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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Boncoraglio, Gabriele Farhat, Charbel Stanford Univ Dept Aeronaut & Astronaut Durand BldgRoom 2242 Stanford CA 94305 USA Stanford Univ Dept Aeronaut & Astronaut Aircraft Struct Durand BldgRoom 257 Stanford CA 94305 USA

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

A computational framework is proposed for efficiently solving multidisciplinary analysis and optimization (MDAO) problems in a relatively high-dimensional design space. It relies on the appropriate blend of linear and nonlinear projection-based model order reduction to accelerate analysis and optimization;and on a new concept of nonlinear active manifold (AM) to mitigate the curse of dimensionality during the training of projection-based reduced-order models (PROMs). The method of AM relies on the nonlinear concept of a deep convolutional autoencoder for dimensionality reduction: it is proposed as a superior alternative to the concept of an active subspace whose capabilities are limited by the capabilities of an affine approximation. The proposed computational framework also blends the concept of a global PROM as a surrogate model of a discretized nonlinear partial differential equation (PDE)based behavior function with that of a database of local, linear PROMs for approximating, wherever appropriate, a linear PDE-based behavior function. Adaptive parameter sampling is performed for training the global, nonlinear PROM as well as for populating the database of local, linear PROMs. The proposed computational framework is demonstrated for the solution of an MDAO problem formulated for a flexible instance of NASA's Common Research Model, where the objective function pertains to aerodynamics in the transonic regime, the constraint relates to flutter, and the design space contains 58 structural and aerodynamic shape design variables. The obtained numerical results illustrate the potential of the AM method for addressing the curse of dimensionality. They also demonstrate the feasibility of the computational framework for realistic MDA Oproblems and highlight its potential for reducing solution time.

关键词： Model Order Reduction Aerodynamic Configurations Computational Fluid Dynamics Fuselages Computational Structural Dynamics Greedy algorithm Structural Modeling Damping Ratio optimization algorithm Sweep Angle

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System Architecture optimization: An Open Source Multidisciplinary Aircraft Jet Engine Architecting Problem

System Architecture Optimization: An Open Source Multidiscip...

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AIAA Aviation Forum and Exposition

作者： Bussemaker, J. H. De Smedtt, T. La Rocca, G. Ciampa, P. D. Nagel, B. DLR German Aerosp Ctr Inst Syst Architectures Aeronaut MDO Grp Aircraft Design & Syst Integrat Hamburg Germany Delft Univ Technol Fac Aerosp Engn Flight Performance & Prop Delft Netherlands

ISBN: (数字)9781624106101

ISBN: (纸本)9781624106101

Decisions regarding the system architecture are important and taken early in the design process, however suffer from large design spaces and expert bias. Systematic design space exploration techniques, like optimization, can be applied to system architecting. Realistic engineering benchmark problems are needed to enable development of optimization algorithms that can successfully solve these black-box, hierarchical, mixed-discrete, multi-objective architecture optimization problems. Such benchmark problems support the development of more capable optimization algorithms, more suitable methods for modeling system architecture design space, and educating engineers and other stakeholders on system architecture optimization in general. In this paper, an engine architecting benchmark problem is presented that exhibits all this behavior and is based on the open-source simulation tools pyCycle and OpenMDAO. Next to thermodynamic cycle analysis, the proposed benchmark problem includes modules for the estimation of engine weight, length, diameter, noise and NOx emissions. The problem is defined using modular interfaces, allowing to tune the complexity of the problem, by varying the number of design variables, objectives and constraints. The benchmark problem is validated by comparing to pyCycle example cases and existing engine performance data, and demonstrated using both a simple and a realistic problem formulation, solved using the multiobjective NSGA-II algorithm. It is shown that realistic results can be obtained, even though the design space is subject to hidden constraints due to the engine evaluation not converging for all design points.

关键词： Aircrafts Afterburner Thermodynamic Cycles optimization algorithm Black Box Space Exploration Thrust Specific Fuel Consumption Turbofan Engines Airframes Model Based System Engineering

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Impact of Turbulence Models on Robust Aerodynamic Shape optimization of 3-D Wing Geometries

Impact of Turbulence Models on Robust Aerodynamic Shape Opti...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Vuruskan, Aslihan Hosder, Serhat Florida Polytech Univ Mech Engn Dept Mech Engn Lakeland FL 33805 USA Missouri Univ Sci & Technol Aerosp Engn Dept Mech & Aerosp Engn Rolla MO 65409 USA

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

The objective of this paper is to investigate the impact of two commonly used turbulence models in Reynolds-Averaged Navier-Stokes simulations (the Spalart-Allmaras and the Menter's Shear Stress Transport models) on the three-dimensional optimum wing design obtained with the gradient-based deterministic and robust aerodynamic shape optimization in transonic, viscous, turbulent flow. In particular, the main contribution of this study to aerodynamic design area is to evaluate the impact of turbulence models and different weight distributions in the multi-objective function (equal, mean-biased, and variance-biased) on the computational cost, optimal shape and its performance under Mach number uncertainty obtained with robust optimization. The results of the study show that the effect of the weight distribution in the objective function is more significant than the effect of turbulence model on the final shape obtained with robust design at lower off-design Mach numbers. Robust design tends to mitigate the impact of the turbulence model selection on the optimum shape and performance over the uncertain Mach number range, whereas the choice of the turbulence model becomes significant at off-design conditions for the optimal shapes obtained with deterministic design. This study also demonstrates the effectiveness of using stochastic expansions in robust aerodynamic shape optimization of three-dimensional wings.

关键词： Turbulence Models Aerodynamic Shape optimization Wing Geometry Robust Design Reynolds Averaged Navier Stokes Aerodynamic Flows Shear Stress Lift Coefficient Computational Fluid Dynamics optimization algorithm

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Building Geometry as a Variable in Energy, Comfort, and Environmental Design optimization-A Review from the Perspective of Architects

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BUILDINGS 2022年第1期12卷 69页

作者： Kistelegdi, Istvan Horvath, Kristof Roland Storcz, Tamas Ercsey, Zsolt Univ Pecs Janos Szentagothai Res Ctr Fac Engn & Informat Technol Dept Bldg Struct & Energy DesignEnergia Design R H-7624 Pecs Hungary Univ Pecs Fac Engn & Informat Technol Dept Syst & Software Technol H-7624 Pecs Hungary

Due to negative environmental impacts caused by the building industry, sustainable buildings have recently become one of the most investigated fields in research. As the design technique itself is mainly responsible for building performance, building energy design optimization is of particular interest. Several studies concentrate on systems, operation, and control optimization, complemented by passive strategies, specifically related to the envelope. In building physics, different architectural considerations, in particular, the building's shape, are essential variables, as they greatly influence the performance of a building. Most scientific work that takes into consideration building geometry explores spaces without any energy optimization or calculates optimization processes of a few basic variables of simplified space geometries. Review studies mainly discuss the historic development of optimization algorithms, building domains, and the algorithm-system and software framework performance with coupling issues. By providing a systemized clustering of different levels of shape integration intensities, space creation principals, and algorithms, this review explores the current status of sustainability related shape optimization. The review proves that geometry design variable modifications and, specifically, shape generation techniques offer promising optimization potential;however, the findings also indicate that building shape optimization is still in its infancy.

关键词： building shape optimization building geometry design variables energy efficiency comfort performance building physics simulation optimization algorithm design method

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Multi-Working Point Performance optimization of Variable Cycle Engine Based on Ensemble Radial Basis Function Neural Network

Multi-Working Point Performance Optimization of Variable Cyc...

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AIAA Propulsion and Energy Forum / IEEE Electric Aircraft Technologies Symposium (EATS)

作者： Ye, Yifan Wang, Zhanxue Zhang, Xiaobo Northwestern Polytech Univ Sch Power & Energy Xian 710129 Shaanxi Peoples R China

ISBN: (数字)9781624106118

ISBN: (纸本)9781624106118

This paper proposed a multi-working point performance optimization algorithm based on the radial basis function neural network (RBFNN) for a variable cycle engine. The ensemble RBFNN is used to replace the engine performance simulation model during the optimization process to improve the optimization efficiency. Also, an enhanced multi-objective differential evolution algorithm is proposed to improve the Pareto solutions quality and convergence rate. Finally, a sample for three working points performance optimization of a double-bypass variable cycle engine is presented. This work optimizes a series of variable cycle engine design parameters and adjusting schedules, which consider high takeoff thrust and low cruise fuel consumption.

关键词： Neural Networks Variable Cycle Engines optimization algorithm Fuel Consumption Numerical Modeling High Pressure Turbine Bypass Ratio Particle Swarm optimization Air Flow Rate Compressor Surge

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Structural Design optimization Based on the Moving Baseline Strategy

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Acta Mechanica Solida Sinica 2020年第3期33卷 307-326页

作者： Xiaojun Wang Qiang Ren Wenpin Chen Yisi Liu Lei Wang Xuyun Ding Institute of Solid Mechanics School of Aeronautic Science and EngineeringBeihang UniversityBeijing 100083China Shanghai Marine Equipment Research Institute(SMERI) Shanghai 200031China

Structural design optimization has always been a topic of concern in industry because good design can improve the safety and economic efficiency of structures during their service *** the appropriate optimization algorithm is the key to solving structural optimal design *** this study,a new global optimization idea is proposed and named the moving baseline strategy.A baseline is initially set and will be repeatedly moving upward or downward to approach the optimal *** proposed strategy is a simple but effective,general,and stable algorithm that can be used to solve constrained and unconstrained structural optimization *** from traditional gradient-based,stochastic and heuristic algorithms,the developed algorithm provides a completely new idea to solve global or local optimization *** unconstrained and constrained numerical benchmark examples are used to test the proposed *** addition,structural optimal design problems of a ten-bar planar truss structure and a hypersonic wing structure(X-37B)are utilized to verify the effectiveness of the developed strategy in addressing structural design optimization problems in engineering.

关键词： Structural design Global optimization optimization algorithm Moving baseline strategy Constrained optimization

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A systematic literature review on software security testing using metaheuristics

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AUTOMATED SOFTWARE ENGINEERING 2024年第2期31卷 44-44页

作者： Ahsan, Fatma Anwer, Faisal Aligarh Muslim Univ Dept Comp Sci Aligarh 202002 Uttar Pradesh India

The security of an application is critical for its success, as breaches cause loss for organizations and individuals. Search-based software security testing (SBSST) is the field that utilizes metaheuristics to generate test cases for the software testing for some pre-specified security test adequacy criteria This paper conducts a systematic literature review to compare metaheuristics and fitness functions used in software security testing, exploring their distinctive capabilities and impact on vulnerability detection and code coverage. The aim is to provide insights for fortifying software systems against emerging threats in the rapidly evolving technological landscape. This paper examines how search-based algorithms have been explored in the context of code coverage and software security testing. Moreover, the study highlights different metaheuristics and fitness functions for security testing and code coverage. This paper follows the standard guidelines from Kitchenham to conduct SLR and obtained 122 primary studies related to SBSST after a multi-stage selection process. The papers were from different sources journals, conference proceedings, workshops, summits, and researchers' webpages published between 2001 and 2022. The outcomes demonstrate that the main tackled vulnerabilities using metaheuristics are XSS, SQLI, program crash, and XMLI. The findings have suggested several areas for future research directions, including detecting server-side request forgery and security testing of third-party components. Moreover, new metaheuristics must also need to be explored to detect security vulnerabilities that are still unexplored or explored significantly less. Furthermore, metaheuristics can be combined with machine learning and reinforcement learning techniques for better results. Some metaheuristics can be designed by looking at the complexity of security testing and exploiting more fitness functions related to detecting different vulnerabilities.

关键词： SBSST Meta-heuristic optimization algorithm Evolutionary algorithm Software security testing Code coverage XSS SQLI XMLI Program crash

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Prediction of wetland soil carbon storage based on near infrared hyperspectral imaging and deep learning

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INFRARED PHYSICS & TECHNOLOGY 2024年 139卷

作者： Jia, Liangquan Yang, Fu Chen, Yi Peng, Liqiong Leng, Huanan Zu, Weiwei Zang, Ying Gao, Lu Zhao, Mingxing Huzhou Univ Sch Informat Engn Huzhou 313000 Peoples R China Fujian Med Univ Fuzhou 350122 Peoples R China Huzhou Nat Resources & Planning Bur Huzhou 313099 Peoples R China

To enhance the accuracy of predicting wetland soil carbon storage and address the issues associated with traditional time-consuming, labor-intensive, and costly detection methods, we adopted a novel approach integrating near-infrared hyperspectral imaging technology with deep learning. Initially, near-infrared hyperspectral imaging was utilized to capture spectral images, from which soil reflectance features were extracted. Subsequently, the data processing phase involved extracting average spectral band reflectance values, serving as inputs for the model with carbon storage values, detected through chemical methods, used as outputs. The continuous projection algorithm (SPA) was applied to eliminate redundant band information, thereby retaining only the bands correlated with carbon storage. In the model establishment phase, four different preprocessing methods were applied to build the models, followed by the utilization of an improved particle swarm optimization (PSO) algorithm to optimize the long-short term memory (LSTM) neural network. It was the improved PSO-LSTM model that yielded significant predictive performance, achieving a coefficient of determination (R2) of 0.95, a root mean square error (RMSE) of 1.23%, and a ratio of performance to deviation (RPD) of 4.47. This method provides crucial support for the rapid, accurate, and non-destructive detection of soil carbon storage in modern wetlands, thereby contributing to the global carbon cycling and climate change studies and supporting future agricultural production efforts.

关键词： Near-infrared hyperspectral imaging Soil carbon storage prediction Deep learning Machine learning optimization algorithm

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Novel GIL mechanical fault diagnosis method based on multi-sensor data feature fusion and TDEAVOA-ELM

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COMPUTERS & ELECTRICAL ENGINEERING 2024年第PartB期119卷

作者： Hu, Tianyu Ma, Hongzhong Duan, Dawei Ge, Wei Hohai Univ Sch Elect & Power Engn Nanjing 211100 Peoples R China Anhui Sci & Technol Univ Sch Elect & Elect Engn Chuzhou 233100 Peoples R China

The gas insulated transmission line (GIL), a crucial component of the power system, faces challenges in mechanical fault diagnosis including difficulties in quantifying sensor signal selection, balancing recognition accuracy, and model training speed. This paper proposes a GIL mechanical fault diagnosis method based on the feature fusion of multi-channel vibration sensor signals and an improved optimization extreme learning machine (ELM). Initially, three optimal signals are selected based on the correlation coefficients of multiple signals, and their time-frequency domain features are extracted. These features are then fused using neighborhood preserving embedding (NPE) and input into the improved optimized ELM model. The enhanced optimization algorithm reduces the likelihood of falling into local optima, thereby improving the model's recognition performance. Experimental results demonstrate that this method effectively enhances the recognition accuracy of mechanical faults in GIL equipment and enables rapid diagnosis.

关键词： Gas insulated transmission lines Signal selection Multi-sensor data fusion Artificial intelligence Mechanical fault optimization algorithm

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Observations on Impedance Eduction in a Grazing Flow Duct Facility 28

Observations on Impedance Eduction in a Grazing Flow Duct Fa...

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28th AIAA/CEAS Aeroacoustics Conference

作者： Eversman, Walter Drouin, Mary Missouri Univ Sci & Technol Rolla MO 65401 USA Spirit AeroSyst Inc Wichita KS 67210 USA

ISBN: (数字)9781624106644

ISBN: (纸本)9781624106644

This paper focuses on details of impedance eduction based on grazing flow duct testing. In this indirect approach the impedance of a sample of acoustic material, imbedded in one wall of the duct, is deduced (educed) from measurements of the acoustic field in the duct and comparison with a theoretical model of the acoustic field in the duct. The theoretical model is adjusted with a systematic variation of impedance to search for and achieve the best match of measurement and prediction. For measurement accuracy the mean flow is limited to approximately 0 <= M <= 0.4, and to a maximum frequency dependent on the duct geometry and grazing flow Mach number. The goal of the eduction procedure reported here is to provide a research quality capability to determine the impedance of samples of uniform normally reacting acoustic liners designed for turbo-fan engine inlet and by-pass ducts. Additional goals include relatively low cost of instrumentation and rapid turnaround of testing and data post-processing. Instrumentation includes only two microphones, one fixed reference microphone and one traversing microphone. The measured acoustic field is defined by FFT processing at multiple dwell points of the traversing microphone on the duct wall opposite to the lining sample to produce an acoustic transfer function. A finite element propagation code is used for theoretical prediction of the acoustic transfer function. Specific details addressed here include a unique implementation of the finite element method for the convected wave equation tailored for the duct geometry and frequency range. The propagation model allows the duct termination plane to be represented by measured acoustic pressure or by a separately educed termination impedance. Comparisons are made of educed impedance spectra obtained with the two termination choices and it is found that the best quality impedance spectra are obtained with the termination impedance boundary condition. A supporting analysis is provided

关键词： Mach Numbers Finite Element Formulation Engine Inlet Turbo Fan Overall Sound Pressure Level Acoustic Propagation Numerical Modeling Flow Conditions optimization algorithm Acoustic Measurement

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