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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Correlating Impacts of Injected Fuels on Carbon Emissions in Blast Furnace with Computational Fluid Dynamics Modeling

A major challenge for steelmaking is the reduction of CO 2 emissions. In this regard, the blast furnace (BF) is critical due to the high associated CO 2 levels. This investigation assesses the impact of tuyere‐injected fuels on BF CO 2 emissions. Specifically, computational fluid dynamics results obtained previously at Purdue University Northwest are analyzed to obtain CO 2 emissions when natural gas (NG), syngas, hydrogen, or hydrogen/NG are injected. CO 2 emissions are compared with those produced when 95 kg of NG/thm is injected. Among these scenarios, the largest CO 2 reduction occurs when 102 kg of syngas/thm (COG feedstock #1) is injected at 973 K, reducing CO 2 by 190.6 kg thm −1 . The largest CO 2 reduction obtained with NG occurs when 130 kg thm −1 is injected at 600 K, reducing emissions by 65 kg thm −1 . H 2 injection also reduces CO 2 , but requires careful adjusting to reach stable operation. For instance, injecting 35 kg of H 2 /thm reduces CO 2 by 52 kg thm −1 . Increasing gaseous injection rates can significantly reduce CO 2 emissions, with fuel preheating providing an addendum, but high injection rates can lead to unstable operation. Furthermore, results show a correlation between CO 2 emissions and average temperature of shaft region for multiple fuels and injection conditions.

Metallurgy & Metallurgical Engineering↗
DOE OSTI2025

Ground-state-based model reduction with unitary circuits

Here, we present a method to numerically obtain low-energy effective models based on a unitary transformation of the ground state. The algorithm finds a unitary circuit that transforms the ground state of the original model to a projected wavefunction with only the low-energy degrees of freedom. The effective model can then be derived using the unitary transformation encoded in the circuit. We test our method on the one-dimensional and two-dimensional square-lattice Hubbard model at half-filling, and obtain more accurate effective spin models than the standard perturbative approach.

Hubbard model↗
DOE OSTI2024

An Analysis of the Spatial Variations in the Relationship Between Built Environment and Severe Crashes

Traffic crashes significantly contribute to global fatalities, particularly in urban areas, highlighting the need to evaluate the relationship between urban environments and traffic safety. This study extends former spatial modeling frameworks by drawing paths between global models, including spatial lag (SLM), and spatial error (SEM), and local models, including geographically weighted regression (GWR), multi-scale geographically weighted regression (MGWR), and multi-scale geographically weighted regression with spatially lagged dependent variable (MGWRL). Utilizing the proposed framework, this study analyzes severe traffic crashes in relation to urban built environments using various spatial regression models within Leon County, Florida. According to the results, SLM outperforms OLS, SEM, and GWR models. Local models with lagged dependent variables outperform both the global and generic versions of the local models in all performance measures, whereas MGWR and MGWRL outperform GWR and GWRL. Local models performed better than global models, showing spatial non-stationarity; so, the relationship between the dependent and independent variables varies over space. The better performance of models with lagged dependent variables signifies that the spatial distribution of severe crashes is correlated. Finally, the better performance of multi-scale local models than classical local models indicates varying influences of independent variables with different bandwidths. According to the MGWRL model, census block groups close to the urban area with higher population, higher education level, and lower car ownership rates have lower crash rates. On the contrary, motor vehicle percentage for commuting is found to have a negative association with severe crash rate, which suggests the locality of the mentioned associations.

Alisan, Onur (ORCID:0000000193113984)↗
DOE OSTI2025

Hierarchical Gaussian process-based Bayesian optimization for materials discovery in high entropy alloy spaces

Bayesian optimization (BO) is a powerful and data-efficient method for iterative materials discovery and design, particularly valuable when prior knowledge is limited, underlying functional relationships are complex or unknown, and the cost of querying the materials space is significant. Traditional BO methodologies typically utilize conventional Gaussian Processes (cGPs) to model the relationships between material inputs and properties, as well as correlations within the input space. However, cGP-BO approaches often fall short in multi-objective optimization scenarios, where they are unable to fully exploit correlations between distinct material properties. Leveraging these correlations can significantly enhance the discovery process, as information about one property can inform and improve predictions about others. Here, this study addresses this limitation by employing advanced kernel structures to capture and model multi-dimensional property correlations through multi-task (MTGPs) or deep Gaussian Processes (DGPs), thus accelerating the discovery process. We demonstrate the effectiveness of MTGP-BO and DGP-BO in rapidly and robustly solving complex materials design challenges that occur within the context of complex multi-objective optimization over FCC FeCrNiCoCu high entropy alloy (HEA) spaces, where traditional cGP-BO approaches fail. Furthermore, we highlight how the differential costs associated with querying various material properties can be strategically leveraged to make the materials discovery process more cost-efficient.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Carbon- and Oxygen-rich stars in MaStar: identification and classification

ABSTRACT Carbon- and Oxygen-rich stars populating the Thermally Pulsing Asymptotic Giant Branch (TP-AGB) phase of stellar evolution are relevant contributors to the spectra of ∼1 Gyr old populations. Atmosphere models for these types are uncertain, due to complex molecules and mass-loss effects. Empirical spectra are then crucial, but samples are small due to the short (∼3 Myr) TP-AGB lifetime. Here we exploit the vastness of the MaNGA Stellar library MaStar (∼60 000 spectra) to identify C and O-rich type stars. We define an optical colour selection with cuts of (g − r) > 2 and (g − i) < 1.55(g − r) − 0.07, calibrated with known C- and O-rich spectra. This identifies C- and O-rich stars along clean, separated sequences. An analogue selection is found in V, R, I bands. Our equation identifies C-rich and O-rich spectra with predictive performance metric F1-scores of 0.72 and 0.74 (over 1), respectively. We finally identify 41 C- and 87 O-rich type AGB stars in MaStar, 5 and 49 of which do not have a SIMBAD counterpart. We also detect a sample of non-AGB, dwarf C-stars. We further design a fitting procedure to classify the spectra into broad spectral types, by using as fitting templates empirical C- and O-rich spectra. We find remarkably good fits for the majority of candidates and categorize them into C- and O-rich bins following existing classifications, which correlate to effective temperature. Our selection models can be applied to large photometric surveys (e.g. Euclid, Rubin). The classified spectra will facilitate future evolutionary population synthesis models.

Hill, Lewis (ORCID:0000000180528080)↗
DOE OSTI2026

CAD-VAE: Leveraging Correlation-Aware Latents for Comprehensive Fair Disentanglement

While deep generative models have significantly advanced representation learning, they may inherit or amplify biases and fairness issues by encoding sensitive attributes alongside predictive features. Enforcing strict independence in disentanglement is often unrealistic when target and sensitive factors are naturally correlated. To address this challenge, we propose CAD-VAE(Correlation-Aware Disentangled VAE), which introduces a correlated latent code to capture the information shared between the target and sensitive attributes. Given this correlated latent, our method effectively separates over-lapping factors without extra domain knowledge by directly minimizing the conditional mutual information between target and sensitive codes. A relevance-driven optimization strategy refines the correlated code by efficiently capturing essential correlated features and eliminating redundancy. Extensive experiments on benchmark datasets demonstrate that CAD-VAE produces fairer representations, realistic counterfactuals, and improved fairness-aware image editing.

Ma, Chenrui [University of California Irvine]↗
DOE OSTI2025

Physics-Driven Construction of Compact Primitive Gaussian Density Fitting Basis Sets

We present a model-assisted density fitting (MADF) basis set generator, an algorithm for generating primitive atomic Gaussian density fitting (DF) basis sets (DFBSs) from a contracted Gaussian orbital basis set (OBS). The MADF algorithm produces DFBSs suitable for accurate robust DF approximation of 2-particle interactions in mean-field and correlated electronic structures. The algorithm is designed to (a) saturate the OBS product space by a large regularized set of primitive solid-harmonic Gaussian shells with nonuniform distribution of exponents, followed by (b) pruning of the shells according to their contributions to the 2- body energy of a correlated atomic ensemble. Building the DFBS generator model almost exclusively on mathematical and physical principles allows one to limit the number of parameters that control the density fitting error to three, with a single set of parameters sufficient for computations with all basis cardinal numbers, with and without correlation of core electrons, with and without scalar and spin-dependent relativistic effects, spanning almost all of the Periodic Table. Performance assessment included basis sets up to quadruple-ζ quality from several major basis set families, using molecules composed of main-group, d-block, and f-block elements. The resulting DF errors in Hartree−Fock and second-order MP2 energies (with relativistic all-electron treatments, when appropriate) were on the order of 20 and 10 μE h per electron, respectively.

Approximation↗
DOE OSTI2024

The secret structure of the gravitational vacuum

We argue that the vacuum of quantum gravity must contain a hierarchical structure of correlations spanning all length scales. These correlated domains (called “vecros”) correspond to virtual fluctuations of black hole microstates. Larger fluctuations are suppressed by their larger action, but this suppression is offset by a correspondingly larger phase space of possible configurations. We give an explicit lattice model of these vecro fluctuations, noting how their distribution changes as the gravitational pull of a star becomes stronger. At the threshold of formation of a closed trapped surface, these virtual fluctuations transition into on-shell black hole microstates (fuzzballs). Fuzzballs radiate from their surface like normal bodies, resolving the information paradox. Here, we also argue that any model without vecro-type extended vacuum correlations cannot resolve the paradox.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Characterizing the multisectoral impacts of future global hydrologic variability

There is significant uncertainty in how global water supply will evolve in the future, due to uncertain climate, socioeconomic, and land use change drivers and variability of hydrologic processes. It is critical to characterize the potential impacts of uncertainty in future water supply given its importance for food and energy production. In this work, we introduce a framework that integrates stochastic hydrology and human-environmental systems to characterize uncertainty in future water supply and its multisector impacts. We develop a global stochastic watershed model and demonstrate that this model can generate a large ensemble of realizations of basin-scale runoff with global coverage that preserves the mean, variance, and spatial correlation of a historical benchmark. We couple this model with a well-known human-environmental systems model to explore the impacts of runoff variability on the water and agricultural sectors across spatial scales. We find that the impacts of future hydrologic variability vary across sectors and regions. Impacts are felt most strongly in the water and agricultural sectors for basins that are expected to have unsustainable water use in the future, such as the Indus River basin. For this basin, we find that the variability in future irrigation water withdrawals and irrigated cropland increase over time due to uncertainty in renewable water supply. We also use the Indus basin to show how our stochastic ensemble can be leveraged to explore the global multisector consequences of local extreme runoff conditions. This work introduces a novel technique to explore the propagation of future hydrologic variability across human and natural systems and spatial scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluating the bond strength and fracture mechanisms of cold-sprayed zinc coating on AZ91 magnesium substrate via a combined experimental and computational approach

Magnesium (Mg) alloys are ideal candidates for automotive applications due to their high strength to weight ratio, castability, recyclability etc., however, they lack corrosion and oxidation resistance. Solid-state deposition techniques, such as cold spray, have been demonstrated to enhance their corrosion resistance as it relies on the severe plastic deformation of powder particles upon impact with the substrate to form a metallurgical bond with the substrate and within the coating. At cold sprayed interfaces, a heterogeneous microstructure is formed that includes some porosity, oxides and intermetallics which can significantly affect coating performance. Thus, establishing a direct correlation between the interface microstructure and its properties can aid in designing optimal cold spray parameters. In this study, we investigated the microstructure and mechanical properties of a zinc (Zn) coating deposited on a high pressure die cast (HPDC) AZ91 Mg substrate via high resolution scanning transmission electron microscopy, in situ micro-tensile testing, and finite element method (FEM) modeling. Micro-tensile pillars fabricated using the plasma focused ion beam (PFIB) successfully isolates the coating-substrate interface within the gauge length. The average bond strength of Zn-Mg interface was determined to be ∼140 MPa with failure occurring partially at the interface and mostly into the coatings. A detailed microstructural characterization revealed evidence of a strong metallurgical bonding at the Zn-Mg interface and formation of the C14 MgZn 2 laves phase interlayer resulting in a mixed mode of fracture during the micro-tensile experiments. FEM modeling reveals the stress distribution along the interfaces and suggests that a MgZn 2 layer thickness between 200–400 nm is optimum to increase the bond strength and minimize the triaxiality. Such a site-specific interfacial analysis with correlative computational modeling provides crucial insight into the overall performance of cold spray interfaces.

Bond strength↗
DOE OSTI2025

Effect of alloying on intrinsic ductility in WTaCrV high entropy alloys

Tungsten (W) exhibits desirable properties for extreme applications, such as the divertor in magnetic fusion reactors, but its practicality remains limited due to poor formability and insufficient irradiation resistance. In this work, we study the intrinsic ductility of body-centered cubic WTaCrV based high entropy alloys (HEAs), which are known to exhibit excellent irradiation resistance. The ductility evaluations are carried out using a criterion based on the competition between the critical stress intensity factors for emission (K Ie ) and cleavage (K Ic ) in the {110} slip planes and {110} crack planes, which are evaluated within the linear elastic fracture mechanics framework and computed using density functional theory. The results suggest that increasing the alloying concentrations of V and reducing the concentrations of W can significantly improve the ductility in these HEAs. The elastic anisotropy for these HEAs is analyzed using the Zener anisotropy ratio and its correlation with the concentration of W in the alloys is studied. Results indicate that these alloys tend to be fairly isotropic independently from the concentration of W in them. The computed data for the elastic constants of these HEAs is also compared against available experimental data. The results are in good agreement, validating the robustness and accuracy of the computational methods. Multiple phenomenological ductility metrics were also computed and analyzed against the analytical model. Some metrics, mainly the surrogate D parameter, show a good correlation with the Rice model. The potential of these empirical metrics to serve as surrogate screening models for optimizing the compositional space is also discussed.

Anisotropy↗
DOE OSTI2025

Thermo-hydraulic steam pipe models for district heating simulations: Simplifications to balance accuracy and simulation speed

Steam piping networks are essential for optimizing performance in industrial processes and district heating systems. However, dynamic models that balance thermo-hydraulic accuracy with computational efficiency remain limited. In response, this paper presents a new discretized steam pipe model based on the plug flow approach, capturing key thermo-hydraulic behaviors while simplifying steam phase change processes. Implemented in Modelica, the model accurately calculates temperature and pressure distributions along steam pipelines. To improve computational efficiency for district-scale simulations, five model simplifications are introduced: lumped thermo-hydraulic functions, empirical correlations, fluid state approximations, steady-state dynamics and inclusion of flow derivatives. These simplified models achieve 85%-98% accuracy in predicting pressure drop and condensation losses, including dynamic condensate behavior during pipe warm-up—a factor often overlooked in existing models. The models support diverse network configurations, scaling effectively to systems with multiple distribution pipes and connected building loads. Discrete models provide detailed insights but exhibit a cubic increase in simulation time as the network scales by N connected building O(N 2.42 ). In contrast, lumped models simulate 10–28 times faster than discrete, offering quadratic scaling of simulation time O(N 1.73 ). However, they still require 6 times more computation time than a lossless network, highlighting the inherent computational challenges of modeling compressible fluid flow. In conclusion, the steady-state lumped variant, with its near-linear scalability in computational time O(N 1.01 ), emerges as an efficient solution for preliminary design evaluations and extensive parametric studies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Ultrasonic-Assisted Extrusion Processing for Enhancing Physical Properties of High-Density Polyethylene by Flow-Induced Crystallization

The evolution of crystallinity resulting from stress imposed on a melt, known as flow-induced crystallinity, can strongly influence the mechanical and physical properties of semicrystalline polymers. This study investigates shear-induced crystallization by applying an ultrasonic field to the melt flow as it passes through dies with various geometries. A custom-built sonication die is employed for controlling the dynamic temperature and shear environment, resulting in molecular alignment and potential for flow-induced crystallization. Application of both conventional and ultrasonic shear rates at the equilibrium melt temperature of high-density polyethylene (HDPE) was investigated to accelerate crystallinity and manipulate the crystal morphology across the film in pursuit of improved mechanical and gas barrier properties without the need for additives or other polymer layers. The relationships among ultrasonic-assisted extrusion processing, polymer structure, and performance were analyzed using wide- and small-angle X-ray scattering (WAXS and SAXS), tensile testing, and oxygen transmission rate (OTR) analysis. Multiple linear regression models were implemented to predict the correlation among HDPE structure, process, and properties. Structural analysis revealed that both conventional and ultrasonic shear rates had the most significant influence on lamellar spacing and redistribution of rigid and soft amorphous fractions within the crystalline domains, ultimately dictating the mechanical and physical properties of the films. The goal is to explore the potential of the ultrasonic-assisted high crystallinity monolayer that can replace some of the functionality of complex, heterogeneous multilayer packaging with a single-material film having enhanced oxygen barrier properties.

crystallinity↗
DOE OSTI2025

Tumor-promoting UBR4 coordinates impaired mitophagy–associated senescence and lung adenocarcinoma pathogenesis

Cellular senescence, an irreversible cell cycle arrest, plays a pivotal role in development, aging, and tumor suppression. However, the fundamental pathway coordinating senescence and neoplastic transformation remains unclear. Here, we describe the tumorigenic involvement of ubiquitin protein ligase E3 component n-recognin 4 (UBR4), an E3 ubiquitin ligase of the N-degron pathway, in lung adenocarcinoma (LUAD). Public genome databases revealed high UBR4 expression in LUAD patients, associated with a dysregulated cell cycle and impaired mitochondrial homeostasis.UBR4knockout (ΔUBR4) in A549 lung cancer cells induced cellular senescence with defective mitochondria. Restoration of UBR4 or antioxidant treatment reversed the ΔUBR4 phenotypes caused by impaired mitophagy. Mitochondrial stress exacerbated mitochondrial dysfunction in ΔUBR4 cells, contributing to diverse cellular phenotypes. Additionally, ΔUBR4 cells exhibited substantially slow tumor growth in mouse xenograft models. In LUAD patients, UBR4 levels correlated with tumor stage, mitophagy markers, and poor survival. These findings suggest a tumor-promoting function of UBR4 in LUAD by regulating mitochondrial quality control. Further research into the pharmacological inhibition of UBR4 could open promising avenues for developing effective antitumor therapies targeting LUAD.

Science & Technology - Other Topics↗
DOE OSTI2025

Quantum Ornstein-Zernike theory for two-temperature two-component plasmas

Laboratory plasma production almost always preferentially heats either the ions or electrons, leading to a two-temperature state. In this state, density functional theory molecular dynamic simulation is the state of the art for modeling bulk material properties. We construct a statistical mechanics model for the two temperature limit that is theoretically consistent with the molecular dynamics method. We proceed to derive the electron-ion multi-temperature quantum Ornstein-Zernike equations for the first time. This allows the construction of a two-temperature two-component plasma model using the average atom from which we can compute bulk material properties at a fraction of the computation time of the two-temperature density functional theory simulation. The accuracy of the model is benchmarked against ion pair correlation and self-diffusion results from ab initio simulation. Here, we proceed to compute the viscosity and ion thermal conductivity as a function of both ion and electron temperature.

Ab initio molecular dynamics↗
DOE OSTI2026

Magnetic field dependence of the spin fluctuations in CeCu 5.8 ⁢Ag 0.2

Quantum phase transitions are among the most intriguing phenomena that can occur when the electronic ground state of correlated metals are tuned by external parameters such as pressure, magnetic field, or chemical substitution. Such transitions between distinct states of matter are driven by quantum fluctuations, and can give rise to macroscopically coherent phases that are at the forefront of condensed matter research. However, the nature of the critical fluctuations, and thus the fundamental physics controlling many quantum phase transitions, remain poorly understood in numerous strongly correlated metals. Here we study the model material CeCu 5.8⁢ Ag 0.2 to gain insight into the implications of critical fluctuations originating from different regions in reciprocal space. By employing an external magnetic field along the crystallographic 𝑎 and 𝑐 axis as auxiliary tuning parameter, we observe a pronounced anisotropy in the suppression of the quantum critical fluctuations, reflecting the spin anisotropy of the long-range ordered ground state at larger silver concentration. Coupled with the temperature dependence of the quantum fluctuations, these results suggest that the quantum phase transition in CeCu 5.8⁢ Ag 0.2 is driven by three-dimensional spin-density wave fluctuations.

Boraley, Xavier [Paul Scherrer Inst. (PSI), Villig↗
DOE OSTI2026

Electromagnetic Tomography of Radial Flow in the Quark-Gluon Plasma

We present a novel multimessenger approach to extract the effective radial flow of the quark-gluon plasma (QGP) by jointly analyzing thermal photon and dilepton spectra in heavy-ion collisions. A key feature of this method is that it circumvents the need for a directly unmeasurable reference—the photon temperature in the absence of flow—by establishing, within a calibrated model framework, a stable, approximately linear correlation with the dilepton-inferred temperature. This construction defines an experimentally constructible quantity, 𝑣$^{eff}_{𝑟}$, which reflects early-time collectivity and exhibits a strong correlation with the spacetime-averaged radial velocity of the QGP. Together with previous results linking dilepton slopes to the initial QGP temperature, our work establishes a consistent framework for electromagnetic tomography of the QGP. Here, our framework quantifies the experimental precision target, thereby providing a concrete road map for future measurements at RHIC and the LHC and opening a new avenue to probe the early-time dynamics of hot QCD matter.

Collective flow↗
DOE OSTI2024

Data Driven Commercial Building Energy Code Compliance and Technology Inventory for New York City

Building Performance Standards (BPS) are gaining national traction. A BPS will require new processes in the design, construction, and operation of buildings that take the occupants into account and enable predictive analysis to ensure compliance with current and future GHG emissions caps. In New York City, most buildings over 25,000 square feet will be regulated by a BPS starting in 2024, regardless of whether it is new construction permitted under current energy codes or an existing building. This research is one of the first to begin the evaluation of a long-term series of building policies in the context of an open data ecosystem, in cooperation with city agencies. Existing building policies enacted in NYC have ranged from building energy benchmarking and labeling to energy audits to the regulation of GHG emission in buildings. Through the development of a dataset related to building technologies and energy consumption, this project can help to evaluate if meaningful conclusions can be drawn for the data that has been largely self-reported in compliance with city regulations. This project will also provide lessons learned from a deep dive into these types of datasets to provide best practices for municipalities or states seeking to embark on policies like those enacted in NYC. In addition, a Building Automation System (BAS) Stretch Standard of Care (SSOC) for owners, designers, and building operators will enable the measurement and predictive analysis of energy consumption and GHG emissions at the plant, system, or component level, in anticipation of regulated GHG limits on buildings based on energy use. The SSOC is expected to be suitable for use on a national level. The primary feature of an SSOC is a standardized format for a set of BAS points that can be used to control and to gather data from individual plants, systems, or components that are related to building energy consumption. This project examined how measurements compare to prescriptive or simulation-based energy code targets, finding little correlation between predictive 8760-hour energy modeling and actual energy consumption for a small sample (n=27) of buildings constructed after 2015. Other analysis found that, while large multifamily housing (MFH) buildings showed a general trend similar to predicted reductions in energy use from the implementation of model commercial energy codes, this trend was not evident in the office, K-12 school, and hotel use groups in NYC. No upward or downward trends in energy consumption were found when buildings were grouped by size. Energy audit data were analyzed and it appears that there is bias by audit company on measures recommended to clients. Further research should be performed to cross-analyze this with other attributes, such as building size, vintage, and number of stories. Analysis found that for 281 buildings that were permitted and completed after 2015 and had submitted benchmarking data in 2022, between 81% and 96% (by use group) were found to be in compliance with the 2024 to 2029 NYC BPS emission caps, and between 55% and 89% were in compliance with the 2030-2034 caps. This work is beneficial to the public in helping policymakers and building stakeholders better understand the wide-ranging implications of a BPS.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗