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Nontangent, developed contour bulkheads for a single-stage launch vehicle

JOURNAL OF SPACECRAFT AND ROCKETS 2000年第1期37卷 114-121页

作者： Wu, KC Lepsch, RA NASA Langley Res Ctr Met & Thermal Struct Branch Hampton VA 23681 USA NASA Langley Res Ctr Vehicle Anal Branch Hampton VA 23681 USA

Dry weights for single-stage launch vehicles that incorporate nontangent, developed contour bulkheads are estimated and compared to a baseline vehicle with 1,414 aspect ratio ellipsoidal bulkheads. Weights, volumes, and heights of optimized bulkhead designs are computed using a preliminary design bulkhead analysis code. The dry weights of vehicles that Incorporate the optimized bulkheads are predicted using a vehicle weights and sizing Code. Two optimization approaches are employed. A structural-level method, where the vehicle's three major bulkhead regions are optimized separately and then incorporated into a model for computation of the vehicle dry weight, predicts a reduction of 4365 lb (2.2%) from the 200,679-lb baseline vehicle dry weight, in the second, vehicle-level, approach, the vehicle dry weight is the objective function for the optimization. For the vehicle-level analysis, modified bulkhead designs are analyzed and incorporated into the weights model for computation of a dry weight. The optimizer simultaneously manipulates design variables for all three bulkheads to reduce the dry weight. The vehicle-level analysis predicts a dry weight reduction of 5129 lb, a 2.6% reduction from the baseline weight. Based on these results, nontangent, developed contour bulkheads may provide substantial weight savings for single stage vehicles.

关键词： Expendable Launch Vehicle Aspect Ratio Structural Analysis Propellant Circumferential Stress sequential quadratic programming International Space Station Lockheed Martin Corporation Material Properties Internal Pressure

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Iterative response surface based optimization scheme for transonic airfoil design

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JOURNAL OF AIRCRAFT 2007年第2期44卷 365-376页

作者： Vavalle, Armando Qin, Ning GKN Aerosp Aerodynam & Aeromech Syst E Cowes PO32 6LR Isle Of Wight England Univ Sheffield Dept Engn Mech Sheffield S1 3JD S Yorkshire England

An improved response surface based optimization technique is presented for two-dimensional airfoil design at transonic speed. The method is based on an iterative scheme where least-square fitted quadratic polynomials of objective function and constraints are repeatedly corrected locally, about the current minimum, by adding the actual function value to the data set used to construct the polynomials. When no further cost function reduction is achieved, the design domain upon which the optimization is initially performed is changed, preserving its initial size, by updating the center point with the position of the last minimum found. The optimization is then conducted by using the same approximations built over the initial design space, which are again iteratively corrected until convergence to a given tolerance. To construct the response surfaces, the design space is explored by using a uniform Latin hypercube, aiming at reducing the bias error, in contrast with previous techniques based on D-optimality criterion. The geometry is modeled by using the PARSEC parameterization. The multiple tests conducted to validate the methodology, regarding a redesign of the outer wing of a blended wing body configuration and the optimization of the RAE2822 airfoil, suggest that the present technique can be more efficient and effective than some other published methods proposed for numerical aerodynamic optimization.

关键词： Two Dimensional Airfoil Blended Wing Body Configurations Transonic Speed Aerodynamic Shape Optimization Thickness to Chord Ratio Reynolds Averaged Navier Stokes Aerodynamic Efficiency Analysis of Variance sequential quadratic programming Probability Density Functions

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Physics-Informed Neural Networks for Optimal Planar Orbit Transfers

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JOURNAL OF SPACECRAFT AND ROCKETS 2022年第3期59卷 834-849页

作者： Schiassi, Enrico D'Ambrosio, Andrea Drozd, Kristofer Curti, Fabio Furfaro, Roberto Univ Arizona Dept Syst & Ind Engn Syst & Ind Engn 1127 East James E Rogers Way Tucson AZ 85721 USA Univ Arizona Dept Syst & Ind Engn 1127 East James E Rogers Way Tucson AZ 85721 USA Univ Roma La Sapienza Sch Aerosp Engn Via Salaria 851 I-00138 Rome Italy

This paper presents a novel framework, combining the indirect method and Physics-Informed Neural Networks (PINNs), to learn optimal control actions for a series of optimal planar orbit transfer problems. According to the indirect method, the optimal control is retrieved by directly applying the Pontryagin minimum principle, which provides the first-order necessary optimality conditions. The necessary conditions result in a two-point boundary value problem (TPBVP) in the state-costate pair, constituting a system of ordinary differential equations, representing the physics constraints of the problem. More precisely, the goal is to model a neural network (NN) representation of the state-costate pair for which the residuals of the TPVBP are as close to zero as possible. This is done using PINNs, which are particular NNs where the training is driven by the problem's physics constraints. A particular PINN method will be used, named Extreme Theory of Functional Connections (X-TFC), which is a synergy of the classic PINN and the Theory of Functional Connections. With X-TFC, the TPBVP's boundary conditions are analytically satisfied. This avoids having unbalanced gradients during the network training. The results show the feasibility of employing PINNs to tackle this class of optimal control problems for space applications.

关键词： Neural Networks Transversality Condition Boundary Value Problems Solar Sail Earth Adaptive Mesh Refinement Nonlinear programming Astronomical Unit sequential quadratic programming Particle Swarm Optimization

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A self-scaling implicit SQP method for large scale structural optimization

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING 1996年第11期39卷 1933-1953页

作者： Huang, MW Arora, JS Optimal Design Laboratory College of Engineering The University of Iowa Iowa City IA 52242 U.S.A.

The basic idea bf an implicit sequential quadratic programming (ISQP) method for constrained problems is to use the approximate Hessian of the Lagrangian without explicitly calculating and storing it. This overcomes one of the major drawbacks of the traditional SQP method where a large matrix needs to be calculated and stored. This concept of an implicit method is explained and an algorithm based on it is presented. The proposed method extends a similar algorithm for unconstrained problems where a two-loop recursion formula is used for the inverse Hessian matrix. The present paper develops a similar algorithm for not only the constrained problem but also the direct Hessian updates. Several scaling procedures for the Hessian are also presented and evaluated. The basic method and some of its variations are evaluated using a set of mathematical programming test problems, and a set of structural design test problems-small to larger scale. The ISQP method performs much better than a method that does not use any approximate Hessian matrix. Its performance is better than the full SQP method for larger scale problems. The test results also show that an appropriate scaling of the Hessian can improve both efficiency and reliability substantially.

关键词： structures optimization large scale structural design sequential quadratic programming self-scaling numerical algorithms algorithm evaluation test problems

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Adjoint-based SQP method with block-wise quasi-Newton Jacobian updates for nonlinear optimal control

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OPTIMIZATION METHODS & SOFTWARE 2021年第5期36卷 1030-1058页

作者： Hespanhol, Pedro Quirynen, Rien Mitsubishi Elect Res Labs Control & Dynam Syst Cambridge MA 02139 USA Univ Calif Berkeley Dept Ind Engn & Operat Res Berkeley CA 94720 USA

Nonlinear model predictive control (NMPC) generally requires the solution of a non-convex dynamic optimization problem at each sampling instant under strict timing constraints, based on a set of differential equations that can often be stiff and/or that may include implicit algebraic equations. This paper provides a local convergence analysis for the recently proposed adjoint-based sequential quadratic programming (SQP) algorithm that is based on a block-structured variant of the two-sided rank-one (TR1) quasi-Newton update formula to efficiently compute Jacobian matrix approximations in a sparsity preserving fashion. A particularly efficient algorithm implementation is proposed in case an implicit integration scheme is used for discretization of the optimal control problem, in which matrix factorization and matrix-matrix operations can be avoided entirely. The convergence analysis results as well as the computational performance of the proposed optimization algorithm are illustrated for two simulation case studies of NMPC.

关键词： Nonlinear model predictive control sequential quadratic programming quasi-Newton updates convergence analysis direct collocation multiple shooting

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Framework for Low-Observable Trajectory Generation in Presence of Multiple Radars

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2008年第6期31卷 1740-1749页

作者： Inanc, Tamer Muezzinoglu, Mehmet K. Misovec, Kathleen Murray, Richard M. Univ Louisville Dept Elect & Comp Engn Louisville KY 40292 USA BAE Syst Adv Informat Technol Div Burlington MA 01803 USA CALTECH Control & Dynam Syst Dept Pasadena CA 91125 USA

This paper explores the problem of finding a real-time optimal trajectory for unmanned aerial vehicles to minimize their probability of detection by opponent multiple radar detection systems. The problem is handled using the nonlinear trajectory generation method developed by Milam et al. (Milam, M., Mushambi, K., and Murray, R., "New Computational Approach to Real-Time Trajectory Generation for Constrained Mechanical Systems," Proceedings of the 39th IEEE Conference on Decision and Control, Vol. 1, Institute of Electrical and Electronics Engineers, New York, Dec. 2000, pp. 845-851.) The paper presents a formulation of the trajectory generation task as an optimal control problem, where temporal constraints allow periods of high observability interspersed with periods of low observability. This feature can be used strategically to aid in avoiding detection by an opponent radar. The guidance is provided in the form of sampled tabular data. It is then shown that the success of nonlinear trajectory generation on the proposed low-observable trajectory generation problem depends upon an accurate parameterization of the guidance data. In particular, such an approximator is desired to have a compact architecture, a minimum number of design parameters, and a smooth continuously differentiable input-output mapping. Artificial neural networks as universal approximators are known to possess these features, and thus are considered here as appropriate candidates for this task. Comparison of artificial neural networks against B-spline approximators is provided, as well. Numerical simulations on multiple radar scenarios illustrate unmanned air vehicle trajectories optimized for both detectability and time.

关键词： Radar Detection UAV Neural Networks Nonlinear programming Obstacle Avoidance B Splines sequential quadratic programming Optimal Control Defense Advanced Research Projects Agencies Bank Angle

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Modelization and kinematics optimization for a flapping-wing microair vehicle

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JOURNAL OF AIRCRAFT 2007年第1期44卷 217-231页

作者： Rakotomamonjy, Thomas Ouladsine, Mustapha Le Moing, Thierry Off Natl Etud & Rech Aerosp Syst Control & Flight Dynam Dept F-13661 Salon De Provence France Lab Sci Informat & Syst F-13397 Marseille France Off Natl Etud & Rech Aerosp F-31055 Toulouse France

OSCAB, a flight-dynamics-oriented simulation model of a flapping-wing microair vehicle, is presented here. This concept is based on flapping flight performed in nature by insects or hummingbirds. The model features two independent wings and integrates the aerodynamic forces computed along each wing to determine the global motion of the microair vehicle with respect to an inertial reference frame. A comparison between our simulation model and previous experimental measurements is presented, showing that it can reproduce the influence of the wing rotation phasing on the total lift. An optimization of the flapping kinematics of the wing has also been conducted to maximize the mean lift. A neural network has been designed to reproduce various function shapes modeling the wing movements. The parameters of this network have been optimized with a genetic algorithm to avoid local extrema. Results show a lift gain from 30 to 40%, corroborating previous experiments.

关键词： Flapping Frequency Kinematics Optimization Aerodynamic Coefficients Aerodynamic Angle Flapping Kinematics Neural Networks sequential quadratic programming Genetic Algorithm Yaw Newton's Second Law

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Parameter Estimation of Aircraft Dynamics via Unscented Smoother with Expectation-Maximization Algorithm

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2011年第2期34卷 426-436页

作者： Yokoyama, Nobuhiro Natl Def Acad Dept Aerosp Engn Yokosuka Kanagawa 2398686 Japan

This paper proposes a new method for parameter estimation of aircraft dynamics modeled in state space. The developed method employs the smoother, which estimates the state and unknown parameters, combined with expectation-maximization algorithm to estimate unknown statistics in the problem, i.e., the mean and covariance of an initial state, and the noise covariances. To approximate the expectation values in the expectation-maximization with a reasonable computational cost, an unscented transform based on the estimates obtained by the unscented Rauch-Tung-Striebel smoother is employed. Moreover, nonconvex numerical optimization algorithms are not necessary in the maximization step of the expectation-maximization, because the optimums of the unknown statistics are given in analytical forms. Thus, the developed method achieves low computational cost and high robustness. Its effectiveness is demonstrated through two problems of estimating aircraft aerodynamic parameters.

关键词： Aircraft Dynamics sequential quadratic programming Rauch Tung Striebel Smoother Unscented Kalman Filter Flight Testing Numerical Optimization Ailerons Number of Particles Elevator Deflection Flight Control System

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Structural Dynamics Optimization of Rotor Systems for a Small-Size Turboprop Engine

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JOURNAL OF PROPULSION AND POWER 2015年第4期31卷 1083-1093页

作者： Pugachev, Alexander O. Sheremetyev, Alexander V. Tykhomirov, Viktor V. Shpilenko, Oleg I. Tech Univ Munich Inst Energy Syst D-85748 Garching Germany Ivchenko Progress Strength Dept UA-69068 Zaporozhe Ukraine

This paper presents the results of the rotordynamic analysis and design optimization performed for a turboprop engine-rotor system consisting of a free-power turbine and a gas generator. Predictions of static stresses, natural frequencies, and amplitudes are obtained by the in-house finite-element code. A detailed analysis of the finite-element method model (various beam formulations, three-dimensional axisymmetric model, point masses, and one-dimensional rigid disk elements) is provided. A sizing optimization problem of total shaft mass minimization is considered. Design variables are inner radii and wall thicknesses of shaft sections. Constraints are imposed on static stresses, natural frequencies, and amplitudes of the unbalance response. Changes in design variables are constrained to avoid contacts between the shafts of the twin-spool configuration. An in-house implementation of the sequential quadratic programming method is coupled with hybrid, analytical and numerical, sensitivity analysis. Results of the optimization show that a significant mass reduction in comparison with the baseline configuration can be achieved with all constraints being simultaneously satisfied. The optimal designs, however, must be finalized to meet other requirements not considered during the optimization. The Campbell diagram of the free-power turbine rotor system is significantly affected by the optimization.

关键词： Turboprop Engine Rotor Systems Structural Dynamics Two Dimensional Elements Sensitivity Analysis sequential quadratic programming Campbell Diagram Critical Speed High Pressure Turbine Lightly Damped Structures

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Decomposition algorithm for performance optimization of a launch vehicle

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JOURNAL OF SPACECRAFT AND ROCKETS 1996年第2期33卷 214-221页

作者： Rahn, M Schottle, UM UNIV STUTTGART SYST & MISS ANAL DEPTD-70550 STUTTGARTGERMANY

A decomposition algorithm is described that performs both the flight path and system design optimization tasks for a space transportation system by a two-level optimization scheme, partitioning the entire mission into a sequence of Right segments. These segments are optimized separately on a subproblem level employing nonlinear programming methods, whereas a main iteration controller subsequently combines the subarc solutions, determining subproblem targets and master problem controls that optimize the general objective In addition, trajectory optimization is performed on a parallel processing workstation cluster to reduce the overall computational time. Optimization examples are presented first to verify the coupled trajectory and design optimization procedure applied to the mission of an airbreathing Sanger-type space transportation system and second to demonstrate the enhanced performance of the parallel algorithm.

关键词： Launch Vehicle Configurations Space Transportation System Propellant Nonlinear programming Trajectory Optimization Angle of Attack Aerodynamic Lift Flight Phases sequential quadratic programming Perigee

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