SEARCH · Search NASA

Results for “computational efficiency”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 775 records · Page 43

All sources NASA NTRS DOE OSTI

NASA NTRS1997

Identification of Linear and Nonlinear Aerodynamic Impulse Responses Using Digital Filter Techniques

This paper discusses the mathematical existence and the numerically-correct identification of linear and nonlinear aerodynamic impulse response functions. Differences between continuous-time and discrete-time system theories, which permit the identification and efficient use of these functions, will be detailed. Important input/output definitions and the concept of linear and nonlinear systems with memory will also be discussed. It will be shown that indicial (step or steady) responses (such as Wagner's function), forced harmonic responses (such as Theodorsen's function or those from doublet lattice theory), and responses to random inputs (such as gusts) can all be obtained from an aerodynamic impulse response function. This paper establishes the aerodynamic impulse response function as the most fundamental, and, therefore, the most computationally efficient, aerodynamic function that can be extracted from any given discrete-time, aerodynamic system. The results presented in this paper help to unify the understanding of classical two-dimensional continuous-time theories with modern three-dimensional, discrete-time theories. First, the method is applied to the nonlinear viscous Burger's equation as an example. Next the method is applied to a three-dimensional aeroelastic model using the CAP-TSD (Computational Aeroelasticity Program - Transonic Small Disturbance) code and then to a two-dimensional model using the CFL3D Navier-Stokes code. Comparisons of accuracy and computational cost savings are presented. Because of its mathematical generality, an important attribute of this methodology is that it is applicable to a wide range of nonlinear, discrete-time problems.

Silva, Walter A.↗

NASA NTRS1989

An interpretation and solution of ill-conditioned linear equations

Data insufficiency, poorly conditioned matrices and singularities in equations occur regularly in complex optimization, correlation, and interdisciplinary model studies. This work concerns itself with two methods of obtaining certain physically realistic solutions to ill-conditioned or singular algebraic systems of linear equations arising from such studies. Two efficient computational solution procedures that generally lead to locally unique solutions are presented when there is insufficient data to completely define the model, or a least-squares error formulation of this system results in an ill-conditioned system of equations. If it is assumed that a reasonable estimate of the uncertain data is available in both cases cited above, then we shall show how to obtain realistic solutions efficiently, in spite of the insufficiency of independent data. The proposed methods of solution are more efficient than singular-value decomposition for dealing with such systems, since they do not require solutions for all the non-zero eigenvalues of the coefficient matrix.

Ojalvo, I. U.↗

NASA NTRS2007

Structural Analysis Methods for Structural Health Management of Future Aerospace Vehicles

Two finite element based computational methods, Smoothing Element Analysis (SEA) and the inverse Finite Element Method (iFEM), are reviewed, and examples of their use for structural health monitoring are discussed. Due to their versatility, robustness, and computational efficiency, the methods are well suited for real-time structural health monitoring of future space vehicles, large space structures, and habitats. The methods may be effectively employed to enable real-time processing of sensing information, specifically for identifying three-dimensional deformed structural shapes as well as the internal loads. In addition, they may be used in conjunction with evolutionary algorithms to design optimally distributed sensors. These computational tools have demonstrated substantial promise for utilization in future Structural Health Management (SHM) systems.

Tessler, Alexander↗

NASA NTRS2015

3-D CFD Simulation and Validation of Oxygen-Rich Hydrocarbon Combustion in a Gas-Centered Swirl Coaxial Injector using a Flamelet-Based Approach

Injector design is a critical part of the development of a rocket Thrust Chamber Assembly (TCA). Proper detailed injector design can maximize propulsion efficiency while minimizing the potential for failures in the combustion chamber. Traditional design and analysis methods for hydrocarbon-fuel injector elements are based heavily on empirical data and models developed from heritage hardware tests. Using this limited set of data produces challenges when trying to design a new propulsion system where the operating conditions may greatly differ from heritage applications. Time-accurate, Three-Dimensional (3-D) Computational Fluid Dynamics (CFD) modeling of combusting flows inside of injectors has long been a goal of the fluid analysis group at Marshall Space Flight Center (MSFC) and the larger CFD modeling community. CFD simulation can provide insight into the design and function of an injector that cannot be obtained easily through testing or empirical comparisons to existing hardware. However, the traditional finite-rate chemistry modeling approach utilized to simulate combusting flows for complex fuels, such as Rocket Propellant-2 (RP-2), is prohibitively expensive and time consuming even with a large amount of computational resources. MSFC has been working, in partnership with Streamline Numerics, Inc., to develop a computationally efficient, flamelet-based approach for modeling complex combusting flow applications. In this work, a flamelet modeling approach is used to simulate time-accurate, 3-D, combusting flow inside a single Gas Centered Swirl Coaxial (GCSC) injector using the flow solver, Loci-STREAM. CFD simulations were performed for several different injector geometries. Results of the CFD analysis helped guide the design of the injector from an initial concept to a tested prototype. The results of the CFD analysis are compared to data gathered from several hot-fire, single element injector tests performed in the Air Force Research Lab EC-1 test facility located at Edwards Air Force Base.

Richardson, Brian↗

NASA NTRS2018

Reliability-Based Design of Thermal Protection Systems with Support Vector Machines

The primary objective of this work was to develop a computationally efficient and accurate approach to reliability analysis of thermal protection systems using support vector machines. An adaptive sampling approach was introduced informs a iterative support vector machine approximation of the limit state function used for measuring reliability. The proposed sampling approach efficient adds samples along the limit state function until the reliability approximation is converged. This methodology is applied to two samples, mathematical functions to test and demonstrate the applicability. Then, the adaptive sampling-based support vector machine approach is applied to the reliability analysis of a thermal protection system. The results of all three problems highlight the potential capability of the new approach in terms of accuracy and computational saving in determining thermal protection system reliability.

White, Laura M.↗

NASA NTRS1987

A combined Eulerian-Lagrangian analysis for computation of two-phase flows

A combined Eulerian-Lagrangian analysis, which combines a linearized block implicit Navier-Stokes analysis for the continuous phase with a Lagrangian analysis for the motion of the droplet phase to simulate evaporating two-phase flows, has been developed. A unique aspect of this analysis is that the Lagrangian equations for the droplet motion have been transformed into the Eulerian computational space using coordinate transformation resulting in better computational efficiency. Use of the present implicit procedure for the continuous phase makes it possible to efficiently use a locally highly refined mesh. The coupling of the continuous phase and the droplet phase analyses is such that it allows the use of convergence acceleration techniques for steady-state problems, as well as making it possible to simulate transient flows. A rapidly evaporating two-phase flow caused by spray of liquid nitrogen in gaseous nitrogen has been used as a test problem to demonstrate the computer code using this analysis.

Sabnis, J. S.↗

NASA NTRS1996

Optimization Methodology for Unconventional Rocket Nozzle Design

Several current rocket engine concepts such as the bell-annular tripropellant engine, and the linear aerospike being proposed for the X-33, require unconventional three-dimensional rocket nozzles which must conform to rectangular or sector-shaped envelopes to meet integration constraints. These types of nozzles exist outside the current experience database, therefore, development of efficient design methods for these propulsion concepts is critical to the success of launch vehicle programs. Several approaches for optimizing rocket nozzles, including streamline tracing techniques, and the coupling of CFD analysis to optimization algorithms are described. The relative strengths and weaknesses of four classes of optimization algorithms are discussed: Gradient based methods, genetic algorithms, simplex methods, and surface response methods. Additionally, a streamline tracing technique, which provides a very computationally efficient means of defining a three-dimensional contour, is discussed. The performance of the various optimization methods on thrust optimization problems for tripropellant and aerospike concepts is assessed and recommendations are made for future development efforts.

Follett, W.↗

DOE OSTI2024

Privacy-Preserving Artificial Intelligence on Edge Devices: A Homomorphic Encryption Approach

Recent advancements in privacy-preserving artificial intelligence (AI) have paved the way for enhanced privacy in computational processes. A standing challenge, however, is the robust privacy preservation in AI algorithms, especially when integrated into edge devices and Internet-of-Thing (IoT) infrastructures. Most prevailing solutions have adopted traditional encryption methods which, though secure, often introduce significant overhead and potential dips in accuracy. In this study, we put forth an innovative approach, utilizing the CKKS encryption scheme, aiming to harmoniously balance computational efficiency with stringent data privacy. By harnessing the capabilities of Full Homomorphic Encryption (FHE) under the CKKS scheme, we ensure the preservation of privacy, successfully curbing the inherent noise traditionally linked with accuracy reductions in similar encryption-oriented solutions. Through comprehensive experiments, our approach showcased its potential as a strong contender for privacy preservation, demonstrating commendable performance across all tests, affirming that FHE is indeed viable for devices with constrained computational power and energy resources.

Khan, Muhammad Jahanzeb↗

DOE OSTI2024

A novel conditional generative model for efficient ensemble forecasts of state variables in large-scale geological carbon storage

Integrating monitoring data to efficiently update reservoir pressure and CO 2 plume distribution forecasts presents a significant challenge in geological carbon storage (GCS) applications. Inverse modeling techniques are commonly used to fuse observational data and refine reservoir model parameters, thereby improving state variable forecasts. However, these techniques often rely on linear or Gaussian assumptions, which can limit their effectiveness in accurately predicting state variables. Moreover, simulating large-scale three-dimensional (3D) GCS problems is computationally expensive, making iterative runs in inverse problems prohibitive. To address these challenges, we propose a conditional generative model utilizing the score-based diffusion method for real-time 3D pressure and saturation field distribution predictions. Our approach involves solving the score function with a mini-batch-based Monte Carlo estimator to generate labeled data. This data is subsequently employed to train a fully connected neural network, enabling it to learn the conditional sample generator within a supervised learning framework. This method enables the rapid generation of a large ensemble of predictions, facilitating comprehensive uncertainty quantification of state variables. Here we applied our method to forecast the dynamic 3D distributions of pressure and saturation fields over a 30-year injection period. The statistical assessment with low root mean square error (RMSE) values demonstrates that our method can accurately predict the spatiotemporal distributions of both pressure and saturation fields. Moreover, the developed conditional generative model shows high computational efficiency by generating 100 ensemble forecasts of 3D state variables in less than 10 min. The consistency between ensemble averages and ground truth values further illustrates the model’s capability to capture state variable dynamics during the CO 2 plume injection process. Notably, the ground truth values fall within the ensemble forecasts, indicating that our uncertainty quantification effectively captures variability and potential noise in the observations. Thus, the developed conditional generative model proves to be a more efficient, accurate, and practical tool for GCS applications, facilitating timely risk analysis and informed decision-making.

58 GEOSCIENCES↗

DOE OSTI2024

COSMIC DAWN: Distributed Analysis of Wireless at Nextscale

Distributed Analysis of Wireless at Nextscale (DAWN) is a novel simulation framework for large-scale design-space exploration (DSE) of unmodified software-defined radio (SDR) applications interacting in a scalable, high-fidelity, virtual physics environment. The software-defined nature of the coupled software-physics simulation leverages hardware emulation to permit in-depth examination and modification of not only the electromagnetic environment, including each signal in flight, but also the precise state of system software and components. DAWN supports modular, customizable physics environments allowing realistic propagation effects so that computationally efficient empirical models, reduced order/surrogate models, or large-scale, high-fidelity, site-specific simulations can be used as a propagation medium based on scenario requirements. This paper introduces DAWN’s design and initial implementation, detailing key architectural components, including the Physics Realization Engine (PhyRE), Runtime Infrastructure for Simulation Environments (RISE), and the design space exploration (DSE) suite. It concludes with demonstrations using unmodified 4G/LTE software available from srsRAN on computing resources ranging from a small cluster to ORNL’s Frontier Exascale system.

Wise, Mike [ORNL] (ORCID:0000000266120641)↗

NASA NTRS2021

Micromechanics Modeling of Textiles for Re-Entry Parachute Applications

Recent flight test projects and NASA missions have highlighted the challenges associated with accurately and efficiently modeling the behavior of parachute deployment systems needed for parachute design. Moreover, parachute deployment has been identified as one of the higher risk components for such missions. The analysis of textile fabrics used for atmospheric entry is inherently complex due to the multiple scales present in the fabric structure, including individual fiber filaments at the microscale, yarn bundles of fibers at the mesoscale, and the overall woven fabric at the macroscale. Computational tools for simulating fabric behavior must be able to account for the different mechanisms present at each scale without sacrificing computational efficiency. This work examines the generalized multiscale method of cells micromechanics theory, which has previously been used for the analysis of reinforced composite structures, to unreinforced textile fabrics. Modifications to the existing composite multiscale framework, implemented in NASA’s Multiscale Analysis Tool (NASMAT), include the specific mechanics unique to unreinforced textile fabrics, and overcoming the assumptions of a fixed fiber angle. It looks to assess the feasibility of using the NASMAT tool for efficient prediction of the response of unreinforced fabrics to loading such that it can ultimately be applied to fluid structure interaction tools for the prediction of parachute deployment systems. In this work, fabric behavior is simulated in NASMAT through homogenization of a triply periodic repeating unit cell, where the geometry of the subcells can change as a function of loading to represent the relative rotation and uncrimping that can occur in fabric tows. Predictions from the amended NASMAT code are compared to experimental data for uniaxial and off-axis tension to verify the ability of the code to incorporate lower-scale mechanics in prediction of unreinforced fabrics under loading.

Micromechanics↗

NASA NTRS2008

Efficient Modal Basis Selection Criteria for Reduced-Order Nonlinear Simulation

A modal basis selection technique for a reduced-order nonlinear numerical simulation with application to two-dimensional structures is presented as a two-step procedure. A system identification analysis is first performed using proper orthogonal decomposition. Using these results, a set of load-invariant bases consisting of the normal modes is next selected. Two criteria for making the basis selection are offered; one using the modal assurance criterion and the other using the modal expansion theorem. The quality of the subsequent reduced-order analyses are examined through comparison with computationally intensive finite element nonlinear simulations in physical degrees-of-freedom. A clamped flat isotropic plate under a random acoustic loading is considered to demonstrate the procedure. It is found that the subject procedure enables formation of an accurate and computationally efficient reduced-order system applicable to a broad range of loading conditions.

Przekop, Adam↗

NASA NTRS1987

A Lanczos eigenvalue method on a parallel computer

Eigenvalue analyses of complex structures is a computationally intensive task which can benefit significantly from new and impending parallel computers. This study reports on a parallel computer implementation of the Lanczos method for free vibration analysis. The approach used here subdivides the major Lanczos calculation tasks into subtasks and introduces parallelism down to the subtask levels such as matrix decomposition and forward/backward substitution. The method was implemented on a commercial parallel computer and results were obtained for a long flexible space structure. While parallel computing efficiency for the Lanczos method was good for a moderate number of processors for the test problem, the greatest reduction in time was realized for the decomposition of the stiffness matrix, a calculation which took 70 percent of the time in the sequential program and which took 25 percent of the time on eight processors. For a sample calculation of the twenty lowest frequencies of a 486 degree of freedom problem, the total sequential computing time was reduced by almost a factor of ten using 16 processors.

Bostic, Susan W.↗

NASA NTRS2007

Decision-Tree Formulation With Order-1 Lateral Execution

A compact symbolic formulation enables mapping of an arbitrarily complex decision tree of a certain type into a highly computationally efficient multidimensional software object. The type of decision trees to which this formulation applies is that known in the art as the Boolean class of balanced decision trees. Parallel lateral slices of an object created by means of this formulation can be executed in constant time considerably less time than would otherwise be required. Decision trees of various forms are incorporated into almost all large software systems. A decision tree is a way of hierarchically solving a problem, proceeding through a set of true/false responses to a conclusion. By definition, a decision tree has a tree-like structure, wherein each internal node denotes a test on an attribute, each branch from an internal node represents an outcome of a test, and leaf nodes represent classes or class distributions that, in turn represent possible conclusions. The drawback of decision trees is that execution of them can be computationally expensive (and, hence, time-consuming) because each non-leaf node must be examined to determine whether to progress deeper into a tree structure or to examine an alternative. The present formulation was conceived as an efficient means of representing a decision tree and executing it in as little time as possible. The formulation involves the use of a set of symbolic algorithms to transform a decision tree into a multi-dimensional object, the rank of which equals the number of lateral non-leaf nodes. The tree can then be executed in constant time by means of an order-one table lookup. The sequence of operations performed by the algorithms is summarized as follows: 1. Determination of whether the tree under consideration can be encoded by means of this formulation. 2. Extraction of decision variables. 3. Symbolic optimization of the decision tree to minimize its form. 4. Expansion and transformation of all nested conjunctive-disjunctive paths to a flattened conjunctive form composed only of equality checks when possible. If each reduced conjunctive form contains only equality checks and all of these forms use the same variables, then the decision tree can be reduced to an order-one operation through a table lookup. The speedup to order one is accomplished by distributing each decision variable over a surface of a multidimensional object by mapping the equality constant to an index

James, Mark↗

DOE OSTI2024

A collision-based hybrid method for the BGK equation

In this article, we apply the collision-based hybrid method introduced by Hauck and McClarren to the Boltzmann equation with the BGK operator and a hyperbolic scaling. An implicit treatment of the source term is used to handle stiffness associated with the BGK operator. Although it helps the numerical scheme become stable with a large time step size, it is still not obvious to achieve the desired order of accuracy due to the relationship between the size of the spatial cell and the mean free path. Without asymptotic preserving property, a very restricted grid size is required to resolve the mean free path, which is not practical. Our approaches are based on the noncollision-collision decomposition of the BGK equation. We introduce the arbitrary order of nodal discontinuous Galerkin (DG) discretization in space with a semi-implicit time-stepping method; we employ the backward Euler time integration for the uncollided equation and the 2nd order predictor-corrector scheme for the collided equation, i.e., both source terms in uncollided and collided equations are treated implicitly and only streaming term in the collided equation is solved explicitly. This improves the computational efficiency without the complexity of the numerical implementation. Numerical results are presented for various Knudsen numbers to present the effectiveness and accuracy of our hybrid method. Also, we compare the solutions of the hybrid and non-hybrid schemes.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1988

Navier-Stokes simulation of 3-D hypersonic equilibrium air flow

A computationally efficient three-dimensional conservative supracharacteristic Navier-Stokes method has been extended to simulate complex external chemically reacting flows of hypersonic reentry vehicles at angle-of-attack. Numerical simulation results of the flow around a sphere-cone-cone-flare reentry vehicle at 10 deg angle-of-attack are presented, in addition to the results of a well-validated two-dimensional code with which the 0-deg axisymmetric flow has been computed. A method for obtaining compositions of species in equilibrium ionized air is proposed.

Nagaraj, N.↗

NASA NTRS1988

Three-dimensional self-adaptive grid method for complex flows

A self-adaptive grid procedure for efficient computation of three-dimensional complex flow fields is described. The method is based on variational principles to minimize the energy of a spring system analogy which redistributes the grid points. Grid control parameters are determined by specifying maximum and minimum grid spacing. Multidirectional adaptation is achieved by splitting the procedure into a sequence of successive applications of a unidirectional adaptation. One-sided, two-directional constraints for orthogonality and smoothness are used to enhance the efficiency of the method. Feasibility of the scheme is demonstrated by application to a multinozzle, afterbody, plume flow field. Application of the algorithm for initial grid generation is illustrated by constructing a three-dimensional grid about a bump-like geometry.

Djomehri, M. Jahed↗

NASA NTRS1988

Three-dimensional self-adaptive grid method for complex flows

Djomehri, M. Jahed↗