Continuous-time emulation of large distributed parameter dispersion models
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The paper discusses the emulation of large, distributed parameter, computer models by low order, continuous-time, transfer function models obtained using the SRIVC method of identification and estimation for continuous-time models. This yields a minimally parameterized, reduced order, 'nominal' emulation model that often reproduces the dynamic behavior of the large model to a remarkable degree. In full Dynamic Model Emulation (DEM), the objective is to emulate the high order model over a whole,...[Show more]
dc.contributor.author | Young, Peter C | |
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dc.coverage.spatial | Bruxelles Belgium | |
dc.date.accessioned | 2015-12-10T23:32:22Z | |
dc.date.created | July 11-13 2012 | |
dc.identifier.isbn | 9783902823069 | |
dc.identifier.uri | http://hdl.handle.net/1885/68814 | |
dc.description.abstract | The paper discusses the emulation of large, distributed parameter, computer models by low order, continuous-time, transfer function models obtained using the SRIVC method of identification and estimation for continuous-time models. This yields a minimally parameterized, reduced order, 'nominal' emulation model that often reproduces the dynamic behavior of the large model to a remarkable degree. In full Dynamic Model Emulation (DEM), the objective is to emulate the high order model over a whole, user-defined range of parameter values, so that it can act as a surrogate for the high order model in applications that demand fast, repeated solution, as in Monte Carlo simulation and sensitivity analysis, or be used as a low order model in automatic control system design and adaptive forecasting applications. Most of the paper deals with the 'stand-alone' emulation of two high order, distributed parameter, computer models for the transport and dispersion of solutes in water systems. | |
dc.publisher | Conference Organising Committee | |
dc.relation.ispartofseries | IFAC Symposium on System Identification 2012 | |
dc.source | IFAC Proceedings Volumes (IFAC-PapersOnline) | |
dc.subject | Keywords: Adaptive forecasting; Computer models; Continuous time; Continuous time models; Dispersion of solutes; Distributed parameter; Dynamic behaviors; Emulation model; High order model; Low order; Low order models; Monte Carlo Simulation; Parameter values; Para | |
dc.title | Continuous-time emulation of large distributed parameter dispersion models | |
dc.type | Conference paper | |
local.description.notes | Imported from ARIES | |
local.description.refereed | Yes | |
dc.date.issued | 2012 | |
local.identifier.absfor | 010204 - Dynamical Systems in Applications | |
local.identifier.absfor | 091302 - Automation and Control Engineering | |
local.identifier.absfor | 040608 - Surfacewater Hydrology | |
local.identifier.ariespublication | f5625xPUB1837 | |
local.type.status | Published Version | |
local.contributor.affiliation | Young, Peter C, College of Medicine, Biology and Environment, ANU | |
local.description.embargo | 2037-12-31 | |
local.bibliographicCitation.startpage | 1055 | |
local.bibliographicCitation.lastpage | 1060 | |
local.identifier.doi | 10.3182/20120711-3-BE-2027.00099 | |
local.identifier.absseo | 961010 - Natural Hazards in Urban and Industrial Environments | |
local.identifier.absseo | 960608 - Rural Water Evaluation (incl. Water Quality) | |
dc.date.updated | 2016-02-24T08:51:11Z | |
local.identifier.scopusID | 2-s2.0-84867057737 | |
Collections | ANU Research Publications |
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