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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

CG-Kit: Code Generation Toolkit for performant and maintainable variants of source code applied to Flash-X hydrodynamics simulations

CG-Kit is a new Code Generation tool-Kit that we have developed as a part of the solution for portability and maintainability for multiphysics computing applications. The development of CG-Kit is rooted in the urgent need created by the shifting landscape of high-performance computing platforms and the algorithmic complexities of a particular large-scale multiphysics application: Flash-X. To efficiently use computing resources on a heterogeneous node, an application must have a map of computation to resources and a mechanism to move the data and computation to the resources according to the map. Most existing performance portability solutions are focussed on abstracting the expression of computations so that a unified source code can be specialized to run on different resources. However, such an approach is insufficient for a code like Flash-X, which has a multitude of code components that can be assembled in various permutations and combinations to form different instances of applications. Similar challenges apply to any code that has composability, where a single specified way of apportioning work among devices may not be optimal. Additionally, use cases arise where the optimal control flow of computation may differ for different devices while the underlying numerics remain identical. This combination leads to unique challenges including handling an existing large code base in Fortran and/or C/C++, subdivision of code into a great variety of units supporting a wide range of physics and numerical methods, different parallelization techniques for distributed and shared memory systems and accelerator devices, and heterogeneity of computing platforms requiring coexisting variants of parallel algorithms. All of these challenges demand that scientific software developers apply existing knowledge about domain applications, algorithms, and computing platforms to determine custom abstractions and granularity for code generation. There is a critical lack of tools to tackle those problems. CG-Kit is designed to fill this gap by providing a user with the ability to express their desired control flow and computation-to-resource map in the form a pseudocode-like recipe. It consists of standalone tools that can be combined into highly specific and, we argue, highly effective portability and maintainability toolchains. Here we present the design of our new tools: parametrized source trees, control flow graphs, and recipes. The tools are implemented in Python. They are agnostic to the programming language of the source code targeted for code generation. In conclusion, we demonstrate the capabilities of the toolkit with two examples, first, multithreaded variants of the basic AXPY operation, and second, variants of parallel algorithms within a hydrodynamics solver, called Spark, from Flash-X that operates on block-structured adaptive meshes.

Algorithmic portability↗
DOE OSTI2023

Fast approximation to supershell partition functions: Explicit forms of the coefficients

In a previous work, we derived a formula for supershell partition functions, which are the cornerstone of the Super-Transition-Array approach to radiative-opacity calculations. The new expression takes the form of a functional of the distribution of energies within the supershell and allows for fast and accurate computations, truncating the number of terms in the expansion. The latter involves coefficients (denoted Γ $k$ ) for which we obtained a recursion relation. In the present short paper, as a complement of the previous one, we give an explicit formula for the coefficients Γ $k$ . Another recursion relation is also provided, as well as an alternative expression involving Bell polynomials. In conclusion, the connections with cycle indexes of permutation groups and with elementary symmetric polynomials are outlined.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Separate sensible and latent cooling: Carnot limits and system taxonomy

Energy efficiency and thermal comfort can be improved by independent control of temperature and humidity. In recent work, an emerging class of cooling systems known as separate sensible and latent cooling (SSLC) systems have demonstrated 14 to 47% energy savings, while simultaneously providing better comfort control than conventional air conditioning. In this work, SSLC is defined as using two or more cooling processes with unequal sensible heat ratio (SHR) to control temperature and humidity independently and dynamically. Fundamental efficiency limits of SSLC systems are developed for the first time, demonstrating that the Carnot efficiency for SSLC is higher than that of conventional systems. It is revealed that SSLC has particularly high potential for moderate outdoor temperatures, high sensible building loads, and drier indoor conditions. Additionally, this work proposes a classification scheme for SSLC systems, revealing an enormous number of feasible cycle permutations. This provides a framework for the important research task of developing SSLC systems that will save energy, be practical to construct, and be controllable.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

A Voxel-Based Approach to Explore Local Dose Differences Associated With Radiation-Induced Lung Damage

Purpose: To apply a voxel-based (VB) approach aimed at exploring local dose differences associated with late radiation-induced lung damage (RILD). Methods and Materials: An interinstitutional database of 98 patients who were Hodgkin lymphoma (HL) survivors treated with postchemotherapy supradiaphragmatic radiation therapy was analyzed in the study. Eighteen patients experienced late RILD, classified according to the Radiation Therapy Oncology Group scoring system. Each patient's computed tomographic (CT) scan was normalized to a single reference case anatomy (common coordinate system, CCS) through a log-diffeomorphic approach. The obtained deformation fields were used to map the dose of each patient into the CCS. The coregistration robustness and the dose mapping accuracy were evaluated by geometric and dose scores. Two different statistical mapping schemes for nonparametric multiple permutation inference on dose maps were applied, and the corresponding P<.05 significance lung subregions were generated. A receiver operating characteristic (ROC)-based test was performed on the mean dose extracted from each subregion. Results: The coregistration process resulted in a geometrically robust and accurate dose warping. A significantly higher dose was consistently delivered to RILD patients in voxel clusters near the peripheral medial-basal portion of the lungs. The area under the ROC curves (AUC) from the mean dose of the voxel clusters was higher than the corresponding AUC derived from the total lung mean dose. Conclusions: We implemented a framework including a robust registration process and a VB approach accounting for the multiple comparison problem in dose-response modeling, and applied it to a cohort of HL survivors to explore a local dose–RILD relationship in the lungs. Patients with RILD received a significantly greater dose in parenchymal regions where low doses (∼6 Gy) were delivered. Interestingly, the relation between differences in the high-dose range and RILD seems to lack a clear spatial signature.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Screening and evaluation of biomass upgrading strategies for sustainable transportation fuel production with biomass-derived volatile fatty acids

Biomass conversion to fuels and chemicals is crucial to decarbonization, but choosing an advantageous upgrading pathway out of many options is challenging. Rigorously evaluating all candidate pathways (process simulation, product property testing) requires a prohibitive amount of research effort; even simple upgrading schemes have hundreds of possible permutations. We present a method enabling high-throughput screening by approximating upgrading unit operations and drop-in compatibility of products (e.g., fuel properties) and apply it to volatile fatty acid (VFA) conversion to liquid transportation fuels via a MATLAB script, VFA Upgrading to Liquid Transportation fUels Refinery Estimation (VULTURE). VULTURE selects upgrading configurations that maximize fuel blend bio-derived content. We validate VULTURE's approximations through surrogate fuel property testing and process simulation. Techno-economic and life cycle analyses suggest that VFA upgrading processes down-selected by VULTURE are profitable and have low carbon intensities, demonstrating the potential for the strategy to accelerate process development timelines at decreased costs.

09 BIOMASS FUELS↗
DOE OSTI2018

Continuous condensed-phase ethanol conversion to higher alcohols: Experimental results and techno-economic analysis

The conversion of ethanol to n-butanol and higher alcohols is carried out in a continuous fixed bed reactor over a Ni/La 2 O 3 /γ-Al 2 O 3 catalyst at 210–250 °C and 100 bar. At these conditions, the reaction mixture is a condensed, near-critical phase that facilitates higher reaction rates than corresponding vapor phase reactions. Combined selectivity to n-butanol and C 6 + higher alcohols reached 74% at 41% ethanol conversion. A process concept is presented in which n-butanol and mixed C 6 + alcohols are produced as saleable products; ethanol is recycled to achieve nearly 100% overall conversion and minor byproducts are burned to provide process energy. A process design is conducted using Aspen Plus V8.4 process simulation software, and economic analyses are carried out for several cases of ethanol conversion and alcohol selectivities. Several additional cases involving permutations of the base process configuration are also examined in attempts to improve process economics. At n-butanol selectivities achieved experimentally, a facility producing 75 million kg n-butanol per year has a total capital cost of 38.1 million with a required n-butanol selling price of 1.55/kg to 1.60/kg for typical values of expected return on investment.

09 BIOMASS FUELS↗
DOE OSTI2025

Anti-symmetric barron functions and their approximation with sums of determinants

A fundamental problem in quantum physics is to encode functions that are completely anti-symmetric under permutations of identical particles. The architecture of neural network models for the electron wave function typically comprises an equivariant component followed by a summation of determinants. The recently introduced Generic Antisymmetric (GA) block is designed to enhance the expressivity of such neural wave functions, and it was found that the 2-layer GA block achieved more accurate energies than the corresponding single-determinant FermiNet architecure, suggesting its promise as a way to improve the expressivity of neural wave functions. In this paper we show how the function expressed by the 2-layer GA block can be decomposed into a sum of determinants. We formalize this result by defining the antisymmetric Barron space as a generalized version of the 2-layer GA block and providing an appromation theorem for this function class. This result can be viewed as a negative result showing that the 2-layer GA block is not more expressive than using multiple determinants.

Abrahamsen, Nilin↗
DOE OSTI2020

Syntheses, solution behavior, and computational bond length analyses of trifluoromethyl and perfluoroethyl cuprate salts

Heteroleptic cuprate salts of the form [X-Cu-Y][(SIMes) 2 Cu] (SIMes = 1,3-dimesitylimidazolin-2-ylidene, X and Y = permutations of Cl, CF 3 , C 2 F 5 ) were generated by equilibrating mixtures of [(SIMes)Cu-X] and [(SIMes)Cu-Y] in CD 2 Cl 2 solvent. Here, the solubility features of the cuprates relative to the neutral species permitted the structural characterizations of new cuprates, including the first example of a mixed fluoroalkylated cuprate [(CF 3 )Cu(C 2 F 5 )][(SIMes) 2 Cu]. Computational studies (DFT and CCSD(T)) of fluoroalkylated cuprates generate Cu-C distances that are shorter than those in hydrocarbon analogues – a trend that in some cases may conflict with crystallographically determined values.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Microbial distributions detected by an oligonucleotide microarray across geochemical zones associated with methane in marine sediments from the Ulleung Basin

The biogeochemical processes that occur in marine sediments on continental margins are complex; however, from one perspective they can be considered with respect to three geochemical zones based on the presence and form of methane: sulfate–methane transition (SMTZ), gas hydrate stability zone (GHSZ), and free gas zone (FGZ). These geochemical zones may harbor distinct microbial communities that are important in biogeochemical carbon cycles. The objective of this study was to describe the microbial communities in sediments from the SMTZ, GHSZ, and FGZ using molecular ecology methods (i.e. PhyloChip microarray analysis and terminal restriction fragment length polymorphism (T-RFLP)) and examining the results in the context of non-biological parameters in the sediments. Non-metric multidimensional scaling and multi-response permutation procedures were used to determine whether microbial community compositions were significantly different in the three geochemical zones and to correlate samples with abiotic characteristics of the sediments. This analysis indicated that microbial communities from all three zones were distinct from one another and that variables such as sulfate concentration, hydrate saturation of the nearest gas hydrate layer, and depth (or unmeasured variables associated with depth e.g. temperature, pressure) were correlated to differences between the three zones. The archaeal anaerobic methanotrophs typically attributed to performing anaerobic oxidation of methane were not detected in the SMTZ; however, the marine benthic group-B, which is often found in SMTZ, was detected. Within the GHSZ, samples that were typically closer to layers that contained higher hydrate saturation had indicator sequences related to Vibrio-type taxa. These results suggest that the biogeographic patterns of microbial communities in marine sediments are distinct based on geochemical zones defined by methane.

03 NATURAL GAS↗
DOE OSTI2018

A multi-prong approach towards the development of high performance Temperature sensor using MWCNTs/Al2O3 composite film

Highlights: • MWCNTs/Al{sub 2}O{sub 3} composite based self-standing highly sensitive temperature sensor. • Optimization of film thickness to achieve best performance of the device. • Effect of carrier drift under strong electron-phonon coupling is examined. • Non-contact measurements reveal possibilities of ambient environment monitoring. • Highly stable and reproducible response bidding commercial scale production. - Abstract: We report fabrication of high-performance temperature sensor from free-standing MWCNTs/Al{sub 2}O{sub 3} composite film where a multi-prong approach was undertaken to enhance sensor performance. Film thickness was varied until a threshold value was reached by playing surface-area to volume-ratio factor leading to enormous improvements in crucial sensor parameters–temperature coefficient of resistance (TCR) and thermal hysteresis. Increased TCR resulted in high sensitivity, ultrafast response and negligible hysteresis loss. Besides, effect of carrier-drift transport under strong electron-phonon scattering is investigated by permutation of channel-length and external DC bias. Observed TCR value is −0.96%/°C for 16.53 μm thick film, at 2 V applied-bias voltage for 1 mm channel-length. Moreover, the sensor exhibited negligible hysteresis loss along-with response and recovery times of ∼40 s and ∼185 s respectively. Non-contact mode temperature measurements also demonstrated excellent performance. Fabricated sensors exhibited good stability and negligible drift for six-months. These studies are significant towards fabricating simple, highly-sensitive, economic heat sensor with high reproducibility.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Database development and exploration of process–microstructure relationships using variational autoencoders

The paper demonstrates graphical representation of a large database containing process–microstructure relationships using an unsupervised machine learning algorithm. Correlating microstructural features to processing is an essential first step to answer the difficult problem of process sequence design. Here, a large database of 346,200 orientation distribution functions resulting from a variety of process sequences is constructed, where each sequence comprises up to four stages of tension, compression and rolling along different directions in various permutations. This open-source database is constructed for collaborative development of process design algorithms. The paper demonstrates a novel application of the large database: graphical representation of texture–process relationships. A variational autoencoder is used to reduce the entire database to a two dimensional latent space where variations in processes and properties can be visualized. Using proximity analysis in this latent space, we can quickly unearth multiple process solutions to the problem of texture or property design.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Neuroanatomical and cognitive correlates of visual hallucinations in Parkinson’s disease and dementia with Lewy bodies: Voxel-based morphometry and neuropsychological meta-analysis

Visual hallucinations (VH) are common in Parkinson's disease and dementia with Lewy bodies, two forms of Lewy body disease (LBD), but the neural substrates and mechanisms involved are still unclear. We conducted meta-analyses of voxel-based morphometry (VBM) and neuropsychological studies investigating the neuroanatomical and cognitive correlates of VH in LBD. For VBM (12 studies), we used Seed-based d Mapping with Permutation of Subject Images (SDM-PSI), including statistical parametric maps for 50% of the studies. For neuropsychology (35 studies), we used MetaNSUE to consider non-statistically significant unreported effects. VH were associated with smaller grey matter volume in occipital, frontal, occipitotemporal, and parietal areas (peak Hedges' g -0.34 to -0.49). In patients with Parkinson's disease without dementia, VH were associated with lower verbal immediate memory performance (Hedges' g -0.52). Both results survived correction for multiple comparisons. Here, abnormalities in these brain regions might reflect dysfunctions in brain networks sustaining visuoperceptive, attention, and executive abilities, with the latter also being at the basis of poor immediate memory performance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

SympGNNs: Symplectic Graph Neural Networks for identifying high-dimensional Hamiltonian systems and node classification

Existing neural network models to learn Hamiltonian systems, such as SympNets, although accurate in low-dimensions, struggle to learn the correct dynamics for high-dimensional many-body systems. Herein, we introduce Symplectic Graph Neural Networks (SympGNNs) that can effectively handle system identification in high-dimensional Hamiltonian systems, as well as node classification. SympGNNs combine symplectic maps with permutation equivariance, a property of graph neural networks. Specifically, we propose two variants of SympGNNs: (i) G-SympGNN and (ii) LA-SympGNN, arising from different parameterizations of the kinetic and potential energy. We demonstrate the capabilities of SympGNN on two physical examples: a 40-particle coupled Harmonic oscillator, and a 2000-particle molecular dynamics simulation in a two-dimensional Lennard-Jones potential. Furthermore, we demonstrate the performance of SympGNN in the node classification task, achieving accuracy comparable to the state-of-the-art. Finally, we also empirically show that SympGNN can overcome the oversmoothing and heterophily problems, two key challenges in the field of graph neural networks.

Deep learning↗
DOE OSTI2017

PSelInv – A distributed memory parallel algorithm for selected inversion: The non-symmetric case

This paper generalizes the parallel selected inversion algorithm called PSelInv to sparse non-symmetric matrices. We assume a general sparse matrix A has been decomposed as $PAQ$ = $LU$ on a distributed memory parallel machine, where L, U are lower and upper triangular matrices, and P, Q are permutation matrices, respectively. The PSelInvmethod computes selected elements of $A$ -1 . The selection is confined by the sparsity pattern of the matrix A T . Here our algorithm does not assume any symmetry properties of A, and our parallel implementation is memory efficient, in the sense that the computed elements of $A$ -T overwrites the sparse matrix $L + U$ in situ . PSelInv involves a large number of collective data communication activities within different processor groups of various sizes. In order to minimize idle time and improve load balancing, tree-based asynchronous communication is used to coordinate all such collective communication. Numerical results demonstrate that PSelInv can scale efficiently to 6,400 cores for a variety of matrices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Equilibrium Structure of a Triblock Copolymer System Revealed by Mesoscale Simulation and Neutron Scattering

We have performed both mesoscale simulations and neutron scattering experiments on Pluronic L62, a poly(ethylene oxide)-poly(propylene oxide)-poly(ethylene oxide) (PEO-PPO-PEO) triblock copolymer system in aqueous solution. The influence of simulation variables such PEO/PPO block ratio, interaction parameters, and coarse-graining methods is extensively investigated by covering all permutations of parameters found in the literatures. Upon increasing the polymer weight fraction from 50 wt% to 90 wt%, the equilibrium structure of the isotropic, reverse micellar, bicontinuous, worm-like micelle network, and lamellar phases are respectively predicted from the simulation depending on the choices of simulation parameters. Small angle neutron scattering (SANS) measurements show that the same polymer systems exhibit the spherical micellar, lamellar, and reverse micellar phases with the increase of the copolymer concentration at room temperature. Detailed structural analysis and comparison with simulations suggest that one of the simulation parameter sets can provide reasonable agreement with the experimentally observed structures.

Do, Changwoo↗
DOE OSTI2025

Five-body systems with Bethe-Salpeter equations

We extend the Bethe-Salpeter formalism to systems made of five valence particles. Restricting ourselves to two-body interactions, we derive the subtraction terms necessary to prevent overcounting. We solve the five-body Bethe-Salpeter equation numerically for a system of five scalar particles interacting by a scalar exchange boson. To make the calculations tractable, we implement properties of the permutation group S 5 and construct an approximation based on intermediate two- and three-body poles. We extract the five-body ground and excited states along with the spectra obtained from the two-, three-, and four-body equations. In the limit of a massless exchange particle, the two-, three, four- and five-body states coexist within a certain range of the coupling strength, whereas for heavier exchange particles the five-body system becomes Borromean. Our study serves as a building block for the calculation of pentaquark properties using functional methods.

Eichmann, Gernot [Univ. of Graz (Austria)] (ORCID:↗
DOE OSTI2024

Ensemble methods for quantification of potassium oxide in ChemCam Mars and laboratory spectra

In this paper we test new approaches for predicting the amount of element oxides in rock samples from the ChemCam instrument suite onboard the NASA Curiosity rover by focusing on K 2 O. Using the expanded dataset compiled by Gasda et al. (2021) with and without the Earth to Mars (E2M and NoE2M) transformation discussed in Clegg et al. (2017) we trained blended submodels using the “double blending” technique and compared these to ensemble methods (Random Forest, ExtraTrees, and Gradient Boosting Regression). We found that ensemble methods performed similar to blended submodels when looking at RMSE-P on the laboratory spectra and provided significant advantages when looking at spectra coming from Mars. For the full model, blended submodels achieved an RMSE-P of 0.62 and 0.60 (E2M and NoE2M respectively) while Gradient Boosting Regression resulted in a slightly improved RMSE-P of 0.59 and 0.60. More importantly, by employing a local RMSE-P estimation technique where model performance is evaluated based on nearby test samples we found that using ensemble methods can lower the quantification limit for K 2 O from the current value of ≈0.6 wt% to ≈0.08 wt% using Extra Trees and Random Forest. This would allow for a much larger range of K 2 O values to be quantified on Mars with greater certainty given that most targets seen on Mars tend to have <1 wt% K2O. Finally, we used both Mean Decrease in Impurity (MDI) and permutation importance techniques to investigate the wavelengths used by the ensemble methods and found that they correspond to known potassium emission lines. This suggests that ensemble methods can provide an easier to train and improved alternative to blended submodels for predicting potassium compositions from Laser Induced Breakdown Spectroscopy (LIBS) data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

A comparative study of multimodal data fusion strategies for planetary spectroscopy

Integrating heterogeneous data sources can improve scientific inference when different modalities capture complementary information, but doing so is challenging in high-dimensional, small-sample settings. In spectroscopy for planetary exploration, Laser-Induced Breakdown Spectroscopy (LIBS), Raman Spectroscopy (Raman), Visible Infrared Spectroscopy (VISIR), and Mid-Infrared Spectroscopy (MIR) each examine different aspects of composition and mineralogy, raising fundamental questions about when and how data fusion improves predictive performance. Using a Mars-relevant set of geologic standards with measurements from all four modalities, we present a rigorous systematic evaluation of four data fusion strategies: low-level (data) fusion, mid-level (feature) fusion, high-level (decision) fusion, and residual-boosting (sequential) fusion. We assess performance in predicting oxide composition via nested cross-validation and corrected significance testing to evaluate whether data fusion improves upon single-modality baselines. We show that data fusion does not uniformly improve accuracy, and that observed gains are modest, oxide-dependent, and sensitive to modality and model structure. To move beyond aggregate accuracy metrics, we use model coefficients, permutation importance, and residual gain analysis to examine how the fusion models weight individual modalities and to identify patterns of apparent complementarity or redundancy. Though focused on spectroscopy for planetary exploration, our framework for data fusion evaluation and interpretation extends to other scientific domains with heterogeneous and scarce data and provides a principled approach evaluating data fusion strategies, interpreting modality contributions, and understanding tradeoffs among data fusion strategies.

97 MATHEMATICS AND COMPUTING↗