Surrogate-Based Optimization Using Multifidelity Models with Variable Parameterization and Corrected Space Mapping

Surrogate-based-optimization methods provide a means to achieve high-fidelity design optimization at reduced computational cost by using a high-fidelity model in combination with lower-fidelity models that are less expensive to evaluate. This paper presents a provably convergent trust-region model-management methodology for variableparameterization design models: that is, models for which the design parameters are defined over different spaces. Corrected space mapping is introduced as a method to map between the variable-parameterization design spaces. It is then used with a sequential-quadratic-programming-like trust-region method for two aerospace-related design optimization problems. Results for a wing design problem and a flapping-flight problem show that the method outperforms direct optimization in the high-fidelity space. On the wing design problem, the new method achieves 76% savings in high-fidelity function calls. On a bat-flight design problem, it achieves approximately 45% time savings, although it converges to a different local minimum than did the benchmark.

Uncertainty and Sensitivity Analysis in Aircraft Operating Costs in Structural Design Optimization

The paper focuses on the development of an aircraft design optimization methodology that models uncertainty and sensitivity analysis in the tradeoff between manufacturing cost, structural requirements, andaircraft direct operating cost.Specifically,ratherthanonlylooking atmanufacturingcost, direct operatingcost is also consideredintermsof the impact of weight on fuel burn, in addition to the acquisition cost to be borne by the operator. Ultimately, there is a tradeoff between driving design according to minimal weight and driving it according to reduced manufacturing cost. Theanalysis of cost is facilitated withagenetic-causal cost-modeling methodology,andthe structural analysis is driven by numerical expressions of appropriate failure modes that use ESDU International reference data. However, a key contribution of the paper is to investigate the modeling of uncertainty and to perform a sensitivity analysis to investigate the robustness of the optimization methodology. Stochastic distributions are used to characterize manufacturing cost distributions, andMonteCarlo analysis is performed in modeling the impact of uncertainty on the cost modeling. The results are then used in a sensitivity analysis that incorporates the optimization methodology. In addition to investigating manufacturing cost variance, the sensitivity of the optimization to fuel burn cost and structural loading are also investigated. It is found that the consideration of manufacturing cost does make an impact and results in a different optimal design configuration from that delivered by the minimal-weight method. However, it was shown that at lower applied loads there is a threshold fuel burn cost at which the optimization process needs to reduce weight, and this threshold decreases with increasing load. The new optimal solution results in lower direct operating cost with a predicted savings of 640=m2 of fuselage skin over the life, relating to a rough order-of-magnitude direct operating cost savings of $500,000 for the fuselage alone of a small regional jet. Moreover, it was found through the uncertainty analysis that the principle was not sensitive to cost variance, although the margins do change.

Design optimization of cold-formed steel portal frames taking into account the effect of building topology

Cold-formed steel portal frames are a popular form of construction for low-rise commercial, light industrial and agricultural buildings with spans of up to 20 m. In this article, a real-coded genetic algorithm is described that is used to minimize the cost of the main frame of such buildings. The key decision variables considered in this proposed algorithm consist of both the spacing and pitch of the frame as continuous variables, as well as the discrete section sizes.A routine taking the structural analysis and frame design for cold-formed steel sections is embedded into a genetic algorithm. The results show that the real-coded genetic algorithm handles effectively the mixture of design variables, with high robustness and consistency in achieving the optimum solution. All wind load combinations according to Australian code are considered in this research. Results for frames with knee braces are also included, for which the optimization achieved even larger savings in cost.

Algorithmic method for scraper load-time optimization

Scrapers have established an important position in the earthmoving field as they are independently capable of accomplishing an earthmoving operation. Given that loading a scraper to its capacity does not entail its maximum production, optimizing the scraper’s loading time is an essential prerequisite for successful operations management. The relevant literature addresses the loading time optimization through a graphical method that is founded on the invalid assumption that the hauling time is independent of the load time. To correct this, a new algorithmic optimization method that incorporates the golden section search and the bisection algorithm is proposed. Comparison of the results derived from the proposed and the existing method demonstrates that the latter entails the systematic needless prolongation of the loading stage thus resulting in reduced hourly production and increased cost. Therefore, the proposed method achieves an improved modeling of scraper earthmoving operations and contributes toward a more efficient cost management.

Parametric design optimization of hard turning of AISI 4340 steel (69 HRC)

Continuous research endeavors on hard turning (HT), both on machine tools and cutting tools, have made the previously reported daunting limits easily attainable in the modern scenario. This presents an opportunity for a systematic investigation on finding the current attainable limits of hard turning using a CNC turret lathe. Accordingly, this study aims to contribute to the existing literature by providing the latest experimental results of hard turning of AISI 4340 steel (69 HRC) using a CBN cutting tool. An orthogonal array was developed using a set of judiciously chosen cutting parameters. Subsequently, the longitudinal turning trials were carried out in accordance with a well-designed full factorial-based Taguchi matrix. The speculation indeed proved correct as a mirror finished optical quality machined surface (an average surface roughness value of 45 nm) was achieved by the conventional cutting method. Furthermore, Signal-to-noise (S/N) ratio analysis, Analysis of variance (ANOVA), and Multiple regression analysis were carried out on the experimental datasets to assert the dominance of each machining variable in dictating the machined surface roughness and to optimize the machining parameters. One of the key findings was that when feed rate during hard turning approaches very low (about 0.02mm/rev), it could alone be most significant (99.16%) parameter in influencing the machined surface roughness (Ra). This has, however also been shown that low feed rate results in high tool wear, so the selection of machining parameters for carrying out hard turning must be governed by a trade-off between the cost and quality considerations.

A self-learning teaching-learning based optimization for dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints, such as the valve point effect, power balance and ramp rate limits. The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model. Self-learning teaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.

A First Step Towards Cost Functions for Quantum-dot Cellular Automata Designs

Quantum-dot cellular automata (QCA) is potentially a very attractive alternative to CMOS for future digital designs. Circuit designs in QCA have been extensively studied. However, how to properly evaluate the QCA circuits has not been carefully considered. To date, metrics and area-delay cost functions directly mapped from CMOS technology have been used to compare QCA designs, which is inappropriate due to the differences between these two technologies. In this paper, several cost metrics specifically aimed at QCA circuits are studied. It is found that delay, the number of QCA logic gates, and the number and type of crossovers, are important metrics that should be considered when comparing QCA designs. A family of new cost functions for QCA circuits is proposed. As fundamental components in QCA computing arithmetic, QCA adders are reviewed and evaluated with the proposed cost functions. By taking the new cost metrics into account, previous best adders become unattractive and it has been shown that different optimization goals lead to different “best” adders.

An efficient genetic algorithm for the design optimization of cold-formed steel portal frame buildings

The design optimization of a cold-formed steel portal frame building is considered in this paper. The proposed genetic algorithm (GA) optimizer considers both topology (i.e., frame spacing and pitch) and cross-sectional sizes of the main structural members as the decision variables. Previous GAs in the literature were characterized by poor convergence, including slow progress, that usually results in excessive computation times and/or frequent failure to achieve an optimal or near-optimal solution. This is the main issue addressed in this paper. In an effort to improve the performance of the conventional GA, a niching strategy is presented that is shown to be an effective means of enhancing the dissimilarity of the solutions in each generation of the GA. Thus, population diversity is maintained and premature convergence is reduced significantly. Through benchmark examples, it is shown that the efficient GA proposed generates optimal solutions more consistently. A parametric study was carried out, and the results included. They show significant variation in the optimal topology in terms of pitch and frame spacing for a range of typical column heights. They also show that the optimized design achieved large savings based on the cost of the main structural elements; the inclusion of knee braces at the eaves yield further savings in cost, that are significant.

Advanced design optimization of cold-formed steel portal frame buildings

The design optimization of cold-formed steel portal frame buildings is considered in this paper. The objective function is based on the cost of the members for the main frame and secondary members (i.e., purlins, girts, and cladding for walls and roofs) per unit area on the plan of the building. A real-coded niching genetic algorithm is used to minimize the cost of the frame and secondary members that are designed on the basis of ultimate limit state. It iis shown that the proposed algorithm shows effective and robust capacity in generating the optimal solution, owing to the population's diversity being maintained by applying the niching method. In the optimal design, the cost of purlins and side rails are shown to account for 25% of the total cost; the main frame members account for 27% of the total cost, claddings for the walls and roofs accounted for 27% of the total cost.

Multilayer optimization in radio resource allocation for the packet transmission in wireless networks

Na última década tem-se assistido a um crescimento exponencial das redes de comunicações sem fios, nomeadamente no que se refere a taxa de penetração do serviço prestado e na implementação de novas infra-estruturas em todo o globo. É ponto assente neste momento que esta tendência irá não só continuar como se fortalecer devido à convergência que é esperada entre as redes móveis sem fio e a disponibilização de serviços de banda larga para a rede Internet fixa, numa evolução para um paradigma de uma arquitectura integrada e baseada em serviços e aplicações IP. Por este motivo, as comunicações móveis sem fios irão ter um papel fundamental no desenvolvimento da sociedade de informação a médio e longo prazos. A estratégia seguida no projecto e implementação das redes móveis celulares da actual geração (2G e 3G) foi a da estratificação da sua arquitectura protocolar numa estrutura modular em camadas estanques, onde cada camada do modelo é responsável pela implementação de um conjunto de funcionalidades. Neste modelo a comunicação dá-se apenas entre camadas adjacentes através de primitivas de comunicação pré-estabelecidas. Este modelo de arquitectura resulta numa mais fácil implementação e introdução de novas funcionalidades na rede. Entretanto, o facto das camadas inferiores do modelo protocolar não utilizarem informação disponibilizada pelas camadas superiores, e vice-versa acarreta uma degradação no desempenho do sistema. Este paradigma é particularmente importante quando sistemas de antenas múltiplas são implementados (sistemas MIMO). Sistemas de antenas múltiplas introduzem um grau adicional de liberdade no que respeita a atribuição de recursos rádio: o domínio espacial. Contrariamente a atribuição de recursos no domínio do tempo e da frequência, no domínio espacial os recursos rádio mapeados no domínio espacial não podem ser assumidos como sendo completamente ortogonais, devido a interferência resultante do facto de vários terminais transmitirem no mesmo canal e/ou slots temporais mas em feixes espaciais diferentes. Sendo assim, a disponibilidade de informação relativa ao estado dos recursos rádio às camadas superiores do modelo protocolar é de fundamental importância na satisfação dos critérios de qualidade de serviço exigidos. Uma forma eficiente de gestão dos recursos rádio exige a implementação de algoritmos de agendamento de pacotes de baixo grau de complexidade, que definem os níveis de prioridade no acesso a esses recursos por base dos utilizadores com base na informação disponibilizada quer pelas camadas inferiores quer pelas camadas superiores do modelo. Este novo paradigma de comunicação, designado por cross-layer resulta na maximização da capacidade de transporte de dados por parte do canal rádio móvel, bem como a satisfação dos requisitos de qualidade de serviço derivados a partir da camada de aplicação do modelo. Na sua elaboração, procurou-se que o standard IEEE 802.16e, conhecido por Mobile WiMAX respeitasse as especificações associadas aos sistemas móveis celulares de quarta geração. A arquitectura escalonável, o baixo custo de implementação e as elevadas taxas de transmissão de dados resultam num processo de multiplexagem de dados e valores baixos no atraso decorrente da transmissão de pacotes, os quais são atributos fundamentais para a disponibilização de serviços de banda larga. Da mesma forma a comunicação orientada à comutação de pacotes, inenente na camada de acesso ao meio, é totalmente compatível com as exigências em termos da qualidade de serviço dessas aplicações. Sendo assim, o Mobile WiMAX parece satisfazer os requisitos exigentes das redes móveis de quarta geração. Nesta tese procede-se à investigação, projecto e implementação de algoritmos de encaminhamento de pacotes tendo em vista a eficiente gestão do conjunto de recursos rádio nos domínios do tempo, frequência e espacial das redes móveis celulares, tendo como caso prático as redes móveis celulares suportadas no standard IEEE802.16e. Os algoritmos propostos combinam métricas provenientes da camada física bem como os requisitos de qualidade de serviço das camadas superiores, de acordo com a arquitectura de redes baseadas no paradigma do cross-layer. O desempenho desses algoritmos é analisado a partir de simulações efectuadas por um simulador de sistema, numa plataforma que implementa as camadas física e de acesso ao meio do standard IEEE802.16e.

Application-specific modified particle swarm optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding he management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Optimization of bioplastics production from cheese whey

Dissertation presented in partial fulfillment of the requirements for the degree of Master in Biotechnology

A MOSFET-only wideband LNA exploiting thermal noise canceling and gain optimization

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

Downstream processing development of enveloped viruses for clinical applications: innovative tools for rational process optimization

Dissertation presented to obtain a Ph.D. degree in Engineering and Technology Sciences, Biotechnology at the Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.