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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Hybrid-energy approach enabled by heat storage and oxy-combustion to generate electricity with near-zero or negative CO 2 emissions

We assess a hybrid-energy approach that modifies a steam-turbine power plant to use renewable energy sources (electricity, plus options for geothermal and solar heat), plus fossil fuel (natural gas and coal) and/or waste biomass (e.g., Douglas fir woodchips). Heat storage allows heat to be created during periods of excess energy supply and for that heat to be converted to electricity when demanded. Excess electricity, such as from variable renewable energy (VRE), is used to generate oxygen for oxy-combustion furnaces that create very-hot, high-purity CO 2 that heats granular rock beds in insulated vessels. Cool CO 2 leaving the beds is dried, sent to compressors powered by excess VRE electricity, before being sent by pipeline to geologic CO 2 storage. Very-hot CO 2 transfers high-grade heat from storage to the power plant in a closed loop that returns medium-grade heat back to storage, allowing low- and medium-grade renewable-heat sources to be stacked beneath combustion heat, with all heat sources being converted to electricity at the same (high) thermal efficiency. Reliable, on-demand power may be generated with near-zero CO 2 emissions with fossil fuel and with negative CO 2 emissions with waste biomass. Our analyses show that with fossil fuel, up to 35% of gross power can be derived from renewable sources, while for waste biomass, it can be entirely derived from renewable sources. Because our approach has the potential to ensure that grids have a continuous supply of clean energy and because electricity is only generated once, when demanded, it could serve as an efficient alternative to bulk energy storage.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Evaluation of Performance Variables to Accelerate the Deployment of Sustainable Aviation Fuels at a Regional Scale

An increase in jet fuel consumption and its associated emissions across the world have led to the need for alternative technologies to produce sustainable aviation fuels (SAF). One option to produce SAFs is to utilize waste or biomass-based feedstocks that has the potential to reduce greenhouse gas emissions by 50% or more compared to conventional jet fuel. However, there is a lack of understanding of how the synergistic effects of key performance variables could hinder or help the deployment of aviation fuels on a regional scale. Here, we assess the implications of key variables-including type and quantity of waste/biomass feedstock availability near the airport, cost of SAF production, life cycle greenhouse gas (GHG) emissions, policies, and fuel/infrastructure logistics-on the deployment of SAF at Chicago's O'Hare International Airport. We consider three ASTM International-approved SAF technologies (Hydroprocessed Esters and Fatty Acids, Fischer-Tropsch, and Alcohol to Jet) that can be blended up to 50% with petroleum-based jet fuel. Results from our analysis show that woody biomass-based Fischer-Tropsch technology has the lowest fuel production costs ($2.31-$2.81/gallon gasoline equivalent) of all pathways, and it reduces life cycle GHG emissions by 86% compared to conventional jet fuel despite the higher availability of crop residues compared to either woody biomass or fats, oils, and greases. Also, infrastructure is available at O'Hare International Airport to blend SAF with Jet A fuel through three terminals directly connected to the airport via pipelines. Our sensitivity analysis shows renewable fuel incentives and feedstock price to be key performance variables affecting the production cost and deployment of SAF.

alcohol-to-jet↗
DOE OSTI2023

Plasma catalytic non-oxidative methane conversion to hydrogen and value-added hydrocarbons on zeolite 13X

Non-thermal plasma has unfolded highly efficient, safe to operate novel routes for methane conversion to hydrogen. In this work, methane conversion is performed under atmospheric dielectric barrier discharge (DBD) plasma with and without 13X zeolite-based catalysts i.e., 13X, Ga/13X, Pd/13X, and Pd-Ga/13X. Experimental results indicate that the plasma catalytic process delivered almost twofold higher product yield than the plasma only route. The binary Pd-Ga catalyst possesses highest catalytic performance with about 40 % CH 4 conversion at an input flowrate of 5 cm 3 min -1 and 2W due to the formation of the Pd-Ga alloy, which acts as catalytic active centre for activating C–H bonds. Product yield can be tailored by the catalyst design where the bimetallic Pd-Ga/13X preferably favours the hydrocarbon formation while H 2 is the dominant product obtained over the Pd/13X. The cleavage of C–H bonds of methane molecule over the plasma only route is mainly governed by energetic electrons in the gaseous phase the catalyst activity, and plasma-catalyst synergism play a significant role in the plasma catalysis process. Further, the findings from this work provide significant insights into the methane activation for subsequent optimization of the methane conversion processes operated on floating production, storage, and offloading vessels (FPSOVs). Exploiting untapped offshore natural gas reserves, where conventional pipeline systems are less economical.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Understanding the impacts of inorganic species in woody biomass for preprocessing and pyrolysis–A review

Woody biomass represents an abundant resource for sustainable biofuels, biochemicals, and bioproducts. Technologies for converting woody biomass have been established for decades, and research consistently highlights the critical role of inorganic species and ash plays in feedstock handling and conversion processes, including equipment plugging, corrosion, and catalyst deactivation. A thorough understanding of the variability, transport behavior, and downstream impact of inorganic species in woody biomass is essential for defining feedstock quality specifications and developing effective management strategies for conversion processes. This review compiles critical information in five main sections: 1) inorganic species concentration in woody biomass, based on anatomical fractions and their sources of variability; 2) technique features for quantifying inorganic elemental chemical analysis; 3) impacts of inorganic species on biomass preprocessing; 4) impacts of inorganic species on pyrolysis, and 5) mitigation strategies. Additionally, this review explores future challenges and opportunities in addressing the impacts of inorganic species on biomass quality. These insights aim to support the sustainable development of the biomass-to-bioenergy pipeline and ensure high-quality lignocellulosic feedstocks for efficient downstream conversions. The findings offer valuable guidance to policy makers, industry stakeholders, and researchers in developing effective strategies for managing inorganic species in woody biomass and fostering the sustainable processes for lignocellulosic biorefineries.

09 BIOMASS FUELS↗
DOE OSTI2022

Constraint-dependent CTOA determination for stable ductile crack growth

The crack tip opening angle (CTOA) has been used as a reliable fracture toughness to characterize stable crack growth for thin-wall structures in low-constraint conditions. Recently, it is found that CTOA can also be utilized as a robust fracture parameter to describe the arrest fracture toughness for gas transmission pipelines in ductile steels. For better understanding the concept of CTOA, this paper presents a brief review on the CTOA definitions and its test methods. And then, four CTOA models are developed for determining the CTOA toughness using conventional single edge notched bend (SENB) specimens, including the load–displacement linear fit model, the logarithmic load–displacement linear fit model, the stable tearing energy model, and the J-differentiation model. Here fracture test data from SENB specimens in A285 carbon steel are employed to evaluate the proposed CTOA models. The results show that these CTOA models can determine either identical or comparable critical CTOA values for stable ductile crack growth using the SENB specimens. A constraint-corrected CTOA resistance curve and a constraint-corrected critical CTOA equation are also obtained for A285 carbon steel.

42 ENGINEERING↗
DOE OSTI2026

Effect of Specimen Thickness on Fracture Toughness and Plane Stress to Plane Strain Transition in Medium-Density Polyethylene

This study investigates the effect of specimen thickness on fracture toughness and the transition from plane stress to plane strain conditions in Medium-Density Polyethylene (MDPE) using Single Edge Notched Bend (SENB) specimens. Three thickness groups (7.5 mm, 9.0 mm, and 12.1 mm) were tested following ASTM D5045 protocol. Conditional stress intensity factors (KQ) increased from 2.2 MPavm to 2.8 MPavm with increasing thickness, demonstrating significant size dependency. Confocal microscopy revealed a 34% reduction in maximum crack tip opening displacement (CTOD) from 0.478 mm to 0.314 mm as thickness increased, with plastic zone lateral extent decreasing by 32%. This quantitative evidence validates Irwin's theoretical prediction of plastic zone size transition from plane stress r_y˜ (1/2p) (K_I/s_y )^2 to plane strain conditions r_y˜ (1/6p) (K_I/s_y )^2.Scanning electron microscopy confirmed progressive suppression of shear lips and evolution from ductile tearing with extensive polymer chain drawing in thin specimens to localized crazing in thick specimens. Despite all specimens satisfying the ASTM D5045 criterion (B = 2.5(KQ/s_y )^2 ) ,residual shear lips persisted even in the thickest specimens, demonstrating that nominal size requirements alone are insufficient for achieving complete plane strain conditions in highly ductile polymers. The findings emphasize the necessity of integrated analytical and morphological assessment for valid fracture toughness characterization, with critical implications for life assessment and integrity prediction in thick-section polymer components such as natural gas distribution pipelines.

Medium-Density Polyethylene↗
DOE OSTI2024

Private, public, and bottled drinking water: Shared contaminant-mixture exposures and effects challenge

Background: Humans are primary drivers of environmental–contaminant exposures worldwide, including in drinking-water (DW). In the United States, point-of-use DW (POU–DW) is supplied via private tapwater (TW), public-supply TW, and bottled water (BW). Differences in management, monitoring, and messaging and lack of directly–intercomparable exposure data influence the actual and perceived quality and safety of different DW supplies and directly impact consumer decision–making. Objectives: The purpose of this paper is to provide a meta-analysis (quantitative synthesis) of POU–DW contaminant–mixture exposures and corresponding potential human–health effects of private-TW, public-TW, and BW by aggregating exposure results and harmonizing apical–health–benchmark–weighted and bioactivity–weighted effects predictions across previous studies by this research group. Discussion: Simultaneous exposures to multiple inorganic and organic contaminants of known or suspected human-health concern are common across all three DW supplies, with substantial variability observed in each and no systematic difference in predicted cumulative risk between supplies. Differences in contaminant or contaminant–class exposures, with important implications for DW–quality improvements, were observed and attributed to corresponding differences in regulation and compliance monitoring. Conclusion: The results indicate that human-health risks from contaminant exposures are common to and comparable in all three DW–supplies, including BW. Importantly, this study’s target analytical coverage, which exceeds that currently feasible for water purveyors or homeowners, nevertheless is a substantial underestimation of the breadth of contaminant mixtures in the environment and potentially present in DW. Thus, the results emphasize the need for improved understanding of the adverse human-health implications of long-term exposures to low–level inorganic–/organic–contaminant mixtures across all three distribution pipelines and do not support commercial messaging of BW as a systematically safer alternative to public-TW. Regardless of the supply, increased public engagement in source-water protection and drinking–water treatment is necessary to reduce risks associated with long-term DW–contaminant exposures, especially in vulnerable populations, and to reduce environmental waste and plastics contamination.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Inductive predictions of hydrologic events using a Long Short-Term Memory network and the Soil and Water Assessment Tool

We present machine learning methods to predict hydrologic features such as streamflow and soil moisture from spatially and temporally varying hydrological and meteorological data. Here, we used a temporal reduction technique to reduce computation and memory requirements and trained a Long Short-Term Memory (LSTM) network to predict soil moisture and streamflow over multiple watersheds. We show LSTM networks can be trained in a fraction of the time required by complex process-based and attention-based models such as Soil and Water Assessment Tool (SWAT) and GeoMAN without sacrificing accuracy. We also demonstrate that outside data - sourced from a watershed other than the target - can be used to train LSTM to comparable or even superior prediction accuracy. The success of LSTM in such spatially-inductive settings shows hydrologic features can be predicted with minimal prior knowledge of the watershed in question. Finally, we make all methodologies of this work publicly available as an end-to-end software pipeline that facilitates rapid prototyping of hydrologic learners.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A systematic method for selecting molecular descriptors as features when training models for predicting physiochemical properties

Machine learning has proven to be a powerful tool for accelerating biofuel development. Although numerous models are available to predict a range of properties using chemical descriptors, there is a trade-off between interpretability and performance. Neural networks provide predictive models with high accuracy at the expense of some interpretability, while simpler models such as linear regression often lack in accuracy. In addition to model architecture, feature selection is also critical for developing interpretable and accurate predictive models. We present a method for systematically selecting molecular descriptor features and developing interpretable machine learning models without sacrificing accuracy. Our method simplifies the process of selecting features by reducing feature multicollinearity and enables discoveries of new relationships between global properties and molecular descriptors. To demonstrate our approach, we developed models for predicting melting point, boiling point, flash point, yield sooting index, and net heat of combustion with the help of the Tree-based Pipeline Optimization Tool (TPOT). For training, we used publicly available experimental data for up to 8351 molecules. Our models accurately predict various molecular properties for organic molecules (mean absolute percent error (MAPE) ranges from 3.3% to 10.5%) and provide a set of features that are well-correlated to the property. This method enables researchers to explore sets of features that significantly contribute to the prediction of the property, offering new scientific insights. To help accelerate early stage biofuel research and development, we also integrated the data and models into a open-source, interactive web tool.

09 BIOMASS FUELS↗
DOE OSTI2022

Perspective on the oil-dominated gas hydrate plugging conceptual picture as applied to transient Shut-In/Restart

Gas hydrate formation is rarely an issue during continuous production conditions. Unplanned and/or extended shut-downs comprise the majority of scenarios where overlapping concern and uncertainty for gas hydrate blockages exist. To date, most experimental studies and theoretical analysis of gas hydrate formation derive from the steady-state conceptual picture. Yet, significant distinctions induced by phase separation and prolonged cooling suggest an altered conceptual picture is required for transient shut-in/restart operations. To address this outstanding issue, this work presents a conceptual picture for the base case of a non-surface active hydrocarbon undergoing cold restart without enacted mitigation efforts. First, phase separation upon shut-in leads to the localization of gas hydrate ingredients due to fluids density differences and pipeline inclination. Cooling to the seafloor temperature, then, induces gas hydrate film formation at the hydrocarbon interface and maximizes subcooling, resulting in rapid and confined gas hydrate growth occurring upon restart. Furthermore, this ultimately leads to plugging dominated by the deposition of porous, low conversion gas hydrate masses. Direct visual observations and results from Raman spectroscopy, hydrate contact angle and film growth, micromechanical force apparatus, rocking cell, and flow loop studies support the mechanisms outlined within each step. Similar to the influence provided by the original Colorado School of Mines steady-state conceptual picture that was introduced about 15 years ago, this transient counterpart builds a foundation that could inspire new directions of hydrate research and analysis in areas where uncertainty abounds.

02 PETROLEUM↗
DOE OSTI2026

Real-time plasma monitoring framework for advanced plasma control and ML-research in DIII-D

Real-time and adaptive plasma control is crucial for robust tokamak operation, requiring sensitivity and tolerance measurements of the plasma state. This paper presents the implementation of an integrated real-time plasma monitoring framework on the DIII-D tokamak to support advanced control approaches, including machine-learning (ML) methods. The system is built on the SHIELD framework, a high-performance modular architecture that provides a unified pipeline for integrating diverse diagnostics. The framework leverages high-bandwidth digitizers, fast numerical processing, and deterministic, low-latency interconnects to stream high-fidelity data from diagnostics such as electron cyclotron emission (ECE), beam emission spectroscopy (BES), CO interferometers, and a visible tangential divertor camera (TangTV). The system’s validity is demonstrated through direct comparisons of real-time and offline data. Furthermore, we present two key applications of the developed plasma monitoring system with ML-based plasma control strategies, including real-time divertor detachment and active Alfvén Eigenmode control. As a result, this work presents a robust and scalable approach for integrating high-frequency, multidimensional diagnostics into advanced control algorithms for future fusion devices.

AI/ML↗
DOE OSTI2023

Economic and operational investigation of CO 2 sequestration through enhanced oil recovery in unconventional reservoirs in Colorado, USA

The ongoing CCUS commercial projects are highly relied on the support of government incentives due to massive capital investment. Here, this study analyzes the economics of carbon capture utilization and sequestration (CCUS) projects, shows a state-wide CCUS deployment exercise, followed by simulation results of enhanced oil recovery (EOR) based CO 2 storage in unconventional reservoirs. The comprehensive economic analysis of capture, transportation, sequestration costs, enhanced 45Q tax credits, and EOR revenue implies the practicality of CO 2 -EOR to offset the high CCUS costs. With the economics understanding, we study the top CO 2 sources, existing CO 2 pipelines, and sequestration sinks in the state of Colorado, USA. This paper next presents results from EOR simulation in one section of the unconventional Denver-Julesburg (DJ) Basin Niobrara and Codell reservoirs. The simulation model is based on a geological static model, incorporated with hydraulic fracture stimulation, history matched to production, and calibrated to the microseismic and time-lapse surface seismic data. The CO 2 -EOR simulation results show that oil production can be increased and more CO 2 stored with: a longer primary production period; the presence of a shut-in period; higher injection rates; and multi-well injectors. The modeling results show that about 7–10 Mscf of CO 2 will be stored when recovering 1 stb of EOR oil. By adding the enhanced oil revenue and the carbon credits together, it is estimated that the most economic case can generate $\$13$ MM when oil price is assumed to be $\$80$/stb, and the EOR oil revenue is 3.4 times greater than that generated from 45Q incentives. It corresponds to the scenario that a five-year primary production is followed by CO 2 injection into four wells with the sequence of injection (4 MMscf/day for 6 months), shut-in (6 months) and production (12 months). The best practices in this study will provide valuable insights for similar CCUS projects in other unconventional fields. Furthermore, this study defines a term named “Carbon Neutrality Index (CNI)” by comparing the amount of CO 2 stored with that burned by EOR oil. The CNI value of 0 indicates the enhanced oil is carbon neutral; a negative CNI value implies there is a net reduction in carbon emission. The 4-year huff-n-puff (HnP) simulation leads to a positive CNI value, indicating that the EOR oil generated in this process is not carbon neutral yet.

03 NATURAL GAS↗
DOE OSTI2025

Bridging the gap: Deploying AI-based Models in Real-Time Fusion Plasma Control Systems

Achieving reliable real-time control in fusion plasma experiments requires strict timing guarantees across entire control algorithms. In earlier work by Abbate et al. (2023), we demonstrated the feasibility of neural-network-based control algorithms on the DIII-D tokamak using the internally developed open-source Keras2C library for model conversion into C (Conlin et al. (2021)). However, the initial implementations relied on data buffering and branching logic outside the neural network code, causing variability in execution times. Subsequent deployments on DIII-D and KSTAR—including the RTCAKENN algorithm for kinetic profile reconstruction—proved that minimizing branching and buffering throughout the pipeline yields consistent millisecond-level cycle times under real experimental conditions (Shousha et al. (2023)). However, keeping pace with rapidly evolving AI frameworks (e.g. PyTorch) is challenging. Finally, we, therefore, propose a community-driven open-source effort to expand the tool, enabling real-time deployment across diverse systems that require strictly bounded execution times.

AI-based models↗
DOE OSTI2022

Feasibility of critical infrastructure protection using network functions for programmable and decoupled ICS policy enforcement over WAN

Industrial control systems (ICS) represent a major component of our critical infrastructure. With the increasing need for more control and monitoring of such systems, ICS have seen an increase in connectivity to wide area networks (WAN) exposing aging equipment to rapidly evolving cybersecurity threats. Furthermore, the ICS data requires a reliability measure from the networks for critical functions for infrastructure monitoring and control. Especially when remote plant sites are involved such as pipelines, energy distribution networks, and transportation, WAN transport impairments most often provide a best effort delivery with no strict reliability guarantees. Network functions can provide a vendor agnostic, programmable critical infrastructure protection with a single maintenance, policy determination, and reliability assurance surface. A network function (NF) can be utilized for policy enforcement over the communication between remote entities and the main control office. This paper presents the research on transparent integration with existing ICS without disrupting communications, resulting in minimal downtime while decoupling the fast paced evolution of defensive security measures from the upgrade cycle of expensive long term hardware. We report our measurements on the resource requirements and overhead in the network for successful NF insertion under a wide variety of network impairments (network packet delay, reordering, and loss). Our paired NF implementation provides a policy enforcement platform extensible to cover myriad cybersecurity-related communication goals, including packet signing for verification, encryption for data privacy, packet filtering and data diode operation (i.e. protecting against eavesdropping, packet injection, and denial-of-service). Furthermore, bundling communication specifications into packet flows allows for tunability in applying policies as coarse- or fine-grained as the needs of the operator. We report on network function resource requirements in the form of required queue depth and network utilization overhead to inform the decision making against hardware cost constraints.

42 ENGINEERING↗
DOE OSTI2020

Application of emerging monitoring techniques at the Illinois Basin – Decatur Project

The Illinois Basin – Decatur Project is a large-scale carbon capture and storage demonstration project located in Decatur, Illinois, USA. In this project, one million metric tons of carbon dioxide (CO 2 ) was captured from an ethanol production facility and successfully injected into a deep saline reservoir over a period of three years. The scale of this project presented an opportunity to explore emerging technologies for effective long-term monitoring of the carbon capture and storage site. This research documents the application of three emerging monitoring techniques: (1) a prototype “open-path” sensor, a method of continuously monitoring atmospheric CO 2 by applying tunable diode laser absorption spectroscopy to provide a warning system for personnel safety along pipelines and at wellheads; (2) the Greenhouse Gas Laser Imaging Tomography Experiment (GreenLITE), an automated system for measuring two-dimensional spatial distribution of atmospheric CO 2 concentrations; and (3) periodic aerial imagery, a method of documenting surface-vegetation dynamics to detect vegetative responses to CO 2 leaks. The objective of this work was to assess the advantages and limitations of these monitoring techniques and quantify their reliability over an extended deployment at an active industrial site. These results will aid in determining viability of long-term monitoring on a commercial scale.

42 ENGINEERING↗
DOE OSTI2022

A multi-criteria CCUS screening evaluation of the Gulf of Mexico, USA

Continued research into reservoir characterization along with offshore carbon dioxide (CO 2 ) transportation and infrastructure assets is needed to facilitate development of safe and successful carbon capture, utilization, and storage (CCUS) projects. This paper outlines a multi-criteria evaluation methodology that incorporates disparate sets of quantitative, spatially variable data into a decision-making framework for screening the Gulf of Mexico (GOM) outer continental shelf (OCS) for potentially viable CO 2 storage and enhanced oil recovery (EOR) sites. Criteria categories include favorable geologic characteristics, logistics, and potential risks. Data compiled for 14 criteria from several publicly available geographic information system (GIS) layers was aggregated over 2559 spatially balanced points across the study area using the National Energy Technology Laboratory (NETL)-developed Cumulative Spatial Impact Layers™ (CSIL) GIS tool. Criteria are weighted by qualitative expert opinion relative to their perceived importance to given scenarios— the output of combined criteria values and weights enables regional CO 2 storage suitability differentiation. The methodology considers both technical and non-technical factors impacting CCUS decision-making. The flexible methodology enables a systematic approach to regional ranking at high spatial resolution over a large study domain. Additionally, the framework enables high-grading of priority sites that warrant further characterization and follow-on analysis. Areas along the Louisiana coast and Mississippi River Delta consistently rank high for all scenarios largely a result of the favorable geology with the potential for stacked storage, as well as the density of existing pipelines and platforms, and proximity to several onshore CO 2 sources. High-graded regions for the CO 2 EOR-related scenarios are typically located further offshore towards the middle and edge of the OCS compared to higher priority regions for the geologic storage scenarios which fall closer to the Louisiana coastline.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Characterization of a selective, zero power sensor for distributed sensing of hydrogen in energy applications

The use of hydrogen as a clean and renewable energy source is increasing rapidly for both vehicle and stationary applications. There are safety concerns for the locations in which hydrogen is made, used, and transported (i.e., pipelines and tanker trucks). Sensors are needed to comply with safety regulations and to enable a smooth and safe rollout of hydrogen as an alternative energy. However, hydrogen sensors do not yet exist that have the combined features of small size and low power for easy deployment coupled with high-volume manufacturability and low cost. This is necessary to accommodate the emerging fixed and mobile markets while retaining critical metrological metrics, including measurement range, detection limits, selectivity, fast response, stability, and long lifetime. An amperometric gas sensor for hydrogen (AGS) has been developed using an innovative manufacturing method. The sensor was designed using scalable fabrication strategies based on “Printed Electronics” (PE) methodology which are compatible with large-scale production. Prototype sensors were batch fabricated with multiple individual elements on a substrate compatible in size with a standard 8-inch wafer to enable high-volume, low-cost manufacturing, thereby leveraging PE and semiconductor fabrication infrastructure. This novel AGS was instrumented with control circuitry and evaluated for hydrogen detection in the range of 0–5000 ppmv H2 in air. Specific performance evaluations included assessment of the sensor measurement range, repeatability, and selectivity.

08 HYDROGEN↗
DOE OSTI2025

Assessing hydrogen supply chains: An integrated review of leakage and energy efficiency studies

This paper examines hydrogen leakage and efficiency across the supply chain for liquid, gaseous, and mixed hydrogen systems. These factors are crucial for assessing hydrogen's role in mitigating emissions and facilitating a clean energy transition. Drawing on a comprehensive review of existing literature and model-based analysis, the study compiles leakage rates and efficiency metrics at each stage of the supply chain: production, storage, transmission, distribution, and end-use. These data inform system scenarios that estimate the impact of leakage on overall performance and climate benefits. The analysis also identifies persistent data gaps, particularly for liquid and mixed system configurations, and outlines priorities for future research. A comparison of hydrogen system types shows that gaseous pathways generally achieve the highest efficiencies (28 %–39 %) and the lowest leakage rates (∼4.5 %) across the supply chain. Liquid hydrogen systems, while favorable for long-distance and high-volume transport due to their higher energy density, exhibit lower efficiency (∼28 %) and a greater leakage potential (∼12 %). Mixed systems, which combine gaseous and liquid elements (e.g., pipeline transmission followed by liquefaction and truck distribution), show compounded energy losses and moderate-to-high leakage rates (6.8 %–9.4 %), highlighting trade-offs associated with added system complexity. The study highlights opportunities for technological advancements, including optimizing liquefaction, enhancing insulation for storage and transportation, and refining refueling equipment. These improvements are crucial for maximizing the climate benefits of hydrogen. The results offer actionable insights for researchers, industry, and policymakers working to develop low-leakage, high-efficiency hydrogen infrastructure.

08 HYDROGEN↗