The Experts below are selected from a list of 270 Experts worldwide ranked by ideXlab platform
Chen Lu - One of the best experts on this subject based on the ideXlab platform.
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Interplay of Sensor Quantity, Placement and System Dimension in POD-Based Sparse Reconstruction of Fluid Flows
Fluids, 2019Co-Authors: Balaji Jayaraman, S M Abdullah Al Mamun, Chen LuAbstract:Sparse linear estimation of fluid flows using data-driven proper orthogonal decomposition (POD) basis is Systematically explored in this work. Fluid flows are manifestations of nonlinear multiscale partial differential equations (PDE) dynamical Systems with inherent scale separation that impact the System Dimensionality. Given that sparse reconstruction is inherently an ill-posed problem, the most successful approaches require the knowledge of the underlying low-Dimensional space spanning the manifold in which the System resides. In this paper, we adopt an approach that learns basis from singular value decomposition (SVD) of training data to recover sparse information. This results in a set of four design parameters for sparse recovery, namely, the choice of basis, System Dimension required for sufficiently accurate reconstruction, sensor budget and their placement. The choice of design parameters implicitly determines the choice of algorithm as either l 2 minimization reconstruction or sparsity promoting l 1 minimization reconstruction. In this work, we Systematically explore the implications of these design parameters on reconstruction accuracy so that practical recommendations can be identified. We observe that greedy-smart sensor placement, particularly interpolation points from the discrete empirical interpolation method (DEIM), provide the best balance of computational complexity and accurate reconstruction.
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Interplay of Sensor Quantity, Placement and System Dimension in POD-based Sparse Reconstruction of Fluid Flows
2019Co-Authors: Balaji Jayaraman, S M Abdullah Al Mamun, Chen LuAbstract:Sparse recovery of fluid flows using data-driven proper orthogonal decomposition (POD) basis is Systematically explored in this work. Fluid flows are manifestations of nonlinear multiscale PDE dynamical Systems with inherent scale separation that impact the System Dimensionality. Given that sparse reconstruction is inherently an ill-posed problem, the most successful approaches require the knowledge of the underlying basis space spanning the manifold in which the System resides. In this study, we employ an approach that learns basis from singular value decomposition (SVD) of training data to reconstruct sparsely sensed information. This results in a set of four control parameters for sparse recovery, namely, the choice of basis, System Dimension required for sufficiently accurate reconstruction, sensor budget and their placement. The choice of control parameters implicitly determines the choice of algorithm as either $l_2$ minimization reconstruction or sparsity promoting $l_1$ norm minimization reconstruction. In this work, we Systematically explore the implications of these control parameters on reconstruction accuracy so that practical recommendations can be identified. We observe that greedy-smart sensor placement provides the best balance of computational complexity and robust reconstruction for marginally oversampled cases which happens to be the most challenging regime in the explored parameter design space.
Sandip K. Saha - One of the best experts on this subject based on the ideXlab platform.
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Numerical analysis of latent heat thermal energy storage using encapsulated phase change material for solar thermal power plant
Renewable Energy, 2016Co-Authors: Kunal Bhagat, Sandip K. SahaAbstract:Thermal energy storage improves the load stability and efficiency of solar thermal power plants by reducing fluctuations and intermittency inherent to solar radiation. This paper presents a numerical study on the transient response of packed bed latent heat thermal energy storage System in removing fluctuations in the heat transfer fluid (HTF) temperature during the charging and discharging period. The packed bed consisting of spherical shaped encapsulated phase change materials (PCMs) is integrated in an organic Rankine cycle-based solar thermal power plant for electricity generation. A comprehensive numerical model is developed using flow equations for HTF and two-temperature non-equilibrium energy equation for heat transfer, coupled with enthalpy method to account for phase change in PCM. Systematic parametric studies are performed to understand the effect of mass flow rate, inlet charging System, storage System Dimension and encapsulation of the shell diameter on the dynamic behaviour of the storage System. The overall effectiveness and transient temperature difference in HTF temperature in a cycle are computed for different geometrical and operational parameters to evaluate the System performance. It is found that the ability of the latent heat thermal energy storage System to store and release energy is significantly improved by increasing mass flow rate and inlet charging temperature. The transient variation in the HTF temperature can be effectively reduced by decreasing porosity.
Balaji Jayaraman - One of the best experts on this subject based on the ideXlab platform.
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Interplay of Sensor Quantity, Placement and System Dimension in POD-Based Sparse Reconstruction of Fluid Flows
Fluids, 2019Co-Authors: Balaji Jayaraman, S M Abdullah Al Mamun, Chen LuAbstract:Sparse linear estimation of fluid flows using data-driven proper orthogonal decomposition (POD) basis is Systematically explored in this work. Fluid flows are manifestations of nonlinear multiscale partial differential equations (PDE) dynamical Systems with inherent scale separation that impact the System Dimensionality. Given that sparse reconstruction is inherently an ill-posed problem, the most successful approaches require the knowledge of the underlying low-Dimensional space spanning the manifold in which the System resides. In this paper, we adopt an approach that learns basis from singular value decomposition (SVD) of training data to recover sparse information. This results in a set of four design parameters for sparse recovery, namely, the choice of basis, System Dimension required for sufficiently accurate reconstruction, sensor budget and their placement. The choice of design parameters implicitly determines the choice of algorithm as either l 2 minimization reconstruction or sparsity promoting l 1 minimization reconstruction. In this work, we Systematically explore the implications of these design parameters on reconstruction accuracy so that practical recommendations can be identified. We observe that greedy-smart sensor placement, particularly interpolation points from the discrete empirical interpolation method (DEIM), provide the best balance of computational complexity and accurate reconstruction.
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Interplay of Sensor Quantity, Placement and System Dimension in POD-based Sparse Reconstruction of Fluid Flows
2019Co-Authors: Balaji Jayaraman, S M Abdullah Al Mamun, Chen LuAbstract:Sparse recovery of fluid flows using data-driven proper orthogonal decomposition (POD) basis is Systematically explored in this work. Fluid flows are manifestations of nonlinear multiscale PDE dynamical Systems with inherent scale separation that impact the System Dimensionality. Given that sparse reconstruction is inherently an ill-posed problem, the most successful approaches require the knowledge of the underlying basis space spanning the manifold in which the System resides. In this study, we employ an approach that learns basis from singular value decomposition (SVD) of training data to reconstruct sparsely sensed information. This results in a set of four control parameters for sparse recovery, namely, the choice of basis, System Dimension required for sufficiently accurate reconstruction, sensor budget and their placement. The choice of control parameters implicitly determines the choice of algorithm as either $l_2$ minimization reconstruction or sparsity promoting $l_1$ norm minimization reconstruction. In this work, we Systematically explore the implications of these control parameters on reconstruction accuracy so that practical recommendations can be identified. We observe that greedy-smart sensor placement provides the best balance of computational complexity and robust reconstruction for marginally oversampled cases which happens to be the most challenging regime in the explored parameter design space.
Ishwar K Puri - One of the best experts on this subject based on the ideXlab platform.
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heat conduction across a solid solid interface understanding nanoscale interfacial effects on thermal resistance
Applied Physics Letters, 2011Co-Authors: Ganesh Balasubramanian, Ishwar K PuriAbstract:Phonons scatter and travel ballistically in Systems smaller than the phonon mean free path. At larger lengths, the transport is instead predominantly diffusive. We employ molecular dynamics simulations to describe the length dependence of the thermal conductivity. The simulations show that the interfacial thermal resistance Rk for a Si-Ge superlattice is inversely proportional to its length, but reaches a constant value as the System Dimension becomes larger than the phonon mean free path. This nanoscale effect is incorporated into an accurate continuum model by treating the interface as a distinct material with an effective thermal resistance equal to Rk.
Kunal Bhagat - One of the best experts on this subject based on the ideXlab platform.
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Numerical analysis of latent heat thermal energy storage using encapsulated phase change material for solar thermal power plant
Renewable Energy, 2016Co-Authors: Kunal Bhagat, Sandip K. SahaAbstract:Thermal energy storage improves the load stability and efficiency of solar thermal power plants by reducing fluctuations and intermittency inherent to solar radiation. This paper presents a numerical study on the transient response of packed bed latent heat thermal energy storage System in removing fluctuations in the heat transfer fluid (HTF) temperature during the charging and discharging period. The packed bed consisting of spherical shaped encapsulated phase change materials (PCMs) is integrated in an organic Rankine cycle-based solar thermal power plant for electricity generation. A comprehensive numerical model is developed using flow equations for HTF and two-temperature non-equilibrium energy equation for heat transfer, coupled with enthalpy method to account for phase change in PCM. Systematic parametric studies are performed to understand the effect of mass flow rate, inlet charging System, storage System Dimension and encapsulation of the shell diameter on the dynamic behaviour of the storage System. The overall effectiveness and transient temperature difference in HTF temperature in a cycle are computed for different geometrical and operational parameters to evaluate the System performance. It is found that the ability of the latent heat thermal energy storage System to store and release energy is significantly improved by increasing mass flow rate and inlet charging temperature. The transient variation in the HTF temperature can be effectively reduced by decreasing porosity.
