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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A multi-phase-field model of topological pattern formation during electrochemical dealloying of binary alloys

Porous metal structures produced by electrochemical dealloying have been extensively studied for use in various applications that require high surface areas. A new comprehensive multi-phase-field (MPF) model is proposed to study topological porous patterns formed by spontaneously etching a bulk binary alloy that involves electrochemical reactions, bulk and surface diffusion, ion transport, applied electrode potential, and charge conservation. The governing equations for the alloy-porous cluster-electrolyte system account for a generalized Butler-Volmer electrochemical reaction and are in accordance with the classical nucleation theory. Based on a quantitative examination of the effects of electrode potential and precursor composition, the simulation results reproduce typical phenomena including passive surface dealloying, active porosity evolution, critical potential, and characteristic length scale in two-dimension (2-D) and three-dimension (3-D). To consider more complicated dealloying systems, the evolutions of a bimodal porous metal structure, nanocomposite, and nested porous network with a structured hierarchy are investigated. Finally, the proposed model can be a useful tool for understanding and predicting the morphology evolution of diverse porous structures during electrochemical dealloying.

Hierarchical porous structure↗
NASA NTRS2008

Model Error Budgets

An error budget is a commonly used tool in design of complex aerospace systems. It represents system performance requirements in terms of allowable errors and flows these down through a hierarchical structure to lower assemblies and components. The requirements may simply be 'allocated' based upon heuristics or experience, or they may be designed through use of physics-based models. This paper presents a basis for developing an error budget for models of the system, as opposed to the system itself. The need for model error budgets arises when system models are a principle design agent as is increasingly more common for poorly testable high performance space systems.

error budgets↗
DOE OSTI2020

SO(3)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials

This work examines the frame-invariance (and the lack thereof) exhibited in simulated anisotropic elasto-plastic responses generated from supervised machine learning of classical multi-layer and informed-graph-based neural networks, and proposes different remedies to fix this drawback. The inherent hierarchical relations among physical quantities and state variables in an elasto-plasticity model are first represented as informed, directed graphs, where three variations of the graph are tested. While feed-forward neural networks are used to train path-independent constitutive relations (e.g., elasticity), recurrent neural networks are used to replicate responses that depends on the deformation history, i.e. or path dependent. In dealing with the objectivity deficiency, we use the spectral form to represent tensors and, subsequently, three metrics, the Euclidean distance between the Euler Angles, the distance from the identity matrix, and geodesic on the unit sphere in Lie algebra, can be employed to constitute objective functions for the supervised machine learning. In this, the aim is to minimize the measured distance between the true and the predicted 3D rotation entities. Following this, we conduct numerical experiments on how these metrics, which are theoretically equivalent, may lead to differences in the efficiency of the supervised machine learning as well as the accuracy and robustness of the resultant models. Neural network models trained with tensors represented in component form for a given Cartesian coordinate system are used as a benchmark. Our numerical tests show that, even given the same amount of information and data, the quality of the anisotropic elasto-plasticity model is highly sensitive to the way tensors are represented and measured. The results reveal that using a loss function based on geodesic on the unit sphere in Lie algebra together with an informed, directed graph yield significantly more accurate rotation prediction than the other tested approaches.

42 ENGINEERING↗
DOE OSTI2021

Identification of Active Metal Carbide and Nitride Catalytic Facets for Hydrodeoxygenation Reactions

The catalytic hydrodeoxygenation (HDO) reaction is of considerable interest for biomass conversion to valuable chemicals and fuels, where one of the critical bottlenecks is the lack of cost-effective and efficient catalysts. To discover cost-efficient catalysts for the HDO reaction, we employed a density functional theory-based hierarchical catalyst design strategy based on catalytic descriptors, reaction energy profiles, and microkinetic modeling (MKM). We focused on the carbide and nitride catalyst space, for which we calculated 121 catalyst surfaces of Mo 2 C, MoC, Mo 2 N, W 2 C, NbC, VC, VN, and NbN catalysts. Based on the computed surface energies, reaction energies of oxygen removal, carbon binding strength, and the surface area of nanoparticles, the likely active facets are the Mo 2 C(111), MoC(011), VN(100), Mo 2 N(001), Mo 2 N(011), and Mo 2 N(100) surfaces. Further, detailed energy profiles were obtained, and MKM was performed for a model reaction (glycolaldehyde + 2H 2 . ethylene + 2H 2 O) on the Mo 2 C(111), VN(100), and MoC(100) surfaces. Based on the computed volcano map obtained from MKM, the predicted active facets for this HDO reaction are the Mo 2 C(111), MoC(011), VN(011), Mo 2 N(001), Mo 2 N(011), and Mo 2 N(100) surfaces. Additionally, none of the carbide and nitride catalyst surfaces are located in the optimal catalytic activity part. Therefore, it is essential to modify the catalyst via adding dopants or alloying to improve the catalytic activity. Catalytic modifications that can destabilize the surface adsorption of O*/H 2 O* and decrease the energy barriers of O-H bond formation are recommended to facilitate the HDO on the carbide and nitride catalysts. These a priori investigations provide guidelines for future low-cost HDO catalyst development.

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

Band gap predictions of double perovskite oxides using machine learning

Abstract The compositional and structural variety inherent to oxide perovskites spawn wide-ranging applications. In perovskites, the band gap E g , a key material parameter for these applications, can be optimally controlled by varying the composition. Here, we implement a hierarchical screening process in which two cross-validated and predictive machine learning models for band gap classification and regression, trained using exhaustive datasets that span 68 elements of the periodic table, are applied sequentially. The classification model separates wide band gap materials, with E g ≥ 0.5 eV, from materials which have zero or relatively small band gaps, namely E g < 0.5 eV, and the second regression model quantitatively predicts the gap value of the wide band gap compounds. The study down-selects 13,589 cubic oxide perovskite compositions that are predicted to be experimentally formable, thermodynamically stable, and have a wide band gap. Of these, a subset of 310 compounds, which are predicted to be stable and formable with a confidence greater than 90%, are identified for further investigation. Our models are methodically analyzed via performance metrics and inter-dependence of model features to gain physical insight into the band gap prediction problem. Design maps to identify the variation of band gap with substitution of different elements are also presented.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Modeling and experimental validation of dynamical effects in Bragg coherent x-ray diffractive imaging of finite crystals

Bragg coherent diffractive imaging (BCDI) is a noninvasive microscopy technique that can visualize the shape and internal lattice deviations of crystals with nanoscale spatial resolution and picometer deformation sensitivity. Its strain imaging capability relies on Fourier transform–based iterative phase retrieval algorithms, which are mostly developed under the kinematical approximation. Such approximation prohibits the application of BCDI on larger crystals, which are commonly seen in most emerging functional materials. Understanding the dynamical effect in BCDI, as well as developing a validated method for modeling BCDI at the dynamical diffraction limit, is crucial for applying BCDI to hierarchical systems that contain micron-sized crystals and grains. Thus we report a comparative study on the impact of dynamical diffraction effects by comparing the reconstruction results from two measurements of the same crystal. Forward simulation is implemented to show subtle changes of interference fringes in the diffraction pattern due to the dynamical diffraction, and is compared directly with the experimental data.

36 MATERIALS SCIENCE↗
NASA NTRS1986

Interfacing intelligent software to robotic peripherals

A paradigm for the specification of robotic behavior is proposed, based on a template of control and data elements replicated in a hierarchical lattice. Considerations from physiological psychology are proposed as control elements for the model. The resulting model allows freedom in implementational details within a symmetrical design, and should also provide a framework for evolving robotic behavior from relatively primitive to extremely complex. The model's implications for realizing learning capabilities in robotic systems are discussed, and its implementation and demonstration are described.

Orlando, N. E.↗
NASA NTRS1991

Galaxy formation through hierarchical clustering

Analytic methods for studying the formation of galaxies by gas condensation within massive dark halos are presented. The present scheme applies to cosmogonies where structure grows through hierarchical clustering of a mixture of gas and dissipationless dark matter. The simplest models consistent with the current understanding of N-body work on dissipationless clustering, and that of numerical and analytic work on gas evolution and cooling are adopted. Standard models for the evolution of the stellar population are also employed, and new models for the way star formation heats and enriches the surrounding gas are constructed. Detailed results are presented for a cold dark matter universe with Omega = 1 and H(0) = 50 km/s/Mpc, but the present methods are applicable to other models. The present luminosity functions contain significantly more faint galaxies than are observed.

White, Simon D. M.↗
NASA NTRS1993

The soft X-ray background and galaxy clusters

We show that the background flux observed in X-rays around 1 keV sets a tight upper limit to the evolution of X-ray-emitting clusters of galaxies, once the contribution of the sources now resolved is subtracted. Specifically, if the clustering proceeds up a scale-invariant hierarchy in a critical universe, the intracluster gas content must increase faster than the dynamical mass. We model such evolution in terms of infall of the intergalactic medium into the hierarchically growing potential wells, limited by the intergalactic temperature. Thus the soft X-ray background provides constraints to the dynamical clustering and to the history of the intergalactic medium, which extend those being provided by the z-resolved luminosity functions in X-rays and complement those expected from measuring the Comptonization parameter in the microwave and far-IR bands.

Burg, R.↗
NASA NTRS2000

GENOA-PFA: Progressive Fracture in Composites Simulated Computationally

GENOA-PFA is a commercial version of the Composite Durability Structural Analysis (CODSTRAN) computer program that simulates the progression of damage ultimately leading to fracture in polymer-matrix-composite (PMC) material structures under various loading and environmental conditions. GENOA-PFA offers several capabilities not available in other programs developed for this purpose, making it preferable for use in analyzing the durability and damage tolerance of complex PMC structures in which the fiber reinforcements occur in two- and three-dimensional weaves and braids. GENOA-PFA implements a progressive-fracture methodology based on the idea that a structure fails when flaws that may initially be small (even microscopic) grow and/or coalesce to a critical dimension where the structure no longer has an adequate safety margin to avoid catastrophic global fracture. Damage is considered to progress through five stages: (1) initiation, (2) growth, (3) accumulation (coalescence of propagating flaws), (4) stable propagation (up to the critical dimension), and (5) unstable or very rapid propagation (beyond the critical dimension) to catastrophic failure. The computational simulation of progressive failure involves formal procedures for identifying the five different stages of damage and for relating the amount of damage at each stage to the overall behavior of the deteriorating structure. In GENOA-PFA, mathematical modeling of the composite physical behavior involves an integration of simulations at multiple, hierarchical scales ranging from the macroscopic (lamina, laminate, and structure) to the microscopic (fiber, matrix, and fiber/matrix interface), as shown in the figure. The code includes algorithms to simulate the progression of damage from various source defects, including (1) through-the-thickness cracks and (2) voids with edge, pocket, internal, or mixed-mode delaminations.

Murthy, Pappu L. N.↗
NASA NTRS1991

Application of an integrated flight/propulsion control design methodology to a STOVL aircraft

The application of an emerging Integrated Flight/Propulsion Control design methodology to a STOVL aircraft in transition flight is reported. The methodology steps consist of: (1) design of a centralized feedback controller to provide command tracking and stability and performance robustness considering the fully integrated airframe/propulsion model as one high-order system; (2) partition of the centralized controller into a decentralized, hierarchical form compatible with implementation requirements; and (3) design of command shaping prefilters from pilot control effectors to commanded variables to provide the overall desired response to pilot inputs. Intermediate design results using this methodology are presented, the complete point control design with the propulsion system operating schedule and limit protection logic included is evaluated for sample pilot control inputs, and the response is compared with that of an 'ideal response model' derived from Level I handling qualities requirements.

Garg, Sanjay↗
DOE OSTI2021

Faint galaxies and small halos : probes of galaxy formation and dark matter

This thesis addresses the intersection of dark matter and galaxy formation physics by modeling the faintest galaxies in the Universe in a cosmological context. In the first part of this work, I present an empirical model for the connection between faint galaxies and small dark matter halos. Combining this model with tailored cosmological simulations of Milky Way analogs and state-of-the-art satellite galaxy observations yields new insights into dwarf galaxy formation and hierarchical structure formation. Next, I show that these same dwarf galaxy observations place stringent constraints on microphysical dark matter properties including its warmth, self-interactions, Standard Model interactions, minimum particle mass, and formation epoch. Finally, I describe a unified framework that combines dark matter constraints from dwarf galaxies and strong gravitational lensing, paving the way for next-generation surveys of small-scale cosmic structure to deliver unprecedented insights into dark matter physics.

Nadler, Ethan Oliver↗
DOE OSTI2023

Embracing fine-root system complexity in terrestrial ecosystem modeling

Projecting the dynamics and functioning of the biosphere requires a holistic consideration of whole-ecosystem processes. However, biases toward leaf, canopy, and soil modeling since the 1970s have constantly left fine-root systems being rudimentarily treated. As accelerated empirical advances in the last two decades establish clearly functional differentiation conferred by the hierarchical structure of fine-root orders and associations with mycorrhizal fungi, a need emerges to embrace this complexity to bridge the data-model gap in still extremely uncertain models. In this work, we propose a three-pool structure comprising transport and absorptive fine roots with mycorrhizal fungi (TAM) to model vertically resolved fine-root systems across organizational and spatial–temporal scales. Emerging from a conceptual shift away from arbitrary homogenization, TAM builds upon theoretical and empirical foundations as an effective and efficient approximation that balances realism and simplicity. A proof-of-concept demonstration of TAM in a big-leaf model both conservatively and radically shows robust impacts of differentiation within fine-root systems on simulating carbon cycling in temperate forests. Theoretical and quantitative support warrants exploiting its rich potentials across ecosystems and models to confront uncertainties and challenges for a predictive understanding of the biosphere. Echoing a broad trend of embracing ecological complexity in integrative ecosystem modeling, TAM may offer a consistent framework where modelers and empiricists can work together toward this grand goal.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Self-Supervised and Interpretable Anomaly Detection Using Network Transformers

Machine learning and deep neural networks (DNNs) have been proposed as a tool to identify anomalies in computer network communications. However, due the obfuscated nature of off-the-shelf machine learning models, their output often does not provide enough information to isolate the source of the anomaly to take corrective measures. In this article, we introduce the network transformer (NeT), a DNN model for anomaly detection that incorporates the graph structure of the communication network in order to improve interpretability. Further, the presented approach has the following advantages: first, enhanced interpretability by incorporating the graph structure of computer networks; second, provides a hierarchical set of features that enables analysis at different levels of granularity; second, self-supervised training that does not require labeled data. The NeT model was evaluated on a set of anomalous scenarios executed in a real industrial control system. The presented approach successfully identified the anomalies, the devices affected, and the specific connections causing the anomalies, providing a data-driven hierarchical approach to analyze the behavior of a cyber network.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Local-scale Arctic tundra heterogeneity affects regional-scale carbon dynamics

In northern Alaska nearly 65% of the terrestrial surface is composed of polygonal ground, where geomorphic tundra landforms disproportionately influence carbon and nutrient cycling over fine spatial scales. Process-based biogeochemical models used for local to Pan-Arctic projections of ecological responses to climate change typically operate at coarse-scales (1km 2 –0.5°) at which fine-scale (<1km 2 ) tundra heterogeneity is often aggregated to the dominant land cover unit. Here, we evaluate the importance of tundra heterogeneity for representing soil carbon dynamics at fine to coarse spatial scales. We leveraged the legacy of data collected near Utqiagvik, Alaska between 1973 and 2016 for model initiation, parameterization, and validation. Simulation uncertainty increased with a reduced representation of tundra heterogeneity and coarsening of spatial scale. Hierarchical cluster analysis of an ensemble of 21 st -century simulations reveals that a minimum of two tundra landforms (dry and wet) and a maximum of 4km 2 spatial scale is necessary for minimizing uncertainties (<10%) in regional to Pan-Arctic modeling applications.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1984

Data flow modeling techniques

There have been a number of simulation packages developed for the purpose of designing, testing and validating computer systems, digital systems and software systems. Complex analytical tools based on Markov and semi-Markov processes have been designed to estimate the reliability and performance of simulated systems. Petri nets have received wide acceptance for modeling complex and highly parallel computers. In this research data flow models for computer systems are investigated. Data flow models can be used to simulate both software and hardware in a uniform manner. Data flow simulation techniques provide the computer systems designer with a CAD environment which enables highly parallel complex systems to be defined, evaluated at all levels and finally implemented in either hardware or software. Inherent in data flow concept is the hierarchical handling of complex systems. In this paper we will describe how data flow can be used to model computer system.

Kavi, K. M.↗
NASA NTRS2003

Developing Generic Software for Spacecraft Avionics

A proposed approach to the development of software for spacecraft avionics is based partly on a concept of generic software that could be tailored to satisfy requirements for specific missions. The proposed approach would stand in contrast to the conventional approach of first defining avionics requirements for a specific mission, then developing software specific to those requirements. The proposed approach might also be adaptable to programming computers that control and monitor other complex equipment systems that range in scale from automobiles to factories. The concept of a spacecraft avionics functional model (SAFM) is a major element of the proposed approach. An SAFM would be, essentially, a systematic and hierarchical description of the functionality required of the avionics software (and hardware) for a given mission. Although the initial input information used to start the construction of an SAFM would typically amount to a high-level description, the SAFM would thereafter be decomposed to a low level. The resulting low-level version of the model would be used to develop a set of generic requirements that could be expected to include a large fraction of all requirements for a large fraction of all missions. The generic requirements would be used to develop software modules that could be included in, or excluded from, the final flight software to satisfy the requirements of a specific mission.

Smith, Joseph↗
DOE OSTI2022

The nano- and meso-scale structure of amorphous calcium carbonate

Abstract Understanding the underlying processes of biomineralization is crucial to a range of disciplines allowing us to quantify the effects of climate change on marine organisms, decipher the details of paleoclimate records and advance the development of biomimetic materials. Many biological minerals form via intermediate amorphous phases, which are hard to characterize due to their transient nature and a lack of long-range order. Here, using Monte Carlo simulations constrained by X-ray and neutron scattering data together with model building, we demonstrate a method for determining the structure of these intermediates with a study of amorphous calcium carbonate (ACC) which is a precursor in the bio-formation of crystalline calcium carbonates. We find that ACC consists of highly ordered anhydrous nano-domains of approx. 2 nm that can be described as nanocrystalline. These nano-domains are held together by an interstitial net-like matrix of water molecules which generate, on the mesoscale, a heterogeneous and gel-like structure of ACC. We probed the structural stability and dynamics of our model on the nanosecond timescale by molecular dynamics simulations. These simulations revealed a gel-like and glassy nature of ACC due to the water molecules and carbonate ions in the interstitial matrix featuring pronounced orientational and translational flexibility. This allows for viscous mobility with diffusion constants four to five orders of magnitude lower than those observed in solutions. Small and ultra-small angle neutron scattering indicates a hierarchically-ordered organization of ACC across length scales that allow us, based on our nano-domain model, to build a comprehensive picture of ACC formation by cluster assembly from solution. This contribution provides a new atomic-scale understanding of ACC and provides a framework for the general exploration of biomineralization and biomimetic processes.

59 BASIC BIOLOGICAL SCIENCES↗