Relevant bibliographies by topics / Mechanical optimizations

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Academic literature on the topic 'Mechanical optimizations'

Author: Grafiati
Published: 9 June 2023

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Journal articles on the topic "Mechanical optimizations"

1

Vlahopoulos, N., and C. G. Hart. "A Multidisciplinary Design Optimization Approach to Relating Affordability and Performance in a Conceptual Submarine Design." Journal of Ship Production and Design 26, no. 04 (2010): 273–89. http://dx.doi.org/10.5957/jspd.2010.26.4.273.

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A multidisciplinary design optimization (MDO) framework is used for a conceptual submarine design study. Four discipline-level performances—internal deck area, powering, maneuvering, and structural analysis—are optimized simultaneously. The four discipline-level optimizations are driven by a system level optimization that minimizes the manufacturing cost while at the same time coordinates the exchange of information and the interaction among the discipline-level optimizations. Thus, the interaction among individual optimizations is captured along with the impact of the physical characteristics of the design on the manufacturing cost. A geometric model for the internal deck area of a submarine is created, and resistance, structural design, and maneuvering models are adapted from theoretical information available in the literature. These models are employed as simulation drivers in the discipline-level optimizations. Commercial cost-estimating software is leveraged to create a sophisticated, automated affordability model for the fabrication of a submarine pressure hull at the system level. First, each one of the four discipline optimizations and also the cost-related top level optimization are performed independently. As expected, five different design configurations result, one from each analysis. These results represent the "best" solution from each individual discipline optimization, and they are used as reference for comparison with the MDO solution. The deck area, resistance, structural, maneuvering, and affordability models are then synthesized into a multidisciplinary optimization statement reflecting a conceptual submarine design problem. The results from this coordinated MDO capture the interaction among disciplines and demonstrate the value that the MDO system offers in consolidating the results to a single design that improves the discipline-level objective functions while at the same time produces the highest possible improvement at the system level.
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2

Mo, Yu Zhen, and Jia Chu Xu. "Studies on Mechanical Properties and Optimization Model of PI/SiO2 Nanocomposite Based on Materials Studio." Advanced Materials Research 1049-1050 (October 2014): 54–57. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.54.

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The unit cell models of PI/SiO2 nanocomposite was built by Materials Studio. The stiffness matrix and mechanical properties parameters such as Young modulus, shear modulus, bulk modulus and Poisson ratio of the unit cells were achieved after molecular dynamic (MD) optimizations and calculations. The influence factors on the mechanical properties of nanocomposite were analyzed. Finally, the optimization model was achieved.
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3

Xu, Haoran, Lingen Chen, Yanlin Ge, and Huijun Feng. "Four-Objective Optimization of an Irreversible Stirling Heat Engine with Linear Phenomenological Heat-Transfer Law." Entropy 24, no. 10 (2022): 1491. http://dx.doi.org/10.3390/e24101491.

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This paper combines the mechanical efficiency theory and finite time thermodynamic theory to perform optimization on an irreversible Stirling heat-engine cycle, in which heat transfer between working fluid and heat reservoir obeys linear phenomenological heat-transfer law. There are mechanical losses, as well as heat leakage, thermal resistance, and regeneration loss. We treated temperature ratio x of working fluid and volume compression ratio λ as optimization variables, and used the NSGA-II algorithm to carry out multi-objective optimization on four optimization objectives, namely, dimensionless shaft power output P¯s, braking thermal efficiency ηs, dimensionless efficient power E¯p and dimensionless power density P¯d. The optimal solutions of four-, three-, two-, and single-objective optimizations are reached by selecting the minimum deviation indexes D with the three decision-making strategies, namely, TOPSIS, LINMAP, and Shannon Entropy. The optimization results show that the D reached by TOPSIS and LINMAP strategies are both 0.1683 and better than the Shannon Entropy strategy for four-objective optimization, while the Ds reached for single-objective optimizations at maximum P¯s, ηs, E¯p, and P¯d conditions are 0.1978, 0.8624, 0.3319, and 0.3032, which are all bigger than 0.1683. This indicates that multi-objective optimization results are better when choosing appropriate decision-making strategies.
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4

Lee, Youngmyung, Yong-Ha Han, Sang-ok Park, and Gyung-Jin Park. "Vehicle crash optimization considering a roof crush test and a side impact test." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, no. 10 (2018): 2455–66. http://dx.doi.org/10.1177/0954407018794259.

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The vehicle performances for the side impact test and the roof crush test are dependent on the side structure design of a vehicle. Crash optimization can be employed to enhance the performances. A meta-model-based structural optimization technique is generally utilized in the optimization process since the technique is simple to use. However, the meta-model-based optimization is not suitable for problems with many design variables such as topology and topometry optimizations. A crash optimization methodology is proposed to consider both the side impact test and the roof crush test. The equivalent static loads method is adopted for the side impact test and the enforced displacement method is adopted for the roof crush test, and the two methods are integrated. A design formulation is defined. The survival distance from the side impact test and the roof strength for the roof crush test are used for the design constraints. Crash optimization is performed for a practical large-scale structure. For conceptual design, reinforcement of the B-pillar is determined by using topometry optimization, and size and shape optimizations are employed for a detailed design to satisfy the design constraints while the mass is reduced.
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5

Sun, S., Z. Fan, Y. Wang, and J. Haliburton. "Organic solar cell optimizations." Journal of Materials Science 40, no. 6 (2005): 1429–43. http://dx.doi.org/10.1007/s10853-005-0579-x.

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6

Du, Yun Peng, Guo Liang Hu, and Fei Dong. "Study on Structural Design and Optimization Analysis for 2V80 Engine Piston Based on Finite Element Analysis." Advanced Materials Research 753-755 (August 2013): 1188–91. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.1188.

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According to the experiment of engine bench test and piston's temperature field, the thermal boundary conditions and the mechanical conditions of piston are obtained. Under the condition of mechanical load and thermal load playing, the stress and strain of original engine is simulated and analyzed. Stress concentration and weak structure of original engine is found. The parameter of piston structure is optimized and the optimizations are put forward and analyzed to verify the optimizations' effectiveness. These provide a reference for the digital modeling and numerical analysis to solver engineering problems.
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7

Xiong, Zhe, Xiao-Hui Li, Jing-Chang Liang, and Li-Juan Li. "A Multi-Objective Hybrid Algorithm for Optimization of Grid Structures." International Journal of Applied Mechanics 10, no. 01 (2018): 1850009. http://dx.doi.org/10.1142/s1758825118500096.

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In this study, a novel multi-objective hybrid algorithm (MHGH, multi-objective HPSO-GA hybrid algorithm) is developed by crossing the heuristic particle swarm optimization (HPSO) algorithm with a genetic algorithm (GA) based on the concept of Pareto optimality. To demonstrate the effectiveness of the MHGH, the optimizations of four unconstrained mathematical functions and four constrained truss structural problems are tested and compared to the results using several other classic algorithms. The results show that the MHGH improves the convergence rate and precision of the particle swarm optimization (PSO) and increases its robustness.
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8

Wan, Zhi-Qiang, Xiao-Zhe Wang, and Chao Yang. "Integrated aerodynamics/structure/stability optimization of large aircraft in conceptual design." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 4 (2017): 745–56. http://dx.doi.org/10.1177/0954410016687143.

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The multidisciplinary design optimization is suitable for modern large aircraft, and it has the potential in conceptual phase of aircraft design especially. An integrated optimization method considering the disciplines of aerodynamics, structure and stability for large aircraft design in conceptual phase is presented. The objective is the minimum stiffness of a beam-frame wing structure subject to aeroelasticity, aerodynamics, and stability constraints. The aeroelastic responses are computed by commercial software MSC. Nastran, and the cruise stability is evaluated by the linear small-disturbance equations. A viscous-inviscid iteration method, which is composed of a computational fluid dynamics tool solving the Euler equations and a viscous correction method, is used for computing the flow over the model. The method ensures effective and rapid computation. In this paper, a complete aircraft model is optimized, and all the responses are computed in the trim condition with a fixed maximum takeoff weight. Genetic algorithm is utilized for global optimizations, and the optimal jig shape, the elastic axis positions and the stiffness distribution can be attained adequately. The results show that the method has a value of application in engineering optimizations. For the satisfaction of the total drag and stability constraints, the structure weight usually needs a price to pay. The integrated optimization captures the tradeoff between aerodynamics, structure and stability, and the repeated design can be avoided.
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9

López, Luis Fernando de Mingo, Francisco Serradilla García, José Eugenio Naranjo Hernández, and Nuria Gómez Blas. "Speed Proportional Integrative Derivative Controller: Optimization Functions in Metaheuristic Algorithms." Journal of Advanced Transportation 2021 (November 3, 2021): 1–12. http://dx.doi.org/10.1155/2021/5538296.

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Recent advancements in computer science include some optimization models that have been developed and used in real applications. Some metaheuristic search/optimization algorithms have been tested to obtain optimal solutions to speed controller applications in self-driving cars. Some metaheuristic algorithms are based on social behaviour, resulting in several search models, functions, and parameters, and thus algorithm-specific strengths and weaknesses. The present paper proposes a fitness function on the basis of the mathematical description of proportional integrative derivate controllers showing that mean square error is not always the best measure when looking for a solution to the problem. The fitness developed in this paper contains features and equations from the mathematical background of proportional integrative derivative controllers to calculate the best performance of the system. Such results are applied to quantitatively evaluate the performance of twenty-one optimization algorithms. Furthermore, improved versions of the fitness function are considered, in order to investigate which aspects are enhanced by applying the optimization algorithms. Results show that the right fitness function is a key point to get a good performance, regardless of the chosen algorithm. The aim of this paper is to present a novel objective function to carry out optimizations of the gains of a PID controller, using several computational intelligence techniques to perform the optimizations. The result of these optimizations will demonstrate the improved efficiency of the selected control schema.
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10

Nowak, M. "Improved aeroelastic design through structural optimization." Bulletin of the Polish Academy of Sciences: Technical Sciences 60, no. 2 (2012): 237–40. http://dx.doi.org/10.2478/v10175-012-0031-8.

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Abstract. The paper presents the idea of coupled multiphysics computations. It shows the concept and presents some preliminary results of static coupling of structural and fluid flow codes as well as biomimetic structural optimization. The model for the biomimetic optimization procedure was the biological phenomenon of trabecular bone functional adaptation. Thus, the presented structural bio-inspired optimization system is based on the principle of constant strain energy density on the surface of the structure. When the aeroelastic reactions are considered, such approach allows fulfilling the mechanical theorem for the stiffest design, comprising the optimizations of size, shape and topology of the internal structure of the wing.
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