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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

High-Pressure Synthesis of Metal–Inorganic Frameworks Hf 4 N 20 ·N 2 , WN 8 ·N 2 , and Os 5 N 28 ·3 N 2 with Polymeric Nitrogen Linkers

Polynitrides are intrinsically thermodynamically unstable at ambient conditions and require peculiar synthetic approaches. Now, a one-step synthesis of metal–inorganic frameworks Hf 4 N 20 ·N 2 , WN 8 ·N 2 , and Os 5 N 28 ·3 N 2 via direct reactions between elements in a diamond anvil cell at pressures exceeding 100 GPa is reported. The porous frameworks (Hf 4 N 20 , WN 8 , and Os 5 N 28 ) are built from transition-metal atoms linked either by polymeric polydiazenediyl (polyacetylene-like) nitrogen chains or through dinitrogen units. Triply bound dinitrogen molecules occupy channels of these frameworks. Owing to conjugated polydiazenediyl chains, these compounds exhibit metallic properties. The high-pressure reaction between Hf and N 2 also leads to a non-centrosymmetric polynitride Hf 2 N 11 that features double-helix catena-poly[tetraz-1-ene-1,4-diyl] nitrogen chains [–N–N–N=N–] ∞ .

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

Spatial Bayesian models project shifts in suitable habitat for Pacific Northwest tree species under climate change

Abstract We developed spatial Bayesian hierarchical models to assess potential climate change impacts on suitable habitat for five important tree species in the Pacific northwestern United States (California, Oregon, and Washington). Individual‐species models were fit with presence–absence data from forest inventory field plots and spatial relationships were specified through a conditional autoregressive model. This modeling approach allowed us to visualize uncertainty in response curves, map current and future prediction uncertainty, and provide interval estimates for change. Upward elevational or northward latitudinal shifts in climatically suitable habitat were projected for all species. Climate change impacts were the most damaging for noble fir ( Abies procera ), for which 79%–100% of the current range was projected to become climatically unsuitable by the 2080s. Although coastal Douglas‐fir ( Pseudotsuga menziesii var. menziesii ) has been projected by others to gain habitat in Canada, within our study area we projected a net loss of climatically suitable habitat (ca. 8000–31,400 km 2 ) under three of four future climate scenarios. A net loss in habitat was also projected for Oregon white oak ( Quercus garryana ) under three of four scenarios, with 40%–60% of the current range becoming unsuitable. Although there was no net loss of habitat for forest land blue oak under any scenario, other factors like competition may inhibit blue oak ( Quercus douglasii ) and white oak from occupying areas projected to increase in climatic suitability. Additionally, between 13% and 32% of blue oak's current range was projected to become unsuitable; some of these areas aligned with dieback following the 2012–2015 California drought, which our data set predates. Unlike the other four species, we projected a 17%–25% increase in climatically suitable habitat for California black oak ( Quercus kelloggii ), although 1%–20% of the current range was still projected to become unsuitable. Our findings indicate that, although some species will face more pressure in tracking climatically suitable habitat than others, climate change will impact the location of suitable habitat for many species.

Kralicek, Karin↗
DOE OSTI2025

Utilizing plant synthetic biology to accelerate plant-microbe interactions research

Plant-microbe interactions are critical to ecosystem resilience and substantially influence crop production. From the perspective of plant science, two important focus areas concerning plant-microbe interactions include: 1) understanding plant molecular mechanisms involved in plant-microbe interfaces and 2) engineering plants for increasing plant disease resistance or enhancing beneficial interactions with microbes to increase their resilience to biotic and abiotic stress conditions. Molecular biology and genetics approaches have been used to investigate the molecular mechanisms underlying plant responses to various beneficial and pathogenic microbes. While these approaches are valuable for elucidating the functions of individual genes and pathways, they fall short of unraveling the complex cross-talk across pathways or systems that plants employ to respond and adapt to environmental stresses. Also, genetic engineering of plants to increase disease resistance or enhance symbiosis with microbes has mainly been attempted or conducted through targeted manipulation of single genes/pathways of plants. Recent advancements in synthetic biology tool development are paving the way for multi-gene characterization and engineering in plants in relation to plant-microbe interactions. Here, we briefly summarize the current understanding of plant molecular pathways involved in plant interactions with beneficial and pathogenic microorganisms. Then, we highlight the progress in applying plant synthetic biology to elucidate the molecular basis of plant responses to microbes, enhance plant disease resistance, engineer synthetic symbiosis, and conduct in situ microbiome engineering. Lastly, we discuss the challenges, opportunities, and future directions for advancing plant-microbe interactions research using the capabilities of plant synthetic biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Tailoring Microbial Fitness Through Computational Steering and CRISPRi-Driven Robustness Regulation

The widespread application of genetically modified microorganisms (GMMs) across diverse sectors underscores the pressing need for robust strategies to mitigate the risks associated with their potential uncontrolled escape. This study merges computational modeling with CRISPR interference (CRISPRi) to refine GMM metabolic robustness. Utilizing ensemble modeling, we achieved high-throughput in silico screening for enzymatic targets susceptible to expression alterations. Translating these insights, we developed functional CRISPRi, boosting fitness control via multiplexed gene knockdown. Our method, enhanced by an insulator-improved gRNA structure and an off-switch circuit controlling a compact Cas12m, resulted in rationally engineered strains with escape frequencies below National Institutes of Health standards. The effectiveness of this approach was confirmed under various conditions, showcasing its ability for secure GMM management. This research underscores the resilience of microbial metabolism, strategically modifying key nodes to halt growth without provoking significant resistance, thereby enabling more reliable and precise GMM control. A record of this paper's transparent peer review process is included in the supplemental information.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Direct Numerical Simulation of hydrogen combustion at auto-ignitive conditions: Ignition, stability and turbulent reaction-front velocity

Direct Numerical Simulations (DNS) are performed to investigate the process of spontaneous ignition of hydrogen flames at laminar, turbulent, adiabatic and non-adiabatic conditions. Mixtures of hydrogen and vitiated air at temperatures representing gas-turbine reheat combustion are considered. Adiabatic spontaneous ignition processes are investigated first, providing a quantitative characterization of stable and unstable flames. Results indicate that, in hydrogen reheat combustion, compressibility effects play a key role in flame stability and that unstable ignition and combustion are consistently encountered for reactant temperatures close to the mixture’s characteristic crossover temperature. Furthermore, it is also found that the characterization of the adiabatic processes is also valid in the presence of non-adiabaticity due to wall heat-loss. Finally, a quantitative characterization of the instantaneous fuel consumption rate within the reaction front is obtained and of its ability, at auto-ignitive conditions, to advance against the approaching turbulent flow of the reactants, for a range of different turbulence intensities, temperatures and pressure levels.

42 ENGINEERING↗
DOE OSTI2025

RLGBS: Reinforcement Learning-Guided Beam Search for process optimization in a paper machine dryer section

Paper drying is responsible for over two-thirds of energy consumption in the U.S. pulp and paper industry, presenting significant potential for energy savings through optimization of process parameters. Current approaches often assume fixed operating conditions, neglecting dynamic ambient and process variations that limit achievable savings and real-world applicability. To this end, we develop a physics-based simulation environment for a paper machine dryer section and propose a reinforcement learning (RL) framework to minimize overall energy consumption by optimizing drying process parameters under diverse operating conditions. To mitigate overdrying and numerical instabilities caused by suboptimal local RL actions, we introduce Reinforcement Learning-Guided Beam Search (RLGBS), which explores multiple action sequences in parallel using beam search. Instead of making step-by-step decisions, RLGBS prioritizes solutions based on cumulative probability, reducing the impact of individual suboptimal actions. Experiments demonstrate that RLGBS achieves consistent energy savings under unseen operating conditions not encountered during training, outperforming conventional RL methods. While validated in drying optimization, this framework is broadly applicable to other RL-based industrial process control problems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Leveraging SETS resilience capabilities for safe-to-fail infrastructure under climate change

As the rehabilitation of infrastructure is outpaced by changes in the profile, frequency, and intensity of extreme weather events, infrastructure’s service disruptions and failures become increasingly likely. Safe-to-fail approaches for infrastructure planning and design improve the capacity of cities to adapt for uncertain climate futures by identifying social, ecological, and technological systems (SETS) capabilities to prepare for potential failure scenarios. In this work, we argue for transforming infrastructure planning and design to effectively utilize safe-to-fail approaches by navigating the opportunities and trade-offs of SETS resilience capabilities. From a technological vantage point, traditional infrastructure planning approaches account for social and ecological domains as external design conditions rather than embedded system characteristics. Safe-to-fail approaches directly challenge the isolation of the technological domain by necessitating a recognition that SETS domains are interconnected and interdependent in infrastructure systems, as such risks and system capabilities for resilience must be managed cohesively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Experimental investigation on heat transfer performance of drain water heat recovery heat exchangers

Water heating accounts for 18 % of residential energy consumption, and a substantial portion of this energy can be recovered through drain water heat recovery systems. Here, this paper investigates the heat transfer performance of drain water heat recovery heat exchangers. Two heat exchanger configurations were studied: vertical (gravity film exchanger) and horizontal. The heat recovery performance of the exchangers was quantified using sensible effectiveness and characteristic curves following the ε-NTU approach. Depending on the flow conditions, heat exchangers with a vertical configuration offered higher heat transfer effectiveness compared with the horizontal configuration under similar operating conditions. Furthermore, the results showed that properly designed and sized heat exchangers can effectively recover considerable amounts of waste heat from drains, depending on flow rates. Additionally, in the vertical heat exchanger configuration, low flow rates result in partially wet conditions, resulting in lower heat transfer effectiveness. Therefore, two distributor configurations are proposed to improve the heat transfer performance of vertically configured heat exchangers at low flow rates. The proposed distributor configurations and data presented in this paper will be useful in the design and development of drain water heat recovery systems in building applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Direct observation of pore formation mechanisms during LPBF additive manufacturing process and high energy density laser welding

Laser powder bed fusion (LPBF) is a 3D printing technology that can print parts with complex geometries that are unachievable by conventional manufacturing technologies. However, pores formed during the printing process impair the mechanical performance of the printed parts, severely hindering their widespread application. Here, we report six pore formation mechanisms that were observed during the LPBF process. Our results in this study reconfirm three pore formation mechanisms - keyhole induced pores, pore formation from feedstock powder and pore formation along the melting boundary during laser melting from vaporization of a volatile substance or an expansion of a tiny trapped gas. We also observe three new pore formation mechanisms: (1) pore trapped by surface fluctuation, (2) pore formation due to depression zone fluctuation when the depression zone is shallow and (3) pore formation from a crack. The results presented here provide direct evidence and insight into pore formation mechanisms during the LPBF process, which may guide the development of pore elimination/mitigation approaches. Since certain laser processing conditions studied here are similar to the situations in high energy density laser welding, the results presented here also have implications for laser welding.

42 ENGINEERING↗
DOE OSTI2020

Impact of the adhesion-force lever-arm “a” on the Rock ‘n’ Roll resuspension model and how to compute it from contact mechanics

The paper proposes a methodology to fill a gap in the context of mechanistic particle resuspension models related to the adhesion and lift force lever arms ( a ): to date, only one experimental observation exists from a centrifuge experiment with a specific particle/surface pair which found r P / a = 100 and assumed that the contact points were symmetric with respect to the particle center. However, the Reeks and Hall’s (2001) model is highly sensitive to this parameter, which therefore cannot be used as-is for different particle diameters or materials. This paper proposes a methodology to compute the adhesion and lift force lever arms ( a A and a L , respectively) based on Atomic Force Microcopy (AFM) surface roughness measurements and contact mechanics arguments. In practice, a spherical, perfectly smooth particle is brought in contact (computationally) with the measured surface roughness and is then rotated by the point of contact to determine the two or more asperities over which the particle would sit on the surface. A method to compute the asperity radius of curvature is devised to compute the asperity deformation and the correct partitioning of the total deformation between the particle and the asperity is presented. For the glass surface under consideration, the asperity deformation was negligible, i.e., it was below the van der Waals radius, which is the limit of applicability of contact mechanics. The Reeks and Hall’s (2001) model is then extended to account for the joint probability of a A and a L . Significant differences in particle resuspended after a day in common atmospheric conditions are found between the approach that uses the lever-arm distributions or their mean values. Also, important differences are found when comparing the lever-arm distribution and r P / a = 100. Hence, one should be cautious of using r P / a = 100 for surface/particle pairs differing from those in the original study. Finally, limits of applicability of the theories used are presented together with considerations regarding how to extend the method to non-spherical particles and different materials.

42 ENGINEERING↗
DOE OSTI2020

The exit-wave power-cepstrum transform for scanning nanobeam electron diffraction: robust strain mapping at subnanometer resolution and subpicometer precision

Scanning nanobeam electron diffraction (NBED) with fast pixelated detectors is a valuable technique for rapid, spatially resolved mapping of lattice structure over a wide range of length scales. However, intensity variations caused by dynamical diffraction and sample mistilts can hinder the measurement of diffracted disk centers as necessary for quantification. Robust data processing techniques are needed to provide accurate and precise measurements for complex samples and non-ideal conditions. Here we present an approach to address these challenges using a transform, called the exit wave power cepstrum (EWPC), inspired by cepstral analysis in audio signal processing. The EWPC transforms NBED patterns into real-space patterns with sharp peaks corresponding to inter-atomic spacings. We describe a simple analytical model for interpretation of these patterns that cleanly decouples lattice information from the intensity variations in NBED patterns caused by tilt and thickness. With tracking the inter-atomic spacing peaks in EWPC patterns, strain mapping is demonstrated for two practical applications: mapping of ferroelectric domains in epitaxially strained PbTiO 3 films and mapping of strain profiles in arbitrarily oriented core-shell Pt-Co nanoparticle fuel-cell catalysts. The EWPC transform enables lattice structure measurement at sub-pm precision and sub-nm resolution that is robust to small sample mistilts and random orientations.

42 ENGINEERING↗
DOE OSTI2025

A two-dimensional numerical study of the magneto-Rayleigh–Taylor instability with FLASH: Application to the staged Z-pinch concept

Magnetically driven implosions involving a liner collapsing onto a target are inherently vulnerable to the magneto-Rayleigh–Taylor instability (MRTI). Among the various approaches proposed to achieve fusion conditions within the target, the staged Z-pinch (SZP) concept employs a high-Z liner, the advantages of which remain an active area of investigation. Consequently, ongoing design optimization efforts are essential, while critical physical processes such as magnetic field (B-field) diffusion and radiation transport increase the complexity of required simulations. In this study, we utilize the new capabilities of the FLASH code to simulate in 2D, for the first time using this code, staged Z-pinch configurations (designated SZP1 and SZP1*), focusing on their stability. First, a comparison of simulation results with theoretical predictions of MRTI growth provide new insights into the often-overlooked influences of high mesh resolution and initial perturbation seeding on instability dynamics. These findings then lead to a discussion on potential improvements for future SZP experiments. These include the use of an axial B-field stabilization and the optimization of radiation transport processes during the implosion. The results presented herein establish the framework for simulating multi-dimensional Z pinches using the FLASH code and pave the way for the development of innovative experimental configurations leveraging its advanced simulation capabilities.

Adaptive mesh refinement↗
DOE OSTI2026

Enabling accurate chemical modeling of shocked energetic materials using a machine learning interatomic potential

Understanding the complex chemistry of organic materials under dynamic compression is important for many applications, but it is challenging due to the large number of reactions occurring at various time scales. Here, in this study, we develop a machine learning potential based on Chebyshev polynomials to study the insensitive energetic material 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) under detonation. We discuss a strategy for constructing diverse training data needed to capture the complex chemistry of TATB. Our potential demonstrates strong transferability across a wide range of thermodynamic conditions and other explosives, enabling accurate and reliable chemical modeling of organic materials under extreme conditions. The efficiency of our approach allows for simulations over several nanoseconds and for large system sizes, providing detailed insights into the chemistry of shocked TATB. The model accurately reproduces experimental Hugoniot equation of state data, and our simulations reveal the rapid formation of nitrogen-rich carbon clusters following shock. The methods and datasets developed here offer a robust framework for accurate chemical modeling of other shocked organic energetic materials.

Chemistry↗
DOE OSTI2026

Deployment of Traditional and Hybrid Machine Learning for Critical Heat Flux Prediction in the CTF Thermal-Hydraulics Code

Critical heat flux (CHF) marks the transition from nucleate to film boiling, where heat transfer to the working fluid can rapidly deteriorate. Accurate CHF prediction is essential for efficiency, safety, and preventing equipment damage, particularly in nuclear reactors. Although widely used, empirical correlations frequently exhibit discrepancies when compared to experimental data, limiting their reliability in diverse operational conditions. Traditional machine learning (ML) approaches have demonstrated potential for CHF prediction but often suffer from limited interpretability, data scarcity, and insufficient knowledge of physical principles. Hybrid model approaches, which combine data-driven ML with base models, mitigate these concerns by incorporating prior knowledge of the domain. This study integrates an externally trained purely data-driven ML model and two hybrid models (using the Biasi and Bowring CHF correlations) within the CTF subchannel code via a custom Fortran framework. Performance was evaluated using two validation cases: a subset of the Nuclear Regulatory Commission (NRC) CHF database and the Bennett dryout experiments. In both cases, the hybrid models demonstrated significantly lower error metrics compared to conventional empirical correlations, with the best models often reducing relative error by about 5 percentage points. The pure ML model achieved comparable accuracy, outperforming the hybrid Biasi model in the NRC test case (3.3% versus 5.5% relative error) but exhibiting slightly higher error against the hybrid Bowring model in the Bennett test case (7.7% versus 6.1%). Trend analysis of error parity indicated that ML-based models reduced the tendency for CHF overprediction, improving overall accuracy. These results demonstrate that ML-based CHF models can be effectively integrated into subchannel codes and could potentially increase performance compared to conventional methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

A fast, matrix-based method to perform omnidirectional pressure integration

Abstract Experimentally-measured pressure fields play an important role in understanding many fluid dynamics problems. Unfortunately, pressure fields are difficult to measure directly with non-invasive, spatially resolved diagnostics, and calculations of pressure from velocity have proven sensitive to error in the data. Omnidirectional line integration methods are usually more accurate and robust to these effects as compared to implicit Poisson equations, but have seen slower uptake due to the higher computational and memory costs, particularly in 3D domains. This paper demonstrates how omnidirectional line integration approaches can be converted to a matrix inversion problem. This novel formulation uses an iterative approach so that the boundary conditions are updated each step, preserving the convergence behavior of omnidirectional schemes while also keeping the computational efficiency of Poisson solvers. This method is implemented in Matlab and also as a GPU-accelerated code in CUDA-C++. The behavior of the new method is demonstrated on 2D and 3D synthetic and experimental data. Three-dimensional grid sizes of up to 125 million grid points are tractable with this method, opening exciting opportunities to perform volumetric pressure field estimation from 3D PIV measurements.

42 ENGINEERING↗
DOE OSTI2024

Application of functional genomics for domestication of novel non-model microbes

Abstract With the expansion of domesticated microbes producing biomaterials and chemicals to support a growing circular bioeconomy, the variety of waste and sustainable substrates that can support microbial growth and production will also continue to expand. The diversity of these microbes also requires a range of compatible genetic tools to engineer improved robustness and economic viability. As we still do not fully understand the function of many genes in even highly studied model microbes, engineering improved microbial performance requires introducing genome-scale genetic modifications followed by screening or selecting mutants that enhance growth under prohibitive conditions encountered during production. These approaches include adaptive laboratory evolution, random or directed mutagenesis, transposon-mediated gene disruption, or CRISPR interference (CRISPRi). Although any of these approaches may be applicable for identifying engineering targets, here we focus on using CRISPRi to reduce the time required to engineer more robust microbes for industrial applications. One-Sentence Summary The development of genome scale CRISPR-based libraries in new microbes enables discovery of genetic factors linked to desired traits for engineering more robust microbial systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Dual-cone variational calculation of the two-electron reduced density matrix

The computation of strongly correlated quantum systems is challenging because of its potentially exponential scaling in the number of electron configurations. Variational calculation of the two-electron reduced density matrix (2-RDM) without the many-electron wave function exploits the pairwise nature of the electronic Coulomb interaction to compute a lower bound on the ground-state energy with polynomial computational scaling. Recently, a dual-cone formulation of the variational 2-RDM calculation was shown to generate the ground-state energy, albeit not the 2-RDM, at a substantially reduced computational cost, especially for higher $\textit{N}$-representability conditions such as the T2 constraint. Here we generalize the dual-cone variational 2-RDM method to compute not only the ground-state energy but also the 2-RDM. The central result is that we can compute the 2-RDM from a generalization of the Hellmann-Feynman theorem. Specifically, we prove that in the Lagrangian formulation of the dual-cone optimization the 2-RDM is the Lagrange multiplier. Further, we apply the method to computing the energies and properties of strongly correlated electrons—including atomic charges, electron densities, dipole moments, and orbital occupations—in an illustrative hydrogen chain and the nitrogen-fixation catalyst FeMoco. The dual variational computation of the 2-RDM with T2 or higher N -representability conditions provides a polynomially scaling approach to strongly correlated molecules and materials with significant applications in atomic and molecular and condensed-matter chemistry and physics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Role of non-Gaussian quantum fluctuations in neutrino entanglement

The flavor evolution of neutrinos in environments with large neutrino number densities is an open problem at the nexus of astrophysics and neutrino flavor physics. Among the many unanswered questions pertaining to this problem, it remains to be determined whether neutrino-neutrino coherent scattering can give rise to nontrivial quantum entanglement among neutrinos, and whether this can affect the flavor evolution in a meaningful way. To gain further insight into this question, here we study a simple system of two interacting neutrino beams and obtain the exact phase space explored by this system using the Husimi quasiprobability distribution. We observe that the entanglement induced by the coupling leads to strong delocalization in phase-space with largely non-Gaussian quantum fluctuations. The link between the neutrino entanglement and quantum fluctuations is illustrated using the one- and two-neutrino entropy. In addition, we propose an approximate phase-space method to describe the interacting neutrinos problem, where the exact evolution is replaced by a set of independent mean-field evolutions with a statistical sampling of the initial conditions. The phase-space approach provides a simple and accurate method to describe the gross features of the neutrino entanglement problem. Applications are shown using time-independent and time-dependent Hamiltonians in the nonadiabatic regime.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗