Browsing by Author "Akhtar, Imran"

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    Computing Functional Gains for Designing More Energy-Efficient Buildings Using a Model Reduction Framework
    Akhtar, Imran; Borggaard, Jeffrey T.; Burns, John A. (MDPI, 2018-11-23)
    We discuss developing efficient reduced-order models (ROM) for designing energy-efficient buildings using computational fluid dynamics (CFD) simulations. This is often the first step in the reduce-then-control technique employed for flow control in various industrial and engineering problems. This approach computes the proper orthogonal decomposition (POD) eigenfunctions from high-fidelity simulations data and then forms a ROM by projecting the Navier-Stokes equations onto these basic functions. In this study, we develop a linear quadratic regulator (LQR) control based on the ROM of flow in a room. We demonstrate these approaches on a one-room model, serving as a basic unit in a building. Furthermore, the ROM is used to compute feedback functional gains. These gains are in fact the spatial representation of the feedback control. Insight of these functional gains can be used for effective placement of sensors in the room. This research can further lead to developing mathematical tools for efficient design, optimization, and control in building management systems.
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    Neural Network-Based Model Reduction of Hydrodynamics Forces on an Airfoil
    Farooq, Hamayun; Saeed, Ahmad; Akhtar, Imran; Bangash, Zafar (MDPI, 2021-09-15)
    In this paper, an artificial neural network (ANN)-based reduced order model (ROM) is developed for the hydrodynamics forces on an airfoil immersed in the flow field at different angles of attack. The proper orthogonal decomposition (POD) of the flow field data is employed to obtain pressure modes and the temporal coefficients. These temporal pressure coefficients are used to train the ANN using data from three different angles of attack. The trained network then takes the value of angle of attack (AOA) and past POD coefficients as an input and predicts the future temporal coefficients. We also decompose the surface pressure modes into lift and drag components. These surface pressure modes are then employed to calculate the pressure component of lift CLp and drag CDp coefficients. The train model is then tested on the in-sample data and out-of-sample data. The results show good agreement with the true numerical data, thus validating the neural network based model.
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    Parallel Simulations, Reduced-Order Modeling, and Feedback Control of Vortex Shedding using Fluidic Actuators
    Akhtar, Imran (Virginia Tech, 2008-04-28)
    In most of the engineering and industrial flow applications, one encounters fluid-structure interaction. This interaction can lead to some undesirable forces acting on the structure, causing its damage or fatigue. The phenomenon, being complex in nature, requires thorough understanding of the flow physics. Analyzing canonical flows, such as the flow past a cylinder, provides fundamental concepts governing the fluid behavior. Despite a simpler geometry, studying such flows are a building block in an effort to comprehend, model, and control complicated flows. For the flow past a circular cylinder, we examine the phenomenon of vortex shedding observed in many bluff body wakes. We develop a parallel computational fluid dynamics (CFD) code to solve the incompressible Navier-Stokes equations on curvilinear coordinates to analyze vortex shedding. The algorithm is implemented on a distributed-memory, message-passing parallel computer, and a domain decomposition technique is employed to partition the grid into various processors. We validate and verify the numerical results with existing experimental and numerical studies. We analyse the performance of the parallel CFD solver by computing the speed-up and efficiency of the solver. We also show that the algorithm is scalable and can be efficiently employed to study other engineering problems requiring larger grid sizes and computational domains. Various other features of the solver, such as the turbulence model, moving boundary techniques, shear, and other canonical flows are also presented. Direct numerical simulations (DNS) are performed to simulate the flow past a circular cylinder to compute the velocity and pressure fields. Based on the flow realizations of the DNS data, we use the proper orthogonal decomposition (POD) tool to determine the minimum degrees of freedom (or modes) required to represent the flow field. For the current nonlinear problem, the dominant POD modes are used in a Galerkin procedure to project the Navier-Stokes equations onto a low-dimensional space, thereby reducing the distributed-parameter problem into a finite-dimensional nonlinear dynamical system in time. We use long-time integration of the reduced-order model to calculate periodic solutions and alternatively use a shooting technique to home on the system limit cycles. We obtain the pressure-Poisson equation by taking the divergence of the Navier-Stokes equation and then project it onto the pressure POD modes. Then, we decompose the pressure into lift and drag components and compare the results with the CFD results. To reduce the fluctuating forces on the structure, we implement full-state feedback control on the low-dimensional model with suction applied aft of the separation point. The control algorithm is successfully simulated using the CFD code and suppression of vortex-shedding is achieved.
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    Shape Sensitivity Analysis in Flow Models Using a Finite-Difference Approach
    Akhtar, Imran; Borggaard, Jeffrey T.; Hay, Alexander (Hindawi Publishing Corporation, 2010)
    Reduced-order models have a number of practical engineering applications for unsteady flows that require either low-dimensional approximations for analysis and control or repeated simulation over a range of parameter values. The standard method for building reduced-order models uses the proper orthogonal decomposition (POD) and Galerkin projection. However, this standard method may be inaccurate when used "off-design" (at parameter values not used to generate the POD). This phenomena is exaggerated when parameter values describe the shape of the flow domain since slight changes in shape can have a significant influence on the flow field. In this paper, we investigate the use of POD sensitivity vectors to improve the accuracy and dynamical system properties of the reduced-order models to problems with shape parameters. To carry out this study, we consider flows past an elliptic cylinder with varying thickness ratios. Shape sensitivities (derivatives of flow variables with respect to thickness ratio) computed by finite difference approximations are used to compute the POD sensitivity vectors. Numerical studies test the accuracy of the new bases to represent flow solutions over a range of parameter values.