Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “multiple iterations”

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.

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

At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Do You See What I See? Interactive Visualization of Mission Design and Navigation

Mission Design and Navigation (MDNav) is an intensive process requiring advanced computational resources, expert human intuition, and many successive human-in-the-loop iterations to converge on acceptable trajectory designs or navigation solutions. The current bottleneck in MDNav is not the underlying computational algorithms but the human cognitive capacity to prune through a multitude of simulated results to select high-value candidates. One approach to alleviate this burden is through the judicious application of visualizations that allow humans to interactively filter data in multiple dimensions to reveal salient patterns and highlight divergences. When designed efficiently, such interactive visualizations should aid human operators to get familiar with data faster, visually observe correlations, and communicate findings more effortlessly. In this work, we present three visualization case studies that have the potential to increase human operator efficiency in MDNav. While identifying the most critical “pain points” that operators face, and also working on potential solutions, we followed a human-centered design approach. We started with a series of interviews with potential users, and then rapidly created prototypes for alternative solutions, validated outcomes with feedback from users through out development of these proof of concept visualizations. With this survey of our current efforts, we demonstrate the transformative capability of interactive data visualizations for improving mission development and operations, enabling operators to grow intuition, and communicating key concepts across diverse mission teams.

Arora, Nitin↗
DOE OSTI2022

Interlinked tuples in coordination namespace

A system and method for supporting tuple record interlinking in one or more tuple space/coordinated namespace (CNS) extended memory storage systems. A system-wide CNS provides for efficient storing and communicating of data generated by local processes running at the nodes, and coordinated to generate a union/intersection of multiple CNS where tuple records are interlinked in multiple CNS hashtables, and/or share tuple data between two sets of processes that are part of different CNSs. Local node processes further generate multi-key tuples where two or more tuple records are interlinked within the same CNS hash table, thereby permitting a look up of the tuple data by either tuple name/keys. A CNS controller further provides a tuple iterator for a key-value storage in a CNS system that adds more links between tuples enables creation of iterator structures such as linked list or trees etc. of “different” tuples in a tuple database.

Jacob, Philip↗
NASA NTRSDate not supplied

Modal Correlation of Complex Aerospace Joints Using Automated Variable Substitution

A critical task involved with being able to predict flight loads accurately in aerospace finite element models (FEMs) is the prior verification of the FEMs by conducting modal survey testing (MST). Experience comparing dynamic response of initial FEMs to MST data tends to demonstrate that FEMs can have unacceptable accuracy even when best modeling practices are followed. One inherent source of inaccuracy in linear dynamic FEMs is the modeling of nonlinear joints with mechanisms such as spherical bearings. These joints are usually designed to freely translate or rotate under the high levels of loading experienced in flight. Engineers who create linear FEMs conventionally model these joints without any stiffness in the mechanism degrees of freedom to meet this design intent. However, inaccuracy is observed during test validation of these FEMs, which usually relies on low-level force excitation orders of magnitude below flight load levels. This low-level modal test rarely overcomes the joint friction that is present, and thus the mechanism joints are able to react loads. This divide between the test results and the FEM creates a significant challenge to the engineer who is performing the correlation, in that the engineer has no basis for what stiffness value should be used to make the FEM match the test results. A compounding challenge is that complicated built-up aerospace structures commonly have multiple joints through a load path where each joint will “stick” and “slip” at different levels of force input. Explicitly matching the dynamics of a system containing these nonlinear mechanisms would require a nonlinear FEM, which is prohibitively costly for dynamic simulations of most aerospace systems. The objective of this paper is to present a workflow that can efficiently cycle through many iterations of a FEM, allowing a Monte Carlo style examination of the design space to identify candidate stiffness values for nonlinear mechanism joints. The outlined approach is specific to MSC Nastran and utilizes MSC Nastran’s symbolic substitution capabilities, coupled with the IMAT™ and Attune™ software packages developed by ATA Engineering, Inc. (ATA). The workflow is demonstrated with a case study from the correlation effort for The Boeing Company’s Crew Space Transportation (CST)-100 Starliner FEM. Keywords: Correlation, MSC Nastran, IMAT™, Attune™, Finite Element Model, Modal Testing

Correlation↗
DOE OSTI2021

Predicting operational windows of ELMs suppression by resonant magnetic perturbations in the DIII-D and KSTAR tokamaks

A newly developed plasma response model, combining the nonlinear two-fluid MHD code TM1 and toroidal MHD code GPEC run in ideal mode, quantitatively predicts the narrow isolated q 95 windows (Δq 95 ~ 0.1) of ELM suppression by n = 1, 2 and 3 resonant magnetic perturbations (RMPs) in both DIII-D and KSTAR tokamaks across a wide range of plasma parameters. The key physics that unites both experimental observations and our simulations is the close alignment of essential resonant q-surfaces and the location of the top of the pedestal prior to an ELM. This alignment permits an applied RMP to produce field penetration due to the lower E × B rotation at the pedestal top rather than being screened. The model successfully predicts that narrow magnetic islands form when resonant field penetration occurs at the top of pedestal, and these islands are easily screened when q 95 moves off resonance, leading to very narrow windows of ELM suppression (typically Δq 95 ~ 0.1). Furthermore, the observed reduction in the pedestal height is also well captured by the calculated classical collisional transport across the island. We recover observed q 95 , β N and plasma shape dependence of ELM suppression due to the effect of magnetic islands on pedestal transport and Peeling-Ballooning- Mode (PBM) stability. Importantly, experiments do occasionally observe wide windows of ELM suppression (Δq 95 > 0.5). Our model reveals that at low pedestal-top density multiple islands open, leading to wide operational windows of ELM suppression consistent with experiment. The model indicates that wide q95 windows of ELM suppression can be achieved at substantially higher pedestal pressure with less confinement degradation in DIII-D by operating at higher toroidal mode number (n = 4) RMPs. As a result, this can have significant implications for the operation of the ITER ELM control coils for maintaining high confinement together with ELM suppression.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

GPU-Based Static Data-Flow Analysis for Fast and Scalable Android App Vetting

Many popular vetting tools for Android applications use static code analysis techniques. In particular, Interprocedural Data-Flow Graph (IDFG) construction is the computation at the core of Android static data-flow analysis and consumes most of the analysis time. Many analysis tools use a worklist algorithm, an iterative fixed-point approach, to construct the IDFG. In this paper, we observe that a straightforward GPU parallelization of the worklist algorithm leads to significant underutilization of the GPU resources. We identify four performance bottlenecks, namely, frequent dynamic memory allocations, high branch divergence, workload imbalance, and irregular memory access patterns. Accordingly, we propose GDroid, a GPU-based worklist algorithm implementation with multiple fine-grained optimizations tailored to common characteristics of Android applications. The optimizations considered are: matrix-based data structure, memory access-based node grouping, and worklist merging. Our experimental evaluation, performed on 1000 Android applications, shows that the proposed optimizations are beneficial to performance, and GDroid can achieve up to 128X speedups against a plain GPU implementation.

Android security↗
NASA NTRS1990

Method of up-front load balancing for local memory parallel processors

In a parallel processing computer system with multiple processing units and shared memory, a method is disclosed for uniformly balancing the aggregate computational load in, and utilizing minimal memory by, a network having identical computations to be executed at each connection therein. Read-only and read-write memory are subdivided into a plurality of process sets, which function like artificial processing units. Said plurality of process sets is iteratively merged and reduced to the number of processing units without exceeding the balance load. Said merger is based upon the value of a partition threshold, which is a measure of the memory utilization. The turnaround time and memory savings of the instant method are functions of the number of processing units available and the number of partitions into which the memory is subdivided. Typical results of the preferred embodiment yielded memory savings of from sixty to seventy five percent.

Baffes, Paul Thomas↗
NASA NTRS2003

Numerical Models of Human Circulatory System under Altered Gravity: Brain Circulation

A computational fluid dynamics (CFD) approach is presented to model the blood flow through the human circulatory system under altered gravity conditions. Models required for CFD simulation relevant to major hemodynamic issues are introduced such as non-Newtonian flow models governed by red blood cells, a model for arterial wall motion due to fluid-wall interactions, a vascular bed model for outflow boundary conditions, and a model for auto-regulation mechanism. The three-dimensional unsteady incompressible Navier-Stokes equations coupled with these models are solved iteratively using the pseudocompressibility method and dual time stepping. Moving wall boundary conditions from the first-order fluid-wall interaction model are used to study the influence of arterial wall distensibility on flow patterns and wall shear stresses during the heart pulse. A vascular bed modeling utilizing the analogy with electric circuits is coupled with an auto-regulation algorithm for multiple outflow boundaries. For the treatment of complex geometry, a chimera overset grid technique is adopted to obtain connectivity between arterial branches. For code validation, computed results are compared with experimental data for steady and unsteady non-Newtonian flows. Good agreement is obtained for both cases. In sin-type Gravity Benchmark Problems, gravity source terms are added to the Navier-Stokes equations to study the effect of gravitational variation on the human circulatory system. This computational approach is then applied to localized blood flows through a realistic carotid bifurcation and two Circle of Willis models, one using an idealized geometry and the other model using an anatomical data set. A three- dimensional anatomical Circle of Willis configuration is reconstructed from human-specific magnetic resonance images using an image segmentation method. The blood flow through these Circle of Willis models is simulated to provide means for studying gravitational effects on the brain circulation under auto-regulation.

Kim, Chang Sung↗
NASA NTRS2020

Multi-Impulse to Time Optimal Finite Burn Trajectory Conversion

A novel conversion algorithm is presented that combines the fidelity of indirect optimization methods with the generality of direct methods to more easily solve time-optimal, finite-burn pseudo-rendezvous problems. An algorithm is described that converts a set of multiple-impulses, representing the entirety or a portion of a high- or low-thrust maneuver, to an exact time optimal finite-burn trajectory for a thrust limited, constant exhaust velocity spacecraft. A pseudo-rendezvous problem is one that yields a solution whose final time, position and velocity state is equal to that of the original post-impulsive trajectory. An iterative adjoint-control transformation is used to initialize the optimal control two-point boundary value problem. Examples are shown for both high and low-thrust non-coplanar Earth orbit transfers, as well as a low-thrust Hohmann-type Earth-Mars transfer.

Fogel, J.↗
DOE OSTI2024

SLICE

For most of their lifetime, pebble-bed reactors (PBRs) operate at an equilibrium state in which the core is filled with fuel pebbles at various levels of burnup. A fuel pebble travels multiple times in so-called passes through the reactor before it reaches its target discharge burnup and is replaced with a fresh fuel pebble. Given the stochastic nature of the fuel pebble travel paths and consequently the individual fuel pebble histories, it is not possible with standard methods developed for traditional reactor concepts to calculate the fuel inventory in the reactor core. An iterative approach, the SCALE Leap-In method for Cores at Equilibrium (SLICE), was developed to generate region-average fuel inventory for a PBR. The SLICE code enables automatic generation of input files for the SCALE code system (https://www.ornl.gov/scale), management of the SCALE result files, and analysis of results.

Bostelmann, Friederike [Oak Ridge National Laborat↗
DOE OSTI2026

Scientific Core Library Stack (SCLS) v2026

SCLS (Scientific Core Library Stack) is an opinionated build and packaging system for scientific computing libraries developed at Lawrence Berkeley National Laboratory. It produces a coherent, reproducible stack of numerical libraries — including BLAS/LAPACK, MPI, sparse direct and iterative solvers, graph partitioners, and parallel I/O libraries (e.g., PETSc, SLEPc, HDF5, NetCDF, MUMPS, OpenBLAS) — that work together without manual repair by downstream scientific software. From a single recipe-and-flavor model, SCLS produces native RPM packages for RHEL-family Linux, DEB packages for Debian/Ubuntu, direct Unix-style prefix installs for HPC and locked-down environments, and native macOS builds. Multiple build "flavors" (e.g., GCC+OpenBLAS, GCC+MKL, Intel+MKL, debug) coexist in distinct prefixes on the same host. Compared to general-purpose meta-build frameworks, SCLS is deliberately curated rather than infinitely configurable. It enforces deterministic, audit-friendly behavior: explicit build dependencies, no silent feature autodetection, a clear open-source license policy, and rpath-based runtime linkage so installs integrate cleanly with standard package-manager workflows.

Messe, Christian [Lawrence Berkeley National Labor↗
DOE OSTI2021

Clearing a Path to Commercialization of Marine Renewable Energy Technologies Through Public–Private Collaboration

Governments are increasingly turning toward public–private partnerships to bring industry support to improving public assets or services. Here, we describe a unique public–private collaboration where a government entity has developed mechanisms to support public and private sector advancement and commercialization of monitoring technologies for marine renewable energy. These support mechanisms include access to a range of skilled personnel and test facilities that promote rapid innovation, prove reliability, and inspire creativity in technology development as innovations move from concept to practice. The ability to iteratively test hardware and software components, sensors, and systems can accelerate adoption of new methods and instrumentation designs. As a case study, we present the development of passive acoustic monitoring technologies customized for operation in energetic waves and currents. We discuss the value of testing different systems together, under the same conditions, as well as the progression through different test locations. The outcome is multiple, complementary monitoring technologies that are well suited to addressing an area of high environmental uncertainty and reducing barriers to responsible deployment of low-carbon energy conversion systems, creating solutions for the future.

16 TIDAL AND WAVE POWER↗
NASA NTRS1993

Second order distorted born approximation for backscattering from a layer of discrete random medium

In recent years there has been increasing interest in scattering and depolarization characteristics of the vegetation canopies. Scattering models applied to the microwave remote sensing of vegetation canopies showed that multiple scattering effects can be important in simulating the backscattering coefficients correctly. In particular, in most applications, the cross-polarized backscattering coefficients are often underestimated by single scattering models. Recently, there have been concerted efforts to include the second order terms in the radiative transfer models of vegetation canopies in order to account for multiple scattering within the canopy. The coherent wave theory approach is extended to include multiple scattering effects to predict the coherent and incoherent backscattering contributions from a layer of vegetation canopy. The problem is initially formulated in terms of the exact equation for the correlation function of the field, i.e., the Bethe-Salpeter equation. Using fractional volume as a small parameter, a Foldy type approximation is made to obtain a more manageable correlation equation. This equation is iterated to obtain first and second order solutions. The iteration procedure assumes the variance of the field fluctuations are small compared to the coherent intensity. This assumption proved to be particularly successful in computing backscattering coefficients. First and second order backscattering coefficients are calculated from the iterants of the correlation equation. It is shown that the first order coefficients are the same as the distorted Born results used previously by the authors. These results contained enhancement terms in the direct-reflected contributions. The important contributions to second order backscattering are examined and interpreted in terms of scattering diagrams. Examples of situations in which second order backscattering coefficients are important are given.

Lang, Roger H.↗
NASA NTRS1972

Multiple element airfoils optimized for maximum lift coefficient.

Optimum airfoils in the sense of maximum lift coefficient are obtained for incompressible fluid flow at large Reynolds number. The maximum lift coefficient is achieved by requiring that the turbulent skin friction be zero in the pressure rise region on the airfoil upper surface. Under this constraint, the pressure distribution is optimized. The optimum pressure distribution is a function of Reynolds number and the trailing edge velocity. Geometries of those airfoils which will generate these optimum pressure distributions are obtained using a direct-iterative method which is developed in this study. This method can be used to design airfoils consisting of any number of elements. Numerical examples of one- and two-element airfoils are given. The maximum lift coefficients obtained range from 2 to 2.5.

Ormsbee, A. I.↗
DOE OSTI2023

Identification of 2-Hydroxyacyl-CoA Synthases with High Acyloin Condensation Activity for Orthogonal One-Carbon Bioconversion

One-carbon (C1) compounds are emerging as cost-effective and potentially carbon-negative feedstocks for biomanufacturing, which require efficient, versatile metabolic platforms for the synthesis of value-added products. Synthetic formyl-CoA elongation (FORCE) pathways allow diverse product synthesis from C1 compounds via iterative C1 elongation, operating independently from the host metabolism with reduced engineering complexity and improved theoretical yields. However, a major bottleneck was identified as the suboptimal kinetics of the core C1–C1 condensation enzyme, 2-hydroxyacyl-CoA synthase (HACS), catalyzing the acyloin condensation reaction between formaldehyde and formyl-CoA. Furthermore, we used a combinatorial approach of bioprospecting and rational protein engineering to identify multiple HACS variants with significantly improved activities toward C1 substrates. Sequence and structure alignment of the active variants elucidated the key regions for the catalytic function, which were targeted for mutagenesis, leading to improved catalytic efficiency. In parallel, a consecutive round of bioprospecting for homologs with high similarity with active variants revealed a highly active HACS variant exhibiting up to 7-fold improvement in catalytic efficiency (k cat /K M ) and 14-fold improvement in the FORCE pathway flux in vivo compared to the previous reports. Upon further optimization of the downstream pathway, the orthogonal C1-to-product bioconversion system showed a metabolic flux of up to 700 μM glycolate OD –1 h –1 (2.1 mmol gDCW –1 h –1 ) and an industrially relevant glycolate titer, rate, and yield of 5.2 g L –1 (67.8 mM), 0.22 g L –1 h –1 , and 94% carbon yield, respectively.

2-hydroxyacyl-CoA synthase↗
DOE OSTI2026

Probing multi-dimensional composition spaces in search of strong metallic alloys

Refractory complex concentrated alloys (RCCA) offer exceptionally high-temperature strength compared to pure metals and dilute alloys, but predictive theory for RCCA design is lacking. We present large-scale molecular Dynamics (MD) simulations of crystal plasticity to explore alloy compositions for maximum mechanical strength, focusing on Fe-Ta-W and Nb-Ta-Mo-W alloy families modeled with Embedded Atom Model (EAM) and Spectral Neighbor Analysis Potentials (SNAP). To efficiently guide the search for strong alloy compositions, we employ iterative optimization using Gaussian process regression. Many simulated RCCA compositions exhibit pronounced cocktail strengthening, with strengths surpassing their strongest constituent metal, tungsten. Contrary to expectations, the highest strength is found on binary edges of the RCCA composition space. Detailed analyses of atomistic simulations reveal that, similar to pure BCC metals, plastic response in RCCA is primarily governed by screw dislocations. However, at large strains, dislocation multiplication and interactions (Taylor hardening) become the dominant mechanisms contributing to RCCA strength.

Materials science↗
DOE OSTI2021

Neutron radiation hardness of aluminum gallium nitride UV LEDs at various wavelengths

Radiation hard diagnostics are critical to the success of nuclear fusion at National Ignition Facility, Z, ITER, and prolonged space explorations. We have first demonstrated the exceptional proton radiation hardness of initial GaN devices and qualified their space flight and deployment for missions such as the Laser Interferometer Space Antenna and International Space Station. We have further conducted neutron radiation hardness experiments at Los Alamos Neutron Science Center by opening a new high fluence beam station. During 2014–2016, we irradiated multiple Aluminum Gallium Nitride (AlGaN) LEDs with a maximum fluence of 2.4 × 1013 neutrons/cm2 in 3 years and generated 54 161 current–voltage (I–V) scan traces. Our data processing program analyzes each and all I–V traces. In addition, we retrieved local temperature records to analyze and remove temperature effects in the outdoor environment. The I–V curve families of AlGaN UV LEDs with emitting wavelengths of 265, 275, and 310 nm were compared. The I–V curves of 265 nm AlGaN UV LEDs have the smallest deviations from the average value, while the I–V curves for 310 nm AlGaN LEDs showed the largest deviations from the average value. We have reached another important recommendation for the optimal use of multiple AlGaN optoelectronic devices or imaging arrays for inertially confined fusion diagnostics: Shorter wavelength devices at 265 nm exhibit more consistent radiation hardness performance than the 310 nm devices. Higher aluminum content LEDs or AlxGa1–xN devices with higher mole fraction x for generating shorter wavelengths have better radiation hardness for fusion diagnostics.

Sun, K. -X. (ORCID:0000000224659235)↗
DOE OSTI2025

A time-parallel multiple-shooting method for large-scale quantum optimal control

Quantum optimal control plays a crucial role in quantum computing by providing the interface between compiler and hardware. Solving the optimal control problem is particularly challenging for multi-qubit gates, due to the exponential growth in computational complexity with the system's dimensionality and the deterioration of optimization convergence. To ameliorate the computational complexity of time-integration, this paper introduces a multiple-shooting approach in which the time domain is divided into multiple windows and the intermediate states at window boundaries are treated as additional optimization variables. Further, this enables parallel computation of state evolution across time-windows, significantly accelerating objective function and gradient evaluations. Since the initial state matrix in each window is only guaranteed to be unitary upon convergence of the optimization algorithm, the conventional gate trace infidelity is replaced by a generalized infidelity that is convex for non-unitary state matrices. Continuity of the state across window boundaries is enforced by equality constraints. A quadratic penalty optimization method is used to solve the constrained optimal control problem, and an efficient adjoint technique is employed to calculate the gradients in each iteration. We demonstrate the effectiveness of the proposed method through numerical experiments on quantum Fourier transform gates in systems with 2, 3, and 4 qubits, noting a speedup of 80x for evaluating the gradient in the 4-qubit case, highlighting the method's potential for optimizing control pulses in multi-qubit quantum systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations

Predictive modeling typically relies on Bayesian model calibration to provide uncertainty quantification. Variational inference utilizing fully independent (“mean-field”) Gaussian distributions are often used as approximate probability density functions. This simplification is attractive since the number of variational parameters grows only linearly with the number of unknown model parameters. However, the resulting diagonal covariance structure and unimodal behavior can be too restrictive to provide useful approximations of intractable Bayesian posteriors that exhibit highly non-Gaussian behavior, including multimodality. High-fidelity surrogate posteriors for these problems can be obtained by considering the family of Gaussian mixtures. Gaussian mixtures are capable of capturing multiple modes and approximating any distribution to an arbitrary degree of accuracy, while maintaining some analytical tractability. Unfortunately, variational inference using Gaussian mixtures with full-covariance structures suffers from a quadratic growth in variational parameters with the number of model parameters. The existence of multiple local minima due to strong nonconvex trends in the loss functions often associated with variational inference present additional complications, These challenges motivate the need for robust initialization procedures to improve the performance and computational scalability of variational inference with mixture models. In this work, we propose a method for constructing an initial Gaussian mixture model approximation that can be used to warm-start the iterative solvers for variational inference. The procedure begins with a global optimization stage in model parameter space. In this step, local gradient-based optimization, globalized through multistart, is used to determine a set of local maxima, which we take to approximate the mixture component centers. Around each mode, a local Gaussian approximation is constructed via the Laplace approximation. Finally, the mixture weights are determined through constrained least squares regression. The robustness and scalability of the proposed methodology is demonstrated through application to an ensemble of synthetic tests using high-dimensional, multimodal probability density functions. Here, the practical aspects of the approach are demonstrated with inversion problems in structural dynamics.

97 MATHEMATICS AND COMPUTING↗