Comparison and combination of NLPQL and MOGA algorithms for a marine medium-speed diesel engine optimisation

Hu, Nao; Zhou, Peilin; Yang, Jianguo

doi:10.1016/j.enconman.2016.11.066

Cited by 27 publications

(15 citation statements)

References 35 publications

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“…As shown in Figure 7, a series of sampling points were generated through Latin Hypercube Sampling (LHS) of the DOE, and used to construct the response surface model between parameters and objective functions. Then, the optimal solution to the model was searched for by the multi-objective genetic algorithm (MOGA) of the GDO [15][16][17][18].…”

Section: Figure 7 the Optimal Design Routementioning

confidence: 99%

Multi-Goal Driven Optimization of the Beam in Straight-Side Two-Point Press

Yang¹

2020

JESA

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Traditionally, the beams of mechanical presses are designed and optimized empirically. The empirical design cannot satisfy the increasingly strict requirements on product performance, quality, cost and appearance. This paper attempts to develop a lightweight design for the beam of a straight-side two-point press without sacrificing the strength and stiffness of the structure, thus saving materials and production cost. Our design was mainly developed through multi-goal-driven optimization (multi-GDO) based on ANSYS Workbench. Firstly, the finite-element model of the target beam was established with the aid of ANSYS Workbench and SolidWorks, followed by the static structural analysis on the beam structure. Next, the main dimensional parameters of the beam were subjected to sensitivity analysis, revealing the key factors affecting the beam dimensions. Finally, the multi-objective genetic algorithm (MOGA) was called to optimize the main design variables, and finalize the lightweight design of the beam. The lightweight design was found to greatly save the material and production cost. The research results lay the theoretical basis for beam improvement, and provide a reference for the optimization of other parts of mechanical press and other molding and forging machines.

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Section: Figure 7 the Optimal Design Routementioning

confidence: 99%

Multi-Goal Driven Optimization of the Beam in Straight-Side Two-Point Press

Yang¹

2020

JESA

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“…Here, the global search capability of an intelligent optimization algorithm (particle swarm optimization: PSO) [23,24] and the local search capability based on a sensitivity analysis gradient method (NLPQL) are used to achieve a balance between optimization quality and optimization efciency [25,26]. An optimized Latin hypercube test is used to design sample points to prevent running the NASTRAN nite element program every time in the iterative process.…”

Section: Sensitivity Analysis and Model Updatingmentioning

confidence: 99%

Finite Element Model Updating of Satellite Sailboard Based on Sensitivity Analysis

Luo

Wang

et al. 2019

Shock and Vibration

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The modal analysis of a satellite sailboard finite element model is carried out to accurately investigate the response of a satellite sailboard in a complex loaded space environment through simulation. The basic excitation vibration test of the satellite sailboard is used to perform model matching and a correlation test. Appropriate design variables are selected through sensitivity analysis. Modal analysis data and vibration table excitation test response data are used to modify the finite element model. After optimization, the orthogonality of the simulated vibration mode and experimental vibration mode is good. The low-order frequency errors in the simulation model are less than 5%, the high-order errors are less than 10%, and the modal confidence MAC values are above 0.8. The modal frequency and mode shape are closer to the experimental modal frequency and mode shape, respectively. The simulation and test acceleration response of the modified finite element model of a honeycomb panel are compared under the two conditions of sine sweep and random vibration. The acceleration response curves of reference points are consistent, and amplitude and frequency errors are within acceptable limits. The model updating effect is evident, which provides good reference for research on satellites and other aerospace products.

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“…5,6 Engine optimization in general is rated as evolutionary or heuristic as in genetic algorithm (GA) 7 or nonevolutionary or gradient-based method as nonlinear programming by quadratic Lagrangian (NLPQL) 8 or Nelder-Mead algorithm. 9 Hu et al [10][11][12] have performed an optimization work on a medium speed marine diesel engine where it serves as an effective tool to reduce emissions and increase the engine operation metrics. Their study proved the robustness and efficiency of NLPQL in order to have NOx and soot emissions reduced as well as fuel consumption reduction through manipulation in injection schemes.…”

Section: Exergy Analysis Of the Baseline And Optimized Engine Charactmentioning

confidence: 99%

“…Hu et al have performed an optimization work on a medium speed marine diesel engine where it serves as an effective tool to reduce emissions and increase the engine operation metrics. Their study proved the robustness and efficiency of NLPQL in order to have NOx and soot emissions reduced as well as fuel consumption reduction through manipulation in injection schemes.…”

Section: Introductionmentioning

confidence: 99%

Optimization of a DI diesel engine to reduce emission and boost power by exergy and NLPQL method

Taghavifar

Anvari

2019

Env Prog and Sustain Energy

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The design and manufacture of efficient diesel engines, which can convert the majority of fuel combustion into a mechanical rotation movement is still growing. Simultaneous investigation of optimum engine configuration and the second law of thermodynamics consideration can best achieve this goal. A baseline 1.8 Ford diesel engine is simulated via AVL Fire software and then the multiobjective nonlinear programming by quadratic Lagrangian method have been applied to find the global optimal design. Then for exergy analysis of the baseline and optimized engine characteristics, a FORTRAN developed code is used. Two basic optimization cases are detected for exergy assessment that are RunID41 (feasible design) and RunID35 (infeasible design) among 55 run points. It was determined that the optimum feasible solution should characterize with low inner wall diameter (Di) with wider half spray cone angle to avoid wall collision in order to stay in the constraint zone and feasible area. The results of exergy also indicates that RunID41 is fuel efficient since the fuel burnt exergy of three design of ID41, ID35, and baseline are 719.52, 694.77, and 643.07 J, respectively. It has also been pointed out that the feasible optimum gives the higher thermo‐mechanical exergy, work exergy, and total exergy, while the infeasible optimum yields a higher heat release rate exergy and chemical exergy as there are no restrictions of injection and combustion chamber geometry set by the designer.

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Comparison and combination of NLPQL and MOGA algorithms for a marine medium-speed diesel engine optimisation

Cited by 27 publications

References 35 publications

Multi-Goal Driven Optimization of the Beam in Straight-Side Two-Point Press

Multi-Goal Driven Optimization of the Beam in Straight-Side Two-Point Press

Finite Element Model Updating of Satellite Sailboard Based on Sensitivity Analysis

Optimization of a DI diesel engine to reduce emission and boost power by exergy and NLPQL method

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