced Fourier model reduction (FMR) for largescale linear timeinvariant systems. FMR uses a finite number of discretetime Fourier coefficients, yielding a reduced model with a theoretical error bound. In comparison with standard POD, for smooth transfer functions, FMR is computationally more efficient, yields models with rigorous guarantees of accuracy/stability, and exploits both input and output information. These approaches have been applied to a range of problems including turbomachinery aeroelasticity, compressor mistuning, supersonic inlet flow dynamics, and active flow controller design. Gappy POD for flow reconstruction, flow sensing, and nonlinear model reduction The gappy POD is a modification of the basic POD method that handles incomplete or "gappy" data sets. An incomplete data vector can be reconstructed by representing it as a linear combination of known POD basis vectors. The modal content is determined by solving a small linear system. Further, if the snapshots themselves are damaged or incomplete, an iterative method can be used to derive the POD basis vectors. This method was developed by Everson and Sirovich in the context of reconstruction of images, such as human faces, from partial data. The gappy POD is relevant for flow problems where incomplete data is available. For example, in experiments, data may only be available on the airfoil surface. Our research has shown that the gappy POD can be used to reconstruct steady and unsteady flowfield data from limited surface pressure measurements. We have also used the gappy POD to develop the Missing Point Estimation approach for efficient reduction of nonlinear systems. Model reduction for optimal design and inverse problem applications
We have developed enabling methodology to advance model reduction from simulation to optimization applications. For largescale optimal design, optimal control, and inverse problem applications, a key challenge is deriving reduced models to capture variation over a parametric input space, which, for many optimization applications, is of high dimension. We have proposed a new modelconstrained optimization methodology, building on the work of Patera et al. This methodology provides a systematic approach to sample highdimensional input spaces by solving a sequence of modelconstrained optimization problems. Integrated performance/cost optimization for aircraft design As the aerospace industry moves from the era of "Higher, Faster, Farther" to the challenge of "Leaner, Meaner, Greener" the definition of best design has evolved considerably. The balance between performance and cost in commercial aircraft design is increasingly important, at both the conceptual and the preliminary design level. This research developed quantitative methods for including programmatic decisions and financial models in the conceptual and preliminary design phase, and implemented them in an integrated cost/performance design tool. The work is directed towards new aircraft concepts, such as the BlendedWingBody. Using a real options valuation approach combined with an MDO framework, we demonstrated the importance of emphasizing longterm cash flows over development costs for a commercial aircraft program and introduced new methodology to quantify the impact of technical and financial uncertainty and to combine technical and programmatic decisions. 