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DOE OSTI2023

Designing shape-memory-like microstructures in intercalation materials

During the reversible insertion of ions, lattices in intercalation materials undergo structural transformations. These lattice transformations generate misfit strains and volume changes that, in turn, contribute to the structural decay of intercalation materials and limit their reversible cycling. In this paper, we draw on insights from shape-memory alloys, another class of phase transformation materials, that also undergo large lattice transformations but do so with negligible macroscopic volume changes and internal stresses. We develop a theoretical framework to predict structural transformations in intercalation compounds and identify crystallographic design rules necessary for forming shape-memory-like microstructures in intercalation materials. We use our approach to systematically screen open-source structural databases comprising $n$ > 5000 pairs of intercalation compounds. We identify candidate compounds, such as Li x Mn 2 O 4 (Spinel), Li x Ti 2 (PO 4 ) 3 (NASICON), that approximately satisfy the crystallographic design rules and can be precisely doped to form shape-memory-like microstructures. Throughout, we compare our analytical results with experimental measurements of intercalation compounds. We find a direct correlation between structural transformations, microstructures, and increased capacity retention in these materials. These results, more generally, show that crystallographic designing of intercalation materials could be a novel route to discovering compounds that do not decay with continuous usage.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Environmental Impacts of Closed-Loop Pumped Storage Hydropower

The goal of this report is to help license applicants, resource agencies, and other members of the hydropower community involved in closed-loop pumped storage hydropower permitting and licensing process, focus the scope of environmental reviews, and more quickly identify impacts with project nexus and potential mitigation measures for these impacts. Pumped storage hydropower (PSH) is an energy storage technology that uses energy to pump water up from a lower reservoir to an upper reservoir where water is stored until electricity is needed and the water is released to a lower reservoir passing through turbines. Closed-loop PSH—PSH that is not continuously connected to a naturally flowing water feature—is one of the lowest greenhouse gas emitting energy storage technologies and is therefore a critical part of the transition to renewable energy (Simon et al. 2023). Proposals for closed-loop PSH facilities in the United States currently account for more than 40% of original licenses and 99% of potential generation capacity in the Federal Energy Regulatory Commission (FERC) hydropower licensing pipeline (Johnson et al. 2023). While closed-loop PSH facilities can have lower environmental impacts than open-loop PSH facilities, no closed-loop facilities have been constructed in the United States to enable direct accounting of project impacts and efficacy of mitigations. Many proposals for closed-loop PSH submitted to FERC are abandoned early in the permitting and licensing process prior to license applications and environmental assessments, so there is little documentation describing potential project impacts and proposed mitigations. The newness of closed-loop PSH proposals in the United States may mean that tribal, federal, and state agencies with authorities for cultural and natural resources protection and management involved in the FERC licensing process may not have experience with closed-loop PSH regulation. Moreover, many proposed closed-loop PSH facilities are in areas that do not have high concentrations of conventional hydropower, so these agencies may also be unfamiliar with the FERC hydropower licensing process. The goal of this report is to help license applicants, resource agencies, and other members of the hydropower community focus the scope of environmental review for the closed-loop PSH development, licensing, and federal authorization process enabling quicker identification of potential impacts, mitigations, and situations where mitigation may not be possible. We found that environmental impacts of closed-loop PSH are highly site-specific, and generalizations about the types of environmental impacts across closed-loop PSH projects are difficult to make. Environmental impacts of closed-loop PSH are like those for open-loop PSH with a few exceptions including water sourcing, which can lead to delays and contention due to potential complexities with water rights, impacts to aquatic resources, and greenhouse gas emission potential. Cultural resource impacts were commonly reported in National Environmental Policy Act (NEPA) documents reviewed and discussed in interviews, but in many cases such impacts cannot be mitigated.

13 HYDRO ENERGY↗

NASA NTRSDate not supplied

RadLab: Graphical and Programming Interfaces for Interrogation of Space Telemetry Data

Sensors on multiple spacecraft in and beyond low Earth orbit continuously monitor and collect space radiation data and transmit it back to Earth. These data are of vast importance to space biology research, as ionizing radiation affects living organisms—astronauts and non-human experiment subjects alike—placing them at higher risk of carcinogenesis, degenerative diseases, and radiation sickness. Therefore, knowledge of the biological effects of space radiation is essential for planning future crewed missions beyond low Earth orbit. The RadLab project, initiated by GeneLab and ALSDA (the Open Science Data Repository; OSDR) and sponsored by the NASA Human Research Program, is a new effort aimed at connecting dosimetry data from radiation detectors located on the International Space Station (ISS), as well as other spacecraft. To date, access to these data has been fragmented across space agencies and databases; to address this issue, we have developed an application programming interface (API) and an associated graphical user interface (GUI) designed to provide a single point of access to the data. As of now, OSDR has focused on the detectors located on the ISS, with the long-term goal to establish a self-sustained portal receiving continuous updates through APIs connecting to multiple radiation databases of varying scope, as well as individual investigator contributions. The RadLab API implements a request syntax enabling users to query data by craft, sensor type, timespan, etc, allowing for arbitrary combinations of original source data, thus providing programmatic access for use in computational pipelines, while the GUI facilitates data visualization and exploration, making these data FAIR (Findable, Accessible, Interoperable, and Reusable), complementing the biological data contained in OSDR, and providing the space science community with a valuable resource for scientific analyses.

radiation↗

DOE OSTI2023

Symmetry-mode analysis for local structure investigations using pair distribution function data

Symmetry-adapted distortion modes provide a natural way of describing distorted structures derived from higher-symmetry parent phases. Structural refinements using symmetry-mode amplitudes as fit variables have been used for at least ten years in Rietveld refinements of the average crystal structure from diffraction data; more recently, this approach has also been used for investigations of the local structure using real-space pair distribution function (PDF) data. Here, the value of performing symmetry-mode fits to PDF data is further demonstrated through the successful application of this method to two topical materials: TiSe2, where a subtle but long-range structural distortion driven by the formation of a charge-density wave is detected, and MnTe, where a large but highly localized structural distortion is characterized in terms of symmetry-lowering displacements of the Te atoms. Here, the analysis is performed using fully open-source code within the DiffPy framework via two packages developed for this work: isopydistort, which provides a scriptable interface to the ISODISTORT web application for group theoretical calculations, and isopytools, which converts the ISODISTORT output into a DiffPy-compatible format for subsequent fitting and analysis. These developments expand the potential impact of symmetry-adapted PDF analysis by enabling high-throughput analysis and removing the need for any commercial software.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Prototyping a self-learning digital twin platform for personalized treatment in melanoma patients (Final Report)

We completed all the Milestones and disseminated results at the 2021 Computational Approaches for Cancer Workshop (CAFCW21) at the SuperComputing21 conference. As part of Milestone 1, we developed and implemented a prototype digital twin of metastatic melanoma patients, performed quality checks on the code and released it as open source. The work has directly driven major grant proposals by the team on computational digital twins and supporting software infrastructure, including a successful fellowship application by the PI. Work was lead by Paul Macklin (Contact PI, Indiana University), in collaboration with Ilya Shmulevich (PI, Institute for Systems Biology), Tina Hernandez-Boussard (PI, Stanford Univ.), Jeffrey Bryan (Co-I, University of Missouri), Snigdhansu Chatterjee (Co-I, University of Minnesota), and Mohammad Fallahi-Sichani (Co-I, U. of Virginia). Postdoctoral student Heber L. Rocha (Macklin lab, IU) performed intensive computational work, in collaboration with Senior Research Scientist Boris Aguilar (Shmulevich lab, ISB).

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

PPI DataHub Project Data Package: S. elongatus PCC 7942 Circadian Control Bioproduction Transcriptomics (PB-DP3)

The purpose of this experiment was to evaluate how circadian clock regulation impacts carbon partitioning between storage, growth, and product synthesis in Synechococcus elongatus PCC 7942 in providing insights to strategies for enhanced bioproduction. Sample data was acquired using a Illumina HiSeq sequencer system and processed for RNA sequencing (RNA-Seq) expression analysis. Transcriptomic differential expression analysis revealed coordinated circadian clock-driven adjustment of the cell cycle and rewiring of energy and carbon metabolism. Processed RNA-Seq datasets are openly accessible from the PNNL DataHub project dataset download page and contain secondary processed RNA-seq results files and supporting metadata materials linked to relevant source code information supporting data transparency and reuse.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Toward achieving cost-effective hexagonal BN semi-bulk crystals and BN neutron detectors via halide vapor phase epitaxy

Presently, thermal neutron detectors fabricated from boron-10 enriched hexagonal boron nitride (h-10BN) ultrawide bandgap semiconductor grown by metal organic chemical vapor deposition (MOCVD) hold the record high detection efficiency among all solid-state detectors at 59%. To overcome the short comings of MOCVD growth, including inherently low growth rate and unavoidable impurities such as carbon in metal organic source, we demonstrate here the growth of natural hexagonal boron nitride (h-BN) semi-bulk wafers using halide vapor phase epitaxy (HVPE), which is an established technique for producing GaN semi-bulk crystals at a high growth rate. Electrical transport characterization results revealed that these HVPE grown materials possess an electrical resistivity of 1 × 1013 Ω cm, and a charge carrier mobility and lifetime product of 2 × 10−4 cm2/V s. Detectors fabricated from a 100 μm thick h-BN wafer have demonstrated a thermal neutron detection efficiency of 20%, corresponding to a charge collection efficiency of ∼60% at an operating voltage of 500 V. This initial demonstration opens the door for mass producing high efficiency h-BN semiconductor neutron detectors at a reduced cost, which could create unprecedented applications in nuclear energy, national security, nuclear waste monitoring and management, the health care industry, and material sciences.

Physics↗

DOE OSTI2020

Enabling Catalog Simulations of Transient and Variable Sources Based on LSST Cadence Strategies

The American Astronomical Society. All rights reserved.. The Large Synoptic Survey Telescope (LSST) project will conduct a 10 year multi-band survey starting in 2022. Observing strategies for this survey are being actively investigated, and the science capabilities can be best forecasted on the basis of simulated strategies from the LSST Operations Simulator (OpSim). This paper describes the way in which OpSim simulates a stochastic realization of the sequence of LSST pointings over the survey duration, and is based on a model of the observatory (including telescope) and historical data of observational conditions. OpSim outputs contain a record of each simulated pointing of the survey along with a complete characterization of the pointing in terms of observing conditions, and some useful quantities derived from the characteristics of the pointing. Thus, each record can be efficiently used to derive the properties of observations of all astrophysical sources found in that pointing. However, in order to obtain the time series of observations (light curves) of a set of sources, it is often more convenient to compute all observations of an astrophysical source, and iterate over sources. In this document, we describe the open source python package OpSimSummary, which allows for a convenient reordering. The objectives of this package are to provide users with an Application Programming Interface for accessing all such observations and summarizing this information in the form of intermediate data products usable by third party software such as SNANA, thereby also bridging the gap between official LSST products and preexisting simulation codes.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Radiation-Hardened GaN HEMT and Cell Design, Modeling, and Fabrication for Nuclear Instrumentation Applications

Recent advances in nuclear power generation technologies show great promise for efficient, safe generation of carbon-free power in the near future. Consequently, many new generation reactor designs are being pursued by government and industry groups, including both fission- and fusion-based technologies. Radiation- and temperature-tolerant sensor technology continues to advance, yet development of the electronics/instrumentation technologies suitable for these environments have fallen behind. Industry has adequately addressed harsh environment electronics needs for low earth orbit satellites, but the much more extreme conditions associated with electronics placed near or in a reactor core remain unaddressed. This project investigates the use of gallium nitride (GaN) circuit technology to address the unique needs for sensor interface electronics and communications in reactor environments. This report provides a summary of the first year’s project activities related to developing and optimizing devices and circuits for this GaN high electronic mobility transistor (HEMT) process. Activities reported include multiple analog and digital circuit designs simulated using custom Verilog-A models generated from measured GaN devices fabricated in the target process. Digital circuit designs included fundamental logic circuits such as an inverter, NAND, NOR, AND and OR gates, as well as a 5-stage ring oscillator. Each design was simulated using an open-source SPICE simulator, and an integrated circuit layout was produced of each design for use in future fabrication. Analog circuit designs focused on the fundamental building blocks to be used to construct sensor interface and communications circuits. These included current mirrors, matched differential pairs, and a Gilbert cell mixer design, each with an associated integrated circuit layout. Layout tools used for these designs included two open-source packages—KLayout and Magic—which were customized for specific use with the OSU GaN process layers. Design rules and preliminary device extraction capabilities were built for the Magic tool to enable device extraction and subsequent netlist generation for SPICE simulation. Finally, future research directions for year two are summarized. When the year-two directions are implemented, the project will be in position to advance the state of the art in electronics for near- or in-reactor sensor interfacing and communications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2019

ROBOT: A Tool for Automating Ontology Workflows

BACKGROUND:Ontologies are invaluable in the life sciences, but building and maintaining ontologies often requires a challenging number of distinct tasks such as running automated reasoners and quality control checks, extracting dependencies and application-specific subsets, generating standard reports, and generating release files in multiple formats. Similar to more general software development, automation is the key to executing and managing these tasks effectively and to releasing more robust products in standard forms. For ontologies using the Web Ontology Language (OWL), the OWL API Java library is the foundation for a range of software tools, including the Protégé ontology editor. In the Open Biological and Biomedical Ontologies (OBO) community, we recognized the need to package a wide range of low-level OWL API functionality into a library of common higher-level operations and to make those operations available as a command-line tool. RESULTS:ROBOT (a recursive acronym for "ROBOT is an OBO Tool") is an open source library and command-line tool for automating ontology development tasks. The library can be called from any programming language that runs on the Java Virtual Machine (JVM). Most usage is through the command-line tool, which runs on macOS, Linux, and Windows. ROBOT provides ontology processing commands for a variety of tasks, including commands for converting formats, running a reasoner, creating import modules, running reports, and various other tasks. These commands can be combined into larger workflows using a separate task execution system such as GNU Make, and workflows can be automatically executed within continuous integration systems. CONCLUSIONS:ROBOT supports automation of a wide range of ontology development tasks, focusing on OBO conventions. It packages common high-level ontology development functionality into a convenient library, and makes it easy to configure, combine, and execute individual tasks in comprehensive, automated workflows. This helps ontology developers to efficiently create, maintain, and release high-quality ontologies, so that they can spend more time focusing on development tasks. It also helps guarantee that released ontologies are free of certain types of logical errors and conform to standard quality control checks, increasing the overall robustness and efficiency of the ontology development lifecycle.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

NASA NTRS2017

NeMO-Net: The Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 percent error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets. We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

NeMO-Net↗

NASA NTRS2017

NeMO-Net The Neural Multi-Modal Observation Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets.We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Remote Sensin↗

DOE OSTI2026

Electrostatic Superlattices Beyond 1:1 Stoichiometry

ABSTRACT Exotic nanoparticle superstructures can be accessed by harnessing nanoparticle softness and charge regulation, features often viewed as obstacles to structural control. Here, we show that regulated charge mismatch in polymer‐grafted nanoparticles enables the assembly of high‐stoichiometry cubic superlattices. By co‐tuning grafting density, particle size, and bulk composition, we realize ionic‐lattice analogues, such as and , as well as single‐component and superlattices without atomic counterparts. The superlattice has recently been identified theoretically as a photonic band‐gap lattice. These phases emerge from a 1:1 “parent” lattice when local charge neutrality cannot be satisfied, driving either progressive interstitial filling or reorganization into a larger basis. For instance, the systematic occupation of ZnS tetrahedral sites yields , while ligand‐swapping symmetry breaking converts CsCl into . Upon heating, the assemblies exhibit reversible lattice contraction and pronounced negative thermal expansion. Furthermore, the energetic penalty for defects increases with nanoparticle size, facilitating the scalable production of high‐quality, open superlattices for photonic applications.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Sample shape and boundary dependence of measured transverse thermal properties

In spite of increased interest in thermal Hall measurements for the analysis of insulating quantum materials, there remains large uncertainty in such measurements due to contact misalignment. In this paper, we propose that sample geometry and uncertain boundary conditions provide an additional source of uncertainty in the measurement of $D_{xy}$ or $κ_{xy}$. By running simple simulations in an open source finite-element solver, we demonstrate that measured $D_{xy}$ can be changed by a non-negligible fraction in samples with similar width and length. This geometric corrective factor depends on the distinction between a uniform heat flow and constant temperature boundary couplings to a bath. Sample geometry and boundary conditions can be accounted for through simulation or by using rectangular samples to make thermal Hall measurements more reliable and reproducible. Finally, we detail a contactless optical method for measuring $D_{xy}$ based on an existing photothermal microscope technique. This method is insensitive to the longitudinal diffusivity pollution caused by contact misalignment.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Insights on sources and formation mechanisms of liquid-bearing clouds over MOSAiC examined from a Lagrangian framework

Understanding Arctic stratiform liquid-bearing cloud life cycles and properly representing these life cycles in models is crucial for evaluations of cloud feedbacks as well as the faithfulness of climate projections for this rapidly warming region. Examination of cloud life cycles typically requires analyses of cloud evolution and origins on short time scales, on the order of hours to several days. Measurements from the recent Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition provide a unique view of the current state of the central Arctic over an annual cycle. Here, we use the MOSAiC radiosonde measurements to detect liquid-bearing cloud layers over full atmospheric columns and to examine the cloud-generating air masses’ properties. We perform 5-day (120 h) back-trajectory calculations for every detected cloud and cluster them using a unique set of variables extracted from these trajectories informed by ERA5 reanalysis data. This clustering method enables us to separate between the air mass source regions such as ice-covered Arctic and midlatitude open water. We find that moisture intrusions into the central Arctic typically result in multilayer liquid-bearing cloud structures and that more than half of multilayer profiles include overlying liquid-bearing clouds originating in different types of air masses. Finally, we conclude that Arctic cloud formation via prolonged radiative cooling of elevated stable subsaturated air masses circulating over the Arctic can occur frequently (up to 20% of detected clouds in the sounding data set) and may lead to a significant impact of ensuing clouds on the surface energy budget, including net surface warming in some cases.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Transforming the Future of Marine Aquaculture: A Circular Economy Approach

By mid-century, society will need to significantly intensify the output of its food production system while simultaneously reducing that system’s detrimental impacts on climate, land use, freshwater resources, and biodiversity. This will require finding alternatives to carbon emissions-intensive agriculture, which provides the backbone of today’s global food production system. Here, we explore the hypothesis that marine algae-based aquaculture can help close the projected gap in society’s future nutritional demands while simultaneously improving environmental sustainability. Food production from marine algae-based aquaculture has the potential to contribute more than the total global protein demand projected for 2050, which ranges from 263.8 Mt/yr to 286.5 Mt/yr. It also offers important nutritional and environmental sustainability advantages relative to terrestrial agriculture. Marine algae can provide a better source of high-quality nutritional protein, essential amino acids, and other micronutrients relative to terrestrial plants. In addition, because marine algae do not require soil, irrigation, and the open application of fertilizer, their cultivation does not need to compete with agriculture for arable land and freshwater nor does it lead to fertilizer runoff and downstream eutrophication. Furthermore, by reducing agriculture’s demand for arable land and freshwater, marine algae-based aquaculture can reduce the pressure for deforestation, potentially leading to globally significant reductions in carbon emissions and biodiversity loss.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

SuperScreen: An open-source package for simulating the magnetic response of two-dimensional superconducting devices

Quantitative understanding of the spatial distribution of magnetic fields and Meissner screening currents in two-dimensional (2D) superconductors and mesoscopic thin film superconducting devices is critical to interpreting the results of magnetic measurements of such systems. Here, we introduce SuperScreen, an open-source Python package for simulating the response of 2D superconductors to trapped flux and applied time-independent or quasi-DC magnetic fields for any value of the effective magnetic penetration depth, Λ. Given an applied magnetic field, SuperScreen solves the 2D London equation using an efficient matrix inversion method to obtain the Meissner currents and magnetic fields in and around structures composed of one or more superconducting thin films of arbitrary geometry with spatially nonuniform magnetic penetration depth. SuperScreen can be used to model screening effects and calculate self- and mutual-inductance in superconducting devices, and simulate the magnetic response of inhomogeneous 2D superconductors.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Multi‐Decadal Decarbonization Pathways for U.S. Freight Rail

A‐STEP is a first‐of‐its‐kind, integrated, open‐source software tool aimed at guiding freight rail decarbonization decision‐making. It has tools for studying energy use details for individual trains, networks of trains, battery and hydrogen charging stations, national energy sourcing and pricing, and overall decarbonization costs and environmental impacts. It gives analysts an ability to study the challenges of making such change happen. Completely amenable to analyst specified inputs and parameter values, it can be customized to provide outputs for a wide variety of assumptions about future energy conditions and technological advances. Written in Python, C++, and VB.Net, A‐STEP can be implemented on both Windows and Linux‐based platforms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗