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DOE OSTI2021

Impact of Dry Cooler Air-Side Performance on a sCO2 Power Cycle for a CSP Application

Uncertainty around the design and control of the supercritical CO2 power cycle must be reduced before this technology can be implemented for large-scale grid support. To better understand the day-to-day performance of an sCO2 cycle, off-design performance calculations must be included for all power block components, and performance assumptions must be removed. This study has expanded the modeled scope to include the air-side performance for the dry cooler and has incorporated discretized heat transfer calculations for both streams through the pre-cooler to better predict off-design performance. This study considered a recompression Brayton cycle in a concentrating solar power application. The cycle model utilized fixed sCO2 turbomachinery maps for the main compressor, recompressor, and expander operating to supply approximately 10 MW gross at the design point. Fixed vendor-supplied fan curves were used to calculate the air-side performance of the dry cooler. The primary heater was modeled considering both the sCO2 and heat transfer fluid streams. Off-design performance was predicted for an ambient temperature range of 0-55℃, a HTF temperature range of 705-735℃, and a HTF mass flow range of 50-105% of the design point value. To understand the importance of modeling the air-side performance, the cycle off-design performance was also calculated using a constant CO2 outlet temperature assumption and a constant approach temperature assumption for the dry cooler. Results show that using these assumptions can significantly alter the power output and cycle efficiency predictions.

14 SOLAR ENERGY↗

DOE OSTI2021

Numerical Investigation of Two-Stage Vapor Compression System with Simultaneous Vapor and Liquid Injection

Two-stage vapor compression systems can be advantageous over single-stage systems by providing improved system performance, lower discharge temperature and reduced throttling losses. These systems employ various intermediate configurations, such as liquid injection and vapor injection. This paper presents a configuration for a R1234ze(E) two-stage air-conditioning system using a turbo compressor with two injection ports, one for vapor injection and the second for liquid injection. The liquid injection is used to cool the motor and electronics. A component-based representation and solution approach was used to simulate the two-stage compression system with simultaneous vapor and liquid injection at steady state. The turbo compressor was represented using a customized performance map. The condenser and the evaporator were modeled using finite-volume approach. A parametric study was conducted to assess the impact of the following three variables on the system performance: vapor injection ratio, condenser air flow rate, and discharge pipe pressure drop. The simulation results show that as the vapor injection ratio increased, the system performance undergoes a tradeoff between an enhanced subcooling effect (and thus enhanced unit refrigeration capacity) and a decreased suction mass flow rate. Maximum COP occurs when the vapor injection ratio was 0.1. The results also show that as the condenser air flow rate increased, both the capacity and power consumption (including fan power) increased monotonically, and COP increased first and then decreased. At 75% load, the COP improvement at the optimum flow rate was marginally less than 0.5%. Lastly, higher discharge pipe pressure drop increased the discharge pressure. It showed very small effect on the overall system performance at the condition selected for the current study.

component-based steady-state simulation↗

DOE OSTI2022

A Time-Varying Model for Predicting Formaldehyde Emission Rates in Homes

Recent studies have succeeded in relating emissions of various volatile organic compounds to material mass diffusion transfer using detailed empirical characteristics of each of the individual emitting materials. While significant, the resulting models are often scenario specific and/or require a host of individual component parameters to estimate emission rates. This study developed an approach to estimate aggregated emissions rates based on a wide number of field measurements. We used a multi-parameter regression model based on previous mass transfer models to predict formaldehyde emission rate for a whole dwelling using field-measured, time-resolved formaldehyde concentrations, air exchange rates, and indoor environmental parameters in 63 California single-family houses built between 2011 and 2017. The resulting model provides time-varying formaldehyde emission rates, normalized by floor area, for each study home, assuming a well-mixed mass balance transport model of the home, and a well-mixed layer transport model of indoor surfaces. The surface layer model asserts an equilibrium concentration within the surface layer of the emitted materials that is a function of temperature and RH; the dwelling ventilation rate serves as a surrogate for indoor concentration. We also developed a more generic emission model that is suitable for broad prediction of emission for a population of buildings. This model is also based on measurements aggregated from 27 homes from the same study. We showed that errors in predicting household formaldehyde concentrations using this approach were substantially less than those using a traditional constant emission rate model, despite requiring less unique building information.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Microstructure Evolution During Controlled Solidification of “Fe{sub 2}O{sub 3}”-CaO-SiO{sub 2} Liquids in Air

The principal chemical components in iron ore sintering are Fe{sub 2}O{sub 3}, CaO, and SiO{sub 2}. This sintering process consists of three key steps: heating, holding at peak temperature, and cooling. During the cooling stage, a liquid oxide solidifies to form the final sinter microstructures. To investigate the fundamental processes taking place during the cooling of sinters, a new experimental technique has been developed that allows the stages of solidification to be determined in isolation, rather than inferred from the final microstructures. Fe{sub 2}O{sub 3}-CaO-SiO{sub 2} oxide samples of a bulk composition having a CaO/SiO{sub 2} mass ratio of 3.46 and 73.2 wt pct Fe{sub 2}O{sub 3} were cooled in air from 1623 K (1350 °C) at 2 K/s, quenched at 5 K temperature intervals from 1533 K to 1453 K (1260 °C to 1180 °C), and analyzed using Electron Probe Micro X-Ray Analysis (EPMA). During cooling, four distinct stages were observed, consisting of the phase assemblages Liquid + Hematite (I), Liquid + Hematite + C{sub 2}S(II), Liquid + C{sub 2}S + CF{sub 2}(III), and C{sub 2}S + CF{sub 2} + CF (IV). This solidification sequence differs from that predicted under equilibrium and Scheil–Gulliver Cooling. Importantly, no Silico-Ferrite of Calcium (SFC) phase was observed to form on solidification of the liquid. Based on the microstructures formed and liquid compositions, measured by EPMA, it was demonstrated that kinetic factors play a major role in determining the phases and microstructures formed under the conditions investigated.

36 MATERIALS SCIENCE↗

DOE OSTI2020

High temperature oxidation behaviors of bulk SiC with low partial pressures of air and water vapor in argon

Oxidation behaviors of bulk SiC at 1000–1400 °C in Ar-20 vol% H 2 O-10 vol% air were investigated. The thickness of the passivation oxide layer showed a parabolic increase with time. Dense and uniform layers formed below 1200 °C, whereas pores and cracks were observed at higher temperatures. An analytical model was proposed to estimate the oxide layer growth and SiC mass loss rate. The SiC layer should be effective in retaining the fission products after oxidation by excessive water and air. This work is the first detailed study of SiC layer oxidation during loss of coolant accidents (LOCA) for HTGRs.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Information content of spatially distributed ground-based measurements for hydrologic-parameter calibration in mixed rain-snow mountain headwaters

Parameters in hydrologic models used in mixed rain-snow regions are often uncertain to calibrate and overfitted on streamflow. To contribute addressing these challenges, we used an algorithm that assesses modeling performances through time (Dynamic Identifiability Analysis) to quantify the information content of spatially distributed ground-based measurements for identifying optimal parameter values in the Precipitation Runoff Modeling System (PRMS) model. Including spatially distributed ground-based measurements in Identifiability Analysis allowed us to unambiguously estimate more parameter values than only using streamflow (seven parameters instead of two out of a pool of thirty-three). Peaks in information gain were obtained when using dew-point temperature to identify precipitation phase-partitioning parameters. Multi-attribute identifiability analysis also yielded optimal parameter values that were temporally less variable than those estimated using streamflow alone. Overall, identifying parameter values using ground-based measurements improved the simulation of key drivers of the surface-water budget, such as air temperature and precipitation-phase partitioning. However, parameters simulating surface-to-subsurface mass fluxes like snow accumulation and melt or evapotranspiration were poorly identified by any attribute and so emerged as key sources of predictive uncertainty for this distributed-parameter hydrologic model. In conclusion, this work demonstrates the value of expanded ground-based measurements for identifying parameters in distributed-parameter hydrologic models and so diagnosing their conceptual uncertainty across the water budget.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

High throughput exploration of the oxidation landscape in high entropy alloys

We report high entropy alloys (HEAs) have gained interest for structural applications in extreme environments. With a potentially vast chemical and phase space, there are significant opportunities to discover superior performing alloys. Crucial for most high-temperature applications is understanding and mitigating the oxidation behavior of these chemically complex alloys. Most experimental and computational HEA studies have focused on a limited set of compositions and only a fraction these compositions have been characterized for oxidation. We present a high-throughput framework that utilizes density-functional theory (DFT) in concert with a combined machine-learning model and grand-canonical linear programming for assessing phase stability, phase-fraction, chemical activity and high-temperature survivability of arbitrary HEAs. This framework considers temperature dependent contributions to the Gibbs energy of the competing phases arising from short-range order and vibrational entropy. We demonstrate the effectiveness of the framework by assessing the thermodynamic stability, oxidation behavior, chemical activity, and phase decomposition of body-centered cubic Mo-W-Ta-Ti-Zr refractory HEAs. A total of 51 compositions were analyzed and ranked in order of their survivability based on the Pareto-front analysis. Oxidation was performed at 1373 K on fours samples in air showing the difference in oxidation behavior determined experimentally through scale thickness and their mass changes. The insights on oxidation behavior presented in this work will enable the fast assessment of technologically useful HEAs needed for future structural application in extreme conditions

36 MATERIALS SCIENCE↗

DOE OSTI2026

Measured indoor PM2.5, black carbon, and oxidative potential before and after replacing gas with induction cooking in asthmatic households

Cooking is a major source of fine particulate matter (PM) in homes and evidence to date is inconclusive about the impact of cooking fuel on measures of residential PM exposure. The Cooking Energy and Ventilation Impacts on Children's Asthma (CEVICA) study measured cooking frequency, range hood use, indoor air quality (IAQ) and respiratory health indicators of children with asthma living in homes with gas stoves in California's San Joaquin Valley. Intensive measurements occurred over three 2-week intensive periods: at baseline and at the end of two consecutive 3-month study phases. Participants were randomly assigned to have their gas stoves replaced with electric induction at the start of Phase 1 or Phase 2. As part of the IAQ assessment, we collected particulate matter on Teflon filters using ultrasonic personal air samplers (UPAS). The filters were analyzed to quantify time-integrated PM 2.5 mass concentration, black carbon (BC) oxidative potential (OP). OP was measured using the SLF-OH assay, an acellular chemical assay that quantifies production of OH radicals in simulated lung fluid (SLF). Across all comparisons, PM 2.5 mass tended to be higher during gas cooking than induction electric cooking, although only one transition (Baseline to Phase 1) was statistically discernible at p<0.05. BC and mass-normalized OP showed no measurable differences across stove types or across phases. These preliminary findings suggest that stove fuel alone may not be the dominant determinant of indoor PM 2.5 concentrations in this sample, as previous studies have reported that indoor PM 2.5 during cooking can also be influenced by factors such as cooking practices, food and oil type, kitchen ventilation, and outdoor infiltration.

Lin, Zhuoying↗

DOE OSTI2024

EPCAPE-PT-LANL Measurements: Soot Particle Aerosol Mass Spectrometer

Coastal cities offer a unique environment for studying aerosol-cloud interactions and the effects of urban emissions on cloud properties. As part of the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE), the Partitioning Thrust by Los Alamos National Laboratory (EPCAPE-PT-LANL) was conducted. Our campaign focused on measuring the optical and chemical properties of aerosols and their interactions within marine stratocumulus clouds in La Jolla, California. EPCAPE-PT-LANL enhances the primary goals of EPCAPE through innovative observations of vapor-phase transitions between aerosols and cloud droplets, the impact of black carbon on aerosol-cloud dynamics, and the effects of cloud processing on aerosol optical properties. Instrument: Soot particle-Aerosol Mass Spectrometer. (Aerodyne Inc.) Header: - Organic_GCVIoff[ug/m3]: Concentration of organic compounds measured via the Main Inlet (out-of-cloud) in micrograms per cubic meter. - Nitrate_GCVIoff[ug/m3]: Concentration of nitrate measured via the Main Inlet (out-of-cloud) in micrograms per cubic meter. - Sulfate_GCVIoff[ug/m3]: Concentration of sulfate measured via the Main Inlet (out-of-cloud) in micrograms per cubic meter. - Chloride_GCVIoff[ug/m3]: Concentration of chloride measured via the Main Inlet (out-of-cloud) in micrograms per cubic meter. - Ammonium_GCVIoff[ug/m3]: Concentration of ammonium via the Main Inlet (out-of-cloud) in micrograms per cubic meter. - Organic_GCVIon[ug/m3]: Concentration of organic compounds measured in-cloud via CVI in micrograms per cubic meter. - Nitrate_GCVIon[ug/m3]: Concentration of nitrate measured in-cloud via CVI in micrograms per cubic meter. - Sulfate_GCVIon[ug/m3]: Concentration of sulfate measured in-cloud via CVI in micrograms per cubic meter. - Chloride_GCVIon[ug/m3]: Concentration of chloride measured in-cloud via CVI in micrograms per cubic meter. - Ammonium_GCVIon[ug/m3]: Concentration of ammonium measured in-cloud via CVI in micrograms per cubic meter. - CVI_Flag[bool]: A boolean flag indicating whether the CVI was active (true) or inactive (false) during the measurement.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Assessing the sensitivity of aerosol mass budget and effective radiative forcing to horizontal grid spacing in E3SMv1 using a regional refinement approach

Abstract. Atmospheric aerosols have important impacts on air quality and the Earth–atmospheric energy balance. However, as computing power is limited, Earth system models generally use coarse spatial grids and parameterize finer-scale atmospheric processes. These parameterizations and the simulation of atmospheric aerosols are often sensitive to model horizontal resolutions. Understanding the sensitivities is necessary for the development of Earth system models at higher resolutions with the deployment of more powerful supercomputers. Using the Energy Exascale Earth System Model (E3SM) version 1, this study investigates the impact of horizontal grid spacing on the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols (ERFaer) over the contiguous United States. We examine the resolution sensitivity by comparing the nudged simulation results for 2016 from the low-resolution model (LR) and the regional refinement model (RRM). As expected, the simulated emissions of natural dust, sea salt, and marine organic matter are substantially higher in the RRM than in the LR. In addition, RRM simulates stronger aqueous-phase production of sulfate through the enhanced oxidation of sulfur dioxide by hydrogen peroxide due to increased cloud liquid water content. In contrast, the gas-phase chemical production of sulfate is slightly suppressed. The RRM resolves more large-scale precipitation and produces less convective precipitation than the LR, leading to increased (decreased) aerosol wet scavenging by large-scale (convective) precipitation. Regarding aerosol effects on clouds, RRM produces larger temporal variabilities in the large-scale liquid cloud fractions than LR, resulting in increased microphysical cloud processing of aerosols (more interstitial aerosols are converted to cloud-borne aerosols via aerosol activation) in RRM. Water vapor condensation is also enhanced in RRM compared to LR. Consequently, the RRM simulation produces more cloud droplets, a larger cloud droplet radius, a higher liquid water path, and a larger cloud optical depth than the LR simulation. A comparison of the present-day and pre-industrial simulations indicates that, for this contiguous United States domain, the higher-resolution increases ERFaer at the top of the model by about 12 %, which is mainly attributed to the strengthened indirect effect associated with aerosol–cloud interactions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Deep-Learning based Reconstruction of the Shower Maximum $X_{\mathrm{max}}$ using the Water-Cherenkov Detectors of the Pierre Auger Observatory

The atmospheric depth of the air shower maximum X max is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of X max are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of X max from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of X max . The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov detectors of the Pierre Auger Observatory. The network architecture features recurrent long short-term memory layers to process the temporal structure of signals and hexagonal convolutions to exploit the symmetry of the surface detector array. We evaluate the performance of the network using air showers simulated with three different hadronic interaction models. Thereafter, we account for long-term detector effects and calibrate the reconstructed X max using fluorescence measurements. Finally, we show that the event-by-event resolution in the reconstruction of the shower maximum improves with increasing shower energy and reaches less than 25 g/cm 2 at energies above 2 × 10 19 eV.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Process-based and observation-constrained SOA simulations in China: the role of semivolatile and intermediate-volatility organic compounds and OH levels

Organic aerosol (OA) is a major component of tropospheric submicron aerosol that contributes to air pollution and causes adverse effects on human health. Chemical transport models have difficulties in reproducing the variability in OA concentrations in polluted areas, hindering understanding of the OA budget and sources. Herein, we apply both process-based and observation-constrained schemes to simulate OA in GEOS-Chem. Comprehensive data sets of surface OA, OA components, secondary organic aerosol (SOA) precursors, and oxidants were used for model–observation comparisons. The base models generally underestimate the SOA concentrations in China. In the revised schemes, updates were made on the emissions, volatility distributions, and SOA yields of semivolatile and intermediate-volatility organic compounds (SVOCs and IVOCs) and additional nitrous acid sources. With all the model improvements, both the process-based and observation-constrained SOA schemes can reproduce the observed mass concentrations of SOA and show spatial and seasonal consistency with each other. Our best model simulations suggest that anthropogenic SVOCs and IVOCs are the dominant source of SOA, with a contribution of over 50% in most of China, which should be considered for pollution mitigation in the future. The residential sector may be the predominant source of SVOCs and IVOCs in winter, despite large uncertainty remaining in the emissions of IVOCs from the residential sector in northern China. The industry sector is also an important source of IVOCs, especially in summer. More SVOC and IVOC measurements are needed to constrain their emissions. Besides, the results highlight the sensitivity of SOA to hydroxyl radical (OH) levels in winter in polluted environments. The addition of nitrous acid sources can lead to over 30% greater SOA mass concentrations in winter in northern China. It is important to have good OH simulations in air quality models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A physics-constrained neural ordinary differential equations approach for robust learning of stiff chemical kinetics

The high computational cost associated with solving for detailed chemistry poses a significant challenge for predictive computational fluid dynamics (CFD) simulations of turbulent reacting flows. While deep learning techniques have been explored to develop faster surrogate models, they often fail to integrate reliably with CFD solvers. This instability arises because traditional deep learning approaches optimize for training error without ensuring compatibility with ordinary differential equation (ODE) solvers, resulting in accumulation of errors over time. Recently, neuralODE (NODE) based approaches have been shown to be a promising technique to emulate and accelerate detailed chemistry computations. Here, in the present work, we extend this NODE framework for stiff chemical kinetics by incorporating mass conservation constraints directly into the loss function during training. This ensures that the total mass as well as the individual elemental species masses are conserved in an a-posteriori manner. Proof-of-concept studies are performed with the novel physics-constrained NODE (PC-NODE) approach for homogeneous autoignition of hydrogen-air mixture over a range of composition and thermodynamic conditions. It is demonstrated that the PC-NODE framework not only improves the physical consistency of the resulting data-driven model with respect to mass conservation criteria, but also improves training efficiency. PC-NODE is shown to achieve 2–100× speedup relative to the hydrogen-air detailed chemical mechanism depending on the type of the ODE solver (implicit or explicit) used during autoregressive inference tests. Lastly, a-posteriori studies are performed wherein the trained PC-NODE model is coupled with a CFD solver. It is shown that higher accuracy is achieved with PC-NODE relative to the purely data-driven NODE approach. Moreover, PC-NODE also exhibits robustness and generalizability to unseen initial conditions from within (interpolative capability) as well as outside (extrapolative capability) the training regime.

computational combustion↗

DOE OSTI2021

Fractionation of Oxygen Isotopes in Uranium Oxides during Peroxide Precipitation and Dry Air Calcination

The interaction between uranium and oxygen-containing substrates is nearly ubiquitous within the nuclear fuel cycle. Given the well-known and predictable oxygen stable isotope compositions of atmospheric oxygen and meteoric waters around the world, the use of these isotopes as a potential geolocation or processing signature of uranium compounds has been of interest within the nuclear safeguards community. This study focuses on measuring the oxygen-stable isotope composition of synthetically produced uranium oxides to determine the mechanism and extent of fractionation during materials processing relevant to the nuclear fuel cycle. Here, metastudtite [UO 2 O 2 (H 2 O) 2 ] samples were first produced using water and hydrogen peroxide of a known oxygen isotope composition. This starting material was calcined in dry air at temperatures ranging from 300 °C to 1000 °C for 20 h producing oxides ranging in composition from UO 3 +x (0 ≤ x ≤ 0.5) to U 3 O 8 . In addition, calcinations between 500 and 800 °C were performed at different time intervals to evaluate the rate of isotopic equilibration. The δ 18 O values of water bound to metastudtite were measured by thermogravimetric analysis coupled to isotope ratio infrared spectroscopy (TGA–IRIS). A redesigned fluorination manifold was constructed at the University of Utah and was used to extract the bulk oxygen from precipitated and calcined products using bromine pentafluoride (BrF 5 ). Mineralization water on metastudtite (measured by TGA–IRIS) was closely associated with the aqueous environment of precipitation with a fractionation of +3.5 ± 0.6‰. In contrast, bulk oxygen from metastudtite (measured by fluorination and gas chromatography combined with isotope ratio mass spectrometry) was found to have an inconsistent fractionation with precipitation water. Samples calcined in dry air exhibited a wide range of oxygen isotope compositions which increased from δ 18 O = +1.4 ± 0.9‰ at 300 °C to +17.3 ± 0.9‰ at 1000 °C. This variation in δ 18 O values was a result of oxygen exchange with atmospheric O 2 and was found to be rapid at calcination temperatures greater than 600°C—with equilibration occurring in less than 2 h. These results provide insights into the incorporation and exchange of oxygen isotopes during precipitation and calcination processes comparable to those employed within the nuclear fuel cycle and establish a foundation for future investigations to build upon.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2026

Multiplexed Inertial Coalescence Filters for High-Rate Liquid-Gas Chemistry

The aim of this project is to support the development of a disruptive method for deploying liquids in liquid-gas chemical processes to transform carbon dioxide capture from flue gas and ambient air streams. The proposed project is based on the development of a novel filtration method called the Helix MICRA™ (Multiplexed Inertial Coalescence Refining Apparatus) filters. Helix MICRA™ filters are a novel, patented filter that enable high efficiency, low-pressure drop capture of droplet streams. Liquid droplets have a large net-surface area per unit volume and have correspondingly rapid mass transfer rates. By effectively capturing these droplets after deployment, we enable high-rate carbon dioxide capture from air streams unlike any other technology. This project aims at using Helix MICRA™ filters to create efficient and compact carbon dioxide capture systems that would dramatically reduce system size and capital costs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Location generalizability of image-based air quality models

This paper is to be submitted at the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Computer Vision for Earth Observation workshop. The full paper abstract is below: The ability to rapidly quantify atmospheric pollutants is important both for global emissions monitoring and for mitigating the adverse effects that follow a hazardous chemical release. In the aftermath of a chemical release, imagery is often the only available resource to assess local conditions. Recent work has demonstrated initial success in predicting particulate matter pollution from imagery; however, these results are tied to a specific site and do not generalize to new geographic locations. In this work, we seek to understand how easily deep learning models generalize to new locations in the context of image-based air quality assessments, targeting two distinct tasks: (1) broad measures of particulate matter pollution, and (2) the mass of a given chemical released in hazardous plumes. For the latter, we focus on sulfur dioxide, a toxic aerosol and a major component of particulate matter pollution caused by industrial fossil fuel consumption. To develop a model that operates in the widest possible range of environments, we test different training strategies, including the use of new geolocation foundation models. The best performing models achieve >80% accuracy when evaluating unseen imagery at previously seen sites, but we find significant drops in performance when evaluating imagery from unseen sites, at best 65%. Additionally, we present the public release of the National Parks Air Quality Index Dataset, a new medium-sized dataset that pairs imagery with sensor-based air quality measurements at 15 different national parks.

Byler, Eleanor B. [BATTELLE (PACIFIC NW LAB)]↗

DOE OSTI2021

Heat and mass transfer during a sudden loss of vacuum in a liquid helium cooled tube - Part III: Heat deposition in He II

A sudden loss of vacuum can be catastrophic for superconducting particle accelerators. In such an event, air leaks into the liquid-helium-cooled accelerator beamline tube and condenses on its inner surface, causing rapid boiling of the helium and dangerous pressure build-up. Understanding the coupled heat and mass transfer processes is important for the design of the beamline cryogenic system. Our past experimental study on nitrogen gas propagating in a copper tube cooled by normal liquid helium (He I) has revealed a nearly exponential slowing down of the gas front. A theoretical model that accounts for the interplay of the gas dynamics and the condensation was developed, which successfully reproduced various key observations. However, since many accelerator beamlines are actually cooled by superfluid helium (He II) in which the heat transfer is via a non-classical thermal counterflow mode, we need to extend our work to the He II cooled tube. This paper reports our systematic measurements using He II and the numerical simulations based on a modified model that accounts for the He II heat-transfer characteristics. By tuning the He II peak heat-flux parameter in our model, we have reproduced the observed gas dynamics in all experimental runs. The fine-tuned model is then utilized to reliably evaluate the heat deposition in He II. Finallly, this work not only advances our understanding of condensing gas dynamics but also has practical implications to the design codes for beamline safety.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Experimental Evaluation of Refrigerant Leak Characteristics for Different HVAC&R Equipment Types–Phase 2

The US Department of Energy (DOE) Building Technology Office (BTO) and the Air-conditioning, Heating & Refrigeration Technology Institute (AHRTI) collaborated to sponsor an experimental study of refrigerant leak characteristics at Oak Ridge National Laboratory (ORNL); AHRTI project 9012. The project has been conducted in two phases with objectives to conduct refrigerant leak tests on several heating, ventilating, air-conditioning and refrigeration (HVAC&R) systems under operating conditions representative of actual applications to document the pressure decay rate and the mass flow rate of leaked refrigerant as a function of time. Phase 1 of the project ran from late 2017 to late 2018 with refrigerant leaks imposed on five systems and the collection of data to meet the project objectives. Systems chosen covered three air-conditioning (AC) applications (packaged terminal AC or PTAC, 3-ton residential split system AC, and 5-ton packaged rooftop AC) and two refrigeration applications (split system unit cooler, and standalone display case). AHRTI members donated the test systems used for the project. The systems were tested at two different leak rates (catastrophic high rate simulating full line break and a lower flow rate), two leak locations (high-pressure side and low-pressure side), and two operating conditions (compressor ON or OFF). Phase 1 results were summarized in a project report by Baxter, et al. (2019)1. The leak rates imposed in Phase 1 led to much more rapid charge releases than the more typical much slower rates seen in practice that would take days, weeks, or months to empty a system of its entire charge. For Phase 2, the project sponsors asked that the “higher” leak rate target a total charge release time of about 4 min. This was intended to represent a “reasonable” worst case release scenario to match the refrigerant leak rate assumption used in the International Electrotechnical Commission (IEC) standard IEC 60335-2-40 (2016 version [IEC 2016]. A charge release time of about 20–40 min (leak release rate 5 to 10 times lower than higher rate) was targeted for the “lower” leak rate. This report provides a summary of the Phase 2 effort. Two additional HVAC systems were selected for testing in this phase. First was a multi-split air-conditioning (MS-AC) system with one outdoor section and two indoor units and a nameplate refrigerant charge of 1.75 kg (3.86 lb.) of R-410A. The MS-AC was tested in cooling only or AC operation. Second was a split system heat pump coupled to two different representative supply duct systems (HP-duct) and a nameplate charge of 3.83 kg (8.44 lb.) of R-410A. This system was tested in both space heating and AC modes. As in Phase 1 of the project, all tests were conducted using R-410A.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗