Screening and metamodeling of computer experiments with functional outputs. Application to thermal-hydraulic computations

To perform uncertainty, sensitivity or optimization analysis on scalar variables calculated by a cpu time expensive computer code, a widely accepted methodology consists in first identifying the most influential uncertain inputs (by screening techniques), and then in replacing the cpu time expensive model by a cpu inexpensive mathematical function, called a metamodel. This paper extends this methodology to the functional output case, for instance when the model output variables are curves. The screening approach is based on the analysis of variance and principal component analysis of output curves. The functional metamodeling consists in a curve classification step, a dimension reduction step, then a classical metamodeling step. An industrial nuclear reactor application (dealing with uncertainties in the pressurized thermal shock analysis) illustrates all these steps

Clustering "optimal" dans des espaces fonctionnels

International audienceComputer codes used in support of nuclear industry are more and more complex, and consequently more and more CPU time consuming. We are here interested in such code, in the special case of functional output : the code output represents the evolutions of some physical parameters in time. Those last curves are functions from an interval $I \subset \R$ to $\R$, which will be preprocessed in order to cluster them in a few meaningful groups (clustering, or unsupervised classification). The aim of our work is the estimation of the convergence speed of clustering error estimates. After finding bounds on convergence speeds, we will illustrate this on an example with six distinct groups of curves

Mixmod - Développement, diffusion, valorisation d’un ensemble logiciel de classification de données quantitatives et qualitatives par modèles de mélanges

International audienceClassification des donnéesMixmod et les modèles de mélnagesComment est développé Mixmod

Projection-based curve clustering

This paper focuses on unsupervised curve classification in the context of nuclear industry. At the Commissariat à l'Energie Atomique (CEA), Cadarache (France), the thermal-hydraulic computer code CATHARE is used to study the reliability of reactor vessels. The code inputs are physical parameters and the outputs are time evolution curves of a few other physical quantities. As the CATHARE code is quite complex and CPU-time consuming, it has to be approximated by a regression model. This regression process involves a clustering step. In the present paper, CATHARE output curves are clustered using a k-means scheme, with a projection onto a lower dimensional space. We study the properties of the empirically optimal cluster centers found by the clustering method based on projections, compared to the “true” ones. The choice of the projection basis is discussed, and an algorithm is implemented to select the best projection basis among a library of orthonormal bases. The approach is illustrated on a simulated example and then applied to the industrial problem

Validation expérimentale de la nature tridimensionnelle de l'écoulement des polymères ramifiés dans des contractions planes

4 p.Nous présentons quelques résultats expérimentaux de l'écoulement dans deux contractions planaires pour deux polymères branchés. Il s'agit du PEbd-Iupac A fondu à 150°C et du PEbd-Atofina fondu à 150°C et 160°C. Un des objectifs de ce travail est de mettre en évidence le caractère tridimensionnel de ce type d'écoulement. Les clichés présentés concernent les franges isochromatiques d'un seul matériau pour un seul taux de déformation dans les deux géométries considérées. La détermination du coefficient des contraintes optiques est effectuée suivant deux approches empiriques. Les résultats de ces méthodes sont comparés à ceux issus de la littératur

Inversiones públicas en infraestructura de agua y saneamiento y su impacto socioeconómico en la población de Utcubamba, 2015 - 2020

El título de esta investigación es Inversiones públicas en infraestructuras de agua y saneamiento y su impacto socioeconómico en la población de Utcubamba, 2015 – 2020, su objetivo central fue determinar el impacto socioeconómico en la población de Utcubamba de las inversiones públicas en infraestructuras de agua y saneamiento del período 2015 – 2020. La investigación fue del tipo explicativa, de enfoque cuantitativo y de diseño longitudinal no experimental. La población fue 12 646 personas consideradas beneficiarios directos, y la muestra fue calculada en 137. Sus resultados permitieron demostrar la existencia de brechas sociales en los sectores de salud, trabajo y servicios básicos, tanto a nivel de la región Amazonas como a nivel de la provincia de Utcubamba. La conclusión refiere ser 9 proyectos ejecutados con una inversión pública a nivel rural de S/ 35´207,570.82 desde el 2015 al 2020 atendiendo a 12 646 habitantes de tan sólo 3 distritos y un promedio per cápita de S/ 2,784.09. Los cálculos realizados por especialistas indican el valor de S/ 3,386.50 por infraestructura por habitantes de agua y saneamiento rural contrastada la inversión ejecutada por la Gerencia Sub Regional Utcubamba fue de S/ 2,784.09 y una diferencia de S/ 602.41 per cápita, lo que representa el 17,8% de ahorro para la entidad ejecutora y los efectos producidos son mayor impacto socio económico de la población involucrada al contar con servicios básicos adecuados y disponibles a nivel intra domiciliarios

Atualização em tempo real do modelo HEC-RAS para previsão de vazões utilizando um algoritmo de otimização

Real-time updating of channel flow routing models is essential for error reduction in hydrological forecasting. Recent updating techniques found in scientific literature, although very promising, are complex and often applied in models that demand much time and expert knowledge for their development, posing challenges for using in an operational context. Since powerful and well-known computational tools are currently available, which provide easy-to-use and less time-consuming platforms for preparation of hydrodynamic models, it becomes interesting to develop updating techniques adaptable to such tools, taking full advantage of previously calibrated models as well as the experience of the users. In this work, we present a real-time updating procedure for streamflow forecasting in HEC-RAS model, using the Shuffled Complex Evolution - University of Arizona (SCE-UA) optimization algorithm. The procedure consists in a simultaneous correction of boundary conditions and model parameters through: (i) generation of a lateral inflow, based on Soil Conservation Service (SCS) dimensionless unit hydrograph and; (ii) estimation of Manning roughness in the river channel. The algorithm works in an optimization window in order to minimize an objective function, given by the weighted sum of squared errors between simulated and observed flows where differences in later intervals (start of forecast) are more penalized. As a case study, the procedure was applied in a river reach between Salto Caxias dam and Hotel Cataratas stream gauge, located in the Lower Iguazu Basin. Results showed that, with a small population of candidate solutions in the optimization algorithm, it is possible to efficiently improve the model performance for streamflow forecasting and reduce negative effects caused by lag errors in simulation. An advantage of the developed procedure is the reduction of both excessive handling of external files and manual adjustments of HEC-RAS model, which is important when operational decisions must be taken in relatively short times.A atualização em tempo real de modelos de propagação do escoamento em rios é essencial para a redução de erros na previsão hidrológica. As técnicas de atualização recentes encontradas na literatura, apesar de promissoras, são complexas e geralmente aplicadas em modelos cujo desenvolvimento demanda tempo e conhecimento muito especializado, representando desafios para sua utilização em ambientes operacionais. Dado que atualmente existem ferramentas computacionais amplamente difundidas, que reduzem tempo e simplificam a preparação de modelos hidrodinâmicos, torna-se interessante desenvolver técnicas que sejam facilmente acopladas a estas ferramentas de modo a aproveitar um modelo já calibrado e a experiência dos usuários. Neste trabalho é apresentada uma metodologia de atualização em tempo real do modelo HEC-RAS para previsão de vazões, utilizando o algoritmo de otimização Shuffled Complex Evolution - University of Arizona (SCE-UA). O procedimento consiste na atualização simultânea de condições de contorno e parâmetros no modelo hidrodinâmico, através de: (i) geração de um aporte lateral concentrado, definido por uma adaptação do hidrograma unitário adimensional do Soil Conservation Service - SCS e; (ii) estimativa do coeficiente de Manning no trecho simulado. O algoritmo opera em uma janela de otimização com a minimização de uma função-objetivo, que considera a soma ponderada dos erros quadráticos das vazões dando maior peso aos erros nos últimos intervalos com dados observados (início da previsão). Como estudo de caso, a metodologia foi aplicada em um trecho localizado na bacia do rio Iguaçu, entre a UHE Salto Caxias e o posto fluviométrico de Hotel Cataratas. Os resultados mostraram que, com um conjunto relativamente pequeno de soluções candidatas no algoritmo de otimização, é possível melhorar, de forma eficiente, o desempenho do modelo na previsão de vazões e reduzir efeitos negativos causados por erros de fase nos hidrogramas calculados. Uma vantagem da metodologia desenvolvida é que ela permite reduzir tanto a necessidade de manipulações excessivas de arquivos como de ajustes manuais do modelo HEC-RAS, o que é importante quando decisões operacionais devem ser tomadas em tempo relativamente curto