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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The Early Years and Evolution of the DOE Computational Science Graduate Fellowship Program

The U.S. Department of Energy Computational Graduate Fellowship Program, celebrating 30 years of existence in 2021, is one of the most successful graduate fellowships in the world as well as one of the longest running programs in the U.S. Department of Energy. Furthermore, this article discusses the conception, early years and evolution of the fellowship over the past thirty years.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Modeling Analysis of Ball-Milling Process for Battery-Electrode Synthesis

The mechanical alloying process is a promising method for synthesizing electrode materials for batteries owing to its benefits such as the ability to produce nanostructured, high-performing electrode alloys, no adverse effects on the solid electrolyte for solid-state batteries, stable production of thick electrodes, simple processing steps, and low processing costs. It is gaining intensive attention in the battery industry as one of the best methods to replace the conventional wet-slurry-solvent method, and its application is rapidly increasing these days. However, the operation is currently conducted purely based on trial-and-error methods without fully utilizing the features of its functions. Here, this may be attributed to a lack of understanding of the effect of operating parameters on the alloying process and final products. Surprisingly, there is a scarcity of the literature conducting fundamental research to comprehend the underlying physics of the entire mechanical alloying process, resulting in a significant knowledge gap. To address this knowledge gap, extensive research was conducted. The existing literature on mechanical alloying was reviewed to comprehend the current state of understanding and to discuss the direction for future research. Mathematical expressions were developed to create physics-based models capable of capturing the entire mechanical alloying process, including milling kinetics and defect-enhanced phase evolution. These methods were then applied to investigate the impact of operating parameters such as milling frequency, initial mole ratio of the alloyed materials, density of grinding balls, and energy required for the powders to become amorphous (i.e., the amorphization energy threshold). This research aimed not only to comprehend the direct effects of these operating parameters but also to unveil the physics underlying the ball-milling process. The results of our study can serve as crucial information for the battery industry in designing or operating the ball-milling process.

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

Molecular mechanisms underlying host-induced gene silencing

Abstract Host-induced gene silencing (HIGS) refers to the silencing of genes in pathogens and pests by expressing homologous double-stranded RNAs (dsRNA) or artificial microRNAs (amiRNAs) in the host plant. The discovery of such trans-kingdom RNA silencing has enabled the development of RNA interference-based approaches for controlling diverse crop pathogens and pests. Although HIGS is a promising strategy, the mechanisms by which these regulatory RNAs translocate from plants to pathogens, and how they induce gene silencing in pathogens, are poorly understood. This lack of understanding has led to large variability in the efficacy of various HIGS treatments. This variability is likely due to multiple factors, such as the ability of the target pathogen or pest to take up and/or process RNA from the host, the specific genes and target sequences selected in the pathogen or pest for silencing, and where, when, and how the dsRNAs or amiRNAs are produced and translocated. In this review, we summarize what is currently known about the molecular mechanisms underlying HIGS, identify key unanswered questions, and explore strategies for improving the efficacy and reproducibility of HIGS treatments in the control of crop diseases.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Plants use molecular mechanisms mediated by biomolecular condensates to integrate environmental cues with development

This review highlights recent literature on biomolecular condensates in plant development and discusses challenges for fully dissecting their functional roles. Plant developmental biology has been inundated with descriptive examples of biomolecular condensate formation, but it is only recently that mechanistic understanding has been forthcoming. Here, we discuss recent examples of potential roles biomolecular condensates play at different stages of the plant life cycle. We group these examples based on putative molecular functions, including sequestering interacting components, enhancing dwell time, and interacting with cytoplasmic biophysical properties in response to environmental change. We explore how these mechanisms could modulate plant development in response to environmental inputs and discuss challenges and opportunities for further research into deciphering molecular mechanisms to better understand the diverse roles that biomolecular condensates exert on life.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A small molecule antagonizes jasmonic acid perception and auxin responses in vascular and nonvascular plants

The phytohormone jasmonoyl-L-isoleucine (JA-Ile) regulates many stress responses and developmental processes in plants. A co-receptor complex formed by the F-box protein Coronatine Insensitive 1 (COI1) and a Jasmonate (JA) ZIM-domain (JAZ) repressor perceives the hormone. JA-Ile antagonists are invaluable tools for exploring the role of JA-Ile in specific tissues and developmental stages, and for identifying regulatory processes of the signaling pathway. Using two complementary chemical screens, we identified three compounds that exhibit a robust inhibitory effect on both the hormone-mediated COI–JAZ interaction and degradation of JAZ1 and JAZ9 in vivo. One molecule, J4, also restrains specific JA-induced physiological responses in different angiosperm plants, including JA-mediated gene expression, growth inhibition, chlorophyll degradation, and anthocyanin accumulation. Interaction experiments with purified proteins indicate that J4 directly interferes with the formation of the Arabidopsis (Arabidopsis thaliana) COI1–JAZ complex otherwise induced by JA. The antagonistic effect of J4 on COI1–JAZ also occurs in the liverwort Marchantia polymorpha, suggesting the mode of action is conserved in land plants. Besides JA signaling, J4 works as an antagonist of the closely related auxin signaling pathway, preventing Transport Inhibitor Response1/Aux–indole-3-acetic acid interaction and auxin responses in planta, including hormone-mediated degradation of an auxin repressor, gene expression, and gravitropic response. However, J4 does not affect other hormonal pathways. Altogether, our results show that this dual antagonist competes with JA-Ile and auxin, preventing the formation of phylogenetically related receptor complexes. J4 may be a useful tool to dissect both the JA-Ile and auxin pathways in particular tissues and developmental stages since it reversibly inhibits these pathways. One-sentence summary: A chemical screen identified a molecule that antagonizes jasmonate perception by directly interfering with receptor complex formation in phylogenetically distant vascular and nonvascular plants.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1992

Lightning-Sensor Data Help In Understanding Thunderstorms

NASA technical memorandum discusses research on use of data from network of ground-based magnetic direction-finding ground-strike lightning sensors to diagnose and predict occurrence and evolution of thunderstorms. Purposes of study to explore applicability and limitations of extrapolation techniques used to generate forecasts from data; to examine physically-based, nonlinear mathematical models for applicability to lightning-forecast problem; and to determine valid extrapolation ranges of such models for various weather scenarios.

Goodman, Steven J.↗
NASA NTRS2011

Modeling and Simulation for Multi-Missions Space Exploration Vehicle

Asteroids and Near-Earth Objects [NEOs] are of great interest for future space missions. The Multi-Mission Space Exploration Vehicle [MMSEV] is being considered for future Near Earth Object missions and requires detailed planning and study of its Guidance, Navigation, and Control [GNC]. A possible mission of the MMSEV to a NEO would be to navigate the spacecraft to a stationary orbit with respect to the rotating asteroid and proceed to anchor into the surface of the asteroid with robotic arms. The Dynamics and Real-Time Simulation [DARTS] laboratory develops reusable models and simulations for the design and analysis of missions. In this paper, the development of guidance and anchoring models are presented together with their role in achieving mission objectives and relationships to other parts of the simulation. One important aspect of guidance is in developing methods to represent the evolution of kinematic frames related to the tasks to be achieved by the spacecraft and its robot arms. In this paper, we compare various types of mathematical interpolation methods for position and quaternion frames. Subsequent work will be on analyzing the spacecraft guidance system with different movements of the arms. With the analyzed data, the guidance system can be adjusted to minimize the errors in performing precision maneuvers.

Multi-Mission Space Exploration Vehicle [MMSEV]↗
DOE OSTI2024

A multiphysics coupling framework for exascale simulation of fracture evolution in subsurface energy applications

Predicting the evolution of fractured media is challenging due to coupled thermal, hydrological, chemical and mechanical processes that occur over a broad range of spatial scales, from the microscopic pore scale to field scale. We present a software framework and scientific workflow that couples the pore scale flow and reactive transport simulator Chombo-Crunch with the field scale geomechanics solver in GEOS to simulate fracture evolution in subsurface fluid-rock systems. This new multiphysics coupling capability comprises several novel features. An HDF5 data schema for coupling fracture positions between the two codes is employed and leverages the coarse resolution of the GEOS mechanics solver which limits the size of data coupled, and is, thus, not taxed by data resulting from the high resolution pore scale Chombo-Crunch solver. The coupling framework requires tracking of both before and after coarse nodal positions in GEOS as well as the resolved embedded boundary in Chombo-Crunch. We accomplished this by developing an approach to geometry generation that tracks the fracture interface between the two different methodologies. The GEOS quadrilateral mesh is converted to triangles which are organized into bins and an accessible tree structure; the nodes are then mapped to the Chombo representation using a continuous signed distance function that determines locations inside, on and outside of the fracture boundary. The GEOS positions are retained in memory on the Chombo-Crunch side of the coupling. The time stepping cadence for coupled multiphysics processes of flow, transport, reactions and mechanics is stable and demonstrates temporal reach to experimental time scales. The approach is validated by demonstration of 9 days of simulated time of a core flood experiment with fracture aperture evolution due to invasion of carbonated brine in wellbore-cement and sandstone. We also demonstrate usage of exascale computing resources by simulating a high resolution version of the validation problem on OLCF Frontier.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1987

Task-level robot programming: Integral part of evolution from teleoperation to autonomy

An explanation is presented of task-level robot programming and of how it differs from the usual interpretation of task planning for robotics. Most importantly, it is argued that the physical and mathematical basis of task-level robot programming provides inherently greater reliability than efforts to apply better known concepts from artificial intelligence (AI) to autonomous robotics. Finally, an architecture is presented that allows the integration of task-level robot programming within an evolutionary, redundant, and multi-modal framework that spans teleoperation to autonomy.

Reynolds, James C.↗
NASA NTRS1981

Zero-gravity aerosol behavior

The feasibility and scientific benefits of a zero gravity aerosol study in an orbiting laboratory were examined. A macroscopic model was devised to deal with the simultaneous effects of diffusion and coagulation of particles in the confined aerosol. An analytical solution was found by treating the particle coagulation and diffusion constants as ensemble parameters and employing a transformation of variables. The solution was used to carry out simulated zero gravity aerosol decay experiments in a compact cylindrical chamber. The results demonstrate that the limitations of physical space and time imposed by the orbital situation are not prohibitive in terms of observing the history of an aerosol confined under zero gravity conditions. While the absence of convective effects would be a definite benefit for the experiment, the mathematical complexity of the problem is not greatly reduced when the gravitational term drops out of the equation. Since the model does not deal directly with the evolution of the particle size distribution, it may be desirable to develop more detailed models before undertaking an orbital experiment.

Edwards, H. W.↗
DOE OSTI2021

Defining Computational Emissivity Uncertainty Over Large Temperature Scales Due to Surface Evolution

There is a dearth in the literature on how to capture the uncertainty generated by material surface evolution in thermal modeling. This leads to inadequate or highly variable uncertainty representations for material properties, specifically emissivity when minimal information is available. Inaccurate understandings of prediction uncertainties may lead decision makers to incorrect conclusions, so best engineering practices should be developed for this domain. In order to mitigate the aforementioned issues, this study explores different strategies to better capture the thermal uncertainty response of engineered systems exposed to fire environments via defensible emissivity uncertainty characterizations that can be easily adapted to a variety of use cases. Here, two unique formulations (one physics-informed and one mathematically based) are presented. The formulations and methodologies presented herein are not exhaustive but more so are a starting point and give the reader a basis for how to customize their uncertainty definitions for differing fire scenarios and materials. Lastly, the impact of using this approach versus other commonly used strategies and the usefulness of adding rigor to material surface evolution uncertainty is demonstrated.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

An investigation of a mathematical model of an optically pumped Ti(3+):Al2O3 laser system

During the last several years, solid state lasers were developed that have the potential for meeting rigorous performance requirements for space-based remote sensing of the atmosphere. In order to design a stable and efficient laser and to understand the effect on laser output of changes in the physical and design parameters, an understanding of the development of the dynamical processes of the laser is necessary. Typically, the dynamical processes in a laser system are investigated via rate equations describing the evolution of the occupancy in the electronic levels and of the photon density in the laser cavity. There are two approaches to this type of study. Most often, for the sake of simplicity, the spatial variations of the dynamic variables in the laser system are disregarded and the mathematical model consists of a system of first order nonlinear ordinary differential equations (ODE). The second approach is to take into account both spatial and temporal variations in the dynamic variables in the laser cavity. The resulting model consists of a first order semilinear system of partial differential equations (PDE). The model which was studied was studied was generic in the sense that it was a four-level laser system, but the parameters used in the numerical study were specific to Titanium-doped sapphire. For simplicity, a constant, spatially uniform pumping scheme was considered. In addition, a simplification of the model was made so that it treats a single lasing wavelength with a narrow bandwidth. The purpose was to investigate both versions of the mathematical model and to determine whether the numerical solutions are similar both qualitatively and quantitatively. The systems of ordinary differential equations were solved numerically using a Runge-Kutta-Fehlberg algorithm which was very efficient for typical values of the physical parameters. A numerical scheme, based on the Modified Euler method, for computing solutions to the system of partial differential equations was developed and implemented. The PDE model was solved numerically at the expense of greatly increased computer time.

Roberts, Lila F.↗
NASA NTRS2019

Exobiology Through the Lens of Constraint-Based Metabolic Modeling

A focus of many astrobiology questions resides on the interplay between biochemistry and geochemistry. This biotic-abiotic interface is intimately connected to habitability. Computational systems biology provides a paradigm to characterize this interface. Here we describe the application of constraint-based metabolic modeling to characterize photosynthetic microorganisms inhabiting a modern analog to the Earth’s primordial ocean. Biochemistry is captured in a metabolic network that is converted to a mathematical model. Geochemistry results in constraints that limit the capabilities of the metabolic network. The combination of the two within a single framework can elucidate the co-evolution of life and the environment.

Broddrick, Jared T.↗
DOE OSTI2021

Rodeo Algorithm for Quantum Computing

We present a stochastic quantum computing algorithm that can prepare any eigenvector of a quantum Hamiltonian within a selected energy interval $\ [E-\epsilon, E+\epsilon]$. In order to reduce the spectral weight of all other eigenvectors by a suppression factor δ, the required computational effort scales as $\ O[|\log \delta|/(p \epsilon)]$, where p s the squared overlap of the initial state with the target eigenvector. The method, which we call the rodeo algorithm, uses auxiliary qubits to control the time evolution of the Hamiltonian minus some tunable parameter E . In this manner, we converge to the target eigenvector with exponential accuracy in the number of measurements. In addition to preparing eigenvectors, the method can also compute the full spectrum of the Hamiltonian. We illustrate the performance with several examples. For energy eigenvalue determination with error $\epsilon$, the computational scaling is $\ O[(\log \epsilon)^2/(p \epsilon)]$. For eigenstate preparation, the computational scaling is $\ O(\log \Delta/p)$, where $\Delta$ is the magnitude of the orthogonal component of the residual vector. The speed for eigenstate preparation is exponentially faster than that for phase estimation or adiabatic evolution.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1992

Domain decomposition: A bridge between nature and parallel computers

Domain decomposition is an intuitive organizing principle for a partial differential equation (PDE) computation, both physically and architecturally. However, its significance extends beyond the readily apparent issues of geometry and discretization, on one hand, and of modular software and distributed hardware, on the other. Engineering and computer science aspects are bridged by an old but recently enriched mathematical theory that offers the subject not only unity, but also tools for analysis and generalization. Domain decomposition induces function-space and operator decompositions with valuable properties. Function-space bases and operator splittings that are not derived from domain decompositions generally lack one or more of these properties. The evolution of domain decomposition methods for elliptically dominated problems has linked two major algorithmic developments of the last 15 years: multilevel and Krylov methods. Domain decomposition methods may be considered descendants of both classes with an inheritance from each: they are nearly optimal and at the same time efficiently parallelizable. Many computationally driven application areas are ripe for these developments. A progression is made from a mathematically informal motivation for domain decomposition methods to a specific focus on fluid dynamics applications. To be introductory rather than comprehensive, simple examples are provided while convergence proofs and algorithmic details are left to the original references; however, an attempt is made to convey their most salient features, especially where this leads to algorithmic insight.

Keyes, David E.↗
DOE OSTI2024

Deep operator network surrogate for phase-field modeling of metal grain growth during solidification

A deep operator network (DeepONet) has been constructed that generates accurate representations of phase-field model simulations for evolving two dimensional metal grain morphology growing from melt. These representations serve as lower resolution, computationally efficient stand-ins for quick parameter space exploration of solutions to the the Allen-Cahn equations that dictate the phase-field model simulations. The experimental target for the phase-field model is a uranium casting system cooling a 434 g uranium charge from a maximum temperature of 1400° C at an average rate of 30° C / min , traversing the crystallographic phases of the pure metal. Experimental parameters inform the phase-field model, whose higher resolution computational model solutions are used to train the DeepONet in a given parameter space with the aim of developing a faster, more efficient method for predicting the solidifying metal's microstructure at different potential experimental values. The final DeepONet generates high accuracy, lower resolution predictions with cumulative relative approximation error over all timesteps of less than 0.5%, while ensuring solutions remain within physically feasible ranges. Further, these relative error values are comparable with other state-of-the-art DeepONet models for microstructure evolution, while significantly reducing the amount of training data required. Training a convolutional neural network simultaneously with the DeepONet, enforcing realistic values at the complex metal grain boundaries, and mathematically encoding boundary conditions into the structure of the DeepONet improved prediction accuracy and computational efficiency over a standard DeepONet model.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Pure quantum extension of the semiclassical Boltzmann-Uehling-Uhlenbeck equation

The Boltzmann equation is the traditional framework in which one extends the time-dependent mean field classical description of a many-body system to include the effect of particle-particle collisions in an approximate manner. A semiclassical extension of this approach to quantum many-body systems was suggested by Uehling and Uhlenbeck in 1933 for both Fermi and Bose statistics, and many further developments of this approach are known as the Boltzmann-Uehling-Uhlenbeck (BUU) equations. In this paper I introduce a pure quantum version of the BUU type of equations, which is mathematically equivalent to a generalized time-dependent density functional theory extended to superfluid systems. As expected, during nonequilibrium processes the quantum Boltzmann one-body entropy increases during evolution.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗