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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 145 records · Page 8

Representing Operations Procedures Using Temporal Dependency Algorithms

The research presented in this paper is investigating new ways of specifying operations procedures that incorporate the insight of operations, engineering, and science personnel to improve mission operations. The paper describes the rationale for using Temporal Dependency Networks to represent the procedures, a description of how the data is acquired, and the knowledge engineering effort required to represent operations procedures.

automation knowledge engineering operations↗

Application of the generalized vertical coordinate ocean model for better representing satellite data

It is found that two adaptive parametric functions can be introduced into the basic ocean equations for utilizing the optimal or hybrid features of commonly used z-level, terrain- following, isopycnal, and pressure coordinates in numerical ocean models. The two parametric functions are formulated by combining three techniques: the arbitrary vertical coordinate system of Kasahara (1 974), the Jacobian pressure gradient formulation of Song (1 998), and a newly developed metric factor that permits both compressible (non-Boussinesq) and incompressible (Boussinesq) approximations. Based on the new formulation, an adaptive modeling strategy is proposed and a staggered finite volume method is designed to ensure conservation of important physical properties and numerical accuracy. Implementation of the combined techniques to SCRUM (Song and Haidvogel1994) shows that the adaptive modeling strategy can be applied to any existing ocean model without incurring computational expense or altering the original numerical schemes. Such a generalized coordinate model is expected to benefit diverse ocean modelers for easily choosing optimal vertical structures and sharing modeling resources based on a common model platform. Several representing oceanographic problems with different scales and characteristics, such as coastal canyons, basin-scale circulation, and global ocean circulation, are used to demonstrate the model's capability for multiple applications. New results show that the model is capable of simultaneously resolving both Boussinesq and non-Boussinesq, and both small- and large-scale processes well. This talk will focus on its applications of multiple satellite sensing data in eddy-resolving simulations of Asian Marginal Sea and Kurosio. Attention will be given to how Topex/Poseidon SSH, TRMM SST; and GRACE ocean bottom pressure can be correctly represented in a non- Boussinesq model.

numerical ocean model generalized coordinate syste↗

Representative Agricultural Pathways and Scenarios for Regional Integrated Assessment of Climate Change Impacts, Vulnerability, and Adaptation: Chapter - 5

The global change research community has recognized that new pathway and scenario concepts are needed to implement impact and vulnerability assessment where precise prediction is not possible, and also that these scenarios need to be logically consistent across local, regional, and global scales. For global climate models, representative concentration pathways (RCPs) have been developed that provide a range of time-series of atmospheric greenhouse-gas concentrations into the future. For impact and vulnerability assessment, new socio-economic pathway and scenario concepts have also been developed, with leadership from the Integrated Assessment Modeling Consortium (IAMC).This chapter presents concepts and methods for development of regional representative agricultural pathways (RAOs) and scenarios that can be used for agricultural model intercomparison, improvement, and impact assessment in a manner consistent with the new global pathways and scenarios. The development of agriculture-specific pathways and scenarios is motivated by the need for a protocol-based approach to climate impact, vulnerability, and adaptation assessment. Until now, the various global and regional models used for agricultural-impact assessment have been implemented with individualized scenarios using various data and model structures, often without transparent documentation, public availability, and consistency across disciplines. These practices have reduced the credibility of assessments, and also hampered the advancement of the science through model intercomparison, improvement, and synthesis of model results across studies. The recognition of the need for better coordination among the agricultural modeling community, including the development of standard reference scenarios with adequate agriculture-specific detail led to the creation of the Agricultural Model Intercomparison and Improvement Project (AgMIP) in 2010. The development of RAPs is one of the cross-cutting themes in AgMIP's work plan, and has been the subject of ongoing work by AgMIP since its creation.

agriculture↗

Calibration of a Structural Finite Element Model for a Representative Inflatable Space Structure Utilizing Probabilistic Methods and Surrogate Models

A structural finite element model representing a novel inflatable airlock concept has been calibrated using full‐scale test data. The concept, denoted as the Non‐Axisymmetric Inflatable Pressure Structure (NAIPS), was developed under NASA’s Minimalistic Advanced Softgoods Hatch (MASH) Program. The current studies extended previous numerical efforts by incorporating the midbody section of the NAIPS to the dome section and calibrating the model with test data using a process that included surrogate models. Brief overviews of the finite element model and calibration process are provided. The completion of the calibration process provided a model that adequately replicated the test data. The successful demonstration of calibration of a finite element model representing an inflatable habitat provides confidence in the ability to use numerical simulations and associated surrogate models to support design and certification of inflatable space habitats.

Lyle, Karen H.↗

Simulation of a Representative Future Trajectory-Based Operations Environment

Trajectory-Based Operations in the National Airspace System is a key aspect of advanced air traffic management research. Trajectory-Based Operations focuses on modernizing the current operating paradigm to increase efficiency, predictability, resilience, and flexibility while migrating toward greater operational autonomy across the airspace. Research conducted at the National Aeronautics and Space Administration supports the transition from current airspace operations to Trajectory-Based Operations targeting a 2035-2045 implementation timeframe. Simulation scenarios that demonstrate a representative Trajectory-Based Operations environment in that timeframe, and enable the evaluation of advanced airborne tools such as strategic airborne trajectory management services, are necessary to support this research effort. This report describes characteristics and assumptions made about the future operating environment that were applied to a scenario development methodology to study a representative 2040 Trajectory-Based Operations environment in a simulation use case. This report also includes descriptions of the study design and analysis approach, discussion of the simulation results, and application of these scenarios to future research activities.

Trajectory Based Operations↗

Geometric Requirements for Modeling Polycrystalline Representative Volume Elements Using the Generalized Method of Cells

To accurately predict the mechanical response of advanced metallic parts, the grain structure of the metal must be considered and coupled to the macroscale in a multiscale model. The size of the microscale representative volume element must be sufficiently large, and the grid used to discretize the microscale must be adequately refined, in order to ensure an accurate and converged solution. The generalized method of cells is used to predict the mechanical properties of polycrystalline representative volume elements of copper containing randomly oriented grains with the properties of a single face-centered-cubic crystal. Moreover, the results of a parametric study are used to conclude the geometric requirements for an objective microscale model, with respect to the elastic stiffness.

Multiscale Modeling↗

Building Sustainable Capacity within Under-Represented Communities Through A Deep Partnership Model

Despite varied programmatic inducements and the documented benefits of Diversity Equity & Inclusion (DE&I), participation rates of underrepresented communities within NASA’s planetary sciences activities have remained low and nearly unchanged over the last decade. The lack of success of various efforts may be attributable to factors identified in social science research. First, the proportionality of under-represented groups has been shown to be critical to achieving the positive impacts of DE&I. When under-represented groups make up less than ~30%, group dynamics such as assimilation or tokenism routinely appear, with destructive and counter-productive results. Additionally, if participation only occurs within limited segments of an organization, benefits may be limited. Proportionality must extend across all levels or layers of the organization for the benefits of DE&I to accrue. Second, the inclusion of “Third Spaces” in the planning and support of DE&I efforts is critical to the long-term success or “stickiness” of DE&I activities. Third Spaces are those beyond home (First Space) and work (Second Space) where informal, community-building activities occur. Examples include conferences, after-work activities, and weekend social engagements. Without explicitly acknowledging and including “Third Space” interactions, DE&I efforts can fall short of their long-term goals. In 2014, the Laboratory for Atmospheric and Space Physics (LASP) at the University of Colorado Boulder began a partnership with the United Arab Emirates to develop and fly a space mission to Mars. The scientific mission had an explicit goal of training scientists and engineers, and creating a sustainable ecosystem that would spark and accelerate the UAE’s space economy. To accomplish this, LASP and the UAE set up a program that matched scientists and engineers in one-on-one mentorship relationships, at all levels of the organization. Over the course of the roughly 4-year project, those partnering relationships fostered not only strong professional relationships and knowledge transfer, but also personal friendships that extended into “Third Spaces.” The UAE/LASP partnership now continues through the co-development of an asteroid mission currently in definition phase. In collaboration with NASA Ames Research Center, LASP is developing the conceptual frame work to extend this deep partnership model to domestic opportunities. By implementing a planetary science mission that employs an analogous one-on-one partnership model across all elements of the organization, this project will demonstrate how proportionality and Third Space relationships can enable the development of localized, sustainable communities that are explicitly and deeply engaged with the NASA ecosystem.

Capacity Building↗

Data structures and apparatuses for representing knowledge

Data structures and apparatuses to represent knowledge are disclosed. The processes can comprise labeling elements in a knowledge signature according to concepts in an ontology and populating the elements with confidence values. The data structures can comprise knowledge signatures stored on computer-readable media. The knowledge signatures comprise a matrix structure having elements labeled according to concepts in an ontology, wherein the value of the element represents a confidence that the concept is present in an information space. The apparatus can comprise a knowledge representation unit having at least one ontology stored on a computer-readable medium, at least one data-receiving device, and a processor configured to generate knowledge signatures by comparing datasets obtained by the data-receiving devices to the ontologies.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets: Preprint

Increasing levels of renewable generation motivate a growing interest in data-driven approaches for AC optimal power flow (AC OPF) to manage uncertainty. However, a lack of disciplined dataset creation and benchmarking prohibits useful comparison between approaches in the literature. To instigate confidence, models must be able to reliably predict solutions across a wide range of operating conditions. This paper develops the OPF-Learn package for Julia and Python which uses a computationally efficient approach to create representative datasets that span a wide spectrum of the AC OPF feasible region. Load profiles are uniformly sampled from a convex set that contains the AC OPF feasible set. For each infeasible point found, the convex set is reduced using infeasibility certificates, found by utilizing properties of a relaxed formulation. The framework is shown to generate datasets which are more representative of the entire feasible space versus traditional techniques seen in the literature, improving machine learning model performance.

dataset↗

QONNX: Representing Arbitrary-Precision Quantized Neural Networks

We present extensions to the Open Neural Network Exchange (ONNX) intermediate representation format to represent arbitrary-precision quantized neural networks. We first introduce support for low precision quantization in existing ONNX-based quantization formats by leveraging integer clipping, resulting in two new backward-compatible variants: the quantized operator format with clipping and quantize-clip-dequantize (QCDQ) format. We then introduce a novel higher-level ONNX format called quantized ONNX (QONNX) that introduces three new operators -- Quant, BipolarQuant, and Trunc -- in order to represent uniform quantization. By keeping the QONNX IR high-level and flexible, we enable targeting a wider variety of platforms. We also present utilities for working with QONNX, as well as examples of its usage in the FINN and hls4ml toolchains. Finally, we introduce the QONNX model zoo to share low-precision quantized neural networks.

Pappalardo, Alessandro↗

Qualitative and Quantitative Evaluation for Representative Human Reliability Analysis Methods

The Korea Institute of Nuclear Safety (KINS) is the regulatory expert organization established by the Korean government to strengthen the nation’s technical capabilities relating to nuclear safety regulation. KINS oversees the technical aspects of nuclear safety regulation, including safety reviews, inspections, education, and safety research—all conducted based on technical knowledge and accumulated regulatory experience. In 2023, KINS requested that Idaho National Laboratory (INL) validates representative human reliability analysis (HRA) methods used throughout the world, thus affording KINS with a basis for determining an HRA method adequate for its domestic regulatory purposes. The present paper mainly examines INL’s efforts in this regard. The resulting INL study covered four representative HRA methods widely used by nuclear utilities and regulatory institutes. These methods were qualitatively evaluated by applying specific evaluation criteria and determining how well each method reflected critical HRA issues. For this assessment, INL benchmarked the Halden International HRA Empirical Study. Using the Halden empirical data, along with information on human failure events (HFEs), the present study employed the selected HRA methods to estimate human error probabilities (HEPs) for the HFEs. It also performed statistical analyses to compare the HEPs predicted via the HRA methods against those from the Halden empirical data.

99 - GENERAL AND MISCELLANEOUS↗

Meeting the Radiative Forcing Targets of the Representative Concentration Pathways in a World with Agricultural Climate Impacts

This study assesses how climate impacts on agriculture may change the evolution of the agricultural and energy systems in meeting the end-of-century radiative forcing targets of the Representative Concentration Pathways (RCPs). We build on the recently completed ISI-MIP exercise that has produced global gridded estimates of future crop yields for major agricultural crops using climate model projections of the RCPs from the Coupled Model Intercomparison Project Phase 5 (CMIP5). For this study we use the bias-corrected outputs of the HadGEM2-ES climate model as inputs to the LPJmL crop growth model, and the outputs of LPJmL to modify inputs to the GCAM integrated assessment model. Our results indicate that agricultural climate impacts generally lead to an increase in global cropland, as compared with corresponding emissions scenarios that do not consider climate impacts on agricultural productivity. This is driven mostly by negative impacts on wheat, rice, other grains, and oil crops. Still, including agricultural climate impacts does not significantly increase the costs or change the technological strategies of global, whole-system emissions mitigation. In fact, to meet the most aggressive climate change mitigation target (2.6 W/m2 in 2100), the net mitigation costs are slightly lower when agricultural climate impacts are considered. Key contributing factors to these results are (a) low levels of climate change in the low-forcing scenarios, (b) adaptation to climate impacts, simulated in GCAM through inter-regional shifting in the production of agricultural goods, and (c) positive average climate impacts on bioenergy crop yields.

agricultural climate impacts↗

Spatially resolved estimation of ozone-related mortality in the United States under two representative concentration pathways (RCPs) and their uncertainty

We report that the spatial pattern of the uncertainty in air pollution-related health impacts due to climate change has rarely been studied due to the lack of high-resolution model simulations, especially under the Representative Concentration Pathways (RCPs), the latest greenhouse gas emission pathways. We estimated future tropospheric ozone (O 3 ) and related excess mortality and evaluated the associated uncertainties in the continental United States under RCPs. Based on dynamically downscaled climate model simulations, we calculated changes in O 3 level at 12 km resolution between the future (2057 and 2059) and base years (2001–2004) under a low-to-medium emission scenario (RCP4.5) and a fossil fuel intensive emission scenario (RCP8.5). We then estimated the excess mortality attributable to changes in O 3 . Finally, we analyzed the sensitivity of the excess mortality estimates to the input variables and the uncertainty in the excess mortality estimation using Monte Carlo simulations. O 3 -related premature deaths in the continental U.S. were estimated to be 1312 deaths/year under RCP8.5 (95 % confidence interval (CI): 427 to 2198) and ₋2118 deaths/year under RCP4.5 (95 % CI: ₋3021 to ₋1216), when allowing for climate change and emissions reduction. The uncertainty of O 3 -related excess mortality estimates was mainly caused by RCP emissions pathways. Finally, excess mortality estimates attributable to the combined effect of climate and emission changes on O 3 as well as the associated uncertainties vary substantially in space and so do the most influential input variables. Spatially resolved data is crucial to develop effective community level mitigation and adaptation policy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Uniformity and volume-representativity of consecutive submillimetre wafers of shale for SANS and USANS investigations

(Ultra) Small Angle Neutron Scattering (SANS/USANS) is a non-destructive technique suitable for characterization of porous structures from nano- to micro-pore size (1 nm to 20 μm), which has been widely used to characterize accessible and inaccessible porosity in tight unconventional hydrocarbon reservoirs (e.g., shales). Here, in this study, SANS and USANS are used to examine the degree of nano- to micropore uniformity of the rock body on sub-millimetre to centimetre scale. This study presents a test of the hypothesis of volume representativity of pore structure information measured by SANS and USANS for shale wafers (0.22–1 mm) cut from six North American unconventional shale cores of different maturities (New Albany Shale and Marcellus Shale). Experimental reproducibility of USANS is further investigated for two wafers measured using various acquisition times. All samples show that fluctuations of the relative intensity generally decrease from the USANS Q-range (pore size from around 200 nm to 20 μm) to most of the SANS Q-range (pore size from around 5 nm to 700 nm), and peaks in the SANS large-Q region (for pores smaller than about 5 nm). There is a positive correlation between the spatial fluctuation of the relative SANS intensity and the scattering power of shales. In addition, the scattering intensity and its fluctuations is larger for the less mature shale cores. Shale is known to be highly heterogenous and the hypothesis of uniformity does not apply at sub-millimetre scale even for samples that appear homogeneous at visual inspection. This study presents an approach to minimise the variation through sample selection, data interpretation methodology and instrumental reproducibility tests. The best values of the microstructural spatial uniformity for the apparently homogeneous sections of a core (i.e., the SANS and USANS intensity variation) is less than ±10%.

01 COAL, LIGNITE, AND PEAT↗

Selecting representative geological realizations to model subsurface CO 2 storage under uncertainty

Carbon capture and storage (CCS) is one of the quickest and most effective solutions for reducing carbon emissions. The majority of subsurface storage occurs in saline aquifers, for which geological information is lacking which in turn results in geological uncertainty. To evaluate uncertainty in CO 2 injection projections, the use of multiple geological realizations (GRs) has been practiced very commonly. In this approach, hundreds or thousands of high-resolution GRs is used that quickly becomes computationally expensive. This issue can be addressed with representative geological realizations (RGRs) that preserve the uncertainty domain of the ensemble GRs. Here, in this study, we propose the use of unsupervised machine learning (UML) frameworks, including dissimilarity measurement, dimensionality reduction, clustering and sampling algorithms ta select a predetermined number of RGRs. We compare the simulation outputs of the RGR sets and the ensemble using the Kolmogorov–Smirnov (KS) test to select the best UML. The UML frameworks and their associated selection processes are evaluated using a saline aquifer with a single CO 2 injection well and 200 GRs with varying uncertain petrophysical characteristics. The best UML framework is selected to use only 5% of the GRs while maintaining the uncertainty domain of the ensemble GRs. In addition, the best UML framework is tested using a saline aquifer with three CO 2 injection wells and varied GRs. The results show that our proposed UML framework can be used to choose RGRs, capturing the whole uncertainty domain. Our approach leads to a significant reduction in the computational cost associated with scenario testing, decision-making, and development planning for CO 2 storage sites under geological uncertainty.

58 GEOSCIENCES↗

Observation and Modeling of Dynamic Fracture Behaviors of Battery Cell Under Impact Loading Using Enhanced Representative Volume Element Concept

The burgeoning electric automobile industry has increased interest in battery safety. Battery cells experience significant mechanical stress during operation, including the impact of accidents and vibrations from driving. The potential for thermal runaway reactions in battery cells raises safety concerns. Although numerous researchers have defined the dynamic behavior of battery cells and proposed numerical models to describe it, few studies have focused on the high-strain rate mechanical impact phase correlated with the onset of fracture. In this study, we describe the dynamic behavior of pouch battery cells and propose a modeling method to study their mechanical failure under impact situations. Impact tests are conducted at various velocities and heights. To overcome numerical issues commonly encountered under rapid deformation scenarios, a new finite element model is developed based on the representative volume element model. The proposed approach efficiently simulates continuous crack propagation and brittleness behavior during impact by permitting the individual behavior of the cell components. Therefore, engineers can reliably design safer electric vehicle battery cells by measuring the properties of the cell components.

ENERGY STORAGE↗

A Novel In Situ Flamelet Tabulation Methodology for the Representative Interactive Flamelet Model

A priori flamelet tabulation methods have been used extensively to model premixed and non-premixed combustion in Reynolds Averaged Navier-Stokes (RANS) and Large Eddy Simulations (LES). Pre-tabulation of flamelet libraries have led to significant cost reduction of CFD simulations. However, these tabulation methods rely on certain simplifying assumptions. The dimensionality of the flamelet manifolds and size of flamelet libraries are also constrained due to memory and computational limitations. This limits the applicability and predictive capability of flamelet models. The traditional representative interactive flamelet (RIF) approach with multiple flamelets on the other hand involves online solution of flamelet equations and presumed PDF integration at each computational cell making it attractive but computationally expensive and not suitable for LES with large cell counts. The current work discusses the development of a novel methodology that combines the advantages of an online flamelet solver along with the computational efficacy of tabulated models. The novel in situ tabulation approach consists of creating a flamelet table at each computational time step using the unsteady flamelet equations. The flamelets are then integrated in real time based on their respective scalar dissipation rate histories. This approach is capable of including history effects and unsteady chemical kinetic effects as it involves the online solution of unsteady flamelets. Implementation and comprehensive validation of the new framework is carried out against gas jet flames as well as spray flames at high pressures. The approach is first validated for a partially premixed methane gas-jet flame in a RANS framework. The temperature and species mass fractions are compared against experimental results at different locations of a lifted flame. The approach is able to capture the transient flame characteristics. The model is then validated against an n -dodecane spray flame at high pressures in a LES framework with a 103 species n -dodecane chemistry mechanism. The model is able to capture ignition delays and flame liftoff of the n -dodecane spray over varying ambient temperature conditions. Due to the offsetting of presumed PDF integration cost, the proposed framework is shown to be faster than the traditional RIF approach and the speedup over RIF increases with an increase in the cell count. Furthermore, this framework enables use of the RIF model for large cell count LES simulations.

42 ENGINEERING↗

An Ally in Your Corner: Benefits of Using Owner's Representatives

This short guide provides prospective ESPC customers with key considerations for retaining an owner's representative ("owner’s rep"), specifically regarding recommended areas of competency and qualifications. It also provides a summary of resources to help with finding or soliciting owner's rep services. This information may also be valuable for ESPC program administrators considering how to promote the use of owner’s reps for projects in their state.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗