Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “state distribution”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Optimizing temperature distributions for training neural quantum states using parallel tempering

Parametrized artificial neural networks (ANNs) can be very expressive ansatzes for variational algorithms, reaching state-of-the-art energies on many quantum many-body Hamiltonians. Nevertheless, the training of the ANN can be slow and stymied by the presence of local minima in the parameter landscape. One approach to mitigate this issue is to use parallel tempering methods, and in this work, we focus on the role played by the temperature distribution of the parallel tempering replicas. Using an adaptive method that adjusts the temperatures in order to equate the exchange probability between neighboring replicas, we show that this temperature optimization can significantly increase the success rate of the variational algorithm with negligible computational cost by eliminating bottlenecks in the replicas' random walk. Furthermore, we demonstrate this using two different neural networks, a restricted Boltzmann machine and a feedforward network, which we use to study a toy problem based on a permutation invariant Hamiltonian with a pernicious local minimum and the 𝐽 1 −𝐽 2 model on a rectangular lattice.

Neural network simulations↗
DOE OSTI2022

Recursive Gaussian Process over graphs for Integrating Multi-timescale Measurements in Low-Observable Distribution Systems

The transition to a smarter grid is empowered by enhanced sensor deployments and smart metering infrastructure in the distribution system. Measurements from these sensors and meters can be used for many applications, including distribution system state estimation (DSSE). However, these measurements are typically sampled at different rates and could be intermittent due to losses during the aggregation process. These multi timescale measurements should be reconciled in real-time to perform accurate grid monitoring. This paper tackles this problem by formulating a recursive multi-task Gaussian process (RGP-G) approach that sequentially aggregates sensor measurements. Specifically, we formulate a recursive multi-task GP with and without network connectivity information to reconcile the multi time-scale measurements in distribution systems. Here, the proposed framework is capable of aggregating the multi-time scale measurements batch-wise or in real-time. Following the aggregation of the multi time-scale measurements, the spatial states of the consistent time-series are estimated using matrix completion based DSSE approach. Simulation results on IEEE 37 and IEEE 123 bus test systems illustrate the efficiency of the proposed methods from the standpoint of both multi time-scale data aggregation and DSSE.

42 ENGINEERING↗
NASA NTRS1992

Geology and distribution of impact craters on Venus - What are they telling us?

Data on the distribution of impact craters on Venus are analyzed in relation to the geologic history of Venus and the effects of high ambient pressures and temperatures on the cratering process itself. The cratering record of Venus is discussed in terms of size, number, spatial distribution, and state of preservation of craters. The morphology of Venusian craters is examined as a function of the diameter, and features unique to these craters are discussed. Attention is also given to several craters that illustrate important features of Venusian crater; surface 'splotches' that are closely related in origin to impact craters; and implications of the distribution of severely modified craters for Venus' recent geologic history.

Schaber, G. G.↗
NASA NTRS1976

Molecular cluster theory for chemisorption of first row atoms on nickel /100/ surfaces

Self-consistent Hartree-Fock-Slater molecular cluster models for the chemisorption of first-row atoms on Ni(100) surfaces are presented. Energy levels and ground-state charge distributions are given for XNi5 clusters with the adatom X = H, C, N, O located in C4V symmetry at a fixed height of 2.0 au above the surface. The variation of properties with height was studied in detail for the case of oxygen. Theoretical results compare rather well with experimental photoelectron and energy-loss data. Local-densities-of-states diagrams are used to clarify the interaction between adsorbate levels and metal conduction bands.

Ellis, D. E.↗
DOE OSTI2024

Imaging the initial condition of heavy-ion collisions and nuclear structure across the nuclide chart

High-energy nuclear collisions encompass three key stages: the structure of the colliding nuclei, informed by low-energy nuclear physics, the initial condition , leading to the formation of quark–gluon plasma (QGP), and the hydrodynamic expansion and hadronization of the QGP, leading to final-state hadron distributions that are observed experimentally. Recent advances in both experimental and theoretical methods have ushered in a precision era of heavy-ion collisions, enabling an increasingly accurate understanding of these stages. However, most approaches involve simultaneously determining both QGP properties and initial conditions from a single collision system, creating complexity due to the coupled contributions of these stages to the final-state observables. To avoid this, we propose leveraging established knowledge of low-energy nuclear structures and hydrodynamic observables to independently constrain the QGP’s initial condition. By conducting comparative studies of collisions involving isobar-like nuclei—species with similar mass numbers but different ground-state geometries—we can disentangle the initial condition’s impacts from the QGP properties. This approach not only refines our understanding of the initial stages of the collisions but also turns high-energy nuclear experiments into a precision tool for imaging nuclear structures, offering insights that complement traditional low-energy approaches. Opportunities for carrying out such comparative experiments at the Large Hadron Collider and other facilities could significantly advance both high-energy and low-energy nuclear physics. Additionally, this approach has implications for the future electron-ion collider. While the possibilities are extensive, we focus on selected proposals that could benefit both the high-energy and low-energy nuclear physics communities. Originally prepared as input for the long-range plan of U.S. nuclear physics, this white paper reflects the status as of September 2022, with a brief update on developments since then.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Designing a quantum-accurate machine-learning potential to enable large-scale simulations of deuterium under shock

Large-scale molecular dynamics of deuterium under shock can elucidate kinetic processes vital to the target design in inertial confinement fusion and high-energy-density experiments. However, modeling the complex evolution of this material from an insulating molecular state at ambient pressure to an ionized, atomic fluid under strong shock is beyond the capability of simple pair and even bond order potentials. We thus train a quantum-accurate and broadly transferable machine-learning interatomic potential for deuterium using the Chebyshev Interaction Model for Efficient Simulations framework. We show that due to an improved description of the molecular-to-atomic transition, our model is able to better reproduce the ab initio equation of state, radial distribution functions, and principal Hugoniot than bond order potentials. This represents an important step toward large-scale quantum-accurate and nonequilibrium simulations of complicated systems under dynamic changes including phase transitions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1973

Time dependent worldwide distribution of atmospheric neutrons and of their products. I, II, III.

Review of the experimental results obtained in a series of measurements of the fast neutron cosmic ray spectrum by means of high-altitude balloons and aircraft. These results serve as a basis for checking a Monte Carlo calculation of the entire neutron distribution and its products. A calculation of neutron production and transport in the earth's atmosphere is then discussed for the purpose of providing a detailed description of the morphology of secondary neutron components. Finally, an analysis of neutron observations during solar particle events is presented. The Monte Carlo output is used to estimate the contribution of flare particles to fluctuations in the steady state neutron distributions.

Merker, M.↗
DOE OSTI2022

Distributed Wind: Supporting the Clean Energy Transition

Distributed wind technologies have advanced a great deal over the last decade, developing from a very selective market to one that is poised to play a significant role in supporting a clean energy transition. Summarizing technical and modeling advances, most of them supported by program efforts undertaken by the Wind Energy Technology Office of the U.S. Department of Energy, this presentation provides an overview of the current state of distributed wind technologies and how the technology could support the wider distributed energy development. The presentation also provides an short introduction to the challenges and ongoing research to address these challenges.

distributed energy↗
DOE OSTI2022

Elucidating Arsenic and Selenium Speciation in Coal Fly Ashes

Coal fly ash (CFA) is produced during the coal combustion process and contains trace elements (<100 ppm) including selenium (Se) and arsenic (As), both detrimental for environmental and human health. Existing management strategies for CFAs involve disposal in landfills and ash ponds, as well as beneficial usage in other applications such as in concrete. Both the direct disposal and beneficial usage of CFA raise concerns for environmental hazards due to the potential leaching of Se and As. CFAs were classified as reusable material by the U.S. Environmental Protection Agency (EPA) with the first federal regulation (Disposal of Coal Combustion Residuals from Electric Utilities, CCR) for its disposal established six years ago. During coal combustion process, parameters such as furnace firing type, emissions controls, coal rank, use of additives, and particulate matter controls, could contribute to significant variability in As/Se concentrations and their speciation. Although there have been a handful of studies on As/Se speciation on small sets of samples, they do not fully represent the current state of coal-fired electrical generating units. Little information exists on the detailed speciation (e.g. oxidation state, complexation state) of As/Se in large set of fly ash samples. To better investigate the role of the potential contributing parameters, a large-scale survey study with representative samples of typical coal source and combustion conditions is highly desired. The overall goal of this research is to systematically characterize As and Se speciation of representative coal fly ash samples using state-of-the-art synchrotron based spectroscopic and microscopic techniques, in order to develop a correlation between coal source/type, operation condition, As/Se speciation, and As/Se mobility. A detailed survey study was conducted documenting the current state-of-knowledge on fossil power generating units as a function of coal type/source, operating conditions, environmental control systems, additive use, and fly ash handling methods. Based on this survey, a matrix of fly ash samples representing a range of conditions were chosen and collected. The collected ash samples were analyzed for bulk characteristics such as elemental composition, microstructure, chemical and mineralogical composition, surface area, and particle size distribution. State-of-the-art synchrotron X-ray microscopy and spectroscopy techniques were applied to reveal the molecular scale speciation information of As and Se, such as oxidation state, association with other elements/minerals, and complexation states.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Cybersecurity of DER Systems: Cybersecurity Training for State Commissions

NARUC regularly hosts multi-day cybersecurity training events for commissioners and their staff to gain knowledge of IT/OT technologies, understand the spectrum of state and federal approaches to cybersecurity, and explore cybersecurity cost recovery challenges and innovative solutions to address them. About 300 commissioners and staffers typically register for these virtual events.

cybersecurity cost recovery↗
DOE OSTI2023

Community Solar

Community solar, also known as shared solar or solar gardens, is a distributed solar energy deployment model that allows customers to buy or lease part of a larger, offsite shared solar photovoltaic (PV) system and receive benefits of their participation.

achetypes of ownership↗
DOE OSTI2023

Deep reinforcement learning for class imbalance fault diagnosis of equipment in nuclear power plants

In equipment fault diagnosis in nuclear power plants, there may be far more samples in one class (e.g., a health state) than in another class (e.g., a fault state). The distribution of data in each class is highly skewed. Most machine learning algorithms are suitable for balanced training datasets. When faced with imbalanced samples, these algorithms tend to provide good identification for the majority classes and bias for the minority classes. However, the misclassification of minority classes can lead to high costs. To address the above problem, this paper develops a deep reinforcement learning-based diagnosis method that models fault diagnosis as a sequential decision-making process. At each time step, the agent receives the state of the environment represented by the training samples and then takes a diagnosis action guided by a policy. If the action is correct/incorrect, the agent receives a positive/negative reward. The reward for minority classes is higher than that for majority classes. The agent’s goal is to obtain as many cumulative rewards as possible in the process, i.e., to identify the sample as correctly as possible. Six demonstration scenarios are constructed, depending on the selected fault datasets and the designed model structures. Experiments show that the proposed method achieves a higher weighted-averaged F1 score than the classical supervised learning method in most cases of class imbalance. Finally, the proposed method has potential applications in the field of class imbalance fault diagnosis of equipment in nuclear power plants.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Collisional-energy-cascade model for nonthermal velocity distributions of neutral atoms in plasmas

Nonthermal velocity distributions with much greater tails than a Maxwellian have been observed for radical atoms in plasmas for a long time. Historically, such velocity distributions have been modeled by a two-temperature Maxwell distribution. In this paper, I propose a model based on collisional energy cascade, which has been studied in the field of granular materials. In the collisional energy cascade, a particle ensemble undergoes energy input at the high-energy region, entropy production by elastic collisions among particles, and energy dissipation. For radical atoms, energy input may be caused by the Franck-Condon energy of molecular dissociation or charge-exchange collision with hot ions, and the input energy is eventually dissipated by collisions with the walls. Here, I show that the steady-state velocity distribution in the collisional energy cascade is approximated by the generalized Mittag-Leffler distribution, which is a one-parameter extension of the Maxwell distribution. This parameter indicates the degree of the nonthermality and is related to the relative importance of energy dissipation over entropy production. This model is compared with a direct molecular dynamics simulation for simplified gaseous systems with energy input, as well as some experimentally observed velocity distributions of light radicals in plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Physics-guided Deep Learning for Power System State Estimation

In the past decade, dramatic progress has been made in the field of machine learning. This paper explores the possibility of applying deep learning in power system state estimation. Traditionally, physics-based models are used including weighted least square (WLS) or weighted least absolute value (WLAV). These models typically consider a single snapshot of the system without capturing temporal correlations of system states. In this paper, a physics-guided deep learning (PGDL) method is proposed. Specifically, inspired by autoencoders, deep neural networks (DNNs) are used to learn the temporal correlations. The estimated system states from DNNs are then checked against physics laws by running through a set of power flow equations. Hence, the proposed PGDL is both data-driven and physics-guided. The accuracy and robustness of the proposed PGDL method are compared with traditional methods in standard IEEE cases. Simulations show promising results and the applicability is further discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1995

Rotational And Rovibrational Energy Transfer In Electron Collisions With Molecules

Air flows around a hypervelocity reentry vehicle undergo dissociation, rovibrational excitation and ionization. More specifically the air, initially 80% N2 and 20% O2, in the shock layer consists of species such as N, O, N2, O2, NO, N+, O+, N+, O+, NO+ and 2 free electrons. It was pointed out in multi temperature models'' that the temperature of the rotational energy modes and the gas-kinetic translational temperature are quickly equilibrated by a few collisions and rise rapidly to high temperatures as 50000K before falling off to equilibrium value of 10000K. Contrary, the electronic and vibrational temperatures state energy distributions remain low (less than 15000K) because of the slow equilibration. Electron vibrational energy transfer is thought to play a crucial role in such a ionizing flow regime since chemical reaction rates and dissociation depend strongly on the vibrational temperatures. Modeling of these flowfields in principle require the rovibrational excitation and de-excitation cross section data for average electron energies from threshold up to several eV (leV=11605.4 K). In this lecture we focus on theoretical description of rotational effects i.e. energy transfer of electrons to molecules such that the molecular rotational (vojo goes to voj) or vibrational and rotational (v(sub 0)j(sub 0) goes to vj) states are changed. Excitation and de-excitation of electronic states was discussed in a previous talk at this conference.

Thuemmel, Helmar T.↗
NASA NTRS1971

Vibrational population distributions in nonequilibrium nozzle expansion flows

Experimental measurements and theoretical calculations of the vibrational population distribution in nonequilibrium nozzle expansion flows of gas mixtures are reported. These studies were directed toward determining whether vibrational energy exchange pumping could lead to laser action on the vibrational bands of a diatomic molecule. Three different types of experiments were conducted. These showed (1) that vibrational energy was preferentially transferred from N2 to CO in supersonic nozzle flows containing these gases; (2) that under some conditions this vibrational energy exchange pumping mechanism created population inversions in the vibrational levels of CO; and (3) that at large expansion ratios the magnitude of these population inversions was sufficient to sustain lasing in the nozzle. A theoretical model was developed to calculate vibrational state population distributions in gas dynamic expansions of a mixture of diatomic gases. Although only isothermal calculations have been completed, these data indicate that population inversions are predicted for conditions similar to those obtained in the nozzle expansion flows.

Watt, W. S.↗
NASA NTRS1983

Reduction methods for nonlinear steady-state thermal analysis

Hybrid analysis techniques and a problem adaptive computational algorithm are presented for predicting the nonlinear steady state temperature distribution in structures and solids. In the proposed techniques, the structure is discretized by using the finite element method. The vector of nodal temperatures is then expressed as a linear combination of a small number of global temperature modes (or basis vectors), and the Bubnov Galerkin technique is used to compute the amplitudes of the global modes. The global temperature modes (or basis vectors) are chosen to include the various order derivatives of the nodal temperature vector with respect to preselected path parameter(s). The potential of the proposed reduction methods for solution of large scale, nonlinear thermal problems is discussed and the effectiveness of the methods is demonstrated by means of numerical examples, including steady state conduction, convection, and radiation modes of heat transfer.

Noor, A. K.↗