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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Exploiting Identical Generators in Unit Commitment

Here, we present sufficient conditions under which thermal generators can be aggregated in mixed-integer linear programming (MILP) formulations of the unit commitment (UC) problem, while maintaining feasibility and optimality for the original disaggregated problem. Aggregating thermal generators with identical characteristics (e.g., minimum/maximum power output, minimum up/down-time, and cost curves) into a single unit reduces redundancy in the search space induced by both exact symmetry (permutations of generator schedules) and certain classes of mutually non-dominated solutions. We study the impact of aggregation on two large-scale UC instances, one from the academic literature and another based on real-world operator data. Our computational tests demonstrate that when present, identical generators can negatively affect the performance of modern MILP solvers on UC formulations. Further, we show that our reformation of the UC MILP through aggregation is an effective method for mitigating this source of computational difficulty.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Compilation of a Solar Mirror Materials Database and an Analysis of Natural and Accelerated Mirror Exposure and Degradation

The National Renewable Energy Laboratory (NREL) has been conducting exposure experiments on solar reflectors for over four decades. Thousands of mirror samples from over one hundred suppliers have been exposed to and monitored in a range of relevant environments. These test conditions include outdoor test settings and several controlled laboratory environments. These samples have been rigorously individually characterized using a series of reflectance measurements, visual inspections, and in some cases, in-depth composition analysis to identify degradation modes, reflectance losses, and other mirror properties integral to understanding the solar reflector's life cycle. Here, this paper compiles the decades of measurement data into a concise statistical analysis. It includes exposure and degradation data for numerous reflector types, including secondary-surface reflector permutations of polymer and glass superstrates with silver and aluminum reflectors as well as front-surface reflectors. The results herein are intended to analyze environmental stressors and degradation trends among various historical and state-of-the-art solar reflectors. It may be used to support solar reflector design, effective testing methodology, and inform manufacturing decisions moving forward. Presented are the results of the compiled database and an initial analysis for degradation rate modeling using full-spectrum and wavelength-dependent approaches. The database is a growing resource hosted on a live, publicly accessible website. In conjunction with the analysis presented here, it provides a valuable resource to the solar reflector manufacturing and testing industry.

14 SOLAR ENERGY↗
DOE OSTI2021

Quantification of Variation in Combustion Instability Amplitude in a Multi-Nozzle Can Combustor

In this work, we quantify the level of variation in unstable combustion oscillation amplitudes and identify the source of this variation in a multi-nozzle can combustor. At conditions where pollutant emissions are reduced, lean-premixed combustors can undergo thermoacoustic instability when pressure and heat release rate oscillations couple. A commonly used method for suppressing instability is fuel staging, which is a method where fuel is unevenly distributed between nozzles in a multi-nozzle combustor. Our work follows others who have characterized the effect of fuel staging on combustion instability and the mechanisms by which it works during both steady-state and transient operation. One of the outcomes from our previous work was that certain instability operating points display a high level of pressure oscillation amplitude variation. Instead of oscillating at a constant limit-cycle amplitude, the pressure oscillation amplitude varies significantly in time. In this work, we use the concept of permutation entropy to quantify the level of variation in the pressure fluctuation amplitude. We correlate the level of variation with a number of state variables over a range of operating conditions, 291 test cases in all. These state variables include mixture equivalence ratio; transient timescale, amplitude, and direction; hardware temperatures; gas temperatures; and thermoacoustic damping and growth rates. Significant pressure oscillation amplitude variation occurs when the thermoacoustic damping rate is low. The damping and growth rates can be low for a number of reasons, but they are highly correlated with the metal temperature of the centerbody, where the flames are anchored; lower temperatures result in lower damping rates during stable operation and lower growth rates during unstable operation. These results show the importance of the thermal boundary condition on the time-dependent behavior of the thermoacoustic instability.

Westfall, Seth↗
DOE OSTI2024

Optical constants measurements of liquids for modeling aerosolized threats

Spectroscopic identification of aerosolized chemical threats is challenging due to the complex nature of the photon/particle interaction, as well as the diversity of possible particle sizes, morphologies, and compositions. Constructing a database of laboratory - measured transmittance spectra that covers each permutation is not practical. However, calculation of the spectra using the measured optical constants as a function of wavenumber, n(?) and k(?), for each of the liquids and/or solids composing the aerosol particles provides a viable alternative. These synthetic spectra (vis-à-vis laboratory-measured spectra) can be used to identify chemicals of interest and subtract out background interferents in field measurements. Using a well-established multiple pathlength approach, we measure the optical constants n/k for liquids that may be found as chemical species of interest or common background interferents aerosolized in plumes. These measurements are also used to generate several test case aerosol synthetic spectra.

Lockwood, Schuyler↗
DOE OSTI2025

Attention to quantum complexity

The imminent era of error-corrected quantum computing demands robust methods to characterize quantum state complexity from limited, noisy measurements. We introduce the Quantum Attention Network (QuAN), a classical artificial intelligence (AI) framework leveraging attention mechanisms tailored for learning quantum complexity. Inspired by large language models, QuAN treats measurement snapshots as tokens while respecting permutation invariance. Combined with our parameter-efficient miniset self-attention block, this enables QuAN to access high-order moments of bit-string distributions and preferentially attend to less noisy snapshots. We test QuAN across three quantum simulation settings: driven hard-core Bose-Hubbard model, random quantum circuits, and toric code under coherent and incoherent noise. QuAN directly learns entanglement and state complexity growth from experimental computational basis measurements, including complexity growth in random circuits from noisy data. In regimes inaccessible to existing theory, QuAN unveils the complete phase diagram for noisy toric code data as a function of both noise types, highlighting AI’s transformative potential for assisting quantum hardware.

Kim, Hyejin [Cornell Univ., Ithaca, NY (United Sta↗
DOE OSTI2023

Targeted metagenomic assessment reflects critical colonization in battlefield injuries

Current diagnostics and clinical management strategies for combat wounds are based on decisions made by expert clinicians. However, even in the hands of experienced surgeons, wounds from combat injuries can exhibit failed healing and complications related to limitations in the rapid and comprehensive generation of diagnostic information. Previous studies have demonstrated the possible use of genomic sequencing approaches to detect microbial signatures involved in combat casualty care. While effective, whole metagenome sequencing is limited by the depth required to confidently detect all relevant signatures. To address this, we developed a targeted capture sequencing panel to detect microbial signatures relevant to wound healing. These targets include known microbial nosocomial pathogens, wound colonizers, and genes involved in virulence and antimicrobial resistance. A bioinformatics pipeline was built to identify genomic regions of interest and over 8,000 oligonucleotide probes were designed for capture. The panel was synthesized and validated using control reference genomes in human background and on wound-effluent samples from a cohort of combat-injured U.S. service members. Our panel was sensitive against wound-colonizing species, Acinetobacter baumannii and Pseudomonas aeruginosa, and was specific in detecting corresponding virulence and antimicrobial-resistance genes as well as other pathogenic species present in microflora mixtures. Random forest feature permutation confirmed the prevalence of Acinetobacter and Pseudomonas in critically colonized wounds and wounds that failed to heal, respectively. Our results demonstrate the capability of targeted sequencing tools and analysis platforms to profile and deliver information on pathogenic factors influencing wound progression, thereby guiding therapeutic intervention.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Cyclotomic Shuffles

Analogues of 1-shuffle elements for complex reflection groups of type G(m,1,n) are introduced. A geometric interpretation for G(m,1,n) in terms of rotational permutations of polygonal cards is given. We compute the eigenvalues, and their multiplicities, of the 1-shuffle element in the algebra of the group G(m,1,n). Considering shuffling as a random walk on the group G(m,1,n), we estimate the rate of convergence to randomness of the corresponding Markov chain. We report on the spectrum of the 1-shuffle analogue in the cyclotomic Hecke algebra H(m,1,n) for m=2 and small n.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Fast and effective reordering of columns within supernodes using partition refinement

In this paper, we consider the problem of computing a triangular factorization of a sparse symmetric matrix using Gaussian elimination. We assume that the sparse matrix has been permuted using a fill-reducing ordering algorithm. When the matrix is symmetric positive definite, the sparsity structure of the triangular factor can be determined once the fill-reducing ordering has been computed. Thus, an efficient numerical factorization scheme can be designed so that only the nonzero entries are stored and operated on. On modern architectures, the positions of the nonzero entries in the triangular factor play a big role in determining the efficiency. It is desirable to have dense blocks in the factor so that the computation can be cast in terms of level-3 BLAS as much as possible. On architectures with GPUs, for example, it is also desirable for these dense blocks to be as large as possible in order to reduce the times to transfer data between the main CPU and the GPUs. We address the problem of locally refining the ordering so that the number of dense blocks is reduced and the sizes of these dense blocks are increased in the triangular factor.

Jacquelin, Mathias↗
DOE OSTI2019

Software for Sparse Tensor Decomposition on Emerging Computing Architectures

In this paper, we develop software for decomposing sparse tensors that is portable to and performant on a variety of multicore, manycore, and GPU computing architectures. The result is a single code whose performance matches optimized architecture-specific implementations. The key to a portable approach is to determine multiple levels of parallelism that can be mapped in different ways to different architectures, and we explain how to do this for the matricized tensor times Khatri--Rao product (MTTKRP), which is the key kernel in canonical polyadic tensor decomposition. Our implementation leverages the Kokkos framework, which enables a single code to achieve high performance across multiple architectures that differ in how they approach fine-grained parallelism. We also introduce a new construct for portable thread-local arrays, which we call compile-time polymorphic arrays. Not only are the specifics of our approaches and implementation interesting for tuning tensor computations, but they also provide a roadmap for developing other portable high-performance codes. As a last step in optimizing performance, we modify the MTTKRP algorithm itself to do a permuted traversal of tensor nonzeros to reduce atomic-write contention. Lastly, we test the performance of our implementation on 16- and 68-core Intel CPUs and the K80 and P100 NVIDIA GPUs, showing that we are competitive with state-of-the-art architecture-specific codes while having the advantage of being able to run on a variety of architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Lorentz invariance of basis tensor gauge theory

Basis tensor gauge theory (BTGT) is a vierbein analog reformulation of ordinary gauge theories in which the vierbein field describes the Wilson line. After a brief review of the BTGT, we clarify the Lorentz group representation properties associated with the variables used for its quantization. In particular, we show that starting from an SO(1,3) representation satisfying the Lorentz-invariant U(1,3) matrix constraints, BTGT introduces a Lorentz frame choice to pick the Abelian group manifold generated by the Cartan subalgebra of U(1,3) for the convenience of quantization even though the theory is frame independent. This freedom to choose a frame can be viewed as an additional symmetry of BTGT that was not emphasized before. We then show how an [Formula: see text] permutation symmetry and a parity symmetry of frame fields natural in BTGT can be used to construct renormalizable gauge theories that introduce frame-dependent fields but remain frame independent perturbatively without any explicit reference to the usual gauge field.

Physics↗
DOE OSTI2024

PyOED: An Extensible Suite for Data Assimilation and Model-Constrained Optimal Design of Experiments

This article describes PyOED, a highly extensible scientific package that enables developing and testing model-constrained optimal experimental design (OED) for inverse problems. Specifically, PyOED aims to be a comprehensive Python toolkit for model-constrained OED. The package targets scientists and researchers interested in understanding the details of OED formulations and approaches. It is also meant to enable researchers to experiment with standard and innovative OED technologies with a wide range of test problems (e.g., simulation models). OED, inverse problems (e.g., Bayesian inversion), and data assimilation (DA) are closely related research fields, and their formulations overlap significantly. Thus, PyOED is continuously being expanded with a plethora of Bayesian inversion, DA, and OED methods as well as new scientific simulation models, observation error models, and observation operators. These pieces are added such that they can be permuted to enable testing OED methods in various settings of varying complexities. The PyOED core is completely written in Python and utilizes the inherent object-oriented capabilities; however, the current version of PyOED is meant to be extensible rather than scalable. Specifically, PyOED is developed to “enable rapid development and benchmarking of OED methods with minimal coding effort and to maximize code reutilization.” This article provides a brief description of the PyOED layout and philosophy and provides a set of exemplary test cases and tutorials to demonstrate the potential of the package.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

WORM (Write One, Read Many)

WORM (Write One, Run Many) is an easy to use, cross platform, embedded and extensible, functional programming language designed to facilitate the creation of input-decks for computer codes that use standard ASCII text files for input. WORM makes it easy to create generic (yet, complex and powerful) reusable models. Additionally its nature allows for complex calculations and routines to be coded once and easily reused, further simplifying the creation of input decks. WORM (Write One, Run Many) is a powerful and versatile tool designed to improve the efficiency of today’s criticality safety analyst by allowing: + input decks for parametric studies to be created quickly and easily, + calculations and variables to be imbedded into any input deck, thus allowing for meaningful parameter specifications, + problems to be specified using any combination of units, and + complex mathematically defined models to be created. A very simple syntax is employed, and therefore the WORM is easy to learn. A WORM model is essentially a standard input deck with some of its numerical values replaced by WORM code. WORM code may include and evaluate the following mathematical operators and functions: addition, subtraction, multiplication, division, exponentiation, modulus, sine, cosine, tangent, arcsine, arccosine, arctangent, the natural logarithm, logarithm base 10, integer truncation, absolute value, and random number. Several common constants, e.g., pi, e, and Avogadros’s Number (both as 6.022e23 and 0.6022), are predefined in WORM. Additionally, many unit conversion factors are also predefined: millimeters, meters, inches, feet, yards, and mils to centimeters; kilograms, pounds, and ounces to grams; liters, milliliters, gallons, and fluid ounces to cubic centimeters; and angular degrees to radians. For parametric studies, WORM supports various shorthand list specifications: the explicit step size, linear interpolation, and logarithmic interpolation. The list notation sequentially assigns multiple values to a name. WORM creates an input deck for each value of the name. If multiple lists are used, WORM steps through each list individually, i.e., WORM creates input decks corresponding to each and every permutation of the list values. Additionally, a library of standard material definitions and Perl subroutines are included. Any one of these files can be incorporated into the subject model with a simple WORM read command. WORM is completely written in Perl, the Practical Extraction and Reporting Language. Perl is one of the most portable programming languages available today. As such, the WORM works on practically any computer platform.

Sartor, Raymond↗
DOE OSTI2022

PyTREES

PyTREES (Python tool for Training/Testing Robust Explainable Ensembles on Spectra) is software that implements a data-driven approach to predicting the amount of specific oxides present in materials samples of laser-induced breakdown spectroscopy (LIBS); such as from the ChemCam instrument suite onboard the NASA Curiosity rover. PyTREES is designed to input LIBS data in the format provided by the ChemCam team [1]. PyTREES then applies appropriate pre-processing to this data [2], and implements several regression methods for predicting oxides from spectra. The regression methods include: ensemble methods (random forest, extra trees, and gradient boosting regression) and blended submodels using the “double blending” technique. PyTREES additionally implements methods for quantifying the importance of features in regression model: (1) mean decrease in impurity (MDI) and (2) permutation importance to investigate the wavelengths used by the regression methods. [1] Gasda et al. (2021). Spectrochim Acta B, 181, 106223. [2] Clegg et al. (2017). Spectrochim Acta B , 129, 64–85.

Oyen, Diane↗
DOE OSTI2023

PYOED: AN ETENSIBLE SUITE FOR DATA ASSIMILATION AND MODEL-CONSTRAINED OPTIMAL DESIGN OF EXPERIMENTS

SF-23-005 PyOED is a highly extensible scientific package that enables developing and testing model-constrained optimal experimental design (OED) for inverse problems. Specifically, PyOED aims to be a comprehensive Python toolkit for model-constrained OED. The package targets scientists and researchers interested in understanding the details of OED formulations and approaches. It is also meant to enable researchers to experiment with standard and innovative OED technologies with a wide range of test problems (e.g., simulation models). OED, inverse problems (e.g., Bayesian inversion), and data assimilation (DA) are closely related research fields, and their formulations overlap significantly. Thus, PyOED is continuously being expanded with a plethora of Bayesian inversion, DA, and OED methods as well as new scientific simulation models, observation error models, and observation operators. These pieces are added such that they can be permuted to enable testing OED methods in various settings of varying complexities. The PyOED core is completely written in Python and utilizes the inherent object-oriented capabilities; however, PyOED is meant to be extensible rather than scalable. Specifically, PyOED is developed to ``enable rapid development and benchmarking of OED methods with minimal coding effort and to maximize code reutilization.'' PyOED will be continuously expanded with a plethora of Bayesian inversion, DA, and OED methods as well as new scientific simulation models, observation error models, and observation operators.

ATTIA, AHMEDMOHAMED↗
DOE OSTI2023

VEESA R package

SAND2024-04584O R package for applying the VEESA pipeline method is a technique used for explainable machine learning with functional data. The VEESA pipeline makes use of the elastic-shape analysis framework for functional data. It also implements functional principal component analysis and permutation feature importance. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Tucker, James↗
DOE OSTI2023

BARC Element Classifier (barc)

SAND2024-02120O This program enumerates and classifies all possible functional elements in several different categories within the Ballistic Asynchronous Reversible Computing (barc) model of computation. barc software is a research tool that is being used to help document the possible digital behaviors of primitive functional elements in the ABRC a.k.a. BARC model of computation. The method of operation of this software leverages elementary concepts and methods from discrete mathematics: combinatorics (permutations), partitions and equivalence classes, symmetry transformations, and the theory of finite groups. Implementation is in Python programming language. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Frank, Michael↗
DOE OSTI2026

torch-einshard v1.0

torch-einshard is a Python library for describing local and distributed PyTorch tensor computations with compact, einsum-like notation. Its expressions name logical axes, specify how they are sharded across a PyTorch DeviceMesh, and represent partial reductions. The library automatically performs contractions, permutations, reshaping, splitting, gathering, reduction, reduce-scatter, and repartitioning while preserving autograd. Additional features include sharding-aware FFTs, tensor rolls, halo exchange, sliding windows, 1D–3D convolutions, uneven-shard handling, parameter initialization and gradient management, and cost-based execution planning. It is designed for scientific machine learning and large-model workloads, including tensor-, sequence-, and spatial-parallel MLPs, attention, convolutions, and spectral operations. Compared with manually combining torch.einsum and distributed collectives, torch-einshard expresses both the mathematical operation and data placement in one readable formula. This reduces boilerplate and synchronization errors, keeps forward and backward communication consistent, and allows the library to select optimized collective strategies without changing model code.

Morozov, Dmitriy [Lawrence Berkeley National Labor↗
DOE OSTI2018

Verification of a rapid mooring and foundation design tool

Marine renewable energy devices require mooring and foundation systems that suitable in terms of device operation and are also robust and cost effective. In the initial stages of mooring and foundation development a large number of possible configuration permutations exist. Filtering of unsuitable designs is possible using information specific to the deployment site (i.e. bathymetry, environmental conditions) and device (i.e. mooring and/or foundation system role and cable connection requirements). The identification of a final solution requires detailed analysis, which includes load cases based on extreme environmental statistics following certification guidance processes. Static and/or quasi-static modelling of the mooring and/or foundation system serves as an intermediate design filtering stage enabling dynamic time-domain analysis to be focused on a small number of potential configurations. Mooring and foundation design is therefore reliant on logical decision making throughout this stage-gate process. The open-source DTOcean (Optimal Design Tools for Ocean Energy Arrays) Tool includes a mooring and foundation module, which automates the configuration selection process for fixed and floating wave and tidal energy devices. As far as the authors are aware, this is one of the first tools to be developed for the purpose of identifying potential solutions during the initial stages of marine renewable energy design. While the mooring and foundation module does not replace a full design assessment, it provides in addition to suitable configuration solutions, assessments in terms of reliability, economics and environmental impact. This article provides insight into the solution identification approach used by the module and features the verification of both the mooring system calculations and the foundation design using commercial software. Several case studies are investigated: a floating wave energy converter and several anchoring systems. It is demonstrated that the mooring and foundation module is able to provide device and/or site developers with rapid mooring and foundation design solutions to appropriate design criteria.

42 ENGINEERING↗