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optimization for aircraft engines with regression and neural-network analysis approximators

JOURNAL OF PROPULSION AND POWER 2001年第1期17卷 85-92页

作者： Patnaik, SN Guptill, JD Hopkins, DA Lavelle, TM NASA John H Glenn Res Ctr Lewis Field Computat Sci Branch Cleveland OH 44135 USA Ohio Aerosp Inst Brookpark OH 44142 USA NASA John H Glenn Res Ctr Lewis Field Machine Dynam Branch Cleveland OH 44135 USA NASA John H Glenn Res Ctr Lewis Field Prop Syst Anal Off Cleveland OH 44135 USA

The NASA Engine Performance Program (NEPP) can configure and analyze almost any type of gas-turbine engine that can be generated through the interconnection of a set of standard physical components, In addition, the code can optimize engine performance by changing adjustable variables under a set of constraints, For engine-cycle problems at certain operating points, the NEPP code can encounter difficulties: nonconvergence in Powell'sa* optimization algorithm and deficiencies in the Newton-Raphson solver during engine balancing. A project was undertaken to correct the deficiencies, Nonconvergence was avoided through a cascade optimization strategy. Deficiencies associated with engine balancing were eliminated through neural-network and linear-regression methods. An approximation-interspersed cascade strategy was used to optimize engine operation over the Right envelope. Replacement of Powell's algorithm by the cascade strategy improved the optimization segment of the NEPP code. The performance of the linear-regression and neural-network methods as alternate engine analyzers was found to be satisfactory. The paper illustrates the results and insights gained from the improved version of the NEPP code considering two examples: a supersonic mixed-flow turbofan engine and a subsonic waverotor-topped engine.

关键词： Neural Network Methods Overall Pressure Ratio High Pressure Turbine Airbreathing Propulsion optimization algorithm Gas Turbine Engines Subsonic Aircraft Mach Number Sequential Linear Programming Aspect Ratio

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Automatic image annotation using feature selection based on improving quantum particle swarm optimization

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SIGNAL PROCESSING 2015年 109卷 172-181页

作者： Jin, Cong Jin, Shu-Wei Cent China Normal Univ Sch Comp Sci Wuhan 430079 Peoples R China Ecole Normale Super Dept Phys F-75231 Paris 5 France

Automatic image annotation (AIA) is a task of assigning one or more semantic concepts to a given image and a promising way to achieve more effective image retrieval and analysis. It is a typical classification problem. Due to the semantic gap between low-level visual features and high-level image semantic, the performances of many existing image annotation algorithms are not satisfactory. This paper presents a novel AIA scheme based on improved quantum particle swarm optimization (IQPSO) algorithm for visual features selection (VFS) and an ensemble stratagem based on boosting technique to improve performance of image annotation approach. To maintain the population diversity, the measure method of population diversity and improvement operation are proposed. To achieve better performance of ALA scheme, the measure of population diversity is as a control condition of VFS process. The classification result of an ensemble classifier is as the final annotation result rather than individual classifier. The experimental results confirm that the proposed AIA scheme is very effectiveness. When using proposed AIA scheme over three image datasets respectively, the annotation results are satisfactory. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Automatic image annotation Visual feature selection optimization algorithm Improvement operation Ensemble stratagem

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Biologically Inspired Model with Feature Selection for Target Recognition Using Biogeography-Based optimization

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2014年第7期11卷 433-446页

作者： Duan, Haibin Deng, Yimin Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China

To detect salient ground targets precisely and rapidly during aerial reconnaissance, this paper describes a novel object recognition method based on the feature selection of a biologically inspired model and biogeography-based optimization. As a promising approach to object recognition, the biologically inspired model is a hierarchical system of building an increasingly complex and invariant feature representation, which closely follows the process of object recognition in the visual cortex. These scale-and position-tolerant features are constructed by alternating between a template-matching and a maximum-pooling operation. Because of the many patches extracted in the standard biologically inspired model, the random mechanism may extract patches from irrelevant parts of an image and consume a lot of time. In this work, a feature selection method is proposed based on a new population-based evolutionary algorithm called biogeography-based optimization to choose the proper set of patches with high accuracy of classification and recognition. A support vector machine classifier is used for evaluation of the fitness function in biogeography-based optimization and to calculate the recognition rate in testing. A series of experiments are conducted, and the comparative results demonstrate the feasibility and effectiveness of the approach.

关键词： Particle Swarm optimization Support Vector Machine Aerial Reconnaissance Aeroplane Helicopters Aspect Ratio optimization algorithm Feedforward Computing algorithms Automatic Target Recognition

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optimization of Flapping Motion Parameters for Two Airfoils in a Biplane Configuration

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JOURNAL OF AIRCRAFT 2009年第2期46卷 583-592页

作者： Kaya, Mustafa Tuncer, Ismail H. Jones, Kevin D. Platzer, Max F. Middle E Tech Univ Dept Aerosp Engn TR-06531 Ankara Turkey USN Postgrad Sch Dept Mech & Astronaut Engn Monterey CA 93943 USA

Flapping motion parameters of airfoils in a biplane configuration are optimized for maximum thrust and/or propulsive efficiency. Unsteady, viscous flowfields over airfoils flapping in a combined plunge and pitch are computed with a parallel flow solver on moving and deforming overset grids. The amplitudes of the sinusoidal pitch and plunge motions and the phase shift between them are optimized for a range of flapping frequencies. A gradient-baseda* optimization algorithm is implemented in a parallel computing environment. The deforming overset grids employed remove the restriction on the flapping motion of airfoils, and improve the optimization results obtained earlier. In the Strouhal number range 0.17 < Sr < 0.25, an airfoil in a biplane configuration produces more thrust than a single airfoil. Yet, at a higher Strouhal number, the airfoil in a biplane configuration produced less thrust at a significantly lower efficiency than a single flapping airfoil.

关键词： Biplane Flapping Airfoil Propulsive Efficiency Flapping Frequency Strouhal Number Separated Flows Parallel Computing Environment optimization algorithm Aerodynamic Performance Freestream Mach Number

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Flight Trajectory optimization of Sailplane After Rope Break

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JOURNAL OF AIRCRAFT 2021年第6期58卷 1229-1241页

作者： Collazo Garcia, Armando R. Ranjan, Prateek Chen, Kevin J. James, Kai A. Ansell, Phillip J. Univ Illinois Dept Aerosp Engn Urbana IL 61801 USA

A study was performed to characterize an optimum return trajectory for a sailplane after a rope-break failure during an aerotow launch procedure. The performance of an SGS 1-26E sailplane was simulated using the equations of motion for quasi-steady flight in a time-stepping routine and published aerodynamic polar data. A gradient-baseda* optimization algorithm was implemented using the simulated trajectory considering the glide velocity, bank angles, and runway offset angle to determine the minimum rope-break altitude from which a successful return could be theoretically produced. The SGS 1-26E sailplane was used in a flight-test campaign for empirical turn modeling and validation of the performance simulation. The minimum altitude where a return trajectory and downwind runway landing could be successfully completed after a rope-break event was observed to be 76.4 ft above ground level. A headwind and crosswind presence was observed to decrease the minimum rope-break altitude with increasing wind velocity up to a critical value, after which rope-break altitudes began to increase. These minimum rope-break altitudes serve as a theoretical indication that safe trajectories can be performed at lower failure altitudes than the commonly practiced decision altitude of 200 ft for certain wind conditions.

关键词： Agricultural Aircraft Trajectory optimization Runway Alignment Zero Lift Drag Coefficient Flight Testing optimization algorithm Steady Flight Flight Path Angle Critical Phases of Flight Pilots Operating Handbook

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Sparse Flow Sensor Placement optimization for Flight-by-Feel Control of 2D Airfoils

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AIAA JOURNAL 2024年第10期62卷 3803-3812页

作者： Hollenbeck, Alex C. Grandhi, Ramana Hansen, John H. Pankonien, Alexander M. US Air Force Dept Aeronaut & Astronaut Inst Technol Wright Patterson AFB OH 45433 USA US Air Force Aerosp Syst Directorate Res Lab Wright Patterson AFB OH 45433 USA

This research introduces the Sparse Sensor Placement optimization for Prediction algorithm and explores its use in bioinspired flight-by-feel control system design. Flying animals have velocity-sensing structures on their wings and are capable of highly agile flight in unsteady conditions, a proof-of-concept that artificial flight-by-feel control systems may be effective. Constrained by size, weight, and power, a flight-by-feel sensory system should have the fewest optimally placed sensors which capture enough information to predict the flight state. Flow datasets, such as from computational fluid dynamics, are discrete, often highly discontinuous, and ill-suited for conventional sensor placement optimization techniques. The data-driven Sparse Sensor Placement optimization for Prediction approach reduces high-dimensional flow data to a low-dimensional sparse approximation containing nearly all of the original information, thereby identifying a near-optimal placement for any number of sensors. For two or more airflow velocity magnitude sensors, this algorithm finds a placement solution (design point) which predicts angle of attack of airfoils to within 0.10 degrees and ranks within the top 1% of all possible design points validated by combinatorial search. The scalability and adaptability of this algorithm is demonstrated on several 2D model variations in clean and noisy data, and model sensitivities are evaluated and compared against conventional optimization techniques. Applications for this sensor placement algorithm are explored for aircraft design, flight control, and beyond.

关键词： Tactile Sensors Aircraft Flight Control System Aerodynamic Performance Control System Design Aircraft Design Computational Fluid Dynamics algorithms and Data Structures optimization algorithm Bio Inspired Aircraft

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An Efficient Antenna optimization Framework Based on the NP to P Problem

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2024年第6期72卷 4818-4828页

作者： Peng, Fengling Chen, Xing Sichuan Univ Coll Elect & Informat Engn Dept Informat & Commun Engn Chengdu 610041 Peoples R China

We propose a novel antenna optimization framework leveraging an NP-to-P problem approach. Initially, the complex antenna optimization mathematical model is segmented into several simpler submodels, clarifying the relationship between design variables and performance indicators. Upon training these submodels, a substantial dataset is rapidly generated. Subsequently, this dataset is used to train an inverse model, which contrasts with the original mathematical model in terms of input and output relations. This inverse approach enables the direct acquisition of high-quality antenna design solutions tailored to specific requirements, thereby bypassing the need for simulations. Consequently, this method transforms the intricate "NP" problem in antenna optimization into a more tractable "P" problem. These high-quality design solutions are then incorporated as foundational knowledge into the improved differential evolution (IDE) algorithm for further refinement. This integration allows IDE to bypass initial exploratory searches, effectively reducing the journey toward the optimal solution. The experimental results indicate that this method can save approximately 55% of the simulation runs compared to traditional methods, leading to more efficient antenna optimization.

关键词： Antenna optimization design variables optimization algorithm radial basis network

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STRUCTURAL optimization BY MULTILEVEL DECOMPOSITION

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AIAA JOURNAL 1985年第11期23卷 1775-1782页

作者： SOBIESZCZANSKISOBIESKI, J JAMES, BB DOVI, AR KENTRON INT INC KENTRON TECH CTRADV COMP METHODS UNITHAMPTONVA

A method for decomposing an optimization problem into a set of subproblems and a coordination problem that preserves coupling between the subproblems is described. The decomposition is achieved by separating the structural element optimization subproblems from the assembled structural optimization problem. Each element optimization and optimum sensitivity analysis yields the cross-sectional dimensions that minimize a cumulative measure of the element constraint violation as a function of the elemental forces and stiffness. The assembled structural optimization produces the overall mass and stiffness distributions optimized for minimum total mass subject to constraints that include the cumulative measures of the element constraint violations extrapolated linearly with respect to the element forces and stiffnesses. The method is introduced as a special case of a multilevel, multidisciplinary system optimization and its algorithm is fully described for two-level optimization for structures assembled of finite elements of arbitrary type. Numerical results are given as an example of a framework to show that the decomposition method converges and yields results comparable to those obtained without decomposition. It is pointed out that optimization by decomposition should reduce the design time by allowing groups of engineers using different computers to work concurrently on the same large problem. © 1985 American Institute of Aeronautics and Astronautics, Inc., All rights reserved.

关键词： Structural optimization Engineer's Beam Theory Sensitivity Analysis Finite Element Analysis Allowable Stress optimization algorithm Distributed Computing Material Properties Young's Modulus FORTRAN

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TRAJECTORY optimization-BASED ON DIFFERENTIAL INCLUSION

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 1994年第3期17卷 480-487页

作者： SEYWALD, H Analytical Mechanics Associates Inc. Hampton VA

A method for generating finite-dimensional approximations to the solutions of optimal control problems is introduced. By employing a description of the dynamical system in terms of its attainable sets in favor of using differential equations, the controls are completely eliminated from the system model. Besides reducing the dimensionality of the discretized problem compared to state-of-the-art collocation methods, this approach also alleviates the search for initial guesses from where standard gradient search methods are able to converge. The mechanics of the new method are illustrated on a simple double integrator problem. The performance of the new algorithm is demonstrated on a one-dimensional rocket ascent problem (Goddard Problem) in presence of a dynamic pressure constraint.

关键词： Trajectory optimization Dynamic Pressure Sounding Rockets Goddard Problem Collocation Method Nonlinear Programming Aerodynamic Drag Boundary Value Problems optimization algorithm Describing Function

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STRUCTURAL MODEL VERIFICATION WITH LINEAR QUADRATIC optimization THEORY

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AIAA JOURNAL 1990年第8期28卷 1497-1503页

作者： LAPIERRE, H OSTIGUY, G UNIV MONTREAL ECOLE POLYTECHDEPT MECH ENGNMONTREAL H3C 3J7QUEBECCANADA

A graphic tool is developed and validated for the verification and updating of large structure finite element models (FEM) for better correlation with modal test data. The elaborated Linear Quadratic optimization (LQO) theory consists of a linearization and solution of the conventional matrix optimization problem that respect the connectivity of the structure and the symmetry of the stiffness matrix. The LQO theory is validated through a numerical and experimental sensitivity analysis. The results demonstrate the ability of the LQO algorithm to identify and update the stiffness coefficients associated with the inappropriately modeled load paths in the structure. © 1990 American Institute of Aeronautics and Astronautics, Inc., All rights reserved.

关键词： Structural Modeling Finite Element Modeling Lagrange Multipliers Accelerometer Structural Damping Singular Value Decomposition Propellant Nastran optimization algorithm Sensitivity Analysis

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