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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 OSTI2023

Techno-Economic Assessment of Residential Heat Pump Integrated with Thermal Energy Storage

Phase change material (PCM)-based thermal energy storage (TES) can provide energy and cost savings and peak demand reduction benefits for grid-interactive residential buildings. Researchers established that these benefits vary greatly depending on the PCM phase change temperature (PCT), total TES storage capacity, system configuration and location and climate of the building. In this study, preliminary techno-economic performance is reported for a novel heat pump (HP)-integrated TES system using an idealized approach. A simplified HP-TES was modeled for 1 year of space heating and cooling loads for a residential building in three different climates in the United States. The vapor compression system of the HP was modified to integrate with TES, and all heat transfer to and from the TES was mediated by the HP. A single PCM was used for heating and cooling, and the PCT and TES capacity were varied to observe their effects on the building’s energy consumption, peak load shifting and cost savings. The maximum reduction in electric consumption, utility cost and peak electric demand were achieved at a PCT of 30 °C for New York City and 20 °C for Houston and Birmingham. Peak energy consumption in Houston, New York City, and Birmingham was reduced by 47%, 53%, and 70%, respectively, by shifting peak load using a time-of-use utility schedule. TES with 170 MJ storage capacity allowed for maximum demand shift from on-peak to off-peak hours, with diminishing returns once the TES capacity equaled the daily building thermal loads experienced during the most extreme ambient conditions.

25 ENERGY STORAGE↗
DOE OSTI2020

Probabilistic Forecasting of Generators Startups and Shutdowns in the MISO System Based on Random Forest

Solving security constrained unit commitment (SCUC) problems to plan an economical generation schedule for day-head electricity market has been an important research topic in recent years. Mixed integer programming method (MIP), the-state-of-art approach for solving SCUC problem, is known computationally hard when the number of binary status variables is large. In this paper, a machine learning-based algorithm - random forest (RF), was applied to forecast the startups (SU) and shutdowns (SD) hours of generators, based on historical hourly system condition observations in the Midcontinent Independent System Operator (MISO) system. The main purpose is to reduce the number of binary status variables, by fixing the SU/SD hours to a narrow range of high confidence. This would significantly reduce the size of the decision space, and therefore speed up SCUC solutions with reduced uncertainty.

Lin, Xinming↗
DOE OSTI2025

Optimization of an aerostructural machining process using physics-guided Bayesian stability modelling

Existing algorithms for predicting milling chatter have not been widely adopted in industry since they require specialized instruments to measure the stability inputs. This study describes how the machining process for a meter-scale aluminum aerostructure was optimized using a physics-guided Bayesian stability model. The study was performed in collaboration with an industrial partner on production machines to evaluate the practicality of the proposed method under real-world conditions. For each cutting tool, the Bayesian approach automatically selected a small number of cutting tests, which were monitored using a microphone to observe the chatter frequency. The algorithm learned the system dynamics, cutting forces, and stability map from these test results. A novel algorithm for predicting tool bending stress was incorporated into the test selection algorithm to avoid tool breakage. On average, each set of optimized cutting parameters required less than six tests to identify and were 97% more productive than baseline parameters from the cutting tool manufacturer. The machining program was then further optimized using commercial feedrate scheduling software to remove cutting force spikes and reduce air cutting time. Five components were machined using the optimized process. These results demonstrate the potential for physics-guided Bayesian models to improve productivity in industrial settings.

Cornelius, Aaron [UT Knoxville]↗
DOE OSTI2023

Design and Characterization of the Engineering Model of the Spectrometer Onboard LuSEE-Night

The Lunar Surface Electromagnetics Explorer - Night, LuSEE-Night, is a low-frequency radio astronomy experiment that will explore the cosmic Dark Ages signal on the radio quiet farside of the Moon. The LuSEE-Night carries a radio frequency spectrometer consisting of a set of antennas, analog and digital processing electronics, and will be launched by NASA's Commercial Lunar Payload Services in 2025. The spectrometer is designed to observe the spectrum of the radio sky in the 0.5−50MHz band. The engineering model (EM) of the four-channel spectrometer has been developed. The EM has been characterized for linearity, gain, noise, and their temperature dependence, confirming that the EM meets all the requirements for LuSEE-Night. Three mitigation techniques have been implemented and verified to suppress self-induced electromagnetic interference (EMI). The flight model of the spectrometer is currently being developed and is scheduled to be shipped to the integration site in early 2024

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Simulations suggest offshore wind farms modify low-level jets

Abstract. Offshore wind farms are scheduled to be constructed along the East Coast of the US in the coming years. Low-level jets (LLJs) – layers of relatively fast winds at low altitudes – also occur frequently in this region. Because LLJs provide considerable wind resources, it is important to understand how LLJs might change with turbine construction. LLJs also influence moisture and pollution transport; thus, the effects of wind farms on LLJs could also affect the region’s meteorology. In the absence of observations or significant wind farm construction as yet, we compare 1 year of simulations from the Weather Research and Forecasting (WRF) model with and without wind farms incorporated, focusing on locations chosen by their proximity to future wind development areas. We develop and present an algorithm to detect LLJs at each hour of the year at each of these locations. We validate the algorithm to the extent possible by comparing LLJs identified by lidar, constrained to the lowest 200 m, to WRF simulations of these very low LLJs (vLLJs). In the NOW-WAKES simulation data set, we find offshore LLJs in this region occur about 25 % of the time, most frequently at night, in the spring and summer months, in stably stratified conditions, and when a southwesterly wind is blowing. LLJ wind speed maxima range from 10 m s−1 to over 40 m s−1. The altitude of maximum wind speed, or the jet “nose”, is typically 300 m above the surface, above the height of most profiling lidars, although several hours of vLLJs occur in each month in the data set. The diurnal cycle for vLLJs is less pronounced than for all LLJs. Wind farms erode LLJs, as LLJs occur less frequently (19 %–20 % of hours) in the wind farm simulations than in the no-wind-farm (NWF) simulation (25 % of hours). When LLJs do occur in the simulation with wind farms, their noses are higher than in the NWF simulation: the LLJ nose has a mean altitude near 300 m for the NWF jets, but that nose height moves higher in the presence of wind farms, to a mean altitude near 400 m. Rotor region (30–250 m) wind veer is reduced across almost all months of the year in the wind farm simulations, while rotor region wind shear is similar in both simulations.

17 WIND ENERGY↗
DOE OSTI2025

Potential Cost Reduction in New Nuclear Deployments Based on Recent AP1000 Experience

The completion of Vogtle Units 3 and 4, despite significant cost and schedule overruns, a major bankruptcy, and a pandemic, demonstrates that the U.S. nuclear industry can still develop new supply chains, train a highly skilled workforce, and build large nuclear power plants. Vogtle, being a first-of-a-kind (FOAK) plant, faced overruns, but FOAK overruns can decrease with more deployments through a combination of design standardization and modularization, transferring and implementing lessons learned between consecutive projects, and innovation. This report quantifies the potential cost and schedule reductions in future AP1000 plants in the US and identifies the barriers in achieving those reductions. It starts with a summary and analysis of the cost and construction timeline data for AP1000 plants that started construction in the United States (Vogtle) and China. Motivated by the steep schedule reductions from the first to second series of AP1000 plants in China, a cost reduction model was developed to quantify potential reductions for future AP1000 builds in the US. Results show that within about 3 more builds, AP1000s can become economically attractive in large markets in the U.S., without leveraging the Investment Tax Credit (ITC) or Production Tax Credit (PTC), and much sooner if these tax credits are available. Importantly the financial risks for future AP1000 builds in the US might be much smaller than observed in the FOAK plants. The study also identifies various barriers for future builds including the availability of a supply chain and a skilled workforce, and emphasizes the importance of making investments to gather and implement lessons learned between consecutive projects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Argonne Leadership Computing Facility: 2021 Operational Assessment Report

This Operational Assessment Report describes how the Argonne Leadership Computing Facility (ALCF) met or exceeded every one of its goals for calendar year (CY) 2021 as an advanced scientific computing center. In CY 2021, the ALCF operated its production resource, Theta, an Intel-based Cray XC40 system (11.7-petaflops) augmented with 24 NVIDIA DGX A100-based nodes (3.9-petaflops) that supports diverse workloads, integrating data analytics with artificial intelligence (AI) training and learning in a single platform. In 2021, we began deploying Polaris, our newest 40- petaflops system, and augmented this powerful testbed system with an additional 28 nodes to support the integration of real-time experiments and HPC resources. We also deployed our two largest storage systems yet, named Grand and Eagle, that will bring new services to our users and will power data-driven research for years to come. Last year, Theta delivered a total of 20.8 million node-hours to 16 Innovative and Novel Computational Impact on Theory and Experiment (INCITE) projects and 7.2 million node-hours to ASCR Leadership Computing Challenge (ALCC) projects (32 awarded during the 2020–2021 ALCC year and 17 awarded during the 2021–2022 ALCC year), as well as substantial support to Director’s Discretionary (DD) projects (5.5 million node-hours). As Table ES.1 shows, Theta performed exceptionally well in terms of overall availability (95.1 percent), scheduled availability (99.4 percent), and utilization (98.1 percent; Table 2.1). As of the submission date of this document, ALCF’s user community has published 249 papers in high-quality, peer-reviewed journals and technical proceedings. At the 2021 International Conference for High Performance Computing, Networking, Storage and Analysis (SC’21), Argonne researchers won two HPCwire Readers’ Choice Awards and were part of a Gordon Bell Prize finalist team recognized for developing an AI-enabled, multi-resolution simulation framework for studying complex biomolecular machines. Their framework was used to observe the SARS-CoV-2 replication-transcription machinery in action, by directly integrating experimental data. ALCF also provided a comprehensive program of high-performance computing (HPC) support services to help our community make productive use of the facility’s diverse and growing collection of resources. We are now entering the exascale era, with exascale machines being planned for national laboratories across the country, including Aurora at Argonne National Laboratory (Argonne) in 2023. ALCF researchers have been leading and guiding numerous strategic activities that will push the boundaries of what’s possible in computational science and engineering and allow us to deliver science on day one.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Foreword to special issue: Papers from the 63rd annual meeting of the APS Division of Plasma Physics, November 8–12, 2021

The 63rd annual meeting of the APS Division of Plasma Physics (DPP) was held on November 8–12, 2021 in Pittsburgh at the David Lawrence Convention Center with both a live (in person) component and a virtual component. Following guidance from an APS COVID task force, all in-person attendees were fully vaccinated and masked. More than 800 physicists attended, safely, in-person. With both virtual and on-site participants, discussions were lively, and the research presentations showed unmatched mastery in the modern observation, theory, simulation, and manipulation of plasma. The presentations included four invited review talks, 97 invited talks, four tutorials, and four presentations from this year's prize and award recipients. There were more than 1200 contributed poster presentations and 725 contributed oral presentations. Including both in-person and remote attendees, DPP 2021 had a record of 2232 participants. As a hybrid meeting, in-person presentations of all invited presentations were broadcast live and were accompanied by a Q&A discussion. Contributed oral and poster presentations were prerecorded along with options to schedule in-person discussions on demand. Five mini-conferences were held: “Gatekeeper Workshop: Creating a Diverse, Equitable, and Inclusive Pipeline,” “Collisionless Shocks in Laboratory and Space Plasmas,” “The High Repetition Rate Frontier in High-Energy-Density Physics,” “Measuring and Modeling Plasma Surface Interactions,” and “The Second Mini-Conference on Machine Learning, Data Science and Artificial Intelligence in Plasma Research.” Finally, on the day before the official start of the meeting, an afternoon “for students, by students” included lightning talks, plasma trivia, and an informal occasion to connect with other students, learn how to get the most from the DPP Annual Meeting, and share successful ways to connect with colleagues and advance their professional careers.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Distributionally Robust Decision Making Leveraging Conditional Distributions

Distributionally robust optimization (DRO) is a powerful tool for decision making under uncertainty. It is particularly appealing because of its ability to leverage existing data. However, many practical problems call for decision-making with some auxiliary information, and DRO in the context of conditional distributions is not straightforward. We propose a conditional kernel distributionally robust optimization (CKDRO) method that enables robust decision making under conditional distributions through kernel DRO and the conditional mean operator in the reproducing kernel Hilbert space (RKHS). In particular, we consider problems where there is a correlation between the unknown variable y and an auxiliary observable variable x. Given past data of the two variables and a queried auxiliary variable, CKDRO represents the conditional distribution P(y|x) as the conditional mean operator in the RKHS space and quantifies the ambiguity set in the RKHS as well, which depends on the size of the dataset as well as the query point. To justify the use of RKHS, we demonstrate that the ambiguity set defined in RKHS can be viewed as a ball under a metric that is similar to the Wasserstein metric. The DRO is then dualized and solved via a finite dimensional convex program. The proposed CKDRO approach is applied to a generation scheduling problem and shows that the result of CKDRO is superior to common benchmarks in terms of quality and robustness.

Chen, Yuxiao↗
DOE OSTI2023

Using Data-Driven Prediction of Downstream 1D River Flow to Overcome the Challenges of Hydrologic River Modeling

Methods for downstream river flow prediction can be categorized into physics-based and empirical approaches. Although based on well-studied physical relationships, physics-based models rely on numerous hydrologic variables characteristic of the specific river system that can be costly to acquire. Moreover, simulation is often computationally intensive. Conversely, empirical models require less information about the system being modeled and can capture a system’s interactions based on a smaller set of observed data. This article introduces two empirical methods to predict downstream hydraulic variables based on observed stream data: a linear programming (LP) model, and a convolutional neural network (CNN). We apply both empirical models within the Colorado River system to a site located on the Green River, downstream of the Yampa River confluence and Flaming Gorge Dam, and compare it to the physics-based model Streamflow Synthesis and Reservoir Regulation (SSARR) currently used by federal agencies. Results show that both proposed models significantly outperform the SSARR model. Moreover, the CNN model outperforms the LP model for hourly predictions whereas both perform similarly for daily predictions. Although less accurate than the CNN model at finer temporal resolution, the LP model is ideal for linear water scheduling tools.

13 HYDRO ENERGY↗
DOE OSTI2026

The Influence of Shielding Gas on the Wire-Arc Additive Manufacturing (WAAM) of Ni-Based Superalloy Haynes 282

Wire-arc additive manufacturing (WAAM) enables building large near-net-shaped parts using fast deposition rates and is attractive due to the potential cost and schedule savings. Haynes® 282® is a Ni-based superalloy with wide application in advanced power generation systems for its superior high temperature mechanical properties. High-quality WAAM H282 parts are achieved through careful process optimization, hence, we have focused on systematically characterizing the processing-structure-properties relationships over a wide range of wire-feed and travel speeds using a standard Ar30He shielding gas. Here the influence of adding 0.25-3% of H2 and CO2 to Ar-30He standard on our findings is examined. Multi-bead tracks were used to screen 20+ gas combinations to examine impact on wettability, porosity, oxidation, and grain structures. Improved multi-tracks are observed for gases with up to 1% H2 and 0.25% CO2. WAAM H282 builds within this range are examined to reveal differences in microstructure as evaluated with CT, SEM-EDS, and EBSD.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Novel LDPP-MADDPG Approach for Distributed Power Allocation in mmWave Cellular Networks

This paper considers the problem of distributed beam scheduling and power allocation problem in millimeter- Wave (mmWave) cellular networks, in which multiple Base Stations (BSs) operate as individual operators over a shared spectrum. We propose a novel learning-aided approach that integrates the Lyapunov Drift-Plus-Penalty (LDPP) framework and Multi-agent Deep Deterministic Policy Gradient (MADDPG) reinforcement learning algorithms. This offers a powerful approach to learning stable and constraint-aware policies, reaping the joint benefit of both LDPP and MADDPG, in complex multiagent environments. The major challenge for this approach is to integrate these two approaches in a meaningful and effective manner. The key idea to solve this problem is to introduce a novel feature of local observation that incorporates potential negative value of the reward function due to the stochastic constraints introduced by the LDPP framework. Empirical results demonstrate that our proposed scheme outperforms the baseline methods under various conditions.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Direct Measurement of Small Particle Growth and Aging at the Atmospheric Radiation Measurement Southern Great Plains Observatory Field Campaign Report

Two identical Captive Aerosol Growth and Evolution (CAGE) chambers were operated at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Southern Great Plains (SGP) observatory in the late summer and fall of 2021. The field study was scheduled to begin in spring, 2020, but was delayed because of the pandemic. CAGE chambers are designed to expose particles to an environment that mirrors that of the surroundings with or without a controlled perturbation designed to assess a sensitivity. The analysis here focuses on roughly the last two months of the overall study period during which measurements were almost continuous and when several perturbation experiments were conducted. Though the utility of a dual-chamber system is the ability to measure the influence of a single change on top of ambient conditions, both chambers were initially operated in the same way, with ambient air pulled through the gas exchange channel in both and ammonium sulfate particles injected into both. The similar time-dependent particle growth observed in the chambers for those periods provides confidence in differences observed during the subsequent perturbation experiments. The growth rate of particles in the reference chamber into which only ammonium sulfate particles were injected and for which only ambient air was pulled through were used to describe time-of-day averages. The average growth rate was highest in the evening and in the morning after sunrise and lowest in the late afternoon. The sensitivity of particle growth to secondary aerosol precursor gases was studied by adding them at a controlled rate to the ambient air flow pulled through one of the two chambers. Addition of 5 ppb of α-pinene resulted in an average particle growth rate of 4.4 nm hr -1 , compared with that of just 0.8 nm hr -1 in the reference chamber. The added α-pinene also triggered one new particle formation (NPF) event in the early evening just before sunset and another in the morning just after sunrise. Similarly, addition of 5 ppb of SO 2 to one chamber led to a pair of NPF events and to increased particle growth rate, though unlike the impact of added α-pinene, growth was enhanced only during the daytime when OH concentration is highest. The influence of aerosol liquid water on secondary aerosol formation and particle growth was investigated by injecting ammonium sulfate seed particles into one chamber and potassium sulfate particles into the other. Particles were injected into the two chambers four times over a 1.5-day period. The chamber relative humidity (RH) history during and following each injection was used to determine whether each particle type was crystalline or aqueous. For the case when both particle types remained crystalline throughout the period, they were tracked and for the case when they remained aqueous, the magnitude and time-dependence of the growth of both were almost exactly the same. For the other two cases the ammonium sulfate particles deliquesced upon injection and remained aqueous, while the potassium sulfate particles remained crystalline. For those two cases, the aqueous particles grew substantially faster than did the crystalline particles, providing evidence of the role of aerosol liquid water on secondary aerosol formation and particle growth.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters

The industrial sector consumes about one-third of global energy, making them a frequent target for energy use reduction. Variation in energy usage is observed with weather conditions, as space conditioning needs to change seasonally, and with production, energy-using equipment is directly tied to production rate. Previous models were based on engineering analyses of equipment and relied on site-specific details. Others consisted of single-variable regressors that did not capture all contributions to energy consumption. Further, new modeling techniques could be applied to rectify these weaknesses. Applying data from 45 different manufacturing plants obtained from industrial energy audits, a supervised machine-learning model is developed to create a general predictor for industrial building energy consumption. The model uses features of air enthalpy, solar radiation, and wind speed to predict weather-dependency; motor, steam, and compressed air system parameters to capture support equipment contributions; and operating schedule, production rate, number of employees, and floor area to determine production-dependency. Results showed that a model that used a linear regressor over a transformed feature space could outperform a support vector machine and utilize features more representative of physical systems. Using informed parameters to build a reliable predictor will more accurately characterize a manufacturing facility's energy savings opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Structure and Electronic Properties of Mesopores in Si PV Devices with PLEO Contacts

All current state-of-the-art silicon photovoltaic (PV) devices employ some flavor of passivating contact structure to minimize detrimental recombination while providing good conductivity. These properties are most often provided by a dielectric layer (SiNx, Al2O3, or SiOx) that is in contact with the crystalline silicon (c-Si) substrate. The effectiveness of these layers is heavily dependent on the nanoscale structure of both the dielectric layers and the crystalline silicon interface. As an example, the tunneling probability for charge carriers across thin SiOx layers, a process that is critical to maintain high conductivity, is extremely sensitive to the SiOx thickness. SiOx with a thickness below 2 nm readily allows charge carrier tunneling, whereas it is impeded for greater thicknesses. In the latter case it has been shown that modifications to the thermal processing schedule can induce disruptions, or pinholes, in thick SiOx layers that allow transport of charge carriers while maintaining excellent passivation. However, this requires higher temperatures processes that may inhibit widespread adoption by industry. Recently, another option has been presented that may circumvent the necessity for high temperature processing, namely metal assisted chemical etching (MACE). MACE relies on deposition of Ag nanoparticles onto the SiOx layers with subsequent electroless etching to form mesopores in the SiOx layers that are analogous to the pinholes created with high temperature processing. The size and density can be controlled based on the Ag nanoparticle deposition conditions. The resulting contact structure is known as polysilicon on locally etched oxide (PLEO) contacts. The nanoscale structure of the mesopores in PLEO contacts define the device level observables such as recombination current (Jo) and junction resistance. In this work we present atomic resolution transmission electron microscopy (TEM) analysis of mesopores in PLEO contacts formed with a different processing conditions to connect provide insight into how the nanoscale structure of the SiOx layer influences PV device properties. Additionally, we have previously used electron beam induced current (EBIC) to probe local transport properties in Si PV devices with a SiOx layer containing pinholes due to high temperature processing. Using EBIC we were able to directly show the enhance charge carrier transport through the pinholes. Here we also employ EBIC to study non-uniformities in charge carrier recombination and transport associated with mesopores in PLEO contacts and compare these results with our previous work on devices with pinholes in the SiOx formed through high temperature processes. The products of this work provide critical information that is required to both further optimize performance of Si PV devices with PLEO contact and to drive future adoption of this technology by industry.

ENGINEERING,SOLAR ENERGY↗
DOE OSTI2024

Sulfuric Acid New Particle Formation Field Campaign Report

This campaign aimed to measure gaseous sulfuric acid concentrations at the surface level using a newly developed sulfuric acid dimethylamine-reactive condensation particle counter (SAD-RCPC). Sulfuric acid is a key nucleation precursor and is the likely driving compound for new particle formation at the Atmospheric Radiation Measurement (ARM) user facility’s Southern Great Plains (SGP) observatory. The SAD-RCPC will be deployed in the future at ARM SGP (AFC010010: "Vertically Resolved Nucleation Precursors at SGP") scheduled for 2024. The campaign was in conjunction with Eleanor Browne's campaign (AFC010075: Boundary-layer gradients in new particle formation) and took place from 05/7/23 to 5/21/23. The measured sulfuric acid concentrations by the SAD-RCPC will be compared to concentrations measured by Browne's chemical ionization mass spectrometer (CIMS). The SAD-RCPC consisted of a glass flow reactor coupled to a 1-nm diethylene glycol condensation particle counter (DEG CPC). Sampled air was mixed with a high concentration of dimethylamine vapor (~1 ppbv). Sulfuric acid within the sampled gas nucleated with dimethylamine to form detectable 1-nm particles. The measured particle concentration was converted to sulfuric acid concentration using nucleation reaction kinetics. The pulse height distribution of particles detected by the DEG CPC was observed using a multi-channel analyzer. Large pre-existing particles were separated from the freshly formed particles in the SAD-RCPC from the pulse height distribution (PHD). During the short campaign, seven new particle formation events were observed. The SAD-RCPC detected an increase in sulfuric acid concentration, but this also coincided with an increase in pre-existing <3 nm particle concentrations. In addition, Browne's CIMS detect sulfuric acid concentrations in the range of 10 5 -10 6 cm -3 .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Simulation of evapotranspiration and yield of maize: An inter-comparison among 41 maize models

Accurate simulation of crop water use (evapotranspiration, ET) can help crop growth models to assess the likely effects of climate change on future crop productivity, as well as being an aid for irrigation scheduling for today's growers. To determine how well maize (Zea mays L.) growth models can simulate ET, an initial inter-comparison study was conducted in 2019 under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). Herein, we present results of a second inter-comparison study of 41 maize models that was conducted using more comprehensive datasets from two additional sites - Mead, Nebraska, USA and Bushland, Texas, USA. There were 20 treatment-years with varying irrigation levels over multiple seasons at both sites. ET was measured using eddy covariance at Mead and using large weighing lysimeters at Bushland. A wide range in ET rates was simulated among the models, yet several generally were able to simulate ET rates adequately. The ensemble median values were generally close to the observations, but a few of the models sometimes performed better than the median. Many of the models that did well at simulating ET for the Mead site did poorly for drier, windy days at the Bushland site, suggesting they need to improve how they handle humidity and wind. Additional variability came from the approaches used to simulate soil water evaporation. Fortunately, several models were identified that did well at simulating soil water evaporation, canopy transpiration, biomass accumulation, and grain yield. These models were older and have been widely used, which suggests that a larger number of users have tested these models over a wider range of conditions leading to their improvement. These revelations of the better approaches are leading to model improvements and more accurate simulations of ET.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Project Plan: Salt in Situ Heater Test

This project plan gives a high-level description of the US Department of Energy Office of Nuclear Energy (DOE-NE) Spent Fuel and Waste Disposition (SFWD) campaign in situ borehole heater test project planned for the Waste Isolation Pilot Plant (WIPP) site, titled the Brine Availability Test in Salt (BATS). BATS is the first stage in a planned sequence of tests to bolster the technical basis for disposal of heat generating waste in salt. This plan provides an overview of the schedule and responsibilities of the parties involved. This project is a collaborative effort by Sandia, Los Alamos, and Lawrence Berkeley National Laboratories to execute a series of small-diameter borehole heater tests in salt for the DOE-NE SFWD campaign. Design of a heater test in salt at WIPP has evolved over several years. The experiment has begun in January 2020 and the first phase will continue for several months with the possibility for follow on testing. BATS comprises a suite of modular tests, which consist of a group of adjacent horizontal boreholes in the wall of drifts at WIPP. Each test is centered around a packer-isolated heated borehole (12.2 cm [4.81 diameter) containing equipment for water-vapor collection and borehole closure monitoring, surrounded by smaller-diameter (up to 5.3 cm [2.11 diameter) satellite observation boreholes. Observation boreholes contain grouted-in temperature sensors, electrical resistivity tomography (ERT) sensors, and fiber optics; packer-isolated tracer release and sampling intervals; and acoustic emission (AE) piezoelectric sensors. A larger-diameter (12.2 cm [4.81) satellite borehole includes sorel and salt cement plugs, as part of an engineered barrier sealing test. The first two tests, to be implemented in parallel, are heated (target borehole wall temperature of 120 °C) and unheated, with similar arrays of observation borehole monitoring changes. Follow-on tests will be designed using information gathered from the first two tests, and may be conducted at other borehole wall temperatures, use multiple observation boreholes, and may include different measurement types and test designs. This 2020 update of the original 2018 project plan satisfies DOE-NE Spent Fuel and Waste Science and Technology (SFWST) milestone M3SF-205N010303034, as part of the SNL "Salt Disposal R&D" work package.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗