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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Improved fatty acid profiles in seeds of Camelina sativa by artificial microRNA mediated FATB gene suppression

The fatty acid profile of plant oils determines their quality and uses. Saturated fatty acids are often not desirable from the standpoints of nutrition and some industrial applications. Camelina sativa is a re-emerged oilseed crop, however its oil needs to be improved to meet different application requirements. In this study, saturated fatty acids were greatly reduced by down-regulating genes encoding the fatty acyl-ACP thioesterases (FATB). An artificial microRNA (amiFATB) was created by replacing a microRNA sequence in the camelina Csa-miR159a gene with a FATB gene specific sequence. Seed-specific expression of amiFATB caused a 45% reduction of palmitic acid (16:0) and a 38% reduction of stearic acid (18:0) compared to wildtype seeds. The total saturated fatty acid content was decreased by 35% from 14.6% to 9.4% of total fatty acids. When amiFATB was expressed in a high-oleic acid transgenic line, it caused further increased oleic acid content. This work demonstrates that the FATB genes in camelina can be effectively knocked down by an artificial microRNA targeting gene-specific sequences, thus provides an additional tool to improve seed oils for desired properties.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Tunable Stochasticity in an Artificial Spin Network

Metamaterials present the possibility of artificially generating advanced functionalities through engineering of their internal structure. Artificial spin networks, in which a large number of nanoscale magnetic elements are coupled together, are promising metamaterial candidates that enable the control of collective magnetic behavior through tuning of the local interaction between elements. In this work, the motion of magnetic domain-walls in an artificial spin network leads to a tunable stochastic response of the metamaterial, which can be tailored through an external magnetic field and local lattice modifications. This type of tunable stochastic network produces a controllable random response exploiting intrinsic stochasticity within magnetic domain-wall motion at the nanoscale. An iconic demonstration used to illustrate the control of randomness is the Galton board. In this system, multiple balls fall into an array of pegs to generate a bell-shaped curve that can be modified via the array spacing or the tilt of the board. A nanoscale recreation of this experiment using an artificial spin network is employed to demonstrate tunable stochasticity. Furthermore, this type of tunable stochastic network opens new paths toward post-Von Neumann computing architectures such as Bayesian sensing or random neural networks, in which stochasticity is harnessed to efficiently perform complex computational tasks.

Artificial spin network↗
DOE OSTI2020

Disassembling 2D van der Waals crystals into macroscopic monolayers and reassembling into artificial lattices

Two-dimensional materials from layered van der Waals (vdW) crystals hold great promise for electronic, optoelectronic, and quantum devices, but technological implementation will be hampered by the lack of high-throughput techniques for exfoliating single-crystal monolayers with sufficient size and high quality. Here, we report a facile method to disassemble vdW single crystals layer by layer into monolayers with near-unity yield and with dimensions limited only by bulk crystal sizes. The macroscopic monolayers are comparable in quality to microscopic monolayers from conventional Scotch tape exfoliation. The monolayers can be assembled into macroscopic artificial structures, including transition metal dichalcogenide multilayers with broken inversion symmetry and substantially enhanced nonlinear optical response. This approach takes us one step closer to mass production of macroscopic monolayers and bulk-like artificial materials with controllable properties.

Science & Technology - Other Topics↗
DOE OSTI2023

Operation Optimization using Reinforcement Learning with Integrated Artificial Reasoning Framework

In large and complex systems, operational decision-making requires a systematic analysis with a vast amount of data from both process parameters and component status monitoring. In this paper, we present an integrated artificial reasoning approach for system state transition models that can help operational decision-making with explainable and traceable reasoning. The integrated artificial reasoning framework is a physics-based approach of defining the system structure in a Bayesian network, so we leveraged it in a Markov decision process (MDP) for finding optimal operational solutions. In our proposed framework, the MDP is implemented on a dynamic Bayesian network (DBN), which represents causalities in a system. The multilevel flow modeling was utilized in order to extract these causalities in a more efficient and objective manner. Since multilevel flow modeling is based on the fundamental energy and mass conservation laws, the target system is decomposed into several mass, energy, and information structures, which serve as the basis for a DBN. The MDP consists of the processes of finding a solution for the Bellman equation, which can be derived from the conditional probability equations of the constructed DBN. System operators can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from the component degradation process or random failures. We analyzed a simplified example system to illustrate finding an optimal operational policy with this approach.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Understanding oxidation of Fe-Cr-Al alloys through explainable artificial intelligence

Abstract The oxidation resistance of FeCrAl based on alloying composition and oxidizing conditions is predicted using a combinatorial experimental and artificial intelligence approach. A neural network (NN) classification model was trained on the experimental FeCrAl dataset produced at GE Research. Furthermore, using the SHapley Additive exPlanations (SHAP) explainable artificial intelligence (XAI) tool, we explore how the NN can showcase further material insights that are unavailable directly from a black-box model. We report that high Al and Cr content forms protective oxide layer, while Mo in FeCrAl creates thick unprotective oxide scale that is vulnerable to spallation due to thermal expansion. Graphical abstract

Materials Science↗
DOE OSTI2018

A localized artificial diffusivity method to simulate compressible multiphase flows using the stiffened gas equation of state: A localized artificial diffusivity method to simulate compressible multiphase flows using the stiffened gas equation of state

The development of numerical approaches to perform direct numerical simulations of compressible multiphase flows has been an active field of research for several years. Proper treatment of fluid interfaces is crucial as important physics occur in this infinitesimally small region. Furthermore, the compressibility of the fluid requires proper treatment of discontinuities. Artificial diffusivity is among a number of methods widely used for compressible flows. This study develops a general form of consistent artificial diffusion fluxes and extends the localized artificial diffusivity method for high-order central schemes to solve multiphase flows with an interface-capturing method. These fluxes ensure an oscillation-free interface for pressure, velocity, and temperature without employing a sharpening technique. Moreover, the high-order representation of all scales in the flow helps capture the wide range of instabilities inherent in these flows. The goal is to develop an approach capable of performing high-fidelity simulations supported by physics-driven validation. lastly, this is achieved by solving the five-equation model with the stiffened-gas equation of state using the proposed method for multicomponent and multiphase flows on a variety of 1D and 2D problems.

42 ENGINEERING↗
DOE OSTI2024

Artificial Intelligence for the Electron Ion Collider (AI4EIC)

The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Spontaneous Magnetic Superdomain Wall Fluctuations in an Artificial Antiferromagnet.

Collective dynamics often play an important role in determining the stability of ground states for both naturally occurring materials and metamaterials. We studied the temperature dependent dynamics of antiferromagnetically ordered superdomains in a square artificial spin lattice using soft x-ray photon correlation spectroscopy. We observed an exponential slowing down of superdomain wall motion below the antiferromagnetic onset temperature, similar to the behavior of typical bulk antiferromagnets. Using a continuous time random walk model we show that these superdomain walls undergo low-temperature ballistic and high-temperature diffusive motions.

Chen, X. M.↗
DOE OSTI2022

Artificial dynamical effects in quantum field theory

In Newtonian mechanics, studying a system in a non-Galilean reference frame can lead to inertial pseudoforces appearing, such as the centrifugal force that seems to arise in dynamics analysed in a rotating frame. Likewise, artificial effects may arise in relativistic quantum field theory (QFT) if a system is studied in a framework that violates Poincaré invariance. Here, we highlight how such issues complicate the traditional canonical quantization of QFTs and can lead to a subjective description of natural phenomena. By contrast, the treatment of the same problem using light-front quantization is free from spurious pseudoeffects because Poincaré invariance is effectively preserved for all practical intents and purposes. We illustrate these statements using several examples: the Gerasimov-Drell-Hearn (GDH) relation, a fundamental feature of QFT; the absence of any measurable impact of Lorentz contraction in high-energy collisions; and the fictitious character of vacuum fluctuation contributions to the cosmological constant.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Influence of the vertex region on spin dynamics in artificial Kagome spin ice.

We present experimental and theoretical studies of spin-wave mode dynamics in artificial kagome spin ice vertices made of three identical 15-nm thick elongated Ni80Fe20 nanoislands (macrospins). We consider several possible configurations, from completely disjointed macrospins (full dipolar interelement interac-tions) to fully jointed macrospins (full dipolar-exchange interactions). Using angular-resolved magnetic field dependent broadband ferromagnetic resonance (FMR), we demonstrate the occurrence of a mode localized in the vertex region as indicated by the distinct behavior of the FMR spectra at different angles and configurations. Theoretical calculations using micromagnetic simulations support the existence, ori-gin, and behavior of this mode by interpreting it as a localized, quasi-uniform Kittel mode. Our findings pave the way for designing the most appropriate network consisting of ferromagnetic nanomagnets for specific application purposes in magnonics.

Bang, Wonbae↗
DOE OSTI2021

Variational Monte Carlo Calculations of A ≤ 4 Nuclei with an Artificial Neural-Network Correlator Ansatz

Here, the complexity of many-body quantum wave functions is a central aspect of several fields of physics and chemistry where nonperturbative interactions are prominent. Artificial neural networks (ANNs) have proven to be a flexible tool to approximate quantum many-body states in condensed matter and chemistry problems. In this work we introduce a neural-network quantum state ansatz to model the ground-state wave function of light nuclei, and approximately solve the nuclear many-body Schrodinger equation. Using efficient stochastic sampling and optimization schemes, our approach extends pioneering applications of ANNs in the field, which present exponentially scaling algorithmic complexity. We compute the binding energies and point-nucleon densities of A ≤ 4 nuclei as emerging from a leading-order pionless effective field theory Hamiltonian. We successfully benchmark the ANN wave function against more conventional parametrizations based on two- and three-body Jastrow functions, and virtually exact Green's function Monte Carlo results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

The Abrasion of Photovoltaic Glass: A Comparison of the Effects of Natural and Artificial Aging

Natural soiling and the subsequent requisite cleaning of photovoltaic (PV) modules result in abrasion damage to the cover glass. The durability of the front glass has important economic consequences, including determining the use of antireflective and/or antisoiling coatings as well as the method and frequency of operating maintenance (cleaning). The abrasion of coatings and glass has been explored in a field study, including the soiling-prone locations of Dubai (United Arab Emirates), Kuwait City (Kuwait), Mesa (Arizona), Mumbai (India), and Sacramento (California). Dry-brush-cleaned specimens will be compared to those subjected to artificial-brush testing. The characteristics of material integrity, surface energy, optical transmittance, surface roughness, and scratch size were examined using an optical microscope, contact goniometer (for water), spectrophotometer, interferometer, and atomic force microscope (AFM), respectively. The findings of this study will be used to provide feedback regarding the cleaning equipment, cleaning methods, and coatings used in the PV industry. The study here will also be used to aid in developing an abrasion standard for the PV industry.

14 SOLAR ENERGY↗
NASA NTRS1977

A recursively formulated first-order semianalytic artificial satellite theory based on the generalized method of averaging. Volume 1: The generalized method of averaging applied to the artificial satellite problem

A recursively formulated, first-order, semianalytic artificial satellite theory, based on the generalized method of averaging is presented in two volumes. Volume I comprehensively discusses the theory of the generalized method of averaging applied to the artificial satellite problem. Volume II presents the explicit development in the nonsingular equinoctial elements of the first-order average equations of motion. The recursive algorithms used to evaluate the first-order averaged equations of motion are also presented in Volume II. This semianalytic theory is, in principle, valid for a term of arbitrary degree in the expansion of the third-body disturbing function (nonresonant cases only) and for a term of arbitrary degree and order in the expansion of the nonspherical gravitational potential function.

Mcclain, W. D.↗
NASA NTRS1983

An overview of artificial intelligence and robotics. Volume 1: Artificial intelligence. Part A: The core ingredients

Artificial Intelligence (AI) is an emerging technology that has recently attracted considerable attention. Many applications are now under development. The goal of Artificial Intelligence is focused on developing computational approaches to intelligent behavior. This goal is so broad - covering virtually all aspects of human cognitive activity - that substantial confusion has arisen as to the actual nature of AI, its current status and its future capability. This volume, the first in a series of NBS/NASA reports on the subject, attempts to address these concerns. Thus, this report endeavors to clarify what AI is, the foundations on which it rests, the techniques utilized, applications, the participants and, finally, AI's state-of-the-art and future trends. It is anticipated that this report will prove useful to government and private engineering and research managers, potential users, and others who will be affected by this field as it unfolds.

Gevarter, W. B.↗