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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Atmospheric Emitted Radiance Interferometer Optimal Estimation (AERIoe) Value-Added Product Report

The Atmospheric Emitted Radiance Interferometer (AERI) Optimal Estimation (AERIoe) algorithm (Turner and Löhnert 2014, Turner and Blumberg 2019) retrieves boundary-layer profiles of temperature and water vapor mixing ratio, together with cloud properties for a single-layer cloud (i.e., LWP, effective radius), from AERI-observed infrared radiance spectra and other measurements. The current AERIoe Value-Added Product (VAP) uses version 2.8 of David Turner's code, which uses additional inputs to constrain the retrieval including surface meteorology, microwave radiometer brightness temperatures at any number of frequencies (the U.S. Department of Energy Atmospheric Radiation Measurement [ARM] user facility typically used observations at 23.8 and 30.0 GHz), and Rapid Refresh numerical weather prediction model output. The method is a physical iterative retrieval that uses the optimal estimation framework (Rodgers 2000), so a full error covariance of the solution and the information content (in the form of degrees of freedom for signal) is provided for each retrieval.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Anomaly detection for MPC forecast in Fleet of Water Heaters

Among residential devices, water heaters consume 20% of home energy use in the United States. Water heaters possess the capability to store energy within their reservoirs, enabling the ability to decouple energy use from hot water use. This capability can be used to reduce energy usage and costs while also supporting grid services. This requires accurate forecasting of the parameters of the water heater such as upper and lower temperatures. In this study, we analyzed the performance and behavior of a water heater model used in the real-world to predict a control mechanism that is implemented in a smart residential neighborhood. The model forecasts are accurate in most cases but not all. In such scenarios, error correction of the model is necessary to further improve model predictive control accuracy. Anomaly detection is the first step of error correction. This study complements existing research by grouping time series data into two clusters one with anomalies and another without anomalies. To achieve this task, we explored and compared multiple unsupervised machine learning algorithms to perform clustering. Among these algorithms, Ward clustering has the lowest running time and identified the highest number of anomalies for the upper temperature limit. The proposed approach is tested based on the data collected in a neighborhood with 46 townhomes located in Atlanta, GA.

Lebakula, Viswadeep↗
DOE OSTI2022

A Wireless Underground Sensor Network Field Pilot for Agriculture and Ecology: Soil Moisture Mapping Using Signal Attenuation

Wireless Underground Sensor Networks (WUSNs) that collect geospatial in situ sensor data are a backbone of internet-of-things (IoT) applications for agriculture and terrestrial ecology. In this paper, we first show how WUSNs can operate reliably under field conditions year-round and at the same time be used for determining and mapping soil conditions from the buried sensor nodes. We demonstrate the design and deployment of a 23-node WUSN installed at an agricultural field site that covers an area with a 530 m radius. The WUSN has continuously operated since September 2019, enabling real-time monitoring of soil volumetric water content (VWC), soil temperature (ST), and soil electrical conductivity. Secondly, we present data collected over a nine-month period across three seasons. We evaluate the performance of a deep learning algorithm in predicting soil VWC using various combinations of the received signal strength (RSSI) from each buried wireless node, above-ground pathloss, the distance between wireless node and receive antenna (D), ST, air temperature (AT), relative humidity (RH), and precipitation as input parameters to the model. The AT, RH, and precipitation were obtained from a nearby weather station. We find that a model with RSSI, D, AT, ST, and RH as inputs was able to predict soil VWC with an R 2 of 0.82 for test datasets, with a Root Mean Square Error of ±0.012 (m 3 /m 3 ). Hence, a combination of deep learning and other easily available soil and climatic parameters can be a viable candidate for replacing expensive soil VWC sensors in WUSNs.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Project Presentation: Hardware-Based Demonstration of Temperature Control Functions for Reactor Systems

Autonomous thermal regulation in nuclear reactors remains crucial for maintaining stable operation and ensuring the integrity of fuel. To alleviate public skepticism of the safety of nuclear reactors, demonstrating control over this key factor is pivotal. Utilizing electric heat pads to simulate the heat released in a reactor core, thermocouples for temperature monitoring, and an Arduino micro programmable logic controller (PLC) with an embedded proportional-integral-derivative (PID) algorithm for control, a hardware-based demonstration of a reactor heating system will validate the efficacy of reactor control over this key parameter. This report covers internship project presentation as well as relevant experience with nuclear and proposal for project upgrade.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

NREL ASSIST Rhode Island / Thermodynamic retrievals TROPoe

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 11) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height from a collocated NREL Upgraded Galion lidar #1212-60 and temperature, RH, and pressure from collocated PNNL met station. The lidar beta is used to estimate the cloud base height through a modified algorithm by Newsom et al., 2019 used at ARM SGP. The full pipeline for running the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) which provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see e.g. Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Upton, NY.

17 WIND ENERGY↗
DOE OSTI2025

Gas-Gap Calorimeter Sizing and Rating Tools for Heat Pipe Experimentation

Effective gas-gap calorimeter sizing and rating tools are required to design calorimeters that enable well-defined operating conditions, high cooling powers, and accurate calorimetry. The present report describes two tools utilizing Microsoft Excel and MathWorks MATLAB that enable the sizing and rating of gas-gap calorimeters with a He-Ar binary gas mixture in the gap. The Excel tools are two easy-to-use spreadsheets for calculating heat pipe operating temperature or He-Ar mole fractions given the required inputs. The MATLAB tool includes similar models with more robust solution algorithms along with sub-options for full-vacuum, static gas-gap, and forced circulation in the gas-gap. The accuracy of the developed tools were demonstrated by comparing with existing work in literature. The MATLAB tool was used for a parametric study to determine the gas-gap thickness, coolant gap thickness, coolant flow rate, and coolant inlet temperatures for the testing of a 3/4 in outer diameter heat pipe up to powers of 10 kW and temperatures of 1,000°C. The effects of heat pipe and calorimeter inner shell surface emissivities were investigated, considering the inability to control or accurately measure surface emissivities in most applications. It was found that controlling the He-Ar mole fractions provides a wide operating range for gas-gap thicknesses around ~ 0.042--0.090 in (~ 1.07--2.29 mm) for a surface emissivity range of 0.4--0.8, since conduction heat transfer is a significant fraction of the heat transfer across the gas-gap. In addition, it was found that turbulent flow in the coolant gap is needed at high input powers to prevent shell temperatures from approaching the boiling temperature of the water coolant. The parametric study resulted in choosing stainless steel tubes with an outer diameter and thickness of 1 x 0.065 in as the inner shell, and a 1-1/2 x 0.156 in as the outer shell of the calorimeter. Overall, this report presents the necessary information for the sizing and rating of gas-gap calorimeters with He-Ar mixtures for the testing of high-temperature heat pipes (~ 500--1,000°C).

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

A stochastic solver based on the residence time algorithm for crystal plasticity models

Abstract The deformation of crystalline materials by dislocation motion takes place in discrete amounts determined by the Burgers vector. Dislocations may move individually or in bundles, potentially giving rise to intermittent slip. This confers plastic deformation with a certain degree of variability that can be interpreted as being caused by stochastic fluctuations in dislocation behavior. However, crystal plasticity (CP) models are almost always formulated in a continuum sense, assuming that fluctuations average out over large material volumes and/or cancel out due to multi-slip contributions. Nevertheless, plastic fluctuations are known to be important in confined volumes at or below the micron scale, at high temperatures, and under low strain rate/stress deformation conditions. Here, we develop a stochastic solver for CP models based on the residence-time algorithm that naturally captures plastic fluctuations by sampling among the set of active slip systems in the crystal. The method solves the evolution equations of explicit CP formulations, which are recast as stochastic ordinary differential equations and integrated discretely in time. The stochastic CP model is numerically stable by design and naturally breaks the symmetry of plastic slip by sampling among the active plastic shear rates with the correct probability. This can lead to phenomena such as intermittent slip or plastic localization without adding external symmetry-breaking operations to the model. The method is applied to body-centered cubic tungsten single crystals under a variety of temperatures, loading orientations, and imposed strain rates.

42 ENGINEERING↗
DOE OSTI2024

Mesoscale Modeling of the Effects of Accelerated Burnup on UO2 Microstructural Evolution

Accelerating the nuclear fuel qualification process will rely on some combination of advanced modeling and simulation techniques with accelerated irradiation testing and separate effects experiments to enable the development of new fuel concepts in a shorter time frame. One of the key challenges to successfully leveraging accelerated irradiation tests will be understanding the artifacts that may be introduced with accelerated accumulation of dose and/or burnup. This work presents phase field (MARMOT) simulations of the evolution of representative 2D UO2 microstructures up to 40 MWd/kgU. Simulations were performed under both commercial light water reactor fuel conditions as well as those that would be expected for highly accelerated (~10x) burnup conditions similar to those used in the MiniFuel irradiations in Oak Ridge National Laboratory’s High Flux Isotope Reactor. The phase field model was coupled with a discrete nucleation algorithm to model re- structuring at high burnup. The effect of the different fission rates in both microstructures was investigated at two temperatures: 650?C and 800?C. The lower temperature simulations both showed an onset of restructuring at nearly 60 MWd/kgU. More extensive restructuring was obtained in the MiniFuel microstructure compared with that of the PWR fuel. At 800?C, no restructuring was obtained as a result of the thermally activated diffusion of Xe atoms and U vacancies to fission gas bubbles, which reduces the nucleation driving force. These results highlight the importance of using modeling and simulation tools to inform the environmental conditions during targeted accelerated irradiation tests to extract the most useful fuel performance data.

accelerated fuel qualification, Phase Field, Restr↗
DOE OSTI2021

A machine learning degradation model for electrochemical capacitors operated at high temperature

Electrochemical capacitors (ECs) have only recently been considered as an alternative power source for telemetry sensors of drilling equipment for geothermal or oil and gas exploration. The lifecycle analysis and modelling of ECs is underrepresented in literature in comparison to other storage devices e.g. Li-ion batteries. This paper investigates the degradation of ECs when cycled outside the manufacturer-specified operating temperature envelope and proposes a machine learning-based approach for modelling the degradation. Experimental results show that end of life, defined as a 30% decrease in capacitance, occurs at 1,000 cycles when the environmental temperature exceeds the maximum operating temperature by 30%. The life-cycle test data is then used as an input to a Gaussian process regression (GPR) algorithm to predict the capacitance fade trend. The GPR is validated on a total of nine commercial cells from two different manufacturers, achieving an average root mean squared percent error of less than 2% and a mean calibration score of 93% when referenced to a 95% confidence interval. The model can be utilized to determine the EC degradation rate at a range of operating temperature values.

42 ENGINEERING↗
DOE OSTI2025

A Reinforcement Learning Approach to Augment Conventional PID Control in Nuclear Power Plant Transient Operation

The ability of nuclear reactors to operate their power conversion cycles more flexibly will enhance their value to energy grids with variable pricing. Current nuclear control systems are typically classical controllers that are often based on proportional-integral-derivative (PID) control. This paper presents a method of augmenting the existing PID control for difficult transient operations in nuclear power plants using a reinforcement learning–derived feedforward signal applied in real time. The agents, which are trained on a test thermal load-following problem, are designed to improve steam generator outlet temperature control for a range of fast load-following scenarios covering ramp rates from 9%/min to 15%/min. Several reinforcement learning algorithms were initially investigated for the training of the feedforward agents with deep Q-learning (DQN) and proximal policy optimization (PPO) networks, which were found to be the most promising. The DQN controllers utilize discrete actions, giving them a better disturbance rejection at steady state but inconsistent response to initial temperature deviations. In contrast, PPO-trained agents, which take continuous actions except for a dead zone around zero, were shown to have the best combination of high disturbance rejection at steady state and good tracking of the desired temperature value. The ability of the PPO agent was also examined, with the average time of decision making found to be on the order of 1 ms. The fault properties of the controller under the loss of the reinforcement learning agent feedforward signal were also examined. The controller showed strong performance in situations of “no-signal” faults. but was less good at handling “stuck-at” faults, where the feedforward signal remains at a set value. In both cases, however, the PID was able to successfully maintain stability, eventually returning the system to a steady state. It is hoped that this work will allow for the proposed control architecture to be examined for more difficult control problems such that it may eventually be used to adapt existing nuclear plants for more aggressive load-following on grids of the future.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

The Blocking Temperature of an Amorphous Alternate A and B Layers Cylindrical Nanowire

The blocking temperature of an amorphous cylindrical nanowire of length L and radius R is studied within the framework of Monte Carlo simulation based on Metropolis algorithm. The nanowire is formed by alternate layers of atoms A and B. The interlayer coupling J{sub AB} is ferromagnetic. The effects of the exchange couplings J{sub B} and J{sub AB}, the plane anisotropy D{sub xy}, and the amorphization α on the blocking temperature of the system are investigated. We found that the blocking temperature increases with increasing the value of the exchange couplings J{sub B} and J{sub AB} and decreases with increasing the plane anisotropy D{sub xy} and the degree of the amorphization α.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Development and Validation of Algorithms That Analyze Communicating Thermostat Data to Identify Enclosure Retrofit Opportunities

Annual energy savings of up to $\$ 4$ to $\$ 5$ billion could be achieved nationwide through basic insulation and heating system retrofits of existing homes. However, current utility energy efficiency programs are costly and challenging to scale. Customer acquisition occurs primarily through energy bill mailers, mass media, and online advertising that lack specificity about home-specific retrofit opportunities, expected energy savings, and cost-effectiveness. Specific retrofit opportunities are identified via on-site home energy assessments (HEAs) that are inconvenient to homeowners, expensive, and of variable accuracy. We developed computational algorithms that automatically analyze communicating thermostat (CT) heating data that could be used to increase the customer uptake of insulation and air sealing energy conservation measures (ECMs) by identifying homes with the most significant retrofit opportunities, estimating post-retrofit energy savings, and formulating home-specific outreach. The algorithms are based on an extended second-order grey-box model that characterizes a building’s thermal response using lumped elements, coupled with an empirical model of infiltration that accounts for both wind and stack effects. The basic parameters of the model correspond to actual physical parameters of the home, i.e., the home’s overall R-value of and the building envelope ACH50. Unlike the conventional approach, which estimates model parameters based on the best fit to the observed time-dependent room temperature, our approach derives correlations between the daily heating system runtime and temperature difference (indoor-outdoor) that are more robust to data quality issues in real-world applications. We also used HEA data for algorithm development and validation. With the help of our utility partners, Eversource and National Grid, we obtained data sets for hundreds of Massachusetts homes. For each home, these data sets included three sets of information anonymized by the utility: (1) CT data (HVAC runtime, room temperature, and, for some vendors, outdoor temperature and wind speed) collected by the CT vendor (one of three) over a heating season, (2) HEA report performed by the HEA vendor (same vendor for all homes), (3) Monthly utility gas bills coincident with the CT data (3 to 24 per home, depending on availability). For some homes, we also obtained blower-door test results. Initially, we applied the algorithms developed to homes with a single CT and then extended them to homes with two CTs by using an equivalent home approach. Finally, we developed algorithms for prediction of energy savings and a methodology of comparing our predictions with those generated by HEAs. The main technical results indicate that we can reliably identify homes with insulation and/or air sealing retrofit opportunities and provide accurate savings predictions. Our hypothesis is that the algorithms could be applied to utility energy efficiency programs to identify homes that could realize significant energy savings from insulation and/or air sealing retrofits. This information could then be used to reach out to those homes with highly customized outreach, thereby delivering increased program energy savings and cost-effectiveness. This would: Significantly increase the uptake rate of on-site HEAs, and Significantly increase the fraction of HEAs resulting in ECM implementation. To test these hypotheses, we designed and conducted a randomized controlled trial (RCT). The RCT results suggest that personal messaging leads to a two- to five-fold increase in the HEA uptake rate.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Real-time estimation of the electron temperature profile in DIII-D by leveraging neural-network surrogate models

Control of both the magnitude and the shape of tokamak profiles will be necessary to achieve stable, high-performance plasmas. In order to reject disturbances in real time, feedback-control algorithms rely on accurate real-time knowledge of the plasma state. When diagnostics alone are insufficient, either because they are limited in number or their measurements are too noisy, observers can be used to combine diagnostic data with a response model to provide a better estimation of different plasma properties. An observer has been developed to estimate the electron temperature profile in real time using both diagnostic data from the Thomson scattering system and a model based on the electron heat transport equation describing the evolution of the electron temperature profile. Here, neural network surrogate models are leveraged to help improve the overall model prediction while staying within computation time constraints for real-time use. The observer algorithm is shown in offline tests to produce smooth profiles that are consistent with both the diagnostic data and the electron heat transport equation. When implemented into the real-time plasma control system, this observer will provide valuable information on the electron temperature profile to many potential feedback-control applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Reconstructing 3D asymmetries in laser-direct-drive implosions on OMEGA

Three-dimensional reconstruction algorithms have been developed which determine the hot-spot velocity, hot-spot apparent ion temperature distribution, and fuel areal density distribution present in laser-direct-drive inertial con finement fusion implosions on the OMEGA laser. These reconstructions rely on multiple independent measurements of the neutron energy spectrum emitted from the fusing plasma. Measurements of the neutron energy spectrum on OMEGA are made using a suite of quasi-orthogonal neutron time-of-flight detectors and a magnetic recoil spectrometer. These spectrometers are positioned strategically around the OMEGA target chamber to provide unique 3-D measurements of the conditions of the fusing hot spot and compressed fuel near peak compression. Here, the uncertainties involved in these 3-D reconstructions are discussed and are used to identify a new nTOF diagnostic line of sight which when built will reduce the uncertainty in the hot-spot apparent ion temperature distribution from 0.7 to < 0.4 keV.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Polarization-controlled volatile ferroelectric and capacitive switching in Sn 2 P 2 S 6

Abstract Smart electronic circuits that support neuromorphic computing on the hardware level necessitate materials with memristive, memcapacitive, and neuromorphic- like functional properties; in short, the electronic response must depend on the voltage history, thus enabling learning algorithms. Here we demonstrate volatile ferroelectric switching of Sn 2 P 2 S 6 at room temperature and see that initial polarization orientation strongly determines the properties of polarization switching. In particular, polarization switching hysteresis is strongly imprinted by the original polarization state, shifting the regions of non-linearity toward zero-bias. As a corollary, polarization switching also enables effective capacitive switching, approaching the sought-after regime of memcapacitance. Landau–Ginzburg–Devonshire simulations demonstrate that one mechanism by which polarization can control the shape of the hysteresis loop is the existence of charged domain walls (DWs) decorating the periphery of the repolarization nucleus. These walls oppose the growth of the switched domain and favor back-switching, thus creating a scenario of controlled volatile ferroelectric switching. Although the measurements were carried out with single crystals, prospectively volatile polarization switching can be tuned by tailoring sample thickness, DW mobility and electric fields, paving way to non-linear dielectric properties for smart electronic circuits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Emergence of a Spin Microemulsion in Spin-Orbit Coupled Bose-Einstein Condensates

We report the first numerical prediction of a “spin microemulsion”—a phase with undulating spin domains resembling classical bicontinuous oil-water-surfactant emulsions—in two-dimensional systems of spinor Bose-Einstein condensates with isotropic Rashba spin-orbit coupling. Using field-theoretic numerical simulations, we investigated the melting of a low-temperature stripe phase with supersolid character and find that the stripes lose their superfluidity at elevated temperature and undergo a Kosterlitz-Thouless-like transition into a spin microemulsion. Momentum distribution calculations highlight a thermally broadened occupation of the Rashba circle of low-energy states with macroscopic and isotropic occupation around the ring. Here, we provide a finite-temperature phase diagram that positions the emulsion as an intermediate, structured isotropic phase with residual quantum character before transitioning at higher temperature into a structureless normal fluid.

Finite temperature field theory↗