SEARCH · Engineering Papers

Results for “Topic modeling”

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.

SourceResearch connectionFrom yearThrough year

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

At least 577 records · Page 32

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Outcomes of the DOE Workshop on Atmospheric Challenges for the Wind Energy Industry

The U.S. Department of Energy-funded Mesoscale-to-Microscale Coupling (MMC) project team planned and conducted a virtual Workshop on Atmospheric Challenges for the Wind Energy Industry on October 19 and 20, 2020. The goal of the workshop was to forge a dialog with the community, including industry representatives, on how modeling tools are currently being used, the present active atmospheric modeling research in support of wind energy, and required advancements in capabilities and technology to continue to advance wind energy deployment. The workshop was planned in collaboration with an industry advisory panel that included representatives from wind power plant developers, turbine manufacturers, and companies that provide resource assessment and forecasting services. The format of the workshop included panels from government research sponsors, visionaries from industry, and mixed panels of researchers discussing research status and needs. A shared keynote presentation from the Technical University of Denmark experts anchored the second day of the workshop. An emphasis was placed on understanding the research needs in the offshore environment. In addition, breakout opportunities were provided each day. On the first day, the breakout discussions addressed predesigned questions configured to elicit participants’ thoughts on needed research directions. The second-day breakouts treated three important technical topics through a combination of presentations and group conversations. Each workshop participant chose their breakout preference from among downscaling details, modeling for turbines, and using artificial intelligence for atmospheric modeling. The discussions were robust and productive. The outcomes of the workshop include archiving a series of recommendations from industry and the research community on research directions required to further advance wind energy deployment. Discussions confirmed the need for high-fidelity modeling but that there are specific areas of applicability and other areas where the time and cost of computation is prohibitive. In those cases, the high-fidelity models can inform low-order models that are more practical for real-time or widely deployed applications. Industry must consider the financial cost of performing more expensive modeling approaches, but industry engineers and researchers are using these approaches where there appears to be a return on investment. An emerging type of low-order model is based on machine learning (ML). Participants confirmed that there are many atmospheric phenomena that need to be modeled better, including low-level jets, cold air outbreaks, land-sea induced circulations, diurnal variability, thin stable boundary layers, dynamic changes such as from frontal passage, interaction of wakes and blockage, and more. For the offshore environment, there is wide agreement that some level of ocean-wave-atmospheric coupling is necessary to capture variations in rotor-level winds needed to plan and operate offshore wind plants. Another recurring recommendation is that more observations are needed, particularly for the offshore environment. Those observations should consider the needs for model improvement, both for physically based models and for ML models. Observations must capture atmospheric profiles of variables that are important to understanding and modeling atmospheric and oceanic phenomena that impact boundary layer winds. Models must be validated with data and the uncertainty quantified, particularly those that are sensitive to initial and boundary conditions. Finally, a repeated request was to consider the holistic needs of hybrid plants of wind, solar, and storage resources because those types of plants are likely to be the wave of the future. In addition, industry wishes to understand impacts of the resource under a changing climate for long-term planning.

17 WIND ENERGY↗

DOE OSTI2017

A suite of benchmark and challenge problems for enhanced geothermal systems

A diverse suite of numerical simulators is currently being applied to predict or understand the performance of enhanced geothermal systems (EGS). To build confidence and identify critical development needs for these analytical tools, the United States Department of Energy, Geothermal Technologies Office sponsored a Code Comparison Study (GTO-CCS), with participants from universities, industry, and national laboratories. A principal objective for the study was to create a community forum for improvement and verification of numerical simulators for EGS modeling. Teams participating in the study were those representing U.S. national laboratories, universities, and industries, and each team brought unique numerical simulation capabilities to bear on the problems. Two classes of problems were developed during the study, benchmark problems and challenge problems. The benchmark problems were structured to test the ability of the collection of numerical simulators to solve various combinations of coupled thermal, hydrologic, geomechanical, and geochemical processes. This class of problems was strictly defined in terms of properties, driving forces, initial conditions, and boundary conditions. The challenge problems were based on the enhanced geothermal systems research conducted at Fenton Hill, near Los Alamos, New Mexico, between 1974 and 1995. The problems involved two phases of research, stimulation, development, and circulation in two separate reservoirs. The challenge problems had specific questions to be answered via numerical simulation in three topical areas: (1) reservoir creation/stimulation, (2) reactive and passive transport, and (3) thermal recovery. Whereas the benchmark class of problems were designed to test capabilities for modeling coupled processes under strictly specified conditions, the stated objective for the challenge class of problems was to demonstrate what new understanding of the Fenton Hill experiments could be realized via the application of modern numerical simulation tools by recognized expert practitioners. Lastly, we present the suite of benchmark and challenge problems developed for the GTO-CCS, providing problem descriptions and sample solutions.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Root phenotypes for improved nitrogen capture

Abstract Background Suboptimal nitrogen availability is a primary constraint for crop production in low-input agroecosystems, while nitrogen fertilization is a primary contributor to the energy, economic, and environmental costs of crop production in high-input agroecosystems. In this article we consider avenues to develop crops with improved nitrogen capture and reduced requirement for nitrogen fertilizer. Scope Intraspecific variation for an array of root phenotypes has been associated with improved nitrogen capture in cereal crops, including architectural phenotypes that colocalize root foraging with nitrogen availability in the soil; anatomical phenotypes that reduce the metabolic costs of soil exploration, improve penetration of hard soil, and exploit the rhizosphere; subcellular phenotypes that reduce the nitrogen requirement of plant tissue; molecular phenotypes exhibiting optimized nitrate uptake kinetics; and rhizosphere phenotypes that optimize associations with the rhizosphere microbiome. For each of these topics we provide examples of root phenotypes which merit attention as potential selection targets for crop improvement. Several cross-cutting issues are addressed including the importance of soil hydrology and impedance, phenotypic plasticity, integrated phenotypes, in silico modeling, and breeding strategies using high throughput phenotyping for co-optimization of multiple phenes. Conclusions Substantial phenotypic variation exists in crop germplasm for an array of root phenotypes that improve nitrogen capture. Although this topic merits greater research attention than it currently receives, we have adequate understanding and tools to develop crops with improved nitrogen capture. Root phenotypes are underutilized yet attractive breeding targets for the development of the nitrogen efficient crops urgently needed in global agriculture.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

EGS Collab Modeling and Simulation Working Group Teleconference Series (1-98)

This submission contains the presentation slides and recordings from the first 98 EGS Collab Modeling and Simulation Working Group teleconferences. These teleconferences served three objectives for the project: 1) share simulation results, 2) communicate field activities and results to the simulation teams, and 3) hold open scientific discussions on EGS topics.

15 GEOTHERMAL ENERGY↗

DOE OSTI2012

Antibacterial agent triclosan suppresses RBL-2H3 mast cell function

Triclosan is a broad-spectrum antibacterial agent, which has been shown previously to alleviate human allergic skin disease. The purpose of this study was to investigate the hypothesis that the mechanism of this action of triclosan is, in part, due to effects on mast cell function. Mast cells play important roles in allergy, asthma, parasite defense, and carcinogenesis. In response to various stimuli, mast cells degranulate, releasing allergic mediators such as histamine. In order to investigate the potential anti-inflammatory effect of triclosan on mast cells, we monitored the level of degranulation in a mast cell model, rat basophilic leukemia cells, clone 2H3. Having functional homology to human mast cells, as well as a very well defined signaling pathway leading to degranulation, this cell line has been widely used to gain insight into mast-cell driven allergic disorders in humans. Using a fluorescent microplate assay, we determined that triclosan strongly dampened the release of granules from activated rat mast cells starting at 2 μM treatment, with dose-responsive suppression through 30 μM. These concentrations were found to be non-cytotoxic. The inhibition was found to persist when early signaling events (such as IgE receptor aggregation and tyrosine phosphorylation) were bypassed by using calcium ionophore stimulation, indicating that the target for triclosan in this pathway is likely downstream of the calcium signaling event. Triclosan also strongly suppressed F-actin remodeling and cell membrane ruffling, a physiological process that accompanies degranulation. Our finding that triclosan inhibits mast cell function may explain the clinical data mentioned above and supports the use of triclosan or a mechanistically similar compound as a topical treatment for allergic skin disease, such as eczema. -- Highlights: ►The effects of triclosan on mast cell function using a murine mast cell model. ►Triclosan strongly inhibits degranulation of mast cells. ►Triclosan suppresses membrane ruffling of activated mast cells. ►Triclosan's effects persist when early mast cell signaling events are bypassed. ►Supports use of triclosan as a topical treatment for eczema.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

HAIMOS Ensemble Forecasts for Intra-day and Day- Ahead GHI, DNI and Ramps

The objective of this research is to develop a hybrid physics-based/data-driven forecast model to improve direct normal and global horizontal irradiance (DNI and GHI) prediction for horizons ranging from 1 to 72 hours. Project objectives also address key gaps in state-of-the-art solar forecasting: accurate probabilistic solar forecasts and the forecasting of large irradiance ramps (ramp onset and magnitude). The proposed model ensembles Numerical Weather Prediction (NWP) forecasts, determinist physics-based algorithms, and new-generation cloud cover products (high-resolution rapid refresh satellite images and Large Eddy Simulations). The result is the Hybrid Adaptive Input Model Objective Selection (HAIMOS) ensemble model. HAIMOS blends state of the art machine learning methodologies with physics-based models for cloud cover and cloud optical depth forecasts. The technical activities followed a two-pronged strategy. First, the preprocessing of data, the selection of inputs to the nonlinear approximators, the type of approximator and objective functions, and post-processing ensembling techniques included in HAIMOS were all optimized adaptively to find the best model for a specific goal (reduce DNI/GHI forecast error, improve the prediction of ramp onset, etc.). Second, a large effort was put in improving cloud identification and the forecast of cloud cover and cloud optical depth. To this end, new-generation cloud parametrization products were developed in this work. These include improved algorithms to assist in cloud identification, cloud classification and cloud parametrization from satellite images – three key factors in the accuracy of 1 to 6-hours irradiance forecasts and prediction of ramp onset. Furthermore, we also included cloud information extracted from high resolution rapid refresh satellite images (GOES-16) and Large Eddy Simulations (LES). LES was used to model the atmosphere in detail over locations of interest and produce cloud optical depth forecasts. Once these data streams were validated, they were used as input data to the HAIMOS forecast. The model was developed using data from several climatologically distinct locations with potential for high solar penetration. In the last year of the project, we conducted a validation campaign according to the guidelines stipulated by the Topic Area 1 project as described in the FOA. This effort brings, for the first time, proven machine-learning methodologies for generating state-of-the-art solar forecasts interweaved with detailed physics-based models for cloud detection, and cloud optical depth forecasts. HAIMOS will generate accurate irradiance probabilistic forecast to assist in reducing solar generation prediction error. Globally optimized solar forecast models are more likely to impact solar energy stakeholders. The goal of this project was to increase the state-of-the-art forecast skill from their present values of 10 to 35%. At the end of the project, we achieved between 30% and 50% forecast skill across a wide range of horizons for both GHI and DNI.

14 SOLAR ENERGY↗

DOE OSTI2015

Molecular simulation of structure and diffusion at smectite-water interfaces: Using expanded clay interlayers as model nanopores

In geologic settings relevant to a number of extraction and potential sequestration processes, nanopores bounded by clay mineral surfaces play a critical role in the transport of aqueous species. Solution structure and dynamics at clay–water interfaces are quite different from their bulk values, and the spatial extent of this disruption remains a topic of current interest. We have used molecular dynamics simulations to investigate the structure and diffusion of aqueous solutions in clay nanopores approximately 6 nm thick, comparing the effect of clay composition with model Na-hectorite and Na-montmorillonite surfaces. In addition to structural properties at the interface, water and ion diffusion coefficients were calculated within each aqueous layer at the interface, as well as in the central bulk-like region of the nanopore. The results show similar solution structure and diffusion properties at each surface, with subtle differences in sodium adsorption complexes and water structure in the first adsorbed layer due to different arrangements of layer hydroxyl groups in the two clay models. Interestingly, the extent of surface disruption on bulk-like solution structure and diffusion extends to only a few water layers. Additionally, a comparison of sodium ion residence times confirms similar behavior of inner-sphere and outer-sphere surface complexes at each clay surface, but ~1% of sodium ions adsorb in ditrigonal cavities on the hectorite surface. Thus, the presence of these anhydrous ions is consistent with highly immobile anhydrous ions seen in previous nuclear magnetic resonance spectroscopic measurements of hectorite pastes.

36 MATERIALS SCIENCE↗

DOE OSTI2022

EGS Collab: Modeling and Simulation Working Group Teleconference Series (129-130)

This submission contains the presentation slides and recordings from EGS Collab Modeling and Simulation Working Group (MSWG) teleconferences number 129 through 130. These teleconferences served three objectives for the project: 1) share simulation results, 2) communicate field activities and results to the simulation teams, and 3) hold open scientific discussions on EGS topics.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Improved subseasonal-to-seasonal precipitation prediction of climate models with nudging approach for better initialization of Tibetan Plateau-Rocky Mountain Circumglobal wave train and land surface conditions

Abstract Reliable subseasonal-to-seasonal (S2S) precipitation prediction is highly desired due to the great socioeconomical implications, yet it remains one of the most challenging topics in the weather/climate prediction research area. As part of the Impact of Initialized Land Temperature and Snowpack on Sub-seasonal to Seasonal Prediction (LS4P) project of the Global Energy and Water Exchanges (GEWEX) program, twenty-one climate models follow the LS4P protocol to quantify the impact of the Tibetan Plateau (TP) land surface temperature/subsurface temperature (LST/SUBT) springtime anomalies on the global summertime precipitation. We find that nudging towards reanalysis winds is crucial for climate models to generate atmosphere and land surface initial conditions close to observations, which is necessary for meaningful S2S applications. Simulations with nudged initial conditions can better capture the summer precipitation responses to the imposed TP LST/SUBT spring anomalies at hotspot regions all over the world. Further analyses show that the enhanced S2S prediction skill is largely attributable to the substantially improved initialization of the Tibetan Plateau-Rocky Mountain Circumglobal (TRC) wave train pattern in the atmosphere. This study highlights the important role that initial condition plays in the S2S prediction and suggests that data assimilation technique (e.g., nudging) should be adopted to initialize climate models to improve their S2S prediction.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Foundations of Off-Grid Solar in Haiti: 5. Designing and Modeling Off-Grid Solar Systems

This training lends insight into key information and concepts for off-grid solar in Haiti, building foundational knowledge and an ability to catalyze interest in off-grid solar for rural electrification in Haiti. This self-paced course is offered in both English and French and covers a variety of topics related to energy access in Haiti including off-grid solar products, market potential in Haiti, supply and demand side considerations, system design, installation and maintenance, off-grid solar business models, financial modeling, gender and energy access, productive use of energy, and climate adaptation. The training is intended for university students, government partners, NGOs, funders, development partners, professionals, and more. No prior knowledge of off-grid solar or Haiti is required to benefit from this training. See NREL/PR-7A40-89259 for the French translation of this document.

designing↗

DOE OSTI2024

Foundations of Off-Grid Solar in Haiti: 8. Financial Modeling for Off-Grid Solar

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY↗

DOE OSTI2024

Foundations of Off-Grid Solar in Haiti: 7. Elements of Business Models for Off-Grid Solar

business↗

DOE OSTI2012

Cosmic rays, gamma rays and synchrotron radiation from the Galaxy

Galactic cosmic rays (CR), interstellar gamma-ray emission and synchrotron radiation are related topics. CR electrons propagate in the Galaxy and interact with the interstellar medium, producing inverse-Compton emission measured in gamma rays and synchrotron emission measured in radio. I present an overview of the latest results with Fermi/LAT on the gamma-ray diffuse emission induced by CR nuclei and electrons. Then I focus on the recent complementary studies of the synchrotron emission in the light of the latest gamma-ray results. Relevant observables include spectral indices and their variations, using surveys over a wide range of radio frequencies. This paper enphasizes the importance of using the parallel study of gamma rays and synchrotron radiation in order to constrain the low-energy interstellar CR electron spectrum, models of propagation of CRs, and magnetic fields.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

The mobility of small vacancy/helium complexes in tungsten and its impact on retention in fusion-relevant conditions

Tungsten is a promising plasma facing material for fusion reactors. Despite many favorable properties, helium ions incoming from the plasma are known to dramatically affect the microstructure of tungsten, leading to bubble growth, blistering, and/or to the formation of fuzz. In order to develop mitigation strategies, it is essential to understand the atomistic processes that lead to bubble formation and subsequent microstructural changes. In this work, we use large-scale Accelerated Molecular Dynamics simulations to investigate small (N=1,2) V N He M vacancy/helium complexes, which serve as the nuclei for larger helium bubble growth, over timescales reaching into the milliseconds under conditions typical of the operation of fusion reactors. These complexes can interconvert between different I L V N+L He M variants via Frenkel pair nucleation (leading to the creation of a additional vacancy/interstitial pair) and annihilation events; sequences of these events can lead to net migration of these embryonic bubbles. The competition between nucleation and annihilation produces a very complex dependence of the diffusivity on the number of heliums. Finally, through cluster dynamics simulations, we show that diffusion of these complexes provides an efficient pathway for helium release at fluxes expected in fusion reactors, and hence that accounting for the mobility of these complexes is crucial.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Disturbance of hibernating bats due to researchers entering caves to conduct hibernacula surveys

Estimating population changes of bats is important for their conservation. Population estimates of hibernating bats are often calculated by researchers entering hibernacula to count bats; however, the disturbance caused by these surveys can cause bats to arouse unnaturally, fly, and lose body mass. We conducted 17 hibernacula surveys in 9 caves from 2013 to 2018 and used acoustic detectors to document cave-exiting bats the night following our surveys. We predicted that cave-exiting flights (i.e., bats flying out and then back into caves) of Townsend’s big-eared bats (Corynorhinus townsendii) and western small-footed myotis (Myotis ciliolabrum) would be higher the night following hibernacula surveys than on nights following no surveys. Those two species, however, did not fly out of caves more than predicted the night following 82% of surveys. Nonetheless, the activity of bats flying out of caves following surveys was related to a disturbance factor (i.e., number of researchers × total time in a cave). We produced a parsimonious model for predicting the probability of Townsend’s big-eared bats flying out of caves as a function of disturbance factor and ambient temperature. That model can be used to help biologists plan for the number of researchers, and the length of time those individuals are in a cave to minimize disturbing bats.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Context-aware implicit neural representations to compress Earth systems model data

Not provided.

Science & Technology - Other Topics↗

DOE OSTI2017

Conductivity and dissociation in liquid metallic hydrogen and implications for planetary interiors

Liquid metallic hydrogen (LMH) is the most abundant form of condensed matter in our solar planetary structure. The electronic and thermal transport properties of this metallic fluid are of fundamental interest to understanding hydrogen’s mechanism of conduction, atomic or pairing structure, as well as the key input for the magnetic dynamo action and thermal models of gas giants. Here, we report spectrally resolved measurements of the optical reflectance of LMH in the pressure region of 1.4–1.7 Mbar. We analyze the data, as well as previously reported measurements, using the free-electron model. Fitting the energy dependence of the reflectance data yields a dissociation fraction of 65 ± 15%, supporting theoretical models that LMH is an atomic metallic liquid. We determine the optical conductivity of LMH and find metallic hydrogen’s static electrical conductivity to be 11,000–15,000 S/cm, substantially higher than the only earlier reported experimental values. Here, the higher electrical conductivity implies that the Jovian and Saturnian dynamo are likely to operate out to shallower depths than previously assumed, while the inferred thermal conductivity should provide a crucial experimental constraint to heat transport models.

08 HYDROGEN↗

DOE OSTI2024

Chaotic neural dynamics facilitate probabilistic computations through sampling

Cortical neurons exhibit highly variable responses over trials and time. Theoretical works posit that this variability arises potentially from chaotic network dynamics of recurrently connected neurons. Here, we demonstrate that chaotic neural dynamics, formed through synaptic learning, allow networks to perform sensory cue integration in a sampling-based implementation. We show that the emergent chaotic dynamics provide neural substrates for generating samples not only of a static variable but also of a dynamical trajectory, where generic recurrent networks acquire these abilities with a biologically plausible learning rule through trial and error. Furthermore, the networks generalize their experience in the stimulus-evoked samples to the inference without partial or all sensory information, which suggests a computational role of spontaneous activity as a representation of the priors as well as a tractable biological computation for marginal distributions. These findings suggest that chaotic neural dynamics may serve for the brain function as a Bayesian generative model.

60 APPLIED LIFE SCIENCES↗