Data for EMSL Project 61086 from March 2025
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Toyoda, Jason [Environmental Molecular Sciences La↗
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Data for EMSL Project 61086 from March 2025
Explore the source record for details and available documents.
Toyoda, Jason [Environmental Molecular Sciences La↗
Statement of Work Electrical Energy Storage System Installation at Sandia National Laboratories.
Sandia is seeking to procure a 1 MWh energy storage system. It will be installed at the existing Energy Storage Test Pad, which is located at Sandia National Laboratories in Albuquerque, New Mexico. This energy storage system will be a daily operational system, but will also be used as a tool in our Research and development work. The system will be part of a showcase of Sandia distributed energy technologies viewed by many distinguished delegates.
25 ENERGY STORAGE↗
Evaluating Energy Impacts and Costs from PV Component Failures
The PV Reliability PerformanceModel (PV-RPM) tool is used to simulate the cost and energy impacts of component faults and failures for a hypothetical PV system. This model, originally developed by Sandia National Laboratories, is a new feature in the National Renewable Energy Laboratory's System Advisor Model (SAM), which performs stochastic analysissampling of probability distributions for faults, failures, and repairs. One PV system was created for this analysis to be representative of a portfolio of maintenance data maintained by Sandia National Laboratories. Fault,failure, and repair distributions from this system are varied from current baseline conditions to simulate different reliability and repair scenarios and resulting energy and cost impacts. Results highlight ways to express the uncertainty around PV system performance when considering the probabilistic behavior of components inthe system, and ways the PV-RPM model can be used to explore different failure and repair scenarios.
14 SOLAR ENERGY↗
Evaluating Energy Impacts and Costs from PV Component Failures: Preprint
The PV Reliability Performance Model (PV-RPM) tool is used to simulate the cost and energy impacts of component faults and failures for a hypothetical PV system. This model, originally developed by Sandia National Laboratories, is a new feature in the National Renewable Energy Laboratory's System Advisor Model (SAM), which performs stochastic analysis sampling of probability distributions for faults, failures, and repairs. One PV system was created for this analysis to be representative of a portfolio of maintenance data maintained by Sandia National Laboratories. Fault, failure, and repair distributions from this system are varied from current baseline conditions to simulate different reliability and repair scenarios and resulting energy and cost impacts. Results highlight ways to express the uncertainty around PV system performance when considering the probabilistic behavior of components in the system, and ways the PV-RPM model can be used to explore different failure and repair scenarios.
14 SOLAR ENERGY↗
BENEFIT with Northeastern University: HVAC Hardware-in-the-Loop Experimental Testing of a Heat Pump and Air Conditioner
This dataset includes HVAC Hardware-in-the-Loop (HIL) experimental results for a single stage, SEER 16, HSPF 9.5, 3-ton single-speed air source heat pump with 15 kW of backup auxiliary heating tested in both cooling and heating mode, and a two stage, SEER 21, 2-ton central air conditioner tested in cooling mode for a set of outdoor temperatures and indoor setpoint temperatures. In addition to these tests, experimental tests focused on the operation of auxiliary heating for the heat pump for winter condition were also conducted. The laboratory experiments for transient testing of the heat pump and air conditioner were conducted using the two HIL systems in the Systems Performance Laboratory (SPL) at NREL’s Energy Systems Integration Facility (ESIF). Further information on laboratory design and capabilities of the SPL along with the architecture of HVAC HIL system can be found in: Sparn, B. F. 2018. Laboratory Resources and Techniques to Evaluate Smart Home Technology (No. NREL/CP-5500-71696). National Renewable Energy Laboratory (NREL), Golden, CO (United States). https://www.nrel.gov/docs/fy18osti/71696.pdf and the experimental setup and validation of HVAC HIL platform can be found in: Ramaraj, S. and Sparn, B. 2022. Validation of HVAC Hardware-In-the-Loop Simulation for Advanced Control Strategies in Smart Homes (No. NREL/CP-5500-82562). National Renewable Energy Lab (NREL), Golden, CO (United States). https://www.nrel.gov/docs/fy22osti/82562.pdf. These experimental results can be used to validate how we currently model the cycling behavior of heat pumps and air conditioners. Additionally, many demand response programs implement heat pump and air conditioner control by changing the thermostat set point – these data may also be used to verify our models for heat pump and air conditioner demand response control are implemented correctly. The Test_Matrix file describes all the indoor and outdoor test conditions for heat pump and air conditioner and the file names of data sets include information about the test conditions. A wide range of outdoor air temperatures were chosen to accommodate summer and winter conditions. In addition to operating the HVAC equipment with different outdoor temperatures, we also operate the system with different indoor temperature set points to represent different grid signals or different operating conditions. For cooling conditions, the baseline set point is 72°F. To represent Load Up signals, the setpoint is changed to 68°F. The Load Shed set point is 76°F. For heating conditions, the baseline set point was assumed to be 68°F. The Load add set point is 72°F and the Load shed set point is 64°F. The starting indoor temperature for cooling conditions was set ~2°F above the indoor setpoint temperature so that the equipment turned on quickly. Similarly, the initial indoor temperature was set ~2°F lower than setpoint for heating mode tests to ensure that heating began quickly. The return air temperature was assumed to be equal to the indoor setpoint temperature in all cases. The experimental data are sampled at 1-second intervals. The data from ecobee thermostat at 5-minute interval are resampled and added to the corresponding file. The content of each data set is as follows: • T_Return (C): Measured return air temperature [C] • T_Return_SP (C): Return air temperature setpoint from E+ model, sent to HIL [C] • T_Supply (C): Measured supply air temperature at evaporator outlet [C] • T_Outdoor (C): Measured outdoor air temperature [C] • T_Outdoor_SP (C): Outdoor air temperature setpoint from weather file, sent to HIL [C] • T_Indoor (C): Measured indoor air temperature [C] • T_Indoor_SP (C): Indoor air temperature setpoint from E+ model, sent to HIL [C] • Outdoor Unit Power (W): Measured power of the outdoor unit [W] • Indoor Unit Power (W): Measured power of the indoor unit [W] • Evaporator Airflow Rate (CFM): Measured evaporator or indoor unit airflow rate sent to E+ model [CFM] • Cooling/Heating Capacity (kW): Calculated cooling/heating capacity sent to E+ model [kW] • T_SP_Thermostat (C): Thermostat cooling/heating setpoint temperature [C] • T_Indoor_Thermostat (C): Thermostat indoor air temperature [C]
24 POWER TRANSMISSION AND DISTRIBUTION↗
A Low Cost SIPM Evaluation and Control Prototyping System including accurate bias voltage generation, leakage current measurement, and temperature control using Peltier cooling
We describe an electronics system to operate SIPMs in the laboratory. The system is comprised of a single channel amplifier board (SIPM Amp) and a 4-channel control board to which up to 4 SIPM Amp boards can be connected. The control board supplies a common low voltage power for the amplifiers. All channels feature individually regulated bias and Peltier power voltages, along withPt10K RTD readout. A graphical user interface allows control of the bias voltages, amplifier voltage, Peltier voltages, and also provides for readback of leakage currents and other voltages. The GUI app has a feedback loop to measure the SIPM temperature (with the PT10K) and generate correction voltages to the Peltier to regulate the temperature. The temperatures, which can be achieved by Peltier cooling depend on the thermal design of the setup, the practical limit being about 50C below ambient. The SIPM Amp. board is a small (3x5cm) PCB that has a single SIPM readout channel. The SIPM scan be surface mounted on the pcb or connected to a 2-pin 0.100" socket. The area around the SIPM footprint is thermally isolated by perforations in the PCB. The RTD is located inside the thermal break. Onboard is BV and LV filtering. The 10X amplifier is based on MiniCircuits' GALI-S66+ integrated circuit, tweaked for a 6V power. The amplifier was selected for a combination of high bandwidth, low noise, and low power. An accoupled amplifier output is connected to an SMA connector. For best operation of this inverting amplifier the output signal should be in the range of 0 to -1V.The Amplifier board is connected to the Control board with a 10-conductor 0.025" pitch ribbon cable, carrying all the services. Peltier power for currents more than 1A should use a separate 2-contact connector.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
RADIOISOTOPE POWER SYSTEM CAPABILITIES AT THE IDAHO NATIONAL LABORATORY (INL)
--Idaho National Laboratory’s, Space Nuclear Systems and Technology Division established the resources, equipment and facilities required to provide nuclear-fueled, Radioisotope Power Systems (RPS) to Department of Energy (DOE) Customers. RPSs are designed to convert the heat generated by decay of iridium clad, 238PuO2 fuel pellets into electricity that is used to power missions in remote, harsh environments. Utilization of nuclear fuel requires adherence to governing regulations and the INL provides unique capabilities to safely fuel, test, store, transport and integrate RPSs to supply power—supporting mission needs. Nuclear capabilities encompass RPS fueling, testing, handling, storing, transporting RPS nationally, and space vehicle integration. Activities are performed at the INL and in remote locations such as John F. Kennedy Space Center and Cape Canaveral Air Station to support space missions. This paper will focus on the facility and equipment capabilities primarily offered at the INL, Material and Fuel Complex located in a security-protected, federally owned, industrial area on the remote desert site west of Idaho Falls, ID. Nuclear and non-nuclear facilities house equipment needed to perform required activities such as general purpose heat source (GPHS) module pre-assembly and module assembly using nuclear fuel; RPS receipt and baseline electrical testing, fueling, vibration testing to simulate the launch environment, mass properties testing to measure the mass and compute the moment of inertia, electro-magnetic characterizing to determine potential consequences to the operation of vehicle or scientific instrumentation, and thermal vacuum testing to verify RPS power performance in the vacuum and cold temperatures of space.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Initial Investigation of the Small Feature Detection Capability of the ORNL Fast Neutron Tomographic Imaging System
A variety of fast neutron imaging simulations and measurements were performed to investigate the spatial resolution and small feature detection capability of a fast-neutron tomographic imaging system developed at Oak Ridge National Laboratory. The system uses an associated particle DT (deuterium-tritium) neutron generator as the source and 25 mm × 25 mm × 100 mm plastic scintillators as the neutron detectors. Two image quality indicators were examined: 1) a variety of long steel pins embedded axially in the interior of a polyethylene cylinder and 2) a variety of short steels pins embedded radially in the surface of a shielded polyethylene cylinder. Measurements demonstrated detectability down to approximately 3 mm. Further data analysis may show improved detection performance and allow a more precise determination of the spatial resolution and small feature detection limit. The current DT generator has a neutron production spot size of about 3.5 mm, which appears to be the limiting factor in the current system configuration.
Wright, Michael C.↗
Energy Efficient Data Centers
Energy efficient data center strategies include thermal/environmental guidelines, air management, free cooling, and liquid-based cooling. A case study highlighting the data center cooling system at the National Renewable Energy Laboratory Energy Systems Integration Facility illustrates many of these principles.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Energy Efficient Data Centers
Energy efficient data center strategies include thermal/environmental guidelines, air management, free cooling, and liquid-based cooling. A case study highlighting the data center cooling system at the National Renewable Energy Laboratory Energy Systems Integration Facility illustrates many of these principles.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Increasing the Security and Resilience of the Electric Power System
Through its Laboratory Directed Research and Development (LDRD) program, NREL is investigating how the resilience of the electric power system can be measured, evaluated, and visualized - ultimately to provide utilities, government agencies, and communities with tools that can help them prepare for a variety of unpredictable scenarios.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Motion control system of MAX IV Laboratory soft x-ray beamlines
At the MAX IV Laboratory, five new soft x-ray beamlines are under development. The first is Species and it will be used to develop and set the standard of the control system, which will be common across the facility. All motion axes at MAX IV will be motorized using stepper motors steered by the IcePAP motion controller and a mixture of absolute and incremental encoders following a predefined coordinate system. The control system software is built in Tango and uses the Python-based Sardana framework. The user controls the entire beamline through a synoptic overview and Sardana is used to run the scans.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Testing and evaluation of the Sandia Smart Pre-concentrator using a vapor generation system
Sandia National Laboratories (SNL) was contracted by the Defense Threat Reduction Agency (DTRA), through KBRwyle to perform testing and evaluation of the SNL Smart Pre-concentrator (SPC) system and a COTS FTIR system procured by DTRA through KBRwyle. Two common chemical warfare agent simulants, dimethyl methylphosphonate and triethyl phosphate were selected as the compounds of interest. SNL tested both systems using a COTS vapor generation system, capable of delivering known concentrations of specific chemical compounds to both detection systems, with Sandia responsible for the SPC system. Both systems were measured against COTS sorbent collection tubes analyzed by SNL via a laboratory GCMS system. Concentrations measured from tubes upstream from the FTIR system differed from the expected concentrations, while downstream tubes were mostly within 20% of the target concentration.
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
Magnetics Testing on Radioisotope Power Systems at the Idaho National Laboratory
National Aeronautics and Space Administration (NASA) uses Radioisotope Power Systems (RPS) to power deep-space and planetary explorations such as Cassini-Huygens, Galileo, New Horizons, Mars Science Laboratory—Curiosity, and most recently Mars 2020--Perseverance. The Space Nuclear Power and Isotope Technologies (SNPIT) division, at Idaho National Laboratory (INL), fuels, tests, and delivers the RPS to ensure it can provide the electrical power needed to complete mission objectives. Some space crafts have instrumentation specifically designed to study the subject’s magnetic field or may have instruments that can be affected by magnetic fields produced by the RPS. Analyzing and measuring the RPS’s magnetic fields ensures the required thresholds are not exceeded and accurate data can be collected for the mission. Magnetics testing provides numerical values of the RPS’s magnetic field. During Magnetics testing at INL, the RPS is placed on a resistive load to simulate actual spacecraft load characteristics (voltage and current). During this simulation, magnetometers are used to measure the magnetic field of the RPS. Raw data is collected and then analyzed to ensure the field created by the RPS is below NASA thresholds or that the magnitude of the field is accounted for to ensure mission success.
42 ENGINEERING↗
Propagating information content: an example with advection
The mathematical algorithm to derive geophysical information from remote sensing observations is called a retrieval. The mathematics of many retrieval problems are ill-posed, and thus a priori information is used to help constrain the derived geophysical variable to realistic values. One quantity of interest, therefore, is the information content of the observation. Perfect information content in the observation would be achieved if the retrieval were able to capture any perturbation in the desired geophysical variable with the proper magnitude. Many new data products can be derived by combining geophysical variables retrieved from multiple different remote sensors. This paper explores, for the first time, how to derive the information content of these derived products. The approach uses traditional error propagation techniques to derive the uncertainty of the derived field twice, both when the observations are used in the retrieval and also when only the a priori information from each remote sensor is propagated. These two uncertainties are then used to provide an estimate of the information content of the derived geophysical variable. This study demonstrates how to propagate the uncertainties from six different instruments to provide the information content for water vapor and temperature advection. A multi-month analysis demonstrates that, in a mean sense, the information content for temperature advection is nearly unity for all heights below 700 m while, the information content for water vapor advection is somewhat more variable but still larger than 0.6 in the convective boundary layer.
Turner, David D. [National Oceanic and Atmospheric↗
Technical Requirements for Application of Operator Support Technology across Multiple Plant Systems
Argonne National Laboratory (ANL) and Idaho National Laboratory (INL) are jointly developing and evaluating operator aids to: (i) facilitate more timely response to plant faults and grid disturbances; (ii) achieve better management of plant upsets and improved operator performance; and (iii) ultimately improve nuclear plant safety, production, and cost management. The overall objective of the collaborative effort in this third and final year of the project was to conduct human performance tests using nuclear plant operators in the Human Systems Simulation Laboratory (HSSL) control room at INL to assess the value of a computerized operator support system (COSS) as an aid to the operator. This report describes the work performed by ANL. A companion report describes the work performed by INL and the results of the human performance tests.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Identifying Climate Patterns Using Clustering Autoencoder Techniques
Abstract The complexity of growing spatiotemporal resolution of climate simulations produces a variety of climate patterns under different projection scenarios. This paper proposes a new data-driven climate classification workflow via an unsupervised deep learning technique that can dimensionally reduce the vast volume of spatiotemporal numerical climate projection data into a compact representation. We aim to identify distinct zones that capture multiple climate variables as well as their future changes under different climate change scenarios. Our approach leverages convolutional autoencoders combined with k -means clustering (standard autoencoder) and online clustering based on the Sinkhorn–Knopp algorithm (clustering autoencoder) across the conterminous United States (CONUS) to capture unique climate patterns in a data-driven fashion from the Geophysical Fluid Dynamics Laboratory Earth System Model with GOLD component (GFDL-ESM2G). The developed approach compresses 70 years of GFDL-ESM2G simulation at 0.125° spatial resolution across the CONUS under multiple warming scenarios to a lower-dimensional space by a factor of 660 000 and then tested on 150 years of GFDL-ESM2G simulation data. The results show that five climate clusters capture physically reasonable and spatially stable climatological patterns matched to known climate classes defined by human experts. Results also show that using a clustering autoencoder can reduce the computational time for clustering by up to 9.2 times when compared to using a standard autoencoder. Our five unique climate patterns resulting from the deep learning–based clustering of the lower-dimensional space thereby enable us to provide insights on hydrometeorology and its spatial heterogeneity across the conterminous United States immediately without downloading large climate datasets. Significance Statement This paper presents a data-driven climate classification approach using unsupervised deep learning to dimensionally reduce climate model outputs and to identify distinct climate regions for their future changes. Our approach compresses climate information for 70 years of Geophysical Fluid Dynamics Laboratory Earth System Model data across the conterminous United States (CONUS) at 0.125° spatial resolution. The results reveal that five climate clusters capture reasonable and stable climatological patterns matched to known climate patterns. The embedded clustering process in deep learning provides ×9.2 times faster execution than the k -means clustering technique. These results give us insight about climate spatial patterns and heterogeneity of hydrological patterns across the conterminous United States without downloading large climate datasets.
Kurihana, Takuya↗