Aerothermodynamics research at NASA Ames Research Center

Research activity in the aerothermodynamics branch at the NASA Ames Research Center is reviewed. Advanced concepts and mission studies relating to the next generation aerospace transportation systems are summarized and directions for continued research identified. Theoretical and computational studies directed at determining flow fields and radiative and convective heating loads in real gases are described. Included are Navier-Stokes codes for equilibrium and thermochemical nonequilibrium air. Experimental studies in the 3.5-ft hypersonic wind tunnel, the ballistic ranges, and the electric arc driven shock tube are described. Tested configurations include generic hypersonic aerospace plane configurations, aeroassisted orbital transfer vehicle shapes and Galileo probe models

Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms

This book is a reprint of the Special Issue “Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms”,which was published in Applied Sciences

Large Scale Constrained Trajectory Optimization Using Indirect Methods

State-of-the-art direct and indirect methods face significant challenges when solving large scale constrained trajectory optimization problems. Two main challenges when using indirect methods to solve such problems are difficulties in handling path inequality constraints, and the exponential increase in computation time as the number of states and constraints in problem increases. The latter challenge affects both direct and indirect methods. A methodology called the Integrated Control Regularization Method (ICRM) is developed for incorporating path constraints into optimal control problems when using indirect methods. ICRM removes the need for multiple constrained and unconstrained arcs and converts constrained optimal control problems into two-point boundary value problems. Furthermore, it also addresses the issue of transcendental control law equations by re-formulating the problem so that it can be solved by existing numerical solvers for two-point boundary value problems (TPBVP). The capabilities of ICRM are demonstrated by using it to solve some representative constrained trajectory optimization problems as well as a five vehicle problem with path constraints. Regularizing path constraints using ICRM represents a first step towards obtaining high quality solutions for highly constrained trajectory optimization problems which would generally be considered practically impossible to solve using indirect or direct methods. The Quasilinear Chebyshev Picard Iteration (QCPI) method builds on prior work and uses Chebyshev Polynomial series and the Picard Iteration combined with the Modified Quasi-linearization Algorithm. The method is developed specifically to utilize parallel computational resources for solving large TPBVPs. The capabilities of the numerical method are validated by solving some representative nonlinear optimal control problems. The performance of QCPI is benchmarked against single shooting and parallel shooting methods using a multi-vehicle optimal control problem. The results demonstrate that QCPI is capable of leveraging parallel computing architectures and can greatly benefit from implementation on highly parallel architectures such as GPUs. The capabilities of ICRM and QCPI are explored further using a five-vehicle constrained optimal control problem. The scenario models a co-operative, simultaneous engagement of two targets by five vehicles. The problem involves 3DOF dynamic models, control constraints for each vehicle and a no-fly zone path constraint. Trade studies are conducted by varying different parameters in the problem to demonstrate smooth transition between constrained and unconstrained arcs. Such transitions would be highly impractical to study using existing indirect methods. The study serves as a demonstration of the capabilities of ICRM and QCPI for solving large-scale trajectory optimization methods. An open source, indirect trajectory optimization framework is developed with the goal of being a viable contender to state-of-the-art direct solvers such as GPOPS and DIDO. The framework, named beluga, leverages ICRM and QCPI along with traditional indirect optimal control theory. In its current form, as illustrated by the various examples in this dissertation, it has made significant advances in automating the use of indirect methods for trajectory optimization. Following on the path of popular and widely used scientific software projects such as SciPy [1] and Numpy [2], beluga is released under the permissive MIT license [3]. Being an open source project allows the community to contribute freely to the framework, further expanding its capabilities and allow faster integration of new advances to the state-of-the-art

Direct Adaptive Control for Stability and Command Augmentation System of an Air-Breathing Hypersonic Vehicle

In this paper we explore a Direct Adaptive Control scheme for stabilizing a non-linear, physics based model of the longitudinal dynamics for an air breathing hypersonic vehicle. The model, derived from first principles, captures the complex interactions between the propulsion system, aerodynamics, and structural dynamics. The linearized aircraft dynamics show unstable and non-minimum phase behavior. It also shows a strong short period coupling with the fuselage-bending mode. The value added by direct adaptive control and the theoretical requirements for stable convergent operation is displayed. One of the main benefits of the Directive Adaptive Control is that it can be implemented knowing very little detail about the plant. The implementation uses only measured output feedback to accomplish the adaptation. A stability analysis is conducted on the linearized plant to understand the complex aero-propulsion and structural interactions. The multivariable system possesses certain characteristics beneficial to the adaptive control scheme; we discuss these advantages and ideas for future work

Discrete Roughness Transition for Hypersonic Flight Vehicles

The importance of discrete roughness and the correlations developed to predict the onset of boundary layer transition on hypersonic flight vehicles are discussed. The paper is organized by hypersonic vehicle applications characterized in a general sense by the boundary layer: slender with hypersonic conditions at the edge of the boundary layer, moderately blunt with supersonic, and blunt with subsonic. This paper is intended to be a review of recent discrete roughness transition work completed at NASA Langley Research Center in support of agency flight test programs. First, a review is provided of discrete roughness wind tunnel data and the resulting correlations that were developed. Then, results obtained from flight vehicles, in particular the recently flown Hyper-X and Shuttle missions, are discussed and compared to the ground-based correlations