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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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At least 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Parallel interior-point solver for block-structured nonlinear programs on SIMD/GPU architectures

Here, we investigate how to port the standard interior-point method to new exascale architectures for block-structured nonlinear programs with state equations. Computationally, we decompose the interior-point algorithm into two successive operations: the evaluation of the derivatives and the solution of the associated Karush-Kuhn-Tucker (KKT) linear system. Our method accelerates both operations using two levels of parallelism. First, we distribute the computations on multiple processes using coarse parallelism. Second, each process uses SIMD/GPU accelerators locally to accelerate the operations using fine-grained parallelism. The KKT system is reduced by eliminating the inequalities and the state variables from the corresponding equations. We demonstrate our method's capability on the supercomputer Polaris, a testbed for the future exascale Aurora system. Each node is equipped with four GPUs, a setup amenable to our two-level approach. Our experiments on the stochastic optimal power flow problem show that the reduction method is 50x faster than the sparse linear solver HSL MA57 running in serial on the CPU, and 6x faster than Pardiso running in parallel on CPU on the same number of processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Isotope Production Education and Research via a Systematic Study of Photo-nuclear Reaction Yields and Excitation Functions

A new collaboration between the New Mexico Institute of Mining and Technology, Idaho State University, and Idaho National Laboratory builds the domestic workforce in isotope production and related science and technologies by conducting research and education programs related to photo-nuclear reactions. Workforce development was identified by the Department of Energy’s Office of Science in the Reaching a New Energy Sciences Workforce (RENEW) program that seeks to engage underrepresented university students in isotope production activities. This collaboration includes coursework development, student training, and isotope production research utilizing photo-nuclear reactions at electron linear accelerators. Bremsstrahlung-weighted excitation functions and cross-sections will be measured and the use of nanomaterials to exploit kinematic recoil reactions will be investigated. Students will be hosted at the Idaho National Laboratory where mentors will provide opportunities to explore careers in nuclear science and technology. This presentation regards the ongoing progress of the research and education objectives of the collaboration that was performed under the Office of Science Isotopes Program under award number D000-22-2765.

07 ISOTOPE AND RADIATION SOURCES↗
NASA NTRS1982

The MLA program at NASA

The Thematic Mapper (TM) on Landsat-D represents a significant improvement compared to the Multispectral Scanner (MSS) of the current Landsat satellites. However, there is a need for even higher spatial and spectral resolution. Such developments would, for instance, provide a significantly greater capability for mineral identification and the detection of vegetation stress. The pushbroom mode of imaging utilizing solid state linear or area arrays is widely regarded as the best approach to overcome the inherent limitations of a scanning type system. The Multispectral Linear Array (MLA) has become the generic term for the sensor concept and the technology associated with solid state electronically scanned arrays operating in the pushbroom mode. A brief review is provided of the MLA instrument concepts, associated technology developments, program status, and future plans.

Ando, K. J.↗
NASA NTRS2006

DCS-Neural-Network Program for Aircraft Control and Testing

A computer program implements a dynamic-cell-structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated error. A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used. A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate new values. The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning, the error becomes homogeneously distributed over all nodes.

Jorgensen, Charles C.↗
DOE OSTI2026

Joint Optimization of Multimodal Transit Frequency and Shared Autonomous Vehicle Fleet Size with Hybrid Metaheuristic and Nonlinear Programming

Shared autonomous vehicles (SAVs) bring competition to traditional transit services but redesigning multimodal transit network can utilize SAVs as feeders to enhance service efficiency and coverage. This paper presents an optimization framework for the joint multimodal transit frequency and SAV fleet size problem, a variant of the transit network frequency setting problem. The objective is to maximize total transit ridership (including SAV-fed trips and subtracting boarding rejections) across multiple time periods under budget constraints, considering endogenous mode choice (transit, point-to-point SAVs, driving) and route selection, while allowing for strategic route removal by setting frequencies to zero. Due to the problem’s non-linear, non-convex nature and the computational challenges of large-scale networks, we develop a hybrid solution approach that combines a metaheuristic approach (particle swarm optimization) with nonlinear programming for local solution refinement. To ensure computational tractability, the framework integrates analytical approximation models for SAV waiting times based on fleet utilization, multimodal network assignment for route choice, and multinomial logit mode choice behavior, bypassing the need for computationally intensive simulations within the main optimization loop. Applied to the Chicago metropolitan area’s multimodal network, our method illustrates a 33.3% increase in transit ridership through optimized transit route frequencies and SAV integration, particularly enhancing off-peak service accessibility and strategically reallocating resources.

Ng, Max↗
NASA NTRS1973

Development of a two channel linear discriminant function for detecting and identifying surface water using ERTS-1 data

A method for detecting surface water using ERTS-1 multispectral scanner digital data is described. The several computer programs for processing multispectral data are described, along with the manner in which these programs were used. The use of the LARSAA program to test and demonstrate the discriminant function is explained. The discriminant function itself was later implemented in an operational program which performs only those specific operations necessary to test each data point for an indication of surface water in the scene. Several possible sources of difficulty in the use of the function are identified, and suggestions are made for avoiding or correcting those difficulties.

Source record↗
NASA NTRS1985

Linear and nonlinear dynamic analysis of redundant load path bearingless rotor systems

The bearingless rotorcraft offers reduced weight, less complexity and superior flying qualities. Almost all the current industrial structural dynamic programs of conventional rotors which consist of single load path rotor blades employ the transfer matrix method to determine natural vibration characteristics because this method is ideally suited for one dimensional chain like structures. This method is extended to multiple load path rotor blades without resorting to an equivalent single load path approximation. Unlike the conventional blades, it isk necessary to introduce the axial-degree-of-freedom into the solution process to account for the differential axial displacements in the different load paths. With the present extension, the current rotor dynamic programs can be modified with relative ease to account for the multiple load paths without resorting to the equivalent single load path modeling. The results obtained by the transfer matrix method are validated by comparing with the finite element solutions. A differential stiffness matrix due to blade rotation is derived to facilitate the finite element solutions.

Murthy, V. R.↗
NASA NTRS2005

Hard X-ray Spectroscopic, Microwave and H-alpha Linear Polarization Studies with Hard X-Ray Observations from HESSI

The Principal Investigator (P.I.) has been pursuing a three year grant under NASA's Sun-Earth Connection Guest Investigator Program in support of the Ramaty High Energy Solar Spectroscopic Imager (RHESSI). An objective of these efforts is to combine X-ray and other data on solar flares, coronal mass ejections and interplanetary particle events in order to obtain a more comprehensive recognition of signatures, and understanding of interplanetary proton events. Thus, part of these efforts are to investigate if signatures seen in hard X-rays and microwaves can lead to better predictions of interplanetary proton events that can be dangerous to astronauts and spacecraft. The original proposal was written in May, 2000 and it discusses a three-pronged approach for data comparisons with three new types of instrumentation observing at X-ray, microwave and optical wavelengths. The major impetus behind this work and the proposal is that the P.I. discovered a strong correlation between a particular type of hard X-ray signature seen in spectral evolutions and interplanetary proton events (Kiplinger, 1995). The basic signature is that hard X-ray flux peaks either exhibit spectra that soften on their decays (Le. show fewer and fewer high energy X-rays with time) or they harden during decays (i.e. high energy X-rays decay significantly slower that lower energy X-rays). This signature is called progressive hardening. Studies were conducted over an eight-year period of data from the Hard X-Ray Burst Spectrometer (HXRBS) of the Solar maximum mission. Out of the 750 well observed flares studied, 41 flares had major associated proton events. Of these, 29 events were predicted on the basis of progressive hardening for a hit rate of 71%. The 152 largest flares had a hit rate of 82%.

Kiplinger, Alan L.↗
NASA NTRS1990

Finding curvilinear features in speckled images

A method for finding curves in digital images with speckle noise is described. The solution method differs from standard linear convolutions followed by thresholds in that it explicitly allows curvature in the features. Maximum a posteriori (MAP) estimation is used, together with statistical models for the speckle noise and for the curve-generation process, to find the most probable estimate of the feature, given the image data. The estimation process is first described in general terms. Then, incorporation of the specific neighborhood system and a multiplicative noise model for speckle allows derivation of the solution, using dynamic programming, of the estimation problem. The detection of curvilinear features is considered separately. The detection results allow the determination of the minimal size of detectable feature. Finally, the estimation of linear features, followed by a detection step, is shown for computer-simulated images and for a SAR image of sea ice.

Samadani, Ramin↗
DOE OSTI2024

Solving differential‐algebraic equations in power system dynamic analysis with quantum computing

Abstract Power system dynamics are generally modeled by high dimensional non‐linear differential‐algebraic equations (DAEs) given a large number of components forming the network. These DAEs' complexity can grow exponentially due to the increasing penetration of distributed energy resources, whereas their computation time becomes sensitive due to the increasing interconnection of the power grid with other energy systems. This paper demonstrates the use of quantum computing algorithms to solve DAEs for power system dynamic analysis. We leverage a symbolic programming framework to equivalently convert the power system's DAEs into ordinary differential equations (ODEs) using index reduction methods and then encode their data into qubits using amplitude encoding. The system non‐linearity is captured by Hamiltonian simulation with truncated Taylor expansion so that state variables can be updated by a quantum linear equation solver. Our results show that quantum computing can solve the power system's DAEs accurately with a computational complexity polynomial in the logarithm of the system dimension. We also illustrate the use of recent advanced tools in scientific machine learning for implementing complex computing concepts, that is, Taylor expansion, DAEs/ODEs transformation, and quantum computing solver with abstract representation for power engineering applications.

computational complexity↗
NASA NTRS1996

Dose equivalent near the bone-soft tissue interface from nuclear fragments produced by high-energy protons

During manned space missions, high-energy nucleons of cosmic and solar origin collide with atomic nuclei of the human body and produce a broad linear energy transfer spectrum of secondary particles, called target fragments. These nuclear fragments are often more biologically harmful than the direct ionization of the incident nucleon. That these secondary particles increase tissue absorbed dose in regions adjacent to the bone-soft tissue interface was demonstrated in a previous publication. To assess radiological risks to tissue near the bone-soft tissue interface, a computer transport model for nuclear fragments produced by high energy nucleons was used in this study to calculate integral linear energy transfer spectra and dose equivalents resulting from nuclear collisions of 1-GeV protons transversing bone and red bone marrow. In terms of dose equivalent averaged over trabecular bone marrow, target fragments emitted from interactions in both tissues are predicted to be at least as important as the direct ionization of the primary protons-twice as important, if recently recommended radiation weighting factors and "worst-case" geometry are used. The use of conventional dosimetry (absorbed dose weighted by aa linear energy transfer-dependent quality factor) as an appropriate framework for predicting risk from low fluences of high-linear energy transfer target fragments is discussed.

NASA Discipline Number 45-10↗
DOE OSTI2022

An ℓ 0 ℓ 2 -norm regularized regression model for construction of robust cluster expansions in multicomponent systems

In this work we introduce ℓ 0 ℓ 2 -norm regularization and hierarchy constraints into linear regression for the construction of cluster expansions to describe configurational disorder in materials. The approach is implemented through mixed integer quadratic programming (MIQP). The ℓ 2 -norm regularization is used to suppress intrinsic data noise, while the ℓ 0 -norm is used to penalize the number of nonzero elements in the solution. The hierarchy relation between clusters imposes relevant physics and is naturally included by the MIQP paradigm. As such, sparseness and cluster hierarchy can be well optimized to obtain a robust, converged set of effective cluster interactions with improved physical meaning. We demonstrate the effectiveness of ℓ 0 ℓ 2 -norm regularization in two high-component disordered rocksalt cathode material systems, where we compare the cross-validation, convergence speed, and the reproduction of phase diagrams, voltage profiles, and Li-occupancy energies with those of the conventional ℓ 1 -norm regularized cluster expansion models.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Optimal Control of SOEC-Based Hydrogen Production Systems for Demand Response Using Deep Reinforcement Learning in Smart Grids

Solid oxide electrolysis cell (SOEC) hydrogen production technology can range in size from small, appliance-size equipment to large-scale, central production facilities that can be tied directly to renewable or non-greenhouse-gas-emitting forms of electricity production, making it an ideal resource for demand response (DR). The SOEC hydrogen production system is a complex integrated system that encompasses fluid dynamics, electrical dynamics, and electrochemical and thermal dynamics, all of which involve non-linearity and non-convexity. Proper control of the SOEC hydrogen production system is crucial to enable its participation in the DR program. Here, to overcome the difficulty of designing an explicit control law for such nonlinear systems with nonconvex optimization features in DR applications, deep reinforcement learning (DRL) is explored to achieve the optimal control of the SOEC system for DR participation. Specifically, a twin delayed deterministic policy gradient (TD3) control framework is applied to achieve optimal response performance during DR events by considering power tracking error and hydrogen production efficiency with a suitable reward function. Two case studies with grid connections for tracking different DR commands were investigated. The first case study involved operating conditions reaching the boundaries, while the second involved operating conditions within the boundaries. The results showed that the proposed DRL-based control for SOEC can track the DR signal in a timely manner while maintaining high energy efficiency.

08 HYDROGEN↗
DOE OSTI2023

Generalized Bayesian Framework for Evaluation of Integral Benchmark Experiments [Slides]

This presentation is on generalized Bayesian framework for evaluation of integral benchmark experiments. This presentation starts off with assumptions and approximations used with Bayes Theorem. then an overview of approximations used by ORNL codes, and Generalized Bayesian Monto Carlo (GBMC). The presentation then details out a precise framework. This presentation then concludes with considerations.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1977

Hierarchy of simulation models for a turbofan gas engine

Steady-state and transient performance of an F-100-like turbofan gas engine are modeled by a computer program, DYNGEN, developed by NASA. The model employs block data maps and includes about 25 states. Low-order nonlinear analytical and linear techniques are described in terms of their application to the model. Experimental comparisons illustrating the accuracy of each model are presented.

Longenbaker, W. E.↗
NASA NTRS1978

The effect of operations on the ground noise footprints associated with a large multibladed, nonbanging helicopter

In order to expand the data base of helicopter external noise characteristics, a flyover noise measurement program was conducted utilizing the NASA Civil Helicopter Research Aircraft. The remotely operated multiple array acoustics range (ROMAAR) and a 2560-m linear microphone array were utilized for the purpose of documenting the noise characteristics of the test helicopter during flyby and landing operations. By utilizing both ROMAAR concept and the linear array, the data necessary to plot the ground noise footprints and noise radiation patterns were obtained. Examples of the measured noise signature of the test helicopter, the ground noise footprint or contours, and the directivity patterns measured during level flyby and landing operations of a large, multibladed, nonbanging helicopter, the CH-53, are presented.

Hilton, D. A.↗
NASA NTRS1979

A user oriented computer program for the analysis of microwave mixers, and a study of the effects of the series inductance and diode capacitance on the performance of some simple mixers

A user oriented computer program for analyzing microwave and millimeter wave mixers with a single Schottky barrier diode of known I-V and C-V characteristics is described. The program first performs a nonlinear analysis to determine the diode conductance and capacitance waveforms produced by the local oscillator. A small signal linear analysis is then used to find the conversion loss, port impedances, and input noise temperature of the mixer. Thermal noise from the series resistance of the diode and shot noise from the periodically pumped current in the diode conductance are considered. The effects of the series inductance and diode capacitance on the performance of some simple mixer circuits using a conventional Schottky diode, a Schottky diode in which there is no capacitance variation, and a Mott diode are studied. It is shown that the parametric effects of the voltage dependent capacitance of a conventional Schottky diode may be either detrimental or beneficial depending on the diode and circuit parameters.

Siegel, P. H.↗