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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A protein scaffold enables hydrogen evolution for a Ni-bisdiphosphine complex

We find that an artificial metalloenzyme acting as a functional biomimic of hydrogenase enzymes was activated by assembly via covalent attachment of the molecular complex, [Ni(PN glycine P) 2 ] 2- , within a structured protein scaffold. Electrocatalytic H2 production was observed from pH 3.0 to 10.0 for the artificial enzyme, while no electrocatalytic activity was observed for similar [Ni(PNP) 2 ] 2+ systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

In situ microstructural evolution in face-centered and body-centered cubic complex concentrated solid-solution alloys under heavy ion irradiation

This study characterizes the microstructural evolution of single-phase complex concentrated solid-solution alloy (CSA) compositions under heavy ion irradiation with the goal of evaluating mechanisms for CSA radiation tolerance in advanced fission systems. Three such alloys, Cr 18 Fe 27 Mn 27 Ni 28 , Cr 15 Fe 35 Mn 15 Ni 35 , and equimolar NbTaTiV, along with reference materials (pure Ni and E90 for the Cr-FeMnNi family and pure V for NbTaTiV) were irradiated at 50 K and 773 K with 1 MeV Kr ++ ions to various levels of displacements per atom (dpa) using in-situ transmission electron microscopy. Cryogenic irradiation resulted in small defect clusters and faulted dislocation loops as large as 12 nm in face-centered cubic (FCC) CSAs. With thermal diffusion suppressed at cryogenic temperatures, defect densities were lower in all CSAs than in their less compositionally complex reference materials indicating that point defect production is reduced during the displacement cascade stage. High temperature irradiation of the two FCC CSA resulted in the formation of interstitial dislocation loops which by 2 dpa grew to an average size of 27 nm in Cr 18 Fe 27 Mn 27 Ni 28 and 10 nm in Cr 15 Fe 35 Mn 15 Ni 35 . This difference in loop growth kinetics was attributed to the difference in Mn-content due to its effect on the nucleation rate by increasing vacancy mobility or reducing the stacking-fault energy.

36 MATERIALS SCIENCE↗
NASA NTRS2020

Analyzing Changes in the Complexity of Climate in the Last Four Decades Using MERRA-2 Radiation Data

The energy balance of the Earth is controlled by the shortwave and longwave radiation emitted to space. Changes in the thermodynamic state of the system over time afect climate and are noticeable when viewing the system as a whole. In this paper, we study the changes in the complexity of climate in the last four decades using data from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). First, we study the complexity of the shortwave and longwave radiation felds independently using Approximate Entropy and Sample Entropy, observing that the rate of complexity change is faster for shortwave radiation. Then, we study the causality of those changes using Transfer Entropy to capture the non-linear dynamics of climate, showing that the changes are mainly driven by the variations in shortwave radiation. The observed behavior of climatic complexity could be explained by the changes in cloud amount, and we research that possibility by investigating its evolution from a complexity perspective using data from the International Satellite Cloud Climatology Project (ISCCP).

Delgado-Bonal, Alfonso↗
DOE OSTI2021

Multi-scale Coupled Processes Modeling of Fractures as Porous, Interfacial and Granular Systems from Rock Images with the Numerical Manifold Method

Abstract The greatest challenges of rigorously modeling coupled hydro-mechanical processes in fractured rocks at different scales are associated with computational geometry. In addition, selections of continuous or discontinuous models, physical laws, and coupling priorities at different scales based on different geometric features determine the applicability of a numerical model for a certain type of problem. In this study, we present our multi-scale modeling capabilities that have been developed based on the numerical manifold method for analyzing coupled hydro-mechanical processes in fractured rocks. Based on their geometric features, the fractures are modeled as continua—finite-thickness porous zones, and discontinua—discontinuous interfaces and microscale asperities and granular systems. Different governing equations, physical laws, coupling priorities, and approaches for addressing fracture intersections and shearing are then applied to describe these. We applied these models to simulate coupled processes in fractured rocks using realistic geometry obtained from rock images at different scales. We first calculated shearing of a single fracture with different models and demonstrated the impacts of asperities on shearing. We then applied the continuous and discontinuous models to simulate a network of rough fractures, demonstrating that contact dynamics contribute significantly to the geometric, multi-physical evolution of systems where rough fractures are not mineral filled. For a discrete fracture network, our coupled processes modeling demonstrates that shearing of the discrete fractures can have a major impact on stress and pore pressure distribution. Lastly, we applied the discontinuous granular model to simulate evolution of a complex granular system with a deformation band, demonstrating that the deformation band can dominate contact dynamics, the structural and the stress evolution of the granular system.

58 GEOSCIENCES↗
DOE OSTI2023

Evolution of artificial intelligence for application in contemporary materials science

Abstract Contemporary materials science has seen an increasing application of various artificial intelligence techniques in an attempt to accelerate the materials discovery process using forward modeling for predictive analysis and inverse modeling for optimization and design. Over the last decade or so, the increasing availability of computational power and large materials datasets has led to a continuous evolution in the complexity of the techniques used to advance the frontier. In this Review, we provide a high-level overview of the evolution of artificial intelligence in contemporary materials science for the task of materials property prediction in forward modeling. Each stage of evolution is accompanied by an outline of some of the commonly used methodologies and applications. We conclude the work by providing potential future ideas for further development of artificial intelligence in materials science to facilitate the discovery, design, and deployment workflow. Graphical abstract

Materials Science↗
NASA NTRS2013

The Evolution of Software and Its Impact on Complex System Design in Robotic Spacecraft Embedded Systems

The growth in computer hardware performance, coupled with reduced energy requirements, has led to a rapid expansion of the resources available to software systems, driving them towards greater logical abstraction, flexibility, and complexity. This shift in focus from compacting functionality into a limited field towards developing layered, multi-state architectures in a grand field has both driven and been driven by the history of embedded processor design in the robotic spacecraft industry.The combinatorial growth of interprocess conditions is accompanied by benefits (concurrent development, situational autonomy, and evolution of goals) and drawbacks (late integration, non-deterministic interactions, and multifaceted anomalies) in achieving mission success, as illustrated by the case of the Mars Reconnaissance Orbiter. Approaches to optimizing the benefits while mitigating the drawbacks have taken the form of the formalization of requirements, modular design practices, extensive system simulation, and spacecraft data trend analysis. The growth of hardware capability and software complexity can be expected to continue, with future directions including stackable commodity subsystems, computer-generated algorithms, runtime reconfigurable processors, and greater autonomy.

software↗
NASA NTRSDate not supplied

Bayesian Model Selection for Reducing Bloat and Overfitting in Genetic Programming for Symbolic Regression

When performing symbolic regression using genetic programming, overfitting and bloat can negatively impact generalizability and interpretability of the resulting equations as well as increase computation times. A Bayesian fitness metric is introduced and its impact on bloat and overfitting during population evolution is studied and compared to common alternatives in the literature. The proposed approach was found to be more robust to noise and data sparsity in numerical experiments, guiding evolution to a level of complexity appropriate to the dataset. Further evolution of the population resulted not in overfitting or bloat, but rather in slight simplifications in model form. The ability to identify an equation of complexity appropriate to the scale of noise in the training data was also demonstrated. In general, the Bayesian model selection algorithm was shown to be an effective means of regularization which resulted in less bloat and overfitting when any amount of noise was present in the training data.

G F Bomarito↗
NASA NTRSDate not supplied

Bayesian Model Selection for Reducing Bloat and Overfitting in Genetic Programming for Symbolic Regression

When performing symbolic regression using genetic programming, overfitting and bloat can negatively impact generalizability and interpretability of the resulting equations as well as increase computation times. A Bayesian fitness metric is introduced and its impact on bloat and overfitting during population evolution is studied and compared to common alternatives in the literature. The proposed approach was found to be more robust to noise and data sparsity in numerical experiments, guiding evolution to a level of complexity appropriate to the dataset. Further evolution of the population resulted not in overfitting or bloat, but rather in slight simplifications in model form. The ability to identify an equation of complexity appropriate to the scale of noise in the training data was also demonstrated. In general, the Bayesian model selection algorithm was shown to be an effective means of regularization which resulted in less bloat and overfitting when any amount of noise was present in the training data.

Uncertainty quantification↗
NASA NTRS1990

Volume loss and defluidization history of Barbados

A history of pore volume loss and defluidization of accreted sediments of the basal complex of Barbados island is presented. The structural evolution of the basal complex and evidence for past and present volumetric changes are addressed by examining varied phenomena, with emphasis on porosity-depth data. The empirical findings are used to consider questions such as the position-age profile of pore volume loss relative to other deformation components in the growing prism and the type of loading that principally caused pore volume loss.

Speed, R.↗
DOE OSTI2023

Epitaxial Design of Complex Nickelates as Electrocatalysts for the Oxygen Evolution Reaction

Abstract The oxygen evolution reaction (OER) is a crucial process in electrochemical water splitting, a promising technology to renewably yield hydrogen gas from water. Designing and developing earth‐abundant, efficient, and stable OER electrocatalysts to replace the most widely used but scarce RuO 2 and IrO 2 are thus of critical interest. Recently, ABO 3 ‐structured perovskite oxides, especially rare‐earth nickelates, are extensively studied for their potential use as OER electrocatalysts. In particular, the epitaxial synthesis of complex oxide thin films allows flexible and precise control over the materials so that their structure–stability–property relationships can be established. Using nickelate thin films as model systems, this review illustrates how epitaxial design allows researchers to test different hypotheses and proposed descriptors, as well as formulate new design principles. Following a brief introduction to the background of OER mechanisms, proposed activity descriptors, and synthesis methods, various epitaxial design strategies are surveyed including strain tuning, composition control, surface termination/orientation selection, defect engineering, and interface design. These have led to precise control over the atomic structures and electronic properties of nickelates which in turn determine their electrochemical performance. Finally, the remaining challenges and perspectives toward a deeper understanding and use of complex oxides as OER catalysts are discussed.

Choi, Min‐Ju↗
DOE OSTI2020

Toward fusion plasma scenario planning for NSTX-U using machine-learning-accelerated models

One of the most promising devices for realizing power production through nuclear fusion is the tokamak. To maximize performance, it is preferable that tokamak reactors achieve advanced operating scenarios characterized by good plasma confinement, improved magnetohydrodynamic (MHD) stability, and a largely non-inductively driven plasma current. Such scenarios could enable steady-state reactor operation with high \emph{fusion gain} --- the ratio of produced fusion power to the external power provided through the plasma boundary. Precise and robust control of the evolution of the plasma boundary shape as well as the spatial distribution of the plasma current, density, temperature, and rotation will be essential to achieving and maintaining such scenarios. The complexity of the evolution of tokamak plasmas, arising due to nonlinearities and coupling between various parameters, motivates the use of model-based control algorithms that can account for the system dynamics. In this work, a learning-based accelerated model trained on data from the National Spherical Torus Experiment Upgrade (NSTX-U) is employed to develop planning and control strategies for regulating the density and temperature profile evolution around desired trajectories. The proposed model combines empirical scaling laws developed across multiple devices with neural networks trained on empirical data from NSTX-U and a database of first-principles-based computationally intensive simulations. The reduced execution time of the accelerated model will enable practical application of optimization algorithms and reinforcement learning approaches for scenario planning and control development. An initial demonstration of applying optimization approaches to the learning-based model is presented, including a strategy for mitigating the effect of leaving the finite validity range of the accelerated model. The approach shows promise for actuator planning between experiments and in real-time.

machine learning↗
NASA NTRS1998

Satellite-Based Investigations of the Transition from an Oceanic to Continental Transform Margin

Detailed characterization of neotectonics evolution of the Valle de San Felipe and Arroyo Grande regions in northern Baja California. Reoccupied GEOMEX GPS sites, and occupied a regional GPS (Global Positioning System) network. The Baja California peninsula in Mexico offers a unique setting for studying the kinematic evolution of a complex, active strike-slip/rift plate boundary. We are currently conducting remote sensing, geologic, and geodetic studies of this boundary. The combined data sets will yield instantaneous and time integrated views of its evolution. This proposal solicits renewed funding from NASA to support remote sensing and geologic studies. During the late Cenozoic, Baja California has been the locus of changing fault geometry that has accommodated components of the relative motion between the North America and Pacific plates. Contemporary slip between the two plates occurs in a broad zone that encompasses much of southern California and the Baja California Peninsula. The transfer of slip across this zone in southern California is relatively well understood. South of the border, the geometry and role of specific faults and structural provinces in transferring plate margin deformation across the peninsula is enigmatic. Results We use Landsat Thematic Mapper imagery of the Baja California Peninsula to identify recent and active faults, and then conduct field studies that characterize the temporal and spatial structural evolution of the plate margin. These data address questions concerning the neotectonic development of the Gulf of California, the Baja California Peninsula, and their role in evolution of the post-Miocene Pacific - North American plate boundary. Moreover, these studies provide constraints on the geometry of active faults, allowing more exact understanding of the results of ongoing NASA-supported geodetic experiments. In addition, anticipated publication of the TM scenes will provide a widely available geological data base for relatively little-known peninsula California. Achievements include development of an ArcInfo data base of Landsat and SPOT imagery, detailed field studies of Neogene structures in northeastern Baja California, and new constraint on Pacific - North America plate motion at Baja California latitudes. These results are reported in maps, manuscripts and data products which are published or near completion.

Miller, M. Meghan↗
DOE OSTI2026

Fermilab complex upgrades and CLFV

I review the current status and future prospects of charged lepton flavor violation (CLFV) searches at Fermilab, with emphasis on their sensitivity to physics beyond the Standard Model and their complementarity to the mission-critical LBNF/DUNE neutrino program. In this context, strategic considerations for the evolution of the complex in the Linac-II era will be discussed, including synergies between CLFV initiatives and mission-critical commitments such as reliable high-power beam delivery to LBNF/DUNE.

Hedges, Michael [Fermilab] (ORCID:0000000165041872↗
DOE OSTI2020

Evolutionary Variation in MADS Box Dimerization Affects Floral Development and Protein Abundance in Maize

Interactions between MADS-box transcription factors are critical in the regulation of floral development, and shifting MADS-box protein-protein interactions are predicted to have influenced floral evolution. However, precisely how evolutionary variation in protein-protein interactions affects MADS-box protein function remains unknown. To assess the impact of changing MADS-box protein-protein interactions on transcription factor function, we turned to the grasses, where interactions between B-class MADS-box proteins vary. We tested the functional consequences of this evolutionary variability using maize as an experimental system. We found that differential B-class dimerization was associated with subtle, quantitative differences in stamen shape. In contrast, differential dimerization resulted in large-scale changes to downstream gene expression. Differential dimerization also affected B-class complex composition and abundance, independent of transcript levels. This indicates that differential B-class dimerization affects protein degradation, revealing an important consequence for evolutionary variability in MADS-box interactions. Our results highlight complexity in the evolution of developmental gene networks - changing protein-protein interactions could affect not only the composition of transcription factor complexes, but also their degradation and persistence in developing flowers. Here, our results also show how coding change in a pleiotropic master regulator could have small, quantitative effects on development.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2006

Mathematical Modeling of the Origins of Life

The emergence of early metabolism - a network of catalyzed chemical reactions that supported self-maintenance, growth, reproduction and evolution of the ancestors of contemporary cells (protocells) was a critical, but still very poorly understood step on the path from inanimate to animate matter. Here, it is proposed and tested through mathematical modeling of biochemically plausible systems that the emergence of metabolism and its initial evolution towards higher complexity preceded the emergence of a genome. Even though the formation of protocellular metabolism was driven by non-genomic, highly stochastic processes the outcome was largely deterministic, strongly constrained by laws of chemistry. It is shown that such concepts as speciation and fitness to the environment, developed in the context of genomic evolution, also held in the absence of a genome.

Pohorille, Andrew↗