The Experts below are selected from a list of 15048 Experts worldwide ranked by ideXlab platform
W A Silva - One of the best experts on this subject based on the ideXlab platform.
-
reduced order modeling new approaches for Computational physics
Progress in Aerospace Sciences, 2004Co-Authors: David J. Lucia, Philip S Beran, W A SilvaAbstract:In this paper, we review the development of new reduced-order modeling techniques and discuss their applicability to various problems in Computational physics. Emphasis is given to methods ba'sed on Volterra series representations and the proper orthogonal decomposition. Results are reported for different nonlinear systems to provide clear examples of the construction and use of reduced-order models, particularly in the multi-disciplinary field of Computational aeroelasticity. Unsteady aerodynamic and aeroelastic behaviors of two- dimensional and three-dimensional geometries are described. Large increases in Computational efficiency are obtained through the use of reduced-order models, thereby justifying the initial Computational Expense of constructing these models and inotivatim,- their use for multi-disciplinary design analysis.
-
Reduced-order modeling: new approaches for Computational physics
Progress in Aerospace Sciences, 2004Co-Authors: David J. Lucia, Philip S Beran, W A SilvaAbstract:In this paper, we review the development ofnew reduced-order modeling techniques and discuss their applicability to various problems in Computational physics. Emphasis is given to methods based on Volterra series representations, the proper orthogonal decomposition, and harmonic balance. Results are reported for different nonlinear systems to provide clear examples ofthe construction and use ofreduced-order models (ROMs), particularly in the multi- disciplinary field ofComputational aeroelasticity. Unsteady aerodynamic and aeroelastic behaviors oftwo-dimensional and three-dimensional geometries are described. Large increases in Computational efficiency are obtained through the use ofROMs, thereby justifying the initial Computational Expense of constructing these models and motivating their use for multi-disciplinary design analysis. r
David J. Lucia - One of the best experts on this subject based on the ideXlab platform.
-
reduced order modeling new approaches for Computational physics
Progress in Aerospace Sciences, 2004Co-Authors: David J. Lucia, Philip S Beran, W A SilvaAbstract:In this paper, we review the development of new reduced-order modeling techniques and discuss their applicability to various problems in Computational physics. Emphasis is given to methods ba'sed on Volterra series representations and the proper orthogonal decomposition. Results are reported for different nonlinear systems to provide clear examples of the construction and use of reduced-order models, particularly in the multi-disciplinary field of Computational aeroelasticity. Unsteady aerodynamic and aeroelastic behaviors of two- dimensional and three-dimensional geometries are described. Large increases in Computational efficiency are obtained through the use of reduced-order models, thereby justifying the initial Computational Expense of constructing these models and inotivatim,- their use for multi-disciplinary design analysis.
-
Reduced-order modeling: new approaches for Computational physics
Progress in Aerospace Sciences, 2004Co-Authors: David J. Lucia, Philip S Beran, W A SilvaAbstract:In this paper, we review the development ofnew reduced-order modeling techniques and discuss their applicability to various problems in Computational physics. Emphasis is given to methods based on Volterra series representations, the proper orthogonal decomposition, and harmonic balance. Results are reported for different nonlinear systems to provide clear examples ofthe construction and use ofreduced-order models (ROMs), particularly in the multi- disciplinary field ofComputational aeroelasticity. Unsteady aerodynamic and aeroelastic behaviors oftwo-dimensional and three-dimensional geometries are described. Large increases in Computational efficiency are obtained through the use ofROMs, thereby justifying the initial Computational Expense of constructing these models and motivating their use for multi-disciplinary design analysis. r
Philip S Beran - One of the best experts on this subject based on the ideXlab platform.
-
reduced order modeling new approaches for Computational physics
Progress in Aerospace Sciences, 2004Co-Authors: David J. Lucia, Philip S Beran, W A SilvaAbstract:In this paper, we review the development of new reduced-order modeling techniques and discuss their applicability to various problems in Computational physics. Emphasis is given to methods ba'sed on Volterra series representations and the proper orthogonal decomposition. Results are reported for different nonlinear systems to provide clear examples of the construction and use of reduced-order models, particularly in the multi-disciplinary field of Computational aeroelasticity. Unsteady aerodynamic and aeroelastic behaviors of two- dimensional and three-dimensional geometries are described. Large increases in Computational efficiency are obtained through the use of reduced-order models, thereby justifying the initial Computational Expense of constructing these models and inotivatim,- their use for multi-disciplinary design analysis.
-
Reduced-order modeling: new approaches for Computational physics
Progress in Aerospace Sciences, 2004Co-Authors: David J. Lucia, Philip S Beran, W A SilvaAbstract:In this paper, we review the development ofnew reduced-order modeling techniques and discuss their applicability to various problems in Computational physics. Emphasis is given to methods based on Volterra series representations, the proper orthogonal decomposition, and harmonic balance. Results are reported for different nonlinear systems to provide clear examples ofthe construction and use ofreduced-order models (ROMs), particularly in the multi- disciplinary field ofComputational aeroelasticity. Unsteady aerodynamic and aeroelastic behaviors oftwo-dimensional and three-dimensional geometries are described. Large increases in Computational efficiency are obtained through the use ofROMs, thereby justifying the initial Computational Expense of constructing these models and motivating their use for multi-disciplinary design analysis. r
Christoph A Keller - One of the best experts on this subject based on the ideXlab platform.
-
sensitivity of chemistry transport model simulations to the duration of chemical and transport operators a case study with geos chem v10 01
Geoscientific Model Development, 2015Co-Authors: Sajeev Philip, Randall V Martin, Christoph A KellerAbstract:Abstract. Chemistry-transport models involve considerable Computational Expense. Fine temporal resolution offers accuracy at the Expense of computation time. Assessment is needed of the sensitivity of simulation accuracy to the duration of chemical and transport operators. We conduct a series of simulations with the GEOS-Chem chemistry-transport model at different temporal and spatial resolutions to examine the sensitivity of simulated atmospheric composition to operator duration. Subsequently, we compare the species simulated with operator durations from 10 to 60 min as typically used by global chemistry-transport models, and identify the operator durations that optimize both Computational Expense and simulation accuracy. We find that longer continuous transport operator duration increases concentrations of emitted species such as nitrogen oxides and carbon monoxide since a more homogeneous distribution reduces loss through chemical reactions and dry deposition. The increased concentrations of ozone precursors increase ozone production with longer transport operator duration. Longer chemical operator duration decreases sulfate and ammonium but increases nitrate due to feedbacks with in-cloud sulfur dioxide oxidation and aerosol thermodynamics. The simulation duration decreases by up to a factor of 5 from fine (5 min) to coarse (60 min) operator duration. We assess the change in simulation accuracy with resolution by comparing the root mean square difference in ground-level concentrations of nitrogen oxides, secondary inorganic aerosols, ozone and carbon monoxide with a finer temporal or spatial resolution taken as “truth”. Relative simulation error for these species increases by more than a factor of 5 from the shortest (5 min) to longest (60 min) operator duration. Chemical operator duration twice that of the transport operator duration offers more simulation accuracy per unit computation. However, the relative simulation error from coarser spatial resolution generally exceeds that from longer operator duration; e.g., degrading from 2° × 2.5° to 4° × 5° increases error by an order of magnitude. We recommend prioritizing fine spatial resolution before considering different operator durations in offline chemistry-transport models. We encourage chemistry-transport model users to specify in publications the durations of operators due to their effects on simulation accuracy.
Rafail V Abramov - One of the best experts on this subject based on the ideXlab platform.
-
approximate linear response for slow variables of dynamics with explicit time scale separation
Journal of Computational Physics, 2010Co-Authors: Rafail V AbramovAbstract:Many real-world numerical models are notorious for the time scale separation of different subsets of variables and the inclusion of random processes. The existing algorithms of linear response to external forcing are vulnerable to the time scale separation due to increased response errors at fast scales. Here we develop the approximate linear response algorithm for slow variables in a two-scale dynamical system with explicit separation of slow and fast variables, which has improved numerical stability and reduced Computational Expense.
-
linear response for slow variables of deterministic or stochastic dynamics with time scale separation
EGU General Assembly Conference Abstracts, 2009Co-Authors: Rafail V AbramovAbstract:Many real-world numerical models are notorious for the time-scale separation of different subsets of variables and the inclusion of random processes. The existing algorithms of linear response to external forcing are vulnerable to the timescale separation due to increased response errors at fast scales. Here we develop the linear response algorithm for slow variables in a multiscale deterministic or stochastic dynamical system, which has improved numerical stability and reduced Computational Expense.