Adaptive Neural Back-Stepping Control with Constrains for a Flexible Air-Breathing Hypersonic Vehicle

The design of an adaptive neural back-stepping control for a flexible air-breathing hypersonic vehicle (AHV) in the presence of input constraint and aerodynamic uncertainty is discussed. Based on functional decomposition, the dynamics can be decomposed into the velocity subsystem and the altitude subsystem. To guarantee the exploited controller’s robustness with respect to parametric uncertainties, neural network (NN) is applied to approximate the lumped uncertainty of each subsystem of AHV model. The exceptional contribution is that novel auxiliary systems are introduced to compensate both the tracking errors and desired control laws, based on which the explored controller can still provide effective tracking of velocity and altitude commands when the actuators are saturated. Finally, simulation studies are made to illustrate the effectiveness of the proposed control approach in spite of the flexible effects, system uncertainties, and varying disturbances

Uncertainty Propagation in Hypersonic Vehicle Aerothermoelastic Analysis.

Hypersonic vehicles face a challenging flight environment. The aerothermoelastic analysis of its components requires numerous simplifying approximations. Identifying and quantifying the effect of uncertainties pushes the limits of the existing deterministic models, and is pursued in this work. An uncertainty quantification framework is used to propagate the effects of identified uncertainties on the stability margins and performance of the different systems considered. First, the aeroelastic stability of a typical section representative of a control surface on a hypersonic vehicle is examined. Variability in the uncoupled natural frequencies of the system is modeled to mimic the effect of aerodynamic heating. Next, the stability of an aerodynamically heated panel representing a component of the skin of a generic hypersonic vehicle is considered. Uncertainty in the location of transition from laminar to turbulent flow and the heat flux prediction is quantified using CFD. In both cases significant reductions of the stability margins are observed. A loosely coupled airframe--integrated scramjet engine is considered next. The elongated body and cowl of the engine flow path are subject to harsh aerothermodynamic loading which causes it to deform. Uncertainty associated with deformation prediction is propagated to the engine performance analysis. The cowl deformation is the main contributor to the sensitivity of the propulsion system performance. Finally, a framework for aerothermoelastic stability boundary calculation for hypersonic vehicles using CFD is developed. The usage of CFD enables one to consider different turbulence conditions, laminar or turbulent, and different models of the air mixture, in particular real gas model which accounts for dissociation of molecules at high temperature. The system is found to be sensitive to turbulence modeling as well as the location of the transition from laminar to turbulent flow. Real gas effects play a minor role in the flight conditions considered. These studies demonstrate the advantages of accounting for uncertainty at an early stage of the analysis. They emphasize the important relation between heat flux modeling, thermal stresses and stability margins of hypersonic vehicles.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99806/1/nlamorte_1.pd

Hypersonic Aeroelastic and Aerothermoelastic Studies Using Computational Fluid Dynamics

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140678/1/1.j053018.pd

Aircraft Modeling and Simulation

Various aerodynamics, structural dynamics, and control design and experimental studies are presented with the aim of advancing green and morphing aircraft research. The results obtained with an in-house CFD code are compared and validated with those of two NASA codes. The aerodynamical model of the UAS-S45 morphing wing as well as the structural model of a morphing winglet are presented. A new design methodology for oleo-pneumatic landing gear drop impact dynamics is presented as well as its experimental validation. The design of a nonlinear dynamic inversion (NDI)-based disturbance rejection control on a tailless aircraft is presented, including its validation using wind tunnel tests

Aeronautical engineering: A continuing bibliography with indexes (supplement 279)

This bibliography lists 759 reports, articles, and other documents introduced into the NASA scientific and technical information system in May 1992. Subject coverage includes: design, construction, and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Machine Learning in Aerodynamic Shape Optimization

Machine learning (ML) has been increasingly used to aid aerodynamic shape optimization (ASO), thanks to the availability of aerodynamic data and continued developments in deep learning. We review the applications of ML in ASO to date and provide a perspective on the state-of-the-art and future directions. We first introduce conventional ASO and current challenges. Next, we introduce ML fundamentals and detail ML algorithms that have been successful in ASO. Then, we review ML applications to ASO addressing three aspects: compact geometric design space, fast aerodynamic analysis, and efficient optimization architecture. In addition to providing a comprehensive summary of the research, we comment on the practicality and effectiveness of the developed methods. We show how cutting-edge ML approaches can benefit ASO and address challenging demands, such as interactive design optimization. Practical large-scale design optimizations remain a challenge because of the high cost of ML training. Further research on coupling ML model construction with prior experience and knowledge, such as physics-informed ML, is recommended to solve large-scale ASO problems

연속형 측추력기를 사용하는 로켓의 공력 제트 간섭력 수치분석 및 대체 모델링

학위논문 (박사)-- 서울대학교 대학원 공과대학 기계항공공학부, 2017. 8. 이수갑.The supersonic jet interaction generated by a continuous type side-jet thruster of the missile was considered. Firstly, the jet interaction flow field was investigated using numerical simulations. The simulation was made use of the three-dimensional unstructured-based computational fluid dynamics (CFD) solver. The numerical simulation method was validated through comparison with wind tunnel test results. Flow fields investigation and jet interaction effects for various flow conditions, jet magnitude, and jet direction conditions were performed. Secondly, the jet interaction aerodynamic database based on CFD data was developed and assessed. The generation of the jet interaction aerodynamic database for the continuous type side-jet requires a large amount of simulation data owing to the complex nature of jet interaction. To reduce the required number of simulations, seven jet operating conditions were selected using geometrical symmetry at firstthen, three-dimensional numerical simulations were conducted to build the jet interaction aerodynamic database in the reduced design space. Two modeling approaches were used in developing the jet interaction aerodynamic database. One is CFD-based modeling with a full factorial sampling, and the other is surrogate modeling, based on the Latin hypercube sampling and Kriging method, for the interim database. The resulting two aerodynamic databases were assessed through comparison with flight test results. Based on the comparison, both models showed a suitable representation of the aerodynamic coefficients within 10\% error during the jet operation period. This assessment confirms that the jet interaction aerodynamic database for missiles with continuous type side-jet thruster can be constructed using the CFD-based modeling approach. The surrogate model was found to perform well compared with the CFD-based model within an acceptable error level.Chapter 1 Introduction 1 1.1 Research Background 1 1.1.1 Side-jet control of missile 1 1.1.2 Continuous type side-jet 3 1.1.3 Jet interaction aerodynamic database 4 1.2 Literature Review and Scope of Works 5 1.3 Objective of Research 7 1.4 Outline 7 Chapter 2 Numerical Method 9 2.1 Governing Equations 9 2.2 Gas Modeling 11 2.2.1 Calorically perfect gas 11 2.2.2 Thermally perfect gas, Multiple gases 12 2.3 Spatial Discretization 13 2.3.1 Convective uxes 14 2.3.2 Viscous uxes 16 2.4 Temporal Discretization 18 2.5 Turbulence Modeling 18 Chapter 3 Numerical Investigation of Continuous Type side-jet 21 3.1 Conguration and Computational Grid 21 3.2 Jet Interaction Parameters and Evaluation 23 3.3 Jet Direction and Scale of Continuous Type Side-jet 24 3.4 Simulation Conditions 25 3.5 Wind Tunnel Test and Validation of Numerical Method 27 3.5.1 Jet interaction similitude parameter 27 3.5.2 Jet-o cases 29 3.5.3 Jet-on cases 29 3.6 Investigation of Jet Interaction for Continuous Type Side-jet 43 3.6.1 Simulation results of continuous type side-jet 43 3.6.2 Flow Features of Jet Interaction for Continuous Type side-jet 49 3.6.3 Eect of jet interaction parameters 60 Chapter 4 Surrogate Modeling of Jet Interaction Aerodynamic Database for Continuous Type side-jet 66 4.1 Jet interaction aerodynamic database of continuous type side-jet 66 4.2 Dened Jet Direction Conditions 67 4.3 Jet interaction modeling strategy 70 4.4 CFD-Based Modeling of Jet Interaction 73 4.4.1 Numerical simulation for jet interaction modeling 73 4.4.2 CFD-based jet interaction modeling results 74 4.5 Surrogate Modeling Method 77 4.5.1 Design of experiments 77 4.5.2 Kriging predictor 78 4.6 Surrogate Modeling of Jet Interaction 81 4.6.1 Jet interaction modeling and evaluation 81 4.6.2 Surrogate modeling of jet interaction results 83 Chapter 5 Assessment of Jet Interaction Modeling Results 95 5.1 Post Flight Analysis for Jet Interaction Database Identication 95 5.2 Assessment of Jet Interaction Database 99 Chapter 6 Conclusion 106 Appendix Chapter A Extension Rules of Jet Directions 109 Bibliography 113 국문초록 120Docto

A Data Driven Modeling Approach for Store Distributed Load and Trajectory Prediction

The task of achieving successful store separation from aircraft and spacecraft has historically been and continues to be, a critical issue for the aerospace industry. Whether it be from store-on-store wake interactions, store-parent body interactions or free stream turbulence, a failed case of store separation poses a serious risk to aircraft operators. Cases of failed store separation do not simply imply missing an intended target, but also bring the risk of collision with, and destruction of, the parent body vehicle. Given this risk, numerous well-tested procedures have been developed to help analyze store separation within the safe confines of wind tunnels. However, due to increased complexity in store separation configurations, such as rotorcraft and cavity-based separation, there is a growing desire to incorporate computational fluid dynamics (CFD) into the early stages of the store separation analysis. A viable method for achieving this objective is available through data-driven surrogate modeling of store distributed loads. This dissertation investigates the practicality of applying various data-driven modeling techniques to the field of store separation. These modeling methods will be applied to four demonstration scenarios: reduced order modeling of a moving store, design optimization, supersonic store separation, and rotorcraft store separation. For the first demonstration scenario, results are presented for three sub-tasks. In the first sub-task proper orthogonal decomposition (POD), dynamic mode decomposition (DMD), and convolutional neural networks (CNN) were compared for their capability to replicate distributed pressure loads of a pitching up prolate spheroid. Results indicated that POD was the most efficient approach for surrogate model generation. For the second sub-task, a POD-based surrogate model was derived from CFD simulations of an oscillating prolate spheroid subject to varying reduced frequency and amplitude of oscillation. The obtained surrogate model was shown to provide high-fidelity predictions for new combinations of reduced frequency and amplitude with a maximum percent error of integrated loads of less than 3\%. Therefore, it was demonstrated that the surrogate model was capable of predicting accurately at intermediate states. Further analysis showed a similar surrogate model could be generated to provide accurate store trajectory modeling under subsonic, transonic, and supersonic conditions. In the second demonstration scenario, a POD-based surrogate model is derived from a series of CFD simulations of isolated rotors in hover and forward flight. The derived surrogate models for hover and forward flight were shown to provide integrated load predictions within 1% of direct CFD simulation. Additionally, results indicated that computational expense could be reduced from 20 hours on 440 CPUs to less than a second on a single CPU. Given the reduction of cost and high fidelity of the surrogate model, the derived model was leveraged to optimize the twist and taper ratio of the rotor such that the efficiency of the rotor was maximized. For the third demonstration scenario, a POD and CNN surrogate model was derived for fixed-wing based supersonic store separation. Results demonstrated that both models were capable of providing high-fidelity predictions of the store\u27s distributed loads and subsequent trajectory. For the final demonstration scenario, a POD-based surrogate model was derived for the case of a store launching from a rotorcraft. The surrogate model was derived from three CFD simulations while varying ejection force. This surrogate model was then validated against CFD simulation of a new store ejection force. Results indicated that while the surrogate model struggled to provide detailed predictions of store distributed loads, mean load variations could be modeled well at a massively reduced computational cost. For each rotorcraft store separation CFD simulation, the computational cost required 10 days of simulation time across 880. While using the surrogate model, comparable predictions could be produced in under a minute on a single core. Overall findings from this study indicate that massive CFD generated data-sets can be efficiently leveraged to create meaningful surrogate models capable of being deployed to highly iterative design tasks relevant to store separation. Through further improvements, similar surrogate models can be combined with a control strategy to achieve trajectory optimization and control