A service oriented architecture for engineering design

Decision making in engineering design can be effectively addressed by using genetic algorithms to solve multi-objective problems. These multi-objective genetic algorithms (MOGAs) are well suited to implementation in a Service Oriented Architecture. Often the evaluation process of the MOGA is compute-intensive due to the use of a complex computer model to represent the real-world system. The emerging paradigm of Grid Computing offers a potential solution to the compute-intensive nature of this objective function evaluation, by allowing access to large amounts of compute resources in a distributed manner. This paper presents a grid-enabled framework for multi-objective optimisation using genetic algorithms (MOGA-G) to aid decision making in engineering design

Parallelizing multi-objective evolutionary algorithms: cone separation

Evolutionary multi-objective optimization (EMO) may be computationally quite demanding, because instead of searching for a single optimum, one generally wishes to find the whole front of Pareto-optimal solutions. For that reason, parallelizing EMO is an important issue. Since we are looking for a number of Pareto-optimal solutions with different tradeoffs between the objectives, it seems natural to assign different parts of the search space to different processors. We propose the idea of cone separation which is used to divide up the search space by adding explicit constraints for each process. We show that the approach is more efficient than simple parallelization schemes, and that it also works on problems with a non-convex Pareto-optimal front

pdAGMO para configuración inicial de sensores en procesos industriales

En este trabajo se presenta una implementación paralelo-distribuida de un algoritmo genético multiobjetivo (pdAGMO), desarrollado para efectuar la selección de la configuración inicial de sensores en el diseño de instrumentación de plantas de procesos. El pdAGMO fue diseñado empleando el modelo evolutivo de islas y el paradigma masterworker, mientras que para su implementación se empleó la librería de pasaje de mensajes PVM (Parallel Virtual Machine). El desempeño del pdAGMO fue evaluado a través de su aplicación a un caso de estudio industrial correspondiente a una planta de producción de amoníaco. Los resultados alcanzados son muy satisfactorios en términos de speed-up, eficiencia y calidad del diseño de instrumentación.Fil: Asteasuain, Fernando. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; ArgentinaFil: Carballido, Jessica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Vazquez, Gustavo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Brignole, Nélida Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentin

HYDRA: Distributed Multi-Objective Optimization for Designers

Architectural design problems can be quite involved, as there is a plethora of – usually conflicting – criteria that one has to address in order to find an optimal, performative solution. Multi-Objective Optimization (MOO) techniques can thus prove very useful, as they provide solution spaces which can traverse the different trade-offs of convoluted design options. Nevertheless, they are not widely used as (a) they are computationally expensive and (b) the resulting solution space can be proven difficult to visualize and navigate, particularly when dealing with higher dimensional spaces. This paper will present a system, which merges bespoke multi-objective optimization with a parametric CAD system, enhanced by supercomputing, into a single, coherent workflow, in order to address the above issues. The system architecture ensures optimal use of existing compute resources and enables massive performance speed-up, allowing for fast review and delivery cycles. The application aims to provide architects, designers and engineers with a better understanding of the design space, aiding the decision-making process by procuring tangible data from different objectives and finally providing fit (and sometimes unforeseen) solutions to a design problem. This is primarily achieved by a graphical interface of easy to navigate solution spaces of design options, derived from their respective Pareto fronts, in the form of a web-based interactive dashboard. Since understanding high-dimensionality data is a difficult task, multivariate analysis techniques were implemented to post-process the data before displaying it to end users. Visual Data Mining (VDM) and Machine Learning (ML) techniques were incorporated to facilitate knowledge discovery and exploration of large sets of design options at an early design stage. The system is demonstrated and assessed on an applied design case study of a master-planning project, where the benefits of the process are more evident, especially due to its complexity and size

Adaptive Wing/Aerofoil Design Optimisation Using MOEA Coupled to Uncertainty Design Method

The use of adaptive wing/aerofoil designs is being considered as promising techniques in aeronautic/aerospace since they can reduce aircraft emissions, improve aerodynamic performance of manned or unmanned aircraft. The paper investigates the robust design and optimisation for one type of adaptive techniques; Active Flow Control (AFC) bump at transonic flow conditions on a Natural Laminar Flow (NLF) aerofoil designed to increase aerodynamic efficiency (especially high lift to drag ratio). The concept of using Shock Control Bump (SCB) is to control supersonic flow on the suction/pressure side of NLF aerofoil: RAE 5243 that leads to delaying shock occurrence or weakening its strength. Such AFC technique reduces total drag at transonic speeds due to reduction of wave drag. The location of Boundary Layer Transition (BLT) can influence the position the supersonic shock occurrence. The BLT position is an uncertainty in aerodynamic design due to the many factors, such as surface contamination or surface erosion. The paper studies the SCB shape design optimisation using robust Evolutionary Algorithms (EAs) with uncertainty in BLT positions. The optimisation method is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing and asynchronous evaluation. Two test cases are conducted; the first test assumes the BLT is at 45% of chord from the leading edge and the second test considers robust design optimisation for SCB at the variability of BLT positions and lift coefficient. Numerical result shows that the optimisation method coupled to uncertainty design techniques produces Pareto optimal SCB shapes which have low sensitivity and high aerodynamic performance while having significant total drag reduction

Active transonic aerofoil design optimization using robust multiobjective evolutionary algorithms

The use of adaptive wing/aerofoil designs is being considered, as they are promising techniques in aeronautic/ aerospace since they can reduce aircraft emissions and improve aerodynamic performance of manned or unmanned aircraft. This paper investigates the robust design and optimization for one type of adaptive techniques: active flow control bump at transonic flow conditions on a natural laminar flow aerofoil. The concept of using shock control bump is to control supersonic flow on the suction/pressure side of natural laminar flow aerofoil that leads to delaying shock occurrence (weakening its strength) or boundary-layer separation. Such an active flow control technique reduces total drag at transonic speeds due to reduction of wave drag. The location of boundary-layer transition can influence the position and structure of the supersonic shock on the suction/pressure side of aerofoil. The boundarylayer transition position is considered as an uncertainty design parameter in aerodynamic design due to the many factors, such as surface contamination or surface erosion. This paper studies the shock-control-bump shape design optimization using robust evolutionary algorithms with uncertainty in boundary-layer transition locations. The optimization method is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing, and asynchronous evaluation. Two test cases are conducted: the first test assumes the boundary-layer transition position is at 45% of chord from the leading edge, and the second test considers robust design optimization for the shock control bump at the variability of boundary-layer transition positions. The numerical result shows that the optimization method coupled to uncertainty design techniques produces Pareto optimal shock-control-bump shapes, which have low sensitivity and high aerodynamic performance while having significant total drag reduction

Una versión paralela del NSGA II utilizando multi-threads

El trabajo presenta una versión paralela mediante estrategias de multi-threads, del algoritmo evolutivo para optimización multiobjetivo NSGA-II. Se muestran los detalles de diseño e implementación de la versión paralela, en la que se define una estructura de vecindad la cual estipula la interacción entre las distintas sub-poblaciones. Se analiza la calidad de resultados y la eficiencia computacional, comparando con los resultados y tiempos de ejecución de la versión secuencial del algoritmo NSGA-II sobre un conjunto de problemas de prueba estándar.Eje: V - Workshop de agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Una versión paralela del NSGA II utilizando multi-threads

El trabajo presenta una versión paralela mediante estrategias de multi-threads, del algoritmo evolutivo para optimización multiobjetivo NSGA-II. Se muestran los detalles de diseño e implementación de la versión paralela, en la que se define una estructura de vecindad la cual estipula la interacción entre las distintas sub-poblaciones. Se analiza la calidad de resultados y la eficiencia computacional, comparando con los resultados y tiempos de ejecución de la versión secuencial del algoritmo NSGA-II sobre un conjunto de problemas de prueba estándar.Eje: V - Workshop de agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI