A PDE-informed optimization algorithm for river flow predictions

Autor(es)

Birgin, E. G. ; Martinez, J. M.

• Tipo de documento: Artigo Científico
• Fonte: NUMERICAL ALGORITHMS; v. N/A, p. 16-pg., 2023-09-07.

Resumo

An optimization-based tool for flow predictions in natural rivers is introduced assuming that some physical characteristics of a river within a spatial-time domain [x(min), x(max)] x[t(min), t(today)] are known. In particular, it is assumed that the bed elevation and width of the river are known at a finite number of stations in [x(min), xmax] and that the flow-rate at x = x(min) is known for a finite number of time instants in [t(min), t(today)]. Using these data, given t(future) > t(today) and a forecast of the flow-rate at x = x(min) and t = t(future), a regression-based algorithm informed by partial differential equations produces predictions for all state variables (water elevation, depth, transversal wetted area, and flow-rate) for all x. [x(min), x(max)] and t = t(future). The algorithm proceeds by solving a constrained optimization problem that takes into account the available data and the fulfillment of Saint-Venant equations for one-dimensional channels. The effectiveness of this approach is corroborated with flow predictions of a natural river. (AU)

• Processo FAPESP: 18/24293-0 - Métodos computacionais de otimização
• Beneficiário: Sandra Augusta Santos
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 16/01860-1 - Problemas de corte, empacotamento, dimensionamento de lotes, programação da produção, roteamento, localização e suas integrações em contextos industriais e logísticos
• Beneficiário: Reinaldo Morabito Neto
• Modalidade de apoio: Auxílio à Pesquisa - Temático