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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Theoretical predictions

In order to understand the impact of man made chemicals on the atmospheric ozone layer, it is essential to develop models that can perform long term predictions of future ozone changes. An advantage of using two dimensional models is that they can be used to predict latitudinal and seasonal changes in ozone. The formulation and recent improvements are described in 2-D models, which are used herein, along with the three dimensional models that are currently being developed to better simulate transport of chemically active trace gases, especially in polar regions. The range in 2-D model calculations is described. Selected fields calculated by these models are compared with observations. A number of scenarios have been defined, which encompass possible emission rates of different halocarbons. Because of the large uncertainties in the rates for heterogeneous processes, the calculated responses of the models include only the effects of homogeneous chemistry. One important distinction among the models is their ability to account for temperature feedbacks on the calculated ozone changes.

Brasseur, G.↗
DOE OSTI2022

Revenue prediction for integrated renewable energy and energy storage system using machine learning techniques

Revenue estimation for integrated renewable energy and energy storage systems is important to support plant owners or operators’ decisions in battery sizing selection that leads to maximized financial performances. A common approach to optimizing revenues of a hybrid hydro and energy storage system is using mixed-integer linear programming (MILP). Although MILP models can provide accurate production cost estimations, they are typically very computationally expensive. To provide a fast yet accurate first-step information to hydropower plant owners or operators who consider integrating energy storage systems, we propose an innovative approach to predicting optimal revenues of an integrated energy generation and storage system. In this study, we examined the performance of two prediction techniques: Generalized Additive Models (GAMs) and machine learning (ML) models developed based on artificial neural networks (ANN). Predictive equations and models are generated based on optimized solutions from a market participation optimization model, the Conventional Hydropower Energy and Environmental Resource System (CHEERS) model. The two predicting techniques reduce the computational time to evaluate annual revenue for one set of battery configurations from 3 h to 1 to 4 min per run while also being implementable with significantly less data. The model validation prediction errors of developed GAMs and ML models are generally below 5%; for model testing predictions, the ML models consistently outperform the regression equations in terms of root mean square errors. This new approach allows plant owners, operators, or potential investors to quickly access multiple battery configurations under different energy generation and market scenarios. This new revenue prediction method will therefore help reduce the barriers, and thereby promoting the deployment of battery hybridization with existing renewable energy sources.

13 HYDRO ENERGY↗
DOE OSTI2022

Process Interactions Can Change Process Ranking in a Coupled Complex System Under Process Model and Parametric Uncertainty

For a complex hydrologic system with multiple processes and process interactions, global sensitivity analysis is often used to identify important or influential parameters for model development and improvement. The identification is complicated by process model uncertainty, when a system process can be represented by multiple process models. This study develops a new total-effect process sensitivity index to identify influential processes under model uncertainty. This is done by extending Sobol's total-effect parameter sensitivity index for one system model to total-effect process sensitivity index for multiple system models to account for uncertainty in process models and model parameters. The total-effect process sensitivity index includes not only the first-order process sensitivity index for measuring the importance of individual processes but also higher-order indices that account for process interactions. The total-effect process sensitivity index can identify an influential process that itself and its interactions with other processes influence a model output. Here, the total-effect process sensitivity index is applied to two numerical examples: (a) Sobol's G*-functions with analytical solutions of first-order and total-effect process sensitivity indices, and (b) groundwater flow models with interactions between recharge, geology, and snowmelt processes. The second evaluation shows that, due to second-order and higher-order process interactions, the first-order and total-effect process sensitivity indices give different process ranking. It is thus necessary to estimate both first-order and total-effect process sensitivity indices to appreciate the difference between the first-order impact of a process alone and the overall total-effect impact of the process itself and its interactions with other processes on a model output.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

AgMIP Local, National and Global Assessments of Food System Challenges in A Changing Climate

Food systems connect a diverse network of stakeholders around the world, with complex dynamics governing production, processing, transportation, trade and consumption of food products. Extreme conditions can disrupt multiple components of the food system, with systemic connections providing structure that can both buffer and exacerbate risks. Climate change is shifting current practices in agricultural production and the broader food system, encouraging shifts toward more diverse value chains capable of drawing from multiple food producing regions to reduce the chance that an entire value chain be subject to simultaneous impacts. The IPCC Working Group I report assessed that heat waves, fires, floods and severe storms are becoming more pronounced and widespread with each degree of global warming, increasing food system risk. Food system disruptions can also come from outside the climate system, including from viral outbreaks, geopolitical conflict and socioeconomic changes. This presentation will highlight how the Agricultural Model Intercomparison and Improvement Project (AgMIP) develops modeling approaches that connect climate, biophysical and socioeconomic models in order to capture complex responses of the food system and potential interventions by diverse stakeholders in the public and private sectors. Models are applied on a range of scales that match decision contexts. These include households in specific agricultural production regions (e.g., within a country), national-level policymakers considering policies, sustainability and development priorities for a country’s agricultural sector, and global food market models that balance production and consumption around the world with competition for land, water, energy and sustainability priorities. Models are capable of capturing many important responses, but further development is needed to represent key systemic risks and the possibility for additional interventions beyond the farm gate. Improved food system models will provide important insights into our society’s ability to cope with climate change, as well as our ability to identify agricultural adaptation and mitigation opportunities to reduce overall risk.

Food systems↗
DOE OSTI2025

PBE-HIL (Powering the Blue Economy Hardware-in-the-Loop models) [SWR-25-37]

Powering the Blue Economy Hardware-in-the-Loop models (PBE-HIL) is a repository of Power Hardware-in-the-loop models developed for typical Powering the Blue Economy market loads and power requirements. The HIL models were developed to be as generic and functional as possible, meaning that the user can easily configure these models to represent their unique PBE design. These PBE load and power requirement HIL models can then be used to inform marine energy converter (MEC) and power electronics design, as well as be used in laboratory testing using HIL equipment, leading to improved understanding of MEC performance and lower risk prior to open-water MEC deployment.

Labuschagne, Hannes [National Renewable Energy Lab↗
DOE OSTI2022

Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART)

This report contains key findings from a project titled Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART), which was carried out through a collaborative effort of a team of researchers from Texas A&M Engineering Experiment Station, Temple University, and Quanta Technology, LLC. The in-kind support came from OSIsoft (acquired by AVEVA), which provided their PI Historian software to demonstrate the use case of streaming PMU data. The first section of the report describes the project goals and objectives related to the development of Machine Learning (ML) models capable of detecting and classifying events by processing phasor measurements captured in the field by Phasor Measurement Units (PMUs). The data for this study was contributed by the utilities/ISOs from the Western and Eastern interconnects and ERCOT, further referred to as Interconnect B (IC B), Interconnect A (IC A), and Interconnect C (IC C), respectively. The approach that the BDSMART Research Team proposed and the key research tasks defined by the team are outlined in this section. The next section describes the technical approach. We first discuss the data constraints related to the PMU measurements and data interpretation constraints imposed by the data contributors. They provided neither the topological information of the grid nor PMU placement locations and captured recorded data at very few locations in the system with the reporting rate of either 30 or 60 fps. The recordings are mostly positive sequence voltage, frequency, and ROCOF, and in some limited cases, three-phase voltages and currents. We then reflect on the bad data issues that stem from poor recording practices and vague definitions of the PMU status bits to supposedly be used for bad data identification. Finally, the data discovery points to imprecise time stamps with incomplete event start/end time, as well as inconsistent and incomplete event labeling, which combined make the implementation of the data models using supervising learning quite challenging. Following the data discovery study, we hypothesize that because the IC B data has the most complete labels, we should focus our model development on that data and then test it on data from other interconnects. We also define the common metrics used to evaluate the results from the ML algorithm tests. We concluded this section by summarizing the common ML models we used and explaining how we implemented and tested them. The issues from this section are expanded in the Training Dataset Report from this project. The final section of this report deals with the accomplishments and conclusions. As the accomplishments, we formulate the problem we are solving and what is achieved by solving the problem. We then reflect on each of the analytics tools we developed and point out the performance of each tool when applied to solving the mentioned problems. We reference this work for further details to the papers we published on each tool. In the conclusions, we give recommendations on how to improve future PMU recording practices to facilitate the ML algorithm implementation and guidance for the future standardization work aimed at clarifying the ambiguities associated with the PMU status bits. We finally list future tasks that can bring about further improvements in the proposed algorithms. The issues from this section are expanded in the Training, and Test Dataset Report filed at the project completion date.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1994

Development and initial test of the University of Wisconsin global isentropic-sigma model

The description of a global version of the University of Wisconsin (UW) hybrid isentropic-sigma (theta-sigma) model and the results from an initial numerical weather prediction experiment are presented in this paper. The main objectives of this initial test are to (1) discuss theta-sigma model development and computer requirements, (2) demonstrate the ability of the UW theta-sigma model for global numerical weather prediction using realistic orography and parameterized physical processes, and (3) compare the transport of an inert trace constituent against a nominally 'identical' sigma coordinate model. Initial and verifying data for the 5-day simulations presented in this work were supplied by the Goddard Earth Observing System (GEOS-1) data assimilation system. The time period studied is 1-6 February 1985. This validation experiment demonstrates that the global UW theta-sigma model produces a realistic 5-day simulation of the mass and momentum distributions when compared to both the identical sigma model and GEOS-1 verification. Root-mean-square errors demonstrate that the theta-sigma model is slightly more accurate than the nominally identical sigma model with respect to standard synoptic variables. Of particular importance, the UW theta-sigma model displays a distinct advantage over the conventional sigma model with respect to the prognostic simulation of inert trace constituent transport in amplifying baroclinic waves of the extratropics. This is especially true in the upper troposphere and stratosphere where the spatial integrity and conservation of an inert trace constituent is severely compromised in the sigma model compared to the theta-sigma model.

Zapotocny, Tom H.↗
NASA NTRS2015

Characterize Human Forward Contamination Project

Let's face it: wherever we go, we will inevitably carry along the little critters that live in and on us. Conventional wisdom has long held that it's unlikely those critters could survive the space environment, but in 2007 microscopic animals called Tardigrades survived exposure to space and in 2008 Cyanobacteria lived for 548 days outside the International Space Station (ISS). But what about the organisms we might reasonably expect a crewed spacecraft to leak or vent? Do we even know what they are? How long might our tiny hitch-hikers survive in close proximity to a warm spacecraft that periodically leaks/vents water or oxygen-and how might they mutate with long-duration exposure? Unlike the Mars rovers that we cleaned once and sent on their way, crew members will provide a constantly regenerating contaminant source. Are we prepared to certify that we can meet forward contamination protocols as we search for life at new destinations? This project has four technical objectives: 1. TEST: Develop a test plan to leverage existing equipment (i.e. ISS) to characterize the kinds of organisms we can reasonably expect pressurized, crewed volumes to vent or leak overboard; 2. ANALYSIS: Develop an analysis plan to study those organisms in relevant destination environments, including spacecraft-induced conditions; 3. MODEL: Develop a modeling plan to model organism transport mechanisms in relevant destination environments; 4. SHARE: Develop a plan to disseminate findings and integrate recommendations into exploration requirements & ops. In short, we propose a system engineering approach to roadmap the necessary experiments, analysis, and modeling up front--rather than try to knit together disparate chunks of data into a sensible conclusion after the fact.

Rucker, Michelle↗
NASA NTRS2015

Characterize Human Forward Contamination Project

Let's face it: wherever we go, we will inevitably carry along the little critters that live in and on us. Conventional wisdom has long held that it's unlikely those critters could survive the space environment, but in 2007 microscopic animals called Tardigrades survived exposure to space and in 2008 Cyanobacteria lived for 548 days outside the International Space Station (ISS). But what about the organisms we might reasonably expect a crewed spacecraft to leak or vent? Do we even know what they are? How long might our tiny hitch-hikers survive in close proximity to a warm spacecraft that periodically leaks/vents water or oxygen-and how might they mutate with long-duration exposure? Unlike the Mars rovers that we cleaned once and sent on their way, crew members will provide a constantly regenerating contaminant source. Are we prepared to certify that we can meet forward contamination protocols as we search for life at new destinations? This project has four technical objectives: 1. TEST: Develop a test plan to leverage existing equipment (i.e. ISS) to characterize the kinds of organisms we can reasonably expect pressurized, crewed volumes to vent or leak overboard; as part of testing, we'll need to develop an Extravehicular Activity (EVA)-compatible tool that can withstand the pressure and temperature extremes of space, as well as collect, separate, and store multiple samples; 2. ANALYSIS: Develop an analysis plan to study those organisms in relevant destination environments, including spacecraft-induced conditions; 3. MODEL: Develop a modeling plan to model organism transport mechanisms in relevant destination environments; 4. SHARE: Develop a plan to disseminate findings and integrate recommendations into exploration requirements & ops. In short, we propose a system engineering approach to roadmap the necessary experiments, analysis, and modeling up front--rather than try to knit together disparate chunks of data into a sensible conclusion after the fact.

Rucker, Michelle↗
DOE OSTI2021

SAM Developments for MSR Modeling (FY21)

An advanced system analysis tool, SAM, is under development for advanced non-LWR reactor safety analysis, including molten salt reactors (MSR). To support the development and utilization of the SAM code for MSR safety analysis and licensing, continuous efforts have been devoted to enhancing code capabilities and updating reference models for the MSRs. This report documents the FY21 progress in SAM code development, capability enhancements, and reference model development to support transient safety analysis of MSRs, including code enhancements in reactor kinetics and reactivity feedback modeling for liquid fuel reactors, updates of the Molten Salt Reactor Experiment (MSRE) primary system model, developments and updates of the molten salt fast reactor (MSFR) model based on EVOL design, and implementation of a drift flux model for modeling gas transport in MSR systems.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS2020

High Resolution Global Coupled Chemistry-Meteorology Simulations Using the NASA GEOS Composition Forecast System, GEOS-CF

We will give an overview of the NASA Global Earth Observing System Composition Forecast system (GEOS-CF), a high-resolution (0.25 degree) global composition model developed by the NASA Global Modeling and Assimilation Office (GMAO). This system combines the GEOS weather and aerosol model with the GEOS-Chem chemistry module (version 12) to provide a holistic view of atmospheric composition that captures a wide range of air pollutants such as ozone, nitrogen oxides, volatile organic compounds, and fine particulate matter. The spatial resolution of 0.25 degrees (approx. 25 km) is fine enough to resolve local features such as nighttime ozone titration previously resolved only by urban or regional models. Furthermore, since there are no boundary conditions for a global model, the GEOS-CF captures large-scale processes such as long-range transport of air pollutants from forest fires. Comparisons against surface observations highlight the model’s overall capability to reproduce the diurnal variability of air pollutants under a variety of meteorological conditions. In addition, we show how machine learning techniques can be used to correct for sub-grid variability, which further improves model estimates at a given surface observation site. The GEOS-CF system offers a new tool for scientists and the public health community alike and is being developed jointly with several government and non-profit partners. As an example, we will show the use of GEOS-CF during the Satellite Coastal and Oceanic Atmospheric Pollution Experiment (SCOAPE). The campaign, conducted in collaboration between NASA and the Bureau of Ocean Energy Management (BOEM), aims to investigate the response of onshore air quality to Outer Continental Shelf (OCS) oil and gas exploration, development and production. Detailed gas-phase chemistry, as provided by GEOS-CF, is critical to understand the formation of air pollution related to hydrocarbon emissions from offshore oil and gas activities. The accuracy of GEOS-CF can be further improved by incorporating detailed offshore emissions compiled by BOEM.

Knowland, K. Emma↗
DOE OSTI2019

Kinetic Study on Alloying Element Transfer During an Electroslag Remelting Process

Experimental and theoretical studies have been carried out to investigate the effects of slag on the alloying elements in ingots during the electroslag remelting (ESR) process with a focus on developing a mass-transfer model to understand the mechanism of slag-metal reaction. Stainless steel 1Cr21Ni5Ti was used as the electrode and remelted with two different kinds of slags using a 50-kg ESR furnace. The contents of sulfur, aluminum, titanium and silicon along the axial direction of the produced ingots were analyzed. On the basis of the penetration and film theories, the theoretical model developed in this work elucidates the kinetics of the slag-metal reaction revealing the mechanism of alloying element transfer during the ESR process. The calculation results obtained from the model agree well with the experimental results. The model indicates that the resultant [O] coming from the desulfurization reaction of (O{sup 2−}) + [S] = (S{sup 2−}) + [O] causes the oxidation of alloying elements in steel by [M] + [O] = (MO). The distribution ratio of sulfur L{sub S} decreases with the increase of slag temperature in the first slag-temperature-rising period, and the concentration of sulfur in the ingot at the beginning of the ESR process is lower than in the rest of the process because of the combination of the large distribution ratio of sulfur L{sub S} and excellent kinetic conditions. The concentration of aluminum along the height of the ingot has an increasing trend in the first slag-temperature-rising period, while it has a decreasing trend in the rest of the process. Two methods can solve this problem: one is starting up the ESR furnace by high temperature molten slag technology and the other is continually adding extra titania into the molten slag in the first slag-temperature-rising period.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Development of new baseline models for U.S. medium office buildings based on commercial buildings energy consumption survey data

Building energy estimation for the building sector under various scenarios are needed for building energy regulation and policy making. This often starts with representative baselines (either empirical baseline or modeled baseline). Commercial Buildings Energy Consumption Survey (CBECS) data is a widely used empirical baseline for U.S. commercial buildings, but none of the existing baseline model are developed to represent the CBECS data. This paper aims to develop new baseline models for the U.S. medium office buildings, which can produce modeled baselines consistent with the CBECS data. Here, we introduced the methodology to create baseline models and the criteria to evaluate the performance of baseline models. The methodology consists of three phases: (1) identification of model inputs, (2) model calibration, and (3) model validation with uncertainty analysis. The evaluation index is the coefficient of variation of the root-mean-square deviation (CV(RMSD)) of site energy use intensities (EUIs) between the modeled baseline and empirical baseline. Then 30 new baseline models for two vintages (pre- and post-1980) and 15 climate zones were created. The evaluation shows that the CV(RMSD) is lower than 0.05 for the modeled baselines produced by the new baseline models. As a comparison, the CV(RMSD) is higher than 0.1 for the existing modeled baselines generated by DOE Commercial Reference Building Models. Further analysis shows that the new baseline models are able to capture the uncertainties of the representative features of existing buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Multiphysics Simulations of MSRE with NEAMS Thermal Hydraulics Tools

This report documents the benchmarks being developed and simulations performed using tools and codes developed under the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, utilizing MSRE experimental data. In FY23, three main work scopes were investigated under the NEAMS MSR work package at ANL. The first scope investigated the Griffin-SAM coupling model for simulating the pump startup transient experiment of MSRE. The analyses start with a simple model (single-channel, single-lattice), gradually adding more details (multi-channel, full-core) into the model. The results show that the reactivity loss curve is very sensitive to the axial boundary conditions and the radial core discretization. The simple model can predict a similar reactivity trend as that of the more sophisticated model, which is likely due to error cancellation. Accurately modeling the axial boundary condition may further improve the reactivity trend but would require significant efforts to generate the mesh of the MSRE inlet and upper plenum. The core channel radial discretization for the Griffin-SAM coupled model also depends on the flow distribution. Given the complex geometry in the inlet plenum, the flow distribution needed to be calculated from CFD analysis, which was performed using the NekRS code. This analysis employed a MSRE CAD model developed by Copenhagen Atomics. The CAD model was disassembled to keep the inlet plenum region only, which was subsequently cleaned and modified so that the mesh generated is under the memory limit. The results are merged to a few radial regions to show that the flow rate is highest in the central region. This would be useful for future improvement of the Griffin-SAM coupling model of the MSRE core. The last task investigated is tritium transport modeling using the standalone SAM code. This task aimed to initiate the effort to demonstrate and validate the tritium transport model implemented in SAM. The preliminary investigation employed an MSRE model consisting of the primary loop. Three tritium transport pathways were examined including the retention in the graphite, the permeation through the HX tube wall, and the removal from the off-gas system. The results compare well with the MSRE data, but improvements are still needed on the initial conditions (i.e., the present state may not have reached equilibrium), the boundary conditions, the off-gas system modeling, and a better numerical strategy to reach the equilibrium state.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1989

Lubricant Jet Flow Phenomena in Spur and Helical Gears with Modified Addendums; for Radially Directed Individual Jets

This paper develops the mathematical relations for the Virtual Kinetic Model as an improvement over the vectorial model developed earlier. The model solution described provides the most energy efficient means of cooling gears, i.e., it requires the least pressure or pumping power to distribute the coolant onto the tooth surface. Further, this nozzle orientation allows impingement to the root of the tooth if needed and provides the most cooling control when compared to into-mesh and out-of-mesh cooling.

Akin, Lee S.↗
NASA NTRS1989

Lubricant jet flow phenomena in spur and helical gears with modified addendums - For radially directed individual jets

This paper develops the mathematical relations for the Virtual Kinetic Model as an improvement over the vectorial model developed earlier. The model solution described provides the most energy efficient means of cooling gears, i.e., it requires the least pressure or pumping power to distribute the coolant onto the tooth surface. Further, this nozzle orientation allows impingement to the root of the tooth if needed and provides the most cooling control when compared to into-mesh and out-of-mesh cooling.

Akin, L. S.↗
NASA NTRS1989

Research in LMSS propagation

The Virginia Tech Satellite Communications Group has participated in the Land Mobile Satellite System (LMSS) program through JPL sponsorship since 1985. Involvement has mainly been in modeling and simulation of propagation characteristics and effects. Models developed to predict cummulative fade distributions for fading LMSS signals include LMSSMOD and the Simple Models which approximate LMSSMOD. Models to predict the mean and standard deviation of signal attenuation through roadside vegetation, namely the Average Path Model, were developed. In the area of simulation, efforts have centered around the development of a software simulator that uses data bases derived from experimental data to generate simulated data with arbitrary statistical behavior. This work has progressed to the development of an integrated analysis and simulation package, LIPS. The basic theory and results for the models and simulator have been previously documented in reports and papers. All LMSS activities are summarized and details of this year's efforts are given.

Barts, R. M.↗
DOE OSTI2022

The Virtual Test Bed (VTB) repository: a library of multiphysics reference reactor models using NEAMS tools

With the next generation of nuclear reactors under development, modeling and simulation (MS) tools are being developed by the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program in order to support their design, licensing, and future operation. Mirroring the physical test beds currently under construction (i.e., EBR-II and ZPPR), the Virtual Test Bed (VTB) was launched by the National Reactor Innovation Center (NRIC) in collaboration with NEAMS to support the advanced reactor community. This collaborative effort, which involves multiple teams at both Idaho National Laboratory and Argonne National Laboratory aims to use NEAMS tools to model a wide range of reactor designs. Those models are automatically tested to ensure their continued functionality as the tools are further developed. Examples are extensively documented, each acting as a tutorial for applying the relevant NEAMS tools to that reactor design. Currently, five advanced reactor types (with a total of eight specific design variants) are simulated by a variety of different models. These models range from steady-state, core multiphysics simulations to integrated plant analysis during loss-of flow transients. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗