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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Presentation: Reactor Containment Passive Safety Analysis: Steam Condensation in Presence of Non-condensable Gas Scaled Experiment and Modeling

This study presents steam condensation scaled experiments and semi-empirical models in presence of nitrogen--a noncondensable gas (NCG), simulating air in the reactor containment--to support water-cooled small modular reactors (SMRs) passive containment cooling system (PCCS) design and analysis. Previous experimental studies on PCCS are focused on fixed and smaller tube (mostly 2-in.) geometries and specific test condition variations, bringing challenges with geometric scaling and mismatching with SMR prototypic design. To address these challenges, this study presents steam condensation test dataset obtained from three scaled test sections of 1-, 2-, and 4-in.-diameter steam condensers with an annular/jacket cooling of 2-, 3-, and 6 in.-diameter tubes, respectively. Test data were collected for steam ranges from 58 to 63 kg/hr., and NCG flow of 4.4 to 13.3 kg/hr. Annular cooling water flow was varied to obtain required testing conditions of saturated steam inlet and fully condensed outlet. Axial temperature test data of bulk cooling water, steam and condensate were collected by thermocouples for three test sections and various steam-NCG mixing/testing conditions. A standard data reduction method was adopted--utilizing iterative and nodalized mass and heat transfer calculation to estimate axial local heat fluxes, heat transfer coefficients (HTCs), condensation rates, film thickness, and Nusselt number. Based on the obtained dataset semi-empirical model results--a ratio of experimental and Nusselt's theoretical HTC are presented. Such results and findings are supportive of developing scaled-up testing facility, to enable model validations and accelerate next generation of reactors development and deployment.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Data for Determining Spatially Varying Profit-Maximizing Management Practices for Miscanthus and Switchgrass Production in the Rainfed United States

The compiled datasets include county-level variables used for simulating miscanthus and switchgrass production in 2287 counties across the rainfed US including 5-year (2012-2016) averaged growing season degree days (GDD), 5-year (2012-2016) averaged growing season cumulative precipitation, National Commodity Crop Productivity Index (NCCPI) values, regional dummies (only for miscanthus), the regional-level random effect of the yield response function, N price, land cash rent, the first year fixed cost (only for switchgrass), and separate datasets for simulating an alternative model assuming a constant N rate. The GAMS codes are used to run the simulation to obtain the main results including the age-varying profit-maximizing N rate, biomass yields, and annual profits for miscanthus and switchgrass production across counties in the rainfed US. The STATA codes are used to merge and analyze simulation results and create summary statistics tables and key figures.

Age↗
DOE OSTI2024

Data for Optimizing Bioenergy Sorghum Productivity and Nutrient Removal in Illinois: Impact of Nitrogen Fertilization Under Diverse Marginal Conditions

Bioenergy sorghum ( Sorghum bicolor L. Moench) is a promising crop for contributing to the United States bioenergy supply. However, the varying limitations of the marginal lands targeted for its cultivation present a management challenge. This two-year study aimed to investigate how the limitations associated with marginal cropland impact the effects of nitrogen fertilization on the yield of bioenergy sorghum and the uptake of 11 macro- (N, P, K, Ca, Mg, and S) and micronutrients (Fe, Mn, Zn, Cu, and B). The study contrasted prime cropland in central Illinois (Urbana) with three marginal cropland sites in southern (Ewing) and central Illinois (Fairbury and Pesotum). These marginal cropland sites are characterized by varying limitations, including low soil fertility (P and K limitations), leaching and erosion, and flooding, respectively. Four nitrogen rates (0, 56, 112, and 168 kg N ha−1) were tested under eight environments. The average yields and ranges of sorghum biomass were 20.2 (17.0–23.2) Mg ha−1 in Urbana, 18.1 (13.1–19.8) Mg ha−1 in Ewing, 13.8 (9.0–17.3) Mg ha−1 in Fairbury, and 23.3 (14.6–33.0) Mg ha−1 in Pesotum. Optimal N rates were 56 N in Pesotum and 112 N in Urbana, Ewing, and Fairbury. Tissue macronutrient contents in Urbana were generally higher than in the marginal croplands, while micronutrient contents did not show discernible trends. Increasing N rate generally correlated with the macronutrient removal except in Ewing. Comparable sorghum biomass yields were observed between prime and marginal croplands (averaging 18.3 Mg ha−1), but optimal N rates varied between 56 N and 112 N. This suggests that yield gaps can be narrowed by applying the optimal N rates for the respective locations. However, increased removals of macronutrients, especially P and K, with increasing yields indicate the need to revise fertilizer recommendations, particularly for soils deficient in these nutrients. Our study suggests that while sorghum production on marginal cropland is feasible, N management needs to be adapted to the unique limitations associated with various types of marginal cropland.

Biomass Analytics↗
DOE OSTI2026

Semi-Dynamic Leach Testing of Densified Silicon-based Iodine Waste Forms

Iodine waste forms (IWF) require a conceptual corrosion release model (CCRM) to provide iodine (I) release rates for performance modeling nuclear waste disposal repositories. To develop a CCRM, an understanding of the corrosion mechanisms of the IWF are required along with data from consistent test methods to parameterize the model. The present study has advanced both areas by providing and assessing the corrosion resistance of IWF types based on their processing history in minor variations of semi-dynamic leach tests. The test included a series of semi-dynamic leach tests using monolithic IWFs in deionized water (leachant). Several experiments were conducted under alternate test conditions with changes to temperature, leachant replacement, leachant pH, leachant volume, masking, and surface finish to elucidate if varying these conditions impacted IWF corrosion behavior. Tests were conducted on two classes of IWFs: (1) I-bearing silver-mordenite (AgZ) materials processed by hot isostatic pressing (HIP) at different temperatures, pressures, sizes, and times; and (2) I-bearing silver-functionalized silica aerogels (SFA) processed by either HIP or spark plasma sintering (SPS). The corrosion susceptibility of AgZ samples was influenced by HIP temperature and pressure. The SPS SFAs retained I far better than HIP SFAs. Additional findings in this study include: (1) The iodine dissolution rate decreased with decreasing temperature, (2) a common ion effect may occur and slow dissolution of the host phase if the leachant is not regularly replaced, (3) pH controls the dissolution rate, and (4) the iodine dissolution rate slows with extended test time (up to 224 days). Based on this work, these parameters should thus be represented when developing a CCRM.

corrosion↗
DOE OSTI2023

Inversion-based correction of Double-Torsion (DT) subcritical crack growth tests for crack profile geometry

Because of its simplicity and the ability to produce a stable, slow-propagating crack, the Double-Torsion (DT) method has been used widely for investigating the critical and subcritical propagation of a slow-propagating tensile (mode-I) crack. However, to determine the complex relationship between the crack velocity $\mathcal{v_c}$ vs. the strain energy release rate $\mathscr{G}$ (or the stress intensity factor K) from laboratory measurements, several corrections must be made to account for the impact of sample and crack geometry. Particularly, DT test typically produces a crack with a curved edge profile instead of a straight line, causing the local $\mathcal{v_c}$ and $\mathscr{G}$ vary along the crack front. The experimentally measured $\mathcal{v_c}$ and $\mathscr{G}$ data merely reflect collective, averaged behavior of the crack. This makes inversion for the intrinsic, “true” crack growth kinetics necessary, based upon the knowledge of the crack geometry. Simple and effective correction methods have been proposed and validated for the slow, chemical-reaction-controlled part (Region I) of the $\mathcal{v_c}$-$\mathscr{G}$ curve. However, reliable methods for the highly nonlinear, transport-dominated part (Region II) and its sudden transition to the dynamic propagation part (Region III) are still lacking. Here we propose a method for determining the intrinsic $\mathcal{v_c}$-$\mathscr{G}$ relationship across all three Regions based upon DT test data, using a simple model function and its numerical inversion. The performance of this approach is examined and demonstrated using both synthetic and laboratory data for subcritical crack growth in soda lime glass.

42 ENGINEERING↗
DOE OSTI2025

Pulse-by-pulse transient thermal deformation in crystal optics under high-repetition-rate FEL

Time-domain modeling of the thermal deformation of crystal optics can help define acceptable operational ranges across the pulse-energy repetition-rate phase space. In this paper, we have studied the transient thermal deformation of a water-cooled diamond crystal for a cavity-based X-ray free-electron laser (CBXFEL), either an X-ray free-electron laser oscillator (XFELO) or a regenerative amplifier X-ray free-electron laser (RAFEL), by numerical simulations including finite-element analysis and advanced data processing. Pulse-by-pulse transient thermal deformation of a 50 µm-thick diamond crystal has been performed with X-ray pulse repetition rates between 50 kHz and 1 MHz. Results for temperature and thermal deformation have been compared with the results of transient analysis using a continuous wave (CW) power loading. Temperature and thermal deformation results from pulse-by-pulse transient analysis vary with time about the results for the CW case for the same average power. The variation amplitude increases with pulse energy and decreases with repetition rate. When the repetition rate increases to infinity, both temperature and thermal deformation converge to the results for the CW case. Two critical time scales for the operation of crystal optics in a CBXFEL are (1) first-turn time, i.e. the time for the XFEL pulse to complete the first turn around the cavity so that the crystal sees the recirculated XFEL pulse, and (2) period-end time, i.e. the time that the next electron bunch arrives for the amplification, so that the crystal outcouples the amplified FEL power. For the same average power, simulation results show that the crystal thermal deformation seen by the XFEL beam decreases with repetition rate at the first-turn time of a 300 m-long cavity and increases with repetition rate at the period-end time. For the wavefront preservation requirement of the crystal optics, a pulse-energy versus repetition-rate phase space has been established. The upper bounds of the pulse energy at both first-turn and period-end times decreases with repetition rate, especially at the period-end time. The upper bound of the thermal deformation of the crystal at the period-end time for any repetition frequency can be estimated from the CW case. For a water-cooled diamond crystal of dimension 5 mm × 5 mm × 0.05 mm, the time to reach a quasi steady-state is about 50 ms for temperature and 50 µs for thermal deformation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

A Decision Support Tool for Distributed Solar and Storage Investments: A Case Study in Austin, TX

This study builds a decision support tool to evaluate when it is a good economic decision (least cost with minimum discomfort) for the residential customer to invest in distributed energy resources (DERs) based on different electricity rate structures, DER ownership frameworks, and DER rebates offered by electric utilities. Here, the tool is demonstrated using empirical electricity consumption data from Pecan Street Inc. (a non-profit entity based on Austin, Texas), residential rates from Austin Energy (the municipal electric utility in Austin, Texas), DER ownership costs from various nationwide pilot programs, and incentives offered by electric utilities in the United States. Results show that for constant electricity rates, the overall expenditure is least when the customer owns solar panels without storage, while for time-varying pricing structures, the least expensive scenario is one where the customer does not own any DERs. As the capital costs for DERs decline, utilities incentivize customer ownership of DERs, and more residential customers face the decision of whether to invest in DERs, this study aims to be a key tool in aiding that decision-making process.

14 SOLAR ENERGY↗
DOE OSTI2025

Prototype Hanford Barrier Performance Monitoring Report: Fiscal Year 2024

This report provides an annual update on performance monitoring of the Prototype Hanford Barrier (PHB) at the Hanford Site. The PHB is part of a long-term study that serves as the scientific basis for many of the evapotranspiration-capillary barrier designs currently used globally and for future engineered barrier designs planned for remedial actions over waste and demolition sites at the Hanford Site. The PHB allows us to identify potential future issues and develop better monitoring techniques before these engineered barriers are constructed. Surface barriers like the PHB are essential for preventing water infiltration and curbing the spread of contaminants to groundwater. Effective monitoring of soil moisture levels above and below these barriers is crucial given that performance metrics could span up to 1,000 years. From July 2023 to June 2024, the water flux (as measured by tipping buckets) through the 2-m-thick silt loam layer of the PHB – a fine silty material that stores water under high capillary tension – remained well below the 0.5-mm-per-year performance threshold, demonstrating its effectiveness in preventing water penetration. In 2024, degraded tipping bucket gauges were replaced to ensure the accuracy of future data. Additionally, neutron probes revealed that the wetting front from the rainy season only penetrated to a maximum depth of 1.2 m into the silt loam. This further demonstrated the barrier's efficiency, as the wetting front did not fully penetrate the 2-m-thick capillary barrier. The western gravel slope of the PHB, composed of Hanford Site pit gravel of varying sizes, demonstrated low flux rates. In contrast, the eastern riprap slope exhibited higher flux rates. Zhang (2017) found that the riprap side slope of the PHB had the highest drainage rates in January and the lowest in late summer or early fall, indicating significant summer evaporation. Drainage rates increased significantly under enhanced precipitation conditions, far exceeding the design criterion, which could lead to water infiltration into the waste zone. The study introduced the “edge effect,” where elevated drainage rates from the riprap may migrate laterally beneath the barrier, compromising its ability to isolate waste, and recommended expanding the barrier and conducting further research to mitigate this issue. This report provides a review on surface barrier edge effects and their potential impact to subsurface contaminant migration.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Envisioning urban environments resilient to vector-borne diseases: a protocol to study dengue in Vietnam

Transmitted primarily by Aedes aegypti (Ae. aegypti) and Aedes albopictus (Ae. albopictus), arboviral diseases pose a major global public health threat. Dengue, chikungunya, and zika are increasingly prevalent in Southeast Asia. Among other arboviruses, dengue and zika are becoming more common in Central and South America. Given human encroachment into previously uninhabited, often deforested areas, to provide new housing in regions of population expansion, conceptualizing built urban environments in a novel way is urgently needed to safeguard against the growing climate change-driven threat of vector-borne diseases. By understanding the spread from a One Health perspective, enhanced control and prevention can be achieved. This is particularly important considering that climate change is likely to significantly impact the persistence of ponded water where mosquitoes breed due to increasing temperature and shifting rainfall patterns with regard to magnitude, duration, frequency, and season. Models can incorporate aquatic mosquito stages and adult spatial dynamics when habitats are heterogeneously available, thereby including dispersal and susceptible-exposed-infected-recovered (SEIR) epidemiology. Coupled with human population distribution (density, locations), atmospheric conditions (air temperature, precipitation), and hydrological conditions (soil moisture distribution, ponding persistence in topographic depressions), modeling has improved predictive ability for infection rates. However, it has not informed interventional approaches from an urban environment perspective which considers the role of ponds/lakes that support green spaces, the density of population that enables rapid spread of disease, and varying micro-habitats for various mosquito stages under climate change. Here, for an example of dengue in Vietnam, a preventive and predictive approach to design resilient urban environments is proposed, which uses data from rapidly expanding metropolitan communities to learn continually. This protocol deploys computational approaches including simulation and machine learning/artificial intelligence, underpinned by surveillance and medical data for validation and adaptive learning. Its application may best inform urban planning in low-middle income countries in tropical zones where arboviral pathogens are prevalent.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Controls on Barite (BaSO 4 ) Precipitation in Unconventional Reservoirs

Barite (BaSO 4 ) precipitation is one of the most ubiquitous examples of secondary sulfate mineral scaling in shale oil and gas reservoirs. Often, a suite of chemical additives is used during fracturing operations to inhibit the accumulation of mineral scales, though their efficacy is widely varied and poorly understood. This study combines experimental data and multi-component numerical reactive transport modeling to offer a more comprehensive understanding of the geochemical behavior of barite accumulation in shale matrices under conditions typical of fracturing operations. A variety of additives and conditions are individually tested in batch reactor experiments to identify the factors controlling barite precipitation. Our experimental results demonstrate a pH dependence in the rate of barite precipitation, which we use to develop a predictive model including a pH-dependent term that satisfactorily reproduces our observations. Further, this model is then extended to consider the behavior of three major shale samples of highly variable mineralogy (Eagle Ford, Marcellus, and Barnett). This data-validated model offers a reliable tool to predict and ultimately mitigate against secondary mineral accumulation in unconventional shale reservoirs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Comparison of temperature adaptive calibration methods for laser induced fluorescence based fuel-in-oil instrument

Fuel dilution of engine oil (or fuel-in-oil, FiO) is an important issue as multiple and late-cycle fuel injection, integral to many combustion efficiency and emissions improvements (e.g., downsized boosted gasoline engines and catalyst thermal management) increases FiO rate. In addition to causing general wear and corrosion in engine due to decreased oil viscosity and pH buffering, FiO is also believed to cause destructive low speed pre-ignition (or super knock) in boosted SI engines. To understand the effects of engine operating conditions on the FiO rate, an optical diagnostic capable of measuring transient FiO on minute timescales was recently developed and demonstrated on a modified GM Ecotech engine system (Neupane et al., Applied Spectroscopy 2021). The measurement is based on adding a dye to the fuel and monitoring for its presence in oil via laser-induced fluorescence (LIF). The measured LIF signal is related to FiO concentration via pre-determined calibration factors using a multivariate classical least square (CLS) method.Since fluorescence quantum yield is a function of temperature, measured LIF intensity not only depends on FiO concentration but also oil temperature. To expand the applicability of the FiO diagnostic to transient oil-temperature conditions (e.g., cold start in practical engines), this study develops a method to account for oil-temperature variations. The effect of oil temperature (20°C - 95°C) on the LIF spectra of eight FiO samples ranging from ~0.8-15% was investigated. LIF intensities of the FiO samples decreased linearly with increasing temperature; the reductions being more significant at dye peaks. We develop a new calibration model (T-adaptive CLS) incorporating the temperature (T) effects on LIF intensity that enables simultaneous calculation of FiO and oil temperature. The improved FiO diagnostic with T-adaptive calibration is more robust, and applicable to varying oil-temperature conditions. For example, when strategies such as multiple/late fuel injections are applied to overcome cold-start instability due to use of low vaporization bio-based fuels such as ethanol, FiO rate is expected to be very high; the improved T-adaptive FiO diagnostic is hence relevant for engine- and fuel-system calibration and optimization. The diagnostic could also provide validation data for flow-field and spray interaction CFD models, further broadening the diagnostic’s utility for advancing engine technology and efficiency.

Neupane, Sneha↗
DOE OSTI2024

Material Recovery Facilities (MRFs) in the United States: Operations, revenue, and the impact of scale

An analysis was conducted using nationwide survey data to evaluate how material recovery facilities (MRFs) operations vary regionally and with scale. The survey characterized materials, processes, and energy use involved with operations, and revenue for recyclables. This is the first nationwide analysis of MRFs in the US that accounts for mass processed, energy consumed, and revenue. Of a population of 521 MRFs, 48 responses representing MRFs from five US regions were received and analyzed (9.2 % response rate). Responses were analyzed by size according to yearly mass of inbound materials (small: <1,000 Mg/year, medium: 1,000–10,000 Mg/year, and large: >10,000 Mg/year). Most MRFs identify as single-stream; source from residences; utilize tipping floors, picking lines, baling and warehousing; and are powered by electricity. Most revenue and inbound mass (>50%) came from fiber (cardboard and paper). Glass had little revenue, and plastics were difficult to transition to market. Percent residue ranged from 1-39%, averaged <20%, and increased as the mass of inbound material increased. Large MRFs reported more sources of material, employed advanced sorting technology, had greater plastics revenue (33% versus 5% for small MRFs), and had more market access for plastics compared to small MRFs. Large MRFs had two orders of magnitude less annual electricity consumption per Mg recyclables than small MRFs (5–90 kWh/Mg versus ∼300–550 kWh/Mg). Results demonstrate environmental and economic benefits of larger-scale MRFs, which could be implemented more broadly in the US through regional hub-and-spoke arrangements for collecting and processing recyclables, lowering energy consumption and increasing revenue for recyclables.

Hub-and-Spoke↗
DOE OSTI2024

Utah FORGE: Pump and Probe Test on an Intact Westerly Granite Sample

This dataset contains results from a pump and probe experiment conducted on an intact Westerly Granite sample with a diameter of 1 inch and a height of 1 3/8 inches. The experiment was performed within an aluminum triaxial pressure vessel (TEMCO) to investigate the non-linear acoustic parameters of the sample under varying axial pressures. The confining pressure was maintained at 4 MPa, while the axial pressure was incrementally increased from 1 MPa to 17 MPa before being reduced back to 1 MPa. 5 dynamic pressure oscillations of 0.5 MPa were applied over a 10 minute interval. The dataset includes pump controller data, linear variable differential transformer (LVDT) measurements, and acoustic data. The pump controller data tracks the confining and axial pressures, flow rates, and pump volumes. LVDT measurements provide detailed records of the axial displacement of the cell piston. Additionally, acoustic data captured by s-wave transducers is archived in a compressed file.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

A high fidelity and user-friendly equation-oriented optimization model for carbon capture using a novel water-lean solvent

Research Triangle Institute (RTI) International and SLB have developed a novel water-lean solvent technology for carbon capture, demonstrating low specific reboiler duty (SRD) values at capture rates exceeding 90%. At the Technology Centre Mongstad (TCM) pilot plant, the technology achieved an SRD of 2.55 GJ/t-CO2 at 95% capture, utilizing an intercooler and a 5°C temperature approach in the lean/rich solvent cross exchanger. To meet varying carbon capture targets for Front End Engineering and Design (FEED) studies and to enable real-time optimization and advanced process control, an efficient optimization model is required. This model needs to minimize energy demand for a given capture rate and determine optimal operating parameters in response to fluctuating flue gas conditions. While an existing Aspen Plus simulation model, developed by RTI and SLB, accurately matches TCM plant data, its sequential modular (SM) strategy is too slow for real-time applications due to recycle streams and tight heat integration inherent in solvent-based carbon capture processes. Although an equation-oriented (EO) modeling strategy is more suitable for optimizing these processes, its adoption has been limited by several factors: feature limitations in Aspen Plus EO mode (e.g., lack of balance block support), a less user-friendly interface for variable identification and loop solving, complex troubleshooting of convergence issues, and the necessity for accurate initial values.

carbon capture↗
DOE OSTI2026

Inundation and Sediment Supply Predict Annual but Not Centennial Carbon Accumulation in Tidal Wetlands Across Land‐Use Types

Despite growing interest in the contributions of tidal wetlands to natural climate solutions, data remain scarce on how land use affects their carbon accumulation rates (CAR). Additionally, environmental factors driving the large observed variability in CAR among sites are poorly understood. To address these knowledge gaps, we measured short-term (∼1 year) and long-term (∼100 years) CAR using feldspar marker horizons, 210 Pb profiles, and surface elevation tables from 39 sites in seven estuaries in Oregon and Washington, USA. Sites varied in wetland type (tidal marsh, tidal swamp, nontidal pasture), land-use history (reference, restored, and disturbed), and salinity (fresh to polyhaline). Across both timescales, CAR was lowest in disturbed pastures and highest in restored marshes, reflecting patterns in sediment accretion rates. Short-term CAR exceeded long-term CAR by approximately 50% on average and was only weakly correlated with long-term CAR, with differences among hydrogeomorphic setting. Short-term CAR was well predicted by summer inundation frequency and relative sediment load, together explaining 60% of variance, whereas long-term CAR was poorly predicted by contemporary environmental conditions. These findings highlight the importance of aligning predictor and response timescales when modeling blue carbon dynamics, and reinforce the climate mitigation potential of tidal wetland restoration.

land use and land cover change↗
DOE OSTI2021

What is Important? Morphological Asymmetries are Useful Predictors of Star Formation Rates of Star-forming Galaxies in SDSS Stripe 82

The morphology and structure of galaxies reflect their star formation and assembly histories. We use the framework of mutual information (MI) to quantify the interdependence among several structural variables and to rank them according to their relevance for predicting the specific star formation rate (SSFR) by comparing the MI of the predictor variables with the SSFR and penalizing variables that are redundant. We apply this framework to study ~3700 face-on star-forming galaxies (SFGs) with varying degrees of bulge dominance and central concentration and with stellar mass $M$ * ≈ 10 9 $M$ ⊙ at redshift $z$ = 0.02–0.12. We use the Sloan Digital Sky Survey (SDSS) Stripe 82 deep $i$-band imaging data, which improve measurements of asymmetry and bulge dominance indicators. We find that star-forming galaxies are a multiparameter family. In addition to $M$ * , asymmetry emerges as the most powerful predictor of SSFR residuals of SFGs, followed by bulge prominence/concentration. Star-forming galaxies with higher asymmetry and stronger bulges have higher SSFR at a given $M$ * . In conclusion, the asymmetry reflects both irregular spiral arms and lopsidedness in seemingly isolated SFGs and structural perturbations by galaxy interactions or mergers.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Case Study: Hybrid Carbon Conversion Using Low-Carbon Energy Sources in Coal-Producing States

The demand for more carbon efficient power sources and a decrease in natural gas prices has decreased the desire for coal power. This decrease in demand has led to massive job losses in coal mining regions over the past decade. The purpose of this project is to develop a hybrid energy system utilizing both a coal power plant and advanced reactor, which is competitive with natural gas by improving on profitability and decreasing carbon emissions. This report details the problem with a summary of the impact on the coal industry and the availability of renewable energy sources in the Appalachian region. Because of the geography of the region, variable renewable energy sources are not available without significant size and siting restrictions. However, biomass in the form of wood waste is abundant and can be used as a carbon neutral energy source. Combining biomass and coal processing, in addition to thermal power plants, can increase system profits and efficiency by providing peaking power and conversion opportunities for secondary markets. The electric load is based on publicly available demand data from Appalachian Power, which services the western Virginia and southern West Virginia in the Appalachian region. The demand information is combined by service, normalized, and scaled to an average demand of 1000 kW, which will be the basis for sizing the hybrid energy system. A traditional screening curve analysis for a coal plant and advanced reactor shows that the least cost design varies significantly based on the assumed discount rate and capital recovery period. An optimization program to size the design in TEAL based on the load curve gives 10 optimal designs, all with a negative resulting net present value (NPV) and a coal plant capacity of less than 15%. Including profits from selling captured carbon at a flat rate results in a positive NPV; however, the coal capacity factor only increases to about 40%. There are limitations with this optimization as well since the price of CO2 is likely to decrease as more is sold to the conversion market. The suggested design will combine coal power, an advanced reactor, and coal and biomass coprocessing to produce a variety of products that can be sold to the conversion market while increasing system efficiency. The analysis of conversion pathways for coal and biomass reveals that multiple options will need to be included in the analysis to produce the optimal system design. Three systems will be optimized and compared to determine the best design based on the figures of merit of total NPV and cost of carbon avoided. The first system will include a coal power plant and an advanced reactor that will sell electricity to the grid to meet demand and sell captured carbon to the conversion market. The second system adds a high-temperature steam electrolysis plant, which will utilize electricity during times of low demand to produce hydrogen and sell it to the conversion market. The third system adds biomass and coal processing with options for hydrocarbon oils, syngas to be produced for the conversion market, and electricity generation to power components within the system or provide peaking power. This analysis will be based on a new approach that combines traditional screening curve methods with a dispatch algorithm that optimizes the system based on the opportunity cost of different production options. The resulting optimization algorithm should provide results with less processing time than HERON’s decision tree method. The results from this analysis will determine an optimal design and reinforce the benefits of coal power when used in a hybrid energy system. The initial results show that the addition of a secondary market for carbon sales could result in a positive NPV and increases the capacity factor of the coal plant as compared to a design with only sales to the electricity market. The addition of more markets and additional coal consumption from biomass coprocessing could increase NPV further, replace carbon in other markets through the sale of biomass-derived hydrocarbons, and demonstrate the value of coal power technology.

01 COAL, LIGNITE, AND PEAT↗