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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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A theorem and its corollary are presented in which conditions are stated for a global transformation of a nonlinear system to a linear system. An explicit method for constructing such a transformation is given, and the important noncharacteristic matrix is introduced. Examples illustrating the technique are presented.
Sufficient conditions are obtained for global asymptotic stability of linear systems controlled by linear quadratic (LQ) regulators when nonlinearities or unmodeled linear dynamics are present in the loop. The conditions are less conservative than the standard LQ robustness results because they generally yield phase margins greater than 60 deg, and tolerance to nonlinearities having gains greater than 1/2. The conditions also provide a measure of robustness of a given LQ design to unmodeled dynamics and nonlinearities. For open-loop stable plants, it is proved that a class of LQ regulators can always be designed to have plus or minus 90 deg phase margin and tolerance to nonlinearities in the (0, infinity) sector.
The problems of a blade-stiffened panel with a hole subjected to compression, and a deployable space mast subjected to tip loads, are treated through the application of FEM to model generation followed by the solution of a linear system of equations. Direct and iterative approaches to the solution of the linear systems are solved in turn; for the panel problems using varying numbers of processors, the incomplete Cholesky-conjugate gradient method was the fastest iterative method on all but two instances in which the number of processors was large.
This report addresses issues in developing a flight control design for vehicles operating across a broad flight regime and with highly nonlinear physical descriptions of motion. Specifically it addresses the need for reentry vehicles that could operate through reentry from space to controlled touchdown on Earth. The latter part of controlled descent is achieved by parachute or paraglider - or by all automatic or a human-controlled landing similar to that of the Orbiter. Since this report addresses the specific needs of human-carrying (not necessarily piloted) reentry vehicles, it deals with highly nonlinear equations of motion, and then-generated control systems must be robust across a very wide range of physics. Thus, this report deals almost exclusively with some form of dynamic inversion (DI). Two vital aspects of control theory - noninteracting control laws and the transformation of nonlinear systems into equivalent linear systems - are embodied in DI. Though there is no doubt that the mathematical tools and underlying theory are widely available, there are open issues as to the practicality of using DI as the only or primary design approach for reentry articles. This report provides a set of guidelines that can be used to determine the practical usefulness of the technique.
In a fault tolerant control (FTC) system, a parameter varying FTC law is reconfigured based on fault parameters estimated by fault detection and isolation (FDI) modules. FDI modules require some time to detect fault occurrences in aero-vehicle dynamics. In this paper, an FTC analysis framework is provided to calculate the upper bound of an induced-L(sub 2) norm of an FTC system with existence of false identification and detection time delay. The upper bound is written as a function of a fault detection time and exponential decay rates and has been used to determine which FTC law produces less performance degradation (tracking error) due to false identification. The analysis framework is applied for an FTC system of a HiMAT (Highly Maneuverable Aircraft Technology) vehicle. Index Terms fault tolerant control system, linear parameter varying system, HiMAT vehicle.
Design options are described for the multispectral linear arrays which will provide data and facilities beyond those obtained by LANDSAT 1, 2, 3, and D (the thematic mapper) are considered. The features, uses, and risks of a 10M fine resolution pointable imager, a 15M/30M dual resolution pointable imager, a 30M selectable spectral resolution pointing imager, a 30M electronically pointable multi-temporal sampler, and a 40M/80M mixed resolution imager are described. A large format camera scheduled for 1981 shuttle launch which can obtain high spatial ground resolution (approximately 10M), a relative position accuracy of plus or minus 14M, and a relative heightening accuracy, by virtue of stereographic coverage, of between 7 and 28M depending on the base-to-height ratio used. A potential problem of the system is that it will orbit in the shuttle.
A reduced order modeling capability has been developed to reduce the computational burden associated with time-domain solutions of structural dynamic models with linear viscoelastic materials. The discretized equations-of-motion produce convolution integrals resulting in a linear system with nonviscous damping forces. The challenge associated with the reduction of nonviscously damped, linear systems is the selection and computation of the appropriate modal basis to perform modal projection. The system produces a nonlinear eigenvalue problem that is challenging to solve and requires use of specialized algorithms not readily available in commercial finite element packages. This SAND report summarizes the LDRD discoveries of a reduction scheme developed for monolithic finite element models and provides preliminary investigations to extensions of the method using component mode synthesis. In addition, this report provides a background overview of structural dynamic modeling of structures with linear viscoelastic materials, and provides an overview of a new code capability in Sierra Structural Dynamics to output the system level matrices computed on multiple processors.
This paper presents a novel technique for generating a convex system model from an experimentally derived data set which features variance among repeated measurements. The convex system model developed as a test case characterizes the dynamic system losses of a vanadium redox low battery as a function of the active power output and the battery state of charge.
This study presents a parallel preconditioning method for distributed sparse linear systems, based on an approximate inverse of the original matrix, that adopts a general framework of distributed sparse matrices and exploits domain decomposition (DD) and low-rank corrections. The DD approach decouples the matrix and, once inverted, a low-rank approximation is applied by exploiting the Sherman--Morrison--Woodbury formula, which yields two variants of the preconditioning methods. The low-rank expansion is computed by the Lanczos procedure with reorthogonalizations. Numerical experiments indicate that, when combined with Krylov subspace accelerators, this preconditioner can be efficient and robust for solving symmetric sparse linear systems. Comparisons with pARMS, a DD-based parallel incomplete LU (ILU) preconditioning method, are presented for solving Poisson's equation and linear elasticity problems.
This paper presents a novel technique for generating a convex system model from an experimentally derived data set which features variance among repeated measurements. The convex system model developed as a test case characterizes the dynamic system losses of a vanadium redox flow battery as a function of the active power output and the battery state of charge. The technique hinges on a pre-cleaning via clustering procedure which precedes the formation of a planar convex hull comprised of triangular simplices. The clustering procedure efficiently reduces the experimental data set while mitigating variance among repeated measurements and removes outliers. Ultimately, the lower evelope of the planar convex hull serves as the desired convex system model. The proposed technique reduces systematic model error which is otherwise present when directly developing a planar convex hull model based on an unreduced data set.
In this work, we propose a higher-order derivative free family for solving non-linear systems. The local order of convergence of the constructed family is first determined using first-order divided difference operators for functions of several variables and direct computation by Taylor’s series expansion. Computational efficiencies of the developed scheme is considered and compared with their closest competitors. Moreover, numerical experiments are performed on several large and complex non-linear systems. The results are found to be effective and comparable to other existing methods which also support the theoretical results.
The key capability established through the research funded by this award are erasure coded computations for linear systems, in serial and in parallel. This capability enables powerful efficient and scalable alternatives to existing linear system solvers in fault-prone computational systems.
A formulation is presented for identification of linear multivariable from a single set of input-output data. The identification method is formulated with the mathematical framework of learning identifications, by extension of the repetition domain concept to include shifting time intervals. This method contrasts with existing learning approaches that require data from multiple experiments. In this method, the system input-output relationship is expressed in terms of an observer, which is made asymptotically stable by an embedded real eigenvalue assignment procedure. Through this relationship, the Markov parameters of the observer are identified. The Markov parameters of the actual system are recovered from those of the observer, and then used to obtain a state space model of the system by standard realization techniques. The basic mathematical formulation is derived, and numerical examples presented to illustrate.
Piecewise linear switching functions design for suboptimal regulation of linear plants with relay controller in regard to transient response performance criterion
Linear system synthesis method using state variable feedback from existing physical quantities for closed loop response control system design criteria
Performing accurate and efficient numerical simulation of global atmospheric climate models is challenging due to the disparate length and time scales over which physical processes interact. Implicit solvers enable the physical system to be integrated with a time step commensurate with processes being studied. The dominant cost of an implicit time step is the ancillary linear system solves, so we have developed a preconditioner aimed at improving the efficiency of these linear system solves. Our preconditioner is based on an approximate block factorization of the linearized shallow-water equations and has been implemented within the spectral element dynamical core within the Community Atmospheric Model (CAM-SE). Furthermore, in this paper we discuss the development and scalability of the preconditioner for a suite of test cases with the implicit shallow-water solver within CAM-SE.
During my visit to LLNL during July 5{15, 2016, we worked on linear system solvers. The two level hierarchical solver that initiated our study was developed to solve linear system arising from hp adaptive finite element calculations, and is implemented in the PLTMG software package, version 12 [1]. This preconditioner typically requires 3-20% of the space used by the stiffness matrix for higher order elements. It has multigrid like convergence rates for a wide variety of PDEs (self-adjoint positive definite elliptic equations, convection dominated convection-diffusion equations, and highly inde nite Helmholtz equations, among others). The convergence rate is not independent of the polynomial degree p as p ! 1, but but remains strong for p 9, which is the highest polynomial degree allowed in PLTMG, due to limitations of numerical quadrature formulae. A more complete description of the method and some numerical experiments illustrating its effectiveness appear in [2]. Like traditional geometric multilevel methods, this scheme relies on extensive knowledge of the underlying finite element space in order to construct both the smoother and the coarse grid correction components.
During my visit to LLNL during July 17{27, 2017, I worked on linear system solvers. The two level hierarchical solver that initiated our study was developed to solve linear systems arising from hp adaptive finite element calculations, and is implemented in the PLTMG software package, version 12. This preconditioner typically requires 3-20% of the space used by the stiffness matrix for higher order elements. It has multigrid like convergence rates for a wide variety of PDEs (self-adjoint positive de nite elliptic equations, convection dominated convection-diffusion equations, and highly indefinite Helmholtz equations, among others). The convergence rate is not independent of the polynomial degree p as p ! 1, but but remains strong for p 9, which is the highest polynomial degree allowed in PLTMG, due to limitations of the numerical quadrature rules implemented in the software package. A more complete description of the method and some numerical experiments illustrating its effectiveness appear in. Like traditional geometric multilevel methods, this scheme relies on knowledge of the underlying finite element space in order to construct the smoother and the coarse grid correction.