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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Effect of Anoxic Iron Corrosion on WIPP Brine Geochemistry FY23 Final Report (U)

A 280-day study was completed to evaluate the effect of zero-valent iron (Fe 0 ) on the Waste Isolation Pilot Plant (WIPP) brine geochemistry under anticipated reducing conditions. Hydrogen (H 2 ) gas is expected to be present in the repository after closure due to the anoxic corrosion of a vast quantity of iron contained in the waste forms disposed at WIPP; therefore, a background argon atmosphere containing H 2 was chosen for this study. WIPP groundwater brine pH and E h will impact the mobility and fate of plutonium within the repository. Modeling and laboratory results for Castile WIPP brine indicate that equilibrium fa values relative to the standard hydrogen electrode (SHE) are 40 mV more reducing (i.e., more negative) than those for Salado WIPP brine (-480 mV vs. -440 mV, respectively) because of the higher pH of the Castile brine (pH 9 .3 for Castile vs. pH 8.8 for Salado). The E h and pH data were corrected for the effects of high ionic strength. The experimental results for both brines are consistent with thermodynamic predictions using OLI Systems' Mixed Solvent Electrolyte chemical equilibrium model. The measured and corrected pH and E h data from this study are provided in Table ES-I and Table ES-2, respectively. The experimental study, with four test conditions in triplicate, was performed in a dual glovebox with a nominally 3 vol.% H 2 in argon atmosphere (target H 2 range: 3 ± I vol.%). Simulants containing MgO only ( experimental control) and MgO+Fe 0 (WIPP base case) were prepared for both the Salado and Castile brines. MgO was included in all simulants to account for the use of bulk magnesium oxide in the WIPP repository. Fe 0 was included in some simulants to incorporate the effects of the anoxic corrosion of iron and in-situ hydrogen generation in the study. The brine compositions were developed by Sandia National Laboratory (SNL; Xiong, 2008) and have been used in previous WIPP evaluations. The test method (agitation, etc.) is partially based on ASTM D3987-12. Twelve rounds of periodic measurements of pH and E h were performed over the course of the study. Chemical analysis results for liquids and solids (ICP-MS, ICP-ES, IC Anion, TIC, SEM-EDX) are consistent with the pH, E h , and thermodynamic modeling results. This study included the following conditions that deviate from anticipated post-closure conditions following brine intrusion, but were selected to facilitate bench-scale testing to validate modeling of pH and E h for the post-closure WIP P repository: an anoxic glove box atmosphere containing ≤ 4 vol. % H 2 vs. substantially higher H 2 gas concentrations assumed in the WIPP Performance Assessment (PA); a significantly higher liquid-to-solid test ratio compared to the much lower phase ratio anticipated in the WIP P repository; agitation of the simulant bottles to maximize mass transfer; and finally the use of Fe 0 reagents having a much greater surface area than expected in the WIP P repository. Non-representative conditions were chosen for various reasons such as: to provide bounding conservative results, to provide a margin of safety for testing, or to facilitate simulant sub-sampling and analysis. In a parallel effort, aqueous electrolyte thermodynamic models were developed for the synthetic Salado and Castile brines to inform the experimental design, facilitate laboratory data interpretation, and allow extension of evaluations beyond the parameters tested. Thermodynamic modeling simulations including the MgO and Fe 0 additives that are directly relevant to the experimental measurements (e.g., pH calibration curve, ORP corrections) are included in this report. The measured fa of the simulants was close to the OLI model predictions for both brines and was largely controlled by the background H 2 partial pressure in the vapor phase as well as H 2 generated in situ in the aqueous phase by the Fe 0 corrosion. The H 2 gas-phase concentration tested and thermodynamically evaluated was much lower than is assumed in the WIPP PA; however, H 2 (g) concentrations significantly below this level are still predicted to result in very reducing conditions. In conclusion: • The experimental results are consistent with thermodynamic model predictions for fa, pH, and the effects of high ionic strength. • Evidence to date suggests that the H2 concentration in the glovebox atmosphere ultimately determined the final E h values of the simulants and resulted in highly reducing conditions. As a result, little difference was observed between the control simulants containing only MgO and the WIPP base-case simulants that contained MgO and Fe 0 . • This test methodology is recommended for future studies evaluating WIPP repository conditions. The methodology includes: (1) background H 2 in argon with agitation ( or could alternatively include in-situ-generated H 2 in sealed bottles); (2) carefully measured and corrected ORP data ( with much effort focused on allowing the probes to fully stabilize); and (3) ionic-strength-corrected pH data. Other best practices, such as simulant sparging/handling, ORP probe replacement, etc., should also be considered. • The coupling of experimental studies and thermodynamic modeling is also highly recommended because these methods inform and direct one another leading to greater confidence in and understanding of the results.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Building Minimalist Models for Functionalized Metal Nanoparticles

In our opinion, the presented strategy includes all the crucial elements of an optimal low resolution model: the choice of the minimal possible resolution, compatibility between different levels of resolution, a parameterization including the specific coating present on the NP by means of superficial higher resolution interacting sites. The effective charges could be further optimized by directly adopting a RESP procedure for their fitting. This task and the model validation at different concentrations and ionic strengths vs. the aggregation tendency are currently in due course (Brancolini et al., submitted). The following steps will be the use of the model in combination with proteins models at the same CG level (minimalist), to verify their effective capability of preventing the amyloids aggregation. Furthermore, the strategy here outlined is extensible to larger NPs and different functionalization, which opens the possibility of in silico optimization of the NPs size and chemistry for therapeutic use.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Development and modeling of a polar-direct-drive exploding pusher platform at the National Ignition Facility

High-intensity laser facilities, such as the National Ignition Facility (NIF), allow the experimental investigation of plasmas under extreme, high-energy-density conditions. Motivated by validating models for collisional heat-transfer processes in high-energy-density plasmas, we have developed an exploding pusher platform for use at the NIF in the polar-direct-drive configuration. The baseline design employs a 3 mm-diameter capsule, an 18 μm-thick CH ablator, and Ar-doped D 2 gas to achieve several keV electron-ion temperature separations with relatively low convergence ratios. In an initial series of shots at the NIF—N160920–003, -005, and N160921–001—the ratio of the laser intensity at different polar angles was varied to optimize the symmetry of the implosion. In this work, we summarize experimental results from the shot series and present pre- and post-shot analysis. Although the polar-direct-drive configuration is inherently asymmetric, we successfully tuned a post-shot 1D model to a set of key implosion performance metrics. The post-shot model has proven effective for extrapolating capsule performance to higher incident laser drive. Overall, the simplicity of the platform and the efficacy of the post-shot 1D model make the polar-direct-drive exploding pusher platform attractive for a variety of applications beyond the originally targeted study of collisional processes in high-energy-density plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

CleanStart DERMS: Modeling Effort Update

This document describes the model conversion approach taken to transform the Synergee distribution feeder models received from Riverside Public Utility (RPU) to GridLAB-D models. The document briefly introduces the tools and software applications used in this process. Then, the model validation results acquired from GridLAB-D simulations are shown. Finally, several test-cases are presented that the research teams are planning to implement with the GridLAB-D models. The document presents the current state of the project and the planned next steps.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Energy Performance of Awnings in Residential Buildings

Residential buildings consume approximately 20% of the total primary energy in the United States. More than 50% of this energy is spent in heating, cooling, and lighting these buildings. Solar heat gain is one of the largest and most variable sources of cooling load in these buildings, while it can also provide passive heating during the heating season. Shading devices can be used to control the amount of solar heat gain in buildings. Various studies have considered how different shading devices and their applications affect energy and occupant comfort in buildings. However, most of these studies were limited to planar shading devices such as roller shades, cellular shades, and blinds. Although some theoretical studies have been performed for awnings, the energy performance of awnings has rarely been studied via either energy simulation or field measurement. In this study, the authors evaluated the energy performance of typical operable awnings by using field data, aided by simulation. Awnings were installed on a real house, and measurements were performed to evaluate the thermal performance of the awning. The measured data were then used to develop a calibrated energy model and evaluate the awning’s energy performance. The annual simulation of the building model used showed that awnings left in the closed position from April to September can reduce annual HVAC energy consumption by 15% compared with a building without any shades. The validated model was used in US Department of Energy prototype buildings to evaluate awning energy performance in climate zones 1A through 4B via energy simulation. For these prototype buildings, energy savings of up to 1,034 kWh were achieved for a building with a conditioned floor area of 2,377 ft 2 .

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Di-CNN: Domain-Knowledge-Informed Convolutional Neural Network for Manufacturing Quality Prediction

In manufacturing, convolutional neural networks (CNNs) are widely used on image sensor data for data-driven process monitoring and quality prediction. However, as purely data-driven models, CNNs do not integrate physical measures or practical considerations into the model structure or training procedure. Consequently, CNNs’ prediction accuracy can be limited, and model outputs may be hard to interpret practically. This study aims to leverage manufacturing domain knowledge to improve the accuracy and interpretability of CNNs in quality prediction. A novel CNN model, named Di-CNN, was developed that learns from both design-stage information (such as working condition and operational mode) and real-time sensor data, and adaptively weighs these data sources during model training. It exploits domain knowledge to guide model training, thus improving prediction accuracy and model interpretability. A case study on resistance spot welding, a popular lightweight metal-joining process for automotive manufacturing, compared the performance of (1) a Di-CNN with adaptive weights (the proposed model), (2) a Di-CNN without adaptive weights, and (3) a conventional CNN. The quality prediction results were measured with the mean squared error (MSE) over sixfold cross-validation. Model (1) achieved a mean MSE of 6.8866 and a median MSE of 6.1916, Model (2) achieved 13.6171 and 13.1343, and Model (3) achieved 27.2935 and 25.6117, demonstrating the superior performance of the proposed model.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Evaluating turbulence models at high energy densities

The RESHOCK campaign at Lawrence Livermore National Laboratory has been working on increasing our understanding of the evolution of turbulent, unstable plasma interfaces which are applicable in various HED applications including ICF implosions. A common approach to model such interfaces involves the use of Renolds-Averaged-Navier-Stokes (RANS) models whose parameters have been constrained by theory and experiments. These parameter sets are not unique and the applicability of one set of tuned model parameters to systems with differing shock strengths or material densities has not been well tested. Our new NIF experiments, along with related studies of turbulent shear flows, provide data and model validation for plasma interfaces in the HED regime. Our experiments measure mixing-layer width at an unstable interface in the plasma regime and are specifically designed to challenge mix models of the Reynolds-Averaged-Navier-Stokes (RANS) type where turbulence is assumed to be fully developed, i.e. the flow exhibits a broad spectrum of length scales without memory of the initial condition.4 We utilize precise control of the initial interface conditions (densities on both sides of the interface, an initial rippled perturbation pattern consisting of equally weighted wavelengths between 10 and 20 microns, and initial material compositions) along with a repeatable drive history

36 MATERIALS SCIENCE↗
DOE OSTI2018

Validation of Photovoltaic Modeling Tool HelioScope Against Measured Data

HelioScope is a cloud-based solar design software developed by Folsom Labs to model arrays based on physical design. Folsom Labs received a grant from the Department of Energy for the National Renewable Energy Laboratory to validate the performance of HelioScope's simulation engine against measured PV system performance. This study builds upon previous validation work by NREL of multiple other PV modeling tools, using the same fixed-tilt systems with available system specifications. HelioScope designs were set up using the same system specifications and concurrent weather data as were used in previous studies. The predicted performance results from the simulations were compared with measured data and to performance predictions by other PV modeling tools.

14 SOLAR ENERGY↗
DOE OSTI2017

Dynamic Modeling for the Separation of Rare Earth Elements Using Solvent Extraction: Predicting Separation Performance Using Laboratory Equilibrium Data

Industrial rare earth element (REE) separation facilities utilize acidic cation exchange ligands such as 2-ethylhexylphosphonic acid mono-2-ethylhexyl ester (PC88A) for solvent extraction processes. REE separations are costly and difficult due to their chemical similarities and subsequent low separation factors. Several empirical correlations are available in the literature to predict steady state extraction equilibria for various solvent systems. However, complete solvent extraction flowsheet design for REE separations requires complex scrubbing and stripping circuits to separate and produce individual pure species. Furthermore, the sensitive process chemistry is susceptible to process upsets and may manifest as irreversible transients that complicate startup and continuous operation of a solvent extraction plant. Thus, dynamic modeling of REE separations will aid in process design, optimization, and management of process fluctuations. A time-dependent MATLAB/SIMULINK modeling framework has been developed for REE solvent extraction processes using laboratory equilibrium data. The model was used to develop a flowsheet that produced high purity neodymium from a 25 wt% praseodymium and 75 wt% neodymium feed. Laboratory mixer-settlers were used to verify and validate model performance. Results indicated that the model reasonably predicts the dynamic behavior of a counter-current REE separation process, and accurately predicts the steady state REE concentration profiles across the cascade. The modeling framework can be applied to any REE separation or solvent system provided adequate laboratory equilibrium data are available.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Understanding and predicting profile structure and parametric scaling of intrinsic rotation

This study reports on a recent advance in developing physical understanding and a first-principles-based model for predicting intrinsic rotation profiles in magnetic fusion experiments. It is shown for the first time that turbulent fluctuation-driven residual stress (a non-diffusive component of momentum flux) along with diffusive momentum flux can account for both the shape and magnitude of the observed intrinsic toroidal rotation profile. Both the turbulence intensity gradient and zonal flow E×B shear are identified as major contributors to the generation of the k ∥ -asymmetry needed for the residual stress generation. The model predictions of core rotation based on global gyrokinetic simulations agree well with the experimental measurements of main ion toroidal rotation for a set of DIII-D ECH discharges. The validated model is further used to investigate the characteristic dependence of residual stress and intrinsic rotation profile structure on the multi-dimensional parametric space covering the turbulence type, q-profile structure, and up-down asymmetry in magnetic geometry with the goal of developing the physics understanding needed for rotation profile control and optimization. It is shown that in the flat-q profile regime, intrinsic rotations driven by ITG and TEM turbulence are in the opposite direction (i.e., intrinsic rotation reverses). The predictive model also produces reversed intrinsic rotation for plasmas with weak and normal shear q-profiles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Learning continuous models for continuous physics

Abstract Dynamical systems that evolve continuously over time are ubiquitous throughout science and engineering. Machine learning (ML) provides data-driven approaches to model and predict the dynamics of such systems. A core issue with this approach is that ML models are typically trained on discrete data, using ML methodologies that are not aware of underlying continuity properties. This results in models that often do not capture any underlying continuous dynamics—either of the system of interest, or indeed of any related system. To address this challenge, we develop a convergence test based on numerical analysis theory. Our test verifies whether a model has learned a function that accurately approximates an underlying continuous dynamics. Models that fail this test fail to capture relevant dynamics, rendering them of limited utility for many scientific prediction tasks; while models that pass this test enable both better interpolation and better extrapolation in multiple ways. Our results illustrate how principled numerical analysis methods can be coupled with existing ML training/testing methodologies to validate models for science and engineering applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

From deep TLS validation to ensembles of atomic models built from elemental motions. II. Analysis of TLS refinement results by explicit interpretation

TLS modelling was developed by Schomaker and Trueblood to describe atomic displacement parameters through concerted (rigid-body) harmonic motions of an atomic group [Schomaker & Trueblood (1968), Acta Cryst. B24, 63–76]. The results of a TLS refinement are T, L and S matrices that provide individual anisotropic atomic displacement parameters (ADPs) for all atoms belonging to the group. These ADPs can be calculated analytically using a formula that relates the elements of the TLS matrices to atomic parameters. Alternatively, ADPs can be obtained numerically from the parameters of concerted atomic motions corresponding to the TLS matrices. Both procedures are expected to produce the same ADP values and therefore can be used to assess the results of TLS refinement. Here, the implementation of this approach in PHENIX is described and several illustrations, including the use of all models from the PDB that have been subjected to TLS refinement, are provided.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A novel machine learning based identification of potential adopter of rooftop solar photovoltaics

With the proliferation of rooftop solar photovoltaic installations, there is a need to proactively predict consumer potential for solar photovoltaic adoption, for improved electric utility planning and operation. Traditional analytical modeling approaches are limited to a few survey features and a larger part of the survey would remain untouched by the decision model. This article presents a novel, data-driven modeling approach that strategically prunes a large set of consumer profile features using a machine learning framework to train a model for predicting potential solar adoption. The approach utilizes the Gradient Boosting Decision Tree model through a Light Gradient Boosting framework that improves significantly over the poor prediction accuracy of the existing approaches. Model training using focal-loss based supervision is used to overcome the difficulty in identifying the potential adopters that is inherent in conventional data-driven models. In addition, to overcome possible data sparsity in a limited survey sample, a Generative Adversarial Network is presented to create synthetic user samples and its effectiveness on model performance is assessed. A Bayesian optimization approach is used to systematically arrive at the hyperparameters of the proposed model. Validation of the presented approach on a survey data collected by the National Rural Electric Cooperative Association in Virginia in 2018 demonstrates the excellent predictive capability of the machine learning based approach to modeling solar adoption reliably.

14 SOLAR ENERGY↗
DOE OSTI2020

Effects of various parameters of different porous transport layers in proton exchange membrane water electrolysis

Porous transport layers (PTLs) play an important role in proton exchange membrane water electrolysis (PEMWE) cells. The PTL facilitates water and gas transport, as well as thermal and electrical conduction, and is required to sustain good contact with adjacent components. It is expected that using PTLs with variations in material properties such as structure, composition, thickness and wettability results in performance changes of the PEMWE. Here, a general mathematical PEMWE model is developed that separates and analyzes the contributions of ohmic, activation, diffusion and Nernst potentials. For model validation, three inherently different anode PTL structures (carbon paper, sintered titanium particles, and titanium felt) are operated over a range of conditions. Additionally, the effects of PTL wettability were used to verify the model using Polytetrafluoroethylene (PTFE) treated Toray papers with PTFE loading ranging from 0% to 20%. The modeling results of both PTFE treated and untreated materials show good agreement with the experimental data. Mass transport or diffusion loss is the primary reason for performance differences between PTFE treated and untreated PTLs. Sintered titanium PTLs with thicknesses above 1 mm suffer from up to 33% increased ohmic losses without indicating any obvious changes in activation and diffusion losses when compared to untreated PTLs. The losses of the cell increase when using PTFE treated Toray paper. Individual contributions are quantified and assigned to increased ohmic, activation, and diffusion losses. In conclusion, the proposed model offers insights into the overpotential contributions of a PEMWE. It is a useful tool for predicting performance of various PTL materials and can be applied for PTL development and optimization efforts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

FY-2018 FES Joint Research Target

Fusion alphas and injected energetic neutral particle beams provide an important source of heating and current drive in advanced tokamak operating scenarios and burning plasma regimes. Alfvén eigenmode instabilities can cause the redistribution or loss of fast ions and driven currents, as well as potentially decreasing fusion performance and leading to localized losses. Measured fast ion fluxes in DIII-D and NSTX-U plasmas with different levels of Alfvén eigenmode activity will be used to determine the threshold for significant fast ion transport, assess mechanisms and models for such transport, and quantify the impact on beam power deposition and current drive. Measurements will be compared with theoretical predictions, including quantitative fluctuation data and fast ion density, in order to validate models and improve understanding of underlying mechanisms. Model predictions will guide the development of attractive operating regimes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Benchmarking Initiatives

Benchmarking has been recognized as an efficient tool for model validation as well as scientific collaboration. Several workshop series have been established to foster the benchmarking idea: setting up test cases with increasing complexity for method development and code comparison. In addition to representing model complexity, one of the key efforts is to develop codes that are suitable for modern HPC platforms such as PetaFlop supercomputers. It has been realized that those challenges are beyond single team capabilities. The Subsurface Environmental Simulation Benchmarking (SSBench) working group was initiated to develop, publish, and make publicly available well-described benchmark problems that could be used to demonstrate simulator conformance with norms established by the subsurface science and engineering community. SSBench supports the development of environmentally-relevant benchmark problem sets that provide rigorous tests for process model conceptualization, numerical implementation, process coupling, and accuracy. The initial SSBench focus on multicomponent reactive transport modeling reflects the expanding range of mechanisms and increasing complexity needed to address issues relevant to climate change, nuclear waste management, contaminant remediation, and pollution prevention.

benchmarking↗
DOE OSTI2025

Data-driven emulation of modal aerosol microphysics via neural operator-based modeling

The complexity and the small characteristic scales of aerosol microphysical processes pose a big challenge for accurate and efficient Earth system simulations at regional and global scales. In this work, we construct and evaluate a surrogate model: the aerosol deep operator network (ADON), a physics-inspired dual-net architecture for emulating the aerosol microphysics parameterization suite in the version 2 of the Energy Earth System Model (E3SMv2). The current version of the surrogate model is trained on a dataset comprising 9.8 million samples obtained from a global E3SMv2 simulation with the horizontal resolution of about one degree under cloud-free conditions. Incorporating domain spatial and temporal coordinates, as well as principle components extracted from training data, the dual-net surrogate model effectively captures the intricate representations of aerosol and the relationship with atmospheric state variables, achieving an R-squared score over $$95.7\%$$ for all the lognormal aerosol modes in the extrapolated regime. The validated model provides feature importance of input variables and their impact on the predictive capacity of the surrogate model in relation to the E3SM. The computational cost of online inference time deployed on CPUs and GPUs with lower precisions highlights ADON’s efficiency and potential in robust predictive modeling for large-scale Earth system computations.

Bai, Zhe↗
DOE OSTI2022

Continuum Model to Define the Chemistry and Mass Transfer in a Bicarbonate Electrolyzer

Bicarbonate electrolyzers are devices designed to convert CO 2 captured from point sources or the atmosphere into chemicals and fuels without needing to first isolate pure CO 2 gas. In this work, we report here an experimentally validated model that quantifies the reaction chemistry and mass transfer processes within the catalyst layer and cation exchange membrane layer of a bicarbonate electrolyzer. Our results demonstrate that two distinct chemical microenvironments are key to forming CO at high rates: an acidic membrane layer that promotes in situ CO 2 formation and a basic catalyst layer that suppresses the hydrogen evolution reaction. We show that the rate of CO product formation can be increased by modulating the catalyst and membrane layer properties to increase the rate of in situ CO 2 generation and transport to the cathode. These insights serve to inform the design of bicarbonate and BPM-based CO 2 electrolyzers while demonstrating the value of modeling for resolving rate-determining processes in electrochemical systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗