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DOE OSTI2025

Performance Evaluation of Intelligent Solar Control Software Through Hardware-in-the-Loop (CRADA Final Report)

Recent research has highlighted the potential for solar to act as a zero-marginal-cost and zero-emission flexibility resource on the bulk power system when operated with advanced control systems. To increase the performance of these systems, leading technologies, including machine learning (ML) and hierarchical inverter set point allocation, have been developed by Latimer Controls, Inc. to estimate the headroom of large PV plants for grid operation and control; however, these technologies lack comprehensive validation under real-world application scenarios. Latimer Controls, Inc. received two voucher awards for research at a national laboratory from the Department of Energy American Made Solar Prize Round 6. The National Renewable Energy Laboratory (NREL) was selected to collaborate with Latimer staff to conduct a performance evaluation of Latimer PV control software. The NREL team will develop a hardware-in-the-loop (HIL) testbed to perform testing and validation of the Latimer PV control technology in a de-risked yet realistic testbed environment. Latimer and NREL worked together to analyze the test data, draw conclusions from the results, and disseminate the resulting scientific findings. In this CRADA work, we propose to test and validate the real-world application of the Latimer Control solution in an HIL environment. We evaluate the performance of different flexible solar technologies in responding to automatic generation control signals in a closed-loop fashion. In particular, a data-driven potential high limit (PHL) estimation is developed for large solar plants to accurately estimate their headroom so that they have fast and short-time regulation and control capability to participate in grid services and respond to grid signals in real time (e.g., AGC). This PHL estimation algorithm is embedded in a hardware power plant controller (PPC) and tested with an IEEE-39 bus system model developed in RTDS. To account for the varying cloud conditions and diverse inverter dispatches, we developed a 135-MW PV plant with detailed modeling of 27 individual PV modules and inverters using RTDS. The real-world communications used in such big plants, such as ModBus TCP/IP for inverter level and DNP3 for plant level, were developed to emulate the real-world applications in big PV plants. The ML-based PHL estimation method is tested under nine separate weather scenarios against the ‘reference-control’ solution, hereafter referred to as the baseline solution. The baseline method reserves a subset of inverters (reference group) to operate at their PHL at all times and dispatches only the remaining inverters (control group) at curtailed levels to fulfill the flexibility need. Despite being successfully piloted by NREL in California in 2017 and Chile in 2020, there exist two gaps in the state of the art to fully unlock the flexibility of PV plants: a. There is a trade-off between the PHL estimation accuracy and the flexibility range. b. There lacks granularity in the PHL estimation to capture the variation across inverters. The Latimer solution seeks to address these gaps by applying machine learning methods to improve PHL estimation accuracy while accounting for variability at every inverter. Performance metrics were taken from the 2023 Georgia Power CARES utility-scale RFP. The results demonstrate that the ML-based approach outperforms the traditional baseline method in PHL estimation accuracy for 7 of 9 scenarios. The average PHL error across the nine scenarios was 7.40% for the ML-based method, 2.06% less than the 9.46% PHL error average across scenarios that was exhibited by the baseline method. Additionally, the PHL error was below 5% for at least 95% of the testing interval for 3 of 9 tested intervals with the ML approach, whereas it did not achieve this metric for any of the baseline tests. Overall, simulation results indicate the superior performance of an ML-based approach compared to the conventional baseline reference-control approach, showcasing its potential to support grid stability and operational efficiency. This laboratory HIL testing using real PPC, representative power system simulation models in real-time with detailed PV plant and inverter models, and real-world communication protocols gives us confidence that this machine learning based PHL estimation algorithm works well in the hardware PPC and therefore de-risks future field commissioning. The end goal of this project is to advance grid technology to address the grid operation challenges brought by solar plant’s variability and uncertainties in power generation.

14 SOLAR ENERGY↗

DOE OSTI2024

Characterizing model uncertainties in simulated coast-to-offshore wind over the northeast U.S. using multi-platform measurements from the TCAP field campaign

Numerical weather prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, are widely used to provide estimates of the offshore wind energy resource owing to their large spatial coverage compared to available observations. Nevertheless, spatiotemporal distribution of model biases is highly dependent on factors including model configuration, location, and the interplay of multi-scale physical processes. Here, in this study, we focus on the characterization of model uncertainties in simulated coast-to-offshore winds over the northeast U.S., by varying sea surface temperature (SST) forcings, surface layer (SL) and planetary boundary layer (PBL) parameterizations, as well as identifying biases that may be directly passed from initial and boundary conditions. Multiple measurements, including aircraft data collected during the U.S. Department of Energy's Two-Column Aerosol Project (TCAP) experiment, are used to constrain the model results and facilitate quantitative comparisons. Our analysis indicates while SST forcing has notable impacts on simulated air temperature and moisture within PBL, the modeled winds are in general more sensitive to the choices of SL and PBL physics than to SST. The model’s forcing data not only controls the vertical dependence of wind speed errors, but also alters regional variability in wind speed’s spatial correlation. Bias comparisons between ERA5 reanalysis and ensemble simulations revealed significant similarity, particularly in wind speed biases during winter, underscoring their dependency on initial and boundary conditions. Coastal and offshore near-surface wind speed biases tend to exhibit much higher similarity in winter than in summer due to the presence of much stronger and more persistent synoptic wind conditions. This study highlights the importance of accurate atmospheric forcing and parameterization choices in improving wind forecasts and suggests the potential for extrapolating coastal wind biases to offshore locations, aiding wind energy forecasting and informing the Wind Forecast Improvement Project-3 (WFIP3).

17 WIND ENERGY↗

DOE OSTI2026

High-Capacity Enzymatic Degradation of Postconsumer Poly(ethylene terephthalate): Modeling and Experimental Investigations

Enzymatic degradation is considered as one of the key steps for biorecycling of polyethylene terephthalate (PET), a widely used plastic. Recently, we have developed an optimized leaf-branch compost cutinase PelB-LCC ICCG and demonstrated ∼80% degradation of 200 g L −1 recycled PET (RPET) within two days in bioreactors. In this study, continued research efforts were made to achieve a complete degradation of up to 300 g L −1 RPET in bioreactors. First, low-capacity reaction conditions with ≤5 g L −1 PET were examined to identify the key variables for PET degradation with PelB-LCC ICCG , including temperature, enzyme loading, and product inhibition. To better understand and further optimize the enzymatic degradation process, a mechanism-based model was established to describe the kinetics of PET degradation and formation of the main product terephthalic acid (TPA) and byproducts MHET and BHET in high-capacity bioreactors. Model simulation suggested that a minimal enzyme loading of ∼1.4 mg PelB-LCC ICCG g −1 RPET is required to achieve a nearly complete degradation of RPET within 48 h, which was used to guide more high-capacity experiments with 100−300 g L −1 RPET in fully controlled 1 L bioreactors. A higher temperature (≥65 °C) was found not only to enable rapid degradation in the beginning but also to induce a gradual increase in RPET’s crystallinity and significantly slow down the degradation after 48 h. A high loading of RPET solids and the accumulation of the produced insoluble TPA pose a big challenge on mixing and mass transfer in the stirred bioreactor, which can be addressed by increasing the stirring speed. The results pave the way toward biorecycling of PET at a large scale.

enzymatic degradation↗

DOE OSTI2025

Performance of Convection-Permitting and Convection-Parameterized Models in Reproducing the Extreme Precipitation Intensity Relationship with Surface Conditions

Here, this study investigates the warm-season extreme precipitation–temperature scaling relationship in CONUS404, a convection-permitting (4 km) Weather Research and Forecasting (WRF) Model simulation over the conterminous United States for the past four decades, and compares it with the WRF-Thermodynamic Global Warming (WRF-TGW) historical simulation at a coarser resolution (12 km) using parameterized convection. We also analyze the NCEP stage IV and NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) datasets as observational benchmarks. We examine how extreme precipitation intensity (EPI) varies with temperature and saturation deficit over representative regions based on hourly data. The stage IV and IMERG data show a similar pattern of EPI variation with temperature and saturation deficit, except that the EPI peak is lower in IMERG than in stage IV. Under dry and hot conditions, EPI decreases too rapidly with elevated saturation deficit in both CONUS404 and WRF-TGW compared to observations, but the performance of CONUS404 is superior to WRF-TGW. When the near-surface atmosphere is saturated or close to saturated, both CONUS404 and WRF-TGW produce higher peak values of EPI relative to the observational references; IMERG exhibits scaling rates close to the Clausius–Clapeyron (C–C) relationship, while CONUS404, WRF-TGW, and stage IV all demonstrate super-C–C scaling behaviors. Despite marked warming over the past four decades, in both CONUS404 and WRF-TGW, the scaling relationship between EPI and temperature in a saturated atmosphere remains stable and robust. This indicates a strong potential for the EPI–temperature scaling rate under saturation to be used as an emergent constraint in reducing uncertainties of future extreme precipitation projection.

Atmosphere↗

DOE OSTI2025

Machine Learning Techniques for Data Reduction of Climate Applications

Scientists conduct large-scale simulations to compute derived quantities-of-interest (QoI) from primary data. Often, QoI are linked to specific features, regions, or time intervals, such that data can be adaptively reduced without compromising the integrity of QoI. For many spatiotemporal applications, these QoI are binary in nature and represent presence or absence of a physical phenomenon. We present a pipelined compression approach that first uses neural-network-based techniques to derive regions where QoI are highly likely to be present. Then, we employ a Guaranteed Autoencoder (GAE) to compress data with differential error bounds. GAE uses QoI information to apply low-error compression to only these regions. This results in overall high compression ratios while still achieving downstream goals of simulation or data collections. Experimental results are presented for climate data generated from the E3SM Simulation model for downstream quantities such as tropical cyclone and atmospheric river detection and tracking. These results show that our approach is superior to comparable methods in the literature.

Li, Xiao [University of Florida]↗

DOE OSTI2024

Phosphorus recycling by mineral-catalyzed ribonucleotide cleavage on iron and manganese oxides

Abstract Phosphorus is an essential element influencing both food security via plant fertilization, and water pollution through excessive phosphorus use, yet the phosphorus cycle in ecosystems is poorly known. In particular, beyond adsorption, the role of iron and manganese oxides in catalyzing the abiotic dephosphorylation of biomolecules is debated. Here, we studied the reactions of ribonucleotides, containing different phosphate bonding, with goethite, hematite, and birnessite. We employed both high-resolution mass spectrometry of solution species and molecular modeling simulations of ribonucleotide-mineral complexes. Results disclose an up to fivefold preferential hydrolytic cleavage of a phosphoanhydride bond over a phosphoester bond, indicating that mineral-catalyzed reactions reflect the hierarchy reported for the activity of phosphatase enzymes. The fourfold higher catalytic reactivity of goethite and birnessite versus hematite is explained by mineral-specific binding rather than surface area differences. Corresponding simulated adsorbate conformations at the water–mineral interfaces are proposed. Overall, our findings provide new insights on the catalytic recycling of organic phosphorus species by mineral oxides.

58 GEOSCIENCES↗

DOE OSTI2025

Intrepid MCMC: Metropolis-Hastings with exploration

In engineering examples, one often encounters the need to sample from unnormalized distributions with complex shapes that may also be implicitly defined through a physical or numerical simulation model, making it computationally expensive to evaluate the associated density function. For such cases, MCMC has proven to be an invaluable tool. Random-walk Metropolis Methods (also known as Metropolis-Hastings (MH)), in particular, are highly popular for their simplicity, flexibility, and ease of implementation. However, most MH algorithms suffer from significant limitations when attempting to sample from distributions with multiple modes (particularly disconnected ones). Here, in this paper, we present Intrepid MCMC - a novel MH scheme that utilizes a simple coordinate transformation to significantly improve the mode-finding ability and convergence rate to the target distribution of random-walk Markov chains while retaining most of the simplicity of the vanilla MH paradigm. Through multiple examples, we showcase the improvement in the performance of Intrepid MCMC over vanilla MH for a wide variety of target distribution shapes. We also provide an analysis of the mixing behavior of the Intrepid Markov chain, as well as the efficiency of our algorithm for increasing dimensions. A thorough discussion is presented on the practical implementation of the Intrepid MCMC algorithm. Finally, its utility is highlighted through a Bayesian parameter inference problem for a two-degree-of-freedom oscillator under free vibration.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Unique impacts of strong and westward-extended western Pacific subtropical high on ozone pollution over eastern China

As a subtropical anticyclonic high-pressure system that typically forms over the northwestern Pacific Ocean in summer, the Western Pacific subtropical high (WPSH) affects meteorological conditions and ozone pollution in China. The relationship between maximum daily 8-h average ozone (MDA8 O 3 ) concentrations and the extremely strong and westward-extended WPSH occurred in 2022 is investigated using observations, reanalysis data and GEOS-Chem model simulations. The intensity of WPSH has a significant positive correlation with MDA8 O3 over southern China during July-August in 2022, with a correlation coefficient of +0.44, but the correlation is negative (–0.40) in northern China. During the strong WPSH days, MDA8 O 3 increased by 16.5µgm -3 (16.4% relative to July-August average) over southern China and decreased by 19.0µgm -3 (14.5%) in northern China compared to the weak WPSH days. The unique dipole pattern in the relationship between ozone levels and the WPSH in 2022 exhibited a contrast to that during 2015–2021. The difference is primarily due to the extremely strong WPSH intensity and its unusual westward expansion in 2022. In this case, an anomalous anticyclone at 500 hPa dominates over southern China, which creates conditions conducive for ozone formation and accumulation. The anticyclone weakened horizontal winds and reduced the dispersion of ozone, alongside a high temperature and low relative humidity, which favored the chemical production of ozone. In contrast, abnormal northerly winds enhanced ozone diffusion in northern China and the low temperature reduced ozone chemical production. Here, this study reveals the mechanism for the significant impact of strong and westward-extended WPSH on ozone concentrations over China, emphasizing the role of the WPSH location in modulating meteorology and ozone levels.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

A hierarchical framework for aggregating grid-interactive buildings with thermal and battery energy storage

The behind-the-meter (BTM) thermal and battery energy storage can help improve energy efficiency, reduce energy costs, and enhance energy resilience, particularly in rural areas and for disadvantaged communities. Aggregating numerous BTM energy storage systems can act as a price influencer with a significant source of load shifting and peak demand reduction. An integrated and scalable control mechanism is required to effectively utilize energy storage systems and flexible building loads to maximize the economic benefits, considering various distribution system constraints. Here, this paper presents an innovative hierarchical coordination framework for energy storage and flexible load in buildings, considering various factors such as electricity prices, thermal comfort, and distribution system modeling and constraints. At the upper level, a distribution system operator optimizes the power flow to minimize its power procurement costs from the electricity wholesale market, while at the lower level, aggregators determine the optimal dispatch of battery and thermal energy storage systems in multiple buildings on behalf of end-users to minimize operating costs according to the power prices. These problems are solved using a game-theoretic approach through negotiations between the distribution system operator and aggregators as a bi-level decision model. Simulation case studies have been performed for a test distribution network with a number of building end-users using energy storage systems to quantify the performance of aggregators. The results demonstrate that the proposed strategy can reduce peak load for a reliable electricity distribution network while saving electricity bills for customers.

25 ENERGY STORAGE↗

DOE OSTI2024

Capillary-Enhanced Two-Phase Micro-Cooler Using Copper-Inverse-Opal Wick with Silicon Microchannel Manifold for High-Heat-Flux Cooling Application

In this work, we demonstrate a two-phase capillary-fed boiling micro-cooler that consists of a ~ 25-..mu..m-thick copper inverse opal (CIO) porous wicking structure for high-heat-flux boiling and a silicon 3D-manifold for distributed liquid delivery and vapor extraction across a 0.5 cm x 0.5 cm heated area. At low inlet water mass flow rates of 1.5 to 1.9 g(min)-1, the micro-cooler displays nearly two-phase boiling with exit vapor quality ~ 1 and a high critical heat flux (CHF) of 253 to 320 W cm-2 with low superheat of ~ 10 degrees C resulting in a thermal resistance of boiling ~ 0.025 cm2 degrees C W-1 or heat transfer coefficient of 0.4 MW m-2 degrees C-1. For higher flow rates of 5, 10, and 15 g(min)-1, the micro-cooler exhibits a hybrid single-phase and two-phase cooling regime where the contribution of the sensible heat (single-phase) cooling is linearly added to that of the two-phase cooling. For the highest flow rate of 15 g(min)-1, the CHF is increased to ~ 500 W cm-2 resulting in an overall thermal resistance of ~ 0.18 cm2 degrees C W-1. However, the two-phase heat transfer effectiveness, which estimates the utilization level of the inlet mass flow rate for two-phase boiling, is reduced to ~ 0.11. To achieve the best cooling system performances, the micro-cooler must operate entirely within the two-phase boiling regime (exit vapor quality or two-phase heat transfer effectiveness ~ 1). Ideally, the "coolant" should be delivered near its saturation temperature (~ 100 degrees C for water), which provides significant advantages for the energy-efficient operation of data centers and power electronics. We present detailed analysis with Infrared and high speed camera images at various inlet flow rates and heat fluxes to understand complex heat transfer in the micro-cooler. Furthermore, a conjugate thermofluidic simulation model, which incorporates the physics of capillary-fed boiling in a porous copper layer, agrees well with the experimental data.

capillary-enhanced boiling↗

DOE OSTI2025

Process intensification approach to enhancing heat and mass transfer during drying: Ultrasonic (US) assisted drying of paper and board

Drying of paper and board is conventionally achieved through alternating conduction (steam-heated cylinders) and pocket convection (heated air over the paper web surface). These conventional drying systems rely heavily on steam from fossil fuels, resulting in inefficiencies, high energy usage, and thermal losses due to surface-driven mechanisms. Here, to address these challenges, an experimental system, with in-situ drying characteristics measurements, was developed to investigate process intensification using ultrasonic-based dewatering—a volumetric, pressure-driven acoustic energy system—integrated with conventional drying. The objectives of this study are to assess the impact of ultrasonics (US) on dewatering; compare performances to conventional drying systems; identify improvements in drying rate and energy use as a function of moisture content; and gain potential insights on heat and mass transfer mechanisms during US-assisted drying. US performance was evaluated across frequencies, power levels, pulp types, and basis weights. Results show that improvements to ultrasonic applications in conjunction with convection were 30-43% in drying rate and 20-35% in drying time over continuous and intermittent applications. When combined with conduction and convection, ultrasonics yielded up to 20% improvement in both rate and time and up to 20% reduction in energy consumption. Observations support a hypothesis of extension of the constant rate period due to improved capillary flow at higher moisture content and enhancing vapor diffusion and boundary layer disruption at lower moisture contents during falling rate period. These findings will inform future modeling, simulation, design and optimization of advanced drying systems.

42 ENGINEERING↗

DOE OSTI2025

TEM characterization of two variants of fuel cladding chemical interaction in a HT-9 Clad U-10Zr Fuel. Variant 1: FCCI with a Zr Rind

Here, this study investigated the fuel cladding chemical interaction (FCCI), a key factor that limits operational temperature and burnup, in an HT-9 clad U-10Zr nuclear fuel sample irradiated to a high burnup of 13.1 at.% at a time-averaged peak inner cladding temperature (PICT) of 530 °C. Previous results showed this fuel sample exhibited two distinct levels of FCCI at d. This paper analyzed the FCCI at an azimuthal position showing an interdiffusion layer of <10 µm using transmission electron microscopy to examine chemical and crystallographic nature of phases at the fuel-cladding interface at the nanoscale level. A ZrC layer and a Zr 3 Si phase were identified at the interface; these, along with the relatively low local temperature, potentially contributed to limit interdiffusion, behaving as inhibitors for deleterious interactions. Lanthanides (Ln) partially consumed the ZrC layer and interacted with Fe, forming a Zr-Ln compound and a (Zr,Ce)Fe 2+x phase while also infiltrating up to 4 µm into the cladding. Neither U nor Zr were observed in the cladding, whereas Fe diffused up to 3–5 µm in the fuel. Fe infiltration formed a ternary U-Zr-Fe ε-phase and likely promoted the precipitation of a Cr-rich α’ phase on the cladding interface. Additionally, a Cr-rich χ-phase, likely formed by the dissociation of pre-existing M 23 C 6 carbide precipitates, was identified about 2–5 µm from the fuel-cladding interface. Irradiation-induced nano-voids were also observed in the HT-9 bulk. These findings provide critical insights into FCCI mechanisms at representative irradiation conditions, essential for developing models simulating in-pile metallic fuel behaviors for next-generation reactors.

36 - MATERIALS SCIENCE↗

DOE OSTI2025

Bundling measures for food systems transformation: a global, multimodel assessment

Background Current food systems leave one in ten individuals at risk of hunger while driving unsustainable environmental impacts. Inaction risks further exacerbating negative impacts on both human and planetary health. These challenges emerge from complex system interactions, requiring approaches that engage with this complexity and consider how transformation measures interact across food systems. We aimed to quantify the magnitude and uncertainty of the impacts of key food systems transformation measures both individually and in a bundle using an ensemble of global economic models. Methods In this global multimodel assessment, we applied an ensemble of ten state-of-the-art global economic models to evaluate the potential of four key measures in transforming food systems: increasing agricultural productivity, halving food loss and waste, shifting towards healthier diets, and economy-wide climate mitigation policies aligned with limiting warming to 1·5°C. The scenarios used a middle-of-the-road shared socioeconomic pathway for population and gross domestic product growth, climate impact data from Jägermeyr and colleagues, Thornton and colleagues, and Nelson and colleagues, and dietary targets based on the EAT–Lancet healthy reference diet, with model simulations conducted from 2020 to 2050. We then assessed the effect of these measures in isolation and in combination in a bundled scenario. To further understand the interactions between these measures, we conducted a decomposition analysis that distinguishes between the individual effects of a measure (effect when implemented alone), total effects (its contribution within the bundle), and interaction effects (the difference between total and individual effects). This approach aimed to show complementarities and trade-offs that emerge when multiple measures are implemented simultaneously. Findings Our analysis showed that individual measures in isolation are insufficient to achieve high-level environmental objectives and might generate unintended consequences. In contrast, bundling measures produces co-benefits: avoiding 50% of projected agricultural greenhouse gas emissions by 2050 and almost 20% of anticipated land conversion, while moderating food price increases associated with ambitious climate change mitigation policies. Our decomposition analysis further shows that measures can have varying effects across different dimensions. Although dietary shifts and climate mitigation policies are the largest drivers of environmental benefits (each contributing to a median decline of >10 percentage points in non-CO 2 emissions and 5 percentage points in agricultural land use globally), productivity improvements and reducing food loss and waste play essential roles in moderating price increases (each contributing to a median decline of >5 percentage points in average prices). Interpretation This study highlights the importance of implementing coordinated approaches to food system transformation and climate change mitigation rather than relying on isolated interventions. Comprehensive transformation requires understanding how supply-side and demand-side changes can interact with climate mitigation policies, enabling policy makers to design intervention packages that maximise benefits while minimising trade-offs across environmental, economic, and social dimensions.

Sundiang, Marina [Cornell Univ., Ithaca, NY (Unite↗

DOE OSTI2024

International fuel performance study of fresh fuel experiments for PCMI effects during RIA experiments

This paper presents the results of High-burnup Experiments for Reactivity-initiated Accident (HERA) Modeling & Simulation (M&S) exercise. The HERA project under the Nuclear Energy Agency (NEA) Second Framework for Irradiation Experiments (FIDES-II) program is focused on studying Light Water Reactor (LWR) fuel behavior during Reactivity-Initiated Accident (RIA) conditions. The Part I M&S cases are based on a series of tests in the Transient Reactor Test (TREAT) facility in the United States and the Nuclear Safety Research Reactor (NSRR) in Japan. The purpose of this work is to evaluate the test design to accomplish its goals in establishing clearer understanding of the effects of power pulse width during RIA conditions. Further, the blind predictions using various computational tools have been performed and compared amongst to interpret the behaviors of high burnup fuels during RIA. While many international participants evaluate the thermal–mechanical behavior of fuel rod under different conditions, a considerable scatter of outputs comes out for the cases due to the disparity between codes in predicting mechanical behaviors. In general, however, the results of thermal–mechanical analysis elaborate that nominal design conditions the shorter pulse width tests in NSRR should cause cladding failures while the TREAT tests appear to have more split prediction of failure or not. Furthermore, the sensitivity analysis varying key testing parameters reveals the considerable effect of power pulse width and total energy deposition on prediction of fuel rod failure.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Techno-economic and life-cycle analysis of strategies for improving operability and biomass quality in catalytic fast pyrolysis of forest residues

Many of the challenges faced by the first commercial biorefineries were associated with feedstock handling, quality, and cost. Strategies are needed to enable further expansion of biorefineries and meet the growing demand for bio-based fuels and products. Here, we examine 2 key feedstock challenges and mitigation strategies in the context of a catalytic fast pyrolysis (CFP) biorefinery: (1) the operability of the feed system, which may be improved by modifying the minimum particle size fed to the reactor, and (2) the quality of the biomass, which may be improved by employing air classification to remove undesirable material and increase fuel yields. We conduct techno-economic analysis (TEA) and life-cycle analysis for these strategies, employing a discrete event simulation model for biomass preprocessing combined with a series of correlations developed from literature data and a rigorous CFP conversion model. Our results highlight the importance of balancing increased cost and material losses from preprocessing against improved operability and fuel yields. Economics and sustainability were optimized when operating at the lowest minimum particle size, emphasizing the importance of minimizing material losses while maintaining the operability of the process. Economically, additional costs and material losses from air classification could be acceptable due to improved biomass conversion, and an optimum air classification speed was identified; however, the fuel GHG emissions were minimized when air classification was not used. Valorizing material removed during preprocessing as a coproduct could improve economics and sustainability, decreasing the burden of material losses.

09 - BIOMASS FUELS↗

DOE OSTI2025

Glacial geology of the Hudson Mountains, Amundsen Sea sector, West Antarctica

The Hudson Mountains are situated in the eastern Amundsen Sea sector of the West Antarctic Ice Sheet, adjacent to Pine Island Glacier. They form a volcanic field of 17 stratovolcanoes and parasitic vents, preserved as nunataks. Two former tributaries of Pine Island Glacier (Larter and Lucchitta glaciers) flow through the mountains. Here we present a detailed study of the glacial geology of the area. We describe field observations and measurements of geomorphological features from 15 of the nunataks, meltwater ponds found on the surface of three nunataks and supraglacial features (ice dolines) from two sites near the present grounding line. Together these provide constraints on the past ice sheet extent, flow pathways and thermal regime, and enhance our understanding of the present hydrological regime – all of which are important as context for the observed modern ice sheet behaviour. We find evidence suggesting that all nunataks in the Hudson Mountains were covered by ice during the Last Glacial Maximum (defined here as 26.5-19 ka) and have since deglaciated. Faceted and polished erratic cobbles and boulders of exotic lithologies (syenites, alkali granites, granites, granodiorites, tonalites and gabbros) are numerous and perched on nunatak surfaces. A marked difference between the dominant erratic lithologies on nunataks adjacent to Pine Island Glacier (granite) and Lucchitta Glacier (granodiorite-tonalite) indicates that the ice sheet was transporting clasts from at least two distinct upstream source regions. The similarity in degree of weathering suggests, however, that all the erratics were transported by one phase of (warm-based) glaciation; their presence on or close to the summits of all except one nunatak indicates that the ice sheet during that time was at least 700 m thicker than present. These results are consistent with ice sheet model simulations which suggest that all nunataks in the Hudson Mountains were completely submerged by the Last Glacial Maximum ice sheet.

58 GEOSCIENCES↗

DOE OSTI2026

Comparing Delay-, Distance-, and Cordon-Based Congestion Pricing Strategies Via Large-Scale Simulation

This study compares the impacts of delay-, distance-, and cordon-based congestion pricing strategies for Austin, Texas, using the POLARIS agent-based activity-based travel demand simulation model. This approach enables agent-level heterogeneity and realistic choice options (including destination, mode, and activity scheduling) for dynamic traffic assignment and congestion feedbacks across a major metro region, which are features lacking in past work. To ensure comparability, distance-based tolls were set to generate the same revenue as delay-based tolling of $3.5 M/day, averaging $1.17/resident/day or $0.42/vehicle-trip. Delay-based pricing delivers 44% lower network delay and 13% lower VHT compared to the no-toll baseline, levels unmatched by other pricing strategies. At the height of the AM peak, drivers pay up to $0.13/mile on average, though most links in the network remain untolled. Distance-based pricing is the most effective at reducing VMT (by 4%), but VHT reductions (of 6%) primarily stem from drivers selecting closer destinations, achieving only one-fourth the delay reduction of delay-based pricing. Across various implementations of delay- and distance-based pricing, the results suggest that spatial variations of tolls are far more important than temporal variations. Cordon tolls produce minimal impacts at the network-wide level, but offer substantial delay reductions inside the cordon. Other major findings include: 1) delay-based pricing increases trip-making during the PM peak period due to backward shifts in discretionary-activity start times by higher-income residents; and 2) tolls’ spatial impacts, including changes in network flows and tolls paid by residents, vary substantially between delay- and distance-based pricing strategies.

Agent-based modeling↗

DOE OSTI2026

Oil in Alaska North Slope Gas Hydrate Reservoir: Micro-CT and Flow Simulation Insights into Permeability

Gas hydrate-bearing sands on the Alaska North Slope (ANS) host minor volumes of crude oil whose impact on formation permeability has never been quantified relative to a gas hydrate reservoir system. Here, we combine in situ pressure-core microcomputed-tomography (μ-CT), thermogravimetric analysis (TGA), gas-chromatography–mass-spectrometry (GC–MS), scanning-electron microscopy coupled with energy-dispersive spectroscopy (SEM-EDS), and Stokes-flow simulation to (i) quantify oil saturation, (ii) infer its source, and (iii) evaluate its influence on permeability after gas-hydrate dissociation. μ-CT and SEM-EDS imaging identify the host sediment as silty, with some grain-coating clay present. Eight representative 3003-voxel subvolumes extracted from a preserved pressure core (Core 15P-3c, from the B1 sand (unit B), HYDRATE 02 Geo Data Well (GDW), 886.96–887.29 m measured depth, MD) exhibit porosities of 0.34–0.37 and an average oil saturation of 0.09 ± 0.03, in comparison to an independent TGA-based estimate of 0.16. GC-MS fingerprints obtained from an adjacent depressurized core (Core 17P-1, 891.37–891.44 m MD), together with oil pore habit revealed by μ-CT, suggest that the oil is partly native but also includes contributions from mineral oil-based drilling fluid contamination. Further analysis of oil–porewater interactions and flushing tests confirms that native oil saturation in the analyzed samples ranges from 0.04 to 0.08 with an average of approximately 0.06, and the oil is highly immobile. Flow simulations demonstrate that the native oil saturation of ≈0.06 reduces permeability by approximately 50% due to both pore blockage and increased flow-path tortuosity. These findings reveal the potential presence of native oil within the ANS gas hydrate reservoir and suggest that future simulation models may need to account for its impact on permeability to improve long-term performance predictions of gas and water production.

02 PETROLEUM↗