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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Advanced Power Electronics and Electric Machines [Slides]

The presentation provides an overview of NREL's Advanced Power Electronics and Electric Machines (APEEM) group and its research activities. Power electronics thermal management systems for electric-drive vehicles are described and performance metrics are provided. Additionally, advanced cooling technologies developed at NREL are presented. Finally, an overview is provided for electric machine cooling, integrated drives, and thermomechanical research.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Laser powder bed fusion parameter estimation with k-NN

Abstract Laser powder bed fusion (L-PBF) is a technique within additive manufacturing that uses a high power density laser to build parts from fused powdered metal alloy. This technology is well equipped to produce complex parts with otherwise impossible features, such as hidden voids or lattice structures. Alongside capability, reliability and quality are key characteristics considered when choosing a manufacturing method, and these are gaining attention as this method becomes more prevalent in industry. One main indicator of a stable L-PBF process is consistent melt pool geometry, and the properties of which are likely to determine the quality of the part produced. As computing power and sensing technologies become more advanced, this melt pool geometry could be studied in real time. This work addresses the challenge by leveraging a k-nearest neighbor (k-NN) model to identify key features within melt pool imagery and predict the energy density. The k-NN model was trained on data provided by the National Institute of Standards and Technology (NIST). Data preprocessing was performed on the images to extract features that were used in the k-NN model. This approach was used to accurately infer the energy density of unseen layers within the same part. The algorithm was subsequently tested with unique scan strategies and found to reasonably estimate the energy density of different parts. A fivefold cross validation found the algorithm to be consistently predicting the class of 91.4% of the in situ melt pool images.

Jung, Patrick (ORCID:0000000267890859)↗
DOE OSTI2020

Initial Radiation of Nuclear Power Plant Components (CRADA NFE-17-06892 Final Report)

As advanced reactor concepts are developed, instrumentation and control (I&C) cables and sensors will play a significant role in assuring their operation is well understood, and safety-related process information can be collected reliably over the course of their design life. As a first step to develop acceptance criteria and define the limits of currently available nuclear I&C cable and sensor technology, a collaboration between Oak Ridge National Laboratory and Analysis and Measurement Services (AMS) Corporation was initiated under the Gateway for Accelerated Innovation in Nuclear (GAIN) Program. This collaborative effort focused on providing technical information related to the combined thermal and radiation aging of nuclear power plant (NPP) cables, companion NPP cable insulation samples, and two prototype temperature and two pressure sensors. This work was carried out using the instrumented gamma irradiation system that is part of the High Flux Isotope Reactor Gamma Irradiation Facility (HFIR GIF). This system provided the opportunity for the condition of the prototype sensors to be tracked during the irradiation in order to determine their functional limits with greater granularity when compared to a simple irradiation insertion at the HFIRGIF. Experimental assemblies for sensor irradiation were successfully completed, and sensor irradiation to accumulated dose of 3000 kGy at a rate of approximately 6 kGy/hr was carried out. Two prototype temperature assemblies were continuously monitored during exposure to 3000 kGy and found to have no observable degradation from the irradiation. Two pressure sensors were first irradiated to 1000 kGy. At which point, one of the sensors was found to operate normally; however, a second sensor failed the operability test. The operable pressure sensor output was then continuously monitored from 1000 kGy to 3000 kGy. The sensor output exhibited steady trendable drift versus the accumulated radiation dose. At the conclusion of the irradiation, the four test sensors were released for laboratory calibration checks. The calibration check of the temperature sensors found no significant change after irradiation compared to baseline measurements before irradiation; however, both pressure sensors failed the post-test calibration checks and both sensors were not functioning properly. These results will help inform sensor manufacturers and I&C design engineers of potential limitations of these types of sensors for use in high radiation environments associated with the compact configuration of new Small Modular Reactors (SMRs) such as NuScale’s reactor power module. Irradiations on NPP cables and companion samples were not completed due to programmatic delays, however a path forward that was based on lessons learned from this project was defined to accommodate future, comparable instrumented gamma irradiations at the HFIR GIF.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Operating Procedure Extender For Novel Systems

Operating procedures that are used in nuclear power plants are primarily paper. These paper documents are functional for normal operations, but the process has remained relatively unchanged for thirty years. This neglects the digital technology advancements that have been made in that time. There are objectively more efficient ways to present a procedure to an operator using a digital interface. These digital interfaces require structured information to be able to function. This tool takes the procedure that has been written and converts it into the structured from required by the presentation system. This is accomplished through a reliable four phase compilation algorithm.

Lehmer, Jacob P↗
DOE OSTI2025

The Sensor Dilemma in Intelligent Transportation Systems

Intelligent Transportation Systems (ITS) are at the forefront in advancing the way we interact and perceive with the transportation network. This revolution is fueled by the significant advancement in sensor perception technologies such as Radar, LiDAR and Video Imaging which are the most popular modalities for ITS. Real-time perception data from these sensors allows intelligent infrastructure side decision making to improve the energy, efficiency and safety at traffic intersections. As traffic departments across the United States are transitioning from traditional loop detectors / emulators and embracing newer technologies, they are often left with a dilemma in choosing a sensor technology for infrastructure-based perception which is reliable, inexpensive, easy to setup and has robust performance in varying weather conditions. However, choosing a sensor which checks all boxes is not straightforward as every sensor type has unique benefits and drawbacks. Radar is excellent at detecting long range vehicles and weather resistance but lacks high resolution. LiDAR is expensive and weather-sensitive, while cameras provide rich visual data at a low cost but are constrained by lighting and visibility. This study examines Radar, LiDAR and camera sensors capabilities to ascertain whether any of these qualifies as the "best" sensor for ITS perception. Through this evaluation, we hope to draw attention to the necessity of National Renewable Energy Laboratory's (NREL) Infrastructure Perception and Control (IPC) framework which presents a multi-sensor track data fusion engine to assimilate multiple data streams in order to provide robust and reliable perception. While no single sensor can meet all the demands of ITS, a hybrid approach combining multiple sensor modalities like Radar, LiDAR and cameras, offers the most robust solution for enhancing the safety and efficiency in intelligent transportation systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Retail Hydrogen Station Reliability Status and Advances

One of the most vital emerging commercial markets for hydrogen is fuel cell-powered mobility including cars, trucks, and buses. These vehicles are refueled via a network of hydrogen fueling stations, with the highest number of U.S. stations being in California. The numbers of both fuel cell electric vehicles (FCEVs) and hydrogen stations have increased in the last two years, with anecdotal information from FCEV drivers indicating that station reliability is hurting the consumer acceptability of FCEV technologies. Therefore, this study benchmarks the current state of hydrogen station reliability in practice and presents on-going research that is investigating the failures that are contribute to hydrogen station reliability issues. This is accomplished with an analysis of operation, safety, and maintenance data from hydrogen stations and fuel cell electric vehicles to benchmark the maintenance and failure of hydrogen stations and their components. This analysis, of over 5,000 station maintenance events, presents the leading maintenance categories and failure rates, and is a prerequisite to the development of data-driven reliability improvement plans. We present a reliability growth analysis and on-going research into the root causes of failure for dispensers, a particularly failure-prone subsystem.

08 HYDROGEN↗
DOE OSTI2024

CIGS Technology Advancement via Fundamental Modeling of Defect/Impurity Interactions (Final Technical Report)

The primary goals of the proposed work were to provide modeling tools (and the associated insight which comes along with model development) for design and optimization of CuIn x Ga 1-x Se 2 (CIGS) and CdSeTe (CST) solar cell manufacturing processes and to establish the foundation for comprehensive end-to-end predictive modeling tools to enable optimization of thin film photovoltaic technology for performance, cost, yield, and reliability. The initial focus of efforts within this project was to develop coupled process/optical/device models for CIGS PV technology and to work with Siva Power to apply that TCAD (technology computer-aided design) system to improve the efficiency and reduce manufacturing costs for CIGS solar cells. Our approach to that end was to generate an extensive database of DFT calculations and to use those calculations via statistical thermodynamics methods and Monte Carlo simulation to develop and characterize models for the behavior of native defects as well as intentional and unintentional impurities, including the redistribution of the primary components of CIGS films. Increased effort went toward coupling those models for defect behavior and composition evolution to the performance of multicrystalline CIGS solar cells via prediction of doping level and recombination lifetime as function of manufacturing process. In the second budget period, the project pivoted to developing a similar system for the CdSeTe system, focused especially on understanding the role of Se/Te alloy concentration. Execution of the project resulted in the successful development of TCAD systems for both CIGS and CdSeTe thin film PV within the Synopsys Sentaurus framework by utilizing the Alagator interface. In the first budget period of the project, we developed quantitative models for the major components of CIGS PV and implemented them within a framework that couples process, optical, and device simulation. From the insights we have gained, we identified novel opportunities for enhancing CIGS solar cell performance and have laid the groundwork to further optimize the layer structure, composition profile, and thermal cycles for substantially improved efficiency and lower manufacturing costs. For the CIGS system, process changes to achieve greater than 1% absolute enhancement in efficiency were identified, but testing of those approaches was stymied by lack of a domestic CIGS manufacturing partner after the closure of Siva Power as well as Miasole. For CdSeTe, a fully capable TCAD system only became ready to apply near the end of the project period, so substantial opportunities remain to apply those models to enhance the leading thin film PV technology.

14 SOLAR ENERGY↗
DOE OSTI2025

The Sensor Dilemma in Intelligent Transportation Systems: Evaluating Radar, Lidar and Camera: Preprint

Intelligent transportation systems (ITS) are at the forefront in advancing the way we interact with and perceive the transportation network. This revolution is fueled by the significant advancement in sensor perception technologies such as radar, lidar, and video imaging, which are the most popular modalities for ITS. Real-time perception data from these sensors allow intelligent infrastructure-side decision-making to improve the energy, efficiency, and safety at traffic intersections. As traffic departments across the United States transition from traditional loop detectors and emulators and embrace newer technologies, they are often left with a dilemma in choosing a sensor technology for infrastructure-based perception that is reliable, inexpensive, and easy to set up and that has robust performance in varying weather conditions. However, choosing a sensor that checks all these boxes is not straightforward, as every sensor type has unique benefits and drawbacks. Radar is excellent at detecting long-range vehicles and weather resistance but lacks high resolution. Lidar is expensive and weather-sensitive, while cameras provide rich visual data at a low cost but are constrained by lighting and visibility. This study examines radar, lidar, and camera sensor capabilities to ascertain whether any of these qualifies as the "best" sensor for ITS perception. While no single sensor can meet all the demands of ITS, a hybrid approach combining multiple sensor modalities like radar, lidar, and cameras offers the most robust solution for enhancing the safety and efficiency of ITS. Through this evaluation, we hope to draw attention to the necessity of the National Renewable Energy Laboratory's infrastructure perception and control framework, which presents a multisensor track data fusion engine to assimilate multiple data streams in order to provide robust and reliable perception.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

An Integrated Framework for Risk Assessment of Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology Refinement and Exploration

This report documents activities performed by Idaho National Laboratory (INL) during Fiscal Year (FY) 2023 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) risk assessment project. In FY 2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a technical basis to support effective, and secure DI&C technologies for digital upgrades/designs. A risk assessment-informed framework was proposed for this strategy, which aims to (1) provide a best-estimate, risk informed capability to quantitatively estimate the safety margin obtained from plant modernization, especially for safety-related DI&C systems, (2) support and supplement existing risk informed DI&C design guides by providing quantitative risk information and evidence, (3) offer a capability of design architecture evaluation of various DI&C systems, (4) assure the long-term safety and reliability of safety-related DI&C systems, and (5) reduce uncertainty in costs and support integration of DI&C systems in the plant. To achieve these technical goals, the LWRS-developed framework provides a means to address relevant technical issues by: (1) defining a risk informed analysis process for DI&C upgrade that integrates hazard analysis, reliability analysis, and consequence analysis, (2) applying risk informed tools to address common cause failures (CCFs) and quantify corresponding failure probabilities for DI&C technologies, particularly software CCFs, (3) evaluating the impact of digital failures at the component level, system level, and plant level, and (4) providing insights and suggestions on designs to manage the risks, thus to support the development and deployment of advanced DI&C technologies in nuclear power plants (NPPs). Adding diversity within a system or components is the primary means to eliminate and mitigate CCFs, but diversity also increases system complexity and may not address all sources of systematic failures. Optimization of diversity and redundancy applications for the safety-critical DI&C systems remains a challenge. To deal with the technical issues in addressing potential software CCFs in safety-related DI&C systems of NPPs and supporting relevant design optimization, the proposed framework provides: (a) A best-estimate, risk informed capability to address new technical digital issues quantitatively, focusing on software CCFs in safety-related DI&C systems of NPPs; (b) A common and a modularized platform for DI&C designers, software developers, cybersecurity analysts, and plant engineers to predict and prevent risk in the early design stage of DI&C systems; (c) Technical bases and risk informed insights to assist users address the risk informed alternatives for evaluation of CCFs in safety-related DI&C systems of NPPs; and (d) A risk informed tool that offers a capability of design architecture evaluation of various DI&C systems to support system design decisions in diversity and redundancy applications. The research and development efforts of this project in FY 2023 are focused on refining current methods on software CCF modeling and estimation and exploring additional innovative approaches to risk assessment of DI&C systems to enable a more comprehensive and complete assessment of various safety-related DI&C design architectures. The primary audience of this report are DI&C designers, engineers, and probabilistic risk assessment (PRA) practitioners. This includes stakeholders, such as the nuclear utilities and regulators who consider the deployment and upgrade of DI&C systems, DI&C software developers and reviewers, and cybersecurity specialists. It should be noted that all the analyses are performed for the demonstration of the methodology, not for the evaluation of an actual digital control system. Results are obtained based on limited design information and testing data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Simulating Alpha Particles Incident on MKID Chips for Quantum Sensitivity Analysis

Superconducting quantum devices, such as microwave kinetic inductance detectors (MKIDs), are highly sensitive instruments used in quantum computing and advanced sensing technologies. However, their extreme sensitivity also makes them vulnerable to background noise from natural sources like radiation. One significant contributor to this noise is alpha particles emitted by 210Po, a radon decay daughter that accumulates on surfaces near the detector. This project investigates how alpha particles emitted from 210Po interact with MKID chips. These particles can deposit energy on the detector surface, disrupting its operation and generating false signals. Understanding the energy and behavior of these particles is crucial for improving the design and reliability of quantum devices. To explore this, we first modeled the decay chain starting from 210Pb to 210Po using differential equations. This allowed us to predict how the activity of alpha-emitting isotopes changes over time, reaching a steady state after about two years. Next, we simulated alpha particle interactions with the MKID chip using the Geant4 software toolkit. We built a detailed computer model of the detector housing, including the copper lid where alpha particles originate, the silicon chip, and a thin aluminum sensor layer. Alpha particles were emitted isotropically from just beneath the copper lid’s surface, mimicking natural decay conditions. The simulation tracked how these particles deposit energy on the chip, generating electron-hole pairs and phonons. The results provide insight into the behavior of the resultant electron-hole pairs and phonons, giving us a clear understanding of the energy deposition distribution on the chip. This work supports efforts to mitigate background noise in superconducting sensors, advancing their use in quantum computing and sensitive physics experiments.

Hall, Matthew [Fermilab; UCLA]↗
DOE OSTI2024

Rapid Monitoring and Defense Approach for Resilience Improvement of Grid Cyber Security

Cyber-physical systems and electric utilities significantly depend on the reliability and efficiency of information and operational technology. However, false data injection attacks based on synchrophasor measurement data pose a serious threat to the safe and reliable operation of modern power systems. Here, to mitigate this problem, a rapid monitoring and defense approach is proposed to defend against cyber attacks. Initially, the Time and Frequency based Convolutional neural Network (TFCN) is proposed to detect different types of attacks. Within the TFCN, the advances are that both time and frequency domain information can be fused without extra spectrum analysis methods, and can save detection time to speed the calculation efficiency using the developed time-frequency block. Next, a comprehensive defense strategy is developed for multiple cyber attacks to ensure the stability and resilience of the power system according to the feedback detection results. The advances of this strategy are that different control strategies can be automatically selected to recover the stability to the greatest extent according to the detected attacks. To verify the effectiveness of the proposed approach, the high-speed frequency measurements collected from the wide-area monitoring system are used. The results demonstrate that the cyber attack detection performance can reach 95.57% accuracy, outperforming both traditional and some advanced neural networks. Importantly, the defense strategy is conducted and verified in a modified IEEE 39 bus system as well, which illustrates profound performance in faster stability restoration.

Comprehensive defense strategy↗
DOE OSTI2022

An Integrated Framework for Risk Assessment of High Safety Significant Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology and Demonstration

This report documents the activities performed by Idaho National Laboratory (INL) during Fiscal Year (FY) 2022 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) risk assessment project. In FY 2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a technical basis to support effective and secure DI&C technologies for digital upgrades/designs. A framework was proposed for this strategy, which aims to (1) provide a best-estimate, risk-informed capability to quantitatively and accurately estimate the risk impact of plant modernization, considering the introduction of high safety-significant safety-related (HSSSR) DI&C systems, (2) support and supplement existing risk-informed DI&C design guides by providing quantitative risk information and evidence, (3) offer a capability of design architecture evaluation of various DI&C systems, (4) assure the long-term safety and reliability of HSSSR DI&C systems, and (5) reduce uncertainty in costs and support integration of DI&C systems in the plant. To achieve these technical goals, the framework provides a means to address relevant technical issues by: (1) defining a risk-informed analysis process for DI&C upgrade, that integrates hazard analysis, reliability analysis, and consequence analysis, (2) applying risk-informed tools to address common cause failures (CCFs) and quantify corresponding failure probabilities for DI&C technologies, particularly software CCFs, (3) evaluating the impact of digital failures at the component level, system level, and plant level, and (4) providing insights and suggestions on designs to manage the risks, thus to support the development and deployment of advanced DI&C technologies on nuclear power plant (NPPs).

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Accelerating Next-Generation Cybersecurity R&D Using AI Workflows: BADGER Project Development

The Broadband Automation for Distributed Grid Efficiency and Resilience (BADGER) project aligns with national strategic priorities for integrating emerging wireless technologies and advancing AI-driven security. As critical infrastructure modernizes toward increasingly software-defined and interconnected systems, the ability to leverage 5G/NextG networks and AI-enabled control becomes essential. This report outlines work at the National Laboratory of the Rockies (NLR) to develop a NextG-native security architecture powered by AI-RAN concepts and evaluate workflows that enable efficient and reliable architectures. Together, these efforts position the laboratory to accelerate innovation while directly supporting national security and resilience objectives.

5G/6G↗
DOE OSTI2024

Generative AI for Power Grid Operations

Generative artificial intelligence (AI) has captured into the mainstream, demonstrating capabilities that once belonged solely to the realm of human cognition. From defeating world champions in complex games to generating human-quality text and images, Generative AI has proven its potential to revolutionize countless industries. The electric power grid is no exception. Generative AI's ability to process vast amounts of data rapidly, assist decision support and identify patterns could significantly enhance power grid operations. For example, Generative AI could improve state estimation where measurements are not available or integrate renewable energy sources more efficiently with probabilistic forecasting. The key contributions of this whitepaper are outlined below: (1) Comprehensive overview of Generative AI's applications in power grid operations: It highlights the opportunities in areas such as forecasting, state estimation, and demonstrating the potential for enhancing efficiency, reliability, and resilience. (2) Expanding Generative AI's impact through synergies with emerging technologies: The paper introduce NREL developed eGridGPT and explores how AI orchestration, multi-agent systems, and Digital Twins can collaborate to optimize grid operations, addressing the complexities of a decarbonized and electrified future. (3) In-depth analysis of challenges in implementing Generative AI: This includes considerations like data availability and quality, model validation, certification, and ethical concerns, ensuring responsible AI deployment. (4) Emphasizing human-AI collaboration: The whitepaper underscores the importance of trustworthy, transparency, and explainability in AI systems to promote seamless interaction between human operators and AI, ultimately improving decision-making. (5) Exploring future research and development: It identifies critical areas for further advancement to fully realize Generative AI's potential in power grid operations. This whitepaper serves as a valuable resource for researchers, practitioners, and policymakers looking to harness Generative AI for a more reliable, stable, and cost-effective power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Design Optimization for Printed Melt Wire Arrays Encapsulation

As part of the Nuclear Energy Enabling Technology (NEET) Advanced Sensor and Instrumentation (ASI) Program, Idaho National Laboratory (INL) has recently established in-house capabilities to fabricate and test new advanced-manufactured sensors for measuring peak irradiation temperature within a nuclear test reactor. Although methods of real-time temperature monitoring, such as thermocouples, may be used, the complexity of feedthroughs and attachments to collect real-time measurements greatly increases the cost of the experiment. Instead, passive monitoring techniques may be used for peak- temperature measurement that exploit the melting point of well-characterized materials (standard melt wires) to infer peak reactor temperatures. However, limited available space for instrumentation during experiments introduces an additional challenge. To accommodate this, INL has expanded its temperature- detection instrumentation capabilities to include advanced manufactured (AM) melt wires for peak irradiation temperature measurements. These melt wires can determine peak temperatures while also accommodate space limitations in irradiation experiments. In an effort to improve performance reliability of AM meltwire capabilities, a process was developed and tested to identify the significance of entrapping a high purity inert atmosphere within the packaging of printed melt wire arrays. The materials used in this study were aluminum, zinc, and tin encapsulated in high purity helium within a stainless steel (SS) 316 container. Tin, with a low melting point of approximately 230°C, Zn with a mid-melting point of approximately 420°C, and Al with a high melting point of approximately 660°C. This report describes the design, fabrication process, furnace testing and X-ray Computed Tomography (XCT) evaluation. Results show a successful outcome in creating an inert gas encapsulation and high-resolution evaluation methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Crossing the Finish Line: Integration of Data-Driven Process Control for Maximization of Energy and Resource Efficiency in Advanced Water Resource Recovery Facilities

Improvements in process monitoring and control at water resource recovery facilities (WRRFs) could result in reductions in electricity consumption, chemical inputs, and greenhouse gas emissions, as well as improved energy recovery. Many current WRRF data collection, monitoring, and control approaches use 20th century process monitoring and control systems, which require large design safety factors to ensure reliability in the absence of more advanced, precise controls. Implementation of more modern data-driven control tools could lead to more efficient operations that provide intrinsic reliability with better overall process performance at full-scale. This presentation provides an overview of a recently initiated project "Crossing the Finish Line: Integration of Data-Driven Process Control for Maximization of Energy and Resource Efficiency in Advanced Water Resource Recovery Facilities" which will (1) develop and demonstrate data-driven process controls at full-scale facilities for five promising WRRF Applications (i.e., process technologies) that provide whole-plant approaches and offer substantial energy and resource recovery benefits, and (2) create a toolbox of new process control approaches and an implementation guide including five examples for application at utilities. The presentation also provides a detailed overview of the research approach and progress being made on one of the five Applications, namely Application 2: Biological Nutrient Removal (BNR): ammonium-based aeration control (ABAC) / ammonia vs. NOx (AvN) + partial denitration with anammox (PdNA), which is being implemented at Hampton Roads Sanitation District. This project is a collaboration of work being conducted by DC Water, Hampton Roads Sanitation District, Metro Water Recovery, University of Michigan, Northwestern University, US Military Academy - West Point, Black & Veatch, and Oak Ridge National Laboratory. Research partner: U.S. Department of Energy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Compact Hydrogen Generator

GTI Energy (GTI) is developing a One-Step Hydrogen Generation through Sorption Enhanced Reforming (SER) process that provides significant improvements in energy productivity (18% efficiency improvement), environmental performance (90% CO 2 capture and up to 98%), product yield (30% reduction in natural gas consumption), and economic benefit (reduce Levelized Cost of Hydrogen by 28%) as compared to the Steam Methane Reforming process. A 20,000 Standard Cubic Feet per Day pilot plant (or Feasibility Demonstration Unit, FDU) located at the Energy and Environmental Research Center (EERC) was moved to GTI’s Des Plaines, IL facility and reconfigured to utilize an atmospheric calciner which enables CO 2 capture as a high purity product. Between operations at EERC and GTI’s facility, the pilot plant has demonstrated over 110 hours of sorbent enhanced hydrogen production at 80% or higher purity. The primary objective of this project is to advance the development of GTI’s Sorption Enhanced Reforming (SER) hydrogen production technology, known as the Compact Hydrogen Generator (CHG), to allow for a future commercial demonstration. This effort was performed over the course of 51 months. The previous effort demonstrated that previously observed catalyst deactivation can be mitigated with different catalyst substrates and operating conditions. This follow-on effort will improve both the system operational reliability, and system efficiency. Additionally, a novel approach for the calcination process will be demonstrated which can reduce the cost of carbon dioxide capture by up to 60% compared to equivalent commercial approaches.

03 NATURAL GAS↗
DOE OSTI2023

Advances in Binder Jet Additive Manufacturing

Binder Jetting is a powerful additive manufacturing technology due to its low cost and wide range of types of materials it can shape. The industry partner ExOne has a range of binder jet products for applications such as casting tools, composite layup tools, single-alloy metal components, and more. As such, research and development is of high interest in the area of specialized binders for each type of material, processing technology that improves reliability and quality, and materials to enhance the performance of the final parts. Thus, this work focuses on developing novel binders for sand tools and metal prints, new capabilities in multi-material printing, and build monitoring and correction. The result of this work includes a novel binder system that makes binder jet sand stronger than concrete while still being water soluble, a casting binder that is environmentally friendly and produces better surface finishes on castings, and a binder for metals that reduces the residual carbon.

36 MATERIALS SCIENCE↗