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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Carolina Coastal Plain​ Ecological Forecasting

Although the carnivorous plant Venus flytrap (Dionaea muscipula) is recognized globally, its native range is restricted to a small portion of the North and South Carolina Coastal Plain and Sandhills. Within this limited range, Venus flytrap populations are threatened by habitat loss, fire suppression, and poaching. NASA DEVELOP partnered with the North Carolina Botanical Garden (NCBG), the University of North Carolina Herbarium (NCU), and the North Carolina Natural Heritage Program (NCNHP) to support Venus flytrap conservation. The team developed models based on species presence data and environmental variables using the Software for Assisted Habitat Modeling (SAHM) to create a 2021 habitat suitability map for Venus flytrap. These models incorporated Earth observations collected by Landsat 8 Thermal Infrared Sensor (TIRS), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), and Sentinel-2 Multispectral Instrument (MSI). To predict areas at high risk of development, the team produced a 2050 land-use change map using TerrSet Land Change Modeler. The team found potential areas of conflict between predicted habitat and forecasted future development. Many areas of suitable habitat were concentrated along the coast where development was likely to occur, placing populations there at risk of extirpation. Overlaying suitable habitat with forecasted land change also identified suitable habitats with minimal risk of development, which may serve as lasting Venus flytrap habitat. These results can inform the NCBG and NCNHP’s conservation decision-making, including targeted seed banking, reintroduction, and prioritization of enduring habitats for protection and management.

Monika Rock↗
DOE OSTI2017

Temperature compensated transfer function for probability of detection (POD) in high temperature ultrasonic NDE using low temperature signals - 249

Ultrasonic piezoelectric based transducers offer the potential for in-coolant NDT measurements and imaging required at high temperature for the safe operation of advanced small modular reactors (SMRs). Current work estimates a significant 97% reduction in the probability of detection (POD) for an artificial flaw size of 2{lambda} due to degradation of the transducer performance at elevated temperature. The source of variability for POD in the current work is the temperature dependence of material matrix of piezoelectric material PZT-5A used in the sensor. The physics based model developed using finite element method quantifies reduction in the scattering amplitude for the side drilled hole (SDH) from 15 C to 195 C. Using model assisted probability of detection (MAPOD) approach, sensor temperature effect on the POD is demonstrated for SDHs in the low carbon steel specimen. Scattering amplitudes for the room temperature simulated data is validated with the experimental data. A temperature correction factor and transfer factors are reported to transform the simulated temperature effect from physics based model to the experimental data for estimation of high temperature POD using low temperature ultrasonic measurements. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Quantum cryptography and combined schemes of quantum cryptography communication networks

We discuss cryptographic data protection network schemes combining the approaches of quantum cryptography with advanced computer information processing methods. The use of the latter is due to the presence of a number of unsolved problems in the existing quantum key distribution systems and is also associated with projects for the development of network-centric quantum communications and photonic networks, which involves the employment of multi-agent control models. Developments based on the Y-00 protocol with data encoding by the quantum noise of a transmitting laser are considered, and achievements in the field of quantum-optical random number generators are presented. The latter can be also used to design secure multi-valued logic encoding schemes that are promising for increasing the dimension of the key space in the ‘one-time pad’ method, for solving the tasks of position-dependent cryptography and for implementing multi-agent models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Co-optimized machine-learned manifold models for large eddy simulation of turbulent combustion

Many modeling approaches in large eddy simulation (LES) of turbulent combustion employ a projection of the thermochemical state onto a low-dimensional manifold within state space to reduce the number of transported variables and hence computational cost. Flamelet-generated manifolds (FGM) is an example of a well-established, physics-based approach, but increasingly, principal component analysis (PCA) is being used as a data-driven method for generating manifold models. For both approaches, the nonlinear relationship between the location on the predefined manifold and the outputs of interest, such as reaction rates, can be tabulated or encoded in a neural network. This work proposes a new approach for manifold modeling that extends these existing approaches. A modified neural network structure simultaneously encodes the definition of the manifold variables, the nonlinear mapping, and the subfilter closure for LES. This allows all three of these aspects of the model to be co-optimized, generating a model from any source of combustion thermochemical state data. The manifold parameterizing variables are constrained to be linear combinations of species, as in FGM and PCA-based models, to aid in interpretability and implementation. For LES, subfilter variances of the manifold variables are also included as inputs. Two types of a priori analysis are performed to evaluate the new approach. In the first, the model is trained on data from one-dimensional premixed flames. In this case, the approach recovers the behavior of flamelet-based manifold approaches, and in fact slightly improves performance by identifying an optimized progress variable. The approach is also applied to data from direct numerical simulations of spherical ignition kernels in isotropic turbulence. For any specified manifold dimensionality, the new approach provides substantially lower prediction errors than a PCA-based model developed from the same data set. Additionally, the LES formulation of the new approach can provide accurate predictions for filtered reaction rates across a variety of filter widths.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Nonclassical Behavior in Competitive Ion Adsorption at a Charged Solid–Water Interface

Ion adsorption at solid-water interfaces is commonly described by interactions between specific surface sites and adsorbed ions in classical models. However, energetic contributions from non-site-specific ion-ion interactions have not been explored systematically. Here, we report non-classical behaviors observed during competitive adsorption between Sr 2+ and Na + /Rb + at the negatively-charged muscovite mica (001)-water interface, revealing apparent controls of adsorbed ion speciation over the interfacial reactivity. In the absence of competing cations, Sr 2+ adsorbs in approximately equivalent proportions of inner-sphere and outer-sphere complexes, whereas it adsorbs predominantly as an outer-sphere complex in the presence of Na + /Rb + . This transformation of adsorbed Sr 2+ speciation significantly decreases its adsorption strength, as indicated by ~15-fold shift in the Sr 2+ adsorption edge concentration, compared to that calculated from a classical Langmuir isotherm model developed based on site-specific interactions. Finally, these observations highlight the importance of non-site-specific interactions in controlling the energetics of chemical reactions at the charged interface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Edge-Controlled Growth and Etching of Two-Dimensional GaSe Monolayers

Understanding the atomistic mechanisms governing the growth of two-dimensional (2D) materials is of great importance in guiding the synthesis of wafer-sized, single-crystalline, high-quality 2D crystals and heterostructures. Etching, in many cases regarded as the reverse process of material growth, has been used to study the growth kinetics of graphene. In this paper, we explore a growth–etching–regrowth process of monolayer GaSe crystals, including single-crystalline triangles and irregularly shaped domains formed by merged triangles. We show that the etching begins at a slow rate, creating triangular, truncated triangular, or hexagonally shaped holes that eventually evolve to exclusively triangles that are rotated 60° with respect to the crystalline orientation of the monolayer triangular crystals. The regrowth occurs much faster than etching, reversibly filling the etched holes and then enlarging the size of the monolayer crystals. A theoretical model developed based on kinetic Wulff construction (KWC) theory and density functional theory (DFT) calculations accurately describe the observed morphology evolution of the monolayer GaSe crystals and etched holes during the growth and etching processes, showing that they are governed by the probability of atom attachment/detachment to/from different types of edges with different formation energies of nucleus/dents mediated by chemical potential difference Δμ between Ga and Se. Finally, our growth–etching–regrowth study provides not only guidance to understand the growth mechanisms of 2D binary crystals but also a potential method for the synthesis of large, shape-controllable, high-quality single-crystalline 2D crystals and their lateral heterostructures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine learning-enabled discovery of ionic liquid–solvent electrolytes exhibiting high ionic conductivity

Ionic liquids (ILs), which are a class of materials with versatile nature and growing popularity, are facing impediments toward widespread usage as electrolytes due to various factors such as low ionic conductivity, high viscosity, high market price etc. One of the ways these limitations can be addressed is by mixing ILs with a molecular solvent. In a combinatorial sense, there exists an immense number of specific IL–solvent combinations. An exhaustive experimental or even simulation-based investigation of the chemical space spanned by such combinations can be extremely time-consuming, expensive, and nearly impossible. An alternative approach is to employ machine learning-based models developed from available databases. Although there exists prior literature that integrates machine learning to investigate mixtures of specific solvents with ILs, these models lack generalization necessitating development of a large number of ML models to handle various solvents. To remedy this shortcoming, as a part of designing green electrolytes with high ionic conductivity that can have potential applications in next-generation batteries and solar cells, this work aims to develop a unified machine learning model to predict ionic conductivity of any IL–solvent mixture system. In this regard, three models, namely, Random Forest, extreme gradient boosting (XGBoost), and artificial neural network (ANN) were formulated using the NIST ILThermo database. The dataset contained 549 unique ionic liquids from 16 cation families and 81 unique solvents, representing a total of 23 712 datapoints. SHAPLEY additive explanation (SHAP) method was used to assess the impact of various features on model prediction and their significance was compared with literature to gain physical insight about the model behavior. Finally, using the developed models, approximately 2.5 million IL–solvent mixtures at five different compositions were screened at room temperature. The high-throughput screening yielded nearly 19 000 IL–solvent mixtures for which ionic conductivity was found to exceed the ionic conductivity of conventional Li-ion battery electrolyte.

25 ENERGY STORAGE↗
DOE OSTI2013

Multiscale Modeling of Inclusions and Precipitation Hardening in Metal Matrix Composites: Application to Advanced High-Strength Steels

In this study, the strengthening effect of inclusions and precipitates in metals is investigated within a multiscale approach that utilizes models at various length scales, namely, Molecular Mechanics (MM), discrete Dislocation Dynamics (DD), and an Eigenstrain Inclusion Method (EIM). Particularly, precipitates are modeled as hardsoft particles whose stress fields interact with dislocations. The stress field resulting from the elastic mismatch between the particles and the matrix is accounted for through the EIM. While the MM method is employed for the purpose of developing rules for DD for short range interaction between a single dislocation and an inclusion, the DD method is used to predict the strength of the composite resulting from the interaction between ensembles of dislocations and particles. As an application to this method, the mechanical behavior of Advanced High Strength Steel (AHSS) is investigated and the results are then compared to the experimental data. The results show that the finely dispersive precipitates can strengthen the material by pinning the dislocations up to a certain shear stress and retarding the recovery, as well as annihilation of dislocations. The DD results show that strengthening due to nano sized particles is a function of the density and size of the precipitates. This size effect is then explained using a mechanistic model developed based on dislocation-particle interaction.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2014

Evaluation of calibration efficacy under different levels of uncertainty

This study examines how calibration performs under different levels of uncertainty in model input data. It specifically assesses the efficacy of Bayesian calibration to enhance the reliability of EnergyPlus model predictions. A Bayesian approach can be used to update uncertain values of parameters, given measured energy-use data, and to quantify the associated uncertainty.We assess the efficacy of Bayesian calibration under a controlled virtual-reality setup, which enables rigorous validation of the accuracy of calibration results in terms of both calibrated parameter values and model predictions. Case studies demonstrate the performance of Bayesian calibration of base models developed from audit data with differing levels of detail in building design, usage, and operation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Improving net ecosystem CO2 flux prediction using memory-based interpretable machine learning

Terrestrial ecosystems play a central role in the global carbon cycle and affect climate change. However, our predictive understanding of these systems is still limited due to their complexity and uncertainty about how key drivers and their legacy effects influence carbon fluxes. Here, we propose an interpretable Long Short-Term Memory (iLSTM) network for predicting net ecosystem CO 2 exchange (NEE) and interpreting the influence on the NEE prediction from environmental drivers and their memory effects. We consider five drivers and apply the method to three forest sites in the United States. Besides performing the prediction in each site, we also conduct transfer learning by using the iLSTM model trained in one site to predict at other sites. Results show that the iLSTM model produces good NEE predictions for all three sites and, more importantly, it provides reasonable interpretations on the input driver's importance as well as their temporal importance on the NEE prediction. Additionally, the iLSTM model demonstrates good across-site transferability in terms of both prediction accuracy and interpretability. The transferability can improve the NEE prediction in unobserved forest sites, and the interpretability advances our predictive understanding and guides process-based model development.

Liu, Siyan↗
DOE OSTI2014

Modeling of Electric Water Heaters for Demand Response: A Baseline PDE Model

Demand response (DR)control can effectively relieve balancing and frequency regulation burdens on conventional generators, facilitate integrating more renewable energy, and reduce generation and transmission investments needed to meet peak demands. Electric water heaters (EWHs) have a great potential in implementing DR control strategies because: (a) the EWH power consumption has a high correlation with daily load patterns; (b) they constitute a significant percentage of domestic electrical load; (c) the heating element is a resistor, without reactive power consumption; and (d) they can be used as energy storage devices when needed. Accurately modeling the dynamic behavior of EWHs is essential for designing DR controls. Various water heater models, simplified to different extents, were published in the literature; however, few of them were validated against field measurements, which may result in inaccuracy when implementing DR controls. In this paper, a partial differential equation physics-based model, developed to capture detailed temperature profiles at different tank locations, is validated against field test data for more than 10 days. The developed model shows very good performance in capturing water thermal dynamics for benchmark testing purposes

Balancing service, demand response, electric water↗
DOE OSTI2020

EMIS Agent Simulation Model (Electricity Markets Investment Suite) [SWR-19-56]

The Electricity Markets Investment Suite Agent-based Simulation (EMIS-AS) model is an agent-based model developed at NREL for simulating annual investment and retirement decisions of heterogeneous investors in the electricity sector. EMIS-AS is designed to capture the evolution of the electricity generation portfolio resulting from the interactions of heterogeneous investors under different policy and market designs. EMIS-AS not only allows end-users to customize market products and rules, but also to capture investors' heterogeneous financing parameters, technology preferences, beliefs about the future (forecasts), ability to update those forecasts, and risk preferences under uncertainty.

Anwar, Mahammad Bashar↗
DOE OSTI2023

Evaluating Production Implications of Pressure Maintenance in Unconventional Oil and Gas Wells using a Machine Learning Modeling Approach: Case Study in the Permian Basin

This study implements the proprietary machine learning-based model (model) developed under the 2022 study titled “Evaluating the Impact of Proprietary Oil & Gas Data on Machine Learning Model Performance Using a Quasi-Experimental Analytical Approach” for forecasting unconventional oil and gas production using well data from the Permian Basin. The model, developed using an exclusive dataset that includes time series production data from an operator in the Permian Basin, is designed to jointly predict daily oil, gas, and water production for horizontal wells as a function of bottomhole pressure drawdown, spatial placement across the study domain, and well-completion attributes. In this study, the model was explicitly applied to explore its utility to evaluate the impact of varying drawdown strategies on the production forecast of a well in the Permian Basin dataset. Managing pressure drawdown has been identified as a way to improve estimated ultimate recovery (EUR) from unconventional shale wells due to the stress-dependent nature of fractures in shale reservoirs. Research has shown that applying a lower pressure drawdown helps to maintain the reservoir conductivity, resulting in higher productivity over the life of a well. Historic bottomhole pressure data from the well over time was used as a benchmark from which to set more and less aggressive pressure decline rates as bounding modeling cases. All pressure decline rates/strategies were forecasted over 5 years, and the model was used to generate oil, water, and gas prediction over the same timeframe. This report presents the results from the production forecast and discusses potential operational and economic implications, with contrasting perspectives between well productivity and profitability given typical oil and gas economics and the volatility in the oil and gas market.

02 PETROLEUM↗
NASA NTRS1990

Horizontal-branch stellar evolution

The results of canonical theory for the evolution of horizontal-branch (HB) stars are examined. Particular attention is given to how an HB star maintains the appropriate composition distribution within the semiconvective zone and how this composition is affected by the finite time-dependence with which convective boundaries actually move. Newly developed models based on time-dependent overshooting are presented for both the core-helium-exhaustion and main HB phases.

Sweigart, Allen V.↗
NASA NTRS1991

Modeling turbulent boundary layers in adverse pressure gradients

Many of the turbulent layers encountered in practical flows develop in adverse pressure gradients; hence, the dynamics of the thickening and possible separation of the boundary layer has important implications for design practices. What are the key physical processes that govern how a turbulent boundary layer responds to an adverse pressure gradient, and how should these processes be modeled? Despite the ubiquity of such flows in engineering and nature, these equations remain largely unanswered. The turbulence closure models presently used to describe these flows commonly use 'wall functions' that have ad hoc corrections for the effects of pressure gradients. There is, therefore, a practical and theoretical need to examine the effects of adverse pressure gradients on wall bounded turbulent flows in order to develop models based on sound physical principle. The evolution of a turbulent boundary layer on a flat wall with an externally imposed pressure gradient is studied.

Belcher, Stephen E.↗
NASA NTRS2000

An Algorithm to Atmospherically Correct Visible and Thermal Airborne Imagery

The program Watts implements a system of physically based models developed by the authors, described elsewhere, for the removal of atmospheric effects in multispectral imagery. The band range we treat covers the visible, near IR and the thermal IR. Input to the program begins with atmospheric pal red models specifying transmittance and path radiance. The system also requires the sensor's spectral response curves and knowledge of the scanner's geometric definition. Radiometric characterization of the sensor during data acquisition is also necessary. While the authors contend that active calibration is critical for serious analytical efforts, we recognize that most remote sensing systems, either airborne or space borne, do not as yet attain that minimal level of sophistication. Therefore, Watts will also use semi-active calibration where necessary and available. All of the input is then reduced to common terms, in terms of the physical units. From this it Is then practical to convert raw sensor readings into geophysically meaningful units. There are a large number of intricate details necessary to bring an algorithm or this type to fruition and to even use the program. Further, at this stage of development the authors are uncertain as to the optimal presentation or minimal analytical techniques which users of this type of software must have. Therefore, Watts permits users to break out and analyze the input in various ways. Implemented in REXX under OS/2 the program is designed with attention to the probability that it will be ported to other systems and other languages. Further, as it is in REXX, it is relatively simple for anyone that is literate in any computer language to open the code and modify to meet their needs. The authors have employed Watts in their research addressing precision agriculture and urban heat island.

Rickman, Doug L.↗
NASA NTRS2016

Estimating the Rate of Occurrence of Renal Stones in Astronauts

Changes in urine chemistry, during and post flight, potentially increases the risk of renal stones in astronauts. Although much is known about the effects of space flight on urine chemistry, no inflight incidence of renal stones in US astronauts exists and the question "How much does this risk change with space flight?" remains difficult to accurately quantify. In this discussion, we tackle this question utilizing a combination of deterministic and probabilistic modeling that implements the physics behind free stone growth and agglomeration, speciation of urine chemistry and published observations of population renal stone incidences to estimate changes in the rate of renal stone presentation. The modeling process utilizes a Population Balance Equation based model developed in the companion IWS abstract by Kassemi et al. (2016) to evaluate the maximum growth and agglomeration potential from a specified set of urine chemistry values. Changes in renal stone occurrence rates are obtained from this model in a probabilistic simulation that interrogates the range of possible urine chemistries using Monte Carlo techniques. Subsequently, each randomly sampled urine chemistry undergoes speciation analysis using the well-established Joint Expert Speciation System (JESS) code to calculate critical values, such as ionic strength and relative supersaturation. The Kassemi model utilizes this information to predict the mean and maximum stone size. We close the assessment loop by using a transfer function that estimates the rate of stone formation from combining the relative supersaturation and both the mean and maximum free stone growth sizes. The transfer function is established by a simulation analysis which combines population stone formation rates and Poisson regression. Training this transfer function requires using the output of the aforementioned assessment steps with inputs from known non-stone-former and known stone-former urine chemistries. Established in a Monte Carlo system, the entire renal stone analysis model produces a probability distribution of the stone formation rate and an expected uncertainty in the estimate. The utility of this analysis will be demonstrated by showing the change in renal stone occurrence predicted by this method using urine chemistry distributions published in Whitson et al. 2009. A comparison to the model predictions to previous assessments of renal stone risk will be used to illustrate initial validation of the model.

gravitational physiology↗
NASA NTRS2015

Are We There Yet? Determining the Adequacy of Formalized Requirements and Test Suites

Structural coverage metrics have traditionally categorized code as either covered or uncovered. Recent work presents a stronger notion of coverage, checked coverage, which counts only statements whose execution contributes to an outcome checked by an oracle. While this notion of coverage addresses the adequacy of the oracle, for Model-Based Development of safety critical systems, it is still not enough; we are also interested in how much of the oracle is covered, and whether the values of program variables are masked when the oracle is evaluated. Such information can help system engineers identify missing requirements as well as missing test cases. In this work, we add to the idea of checked coverage an analysis of requirements coverage to help provide insight to engineers as to whether the requirements or the test suite need to be improved. We implement our approach based on a dynamic backward slicing technique and evaluate it on several systems developed in Simulink. The results of our preliminary study show that even for systems with comprehensive test suites and good sets of requirements, our approach can identify cases where more tests or more requirements are needed to improve coverage numbers.

Dynamic Slicing↗