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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 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Conceptual design of a noncontacting power transfer device for the ASPS Vernier system

The conceptual of electrical power transfer across a magnetically controlled gap as discussed for several years. The design represents the culmination of the first serious attempt to design a very low force, noncontracting power transfer mechanism. The electromagnetic device advanced herein is an ironless, translatable secondary transformer in which one of the two coils is fixed to the entire magnetic core. The second coil is free to move within the core over the full range of motions required. The specific application considered for this design was the Vernier subsystem of the Annular Suspension and Pointing System (ASPS). The development of and rationale for the electromagnetics design is presented. Similar documentation is provided for the Electronics Design. The Appendices detail the results of small scale model tests, disturbance force calculations, the baseline transformer fabrication drawings, the AVS Converter Parts List, and model schematic diagrams.

Kroeger, J.↗
DOE OSTI2020

Parallel three-dimensional simulations of quasi-static elastoplastic solids

Hypo-elastoplasticity is a flexible framework for modeling the mechanics of many hard materials under small elastic deformation and large plastic deformation. Under typical loading rates, most laboratory tests of these materials happen in the quasi-static limit, but there are few existing numerical methods tailor-made for this physical regime. Here, we extend to three dimensions a recent projection method for simulating quasi-static hypo-elastoplastic materials. The method is based on a mathematical correspondence to the incompressible Navier–Stokes equations, where the projection method of Chorin (1968) is an established numerical technique. We develop and utilize a three-dimensional parallel geometric multigrid solver employed to solve a linear system for the quasi-static projection. Our method is tested through simulation of three-dimensional shear band nucleation and growth, a precursor to failure in many materials. As an example system, we employ a physical model of a bulk metallic glass based on the shear transformation zone theory, but the method can be applied to any elastoplasticity model. We consider several examples of three-dimensional shear banding, and examine shear band formation in physically realistic materials with heterogeneous initial conditions under both simple shear deformation and boundary conditions inspired by friction welding.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Exploring Semantic Search Capability of Graph Convolutions Over a Knowledge Graph Built Using Earth Science Corpora

Traditional knowledge graphs tend to be too generic, and often perform poorly on complex scientific queries. Often times, precedence is given to pop culture over scientific knowledge for queries. This is predominantly due to the use of internet sources for building the knowledge graph. With this work, we aim to explore the effectiveness of combining a knowledge graph generated from earth science corpora with a language model and graph convolutions for the purpose of surfacing latent and related sentences given a natural language query. In this model, sentences are conceptualized in the graph as nodes which are connected through entities—words and phrases of interest found in the text—extracted using Google Cloud’s entity extraction model. The language model we used for this is Bidirectional Encoder Representations from Transformers (BERT).The sentences are given a numeric representation by the BERT model. Graph convolutions are then applied to sentence embeddings in order to obtain a vector representation of the sentence as well as the surrounding graph structure, thereby leveraging the power of adjacency inherently encoded in graph structures. With this presentation, we demonstrate the ability of graph convolutions and their improved ability to surface relevant, latent information based on the subject of the input query.

Muthukumaran Ramasubramanian↗
DOE OSTI2022

Mitigating the Impacts of Uncertain Geomagnetic Disturbances on Electric Grids: A Distributionally Robust Optimization Approach

Severe geomagnetic disturbances (GMDs) increase the magnitude of the electric field on the Earth’s surface (E-field) and drive geomagnetically-induced currents (GICs) along the transmission lines in electric grids. These additional currents can pose severe risks, such as current distortions, transformer saturation and increased reactive power losses, each of which can lead to system unreliability. Today several mitigation actions (e.g., changing grid topology) exist that can reduce the harmful GIC effects on the grids. Making such decisions can be challenging, however, because the magnitude and direction of the E-field are uncertain and non-stationary. In this paper, we model uncertain E-fields using the distributionally robust optimization (DRO) approach that determines optimal transmission grid operations such that the worst-case expectation of the system cost is minimized. We also capture the effect of GICs on the nonlinear AC power flow equations. For solution approaches, we develop an accelerated column-and-constraint generation (CCG) algorithm by exploiting a special structure of the support set of uncertain parameters representing the E-field. Extensive numerical experiments based on “epri-21” and “uiuc-150” systems, designed for GMD studies, demonstrate (i) the computational performance of the accelerated CCG algorithm, (ii) the superior performance of distributionally robust grid operations that satisfy nonlinear, nonconvex AC power flow equations and GIC constraints, in comparison with standard stochastic programming-based methods during the out-of-sample testing.

42 ENGINEERING↗
DOE OSTI2023

Quantum simulations in effective model spaces: Hamiltonian-learning variational quantum eigensolver using digital quantum computers and application to the Lipkin-Meshkov-Glick model

Quantum simulations offer the potential to predict the structure and dynamics of nuclear many-body systems that are beyond the capabilities of classical computing. Generally, preparing the ground state of strongly-interacting many-body systems relevant to nuclear physics is however inefficient, even using ideal quantum computers. In addition, currently available NISQ-era quantum devices possess modest numbers of qubits, limiting the size of quantum many-body systems that can be simulated. In this context, a reformulation of the quantum many-body problems using truncated model spaces and Hamiltonians is desirable to make them more amenable to near-term quantum computers. The importance of symmetries in low-energy theories, including effective field theories (EFTs), lattice quantum chromodynamics (QCD), and effective model spaces for nuclear systems, in particular their interplay with the reduction of active Hilbert spaces, is well known. Lesser known is the fact that the non-commutivity of some symmetries and truncations of the model space can be profitably combined with variational calculations to rearrange the entanglement into localized structures and enable more efficient simulations. Here, the goal of the present study is to explore and utilize the non-commutivity of symmetries and model-space truncations of quantum many-body systems important to nuclear physics, particularly in combination with variational algorithms for quantum simulations and effective Hamiltonian learning. We introduce an iterative hybrid classical-quantum algorithm, Hamiltonian learning variational quantum eigensolver (HL-VQE), that simultaneously optimizes an effective Hamiltonian, thereby rearranging entanglement into the effective model space, and the associated ground-state wavefunction. Quantum simulations, using classical computers and IBM's superconducting-qubit quantum computers, are performed to demonstrate the HL-VQE algorithm, in the context of the Lipkin-Meshkov-Glick (LMG) model of interacting fermions, where the Hamiltonian transformation corresponds to an orbital rotation. We use a mapping where the number of qubits scales with the $\log$ of the size of the effective model space, rather than the particle number. HL-VQE is found to provide an exponential improvement in LMG-model calculations of the ground-state energy and wavefunction, compared to naive truncations without Hamiltonian learning, throughout a significant fraction of the Hilbert space. In the context of EFT, this corresponds to counterterms scaling exponentially with the cut-off as opposed to power law. Implementations on IBM's QExperience quantum computers and simulators for 1- and 2-qubit effective model spaces are shown to provide accurate and precise results, reproducing classical predictions. For a range of parameters defining the LMG model, the HL-VQE algorithm is found to have better scaling of quantum resources requirements than previously explored algorithms. In particular, the HL-VQE scales efficiently over a large fraction of the model space, in contrast to VQE alone. This work constitutes a step in the development of entanglement-driven quantum algorithms for descriptions of nuclear many-body systems. This, in part, leverages the potential of noisy intermediate-scale quantum (NISQ) devices. The exponential scaling of counterterms observed in this study suggests the possibility of more general applicability to other non-perturbative EFTs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1989

Closure of the Africa-Eurasia-North America plate motion circuit and tectonics of the Gloria fault

The current motions of the African, Eurasian, and North American plates are examined. The problems addressed include whether there is resolvable motion of a Spitsbergen microplate, the direction of motion between the African and North American plates, whether the Gloria fault is an active transform fault, and the implications of plate circuit closures for rates of intraplate deformation. Marine geophysical data and magnetic profiles are used to construct a model which predicts about 4 mm/yr slip across the Azores-Gibraltar Ridge, and west-northwest convergence near Gibraltar. The analyzed data are consistent with a rigid plate model with the Gloria fault being a transform fault.

Argus, Donald F.↗
NASA NTRS2018

Practical Aspects of the Frequency Domain Approach for Aircraft System Identification

Practical aspects of the frequency-domain approach for aircraft system identification are explained and demonstrated. Topics related to experiment design, flight data analysis, and dynamic modeling are included. For demonstration purposes, simulated time series data and simulated flight data from an F-16 nonlinear simulation with realistic noise are used. This approach enables detailed evaluations of the techniques and results, because the true characteristics of the data and aircraft dynamics are known for the simulated data. Analytical techniques and practical considerations are examined for the finite Fourier transform, nonparametric frequency response estimation, parametric modeling in the frequency domain, experiment design for frequency-domain modeling, data analysis and modeling in the frequency domain, and real-time calculations. Flight data from a subscale jet transport aircraft are used to demonstrate some of the techniques and technical issues.

Morelli, Eugene A.↗
DOE OSTI2026

Pixel-Resolved Long-Context Learning for Turbulence at Exascale: Resolving Small-scale Eddies Toward the Viscous Limit

Turbulence plays a crucial role in multiphysics applications, including aerodynamics, fusion, and combustion. Accurately capturing turbulence's multiscale characteristics is essential for reliable predictions of multiphysics interactions, but remains a grand challenge even for exascale supercomputers and advanced deep learning models. The extreme-resolution data required to represent turbulence, ranging from billions to trillions of grid points, pose prohibitive computational costs for models based on architectures like vision transformers. To address this challenge, we introduce a multiscale hierarchical Turbulence Transformer that reduces sequence length from billions to a few millions and a novel RingX sequence parallelism approach that enables scalable long-context learning. We perform scaling and science runs on the Frontier supercomputer. Our approach demonstrates excellent performance up to 1.1 EFLOPS on 32,768 AMD GPUs, with a scaling efficiency of 94\%. To our knowledge, this is the first AI model for turbulence that can capture small-scale eddies down to the dissipative range in three-dimensional turbulence at high Reynolds numbers.

Yin, Junqi [ORNL] (ORCID:0000000338435520)↗
DOE OSTI2023

Relaxing the constant molar overflow assumption in distillation optimization

Abstract The constant molar overflow (CMO) framework, while useful for shortcut distillation models, assumes that all components have the same latent heats of vaporization. A simple transformation, from molar flows to latent‐heat flows, allows shortcut models to retain the mathematical simplicity of the CMO framework while accounting for different latent heats, resulting in the constant heat transport (CHT) framework for adiabatic distillation columns. Although several past works have already proposed this transformation in the literature, it has not been well utilized in recent times. In this article, we show the utility of this transformation in upgrading various applications such as identifying energy‐efficient multicomponent distillation configurations based on heat duty rather than surrogate vapor flow. The method transforms the diagram to a diagram. Furthermore, we derive new and insightful analytical results in distillation, such as cumulative latent‐heat stage fractions having monotonic profiles within a distillation column under the CHT framework.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Reanalysis of crackle perception data using logistic and logarithmic fits and sound quality metrics

Mathematical models of human auditory perception (sound quality metrics) are explored for the purpose of fitting human subject data from a prior published study. A variety of linearizing transforms and techniques are employed. Ultimately, a model is identified that predicts 98.9% of the variance in average human subject ratings of crackliness of jet noise waveforms.

Swift, Stephen Hales↗
DOE OSTI2021

Physics-Infused AI/ML Based Digital-Twin Framework for Flow-Induced-Vibration Damage Prediction in a Nuclear Reactor Heat Exchanger

This report summarizes some of the ongoing work related to the development of an expert-elicitation-digital-twin framework for real time damage state prediction in heat exchanger components of a nuclear reactor. The framework is targeted towards predicting damage associated with coupled low cycle fatigue (associated with regular heat-up, cool-down and power operation transients) and high cycle fatigue (associated with flow induced vibration transients). The overall framework will be based on a NoSQL based database, physics-infused-geometry-dependent virtual-sensor data, different AI/ML techniques-based data-driven-predictive-model applications (Apps) and real-time plant sensor measurements available through few existing sensors. Towards this overall goal, this report updates some of the ongoing work, such as on implementation of a NoSQL Database (such as MongoDB), FE based heat transfer analysis of a heat exchanger (e.g. of a PWR steam generator) for generating geometry-dependent virtual sensor data and evaluation of various AI/ML models such as based on multivariate linear regression, ensembled decision-tree based Random-Forest and Gradient-Boosting regression and high-dimensional-kernel-function-transformation based Support-Vector-Machine regression models. The AI/ML models were evaluated for predicting multi-time-series thermal states at thousands of 3D point-clouds

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Raman spectroscopic characterization of SiO 2 phase transformation and Si substrate stress relevant to EBC performance

To accurately model the long-term durability of environmental barrier coatings (EBCs), a more complete understanding of the phase composition and transformations of the thermally grown oxide SiO 2 (TGO) is desired. For the TGO formed during thermal cycling in steam, cristobalite formation and the subsequent β- to α-cristobalite transformation has been identified as a potentially life-limiting mechanism. In this study, Raman micro-spectroscopy was used to quantify the cristobalite transformation on a polycrystalline Si coupon that was exposed to steam at 1350°C for 100 h. The phase transformation was mapped at 200–260°C on the TGO surface at different ramp rates using a heating stage and a micro-positioning stage. The stress in the Si substrate was also determined using Raman spectroscopy by measuring the stress induced peak shift. The α→β phase transformation produced a 300–500 MPa tensile stress in the Si substrate, which compared well to the stress predicted from the volumetric expansion of the cristobalite. In conclusion, quantifying the phase transformation and residual stress are critical tools in developing the next generation of high performance EBCs.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Theory of finite-temperature two-dimensional structural transformations in group-IV monochalcogenide monolayers

An account of two-dimensional (2D) structural transformations in 2D ferroelectrics predicts an evolution from a structure with Pnm 2 1 symmetry into a structure with square P4/nmm symmetry and is consistent with experimental evidence, while another argues for a transformation into a structure with rectangular Pnmm symmetry. An analysis of the assumptions made in these models is provided here, and six fundamental results concerning these transformations are contributed as follows: (i) Softened phonon modes produce rotational modes in these materials. (ii) The transformation to a structure with P4/nmm symmetry occurs at the lowest critical temperature T c . (iii) The hypothesis that one unidirectional optical vibrational mode underpins the 2D transformation is unwarranted. (iv) Being successively more constrained, a succession of critical temperatures ( T c < T c ' < T c " ) occurs in going from molecular dynamics calculations with the NPT and NVT ensembles onto models with unidirectional oscillations. (v) The choice of exchange-correlation functional impacts the estimate of the critical temperature. (vi) Importantly, the correct physical picture of these transformations is one in which rotational modes confer a topological character to the 2D transformation via the proliferation of vortices.

2d ferroelectrics↗
NASA NTRS2010

Kepler Mission's Focal Plane Characterization Models Implementation

The Kepler Mission photometer is an unusually complex array of CCDs. A large number of time-varying instrumental and systemic effects must be modeled and removed from the Kepler pixel data to produce light curves of sufficiently high quality for the mission to be successful in its planet-finding objective. After the launch of the spacecraft, many of these effects are difficult to remeasure frequently, and various interpolations over a small number of sample measurements must be used to determine the correct value of a given effect at different points in time. A library of software modules, called Focal Plane Characterization (FC) Models, is the element of the Kepler Science Data Pipeline (hereafter "pipeline") that handles this. FC, or products generated by FC, are used by nearly every element of the SOC processing chain. FC includes Java components: database persistence classes, operations classes, model classes, and data importers; and MATLAB code: model classes, interpolation methods, and wrapper functions. These classes, their interactions, and the database tables they represent, are discussed. This paper describes how these data and the FC software work together to provide the pipeline with the correct values to remove non-photometric effects caused by the photometer and its electronics from the Kepler light curves. The interpolation mathematics is reviewed, as well as the special case of the sky-to-pixel,pixel-to-sky coordinate transformation code, which incorporates a compound model that is unique in the SOC software.

mission↗
NASA NTRS2017

Model-Driven Development of Safety Architectures

We describe the use of model-driven development for safety assurance of a pioneering NASA flight operation involving a fleet of small unmanned aircraft systems (sUAS) flying beyond visual line of sight. The central idea is to develop a safety architecture that provides the basis for risk assessment and visualization within a safety case, the formal justification of acceptable safety required by the aviation regulatory authority. A safety architecture is composed from a collection of bow tie diagrams (BTDs), a practical approach to manage safety risk by linking the identified hazards to the appropriate mitigation measures. The safety justification for a given unmanned aircraft system (UAS) operation can have many related BTDs. In practice, however, each BTD is independently developed, which poses challenges with respect to incremental development, maintaining consistency across different safety artifacts when changes occur, and in extracting and presenting stakeholder specific information relevant for decision making. We show how a safety architecture reconciles the various BTDs of a system, and, collectively, provide an overarching picture of system safety, by considering them as views of a unified model. We also show how it enables model-driven development of BTDs, replete with validations, transformations, and a range of views. Our approach, which we have implemented in our toolset, AdvoCATE, is illustrated with a running example drawn from a real UAS safety case. The models and some of the innovations described here were instrumental in successfully obtaining regulatory flight approval.

Safety case↗
DOE OSTI2021

Transforming ESM Physical Parameterization Development Using Machine Learning Trained on Global Cloud-Resolving Models and Process Observations

ESMs robustly predict that 21st century greenhouse warming will slowly increase global mean precipitation, rapidly increase extreme precipitation, and increase subtropical drought. ESMs agree less about precipitation trends and extremes over particular land regions critical to human societies, e. g. in semi-arid regions such as California or the Sahel, or in wetter climates prone to monsoonal rainfall (e. g. southeast Asia) or to tropical cyclones and flooding from mesoscale convective systems (e. g. the southeastern U.S.) Deep convective parameterizations and poor representation of orography and complex vegetated land surfaces contribute to this inter-model spread; clouds, aerosols and sea-surface temperature biases are also key. Reducing regional precipitation projection uncertainty has enormous planning value for water supplies, land use, wildfire, hydropower, flood control, etc. IPCC-class ESMs are making painfully slow progress on this.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

Comparison of transform coding methods with an optimal predictor for the data compression of digital elevation models

Statistical encoding techniques enable the reduction of the number of bits required to encode a set of symbols, and are derived from their probabilities. Huffman encoding is an example of statistical encoding that has been used for error-free data compression. The degree of compression given by Huffman encoding in this application can be improved by the use of prediction methods. These replace the set of elevations by a set of corrections that have a more advantageous probability distribution. In particular, the method of Lagrange Multipliers for minimization of the mean square error has been applied to local geometrical predictors. Using this technique, an 8-point predictor achieved about a 7 percent improvement over an existing simple triangular predictor.

Lewis, Michael↗
NASA NTRS2003

Extraction of Qualitative Features from Sensor Data Using Windowed Fourier Transform

In this paper, we use Matlab to model the health monitoring of a system through the information gathered from sensors. This implies assessment of the condition of the system components. Once a normal mode of operation is established any deviation from the normal behavior indicates a change. This change may be due to a malfunction of an element, a qualitative change, or a change due to a problem with another element in the network. For example, if one sensor indicates that the temperature in the tank has experienced a step change then a pressure sensor associated with the process in the tank should also experience a step change. The step up and step down as well as sensor disturbances are assumed to be exponential. An RC network is used to model the main process, which is step-up (charging), drift, and step-down (discharging). The sensor disturbances and spike are added while the system is in drift. The system is allowed to run for a period equal to three time constant of the main process before changes occur. Then each point of the signal is selected with a trailing data collected previously. Two trailing lengths of data are selected, one equal to two time constants of the main process and the other equal to two time constants of the sensor disturbance. Next, the DC is removed from each set of data and then the data are passed through a window followed by calculation of spectra for each set. In order to extract features the signal power, peak, and spectrum are plotted vs time. The results indicate distinct shapes corresponding to each process. The study is also carried out for a number of Gaussian distributed noisy cases.

Amini, Abolfazl M.↗