The present work describes the overall optimization strategy that has been adopted for the enhancement of the aerodynamic performance of a civil tiltrotor empennage surfaces. The optimization process has been designed...
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ISBN:
(数字)9781624106101
ISBN:
(纸本)9781624106101
The present work describes the overall optimization strategy that has been adopted for the enhancement of the aerodynamic performance of a civil tiltrotor empennage surfaces. The optimization process has been designed around GeDEA-II, a multi-objectiveevolutionary Algorithm developed at University of Padua. The optimization algorithm has been used in two different cases: a two-dimensional optimization of the empennage airfoil and a threedimensional optimization of the empennage winglets, patented by Leonardo Helicopters under the name of finlets. Results demonstrate the effectiveness of the optimization strategies for both the cases. A parametric study of the empennage planform has also been conducted with the aid of an artificial neural network, in order to assess the variations in aerodynamic performance for different geometries.
The design of complex system architectures brings with it a number of challenging issues, among others large combinatorial design spaces. Optimization can be applied to explore the design space, however gradient-based...
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ISBN:
(数字)9781624106101
ISBN:
(纸本)9781624106101
The design of complex system architectures brings with it a number of challenging issues, among others large combinatorial design spaces. Optimization can be applied to explore the design space, however gradient-based optimization algorithms cannot be applied due to the mixed-discrete nature of the design variables. It is investigated how effective surrogate-based optimization algorithms are for solving the black-box, hierarchical, mixed-discrete, multiobjective system architecture optimization problems. Performance is compared to the NSGAII multi-objectiveevolutionary algorithm. An analytical benchmark problem that exhibits most important characteristics of architecture optimization is defined. First, an investigation into algorithm effectiveness is performed by measuring how accurately a known Pareto-front can be approximated for a fixed number of function evaluations. Then, algorithm efficiency is investigated by applying various multi-objective convergence criteria to the algorithms and establishing the possible trade-off between result quality and function evaluations needed. Finally, the impact of hidden constraints on algorithm performance is investigated. The code used for this paper has been published.
On the background of growing demand in air traffic, over-capacity of airports occur. When the number of aircraft exceeds the capacity of an airport, aircraft have to take a detour or keep making circles on the particu...
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ISBN:
(数字)9781624105890
ISBN:
(纸本)9781624105890
On the background of growing demand in air traffic, over-capacity of airports occur. When the number of aircraft exceeds the capacity of an airport, aircraft have to take a detour or keep making circles on the particular points to adjust the arrival time. However, these time adjustments trigger not only the arrival delay but also increase of fuel consumption. The objective of this study is to make efficient flight schedules with less congestion and enough resilience against traffic problems. In this study, a multi-objective air traffic optimization is conducted. The objective functions are minimization of averaged arrival-delay, the ratio of the number of delayed aircraft, and the mean fuel consumption. The design variables are the departure-time offsets of each domestic aircraft toward Tokyo International Airport. As a simulator, cellular automaton is adopted. As the result of optimization, a lot of optimal solutions are obtained. These solutions can decrease not only the arrival delay but also fuel consumption. This indicates that the optimization of ground movements in airports contribute to more efficient air-traffic operation.
Systematic modeling of architecture design spaces is needed when architecting complex systems, to support experts in making less biased decisions, and to formulate the optimization problem needed to explore the large ...
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ISBN:
(数字)9781624105982
ISBN:
(纸本)9781624105982
Systematic modeling of architecture design spaces is needed when architecting complex systems, to support experts in making less biased decisions, and to formulate the optimization problem needed to explore the large combinatorial design space. Existing methods do not offer enough compatibility with the Model-Based Systems Engineering (MBSE) approaches, cannot model all needed design scenarios, or are not flexible enough when it comes to architecture evaluation. A new method is presented that provides a semantic representation of the architecture design space, modeled as the Architecture Design Space Graph (ADSG). The ADSG represents three types of architectural decisions: function-component mapping, component characterization, and component connection. The ADSG is constructed from a design space definition, and discrete architectural decisions are automatically inserted according to specified rules. Once decisions and metrics have been defined, the hierarchical, mixed-integer, multi-objective optimization problem can be formulated: decisions are mapped to design variables, and performance metrics are mapped to objectives or constraints. An application of the method to the Apollo mission architecting problem is presented.
Entry, Descent, and Landing (EDL) architecture analysis relies on uncertainty quantification methods, such as Monte Carlo dispersion analysis, to assess system performance, and identify areas of risk. These simulation...
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ISBN:
(数字)9781624105951
ISBN:
(纸本)9781624105951
Entry, Descent, and Landing (EDL) architecture analysis relies on uncertainty quantification methods, such as Monte Carlo dispersion analysis, to assess system performance, and identify areas of risk. These simulations are critical throughout the entire mission lifecycle due to the inherent limitations of system verification and validation on Earth. As a mission lifecycle progresses, fidelity and complexity of these simulations increase, leading to large datasets that have to be manually examined by subject matter experts to identify correlations between parameters and calculate figures of merit. Motivated by the high cognitive load imposed by this process, this paper discusses the use of a cognitive assistant to provide a platform for interactive and collaborative explanation of EDL architecture analysis. The paper starts by describing the process of architecture analysis of EDL systems, identifying some of the main challenges leading to high cognitive workload and motivating the use of cognitive assistants. We then present a short survey of the techniques used to explain complex engineering simulations. The general architecture of the Daphne/EDL cognitive assistant is then described, focusing on the use case of explaining the results of a set of EDL simulations. Three functions are described in detail, namely 1) summarizing the statistics of large datasets (summarization), 2) identifying the input variables that appear to be driving the output variables (sensitivity analysis), and 3) identifying features and behaviors that appear to be common among failing cases or some other region of interest of the design space (association rule mining).
This paper describes the changes done to Daphne, a virtual assistant for architecting earth observing satellite systems, to turn it from a reactive assistant that only acts when asked by the user into a proactive assi...
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ISBN:
(数字)9781624105784
ISBN:
(纸本)9781624105784
This paper describes the changes done to Daphne, a virtual assistant for architecting earth observing satellite systems, to turn it from a reactive assistant that only acts when asked by the user into a proactive assistant that can perform actions without not directly linked to a user request, taking its own initiative. Specifically, the paper describes a new way for Daphne to communicate with the user with Websockets that allows for a broader range of interactivity. The new features enabled by this new communication system are: (1) an agent that searches over the space of designs and shows interesting designs to the user;(2) an agent that tries to encourage the user to diversify their search of the tradespace;and (3) a live recommender system that acts when the user is modifying a certain design by suggesting changes that are likely to improve cost and/or performance. The paper also describes changes in the existing sub-systems as well as the interface to accommodate the new systems and interactivity. Finally, the paper has a short discussion on how a common user case scenario would unfold with all these new features.
With the advancement in high performance computing and numerical optimization techniques, engineering design optimization problems are becoming more complex, larger scale, higher fidelity, and computationally more dem...
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ISBN:
(数字)9781624105784
ISBN:
(纸本)9781624105784
With the advancement in high performance computing and numerical optimization techniques, engineering design optimization problems are becoming more complex, larger scale, higher fidelity, and computationally more demanding, requiring longer run times than ever before. There exists methodologies and techniques that can address some of these challenges but very few can address all, and most are limited in the extent that these concerns can be addressed. With the goal of addressing such challenging engineering problems, we developed a new optimization algorithm, named AMIEGO, that combines concepts from surrogate-based optimization approaches, gradient-based numerical methods, Partial Least Squares, evolutionaryalgorithms, and Branch-and-Bound, providing newer capabilities that were not previously perceived. The effort here builds upon this previously developed optimization algorithm to include multiple infill sampling capability that combines the concept of generalized expected improvement function, unsupervised learning, and multi-objectiveevolutionary technique. To demonstrate, AMIEGO with the multiple infill capability (called AMIEGO-MIMOS) solves a series of increasingly difficult engineering design optimization problems. The results reveal the performance of the new approach is problem dependent. When applied to a ten-bar truss problem, the newly proposed multiple infill strategy consistently leads to a better design solutions when compared to the existing CPTV method (implemented with the context of the AMIEGO algorithm). On the other hand, when applied to a mixed-integer high fidelity wing topology optimization problem - MIMOS, despite showing a steeper convergence at the start, eventually leads to an inferior solution as compared to CPTV approach. These results also reveal that a small number of starting points, in general, are sufficient to lead to a good overall solution.
Parameter extraction in organic thin film transistors (OTFTs) is a labour intensive task when contact effects are present. In this work, a constrained many-objectiveevolutionary parameter extraction procedure is used...
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ISBN:
(纸本)9781538657799
Parameter extraction in organic thin film transistors (OTFTs) is a labour intensive task when contact effects are present. In this work, a constrained many-objectiveevolutionary parameter extraction procedure is used to determine the parameters of a compact model for the current-voltage characteristics of OTFTs that also includes a model for the contacts. This evolutionary procedure ensures that the extracted parameters comply with the physical meaning on which they are based by adding rules in form of optimization objectives and constrains for the different parameters. The evolutionary procedure is applied to experimental output characteristics of OTFTs. Our numerical results show an excellent agreement with the experimental data.
The design of high-lift systems represents a challenging task within the aerospace community, being a multidisciplinary, multi-objective, and multipoint problem. The Design, Simulation and Flight Reynolds Number Testi...
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The design of high-lift systems represents a challenging task within the aerospace community, being a multidisciplinary, multi-objective, and multipoint problem. The Design, Simulation and Flight Reynolds Number Testing for Advanced High-Lift Solution project, funded by the European Commission under the Seventh Framework Programme, aimed at improving the aerodynamics of high-lift systems by developing, in a coordinated approach, both efficient numerical design strategies and measurement techniques for cryogenic conditions. Within the Design, Simulation and Flight Reynolds Number Testing for Advanced High-Lift Solution project, different partners used several numerical automatic optimization strategies for high-lift system design purposes. A realistic multi-objective and multipoint optimization problem was defined and solved by adopting different flow model dimensionality, meshing techniques, geometry parameterization, and optimization strategies. Special attention was devoted to perform a fair comparison of the results, and useful information was obtained about trends, pros, and cons of the approaches used. The outcome of these activities is that an efficient design process can be set up through decoupling of the original multi-objective problem into several, sequential suboptimization processes. Nevertheless, several decoupling possibilities exist, and the most efficient one can be identified only on the bases of preanalysis or preknowledge of the specific problem. Second, the exercise carried out demonstrated the maturity and feasibility of a full three-dimensional automatic high-lift design.
Synthetic biology exploits the of mathematical modeling of synthetic circuits both to predict the behavior of the designed synthetic devices, and to help on the selection of their biological components. The increasing...
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