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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Smart Monitoring and Diagnostic System (SMDS) for Packaged Air Conditioners and Heat Pumps for Small/Medium Commercial Buildings: Preparation for Commercialization: CRADA 478 [Abstract only]

This project will enable Pacific Northwest National Laboratory (PNNL) and industry partner, mCloud Technologies, to work collaboratively to ready the Smart Monitoring and Diagnostic System (SMDS) for commercial deployment and validate its performance under real-world conditions at field sites. This project will specifically focus on 1) implementing the SMDS algorithms in a scalable cloud-based software architecture, 2) designing new, innovative, complimentary commercial services based on the SMDS, 3) enhancing the SMDS energy and cost impact algorithms to reduce uncertainty in estimates, 4) determining the lower limits on SMDS performance degradation detection, 5) validating algorithm performance and default values of adjustable thresholds with existing data from controlled physical testing and from customer packaged air conditioners and heat pumps (commonly referred to as rooftop units or RTUs), 6) field testing to validate the system on multiple customer buildings in diverse environments, and 7) expanding field deployment to a larger set of mCloud’s customer buildings. Project results by validating, enhancing, and quantifying the performance of the SMDS will position mCloud, and potential future licensees, to implement the SMDS in commercial offerings that encourage and enable use of condition-based and predictive maintenance, leading to significant reductions in energy use and greenhouse gas emissions associated with space conditioning by RTUs. Furthermore, these enhancements will increase the value of the SMDS for users and increase the potential market for its use and impacts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Predictive Self-Healing Seals for Gas Transmission

Damaged elastomeric diaphragms within pneumatic controllers used in the oil and gas industry lead to an unintended release of methane. Self-healing microvascular materials capable of healing various types of damage have been fabricated. These microvascular materials are designed to replace currently available commercial diaphragms found in pneumatic controllers and provide a solution to reduce unintended methane leaks. This project demonstrated the ability of these materials to reduce methane release by more than 80% in lab and pilot settings. Additionally, ML approaches successfully identified leaking systems based on training using synthetic and real data.

03 NATURAL GAS↗
DOE OSTI2024

Scaling Up: Demonstrating Risk Reduction and Cost Compression for Commercial Heat Pump Water Heaters - CRADA 625 (Abstract)

Commercial heat pump water heater (CHPWH) systems significantly decarbonize the commercial and multifamily sectors by eliminating the reliance on gas-fired water heating. CHPWH systems are also well suited to include load shift controls that enable load-up and shed commands for supporting grid reliability and time-of-use pricing structure. However, they have not had wide adoption due to factors including price, complexity, and perceived risk. Although CHPWHs have been available in the US for decades, they have not made significant market gains in part because the systems have required significant and costly engineering design expertise and proved lackluster performance. Successful widespread market adoption requires a different approach; a shift from the current custom specialized expertise project design and installation to a repeatable approach that requires little specialized knowledge or expertise and can deliver persistent performance. Using this type of holistic systems approach requires effectively integrating four CHPWH system key components: primary air-to-water heat pumps; primary thermal storage tanks, a temperature maintenance system, and a control system which has capabilities to manage the primary heat pump cycles, any back-up, supplemental, or temperature maintenance heating, alarms, and grid connectivity allowing for demand response (DR), and/or load shifting. The project team has developed and will implement a suite of tools to support faster, less expensive, and more reliable field installations of CHPWH technology and with the resulting data used to further improve the tool set. These tools include: (1) A tool for optimizing system size and costs. (2) A tool that predicts annual energy use and overall system efficiency. (3) The Advanced Water Heater Specification (AWHS 8.0) defining the components of a full CHPWH system addressing performance requirements by climate zone. (4) The Qualified Products List: (QPL) of approved products that meet the specifications requirements. (5) Training materials including online on-demand modules, instructor-led training, and virtual interactive video tours of CHPWH installations in multifamily buildings. Demonstration site identification in low-income buildings in underserved communities is currently underway. Preliminarily, the team anticipates having three demonstrations in the Pacific Northwest and three in the Northeast for a total of six sites. After the demonstration sites are finalized, and M&V instrumentation installations are complete, the team will gather performance data and confirm whether the CHPWH systems perform as predicted and use the data to improve the existing tools.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Virtual Diagnostic Suite for Electron Beam Prediction and Control at FACET-II

We discuss the implementation of a suite of virtual diagnostics at the FACET-II facility currently under commissioning at SLAC National Accelerator Laboratory. The diagnostics will be used for the prediction of the longitudinal phase space along the linac, spectral reconstruction of the bunch profile, and non-destructive inference of transverse beam quality (emittance) while using edge radiation at the injector dogleg and bunch compressor locations. These measurements will be folded into adaptive feedbacks and Machine Learning (ML)-based reinforcement learning controls to improve the stability and optimize the performance of the machine for different experimental configurations. In this paper we describe each of these diagnostics with expected measurement results that are based on simulation data and discuss progress towards implementation in regular operations.

Emma, Claudio (ORCID:0000000247191831)↗
DOE OSTI2022

The Size–Mass Relation of Post-starburst Galaxies in the Local Universe

We present a study of the size–mass relation for local post-starburst (PSB) galaxies at z ≲ 0.33 selected from the Sloan Digital Sky Survey Data Release 8. We find that PSB galaxies with stellar mass (M * ) at 10 9 M ⊙ < M * < 10 12 M ⊙ have a galaxy size smaller than or comparable to those of quiescent galaxies (QGs). After controlling redshift and stellar mass, the sizes of PSBs are ~13% smaller on average than those of QGs; such differences become larger and significant toward the low-M * end, especially at 10 9.5 M ⊙ ≲ M * ≲ 10 10.5 M ⊙ where PSBs can be on average ~19% smaller than QGs. By comparing predictions of possible PSB evolutionary pathways from cosmological simulations, we suggest that a fast quenching of star formation following a short-lived starburst event (which might be induced by a major merger) should be the dominant pathway of our PSB sample. Furthermore, by cross-matching with group catalogs, we confirm that local PSBs at M * ≲ 10 10 M ⊙ are more clustered than more massive ones. PSBs residing in groups are found to be slightly larger in galaxy size and more disk-like compared to field PSBs, which is qualitatively consistent with and thus hints at the environment-driven fast quenching pathway for group PSBs. Taken together, our results support multiple evolutionary pathways for local PSB galaxies: while massive PSBs are thought of as products of fast quenching following a major merger-induced starburst, environment-induced fast quenching should play a role in the evolution of less massive PSBs, especially at M * ≲ 10 10 M ⊙ .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A control oriented comprehensive degradation model for battery energy storage system life prediction

In order to ensure that stationary battery energy storage systems (BESSs) provide reliable energy buffering, both for power quality and economic purposes, the degradation must be considered. Cell degradation involves various side reactions and is highly dependent on its operating conditions. To accurately track cell degradation and predict its impact on battery behavior, a comprehensive physics-based degradation model based on an electrolyte phase-enhanced single particle (SP) model is developed. Key degradation physics, namely solid electrolyte interphase (SEI) layer formation and growth, Li plating on the graphite anode, and Mn dissolution on cathode of nickel-cobalt-manganese oxide (NMC622) are considered. The model is validated against experimental capacity data. The results reveal that the deposition rate of both SEI layer and Li metal increase as the charge voltage increases. At the cathode side, the solvent oxidation rate determines the Mn dissolution rate. As a result, the volume fraction of NMC622 in the cathode continually decreases at a gradually increasing rate.

25 ENERGY STORAGE↗
DOE OSTI2025

In situ Detection of Plasma Induced Surface Interaction based on Deep Learning based Visual Diagnostics (Technical Report)

It is characteristic for many plasma devices to undergo plasma-material interaction leading to surface erosion. These processes, often not easily detectable, lead to changes in device performance and lifespan. State-of-the-art lifetime tests and wear experiments require over 1000s hours. A self-consistent model for accurately predicting the erosion's effects is not available. In situ detection of these processes is not a trivial task since the surface variations at the early stages have a micron scale. Such limitations not only restrict testing and prediction capabilities but also slow the development of new thrusters and limit mission duration. To address these challenges, an in-situ diagnostic for real-time erosion assessment has been developed, aiming to expedite lifetime testing and broaden experimental campaigns. Several works were dedicated to real-time and in situ monitoring of material erosion during plasma exposure using laser holography, microscopy, and with telemicroscopes. However, the applicability of these approaches is limited due to complexity, cost and less flexibility as they often require placing diagnostic equipment inside the vacuum chamber. In collaboration with Princeton Collaborative Research Facility (PCRF), Princeton Plasma Physics Laboratory (PPPL), a new diagnostic approach is developed, where geometry modifications to the ceramic channel walls were introduced that would result in accelerated channel erosion. We employed Long-distance microscope (LDM) imagery, combined with Deep-Learning based Shape from focus or depth from focus (DFF or SFF) approach, that provides an accessible and cost-effective solution. LDM employs focus variation techniques to continuously capture multiple images of the target object at distinct focal planes. DFF, an optical focus variation method, generates a 3D topographical surface depth map from a sequence of variably focused images. Combined with the developed diagnostic, this approach offers a controllable means to study erosion under accelerated conditions. In this work, we develop Neural Network-based DFF algorithm applicable for LDM data to quantitatively evaluate plasma induced surface modification from LDM data. Next, we develop Deep Learning-based super-resolution depth map image reconstruction technique to increase the resolution of depth maps obtained from DFF algorithm to improve the accuracy of erosion measurements. Thirdly, we develop several image processing techniques to remove noise and improve the quality of depth map image. Here we report the results of initial tests for this approach. An experimental setup designed and built in PPPL was employed that consists of a 3-cm gridded ion source that produces a neutralized argon beam with energies up to 600 eV. A hexagonal boron nitride (h-BN) ceramic target, designed based on computational predictions, was used. Tests were conducted to reconstruct the complex geometry of the target under the lighting conditions of the operated ion source.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

The effects of stress, temperature and facet structure on growth of { 10 1 &#xAF; 2 } twins in Mg: a molecular dynamics and phase field study

The present study focuses on the roles of structure, stress and temperature on the mobility of facets bounding twin domains and on the overall impact on the growth kinetics of { 10 1 &#xAF; 2 } twins in Mg. We first use molecular dynamics simulations (MD) to study the relationship between interface structures, stress, temperature and mobility. As facets can exhibit distinct atomic scale structures, we also quantify the effect of stress relaxation mediated by misfit dislocations on the mobility of the basal-prismatic interface. The information gathered for ten different facets is used to calibrate an anisotropic phase field model predicting the effects of temperature and stress on the kinetics and shape of a growing { 10 1 &#xAF; 2 } 3D twin, by comparison with MD data. The comparative analysis suggests that, as compared to simulations of individual facets, the effective mobility of facets can be significantly different during growth of a 3D twin. This can be attributed to the complex three-dimensional internal stress state of the twin and to coupling in facet motion triggered by facet junctions. As such, by directly fitting both the twin growth kinetics and morphology as predicted by MD for different stresses and temperatures, we can identify a limited set of rate-limiting facets that control twin growth and morphology, and measure their effective mobility.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Thermal Adaptation of Enzyme‐Mediated Processes Reduces Simulated Soil CO2 Fluxes Upon Soil Warming

Abstract Understanding factors influencing carbon effluxes from soils to the atmosphere is important in a world experiencing climatic change. Two important uncertainties related to soil organic carbon (SOC) stock responses to a changing climate are (a) whether soil microbial communities acclimate or adapt to changes in soil temperature and (b) how to represent this process in SOC models. To further explore these issues, we included thermal adaptation of enzyme‐mediated processes in a mechanistic SOC model (ReSOM) using the macromolecular rate theory. Thermal adaptation is defined here to encompass all potential responses of soil microbes and microbial communities following a change in temperature. To assess the effects of thermal adaptation of enzyme‐mediated processes on simulated SOC losses, ReSOM was applied to data collected from a 13‐year soil warming experiment. Results show that a model omitting thermal adaptation of enzyme‐mediated processes substantially overestimates observed CO 2 effluxes during the initial years of soil warming. The bias against observed CO 2 effluxes was lower for models including thermal adaptation of enzyme‐mediated processes. In addition, for a simulated linear 3°C soil warming over 100 years, models including thermal adaptation of enzyme‐mediated processes simulated SOC losses of a factor of three smaller than models omitting this process. As thermal adaptation of microbial community characteristics is generally not included in models simulating feedback between the soil, biosphere and atmosphere, we encourage future studies to assess the potential impact that microbial adaptation has on soil carbon – climate feedback representations in models. Plain Language Summary A major uncertainty in projecting how much soil organic carbon (SOC) will be converted to CO 2 as a consequence of climate change is related to how soil microbes may adapt to increasing soil temperatures. While this “microbial thermal adaptation” has been shown to occur in short‐term lab incubation experiments, its effect on SOC cycling on a decadal timescale is not clear. To address this knowledge gap, a mechanistic SOC model was used to simulate data collected from a 13‐year soil warming experiment, to assess how microbial thermal adaptation affects predicted SOC losses upon soil warming. The model results show that incorporating microbial thermal adaptation into the model led to reduced CO 2 effluxes from the soil to the atmosphere compared to the common approach of omitting this mechanism. Our results imply that projected SOC losses for the decades to come may be reduced when this mechanism is incorporated in land models. We therefore advocate for more research on the mechanisms controlling microbial thermal adaptation, and how to implement this mechanism in SOC models. Key Points A crucial aspect of soil organic carbon (SOC) models is the representation of soil microbes Predicted soil CO 2 fluxes upon soil warming are reduced when accounting for microbial thermal adaptation On a centennial time scale, this thermal adaptation results in up to a factor of three lower predicted SOC loss

Van de Broek, Marijn↗
DOE OSTI2023

Shape evolution in even-mass 98-104 Zr isotopes via lifetime measurements using the $\gamma \gamma $-coincidence technique

The Zirconium (Z = 40) isotopic chain has attracted interest for more than four decades. The abrupt lowering of the energy of the first 2 + state and the increase in the transition strength B(E2; 2$^+_1$ →0$^+_1$) going from 98 Zr to 100 Zr has been the first example of “quantum phase transition” in nuclear shapes, which has few equivalents in the nuclear chart. Although a multitude of experiments have been performed to measure nuclear properties related to nuclear shapes and collectivity in the region, none of the measured lifetimes were obtained using the Recoil Distance Doppler Shift method in the γγ-coincidence mode where a gate on the direct feeding transition of the state of interest allows a strict control of systematical errors. Here this work reports the results of lifetime measurements for the first yrast excited states in 98-104 Zr carried out to extract reduced transition probabilities. The new lifetime values in γγ-coincidence and γ-single mode are compared with the results of former experiments. Recent predictions of the Interacting Boson Model with Configuration Mixing, the Symmetry Conserving Configuration Mixing model based on the Hartree–Fock–Bogoliubov approach and the Monte Carlo Shell Model are presented and compared with the experimental data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

A Regime-Oriented Approach to Observationally Constraining Extratropical Shortwave Cloud Feedbacks

The extratropical shortwave (SW) cloud feedback is primarily due to increases in extratropical liquid cloud extent and optical depth. Here, we examine the response of extratropical (35°–75°) marine cloud liquid water path (LWP) to a uniform 4-K increase in sea surface temperature (SST) in global climate models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and variants of the HadGEM3-GC3.1 GCM. Compositing is used to partition data into periods inside and out of cyclones. The response of extratropical LWP to a uniform SST increase and associated atmospheric response varies substantially among GCMs, but the sensitivity of LWP to cloud controlling factors (CCFs) is qualitatively similar. When all other predictors are held constant, increasing moisture flux drives an increase in LWP. Increasing SST, holding all other predictors fixed, leads to a decrease in LWP. The combinations of these changes lead to LWP, and by extension reflected SW, increasing with warming in both hemispheres. Observations predict an increase in reflected SW over oceans of 0.8–1.6 W m -2 per kelvin SST increase (35°–75°N) and 1.2–1.9 W m -2 per kelvin SST increase (35°–75°S). This increase in reflected SW is mainly due to increased moisture convergence into cyclones because of increasing available moisture. The efficiency at which converging moisture is converted into precipitation determines the amount of liquid cloud. Thus, cyclone precipitation processes are critical to constraining extratropical cloud feedbacks

58 GEOSCIENCES↗
DOE OSTI2020

Real-time biomass feedstock particle quality detection using image analysis and machine vision

Abstract A common and costly challenge in the nascent biorefinery industry is the consistent handling and conveyance of biomass feedstock materials, which can vary widely in their chemical, physical, and mechanical properties. Solutions to cope with varying feedstock qualities will be required, including advanced process controls to adjust equipment and reject feedstocks that do not meet a quality standard. In this work, we present and evaluate methods to autonomously assess corn stover feedstock quality in real time and provide data to process controls with low-cost camera hardware. We explore the use of neural networks to classify feedstocks based on actual processing behavior and pixel matrix feature parameterization to further assess particle attributes that may explain the variable processing behavior. We used the pretrained ResNet neural network coupled with a gated recurrent unit (GRU) time-series classifier trained on our image data, resulting in binary classification of feedstock anomalies with favorable performance. The textural aspects of the image data were statistically analyzed to determine if the textural features were predictive of operational disruptions. The significant textural features were angular second moment, prominence, mean height of surface profile, mean resultant vector, shade, skewness, variation of the polar facet orientation, and direction of azimuthal facets. Expansion of these models is recommended across a wider variety of labeled feedstock images of different qualities and species to develop a more robust tool that may be deployed using low-cost cameras within biorefineries.

09 BIOMASS FUELS↗
DOE OSTI2024

Accurate Machine Learning for Predicting the Viscosities of Deep Eutectic Solvents

Deep eutectic solvents (DESs) are emerging as environmentally friendly designer solvents for mass transport and heat transfer processes in industrial applications; however, the lack of accurate tools to predict and thus control their viscosities under both a range of environmental factors and formulations hinders their general application. While DESs may serve as designer solvents, with nearly unlimited combinations, this unfortunately makes it experimentally infeasible to comprehensively measure the viscosities of all DESs of potential industrial interest. To assist in the design of DESs, we have developed several new machine learning (ML) models that accurately and rapidly predict the viscosities of a diverse group of DESs at different temperatures and molar ratios using, to date, one of the most comprehensive data sets containing the properties of over 670 DESs over a wide range of temperatures (278.15–385.25 K). Three ML models, including support vector regression (SVR), feed forward neural networks (FFNNs), and categorical boosting (CatBoost), were developed to predict DES viscosity as a function of temperature and molar ratio and contrasted with multilinear and two-factor polynomial regression baselines. Further, quantum chemistry-based, COSMO-RS-derived sigma profile (σ-profile) features were used as inputs for the ML models. The CatBoost model is excellent at externally predicting DES viscosity, as indicated by high R 2 (0.99) and low root-mean-square-error (RMSE) and average absolute relative deviations (AARD) (5.22%) values for the testing data sets, and 98% of the data points lie within the 15% of AARD deviations. Furthermore, SHapley additive explanation (SHAP) analysis was employed to interpret the ML results and rationalize the viscosity predictions. The result is an ML approach that accurately predicts viscosity and will aid in accelerating the design of appropriate DESs for industrial applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Machine learning substitutional defect formation energies in ABO 3 perovskites

Perovskite oxides are a promising material platform for use in a wide range of technological applications including electronics, sensors, fuel cells, and catalysis. This is owing to the extraordinary tunability of their physical and chemical properties via defect engineering. The feasibility and the stability of a defect, such as a substitutional dopant, in the host lattice is usually obtained via experiments and/or through detailed quantum mechanical calculations. Both of these conventional routes are expensive and time consuming. An alternative is a data-driven machine learning (ML)-based approach. In this work, we have applied ML techniques to identify the factors that influence defect formation energy, which is an important measure of the stability of the defects, in perovskite oxides. Using 13 elemental properties as features and random forest regression, we demonstrate a systematic approach to down-selecting from the larger set of features to those that are important, establishing a framework for accurate predictions of the defect formation energy. We quantitatively show that the most important factors that control the dopant stability are the dopant ionic size, heat of formation, effective tolerance factor, and oxidation state. Our work reveals previously unknown correlations, chemical trends, and the interplay between stability and underlying chemistries. Hence, these results showcase the efficacy of ML tools in identifying and quantifying different feature-dependencies and provide a promising route toward dopant selection in the perovskites. Herein, we have developed a framework that itself is general and can be potentially applied to other material classes.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Towards Development of a Conceptual Model for Mercury in Bear Creek, Oak Ridge, Tennessee (FY23 Update)

Mercury concentrations in fish in Bear Creek are elevated and comparable to concentrations seen in fish in East Fork Poplar Creek (EFPC) on the Oak Ridge Reservation, even though aqueous inorganic mercury concentrations are orders of magnitude lower in Bear Creek than in EFPC. Acknowledging that the relationship between aqueous and fish tissue mercury concentrations is not linear, and that methylmercury (MeHg) concentrations are likely more related to fish tissue concentrations than aqueous total mercury (Hg T ) concentrations, MeHg production is not easily predicted or controlled. In Bear Creek, where aqueous Hg T concentrations are low, factors other than mercury loading drive the transformation of mercury to MeHg and subsequent trophic transfer to fish. Initial development of a waste disposal facility (Environmental Management Disposal Facility; EMDF) in Bear Creek Valley has begun, and operation of the EMDF has the potential to increase mercury inputs to Bear Creek. Consequently, understanding the factors contributing to elevated MeHg concentrations in water and fish in Bear Creek has increased relevance and importance. This report summarizes data from recent and historical compliance and investigatory studies with an eye toward building a conceptual model to understand the processes affecting mercury transport and transformation in the Bear Creek watershed, as well as to highlight key knowledge gaps in our understanding of these processes that warrant further investigation. The conceptual model will provide a strong technical basis for prioritizing and optimizing potential mitigation actions or best management practices to minimize potential negative effects of the EMDF related to mercury in Bear Creek.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Illinois Storage Corridor, CarbonSAFE Phase III: UIC Class VI Permitting Plan

The Illinois Storage Corridor (ISC) project evaluated two distinct sites to determine the feasibility of commercial-scale CO₂ storage at each. The project leveraged the region's exceptional geological characteristics, particularly the well-characterized Cambro-Ordovician Storage Complex, to enable permanent geological storage of more than 50 million tonnes of CO₂ over 30 years. The two storage sites are located at One Earth Energy (OEE) facility in northcentral Illinois and Prairie State Generating Company (PSGC) in southcentral Illinois. Once operational, these facilities will combine to capture and store more than 6.5 million tonnes of CO₂ per year, positioning the ISC among the largest carbon storage regions globally. Preliminary homogeneous dynamic modeling based on regional and site-specific reservoir characteristics indicates promising injection capabilities at both locations. For the OEE site, modeling predicts a maximum allowable injection rate of 3.7 MTPA, with a baseline scenario of 1.7 MTPA over 30 years producing a CO₂ plume radius of 1.4 miles at end of injection. For the PSGC site, incorporating recent well data, modeling indicates a single-well maximum injection rate of 2.1 MTPA, with a plume radius of 4.2 miles at end of injection for the 60 MT over 30 years scenario. The primary objective of this CarbonSAFE Phase III project is to develop and submit Class VI Underground Injection Control (UIC) permit applications to the U.S. Environmental Protection Agency Region 5. The permitting plan outlines comprehensive site characterization, Area of Review delineation, monitoring programs, well construction designs, financial responsibility provisions, and post-injection site care procedures necessary to demonstrate safe, permanent CO₂ storage protective of underground sources of drinking water. Three UIC Class VI permit applications for the OEE site were submitted to EPA in October 2022 and are progressing through technical review, with final permit decision projected by June 2026. Through five rounds of Requests for Additional Information and responses, the applications have been refined to address computational modeling, area of review delineation, well integrity, monitoring protocols, and financial assurance requirements. For the PSGC site, finalized characterization and permitting documentation was delivered directly to the facility in July 2023 due to business constraints precluding formal federal regulatory submission. This comprehensive permitting effort builds upon extensive prior subsurface evaluations and demonstration projects that have confirmed the feasibility of widespread commercial-scale carbon storage in the region.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

Final report: Insights from ARM observations into aerosol processing and transport by extratropical cyclones and aerosol effects on cyclone clouds

This project advanced understanding of aerosol–cloud interactions in extratropical cyclones using observations from the Atmospheric Radiation Measurement (ARM) Eastern North Atlantic (ENA) observatory and ACE-ENA field campaigns, along with satellite datasets and numerical modeling. We have quantified the susceptibility of frontal clouds to aerosols, showing that this susceptibility is similar to that in non-frontal clouds despite substantial (factor 2) differences in droplet concentrations between these cloud types, and also significant differences in cloud albedo. A key finding was that Aitken-mode (here, 60-100nm diameter) aerosols play a disproportionately important role in droplet activation in frontal clouds, and concentrations of these particles are frequently strongly biased in climate models. Activation of aerosols depends strongly on updraft speeds, and via collaboration with Argonne National Laboratory we contributed to quantifying these better with the ARM radar wind profiler at the ENA site. Our studies in the North Atlantic have analogs in the Southern Ocean, and to draw these out we examined data from the MARCUS and SOCRATES field campaigns. We identified new particle formation within extratropical cyclones as a potentially important source of cloud condensation nuclei in these pristine marine environments. By combining ARM observations with perturbed parameter ensemble simulations, we demonstrated that surface observations can effectively constrain aerosol–cloud adjustments and reduce uncertainty in simulated cloud liquid water path responses by approximately 15%, yielding stronger constraints on historical aerosol cooling effects. The work further established precipitation processes as a dominant control on aerosol–cloud interactions and Earth system predictability.

Gordon, Hamish [Department of Chemical Engineering↗
DOE OSTI2022

Investigation of the impact of supplemental reflective films to improve micro-light climate within tomato plant canopy in solar greenhouses

The non-uniform growth and development of crops within Chinese Solar Greenhouses (CSG) is directly related to the micro-light climate within canopy. In practice, reflective films are used to improve micro-light climate within plant canopy by homogenizing light distribution and so increasing total plant light interception. However, as to our knowledge, the contributions to light distribution within canopy have not been investigated for passive reflector like reflective films. Field experiments dealing with light conditions and growth behavior over time, are complicated to carry out, time-consuming and hard to control, while however, accurate measurements of how reflective films influence the micro-light climate of canopy are an essential step to improve the growth conditions for any crop. Here, we propose a supplementary light strategy using reflective films to improve light distribution within plant canopy. Based on the example of CSG, a 3D greenhouse model including a detailed 3D tomato canopy structure was constructed to simulate the influence of supplementary reflective films to improve micro-light climate. Comparison of measured solar radiation intensity with predicted model data demonstrated that the model could precisely predict light radiation intensity over time with different time points and positions in the greenhouse. A series of reflective film configurations were investigated based on features analysis of light distribution in the tomato canopy on sunny days using the proposed model. The reflective film configuration scheme with the highest impact significantly improved the evenness of horizontal and vertical light distribution in tomato canopy. The strategy provided here can be used to configure reflective films that will enhance light conditions in CSG, which can be applied and extended in different scenarios.

Liu, Anhua↗