Multi-Objective Optimization of Transonic Variable Camber Airfoil with Leading- and Trailing-Edge Deflections Using Kriging Surrogate Model
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| Publicado en: | Aerospace vol. 12, no. 8 (2025), p. 659-683 |
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| Otros Autores: | , , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2226-4310 | ||
| 024 | 7 | |a 10.3390/aerospace12080659 |2 doi | |
| 035 | |a 3243962072 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231330 |2 nlm | ||
| 100 | 1 | |a Wang, Wei | |
| 245 | 1 | |a Multi-Objective Optimization of Transonic Variable Camber Airfoil with Leading- and Trailing-Edge Deflections Using Kriging Surrogate Model | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a To investigate the aerodynamic characteristics and multi-objective optimization of the variable camber airfoils, the influence of leading- and trailing-edge deflections on aerodynamic performance is conducted. A novel prediction model is presented using the Kriging surrogate model, with leading and trailing edge deflection angles as inputs and lift coefficients and drag coefficients as outputs. The Non-dominated Sorting Genetic Algorithm II (NSGA II) multi-objective optimization technique is applied to ascertain the ideal deflection parameters. The results show that upward deflection of the leading edge raises the lift, whereas downward deflection increases the value of the critical angle of attack. The deflection of the trailing edge increases the value of the critical angle of attack, while the downward deflection can enhance the lift coefficient. Appropriate upward deflections of both leading and trailing edges can delay the critical Mach number, while downward deflections of both the leading and trailing edges can enhance the value of the critical Mach number. The discrepancies between the Kriging model prediction and the CFD simulation are less than 2%. Compared to the basic airfoil, the aerodynamic performance of the optimized airfoil has been improved, with the lift coefficient increasing by 7.55% and 7.37% and the lift-to-drag ratio rising by 6.97% and 10.27% at two Mach numbers, respectively. The efficiency and reliability of this method have been verified. | |
| 610 | 4 | |a National Aeronautics & Space Administration--NASA | |
| 653 | |a Software | ||
| 653 | |a Accuracy | ||
| 653 | |a Drag coefficients | ||
| 653 | |a Trailing edges | ||
| 653 | |a Investigations | ||
| 653 | |a Aerodynamic coefficients | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Camber | ||
| 653 | |a Airfoils | ||
| 653 | |a Pressure distribution | ||
| 653 | |a Multiple objective analysis | ||
| 653 | |a Sorting algorithms | ||
| 653 | |a Pareto optimum | ||
| 653 | |a Aerodynamic characteristics | ||
| 653 | |a Efficiency | ||
| 653 | |a Aircraft | ||
| 653 | |a Drag | ||
| 653 | |a Deflection | ||
| 653 | |a Design optimization | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Evolution & development | ||
| 653 | |a Prediction models | ||
| 653 | |a Mach number | ||
| 653 | |a Aerodynamics | ||
| 653 | |a Neural networks | ||
| 653 | |a Optimization | ||
| 653 | |a Critical angle | ||
| 653 | |a Variables | ||
| 653 | |a Angle of attack | ||
| 700 | 1 | |a He, Feng | |
| 700 | 1 | |a Cui Shenao | |
| 700 | 1 | |a Li Zhandong | |
| 773 | 0 | |t Aerospace |g vol. 12, no. 8 (2025), p. 659-683 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3243962072/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3243962072/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3243962072/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |