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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Crystalline Structure-Dependent Mechanical and Thermoelectric Performance in Ag 2 Se 1–x S x System

Self-powered wearable electronics require thermoelectric materials simultaneously with a high dimensionless figure of merit (zT) and good flexibility to convert the heat discharged by the human body into electricity. Ag 2 (S,Se)-based semiconducting materials can well satisfy these requirements, and thus, they are attracting great attention in thermoelectric society recently. Ag 2 (S,Se) crystalizes in an orthorhombic structure or monoclinic structure, depending on the detailed S/Se atomic ratio, but the relationship between its crystalline structure and mechanical/thermoelectric performance is still unclear to date. In this study, a series of Ag 2 Se 1–x S x (x = 0, 0.1, 0.2, 0.3, 0.4, and 0.45) samples were prepared and their mechanical and thermoelectric performance dependence on the crystalline structure was systematically investigated. x = 0.3 in the Ag 2 Se 1–x S x system was found to be the transition boundary between orthorhombic and monoclinic structures. Mechanical property measurement shows that the orthorhombic Ag 2 Se 1–x S x samples are brittle while the monoclinic Ag 2 Se 1–x S x samples are ductile and flexible. In addition, the orthorhombic Ag 2 Se 1–x S x samples show better electrical transport performance and higher zT than the monoclinic samples under a comparable carrier concentration, most likely due to their weaker electron-phonon interactions. This study sheds light on the further development of flexible inorganic TE materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Transcription factor FOXC1 positively regulates SFRP1 expression in androgenetic alopecia

Androgenetic alopecia (AGA) is the most common type of hair loss dysfunction. Secreted frizzled related protein 1 (SFRP1) is found to be associated with hair loss, but its role in AGA and the regulation mechanism of its transcription level is unclear. The aim of our study is to explore the expression of SFRP1 in AGA samples and its transcriptional mechanism. Male frontal and occipital scalp hair follicles from AGA patients were collected, and human dermal papilla cells (DPCs) were isolated and cultured. SFRP1 gene was cloned and constructed into recombinant plasmids to perform dual-luciferase reporter assay. Transcription factor binding sites were predicted through the Jaspar website and further confirmed by the chromatin immunoprecipitation (ChIP) assay. Expression of genes in DPCs was determined by immunofluorescence (IF) staining, quantitative real-time PCR (qRT-PCR) and western blotting. Our findings showed that SFRP1 was highly expressed in DPCs of AGA patients. The core promoter region of SFRP1 was from −100 to +50 bp and was found to be positively regulated by forkhead box C1 (FOXC1), a transcription factor related to hair growth, both at mRNA and protein level in DPCs. Our study suggests that FOXC1 plays an important role in regulating SFRP1 transcription, which may provide new insights into the development of therapeutic strategies for the treatment of AGA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Streaming Large-Scale Microscopy Data to a Supercomputing Facility

Data management is a critical component of modern experimental workflows. As data generation rates increase, transferring data from acquisition servers to processing servers via conventional file-based methods is becoming increasingly impractical. The 4D Camera at the National Center for Electron Microscopy generates data at a nominal rate of 480 Gbit s -1 (87,000 frames s -1 ⁠), producing a 700 GB dataset in 15 s. To address the challenges associated with storing and processing such quantities of data, we developed a streaming workflow that utilizes a high-speed network to connect the 4D Camera’s data acquisition system to supercomputing nodes at the National Energy Research Scientific Computing Center, bypassing intermediate file storage entirely. In this work, we demonstrate the effectiveness of our streaming pipeline in a production setting through an hour-long experiment that generated over 10 TB of raw data, yielding high-quality datasets suitable for advanced analyses. Additionally, we compare the efficacy of this streaming workflow against the conventional file-transfer workflow by conducting a postmortem analysis on historical data from experiments performed by real users. Our findings show that the streaming workflow significantly improves data turnaround time, enables real-time decision-making, and minimizes the potential for human error by eliminating manual user interactions.

4D-STEM↗
DOE OSTI2021

Prebiotic properties of Bacillus coagulans MA-13: production of galactoside hydrolyzing enzymes and characterization of the transglycosylation properties of a GH42 β-galactosidase

The spore-forming lactic acid bacterium Bacillus coagulans MA-13 has been isolated from canned beans manufacturing and successfully employed for the sustainable production of lactic acid from lignocellulosic biomass. Among lactic acid bacteria, B. coagulans strains are generally recognized as safe (GRAS) for human consumption. Low-cost microbial production of industrially valuable products such as lactic acid and various enzymes devoted to the hydrolysis of oligosaccharides and lactose, is of great importance to the food industry. Specifically, α- and β-galactosidases are attractive for their ability to hydrolyze not-digestible galactosides present in the food matrix as well as in the human gastrointestinal tract. In this work we have explored the potential of B. coagulans MA-13 as a source of metabolites and enzymes to improve the digestibility and the nutritional value of food. A combination of mass spectrometry analysis with conventional biochemical approaches has been employed to unveil the intra- and extra- cellular glycosyl hydrolase (GH) repertoire of B. coagulans MA-13 under diverse growth conditions. The highest enzymatic activity was detected on β-1,4 and α-1,6-glycosidic linkages and the enzymes responsible for these activities were unambiguously identified as β-galactosidase (GH42) and α-galactosidase (GH36), respectively. Whilst the former has been found only in the cytosol, the latter is localized also extracellularly. The export of this enzyme may occur through a not yet identified secretion mechanism, since a typical signal peptide is missing in the α-galactosidase sequence. A full biochemical characterization of the recombinant β-galactosidase has been carried out and the ability of this enzyme to perform homo- and hetero-condensation reactions to produce galacto-oligosaccharides, has been demonstrated. Probiotics which are safe for human use and are capable of producing high levels of both α-galactosidase and β-galactosidase are of great importance to the food industry. In this work we have proven the ability of B. coagulans MA-13 to over-produce these two enzymes thus paving the way for its potential use in treatment of gastrointestinal diseases.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Rooftop unit comparison calculator: a framework for comparing performance of rooftop units with building energy simulation

The applications of building energy simulation (BES) in designing heating, ventilation, and air conditioning (HVAC) systems are limited by the high costs of developing simulation models and the lack of references for determining the model parameters. This paper presents a software framework for selecting designs for rooftop unit HVAC (RTU) systems with BES. Specifically, this framework reduces the cost of using BES by automating the generation of EnergyPlus models. It also employs a systematic method for determining model parameters based on well-accepted datasets. We applied this framework in a comprehensive assessment of an advanced design of RTU systems in which 478 EnergyPlus models were developed without human involvement. The assessment reveals that replacing a constant-speed fan/coil with a multiple-speed fan/coil may not guarantee better overall performance. In conclusion, it also suggests the benefits of replacing furnace coils with heat pumps are subject to utility cost, weather conditions, and heating load profiles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Discovery of multi-functional polyimides through high-throughput screening using explainable machine learning

Polyimides have been widely used in modern industries because of their excellent mechanical and thermal properties, e.g., high-temperature fuel cells, displays, and aerospace composites. However, it usually takes decades of experimental efforts to develop a successful product. Aiming to expedite the discovery of high-performance polyimides, we utilize computational methods of machine learning (ML) and molecular dynamics (MD) simulations. Our study provides compelling evidence for the effectiveness of a data-driven approach in discovering novel polyimides. We first build a comprehensive library of more than 8 million hypothetical polyimides based on the polycondensation of existing dianhydride and diamine/diisocyanate molecules. Then we establish multiple ML models for the thermal and mechanical properties of polyimides based on their experimentally reported values, including glass transition temperature, Young’s modulus, and tensile yield strength. The obtained ML models demonstrate excellent predictive performance in identifying the key chemical substructures influencing the thermal and mechanical properties of polyimides. The use of explainable machine learning describes the effect of chemical substructures on individual properties, from which human experts can understand the cause of the ML model decision. Applying the well-trained ML models, we obtain property predictions of the 8 million hypothetical polyimides. Then, we screen the whole hypothetical dataset and identify three (3) best-performing novel polyimides that have better-combined properties than existing ones through Pareto frontier analysis. For an easy query of the discovered high-performing polyimides, we also create an online platform https://polyimide-explorer.herokuapp.com/ that embeds the developed ML model with interactive visualization. Furthermore, we validate the ML predictions through all-atom MD simulations and examine their synthesizability. The MD simulations are in good agreement with the ML predictions and the three novel polyimides are predicted to be easy to synthesize via Schuffenhauer’s synthetic accessibility score. Following the proposed ML guidance, we successfully synthesized a novel polyimide and the experimentally obtained high glass transition/thermal decomposition temperature demonstrated its excellent thermal stability. Here our study demonstrates an efficient way to expedite the discovery of novel polymers using ML prediction and MD validation. The high-throughput screening of a large computational dataset can serve as a general approach for new material discovery in other polymeric material exploration problems, such as organic photovoltaics, polymer membranes, and dielectrics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A deep learning model for automatic analysis of cavities in irradiated materials

Transmission electron microscopy (TEM) is a commonly used technique in materials science for defect investigation. Quantitative analysis of defects is important for understanding the properties of a material, but manual analysis of TEM micrographs can be time-consuming and prone to error, especially when the defects have irregular shapes rather than spherical shapes. Many existing methods or deep learning models do not handle a wide range of sizes for the same object type within a single image. In this work, we present a framework that enables users to train an instance segmentation model called Mask R- CNN on any microstructure dataset, perform multi-detection on the same image at different scales, and obtain properties (e.g., size, area) of the objects based on the desired shape (e.g., circle, ellipse, rectangle). Additionally, we have developed a parallel detection module that uses multiple GPUs to increase the efficiency of the object detection process. We demonstrate the capabilities of our framework using a set of TEM images of cavities with different shapes, size distributions, and background contrasts. Finally, we show that the performance of our model in terms of density, size, and swelling of the cavities is comparable to the human average and that our model achieves the highest recall value compared to existing methods due to the use of image multi-rescaling.

36 MATERIALS SCIENCE↗
DOE OSTI2022

What, why and when to go virtual: An international analysis of early adopters of virtual building energy codes inspections

To meet greenhouse gas reduction targets, several countries are pursuing more ambitious policies in their buildings and construction sectors, such as introducing zero net energy/carbon building codes. Countries often report not having enough qualified staff for performing building energy code inspections and many are exploring faster, easier, and more reliable methods to check the compliance of buildings with their codes. Building inspections are a critical element for ensuring code compliance and they have traditionally been performed in person. However, in-person inspections can be labor and travel intensive, costly, and prone to human error. In this paper, the authors explore how virtual inspections, particularly in light of the recent COVID-19 pandemic, have impacted processes for building code compliance checks in jurisdictions and communities around the world. Here, the authors collected data on four key parameters (time and financial savings, scope of inspections, changing practices and technological innovation, and benefits to consumers) from six jurisdictions and communities in five countries (Australia, Canada, Singapore, United Arab Emirates, and the United States) to analyze the impacts of virtual inspections on code compliance checks. The analysis found the greatest value from virtual inspections in geographically dispersed regions and for cities experiencing rapid building construction. The study also explored emerging technologies that are being piloted for virtual inspections. Although many of these technologies hold promise, more resources and capacity are needed to make them viable for use in building energy code inspections.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

In crystallo screening for proline analog inhibitors of the proline cycle enzyme PYCR1

Pyrroline-5-carboxylate reductase 1 (PYCR1) catalyzes the biosynthetic half-reaction of the proline cycle by reducing Δ 1 -pyrroline-5-carboxylate (P5C) to proline through the oxidation of NAD(P)H. Many cancers alter their proline metabolism by up-regulating the proline cycle and proline biosynthesis, and knockdowns of PYCR1 lead to decreased cell proliferation. Thus, evidence is growing for PYCR1 as a potential cancer therapy target. Inhibitors of cancer targets are useful as chemical probes for studying cancer mechanisms and starting compounds for drug discovery; however, there is a notable lack of validated inhibitors for PYCR1. To fill this gap, we performed a small-scale focused screen of proline analogs using X-ray crystallography. Five inhibitors of human PYCR1 were discovered: l-tetrahydro-2-furoic acid, cyclopentanecarboxylate, l-thiazolidine-4-carboxylate, l-thiazolidine-2-carboxylate, and N-formyl l-proline (NFLP). The most potent inhibitor was NFLP, which had a competitive (with P5C) inhibition constant of 100 μm. The structure of PYCR1 complexed with NFLP shows that inhibitor binding is accompanied by conformational changes in the active site, including the translation of an α-helix by 1 Å. These changes are unique to NFLP and enable additional hydrogen bonds with the enzyme. NFLP was also shown to phenocopy the PYCR1 knockdown in MCF10A H-RAS V12 breast cancer cells by inhibiting de novo proline biosynthesis and impairing spheroidal growth. In summary, we generated the first validated chemical probe of PYCR1 and demonstrated proof-of-concept for screening proline analogs to discover inhibitors of the proline cycle.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

The 1.3 Å resolution structure of the truncated group Ia type IV pilin from Pseudomonas aeruginosa strain P1

The type IV pilus is a diverse molecular machine capable of conferring a variety of functions and is produced by a wide range of bacterial species. The ability of the pilus to perform host-cell adherence makes it a viable target for the development of vaccines against infection by human pathogens such as Pseudomonas aeruginosa . Here, the 1.3 Å resolution crystal structure of the N-terminally truncated type IV pilin from P. aeruginosa strain P1 (ΔP1) is reported, the first structure of its phylogenetically linked group (group I) to be discussed in the literature. The structure was solved from X-ray diffraction data that were collected 20 years ago with a molecular-replacement search model generated using AlphaFold ; the effectiveness of other search models was analyzed. Examination of the high-resolution ΔP1 structure revealed a solvent network that aids in maintaining the fold of the protein. On comparing the sequence and structure of P1 with a variety of type IV pilins, it was observed that there are cases of higher structural similarities between the phylogenetic groups of P. aeruginosa than there are between the same phylogenetic group, indicating that a structural grouping of pilins may be necessary in developing antivirulence drugs and vaccines. These analyses also identified the α–β loop as the most structurally diverse domain of the pilins, which could allow it to serve a role in pilus recognition. Studies of ΔP1 in vitro polymerization demonstrate that the optimal hydrophobic catalyst for the oligomerization of the pilus from strain K122 is not conducive for pilus formation of ΔP1; a model of a three-start helical assembly using the ΔP1 structure indicates that the α–β loop and the D-loop prevent in vitro polymerization.

Bragagnolo, Nicholas↗
DOE OSTI2024

A Behavior Tree Approach for Battery-Aware Inspection of Large Structures Using Drones

Electric multi-rotor drones have been used to inspect several structures, including large buildings and dams. In these inspections, energy consumption is a concern. To prevent the drone from running out of battery, commercial drones usually come back to their home position when the battery level reaches a minimum threshold. The pilots then need to replace the battery and use their own experience to restart the inspection mission approximately from where it ended before the drone returned home. Instead of relying on the human operator, in this paper, we automate this process using behavior trees, which is an effective way to perform autonomous mission control and supervision. By integrating battery management strategies into a behavior tree framework, this paper demonstrates the drone’s adaptive and resilient decision-making when confronted with limited power constraints. We implemented our methodology using a commercial drone and tested the proposed ideas in a photogrammetry-based inspection task.

42 ENGINEERING↗
DOE OSTI2022

Improved accuracy of S-value-based dosimetry: a guide to transition from Cristy–Eckerman to ICRP adult phantoms

Background: In 2016, the International Commission on Radiological Protection (ICRP) published the results of Monte Carlo simulations performed using updated and anatomically realistic voxelized phantoms. The resulting specific absorbed fractions are based on more realistic human anatomy than those computed in the stylized, geometrical Cristy–Eckerman (CE) phantom. Despite this development, the ICRP-absorbed fractions have not been widely adopted for radiopharmaceutical dosimetry. To help make the transition, we have established a correspondence between source and target tissues defined in the CE phantom and those defined in the ICRP phantoms. Results: The ICRP phantom has 79 source regions and 43 target regions in comparison with the 23 source and 18 target tissue regions defined in the CE phantom. The ICRP phantom provides tissue regions with greater anatomical detail. Some of this additional detail is focused on radiation protection and dosimetry of inhaled/ingested radioactivity. Some, but not all, of this detail is useful and appropriate for radiopharmaceutical therapy. We have established the correspondence between CE and ICRP phantom source and target regions and attempted to highlight the ICRP source tissues relevant to radiopharmaceutical therapy (RPT). This paper provides tables and figures highlighting the correspondences established. Conclusion: The results provide assistance in transitioning from CE-stylized phantoms to the anatomically accurate voxelized ICRP phantoms. It provides specific guidance for porting the total absorbed activity for regions as defined in the CE phantom to regions within the ICRP phantoms.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

Automated, Unmanned Detection and Quantification of Fugitive Methane Emissions

Compressor stations used to move natural gas are one of the largest sources of fugitive methane emissions in the midstream sector, accounting for approximately 50% of all fugitive emissions (Zimmerle et al., 2015). This problem is most widespread at reciprocating compressors (Subramanian et al., 2015) where faulty seals are a key contributor to methane emissions (Johnson et al., 2015). As such, there is a significant need for a robust technology that could provide an early indication of an unexpected emission. Equally important, the technology needs to be able to account for biogenic versus anthropogenic sources of methane. One means of indirectly making this determination, is to leverage optical technologies that can autonomously pinpoint the source of such leaks. This presentation discusses recent work funded by the U.S. Department of Energy (DOE) National Energy Technology Laboratory (NETL), focused on the development of an innovative remote sensing technology that can reliably and autonomously detect fugitive methane emissions in near real-time, using computer vision and deep learning. The technology called the Smart Methane Leak Detection (SLED/M) system was initially developed to monitor facilities such as compressor stations in a stationary, pan-tilt-zoom configuration. The system has recently been adapted to monitor facilities from an unmanned aerial system (UAS). The speed and maneuverability of UAS platforms are attractive to leak detection and repair program operators, but introduce several challenges. Many existing methane detection algorithms rely on mostly static backgrounds becoming unusable with motion. In addition, top-down views of fugitive methane emissions present differently in Optical Gas Imagers (OGI) compared to looking across the plume. Our work has focused on overcoming these challenges, enhancing the operators ability to detect methane emissions, and pinpoint their sources. Another recent adaptation to SLED/M is the ability to quantify methane emissions using passive sensors (OGI, thermal camera), environmental conditions, plume modeling, and deep learning. SLED/M advances the state-of-the-art for methane emission detection and quantification by focusing on three key critical criteria for effective methane emission mitigation: (1) autonomy (no need for a human to be in the loop), (2) high reliability (low false alarm rates), and (3) real-time performance. Results from this work will be presented.

03 NATURAL GAS↗
DOE OSTI2025

Applications of Nickelate perovskites for neuromorphic computing from electronic structure and Machine Learning

While the limit of Moore's law is presently being reached with current microelectronic technologies, we need to develop new paradigms that overcome this limitation. In that respect, neuromorphic computing is a concept that emulates the neural behavior and response of the human brain, and it has been recognized as a promising alternative approach. In this research project, we will perform multi-fidelity scale bridging to explore the potential use of materials with metal to insulator transition for neuromorphic applications. In particular, rare earth nickelates are promising for such purposes, as the transition in these materials is quite sensitive to a broad set of different external stimuli. Our multi-fidelity approach will bridge the high-fidelity electronic structure calculations with classical potentials. We will bridge dynamical mean field theory with a classical atomistic representation via a deep learning force field. The neural network is trained with energies, charges, and forces obtained by accurate electronic structure theories based on Dynamical Mean Field Theory. The configurational space is generated from known crystal phases, ab initio molecular dynamics with exchange-correlation functionals corrected with the Hubbard model, disordered phases with different concentrations of oxygen vacancies, and nonsymmetrical positions and induced strain by grain interfaces or contact with a substrate. Strategies to train the model with a reduced number of training examples are obtained from active learning methods, and new structures for improving the learning process are generated by using machine learning autoencoders. This classical potential will be validated through a diversity of electronic structure methods and represents an important step to combine the flexibility and accuracy of first-principles with the speed of classical potentials. The generated multi-fidelity surrogate model will be used to understand the role of strain, oxygen vacancies, proton doping, the variation of the crystal phase, substrate effects, vibrational effects as the octahedral rotation, grain boundaries and defect effects on the response of a Metal to Insulator Transition (MIT) in correlated materials. Long time and large-scale simulations will help understand the role of different stimuli to control the hysteresis of the MIT, as it has been experimentally suggested. Selected configurations will be analyzed with higher-level theories to provide an accurate electronic description and to study how the orbitals and charges are rearranged under different conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Molecular basis for catabolism of the abundant metabolite trans-4-hydroxy-L-proline by a microbial glycyl radical enzyme

The glycyl radical enzyme (GRE) superfamily utilizes a glycyl radical cofactor to catalyze difficult chemical reactions in a variety of anaerobic microbial metabolic pathways. Recently, a GRE, trans-4-hydroxy-L-proline (Hyp) dehydratase (HypD), was discovered that catalyzes the dehydration of Hyp to (S)-Δ 1 -pyrroline-5-carboxylic acid (P5C). This enzyme is abundant in the human gut microbiome and also present in prominent bacterial pathogens. However, we lack an understanding of how HypD performs its unusual chemistry. Here, we have solved the crystal structure of HypD from the pathogen Clostridioides difficile with Hyp bound in the active site. Biochemical studies have led to the identification of key catalytic residues and have provided insight into the radical mechanism of Hyp dehydration.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Machine Learning Classification Strategy to Improve Streamflow Estimates in Diverse River Basins in the Colorado River Basin

Streamflow in the Colorado River Basin (CRB) is significantly altered by human activities including land use/cover alterations, reservoir operation, irrigation, and water exports. Climate is also highly varied across the CRB which contains snowpack-dominated watersheds and arid, precipitation-dominated basins. Recently, machine learning methods have improved the generalizability and accuracy of streamflow models. Previous successes with LSTM modeling have primarily focused on unimpacted basins, and few studies have included human impacted systems in either regional or single-basin modeling. We demonstrate that the diverse hydrological behavior of river basins in the CRB are too difficult to model with a single, regional model. We propose a method to delineate catchments into categories based on the level of predictability, hydrological characteristics, and the level of human influence. Lastly, we model streamflow in each category with climate and anthropogenic proxy data sets and use feature importance methods to assess whether model performance improves with additional relevant data. Overall, land use cover data at a low temporal resolution was not sufficient to capture the irregular patterns of reservoir releases, demonstrating the importance of having high-resolution reservoir release data sets at a global scale. On the other hand, the classification approach reduced the complexity of the data and has the potential to improve streamflow forecasts in human-altered regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Blind photovoltaic modeling intercomparison: A multidimensional data analysis and lessons learned

The Photovoltaic (PV) Performance Modeling Collaborative (PVPMC) organized a blind PV performance modeling intercomparison to allow PV modelers to blindly test their models and modeling ability against real system data. Measured weather and irradiance data were provided along with detailed descriptions of PV systems from two locations (Albuquerque, New Mexico, USA, and Roskilde, Denmark). Participants were asked to simulate the plane-of-array irradiance, module temperature, and DC power output from six systems and submit their results to Sandia for processing. The results showed overall median mean bias (i.e., the average error per participant) of 0.6% in annual irradiation and –3.3% in annual energy yield. While most PV performance modeling results seem to exhibit higher precision and accuracy as compared to an earlier blind PV modeling study in 2010, human errors, modeling skills, and derates were found to still cause significant errors in the estimates.

14 SOLAR ENERGY↗
DOE OSTI2024

Modeling Framework for the Assessment of a Sustainable Hydrogen Production and Supply Chain Network in California

The cost-effective and sustainable deployment of hydrogen supply and demand networks, especially in large economic regions like California, can be challenging considering the spatial-temporal availability and variability of the different actors across the network such as production processes, distribution modes, and end-users. In this presentation, we will provide an overview and demonstration of a modeling framework used to assess the environmental, economic, and human health impacts of plausible hydrogen production and supply chain networks in California. Scenarios focus on green hydrogen production pathways using water electrolysis and biomass gasification. End-use applications included in the model are transit, medium and heavy-duty trucking, port authorities, and power and aviation companies that currently consume natural gas, diesel, and aviation fuel for their day-to-day operation. Representative locations for hydrogen production and end-use are based on recent projections of the hydrogen economy in California. All mass and energy flows, as well as estimated emissions, are based on H2A process model designs and projections of technology performance, literature review, and LBNL process, economic and life cycle modeling, and not on company data for the sake of this presentation. Human health impacts are included following methodologies developed for the University of California Irvine HyDeal project. Life cycle phases associated with hydrogen production include feedstock preparation (water and biomass), energy production and consumption (renewable, grid, and combination of renewable and grid electricity), maintenance (chemical utilization in electrolysis and natural gas combustion in gasification), carbon sequestration, hydrogen storage (compression and liquefaction), and distribution (truck and pipeline). We apply the framework utilizing California specific emission factors, financial data, and human health damages and explore the impact of network characteristics on results. Example variations include: the inclusion of policy incentives or not, different representations of the electricity grid and source, electrolysis versus gasification versus combinations of both for production, liquefaction versus compression based on producer capacity cutoffs, transportation truck versus pipeline based on existing infrastructure, and ultimate end use. Comparison of these different scenarios can help inform future projects by demonstrating the trade-offs among environmental, economic, and human health impacts. This model, automated in R, is a starting platform upon which new analysis, modeling capabilities, locations, and emission factors can be rapidly tested and integrated.

Zaki, Mohammed Tamim↗