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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
NASA NTRSDate not supplied

An Artificial Neural Network Approach to Predict Rotor-Airframe Acoustic Waveforms

A surrogate artificial neural network/machine learning model was developed to predict the acoustic interaction for a fixed-pitch rotor in proximity to a downstream cylindrical airframe typical of small Unmanned Aerial System (sUAS) platforms. The model was trained to predict the acoustic waveform under representative hover conditions as a function of rotational speed, airframe proximity, and observer angle. Training data were acquired in an anechoic chamber on both isolated rotors and rotor-airframe configurations. Acoustic amplitude and phase of the revolution-averaged interaction were predicted, which required up to 25 harmonics to capture the impulse event caused by the blade’s approach and departure from the airframe. Prediction performance showed, on average, that the models could estimate the acoustic amplitude and phase over the relevant harmonics for unseen conditions with 86% and 75% accuracy, respectively, enabling a time domain reconstruction of the waveform for the range of geometric and flow parameters tested.

acoustics↗
NASA NTRSDate not supplied

An Artificial Neural Network Approach to Predict Rotor-Airframe Acoustic Waveforms

A surrogate artificial neural network/machine learning model was developed to predict the acoustic interaction for a fixed-pitch rotor in proximity to a downstream cylindrical airframe typical of small Unmanned Aerial System (sUAS) platforms. The model was trained to predict the acoustic waveform under representative hover conditions as a function of rotational speed, airframe proximity, and observer angle. Training data were acquired in an anechoic chamber on both isolated rotors and rotor-airframe configurations. Acoustic amplitude and phase of the revolution-averaged interaction were predicted, which required up to 25 harmonics to capture the impulse event caused by the blade’s approach and departure from the airframe. Prediction performance showed, on average, that the models could estimate the acoustic amplitude and phase over the relevant harmonics for unseen conditions with 86% and 75% accuracy, respectively, enabling a time domain reconstruction of the waveform for the range of geometric and flow parameters tested.

acoustics↗
DOE OSTI2026

Artificial Intelligence Thermostat to Detect Faults

Residential air conditioners and heat pumps often experience faults due to inadequate maintenance, which can severely reduce efficiency or even cause system failure. Common issues include dirty or clogged air filters and refrigerant leaks. These problems degrade performance and increase energy use and operating costs. This study presents a smart thermostat with embedded artificial intelligence to detect such faults and alert homeowners when maintenance is needed. The thermostat uses low-cost measurements—including return-air temperature, relative humidity, supply-air temperature, outdoor-air temperature, and condenser subcooling—to identify abnormal operations. Because different faults produce distinct response patterns, tailored algorithms are developed to recognize characteristic fault signatures. The investigation is built on a detailed co-simulation platform that couples EnergyPlus with the DOE/ORNL Heat Pump Design Model (HPDM). EnergyPlus represents the building’s dynamic environment, while HPDM is a high-fidelity, hardware-based model that can simulate fault-free performance as well as a wide range of faults, including gradual degradation such as minor refrigerant leakage. This platform provides a virtual training and testing environment that helps distinguish fault-induced behavior from normal operation and supports development of robust diagnostic algorithms. Using this framework, a Dynamic Bayesian Network was developed to identify two common faults—gradual refrigerant charge loss and indoor airflow blockage—and the AI-embedded thermostat was verified through annual building simulations.

Shen, Bo [ORNL] (ORCID:0000000336600393)↗
NASA NTRS2003

SpaceDock: A Performance Task Platform for Spaceflight Operations

Preliminary evidence during both short- and long-duration spaceflight indicates that perceptual-motor coordination changes occur and persist in-flight. However, there is presently no in-flight method for evaluating astronaut performance on mission-critical tasks such as docking. We present a portable platform we have developed for attempting and evaluating docking, and describe the results of a pilot study wherein flight novices learned the docking task. Methods: A dual-joystick, six degrees of freedom platform-called SpaceDock-was developed to enable portable, adaptable performance testing in a spaceflight operations setting. Upon this platform, a simplified docking task was created, involving a constant rate of approach towards a docking target and requiring the user to correct translation in two dimensions and attitude orientation along one dimension (either pitch or roll). Ten flight naive subjects performed the task over a 45 min period and all joystick inputs and timings were collected, from which we could successfully reconstruct travel paths, input profiles and relative target displacements. Results: Subjects exhibited significant improvements in docking over the course of the experiment. Learning to compensate for roll-alterations was robust, whereas compensation for pitch-alterations was not in evidence in this population and relatively short training period. Conclusion: The SpaceDock platform can provide a novel method for training and testing subjects, on a spaceflight-relevant task, and can be used to examine behavioral learning, strategy use, and has been adapted for use in brain imaging experiments.

Marshburn, Thomas H.↗
NASA NTRSDate not supplied

Developing a Motion-Based System for Lunar Vehicle Handling Qualities Testing

— Motion and visual cue influences are critical in any simulator system, as they impact multiple aspects of the human’s neurovestibular and visual systems. Cues of real motion proceeds to the brain before cues of visual change. It is important therefore for simulator motion cues to exist and to match those realistically with those of the real vehicle to provide transferable training of the activity for operations. The United Kingdom’s Royal Air Force Institute of Aviation Medicine (1989)[1] stated that motion platforms are the only simulation devices capable of fully stimulating the body motion sensors. They confirmed that motion platforms can impart accelerations to the whole body and therefore exercise the automatic motion feedback-loop that operators are used to. With both visual and motion cues handling the vehicle becomes more realistic. Strachan (2019)[1] confirms motion cueing from a well set-up motion platform has been found to be important especially in conditions such as night or reduced visibility where motion cues may be more relied upon. As of this writing, the only lunar rover motion simulator is housed at the General Motors (GM) Milford Proving Ground, which only simulates the motions of a traditional car. A NASA Test Team proposed to complete a motion-based simulation system for NASA’s Lunar Terrain Vehicle (LTV) which resides at Johnson Space Center’s Systems Engineering Simulator facility. This activity integrated existing fixed base simulation capabilities with a newly procured six-degree of freedom motion base platform for the design, development, evaluation, and training associated with the LTV project. This would incorporate a South Pole Lunar virtual reality simulation using Lunar Reconnaissance Orbiter 5m/pixel high-resolution imagery and Unreal 5.2 Virtual Shadow Maps, combined with a virtual reality (VR) headset, integrated within a rover cockpit on a Mikrolar Motion Platform for evaluating human performance and vehicle handling qualities for concept roving vehicle designs. The motion-based system received its Human Rating Certification on March 2023. To begin testing the new facility, a preliminary handling qualities study was conducted. The objectives were to determine if there is a correlation amongst three handling quality methods of performance for a lunar vehicle and to understand the effects of a simulated 1/6-g loads and visuals with a motion platform on the operator while in a 1-g physical environment. The capability this system provides directly benefits the NASA’s Extravehicular Activity and Human Surface Mobility Program and NASA’s Flight Operations Directorate in evaluating the driving qualities of vehicle concepts, human performance related to the operating of a lunar surface vehicle, and assessment of suit related driving impacts. It would also provide direct operational benefit by providing a first-in class and unique simulation platform for the lunar astronaut training curriculum. The empirical knowledge of rover and human performance on this scale is paramount as there is currently no other lunar surface simulator with these capabilities. The result of this study ensures a broadly applicable method of testing for shirtsleeve, unpressurized and pressurized suited rover handling qualities.

Test Track↗
DOE OSTI2018

Strategic Environmental Assessment, key issues of its effectiveness. The results of the Speedy Project

This paper describes the results of the European Speedy Project, concerning the application of cross-border SEA, a research that had two main objectives. The first has been the definition of cooperation modalities between various public body as well as private entity through the realization of a digital platform. The aims of this innovative platform include sharing of multidisciplinary knowledge, the training, the participation, etc. The second objective has been to draft a proposal for revision of Directive 2001/42/EC based on the criticalities of implementation in individual Member States emerged during the development of the project. This revision has taken, in the final research report, the form of problem areas and suggestions for amending the Directive. A particularly important result, in addition to those related to platform implementation and the SEA Directive revision, is the e-learning section of the same platform. The continuous training provision of the e-learning system, as well as providing a valuable support for professional upgrading, can provide a useful link between the experiences developed by territorial authorities or individual professionals and the construction of a disciplinary and technical corpus that meet new challenges arising from the changes in society and the evolution of the environmental system.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS2005

Rotating Desk for Collaboration by Two Computer Programmers

A special-purpose desk has been designed to facilitate collaboration by two computer programmers sharing one desktop computer or computer terminal. The impetus for the design is a trend toward what is known in the software industry as extreme programming an approach intended to ensure high quality without sacrificing the quantity of computer code produced. Programmers working in pairs is a major feature of extreme programming. The present desk design minimizes the stress of the collaborative work environment. It supports both quality and work flow by making it unnecessary for programmers to get in each other s way. The desk (see figure) includes a rotating platform that supports a computer video monitor, keyboard, and mouse. The desk enables one programmer to work on the keyboard for any amount of time and then the other programmer to take over without breaking the train of thought. The rotating platform is supported by a turntable bearing that, in turn, is supported by a weighted base. The platform contains weights to improve its balance. The base includes a stand for a computer, and is shaped and dimensioned to provide adequate foot clearance for both users. The platform includes an adjustable stand for the monitor, a surface for the keyboard and mouse, and spaces for work papers, drinks, and snacks. The heights of the monitor, keyboard, and mouse are set to minimize stress. The platform can be rotated through an angle of 40 to give either user a straight-on view of the monitor and full access to the keyboard and mouse. Magnetic latches keep the platform preferentially at either of the two extremes of rotation. To switch between users, one simply grabs the edge of the platform and pulls it around. The magnetic latch is easily released, allowing the platform to rotate freely to the position of the other user

Riley, John Thomas↗
DOE OSTI2022

Hybrid Quantum-Classical Neural Networks

Deep learning is one of the most successful and far-reaching strategies used in machine learning today. However, the scale and utility of neural networks is still greatly limited by the current hardware used to train them. These concerns have become increasingly pressing as conventional computers are soon expected to approach the physical limitations that will slow their performance improvements in the near future. For these reasons, scientists have begun to explore alternative computing platforms, like quantum computers, for training neural networks. In recent years, variational quantum circuits have emerged as one of the most successful approaches to quantum deep learning on noisy intermediate scale quantum devices. We propose a hybrid quantum-classical neural network architecture where each neuron is a variational quantum circuit. We empirically analyze the performance of this hybrid neural network on a series of binary classification data sets using a simulated IBM universal quantum computer and a state-of-the-art IBM universal quantum computer. On the simulated hardware, we observe that the hybrid neural network achieves around 10% higher classification accuracy and 20% better minimization of the cost function than an individual variational quantum circuit. On the quantum hardware, we observe that each model only performs well when the qubit and gate count is sufficiently small.

Arthur, Davis↗
NASA NTRS2009

Small Earth Observing Satellites Flying with Large Satellites in the A-Train

This paper/poster presents a real-life example of the benefits of flying small satellites with other satellites, large or small, and vice versa. Typically, most small satellites fly payloads consisting of one or two instruments and fly in orbits that are independent from that of other satellites. The science data from these satellites are either used in isolation or correlated with instrument data from other satellites. Data correlation with other satellites is greatly improved when the measurements of the same point or air mass are taken at approximately the same time. Scientists worldwide are beginning to take advantage of the opportunities for improved data correlation, or coincidental science, offered by the international Earth Observing Constellation known as the A-Train (sometimes referred to as the Afternoon Constellation). Most of the A-Train satellites are small - the A-Train is anchored by two large NASA satellites (EOS-Aqua and EOS-Aura), but consists also of 5 small satellites (CloudSat, CALIPSO, PARASOL, OCO and Glory these last two will join in 2009). By flying in a constellation, each mission benefits from coincidental observations from instruments on the other satellites in the constellation. Essentially, from a data point of view, the A-Train can be envisioned as a single, virtual science platform with multiple instruments. Satellites in the A-Train fly at 705 km in sun-synchronous orbits. Their mean local times at the equator are within seconds to a few minutes of each other. This paper describes the challenges of operating an international constellation of independent satellites from the U.S. and Europe to maximize the coincidental science opportunities while at the same time minimizing the level of operational interactions required between team members. The A-Train mission teams have been able to demonstrate that flying as members of an international constellation does not take away the flexibility to accommodate new requirements. Specific examples will be cited, including CloudSat's relocation (to accommodate a new viewing angle for the CALIPSO satellite), Glory's replan to move closer to PARASOL, and OCO's long term plans to minimize on-orbit operations costs while maintaining safety. In all cases, safety is ensured, science returns are enhanced, and operational flexibility is retained to the maximum extent possible.

Kelly, Angelita C.↗
DOE OSTI2020

Replicating Machine Learning Experiments in Materials Science

Transparency and reproducibility are important aspects of validation for Machine Learning (ML) models that are not fully understood and applies independently of the application domain.We offer a case study of reproducibility that highlights the challenges encountered when attempting to reproduce analyzes obtained with Machine Learning methods in materials informatics. Our study explores prediction results obtained with ML models and issues in training data serving as input. We discuss challenges related to theory-driven and numerical errors in training data, lack of reproducibility across platforms and versions, and effects of randomness when varying hyperparameters. In addition to model accuracy, a main metric of interest in the ML community, our results show that model sensitivity may be equally important for applying ML in domain applications such a materials science.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Scalability analysis of heavy-duty gas turbines using data-driven machine learning

With the increasing integration of variable renewable energy sources into power systems, the role of flexible power generation technologies like gas turbines (GT) in rapid grid balancing remains crucial. This sustained importance underscores the need for scaled and precise modeling of GT to ensure effective integration within evolving energy frameworks. While physics-driven GT models integrate thermodynamics, fluid dynamics, and combustion principles, they often rely on approximate mathematical representations to accommodate scaling that may not capture the actual complex dynamics for GTs and inertial effects associated to GTs with different ratings. In this study, a data-driven model is proposed using machine learning (ML) techniques to conduct GT scalability analysis and performance evaluation with high accuracy. The ML model, trained on data from various operating conditions and performance parameters, aims to uncover intricate relationships and patterns, resembling GT characteristics at different scales (ratings). The model is developed to capture complex system interaction and to adapt to changing operational scenarios at different capacities, providing valuable insights of power system dynamics. In this study, the real-time digital simulator platform was employed to generate training data for the ML model and assess its dynamic characteristics. The ultimate objective was to develop a detailed modeling framework based on governing equations and data-driven ML capable of predicting key performance indicators, in thermal systems such as GTs, including power output, speed, fuel consumption, and exhaust temperature under diverse operating conditions at different scales. The developed ML framework demonstrated high accuracy, with mean relative errors for GT power prediction, reference speed, exhaust temperature, and compressor pressure ratio (CPR) parameters consistently below 0.1% across typical load fluctuation scenarios. Maximum deviations were limited to approximately 0.5 K for exhaust temperature and 0.009 for CPR, underscoring the model’s ability to replicating dynamic GT behavior with high precision. The adaptability of the ML model enables its application across diverse operational conditions and its extension to other thermal systems. By leveraging advanced ML techniques, this study presents a robust and scalable modeling framework that enhances GT simulation precision, facilitating improved integration into evolving power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRSDate not supplied

GL4U: Training the next generation of bioinformaticians, one omics datatype at a time

Spaceflight modifies gene expression in every organism examined to date, including humans. Understanding how these gene expression changes affect physiology is crucial for the development of countermeasures to enable long-duration manned missions. NASA’s GeneLab project provides researchers open access to multi-omics data, including genetic and gene expression data, from spaceflight experiments that can be mined to understand the effects of spaceflight on biological systems. To ensure new knowledge generation through data re-use, it is important to maximize the number of scientists who utilize GeneLab data. Training students on the GeneLab platform is the best way to create long-term adopters of this NASA database and its tools. Turning students into future instructors and advocates will also accelerate the dissemination of these data and tools to the broader scientific community. Therefore, in collaboration with the GeneLab Educational Working Group (EWG), GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – training of trainers), in which participants learn to analyze GeneLab’s space-relevant omics data. During the bootcamp, educators will receive materials and training to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative. The GL4U direct training pilot program was conducted in June 2021 in collaboration with USRA and San Jose State University (SJSU). During the pilot, SJSU students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrates the capacity of GL4U for training young scientists and encouraging data re-use.

Jonathan Matthew Galazka↗
NASA NTRSDate not supplied

Artemis Lunar Surface VR/ARGOS Trainer

This proposal aims to provide insight by identifying potential risks and unknowns of lander egress and surface operations through a Mixed Reality (MR) planning, training, and analysis capability that integrates Virtual Reality (VR) simulations and the Active Response Gravity Offload System (ARGOS) in support of Artemis missions to the moon. The VR simulation will incorporate lunar digital elevation map data and imagery to provide accurate terrain of the south pole and Shackleton Crater. Date specific ephemerides will used to simulate the extreme lighting environment. Virtual representations of a lunar lander vehicle will be represented with a physical mockup of the porch and ladder assembly. Human-in-the-loop engineering test runs within ARGOS will be used to refine performance of the Mixed Reality interface with the mockup platform and define procedures for training.

Lee K Bingham↗
NASA NTRS2007

A-Train Data Depot - Bringing Atmospheric Measurements Together

This paper describes the satellite data processing and services that constitute current functionalities of the A-Train Data Depot. We first provide a brief introduction to the original geometrical intricacies of the platforms and instruments of the A-Train constellation, and then proceed with description of our ATrain collocation processing algorithm that provides subsets that facilitate synergistic use of the various instruments. Finally, we present some sample image products from our web-based Giovanni tool which allows users to display, compare and download coregistered A-Train related data.

Savtchenko, Andrey↗
DOE OSTI2026

MSD CoP Webinar: "Advances in MSD-LIVE to Support the MSD Community of Practice"

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Advances in MSD-LIVE to Support the MSD Community of Practice Presenters: Casey Burleyson and Zoe Guillen (Pacific Northwest National Laboratory) Abstract: The MultiSector Dynamics Living, Intuitive, Value-adding, Environment (MSD-LIVE; msdlive.org) is a cloud-based data management system and advanced computing platform that enables MSD researchers to document and archive their data, run their models and analysis tools, and share their data, software, and workflows within the MSD Community of Practice. Recently, several high-profile datasets have attracted many new users to MSD-LIVE. This webinar has two goals: 1) To refamiliarize the MSD community and new users with the components of the platform (e.g., the data repository, model training notebooks, and data dashboards) and to highlight examples of how these components are advancing MSD science and 2) To demonstrate new features in v3 of the platform, released in late 2025. The main new feature in v3 is the ability to interactively explore data in MSD-LIVE without downloading it. MSD-LIVE users can now click a button in our data repository and launch a blank Jupyter notebook with access to the underlying data on AWS. Users can use the notebook to write analysis, visualization, or subsetting routines that process the data directly on the AWS cloud. We also added a GitHub integration feature that allows users to share analysis or visualization code they develop with the community of MSD-LIVE users. The webinar will wrap up with a look at what's coming next for MSD-LIVE in 2026. Moderator: Patrick M. Reed (MSD CoP Facilitation Team) This webinar was held on: May 12th, 2026 from 1-2 PM EST.

Open Science↗
NASA NTRS2010

Sensory Bias Predicts Postural Stability, Anxiety, and Cognitive Performance in Healthy Adults Walking in Novel Discordant Conditions

We designed a gait training study that presented combinations of visual flow and support surface manipulations to investigate the response of healthy adults to novel discordant sensorimotor conditions. We aimed to determine whether a relationship existed between subjects visual dependence and their scores on a collective measure of anxiety, cognition, and postural stability in a new discordant environment presented at the conclusion of training (Transfer Test). A treadmill was mounted to a motion base platform positioned 2 m behind a large visual screen. Training consisted of three walking sessions, each within a week of the previous visit, that presented four 5-minute exposures to various combinations of support surface and visual scene manipulations, all lateral sinusoids. The conditions were scene translation only, support surface translation only, simultaneous scene and support surface translations in-phase, and simultaneous scene and support surface translations 180 out-of-phase. During the Transfer Test, the trained participants received a 2-minute novel exposure. A visual sinusoidal roll perturbation, with twice the original flow rate, was superimposed on a sinusoidal support surface roll perturbation that was 90 out of phase with the scene. A high correlation existed between normalized torso translation, measured in the scene-only condition at the first visit, and a combined measure of normalized heart rate, stride frequency, and reaction time at the transfer test. Results suggest that visually dependent participants experience decreased postural stability, increased anxiety, and increased reaction times compared to their less visually dependent counterparts when negotiating novel discordant conditions.

Brady, Rachel A.↗
DOE OSTI2022

Anderson Acceleration for Distributed Training of Deep Learning Models

Anderson acceleration (AA) is an extrapolation technique that has recently gained interest in the deep learning (DL) community to speed-up the sequential training of DL models. However, when performed at large scale, the DL training is exposed to a higher risk of getting trapped into steep local minima of the training loss function, and standard AA does not provide sufficient acceleration to escape from these steep local minima. This results in poor generalizability and makes AA ineffective. To restore AA’s advantage to speed-up the training of DL models on large scale computing platforms, we combine AA with an adaptive moving average procedure that boosts the training to escape from steep local minima. By monitoring the relative standard deviation between consecutive iterations, we also introduce a criterion to automatically assess whether the moving average is needed. We applied the method to the following DL instantiations for image classification: (i) ResNet50 trained on the open-source CIFAR100 dataset and (ii) ResNet50 trained on the open-source ImageNet1k dataset. Numerical results obtained using up to 1,536 NVIDIA V100 GPUs on the OLCF supercomputer Summit showed the stabilizing effect of the moving average on AA for all the problems above.

Lupo Pasini, Massimiliano↗
DOE OSTI2016

HySafe research priorities workshop report Summary of the workshop organized in cooperation with US DOE and supported by EC JRC in Washington DC November 10-11 2014.

The HySafe research priorities workshop is held on the even years between the International Conference on Hydrogen Safety (ICHS) which is held on the odd years. The research priorities workshop is intended to identify the state-of-the-art in understanding of the physical behavior of hydrogen and hydrogen systems with a focus on safety. Typical issues addressed include behavior of unintended hydrogen releases, transient combustion phenomena, effectiveness of mitigation measures, and hydrogen effects in materials. In the workshop critical knowledge gaps are identified. Areas of research and coordinated actions for the near and medium term are derived and prioritized from these knowledge gaps. The stimulated research helps pave the way for the rapid and safe deployment of hydrogen technologies on a global scale. To support the idea of delivering globally accepted research priorities for hydrogen safety the workshop is organized as an internationally open meeting. In attendance are stakeholders from the academic community (universities, national laboratories), funding agencies, and industry. The industry participation is critically important to ensure that the research priorities align with the current needs of the industry responsible for the deployment of hydrogen technologies. This report presents the results of the HySafe Research Priorities Workshop held in Washing- ton, D.C. on November 10-11, 2014. At the workshop the participants presented updates (since the previous workshop organized two years before in Berlin, Germany) of their research and development work on hydrogen safety. Following the workshop, participants were asked to provide feedback on high-priority topics for each of the research areas discussed and to rank research area categories and individual research topics within these categories. The research areas were ranked as follows (with the percentage of the vote in parenthesis): 1. Quantitative Risk Assessment (QRA) Tools (23%) 2. Reduced Model Tools (15%) 3. Indoor (13%) 4. Unintended Release-Liquid (11%) 5. Unintended Release-Gas (8%) 6. Storage (8%) 7. Integration Platforms (7%) 8. Hydrogen Safety Training (7%) 9. Materials Compatibility/Sensors (7%) 10. Applications (2%) The workshop participants ranked the need for Quantitative Risk Analysis (QRA) tools as the top priority by a large margin. QRA tools enable an informed expert to quantify the risk asso- ciated with a particular hydrogen system in a particular scenario. With appropriate verification and validation such tools will enable: * system designers to achieve a desired level of risk with suitable risk mitigation strategies, * permitting officials to determine if a particular system installation meets the desired risk level (performance based Regulations, Codes, and Standards (RCS) rather than prescrip- tive RCS), and * allow code developers to develop code language based on rigorous and validated physical models, statistics and standardized QRA methodologies. Another important research topic identified is the development of validated reduced physical models for use in the QRA tools. Improvement of the understanding and modeling of specific release phenomena, in particular liquid releases, are also highly ranked research topics. Acknowledgement The International Association HySafe, represented here by the authors, would like to thank all participants of the workshop for their valuable contributions. Particularly appreciated is the active participation of the industry representatives and the steady support by the European Com- mission's Joint Research Centre (JRC). Deep gratitude is owed for the great support by the United States Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy's Fuel Cell Technologies Office (EERE/FCTO) for the organization of the 2014 version of the hydrogen safety research priorities workshop. This page intentionally left blank.

08 HYDROGEN↗