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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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Analysis of differential scanning calorimetry data for aged plutonium

Differential scanning calorimetry data for samples of a 52 year old plutonium alloy with 3.3 at. % Ga that were heated beyond the melting point is analyzed using transition state theory to find activation energies for the δ to ε and ε to liquid phase transitions. A Bayesian statistical method involving a Gaussian process model is used to find mean values and confidence intervals for the activation energies. The activation energy for the δ to ε phase transition increases by 3.3 ± 3.8% per decade, relative to the case when all age related plutonium lattice point defects have been removed through annealing. The corresponding increase in activation energy for the ε to liquid transition is shown to be 7.1 ± 1.8% per decade. It is postulated that the change in activation energy with age for both phase transitions is caused, in part, by the accumulation of the same type of lattice point defects associated with the observed increase in elastic bulk modulus over time.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A spatially-resolved model of neutron-irradiated tungsten coupling stochastic cluster dynamics and finite deformation plasticity

Structural materials used in nuclear reactors face severe degradation in mechanical properties, such as hardening and embrittlement. At the microscopic scale, this occurs due to creation and accumulation of irradiation-induced defects and their interaction with system dislocations. Although techniques exist which can model evolution of irradiation defects, for instance kinetic transport theory-based models, their interaction with mechanical deformation of the bulk material has not been investigated extensively. In this work, we demonstrate a novel spatially-resolved multiscale coupling between microscopic irradiation defect evolution, modeled using Stochastic Cluster Dynamics (SCD) and macroscopic mechanical deformation modeled using a finite-deformation plasticity model. SCD is used to determine the statistically averaged defect cluster spacing, dependent on operating conditions such as irradiation dose and temperature. This acts as an initial condition that governs the critical resolved shear stress of dislocation glide in the macroscopic plasticity model. This framework is used to predict mechanical behavior in post-mortem test of irradiated Tungsten samples, which has found its importance as structural material used in nuclear reactors. The results obtained using the coupled approach are in good agreement with experimental data of uniaxial tension tests. The model is able to capture the effect of temperature and irradiation dose on the material hardening. Two methods are proposed to estimate hardness – using Tabor's Law relating uniaxial yield stress to hardness and from flat-punch simulations. The results are in reasonable agreement with hardness data from micro-indentation experiments of irradiated Tungsten samples. Finally, the model is also able to reveal microstructural details such as spatial variation in defect density and local stress.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

ComStock Reference Documentation (V.1)

The commercial building sector stock model, or ComStock™, is a highly granular, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual sub-hourly energy consumption of the commercial building stock across the United States. ComStock asks and answers two questions: how is energy used in the U.S. building stock and what are the impact of energy saving technologies. Specifically, ComStock identifies where energy is being consumed geographically, in what building types and end uses, and at what times of day. Simultaneously, it identifies the impact of efficiency measures: how much energy do efficiency measures save; where, or in what use cases do measures save energy; when, or at what time of day do savings occur; and which building stock segments have the biggest savings potential. This document contains the methodology and assumptions behind ComStock and serves as a guide to its use.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Water under hydrophobic confinement: entropy and diffusion

The properties of liquid water are known to change drastically in confined geometries. A most interesting and intriguing phenomenon is that the diffusion of water is found to be strongly enhanced by the proximity of a hydrophobic confining wall relative to the bulk diffusion. We report a molecular dynamics simulation using a classical water model investigating the water diffusion near a non-interacting smooth confining wall, which is assumed to imitate a hydrophobic surface, revealing a pronounced diffusion enhancement within several water layers adjacent to the wall. We present evidence that the observed diffusion enhancement can be accounted for, with a quantitative accuracy, using the universal scaling law for liquid diffusion that relates the diffusion rate to the excess entropy. These results show that the scaling law, which has so far only been used for the description of the diffusion in simple liquids, can successfully describe the diffusion in water. It is shown that the law can be used for the analysis of water dynamics under nanoscale hydrophobic confinement, which is currently a subject of intense research activity.

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

Statistical perspective on embrittling potency for intergranular fracture

Embrittling potency is a thermodynamic metric that assesses the influence of solute segregation to a grain boundary (GB) on intergranular fracture. Historically, authors of studies have reported embrittling potency as a single scalar value, assuming a single segregation site of importance at a GB and a particular cleavage plane. However, the topography of intergranular fracture surfaces is not generally known a priori. Accordingly, we, in this study, present a statistical ensemble approach to compute embrittling potency, where many free surface (FS) permutations are systematically considered to model fracture of a GB. The result is a statistical description of the thermodynamics of GB embrittlement. As a specific example, embrittling potency distributions are presented for Cr segregation to sites at two Ni $\langle 111 \rangle$ symmetric tilt GBs using atomistic simulations. We show that the average embrittling potency for a particular GB site, considering an ensemble of FS permutations, is not equal to the embrittling potency computed using the lowest energy pair of FSs. A mean GB embrittlement is proposed, considering both the likelihood of formation of a particular FS and the probability of solute occupancy at each GB site, to compare the relative embrittling behavior of two distinct GBs.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Accounting for erroneous model structures in biokinetic process models

In engineering practice, model-based design requires not only a good process-based model, but also a good description of stochastic disturbances and measurement errors to learn credible parameter values from observations. However, typical methods use Gaussian error models, which often cannot describe the complex temporal patterns of residuals. Consequently, this results in overconfidence in the identified parameters and, in turn, optimistic reactor designs. Here, we assess the strengths and weaknesses of a method to statistically describe these patterns with autocorrelated error models. This method produces increased widths of the credible prediction intervals following the inclusion of the bias term, in turn leading to more conservative design choices. However, we also show that the augmented error model is not a universal tool, as its application cannot guarantee the desired reliability of the resulting wastewater reactor design.

42 ENGINEERING↗
DOE OSTI2023

Inference finds consistency between a neutrino flavor evolution model and Earth-based solar neutrino measurements

We continue examining statistical data assimilation (SDA), an inference methodology, to infer solutions to neutrino flavor evolution, for the first time using real - rather than simulated - data. The model represents neutrinos streaming from the Sun's center and undergoing a Mikheyev-Smirnov-Wolfenstein (MSW) resonance in flavor space, due to the radially-varying electron number density. The model neutrino energies are chosen to correspond to experimental bins in the Sudbury Neutrino Observatory (SNO) and Borexino experiments, which measure electron-flavor survival probability at Earth. In conclusion, the procedure successfully finds consistency between the observed fluxes and the model, if the MSW resonance - that is, flavor evolution due to solar electrons - is included in the dynamical equations representing the model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Photometry of Outer Solar System Objects from the Dark Energy Survey. II. A Joint Analysis of Trans-Neptunian Absolute Magnitudes, Colors, Light Curves and Dynamics

For the 696 trans-Neptunian objects (TNOs) with absolute magnitudes 5.5 < H r < 8.2 detected in the Dark Energy Survey, we characterize the relationships between their dynamical state and physical properties—namely H r , indicating size; colors, indicating surface composition; and flux variation semiamplitude A, indicating asphericity and surface inhomogeneity. We seek “birth” physical distributions that can recreate these parameters in every dynamical class. We show that the observed colors of these TNOs are consistent with two Gaussian distributions in griz space, “near-infrared bright” (NIRB) and “near-infrared faint” (NIRF), presumably an inner and outer birth population, respectively. We find a model in which both the NIRB and NIRF H r and A distributions are independent of current dynamical states, supporting their assignment as birth populations. All objects are consistent with a common rolling p(H r ), but NIRF objects are significantly more variable. Cold classicals (CCs) are purely NIRF, while hot classical (HC), scattered, and detached TNOs are consistent with ≈ 70% NIRB and the resonance NIRB fractions show significant variation. The NIRB components of the HCs and of some resonances have broader inclination distributions than the NIRFs, i.e. their current dynamics retains information about birth location. We find evidence for radial stratification within the birth NIRB population, in that HC NIRBs are on average redder than detached or scattered NIRBs; a similar effect distinguishes CCs from other NIRFs. We estimate total object counts and masses of each class within our H r range. These results will strongly constrain models of the outer solar system.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Effectiveness of nature-based solutions to reduce flooding in Quad Cities Metro Area (QCMA) using SWMM-HEC based flood model

Nature-based solutions (NbS) have gained significant attention as strategies for addressing urban environmental challenges, particularly since the establishment of the UN Sustainable Development Goals (SDGs) for 2030. However, the current research on NbS for urban flood management lacks comprehensive methodological approaches for identifying suitable areas and evaluating their effectiveness across different urban settings. Here, this study attempts to fill this gap by proposing a methodological framework integrating multi-criteria analysis with a SWMM-HEC-based hydrologic and hydraulic (HH) model to assess the suitability of NbS for the Quad Cities Metro Area (QCMA), consisting of Davenport, Bettendorf, Moline, and Rock Island. Eight NbS options-green roofs, rain gardens, infiltration trenches, permeable pavements, vegetative swales, dry detention basins, retention ponds, and rain barrels/cisterns - were considered based on volumetric efficiency and runoff reduction efficiency. The study reveals that implementing the proposed NbS could have substantially reduced flood depths in key historical flood events by 21% in 1993, 15% in 2008, 16% in 2011, 23% in 2014, 40% in 2019, and 10% in 2023. The findings highlight a critical trade-off between peak runoff and NbS implementation: while NbS effectively reduce flood impacts, they also enhance volumetric efficiency by approximately 43%. In high-density areas of the QCMA, flood depth reductions of around 20% suggest that NbS are a viable solution for dense urban environments with limited space. This shows the potential for integrating NbS into existing infrastructure, offering a promising approach for cities facing increasing flooding risks. The proposed methodology provides a practical framework for incorporating NbS into urban stormwater management, addressing gaps in optimizing NbS performance, and offering a pathway to scale their application in other urban areas with various environmental and social contexts.

CMIP6↗
DOE OSTI2022

Process prediction and detection of faults using probabilistic bidirectional recurrent neural networks on real plant data

Attaining Industry 4.0 for manufacturing operations requires advanced monitoring systems and real-time data analytics of plant data, among other topics. We propose a Probabilistic Bidirectional Recurrent Network (PBRN) for industrial process monitoring for the early detection of faults. The model is based on a Gated Recurrent Unit (GRU) neural network that allows the model to retain long-term dependencies between sensor data along a time horizon, hence learning the dynamic behavior of the process. To reduce the false-positive detection rate of the model, we compel the model to learn from a highly noisy sensor reading while outputting noise-free sensor outputs. The performance of the proposed model is compared to other data-driven statistical process monitoring schemes using real plant data from an industrial Air Separations Unit (ASU) containing noisy sensor readings. We show that the model can learn from noisy data without reducing its performance. Using two different fault cases, we demonstrate the model’s ability to carry out early fault detection with average false-positive rates of 2.9% and 4.9% for both fault cases. The missed detection rates are 0.1% and 0.2%, respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Probing the Kitaev honeycomb model on a neutral-atom quantum computer

Quantum simulations of many-body systems are among the most promising applications of quantum computers. In particular, models based on strongly correlated fermions are central to our understanding of quantum chemistry and materials problems, and can lead to exotic, topological phases of matter. However, owing to the non-local nature of fermions, such models are challenging to simulate with qubit devices. Here we realize a digital quantum simulation architecture for two-dimensional fermionic systems based on reconfigurable atom arrays. We utilize a fermion-to-qubit mapping based on Kitaev’s model on a honeycomb lattice, in which fermionic statistics are encoded using long-range entangled states. We prepare these states efficiently using measurement and feedforward, realize subsequent fermionic evolution through Floquet engineering with tunable entangling gates interspersed with atom rearrangement, and improve results with built-in error detection. Leveraging this fermion description of the Kitaev spin model, we efficiently prepare topological states across its complex phase diagram and verify the non-Abelian spin-liquid phase by evaluating an odd Chern number. We further explore this two-dimensional fermion system by realizing tunable dynamics and directly probing fermion exchange statistics. Finally, we simulate strong interactions and study the dynamics of the Fermi–Hubbard model on a square lattice. These results pave the way for digital quantum simulations of complex fermionic systems for materials science, chemistry and high-energy physics.

atomic and molecular physics↗
DOE OSTI2024

Statistically-driven Experimental Design to Improve Reference-free Quantification of Small Molecules by Liquid Chromatography-Mass Spectrometry

Non-targeted analysis of small molecules and metabolites in unknown, complex samples using liquid chromatography-tandem mass spectrometry remains challenging. One of the main bottlenecks is the extensive unannotated regions of metabolomics mass spectrometry data, resulting in knowledge gaps. Small molecule annotation in mass spectrometry data has conventionally relied on reference standards and libraries for compound identification and confirmation, which can constrain compound identification to those molecules already known, thus limiting the ability to discover new knowledge and new markers. Retention time prediction can facilitate and expedite unknown compound identification in non-targeted analysis of complex metabolomics samples. Additionally, accurate retention time predictions can also inform sample mixture design for LC-MS/MS analyses. However, current machine learning-based methods for retention time prediction are typically developed for specific chromatographic platforms and are not generalizable across scales. And while technologies and methods to improve reference-free metabolite identification for more comprehensive annotation of unknowns has received much attention, development of the same for quantitation without reference standards has been much more limited, despite its importance in toxicological, environmental, food safety, forensics, and clinical applications. We believe that a reference-free quantitation strategy that exploits mass spectrometry data already collected for reference-free identification can provide much more insight on unknowns, and move the metabolomics field for more complete unknowns characterization. As such, we pursue two efforts to improve upon current state-of-the-art methods in non-targeted analysis: (1) machine learning-based retention time prediction and (2) statistical design of experiments framework for reference-free quantitation. In this work, we develop and demonstrate (1) a generalizable retention time prediction capability across chromatographic conditions and scales, and (2) a statistical design-based framework for response factor contribution elucidation and reference-free quantitation. Evaluation of our retention time prediction model, PrediToR, showed approximately 24% improvement over current models, and we observed approximately 10X improvement in concentration estimation accuracy from our statistical design-based response factor model over a primarily ionization efficiency-based model. We expect that future efforts to improve upon these new capabilities will further advance non-targeted analysis of small molecules towards truly reference-free metabolomics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Model-based quantification of margins and uncertainties in metal additive manufacturing for process design and qualification

Laser powder bed fusion (LPBF) Additive Manufacturing (AM) has the potential to enable the production of components with novel designs and material properties unachievable otherwise. However, process repeatability is a challenge, making qualification ill-defined and greatly reducing the utility of what could be an important manufacturing technology. In this work, a combination of modeling, uncertainty quantification (UQ), and experimentation are used in an effort to predict and bound the range of possible outcomes of the LPBF process. Quantities of interest predicted are melt pool dimensions, microstructure features, and mechanical distortions. A combination of high fidelity thermal-fluid models, microstructure growth models, and reduced fidelity, rapid thermal and mechanical models are used. Uncertainty propagation techniques are used to predict probability distributions of quantities of interest from estimates of process uncertainties. Repeated experiments are done to quantify observed probability distributions and compared to predicted distributions to determine if predictions are precise and accurate. Novel modeling methods are microstrucutre characterization techniques are also discussed. It is found that high fidelity models do a generally good job bounding experimentally observed melt pool morphologies for both bead-on-plate and powder bed cases. Microstructure models are able to bound a number of experimentally observed microstructure statistics, but with low precision due to challenges with calibrating the microstructure growth model parameters. A developed modified inherent strain distortion model does not accurately predict observed distortions. A lumped laser distortion model shows promise in being both accurately and precisely bounding observed outcomes from the deflection comb build, but requires further evaluation on more builds and geometries.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Quantitative Insight to Fission Gas Pores Distribution in Irradiated Annular U-10Zr Metallic Fuel Using Machine Learning

Metallic fuels, particularly U-10Zr and its performance in reactor irradiation conditions, have been thoroughly investigated and are a promising candidate for next-generation sodium-cooled fast spectrum nuclear reactors. Irradiation in reactors can lead to the formation of fission gas and increased pore formation which can significantly impact fuel performance. Due to the large number of pores and various phases formed in metallic fuel during irradiation, a quantitative description of fission gas pores as a function of irradiation conditions is not yet available, undermining the fidelity of fuel performance modeling to support fuel qualification. It has been difficult to clearly detect pore boundaries and distinguish matrix phases from fission gas pores using optical microscopy by using simple threshold methods working with low magnification images. The pre-trained deep learning model for fission gas pore detection was applied to ~10,260 high magnification scanning electron microscopy images. The model increased the accuracy of fission gas pore segmentation to obtain statistical features, which cannot be processed manually. A pre-trained decision tree model was used to classify pores as isolated or connected pores, providing new insight into the correlation between the movement of lanthanides, solid fission products, and the radial temperature gradient developed in fuel irradiation conditions. This paper emphasizes the potential that artificial intelligence-based machine learning models have to accelerate qualification and support nuclear fuel development.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Full-stack Quantification of Variability in Predicting Ion Transport Properties using Machine-learned Interatomic Potentials

Machine-learned interatomic potentials (MLIPs) have become the state-of-the-art for performing accurate, scalable molecular dynamics (MD) simulations. It is therefore crucial to understand and quantify the reliability of MLIPs for downstream property predictions. Uncertainty in predicted properties can arise from limitations in first-principles training data, intrinsic MLIP model errors in representing the data, and the statistical noise introduced during subsequent MD simulations. Using ion transport in Li7P3S11 as a case study, we systematically assess the impact of training set size and selection, neural network stochasticity, and MD sampling statistics on predicted diffusivity and activation energy. We find that when using equivariant MLIP architectures with standard MD protocols, uncertainty arising from MD sampling dominates over model-induced errors. In contrast, MLIP errors relative to the underlying first-principles data are consistently minor. Given this, there are two main routes to improving the accuracy of predictions based on MLIP potentials: adopting higher accuracy reference data generation methods, and improving the MD sampling statistics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Effects of sodium and calcium chloride ionic stresses on model yeast membranes revealed by molecular dynamics simulation

As efforts to move a renewable economy grow, it will be necessary to make use of microbial conversion strategies for the production of novel materials or the upgrading of waste to high-value products. One critical technical challenge currently limiting waste upgrading remains the difficulty in obtaining single-pot conversion techniques where physical, chemical, and biological conversion are performed in a single step. To overcome this challenge, a detailed understanding of how different stresses impact microbial membrane stability will be necessary. In this work, using all-atom molecular dynamics simulations, we examine the impacts of moderate concentrations of NaCl and CaCl 2 on a model yeast plasma membrane. Weak, though statistically significant, changes in membrane morphology and dynamics functions are observed that are consistent with swelling and stiffening. Additionally, an examination of the ion-lipid contacts and the behavior of water at the water-membrane interface suggests that the impacts of these common salts may, in part, be mediated through changes to water-membrane hydrogen-bonding and hydration water dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Data-driven projection pursuit adaptation of polynomial chaos expansions for dependent high-dimensional parameters

Uncertainty quantification (UQ) and inference involving a large number of parameters are valuable tools for problems associated with heterogeneous and non-stationary behaviors. The difficulty with these problems is exacerbated when these parameters are statistically dependent requiring statistical characterization over joint measures. Probabilistic modeling methodologies stand as effective tools in the realms of UQ and inference. Among these, polynomial chaos expansions (PCE), when adapted to low-dimensional quantities of interest (QoI), provide effective yet accurate approximations for these QoI in terms of an adapted orthogonal basis. These adaptation techniques have been cast as projection pursuits in Gaussian Hilbert space in what has been referred to as a projection pursuit adaptation (PPA) by Xiaoshu Zeng and Roger Ghanem (2023). The PPA method efficiently identifies an optimal low-dimensional space for representing the QoI and simultaneously evaluates an optimal PCE within that space. The quality of this approximation clearly depends on the size of the training dataset, which is typically a function of the adapted reduced dimension. Here, the complexity of the problem is thus mediated by the complexity of the low-dimensional quantity of interest and not the complexity of the high-dimensional parameter space.

Data-driven↗
DOE OSTI2023

Accelerating Markov Chain Monte Carlo sampling with diffusion models

Global fits of physics models require efficient methods for exploring high-dimensional and/or multimodal posterior functions. We introduce a novel method for accelerating Markov Chain Monte Carlo (MCMC) sampling by pairing a Metropolis-Hastings algorithm with a diffusion model that can draw global samples with the aim of approximating the posterior. We briefly review diffusion models in the context of image synthesis before providing a streamlined diffusion model tailored towards low-dimensional data arrays. We then present our adapted Metropolis-Hastings algorithm which combines local proposals with global proposals taken from a diffusion model that is regularly trained on the samples produced during the MCMC run. Our approach leads to a significant reduction in the number of likelihood evaluations required to obtain an accurate representation of the Bayesian posterior across several analytic functions, as well as for a physical example based on a global fit of parton distribution functions. Our method is extensible to other MCMC techniques, and we briefly compare our method to similar approaches based on normalising flows. A code implementation can be found at https://github.com/NickHunt-Smith/MCMC-diffusion.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗