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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 433 records · Page 24
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NASA NTRSDate not supplied

Inverse Estimation of Mars 2020 Entry Aeroheating Environments Using MEDLI2 Flight Data

The Mars Entry, Descent, and Landing Instrumentation 2 (MEDLI2) sensor suite collected heating and pressure data during entry into Mars' atmosphere of the Mars 2020 Perseverance rover. MEDLI2 included thermocouples, heat flux sensors, and pressure transducers on both the heatshield and the backshell. This paper covers the inverse estimation of heatshield and backshell surface heating based on the MEDLI2 Instrumented Sensor Plugs (MISPs), a network of thermocouples embedded in thermal protection system plugs across the aeroshell. Monte Carlo analysis was conducted to assess the sensitivity of the surface heat rate and temperature to uncertainties in thermocouple depth and material properties such as density, specific heat capacity, and thermal conductivity. Data from each MISP was also used to estimate the local time of transition from laminar to turbulent flow at each plug location.

Hannah S Alpert↗
DOE OSTI2021

TEAL Output Visualization

Tool for Economic Analysis (TEAL) is an open-source economics calculation package written and maintained by Idaho National Laboratory (INL). As a plugin of the Risk Analysis Virtual ENvironment (RAVEN), TEAL provides economics analysis models to RAVEN workflow users. In addition, TEAL is used in other RAVEN plugins such as LOGOS and the Holistic Energy Resource Optimization Network (HERON) to complement other analyses with economic calculations. This report documents the efforts in developing the capability of TEAL output visualization for users to better understand and investigate TEAL simulation results. In the past, outputs (e.g., various cash flows) were generated and printed to screen. According to the need and purpose for visualization, users can select various bar charts and donut charts to visualize the cash flows including inflows, outflows, and net cash flows in each year or in a selected time range, in addition to observing data stored in comma-separated value files (CSVs). The visualization capability is flexible and user-friendly by introducing several user-defined parameters, for instance, the start and the end project year of interest and the type of colors to be assigned to the cash flows.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Surrogate-driven Variance-based Sensitivity Analysis of Thermal Storage Tanks in Integrated Energy Systems

Sensitivity analysis and uncertainty quantification are essential steps for enhancing the accuracy of computational models by identifying and mitigating uncertainties. This study focuses on these steps for the Thermal Energy Delivery System at Idaho National Laboratory, specifically targeting the thermocline tank. Using a Modelica/Dymola simulation model, the study perturbed various design parameters and boundary conditions, including shape factor, porosity, outlet temperature, inlet mass flow rate, and system pressure, to predict and quantify uncertainty in the tank’s ax- ial temperature. A dataset of over 1,000 simulations was generated, and surrogate models were developed using the pyMAISE (Michigan Artificial Intelligence Standard Environment) library, which is an Automatic Machine Learning library for nuclear engineering applications. The optimal model, a feedforward neural network with two hidden layers, achieved an R2 score above 0.99 and a mean absolute error below 1 Kelvin. Sensitivity analyses using Sobol indices and Fourier amplitude sensitivity testing methods on this surrogate model revealed that the inlet mass flow rate at initial timestamps and porosity significantly impacts predicted temperatures across all sensors and time steps.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Large Scale Bilevel Optimization for N-K SCOPF Using Adversarial Robustness

Ensuring a secure dispatch against multiple simultaneous outages has long been desired to maintain grid security in the presence of severe events, such as extreme weather phenomena. Traditionally denoted as N-k security constrained optimal power flow (N-k SCOPF), this problem is intractable to solve due to its size being combinatorial in the number of simultaneous outages and due to the non-convex nature of the AC network constraints. This hinders the use of N-k SCOPF for operating realistic-scale systems. In this paper, we introduce a methodology to scalably solve an AC-feasible dispatch that improves security over k simultaneous outages. Our methodology poses N-k SCOPF as a bilevel optimization problem and solves it using an adversarial robustness approach. We develop new efficient methods to solve each level of the bilevel optimization by employing knowledge of the physics of the underlying system. This yields significant improvements in speed and convergence that enable us to address the N-k SCOPF problem at scale. We demonstrate the effectiveness of our method by conducting a comprehensive analysis of an N-3 SCOPF for a 500-bus network. Furthermore, we emphasize the ability of our physics-driven techniques to handle larger systems by successfully scaling up to 12,000 buses.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Unravelling the Proton Conduction Mechanism from Room Temperature to 553 K in a 3D Inorganic Coordination Framework

We report the preparation of proton-conducting materials that are functional and stable at intermediate temperatures (393–573 K) is a focal point of fuel cell development. The purely inorganic material, HNd(IO 3 ) 4 , which possesses a dense 3D framework structure, can reach a maximum of 4.6 × 10 –4 S·cm –1 at 353 K and 95% relative humidity and exhibit a high conductivity of 8.0 × 10 –5 S·cm –1 from 373 to 553 K under the flow of wet N 2 . HNd(IO 3 ) 4 exhibits a variety of improvements including high thermal stability, low solubility in water, and resistance to reducing atmosphere. The proton conductivity in such a wide temperature range originates from the intrinsic liberated protons in the structure and the resulting 1D hydrogen-bonding network confirmed by bond valence sum calculation and solid-state NMR analysis. Moreover, two different activation energies are observed in different temperature regions (0.23 eV below 373 K and 0.026 eV from 373 to 553 K), indicating that two types of proton motion are responsible for proton diffusion, as further domenstrated by temperature-dependent open-circuit voltage hysteresis in a tested fuel cell assembly as well as variable-temperature and double quantum filtered solid-state NMR measurements.

electrical conductivity↗
DOE OSTI2024

Topological Data Analysis for Particulate Gels

Soft gels, formed via the self-assembly of particulate materials, exhibit intricate multiscale structures that provide them with flexibility and resilience when subjected to external stresses. Here, this work combines particle simulations and topological data analysis (TDA) to characterize the complex multiscale structure of soft gels. Our TDA analysis focuses on the use of the Euler characteristic, which is an interpretable and computationally scalable topological descriptor that is combined with filtration operations to obtain information on the geometric (local) and topological (global) structure of soft gels. We reduce the topological information obtained with TDA using principal component analysis (PCA) and show that this provides an informative low-dimensional representation of the gel structure. We use the proposed computational framework to investigate the influence of gel preparation (e.g., quench rate, volume fraction) on soft gel structure and to explore dynamic deformations that emerge under oscillatory shear in various response regimes (linear, nonlinear, and flow). Our analysis provides evidence of the existence of hierarchical structures in soft gels, which are not easily identifiable otherwise. Moreover, our analysis reveals direct correlations between topological changes of the gel structure under deformation and mechanical phenomena distinctive of gel materials, such as stiffening and yielding. In summary, we show that TDA facilitates the mathematical representation, quantification, and analysis of soft gel structures, extending traditional network analysis methods to capture both local and global organization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Invertible Temper Modeling using Normalizing Flows and the Effects of Structure Preserving Loss

Advanced manufacturing research and development is typically small-scale, owing to costly experiments associated with these novel processes. Deep learning techniques could help accelerate this development cycle but frequently struggle in small-data regimes like the advanced manufacturing space. While prior work has applied deep learning to modeling visually plausible advanced manufacturing microstructures, little work has been done on data-driven modeling of how microstructures are affected by heat treatment, or assessing the degree to which synthetic microstructures are able to support existing workflows. We propose to address this gap by using invertible neural networks (normalizing flows) to model the effects of heat treatment, e.g., tempering. The model is developed using scanning electron microscope imagery from samples produced using shear-assisted processing and extrusion (ShAPE) manufacturing. This approach not only produces visually and topologically plausible samples, but also captures information related to a sample’s material properties or experimental process parameters. We also demonstrate that topological data analysis, used in prior work to characterize microstructures, can also be used to stabilize model training, preserve structure, and improve downstream results. We assess directions for future work and identify our approach as an important step towards end-to-end deep learning system for accelerating advanced manufacturing research and development.

Howland, Sylvia↗
DOE OSTI2019

Sample Complexity of Power System State Estimation Using Matrix Completion

In this paper, we propose an analytical framework to quantify the amount of data samples needed to obtain accurate state estimation in a power system - a problem known as sample complexity analysis in computer science. Motivated by the increasing adoption of distributed energy resources into the distribution-level grids, it becomes imperative to estimate the state of distribution grids in order to ensure stable operation. Traditional power system state estimation techniques mainly focus on the transmission network which involve solving an overdetermined system and eliminating bad data. However, distribution networks are typically underdetermined due to the large number of connection points and high cost of pervasive installation of measurement devices. In this paper, we consider the recently proposed state-estimation method for underdetermined systems that is based on matrix completion. In particular, a constrained matrix completion algorithm was proposed, wherein the standard matrix completion problem is augmented with additional equality constraints representing the physics (namely power-flow constraints). We analyze the sample complexity of this general method by proving an upper bound on the sample complexity that depends directly on the properties of these constraints that can lower number of needed samples as compared to the unconstrained problem. To demonstrate the improvement that the constraints add to state estimation, we test the method on a 141-bus distribution network case study and compare it to the traditional least squares minimization state estimation method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2000

NASA Goddard Space Flight Center

The NASA SLR Operational Center is responsible for: 1) NASA SLR network control, sustaining engineering, and logistics; 2) ILRS mission operations; and 3) ILRS and NASA SLR data operations. NASA SLR network control and sustaining engineering tasks include technical support, daily system performance monitoring, system scheduling, operator training, station status reporting, system relocation, logistics and support of the ILRS Networks and Engineering Working Group. These activities ensure the NASA SLR systems are meeting ILRS and NASA mission support requirements. ILRS mission operations tasks include mission planning, mission analysis, mission coordination, development of mission support plans, and support of the ILRS Missions Working Group. These activities ensure than new mission and campaign requirements are coordinated with the ILRS. Global Normal Points (NP) data, NASA SLR FullRate (FR) data, and satellite predictions are managed as part of data operations. Part of this operation includes supporting the ILRS Data Formats and Procedures Working Group. Global NP data operations consist of receipt, format and data integrity verification, archiving and merging. This activity culminates in the daily electronic transmission of NP files to the CDDIS. Currently of all these functions are automated. However, to ensure the timely and accurate flow of data, regular monitoring and maintenance of the operational software systems, computer systems and computer networking are performed. Tracking statistics between the stations and the data centers are compared periodically to eliminate lost data. Future activities in this area include sub-daily (i.e., hourly) NP data management, more stringent data integrity tests, and automatic station notification of format and data integrity issues.

Carter, David↗
DOE OSTI2019

Sample Complexity of Power System State Estimation using Matrix Completion: Preprint

In this paper, we propose an analytical framework to quantify the amount of data samples needed to obtain accurate state estimation in a power system-a problem known as \emph{sample complexity} analysis in computer science. Motivated by the increasing adoption of distributed energy resources into the distribution-level grids, it becomes imperative to estimate the state of distribution grids in order to ensure stable operation. Traditional power system state estimation techniques mainly focus on the transmission network which involve solving an overdetermined system and eliminating bad data. However, distribution networks are typically \emph{underdetermined} due to the large number of connection points and high cost of pervasive installation of measurement devices. In this paper, we consider the recently proposed state-estimation method for underdetermined systems that is based on \emph{matrix completion}. In particular, a \emph{constrained} matrix completion algorithm was proposed, wherein the standard matrix completion problem is augmented with additional equality constraints representing the physics (namely power-flow constraints). We analyze the sample complexity of this general method by proving an upper bound on the sample complexity that depends directly on the properties of these constraints that can lower number of needed samples as compared to the unconstrained problem. To demonstrate the improvement that the constraints add to state estimation, we test the method on a 141-bus distribution network case study and compare it to the traditional least squares minimization state estimation method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Analytical transport network theory to guide the design of 3-D microstructural networks in energy materials: Part 1. Flow without reactions

Here we present a fully analytical, heuristic model – the “Analytical Transport Network Model” – for steady-state, diffusive, potential flow through a 3-D network. Employing a combination of graph theory, linear algebra, and geometry, the model explicitly relates a microstructural network's topology and the morphology of its channels to an effective material transport coefficient (a general term meant to encompass, e.g., conductivity or diffusion coefficient). The model's transport coefficient predictions agree well with those from electrochemical fin (ECF) theory and finite element analysis (FEA), but are computed 0.5–1.5 and 5–6 orders of magnitude faster, respectively. In addition, the theory explicitly relates a number of morphological and topological parameters directly to the transport coefficient, whereby the distributions that characterize the structure are readily available for further analysis. Furthermore, ATN's explicit development provides insight into the nature of the tortuosity factor and offers the potential to apply theory from network science and to consider the optimization of a network's effective resistance in a mathematically rigorous manner. The ATN model's speed and relative ease-of-use offer the potential to aid in accelerating the design (with respect to transport), and thus reducing the cost, of energy materials.

25 ENERGY STORAGE↗
NASA NTRS2024

Influence of SMAP Soil Moisture Retrieval Assimilation on Runoff Estimation Across South Asia

This study was designed to characterize and quantify the influence of surface soil moisture assimilation on estimated runoff (surface flow and baseflow) and hydraulically-routed streamflow across three large river basins in South Asia that are at risk of impending water stress. Soil Moisture Active Passive (SMAP) surface soil moisture retrievals were assimilated into the Noah-MP land surface model. The gridded runoff was hydraulically routed to obtain volumetric streamflow values for a river network using a runoff routing module (Hydrological Modeling and Analysis Platform, HyMAP). The open loop (OL, model-only) and data assimilated (DA, includes soil moisture retrieval assimilation) Noah-MP runoff estimates highlighted the improvements in estimated total runoff across irrigated areas. Soil moisture assimilation impacted baseflow more relative to surface runoff. The HyMAP-based OL and DA streamflow generally underestimated the streamflow at upstream stations and overestimated the streamflow at downstream stations within the Indus basin due to missing physics related to reservoir operations. For stations located in the Ganges–Brahmaputra basins, the OL and DA estimation performance varied. The OL and DA results showed that the assimilation of soil moisture retrievals improves gridded runoff and volumetric streamflow across irrigated areas. Considerable relative change in streamflow (>70% increase in magnitude relative to OL) is noted after assimilation across the highly irrigated lower Indus basin and high precipitation regions in Bangladesh. Improving the modeling system’s representativeness of ground conditions via inclusion of water management information could improve large-scale streamflow modeling across South Asia.

soil moisture↗
DOE OSTI2019

Framework for Evaluating Grid Readiness for Integrating Electric Vehicles and Utility Scale Storage in India

Electric vehicles (EVs) and distributed solar are poised to grow substantially in India in the coming years following ambitious Government of India targets. The localized impact of these changes, both in terms of infrastructure investments and demand patterns, are not well understood, nor are tools for this type of analysis readily available. This paper describes the development of a framework that can analyze network readiness in terms of feeder impacts for distributed energy resources (DERs) including growing EV penetration, and the potential solutions introduced by utility-scale battery energy storage systems (BESSs). The building blocks of the feeder analysis require multiple data sets to be compiled into a usable network model. The work is driven by feeder head and distribution transformer (DT) loading data along with all the technical specifications and schematics of distribution feeders. These measured data sets are conditioned to remove any afflictions, and the cleaned load profiles are used to perform multi-year quasi-static time-series power flow analyses on detailed three-phase feeder models. EVs translated to grid-tied loads are represented with aggregated EV demand profile estimates based on possible charging scenarios in residential sector, and in workplace and/or public charging stations. Finally, BESSs are evaluated for their cost-effectiveness in the framework through an initial screening that includes the benefits of grid service applications to mitigate present and future overloading scenarios. The outcomes from this framework are expected to help utilities gauge the readiness of their distribution grid for integrating an increasing number of EVs as well as load growth during a projected multi-year time horizon, where BESSs can contribute in making the grid more reliable.

electric vehicles↗
NASA NTRS2006

Characterization of Days Based On Analysis of National Airspace System Performance Metrics

Days of operations in the National Airspace System can be described in term of traffic demand, runway conditions, equipment outages, and surface and enroute weather conditions. These causes manifest themselves in terms of departure delays, arrival delays, enroute delays and traffic flow management delays, Traffic flow management initiatives such as, ground stops, ground delay programs, miles-in-trail restrictions, rerouting and airborne holding are imposed to balance the air traffic demand with respect to the available capacity, In order to maintain operational efficiency of the National Airspace System, the Federal Aviation Administration (FAA) maintains delay sad other statistics in the Air Traffic Operations Network (OPSNET) and the Aviation System Performance Metrics (ASPM) databases. OPSNET data includes reportable delays of fifteen minutes ox more experienced by Instrument Flight Rule (IFR) flights. Numbers of aircraft affected by departure delays, enroute delays, arrival delays and traffic flow delays are recorded in the OPSNET data. ASPM data consist of number of actual departures, number of canceled departures, percentage of on time departures, percentage of on time gate arrivals, taxi-out delays. taxi-in delays, gate delays, arrival delays and block delays. Surface conditions at the major U.S. airports are classified in terms of Instrument Meteorological Condition (IMC) and Visual Meteorological Condition (VMC) as a function of the time of the day in the ASPM data. The main objective of this paper is to use OPSNET and ASPM data to classify the days in the datasets into few distinct groups, where each group is separated from the other groups in terms of a distance metric. The motivations for classifying the days are two-fold, 1) to enable selection of days of traffic with particular operational characteristics for concept evaluation using system-wide simulation systems such as the National Aeronautics and Space Administration's Airspace Concepts Evaluation Tool (ACES) and 2) to enable evaluation of a given day with respect to the characteristics of the classified groups. The first part of the paper is devoted to the analysis of major trends seen in the OPSNET and ASPM data. The second part of the paper is devoted to describing features or measures derived from the OPSNET and ASPM data that are suitable for characterizing days, and the classification algorithm used for grouping the days. Finally, the method for evaluating the characteristics of a given day with respect to the properties of the groups is described.

Chatterji, Gano B.↗
DOE OSTI2015

Behavior-dependent Routing: Responding to Anomalies with Automated Low-cost Measures

This is a conference paper submission describing research and software implementation of a cybersecurity concept that uses behavior models to trigger changes in routing of network traffic. As user behavior deviates more and more from baseline models, traffic is routed through more elevated layers of analysis and control.

information flow controls↗
DOE OSTI2019

Spatial characterization of heterogeneous nanopore surfaces from XCT scans of Niobrara shale

The multiphase fluid flow through natural and geo-architected nanopore structures is fundamentally controlled by the geometry of such nanopore structures, pore wettability and interfacial tension. Furthermore, multiphase fluid flow through the nanopore structures is compromised when the pore surfaces are heterogeneous, having surface regions of different composition, polarity, and wetting characteristics. In this regard, compositional analysis of the pore channel surface has been a big challenge in the past, since 3D analysis is required. Currently, due to advances in nano X-ray CT, with a voxel resolution of 20 nm, it is possible to identify nanopore network structures and quantify their surface composition in 3D. In this paper, the development of procedures for detailed analysis to quantify the composition of the pore channel surface regions, including the corresponding area, is described using nanoCT images. Image analysis procedures, including preprocessing of the 3D tomographic images, correction for the partial volume effect (PVE), and the use of a new algorithm for determination of pore channel surface composition and area are reported on. Videos of the nanopore structure have been provided as supplementary information to further describe the heterogeneity of the nanopore surfaces.

36 MATERIALS SCIENCE↗
NASA NTRS1989

Tools for 3D scientific visualization in computational aerodynamics

The purpose is to describe the tools and techniques in use at the NASA Ames Research Center for performing visualization of computational aerodynamics, for example visualization of flow fields from computer simulations of fluid dynamics about vehicles such as the Space Shuttle. The hardware used for visualization is a high-performance graphics workstation connected to a super computer with a high speed channel. At present, the workstation is a Silicon Graphics IRIS 3130, the supercomputer is a CRAY2, and the high speed channel is a hyperchannel. The three techniques used for visualization are post-processing, tracking, and steering. Post-processing analysis is done after the simulation. Tracking analysis is done during a simulation but is not interactive, whereas steering analysis involves modifying the simulation interactively during the simulation. Using post-processing methods, a flow simulation is executed on a supercomputer and, after the simulation is complete, the results of the simulation are processed for viewing. The software in use and under development at NASA Ames Research Center for performing these types of tasks in computational aerodynamics is described. Workstation performance issues, benchmarking, and high-performance networks for this purpose are also discussed as well as descriptions of other hardware for digital video and film recording.

Bancroft, Gordon↗
NASA NTRSDate not supplied

Analysis of Atmospheric Conditions Responsible for an Ozone Exceedance Event in Southeast Virginia on June 15, 2022

On June 15, 2022, the Virginia Department of Environmental Quality monitoring site at Suffolk/Holland, a rural site in southeast Virginia, recorded its first ozone exceedance since 2016. An ozone exceedance day occurs when the daily maximum 8-hour average surface ozone concentration is greater than 70 ppb. On this day, it was 75 ppb. This event is also noteworthy due to the rapid increase in surface ozone between 6 am and 9 am EDT as well as the hourly maximum ozone concentration of 82 ppb that was measured at 1 pm EDT. In this analysis, we utilize various observational and model data to determine the atmospheric conditions responsible for this ozone exceedance event. The analysis is conducted in two parts: (1) an evaluation of the accuracy of the GEOS-CF and WRF-Chem model forecasts and (2) an investigation of the mechanisms responsible for the high surface ozone at the Virginia DEQ Suffolk/Holland monitoring site. Comparisons of model forecasted ozone time-height cross sections with measured ozone by lidars from the Tropospheric Ozone Lidar Network (TOLNet) at NASA Langley and NASA Goddard reveal that both models forecasted ozone reasonably well near the surface. Model and observational data reveal that an upper-level ridge was present over much of the eastern United States, with a broad anticyclonic circulation near the surface, resulting in north-northeasterly flow over southeast Virginia. Back trajectory calculations using the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) and model data suggest that a low-level plume rich in ozone and precursor species originating from the New York City and New Jersey region was transported along the east coast between June 14 and June 15, arriving in southeast Virginia around 6 am EDT on June 15. In the early morning hours, this plume mixed down to the surface, elevating the background ozone mixing ratio as well as the mixing ratios of several precursor species. Other potential contributions are also explored and discussed. Lastly, the authors note that this work represents the analysis of the authors and not the Virginia Department of Environmental Quality.

Daniel B Phoenix↗