Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Resource Size”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 649 records · Page 36

Simultaneously improving accuracy and computational cost under parametric constraints in materials property prediction tasks

Abstract Modern data mining techniques using machine learning (ML) and deep learning (DL) algorithms have been shown to excel in the regression-based task of materials property prediction using various materials representations. In an attempt to improve the predictive performance of the deep neural network model, researchers have tried to add more layers as well as develop new architectural components to create sophisticated and deep neural network models that can aid in the training process and improve the predictive ability of the final model. However, usually, these modifications require a lot of computational resources, thereby further increasing the already large model training time, which is often not feasible, thereby limiting usage for most researchers. In this paper, we study and propose a deep neural network framework for regression-based problems comprising of fully connected layers that can work with any numerical vector-based materials representations as model input. We present a novel deep regression neural network, iBRNet, with branched skip connections and multiple schedulers, which can reduce the number of parameters used to construct the model, improve the accuracy, and decrease the training time of the predictive model. We perform the model training using composition-based numerical vectors representing the elemental fractions of the respective materials and compare their performance against other traditional ML and several known DL architectures. Using multiple datasets with varying data sizes for training and testing, We show that the proposed iBRNet models outperform the state-of-the-art ML and DL models for all data sizes. We also show that the branched structure and usage of multiple schedulers lead to fewer parameters and faster model training time with better convergence than other neural networks. Scientific contribution: The combination of multiple callback functions in deep neural networks minimizes training time and maximizes accuracy in a controlled computational environment with parametric constraints for the task of materials property prediction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Neural net detection of seismic features related to gas hydrates and free gas accumulations on the northern U.S. Atlantic margin

Bottom-simulating reflections (BSRs) that sometimes mark the base of the gas hydrate stability zone in marine sediments are often identified based on the reverse polarity reflections that cut across stratigraphic layering in seismic amplitude data. On the northern U.S. Atlantic margin (USAM) between Cape Hatteras and Hudson Canyon, legacy seismic data have revealed pronounced BSRs south of the deepwater extension of Hudson Canyon and more subtle ones from offshore Delaware south to Cape Hatteras, where the reflections sometimes follow stratigraphic layering. Using high-resolution seismic data acquired during the 2018 Mid-Atlantic Resource Imaging Experiment and a supervised neural net, we identify seismic features associated with gas hydrates and/or the top of gas between Hudson Canyon and Cape Hatteras. Using seismic attributes especially sensitive to the presence of gas, we train a neural network algorithm on seismic data from an area with strong BSRs and then apply the model to the rest of the data set. The results indicate that gas hydrate and/or shallow free gas are significantly more widespread on the northern part of the USAM than previously known. Seismic indicators of gas extend landward from the 2000 m isobath to the upper continental slope in sectors with (offshore Virginia) and, to a lesser extent, without (offshore New Jersey) pervasive upper slope methane seeps. Higher sand content and intermediate sediment thickness, factors related to the container size and gas charge in a petroleum systems framework, are associated with more robust gas indicators.

Geochemistry & Geophysics↗

Intensifying Renewable Energy Droughts in the Western U.S. Amid Evolving Infrastructure and Climate

If renewable energy resources continue to become a larger part of the generation mix in the United States (U.S.), so does the potential impact of prolonged periods of low wind and solar generation, known as variable renewable energy (VRE) droughts. In such a future, naturally occurring VRE droughts need to be evaluated for their potential impact on grid reliability. This study is the first of its kind to examine the impacts of compound VRE energy droughts in the Western U.S. across a range of potential future climate and infrastructure scenarios. We find that compound VRE drought severity may increase significantly in the future, primarily due to the dramatic increase in wind and solar generation needed in some future infrastructure scenarios. We find that in our future climate scenario, the variability of energy drought severity increases, which has implications for sizing energy storage necessary for mitigating drought events. We also examine the spatial patterns of compound VRE drought events that effect multiple regions of the grid simultaneously. These co-occurring events have distinct spatial patterns depending on the season. We observed overall fewer connected events in the future with the combined effect of potential climate and infrastructure changes, although in the fall we observe a climate-induced shift toward events which impact more regions simultaneously.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Electrochemical Recovery of Iron from Spent Pickle Liquor by Chloride-Based Molten Salt Electrolysis

Spent pickle liquor (SPL) is a waste stream generated in the steel industry that presents both a disposal challenge and a potential resource for metal recovery. This study investigates the electrochemical extraction of high-purity iron metal from SPL using chloride-based molten salt electrolysis (CMSE). Electrochemical characterization of diluted SPL confirmed Fe 2+ as the dominant species. Thermal dehydration of SPL under inert atmosphere yielded FeCl 2 -rich solids, which were directly employed as feedstock for electrolysis in LiCl–KCl eutectic melts at 500 °C. Cyclic voltammetry of FeCl 2 in this melt revealed well-defined Fe 2+ /Fe 0 redox behavior within the electrochemical stability window of the supporting electrolyte. High coulombic efficiency (>85%) electrodeposition was achieved demonstrating that iron metal can be produced from dehydrated SPL by CMSE. The electrodeposited iron exhibited >98 wt% purity, which was further enhanced to 99.9 wt% via arc melting. The resulting iron powder was ferromagnetic, and its size distribution was found to be suitable for powder metallurgy applications. This work demonstrates a scalable, energy-efficient pathway for valorizing SPL into high-purity iron metal, advancing circular economy strategies in the steel industry.

Materials science↗

Seismology in the Cloud: A New Streaming Workflow

Data-intensive research in seismology is experiencing a recent boom, driven in part by large volumes of available data and advances in the growing field of data science. However, there are significant barriers to processing large data volumes, such as long retrieval times from data repositories, complex data management, and limited computational resources. New tools and platforms have reduced the barriers to entry for scientific cluster computing, including the maturation of the commercial cloud as an accessible instrument for research. Here, we build a customized research cluster in the cloud to test a new workflow for large-scale seismic analysis, in which data are processed as a stream (retrieved on-the-fly and acted upon without storing), with data from the Incorporated Research Institutions for Seismology Data Management Center. We use this workflow to deploy a spectral peak detection algorithm over 5.6 TB of compressed continuous seismic data from 2074 stations of the USArray Transportable Array EarthScope network. Using a 50-node cluster in the cloud, we completed the noise survey in 80 hr, with an average data throughput of 1.7 GB per minute. By varying cluster sizes, we find the scaling of our analysis to be sublinear, due to a combination of algorithmic limitations and data center response times. The cloud-based streaming workflow represents an order-of-magnitude increase in acquisition and processing speed compared to a traditional download-store-process workflow, and offers the additional benefits of employing a flexible, accessible, and widely used computing architecture. It is limited, however, due to its reliance on Internet transfer speeds and data center service capacity, and may not work well for repeated analyses or those for which even higher data throughputs are needed. These research applications will require a new class of cloud-native approaches in which both data and analysis are in the cloud.

58 GEOSCIENCES↗

Validating Greater Sage-Grouse Individual-based Model (IBM) Tool (Final Report)

The project focused on validating the previously developed Greater Sage-Grouse Individual-based Model (GrSG IBM; LaGory et al. 2012, 2021). The objective was to transform this predictive, spatially and temporally explicit model into a portable resource to assist siting/resource managers in proactively assessing the cumulative impacts of wind energy development on the greater sage-grouse. Utilizing a bottom-up, individual-based approach, the GrSG IBM accounts for landscape context and species behavior, aiming to reduce uncertainty in estimating development impacts and support ecologically mindful land-based wind energy development. The validation effort covered approximately 6,540 km 2 near the Seven Mile Hill Wind Project in Wyoming. The GrSG IBM tool, built on the NetLogo platform (Tisue and Wilensky 2004), was executed over a 50-year period, with the analysis focusing on years following a 10-year initialization phase. Key results demonstrated the tool’s biological soundness across five key biological metrics: non-chick age class distribution (older than 10 weeks), adult sex ratio, life expectancy, population size, and overall population growth. For instance, the tool estimated that 58.6% of the non-chick population was reproductively immature, while the reference ranges from 51.4% to 57.8% (Patterson 1952, Rogers 1964). Experts confirmed the tool’s estimate was within a reasonable range for the species. The tool estimated average life expectancy of 1.43 years, while the reference ranges from 0.9 years to 1.1 years (Ammann 1957, Hamerstrom 1949). Experts also supported the model’s life-expectancy estimate as ecologically sound for the species in the study area. In terms of population change, the model estimated an annual shift between a 0.6% decline and a 1.0% increase over 50 years. While the reference suggests 2.9% annual decline in range-wide populations (Cortes et al. 2023), that includes many at-risk populations in South Dakota and Washington, for example. Our study area—in the northeastern part of Carbon County and western-edge of Albany County, Wyoming—is one of the remaining greater sage-grouse habitats supporting some of the most stable populations. Experts confirmed that the range of the annual population change spanning from a 0.6% decline to a 1.0% increase estimated by the tool was reasonable for our study area for this reason and confirmed that aligned with population estimates from existing studies on the greater sage-grouse and wind energy development in the study area (LeBeau et al. 2017a, Smith et al. 2024). Furthermore, the project showed that temporally explicit biological metrics generated by the GrSG IBM tool can complement the USGS’ Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET; Duchardt et al. 2021) by incorporating habitat restoration strategies into seasonal habitat suitability models to visualize population responses over time.

17 WIND ENERGY↗

A method for assessing economic, environmental, and reliability tradeoffs of interregional transmission connecting ERCOT (the Texas grid) to the eastern and western grids

Reliable development of the power grid is an evolving concern for humanity due to extreme weather that frequently threatens power sector infrastructure. The state of Texas is a uniquely structured testbed for grid planners to study when looking for solutions to development, innovation, and overcoming such challenges. Because of its size and islanded structure, Texas is small enough to model, but big enough to matter. Texas is a global leader in energy production, energy consumption, and maintains an unusually diverse fuel mix. In addition, the state has experienced winter freezes, heat waves, wind storms, droughts and floods that have threatened power sector infrastructure or caused recent blackouts and calls for demand side conservation. One of the most devastating of these events was the North American winter storm, dubbed “Winter Storm Uri” by the Weather Channel, that froze the region in February 2021 and led to an extended power outage event that put the majority of Texan residents in darkness for days. While preparing to avoid such outage events in the future, various tools have been proposed to improve grid reliability, including energy efficiency, demand response, and distributed energy resources. An additional option would be to develop interregional transmission that connects the Texas grid to other national grids. To assess the merits of this idea, we developed a novel, universally-applicable and internationally-relevant framework to study how the Texas grid would evolve alongside access to various interregional ties. This method allows us to stress the synthetic grid structure and analyze how it would respond to the shock of a simulated winter storm event. Our method leverages open-source modeling tools, such as PowerGenome, pyGRETA, and GenX to synthesize unique zonal grid data, construct a consolidated network of model regions, and simulate different developmental pathways of capacity expansion and operational dispatch. We demonstrate our method with an analysis connecting the Electric Reliability Council of Texas (ERCOT), the grid that serves most of Texas, the Western Electricity Coordinating Council (WECC), the grid that serves the western half of the contiguous U.S., and the Eastern Interconnect, the grid that serves the eastern half of the contiguous U.S. Our results indicate that the cost-optimal capacity of interregional transmission connecting the ERCOT grid to other grids lies between 9–13 GW assuming baseline conditions. Building this amount of connecting capacity in one or multiple directions lowers the costs and emissions of development and operation by up to $16 billion and 257 million metric tonnes (MMT) respectively. Additionally, our results show that the interregional connections between ERCOT and other national grids reduce the amount of total load shed required through mild winter storm events. However, our results also show that there is a threshold of very extreme winter storm conditions, spanning multiple service areas, above which the connections exacerbate resource adequacy problems. Therefore, the results indicate that the connections need to be carefully planned alongside the rest of the grid infrastructure to avoid over-reliance on specific resources or technology options.

24 POWER TRANSMISSION AND DISTRIBUTION↗

MemXCT: Design, Optimization, Scaling, and Reproducibility of X-Ray Tomography Imaging

Here, this work extends our previous research entitled "MemXCT: Memory-centric X-ray CT Reconstruction with Massive Parallelization" that was originally published at SC19 conference (Hidayetoglu et al., 2019) with reproducibility of the computational imaging performance. X-ray computed tomography (XCT) is regularly used at synchrotron light sources to study the internal morphology of materials at high resolution. However, experimental constraints, such as radiation sensitivity, can result in noisy or undersampled measurements. Further, depending on the resolution, sample size and data acquisition rates, the resulting noisy dataset can be in the order of terabytes. Advanced iterative reconstruction techniques can produce high-quality images from noisy measurements, but their computational requirements have made their use an exception rather than the rule. We propose a novel memory-centric approach that avoids redundant computations at the expense of additional memory complexity. We develop a memory-centric iterative reconstruction system, MemXCT, that uses an optimized SpMV implementation with two-level pseudo-Hilbert ordering and multi-stage input buffering. We evaluate MemXCT on various supercomputer architectures involving KNL and GPU. MemXCT can reconstruct a large (11Kx11K) mouse brain tomogram in 10 seconds using 4096 KNL nodes (256K cores). The results presented in our original article at the SC19 were based on large-scale supercomputing resources. The MemXCT application was selected for the Student Cluster Competition (SCC) Reproducibility Challenge and evaluated on a variety of cloud computing resources by universities around the world in the SC20 conference. We summarize the results of the top-ranked SCC Reproducibility Challenge teams and identify the most pertinent measures for ensuring the reproducibility of our experiments in this article.

47 OTHER INSTRUMENTATION↗

Solar Resource and Infrastructure Assessment for the Town of Westhampton

This report is a solar resource and infrastructure assessment for the town of Westhampton, Massachusetts. The assessment was funded through the National Renewable Energy Laboratory, Solar Energy Innovation Network (NREL SEIN) Solar in Rural Communities Program, as part of a project to develop a Community-Informed Proactive Solar Siting and Financing Model. As a first step, the project lead organization, UMass Clean Energy Extension prepared an assessment of existing infrastructure, resources, and potential solar development opportunities in participating municipalities, including Westhampton. This assessment was designed to describe relevant bylaws and infrastructure within the town, identify the types of solar facilities that could be developed, and quantify the total space available for each type of facility. In this report, we reviewed existing electricity grid infrastructure, and the potential to interconnect additional solar facilities. At the present time, both distribution lines providing electricity to Westhampton are oversaturated with authorized and proposed solar projects, and cannot accommodate additional solar projects to interconnect to the grid. Future upgrades could potentially free up additional capacity for new, large projects. Meanwhile, most three-phase lines could likely accommodate additional small-to-medium scale projects (under 200 kW), and most single-phase lines could likely accommodate additional projects under 50 kW in size. This description represents the local grid infrastructure as it is - planning for future scenarios of development could include recommendations for areas of grid infrastructure improvement to allow siting of distributed generation in locations preferred by the community. Future scenarios may also include the addition energy storage and other "non-wires alternatives."

14 SOLAR ENERGY↗

Ornamental origins and genomic frontiers: a review of big-bracted dogwood research

The big-bracted (Benthamidia) dogwood clade consists of small- to medium-sized deciduous trees within the genus Cornus, known for their showy spring-time floral bract display. Cornus is within the family Cornaceae and order Cornales, and as Cornales is one of the earliest diverging asterids, these taxa have been important for phylogenetic research. Three species within the big-bracted clade, flowering (Cornus florida), kousa (C. kousa), and Pacific (C. nuttallii) dogwoods, are popular ornamental landscape plants in North America, with more than 130 cultivars released. Despite their commercial popularity, numerous research gaps have limited the expansion of fundamental research and dogwood breeding programs. In this present review, we aim to provide a thorough overview of our current understanding of 1) the phylogenetic and biogeographic context, 2) plant biology and major pests and pathogens impacting commercialization, 3) historical commercialization and propagation methods, and 4) genetic and genomic resources and how they have been implemented to understand these species. Research gaps and future directions to advance basic research and breeding of big-bracted ornamental dogwoods are discussed throughout.

Cornus florida↗

Learning with Adaptive Conservativeness for Distributionally Robust Optimization: Incentive Design for Voltage Regulation

Information asymmetry between the Distribution System Operator (DSO) and Distributed Energy Resource Aggregators (DERAs) obstructs designing effective incentives for voltage regulation. To capture this effect, we employ a Stackelberg game-theoretic framework, where the DSO seeks to overcome the information asymmetry and refine its incentive strategies by learning from DERA behavior over multiple iterations. We introduce a model-based online learning algorithm for the DSO, aimed at inferring the relationship between incentives and DERA responses. Given the uncertain nature of these responses, we also propose a distributionally robust incentive design model to control the probability of voltage regulation failure and then reformulate it into a convex problem. This model allows the DSO to periodically revise distribution assumptions on uncertain parameters in the decision model of the DERA. Finally, we present a gradient-based method that permits the DSO to adaptively modify its conservativeness level, measured by the size of a Wasserstein metric-based ambiguity set, according to historical voltage regulation performance. The effectiveness of our proposed method is demonstrated through numerical experiments.

adaptation models↗

Learning with Adaptive Conservativeness for Distributionally Robust Optimization: Incentive Design for Voltage Regulation: Preprint

Information asymmetry between the Distribution System Operator (DSO) and Distributed Energy Resource Aggregators (DERAs) obstructs designing effective incentives for voltage regulation. To capture this effect, we employ a Stackelberg game-theoretic framework, where the DSO seeks to overcome the information asymmetry and refine its incentive strategies by learning from DERA behavior over multiple iterations. We introduce a model-based online learning algorithm for the DSO, aimed at inferring the relationship between incentives and DERA responses. Given the uncertain nature of these responses, we also propose a distributionally robust incentive design model to control the probability of voltage regulation failure and then reformulate it into a convex problem. This model allows the DSO to periodically revise distribution assumptions on uncertain parameters in the decision model of the DERA. Finally, we present a gradient-based method that permits the DSO to adaptively modify its conservativeness level, measured by the size of a Wasserstein metric-based ambiguity set, according to historical voltage regulation performance. The effectiveness of our proposed method is demonstrated through numerical experiments.

distribution system operator↗

Williston Basin Resource Study for Commercial-Scale Subsurface Hydrogen Storage

The Energy & Environmental Research Center (EERC), in partnership with the U.S. Department of Energy (DOE) National Energy Technology Laboratory (NETL), the EERC’s State Energy Research Center (SERC), MPLX Operations LLC, and TC Energy Development Holdings Inc. (a subsidiary of TC Energy Corporation), studied the potential for subsurface hydrogen storage and recovery in the Williston Basin of western North Dakota. The project’s goal was to evaluate the feasibility of large-scale, secure geologic H 2 storage to support future hydrogen generation, storage, and use. This work included laboratory testing, H 2 –rock–fluid exposure experiments, literature reviews on H 2 embrittlement, and reservoir modeling and simulations. The study included an assessment of storage potential across three types of storage reservoirs using both reservoir simulation and DOE’s web-based tool SHASTA-HELP (Subsurface Hydrogen Assessment, Storage, and Technology Acceleration – Hydrogen Estimator for Logistical Planning), as well as investigation of potential H 2 production and markets for commercial-scale deployment. Building on prior EERC gas storage research, three storage options were selected for detailed evaluation: the Broom Creek Formation (a clastic saline reservoir), the Dickinson Lodgepole Mounds (DLM) complex (carbonate mud mound structures) of the Lodgepole Formation (an active oil and gas producing reservoir), and the Dunham Salt Interval of the Piper Formation (to be used for engineered salt cavern development). These targets were prioritized based on prior EERC research using datasets related to seal capacity, reservoir quality, mechanical integrity, and injectivity. Exposure tests on Broom Creek and DLM samples showed mineral dissolution and precipitation that increased brine salinity and altered reservoir rock surfaces. Although these results provide useful insight, they are limited by small sample sizes and short-term (30-day) exposure, requiring further study to assess long-term storage integrity. Salt formations were not tested because of their known nonreactivity and established mechanical stability. Results of reservoir simulations performed for a single site demonstrated that the Broom Creek Formation may be capable of receiving up to 42,000 tonnes of injected H 2 over 7 months via one well. H 2 recovery took place over 5 months, resulting in approximately 26,000 tonnes (~62% without cushion gas [CG]). This work suggests water production may be important and subsequent cycles of injection and production may perform more efficiently; however, significant site-specific work in the future is needed to assess actual reservoir performance of injection and withdrawal of H 2 storage. For oil reservoir potential, a multiple-well model was used to simulate injection of approximately 32,000 tonnes of H 2 into a single wellbore while simultaneously producing in place reservoir fluids from four offset wells to maintain reservoir pressure. The simulation results suggested a high recovery (~98%); in addition, cost advantages through existing infrastructure could be realized. Challenges in this reservoir include vi managing gas purity and leakage risks. In both scenarios, production of H 2 takes place in a single-well scenario with 10 cycles (7 months of injection and 5 months of production) over 10 years. Finally, the use of engineered caverns in the Dunhan Salt was evaluated, and the results suggest that while they have a smaller capacity (<1000 tonnes per cavern), they exhibit nearly complete gas recovery (>99%), fast response times, and low purity risk. While caverns in North Dakota may be smaller in capacity, fields can be developed in galleries to accommodate the volumetric needs and rapid turnaround times necessary to meet market demands. Geographic limitations and thin salt intervals in North Dakota may represent less total storage potential than salt domes elsewhere, but significant opportunities exist to expand this market for gas storage in North Dakota. A basinwide assessment was performed to estimate a first-of-its-kind value for H 2 storage on a large scale. DOE’s SHASTA-HELP, combined with EERC simulation work, was used to perform the assessment. Estimated H 2 storage potential varied widely for each formation type. The Broom Creek saline formation was estimated to have a storage potential of approximately 1.7–90.5 million tonnes (MMt). The DLM oil reservoirs were estimated to have 0.07–0.19 MMt of capacity. Notably, each of these estimates relies on significant assumptions regarding reservoir thickness, porosity, permeability, and CG needed for operation. Much research is needed to understand the true site-specific storage resource potential of each formation. Using the Dunham Salt Interval for cavern development may result in as much as 4.79 MMt (up to 2.87 MMt working gas) of H 2 storage potential. An important note for consideration is that the values presented here need significantly more geological characterization and engineering assessments prior to gaining confidence in performance. This will be a focal point for future research and development needs. The basinwide evaluation also indicated that North Dakota has significant H 2 generation potential, with estimates up to a possible 13 MMt annually, suggesting a substantial opportunity for H 2 market development and thus the need for commercial-scale H 2 storage to facilitate growth and resilience. Key Recommendations 1. Conduct detailed site characterization (3D seismic, well logs, core sampling) to reduce geologic uncertainty. 2. Perform techno-economic analyses incorporating market, regulatory, and incentive frameworks. 3. Investigate long-term interactions among H 2 , CGs, well materials, and formations to assess risks. 4. Develop pilot- and field-scale demonstrations to validate models and establish best practices.

03 NATURAL GAS↗

Noisy Intermediate-Scale Quantum Applications on a Pathfinder System

Work performed under this one-year LDRD was concerned with estimating resource requirements for small quantum test beds that are expected to be available in the near future. This work represents a preliminary demonstration of our ability to leverage quantum hardware for solving small quantum simulation problems in areas of interest to the DOE. The algorithms enabling such studies are hybrid quantum-classical variational algorithms, in particular the widely-used variational quantum eigensolver (VQE). Employing this hybrid algorithm, in which the quantum computer complements the classical one, we implemented an end-to-end application-level toolchain that allows the user to specify a molecule of interest and compute the ground state energy using the VQE approach. We found significant limitations attributable to the classical portion of the hybrid system, including a greater than greater-than-quartic power scaling of the classical memory requirements with the system size. Current VQE approaches would require an exascale machine for solving any molecule with size greater than 150 nuclei. Our findings include several improvements that we implemented into the VQE toolchain, including a new classical optimizer that is decades old but hadn't been considered before in the VQE ecosystem. Our findings suggest limitations to variational hybrid approaches to simulation that further motivate the need for a gate-based fault-tolerant quantum processor that can implement larger problems using the fully digital quantum phase estimation algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Hydroelectric Power and Hydrogen Production Integration

Hydropower-based hydrogen production could introduce opportunities for new revenue streams for hydropower plants, including from energy storage and regeneration as well as from sale of the hydrogen product to external markets. Hydrogen-based energy storage and regeneration could also help support Idaho Power’s decarbonization goals by decreasing dependence on fossil-based peaking power plants. Additionally, integration of hydrogen production with hydropower generation could help address the issue of low dissolved oxygen river water conditions that commonly accompany hydropower plant operations by utilizing the oxygen byproduct from an electrolytic hydrogen production process as a resource for mitigation of low dissolved oxygen water conditions. Comprehensive techno-economic analysis of the hybrid hydroelectric and hydrogen energy storage system has revealed critical insights into the pathways and considerations for optimizing the economic value and environmental benefits of such systems. Among the three identified pathways of natural gas blending, regeneration, and direct sale of hydrogen, the direct sale of hydrogen emerges as the most profitable, particularly given the current pricing dynamics of hydrogen and electricity. However, as we anticipate a future grid characterized by higher renewable energy penetration, the landscape may evolve, featuring lower average electricity prices, heightened fluctuations, and more significant seasonal variations. Consequently, the attractiveness of electricity regeneration through hydrogen and the benefits of long-duration hydrogen storage are expected to increase substantially in such a dynamic energy scenario. The careful selection of component sizes within the hybrid system proves to be paramount for ensuring cost-effectiveness. Notably, the size of the hydrogen market has emerged as a critical determinant for the optimal size of the electrolyzer. Hydrogen storage should be sized to meet energy shifting requirements. The appropriate size of the fuel cell/microturbine generator hinges on the shape of electricity prices and the available revenue streams derived from participating in grid services. Striking the right balance among these components is essential for maximizing the overall efficiency and profitability of the hydrogen facility. Furthermore, the by-product of electrolysis, namely oxygen, introduces an additional dimension to the system's functionality. The oxygen generated can be effectively utilized for dissolved oxygen (DO) mitigation, particularly with larger electrolyzer sizes capable of satisfying the complete oxygen demand for this purpose. While the economic benefits derived from saved oxygen purchase costs may be relatively modest compared to other revenue streams, the environmental advantages of repurposing oxygen for DO mitigation could help Idaho Power meet their environmental obligations. In addition to the identified factors shaping the viability of hybrid hydrogen production and hydroelectric generation, it is noteworthy that the integration of hydrogen energy storage offers a unique advantage during unusually wet years. In such periods of increased water inflow, the hydrogen storage capacity serves as a valuable supplement to the reservoir. By utilizing hydrogen energy storage as a complementary reservoir, the system gains flexibility in reservoir management when dealing with fluctuations in water availability.

08 HYDROGEN↗

Comparative genomic and transcriptomic analyses of trans-kingdom pathogen Fusarium solani species complex reveal degrees of compartmentalization

Background: The Fusarium solani species complex (FSSC) comprises fungal pathogens responsible for mortality in a diverse range of animals and plants, but their genome diversity and transcriptome responses in animal pathogenicity remain to be elucidated. We sequenced, assembled and annotated six chromosome-level FSSC clade 3 genomes of aquatic animal and plant host origins. We established a pathosystem and investigated the expression data of F. falciforme and F. keratoplasticum in Chinese softshell turtle (Pelodiscus sinensis) host. Results: Comparative analyses between the FSSC genomes revealed a spectrum of conservation patterns in chromosomes categorised into three compartments: core, fast-core (FC), and lineage-specific (LS). LS chromosomes contribute to variations in genomes size, with up to 42.2% of variations between F. vanettenii strains. Each chromosome compartment varied in structural architectures, with FC and LS chromosomes contain higher proportions of repetitive elements with genes enriched in functions related to pathogenicity and niche expansion. We identified differences in both selection in the coding sequences and DNA methylation levels between genome features and chromosome compartments which suggest a multi-speed evolution that can be traced back to the last common ancestor of Fusarium. We further demonstrated that F. falciforme and F. keratoplasticum are opportunistic pathogens by inoculating P. sinensis eggs and identified differentially expressed genes also associated with plant pathogenicity. These included the most upregulated genes encoding the CFEM (Common in Fungal Extracellular Membrane) domain. Conclusions: The high-quality genome assemblies provided new insights into the evolution of FSSC chromosomes, which also serve as a resource for studies of fungal genome evolution and pathogenesis. This study also establishes an animal model for fungal pathogens of trans-kingdom hosts.

59 BASIC BIOLOGICAL SCIENCES↗

Analyses of transcriptomes and the first complete genome of Leucocalocybe mongolica provide new insights into phylogenetic relationships and conservation

In this study, we report a de novo assembly of the first high-quality genome for a wild mushroom species Leucocalocybe mongolica (LM). We performed high-throughput transcriptome sequencing to analyze the genetic basis for the life history of LM. Our results show that the genome size of LM is 46.0 Mb, including 26 contigs with a contig N50 size of 3.6 Mb. In total, we predicted 11,599 protein-coding genes, of which 65.7% (7630) could be aligned with high confidence to annotated homologous genes in other species. We performed phylogenetic analyses using genes form 3269 single-copy gene families and showed support for distinguishing LM from the genus Tricholoma (L.) P.Kumm., in which it is sometimes circumscribed. We believe that one reason for limited wild occurrences of LM may be the loss of key metabolic genes, especially carbohydrate-active enzymes (CAZymes), based on comparisons with other closely related species. The results of our transcriptome analyses between vegetative (mycelia) and reproductive (fruiting bodies) organs indicated that changes in gene expression among some key CAZyme genes may help to determine the switch from asexual to sexual reproduction. Taken together, our genomic and transcriptome data for LM comprise a valuable resource for both understanding the evolutionary and life history of this species.

54 ENVIRONMENTAL SCIENCES↗

Evaluation of Methods for Causal Discovery in Hydrometeorological Systems

Understanding causal relations is of utmost importance in hydrology and climate research for systems identification, prediction, and understanding systems behavior in a changing climate. Traditionally, researchers in hydrometeorology attempted to study causal questions by conducting controlled experiments using numerical models. This approach, however, in most cases of interest provides uncertain results because the models are approximate representation of the natural system. An alternative approach that has recently drawn significant attention in several fields is to infer causal relations from purely observational data. It possesses several traits to its utility particularly in hydrometeorology due to the rapid accumulation of in situ and remotely sensed data records. The first objective of this study is to present a brief description of four causal discovery methods (Granger causality, Transfer Entropy, graph-based algorithms, and Convergent Cross Mapping) with special emphasis on the assumptions on which they are built. Second, using synthetic data generated from a hydrological model, we assess their performance in retrieving causal information taking into account sensitivity to sample size and presence of noise. Last, we use causal analysis to examine and formulate hypotheses on causal drivers of evapotranspiration in a shrubland region during summer and winter seasons. An interpretation of the hypotheses based on canopy seasonal dynamics and evapotranspiration processes is presented. It is hoped that the results presented here can be useful in guiding researchers studying hydrometeorological systems as to which causal method is most appropriate to the characteristics of the system under study.

54 ENVIRONMENTAL SCIENCES↗