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DOE OSTI2016

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by input files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with software such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focusing on the input structure.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

RAVEN Theory Manual

RAVEN is a software framework able to perform parametric and stochastic analysis based on the response of complex system codes. The initial development was aimed at providing dynamic risk analysis capabilities to the thermohydraulic code RELAP-7, currently under development at Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose stochastic and uncertainty quantification platform, capable of communicating with any system code. In fact, the provided Application Programming Interfaces (APIs) allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by input files or via python interfaces. RAVEN is capable of investigating system response and explore input space using various sampling schemes such as Monte Carlo, grid, or Latin hypercube. However, RAVEN strength lies in its system feature discovery capabilities such as: constructing limit surfaces, separating regions of the input space leading to system failure, and using dynamic supervised learning techniques. The development of RAVEN started in 2012 when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework arose. RAVEN’s principal assignment is to provide the necessary software and algorithms in order to employ the concepts developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just to identify the frequency of an event potentially leading to a system failure, but the proximity (or lack thereof) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. peak pressure in a pipe) is exceeded under certain conditions. Most of the capabilities, implemented having RELAP-7 as a principal focus, are easily deployable to other system codes. For this reason, several side activates have been employed (e.g. RELAP5-3D, any MOOSE-based App, etc.) or are currently ongoing for coupling RAVEN with several different software. The aim of this document is to provide a set of commented examples that can help the user to become familiar with the RAVEN code usage.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analy- sis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncer- tainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is in- terested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assess- ment capability to RELAP-7, currently under develop-ment at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explaination of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analy- sis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncer- tainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is in- terested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assess- ment capability to RELAP-7, currently under develop-ment at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explaination of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

RAVEN User Manual, Revision 4

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assess- ment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

RAVEN Theory Manual

RAVEN is a software framework able to perform parametric and stochastic analysis based on the response of complex system codes. The initial development was aimed at providing dynamic risk analysis capabilities to the thermohydraulic code RELAP-7, currently under development at Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose stochastic and uncertainty quantification platform, capable of communicating with any system code. In fact, the provided Application Programming Interfaces (APIs) allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by input files or via python interfaces. RAVEN is capable of investigating system response and explore input space using various sampling schemes such as Monte Carlo, grid, or Latin hypercube. However, RAVEN strength lies in its system feature discovery capabilities such as: constructing limit surfaces, separating regions of the input space leading to system failure, and using dynamic supervised learning techniques. The development of RAVEN started in 2012 when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework arose. RAVEN’s principal assignment is to provide the necessary software and algorithms in order to employ the concepts developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just to identify the frequency of an event potentially leading to a system failure, but the proximity (or lack thereof) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. peak pressure in a pipe) is exceeded under certain conditions. Most of the capabilities, implemented having RELAP-7 as a principal focus, are easily deployable to other system codes. For this reason, several side activates have been employed (e.g. RELAP5-3D, any MOOSE-based App, etc.) or are currently ongoing for coupling RAVEN with several different software. The aim of this document is to provide a set of commented examples that can help the user to become familiar with the RAVEN code usage.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Assessing Customer Experience and Business Models around Price-to-Device Communication and Smart Control Pathways in CalFlexHub

California is facing three major challenges in electrical grid operation: renewable overgeneration, steep evening ramping, and growing peak demand. The state has identified dynamic retail price response as a key strategy evidenced by CPUC’s Dynamic Rates proceeding and CEC’s Load Management Standards. Furthermore, the CEC launched a $16M “California Load Flexibility Research and Deployment Hub (CalFlexHub)” administered by Berkeley Lab to accelerate price-response flexible load technologies in buildings and EV charging. There are more than 16 laboratory and field demonstration projects in CalFlexHub, each demonstrating innovative automated price-response technologies. CalFlexHub tests various pathways through which hourly price signals and triggered control commands are communicated to load-flexible devices such as smart thermostats, heat pumps, water heaters, and EVs. We identified seven unique communication and control pathways, which involve combinations of third-party cloud, device OEM’s cloud, building central gateway, and local controller in between the price server and the load-flexible devices. It is important for utilities and policy makers to understand the long-term implications of each pathway in designing future programs and creating related policies and mandates for market transformation. We propose an evaluation framework including the following aspects: ● Functionality: connectivity and uptime, resilience, and optimization; ● Customer experience: simplicity in setup, troubleshooting support, continuity, customer choice, first cost, and ongoing cost; ● Business model and scalability: advance interoperability, holistic solution, bridge unique gap, customer base, and value streams and pricing structures. In this paper, we identify emerging business models associated with each communication pathway and discuss their positive features and challenges from the above aspects.

Liu, Jingjing↗
DOE OSTI2018

Methanol Oxidation to Formaldehyde Promoted at the Step Sites of Ultrathin ZnO

Adsorption and oxidation of methanol on ultrathin ZnO layers supported on Au(111) have been investigated using temperature programmed reaction spectroscopy (TPRS) and density functional theory (DFT) calculations. In the TPRS experiments, following adsorption of methanol- 18 O at T = 100 K, only molecular methanol- 18 O desorbed from the planar ZnO bilayer surface at T = 220 and 260 K, whereas a partial oxidation product, formaldehyde- 18 O (~95% selectivity), and a small amount of carbon dioxide (C 16 O 18 O) were produced at T = 580 K at the bilayer–trilayer step sites. The DFT calculations were used to identify the adsorption configurations of methanol on the planar ZnO surface and at the step sites, as well as the reaction pathways to gaseous formaldehyde. The most stable adsorption configuration corresponds to methanol molecule adsorbed at the bilayer–trilayer step sites with its C–O axis parallel to the upper terrace edge, forming a bond between its O atom and a Zn site on the lower terrace, and also a hydrogen bond between its H atom in the OH group and a lattice O anion at the upper terrace edge. Starting from the most stable adsorption configuration at the step sites, formation of gaseous formaldehyde was found to take place preferentially via a methoxy (CH 3 O(ad)) intermediate. This process follows the pathways CH 3 OH(ad) → CH 3 O(ad) + H(ad) → CH 2 O(g) + 2H(ad) and has an overall barrier of 19.0 kcal/mol. The reaction pathway to produce a lattice O-bonded formaldehyde (H 2 COOlattice(ad)), the proposed precursor leading to CO 2 , was found to be energetically less favorable with a barrier of ~38 kcal/mol. As a result, the preference to produce gaseous formaldehyde from the DFT calculations agrees well with the high selectivity toward formaldehyde observed in the TPRS experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Workforce Development Bioenergy Experiential Learning Tool (Final Report)

The "Pathways to Bio-Power" program was designed as a workforce development initiative aimed at addressing the workforce gap in the bioenergy sector while promoting diversity and inclusion. The program incorporated advanced technologies, such as artificial intelligence (AI) and learning algorithms, to provide individualized training plans and pathways for Minority Business Enterprises (MBEs) and the current workforce to enter the bioenergy industry. However, after careful consideration, it was determined that proceeding to Phase II was not feasible due to several reasons related to resource allocation, market demand, and technical challenges.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

Effects of experimentally added salmon subsidies on resident fishes via direct and indirect pathways

Abstract Artificial additions of nutrients of differing forms such as salmon carcasses and analog pellets (i.e. pasteurized fishmeal) have been proposed as a means of stimulating aquatic productivity and enhancing populations of anadromous and resident fishes. Nutrient mitigation to enhance fish production in stream ecosystems assumes that the central pathway by which effects occur is bottom‐up, through aquatic primary and secondary production, with little consideration of reciprocal aquatic‐terrestrial pathways. The net outcome (i.e. bottom‐up vs. top‐down) of adding salmon‐derived materials to streams depend on whether or not these subsidies indirectly intensify predation on in situ prey via increases in a shared predator or alleviate such predation pressure. We conducted a 3‐year experiment across nine tributaries of the N. Fork Boise River, Idaho, USA , consisting of 500‐m stream reaches treated with salmon carcasses (n = 3), salmon carcass analog (n = 3), and untreated control reaches (n = 3). We observed 2–8 fold increases in streambed biofilms in the 2–6 weeks following additions of both salmon subsidy treatments in years 1 and 2 and a 1.5‐fold increase in standing crop biomass of aquatic invertebrates to carcass additions in the second year of our experiment. The consumption of benthic invertebrates by stream fishes increased 110–140% and 44–66% in carcass and analog streams in the same time frame, which may have masked invertebrate standing crop responses in years 3 and 4. Resident trout directly consumed 10.0–24.0 g·m −2 ·yr −1 of salmon carcass and <1–11.0 g·m −2 ·yr −1 of analog material, which resulted in 1.2–2.9 g·m −2 ·yr −1 and 0.03–1.4 g·m −2 ·yr −1 of tissue produced. In addition, a feedback flux of terrestrial maggots to streams contributed 0.0–2.0 g·m −2 ·yr −1 to trout production. Overall, treatments increased annual trout production by 2–3 fold, though density and biomass were unaffected. Our results indicate the strength of bottom‐up and top‐down responses to subsidy additions was asymmetrical, with top‐down forces masking bottom‐up effects that required multiple years to manifest. The findings also highlight the need for nutrient mitigation programs to consider multiple pathways of energy and nutrient flow to account for the complex effects of salmon subsidies in stream‐riparian ecosystems.

Collins, Scott F.↗
DOE OSTI2018

Improved Overall Survival and Locoregional Disease Control With Concurrent PD-1 Pathway Inhibitors and Stereotactic Radiosurgery for Lung Cancer Patients With Brain Metastases

Despite the emerging role of programmed cell death-1 (PD-1) pathway inhibitors for patients with advanced lung cancer, a paucity of data are available on the activity of these agents among patients with brain metastases. We investigated the outcomes of PD-1 pathway inhibitors and stereotactic radiosurgery (SRS) for the treatment of patients with brain metastases from lung cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Systematically programmed adaptive evolution reveals potential role of carbon and nitrogen pathways during lipid accumulation in Chlamydomonas reinhardtii

Background: The concept of adaptive evolution implies underlying genetic mutations conferring a selective advantage to an organism under particular environmental conditions. Thus, a flow cytometry-based strategy was used to study the adaptive evolution in Chlamydomonas reinhardtii wild-type strain CC124 and starchless mutant sta6-1 cells, with respect to lipid metabolism under nitrogen-(N) depleted and -replete conditions. Results: The successive sorting and regeneration of the top 25,000 high-lipid content cells of CC124 and sta6-1, combined with nitrogen starvation, led to the generation of a new population with an improved lipid content when compared to the original populations (approximately 175% and 50% lipid increase in sta6-1 and CC124, respectively). During the adaptive evolution period, the major fatty acid components observed in cells were C16:0, C16:1, C18:0, and C18:1-3, and elemental analysis revealed that cellular carbon to nitrogen ratio increased at the end of adaptive evolution period In order to gain an insight into highly stimulated intracellular lipid accumulation in CC124 and sta6-1 resulting from the adaptive evolution, proteomics analyses of newly generated artificial high-lipid content populations were performed. Functional classifications showed the heightened regulation of the major chlorophyll enzymes, and the enzymes involved in carbon fixation and uptake, including chlorophyll-ab-binding proteins and Rubisco activase. The key control protein (periplasmic L-amino acid oxidase (LAO1)) of carbon-nitrogen integration was specifically overexpressed. Glutathione-S-transferases and esterase, the enzymes involved in lipid-metabolism and lipid-body associated proteins, were also induced during adaptive evolution. Conclusions: Adaptive evolution results demonstrate the potential role of photosynthesis in terms of carbon partitioning, flux, and fixation and carbon-nitrogen metabolism during lipid accumulation in microalgae. This strategy can be used as a new tool to develop C. reinhardtii strains and other microalgal strains with desired phenotypes such as high lipid accumulation.

09 BIOMASS FUELS↗
DOE OSTI2020

The crystal structure of a Polerovirus exoribonuclease-resistant RNA shows how diverse sequences are integrated into a conserved fold

Exoribonuclease-resistant RNAs (xrRNAs) are discrete elements that block the progression of 5' to 3' exoribonucleases using specifically folded RNA structures. A recently discovered class of xrRNA is widespread in several genera of plant-infecting viruses, within both noncoding and protein-coding subgenomic RNAs. The structure of one such xrRNA from a dianthovirus revealed three-dimensional details of the resistant fold but did not answer all questions regarding the conservation and diversity of this xrRNA class. Here, we present the crystal structure of a representative polerovirus xrRNA that contains sequence elements that diverge from the previously solved structure. This new structure rationalizes previously unexplained sequence conservation patterns and shows interactions not present in the first structure. Together, the structures of these xrRNAs from dianthovirus and polerovirus genera support the idea that these plant virus xrRNAs fold through a defined pathway that includes a programmed intermediate conformation. This work deepens our knowledge of the structure-function relationship of xrRNAs and shows how evolution can craft similar RNA folds from divergent sequences.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Continuous Fiber Composite Electrofusion Couplers (Final Report)

The objective of this three-year project was to develop a pipe coupler for high-pressure (100bar service, 350bar burst) composite hydrogen pipelines, without the use of elastomeric sealing mechanisms (e.g. o-rings), thereby avoiding seal maintenance costs and effort associated with digging up subterranean lines over a pipe’s lifetime of up to 50 years. The project aimed to build on past designs at (TRL 3), to adapt their use to hydrogen delivery and test in a relevant environment (TRL 5). This work is toward the DOE’s objective to reduce the technical barrier of high as-installed cost of pipelines, by utilizing fiber reinforced plastic (FRP) pipe. Further DOE targets include those from the Fuel Cell Technologies Office Multi-Year Research, Development, and Demonstration Plan: Milestone 1.5: Coordinating with the H2 Production and Storage sub-programs, identify optimized delivery pathways that meet a H2 delivery and dispensing cost of <\$ 2/gge for use in consumer vehicles. (4Q, 2020) Milestone 6.3: By 2020, reduce the cost of hydrogen delivery from the point of production to the point of use in consumer vehicles to <\$ 2/gge of hydrogen for the gaseous delivery pathway (4Q, 2020). This project headed by Automated Dynamics (now Trelleborg) with partners Savanah River National Laboratory, and NOV Fiber Glass Systems, emerged due to a similar previous project with the DOE, completed by SNRL and NOV Fiber Glass Systems (formerly Fiberspar) in 2014, that evaluated the mechanical suitability of FRP (composite) lines for these same high pressures. That projected concluded with a favorable outlook on using composite lines and required a suitable pipe coupler. The resulting coupler design from this current project employs electrofusion due to its proven use on low pressure unreinforced lines and for its simplicity in the field. This electrofusion coupler design utilizes established concepts for pipe couplers and combines them in a new and unique method. Existing pipe couplers are employed on thermoset pipes in high pressure, with elastomeric seals as the sealing mechanism, and on thermoplastic low-pressure pipes by way of electrofusion. However, no EF coupler is available for composite pipelines for 350bar hydrogen containment.

08 HYDROGEN↗
DOE OSTI2025

Leveraging a Southern California Energy Innovation Cluster to Pilot and Validate Emerging Energy Technologies

The EPIC Pilot Program, led by the Los Angeles Cleantech Incubator (LACI), was designed to help early-stage clean technology startups accelerate technical validation, investment readiness, and market entry. The project focused on supporting startups in designing and deploying small-scale pilots with the guidance of EPIC Partners, a network of regional stakeholders in the Southern California energy ecosystem, including but not limited to: the Los Angeles Department of Water and Power (LADWP), the California Energy Commission (CEC), Los Angeles County Metropolitan Transportation Authority (LA Metro), and Edison International, among others. Through mentorship, funding, and site access for technology deployments, the program provided a structured pathway for startups to test and refine their technologies in real-world settings.

14 SOLAR ENERGY↗
DOE OSTI2024

Transforming Cyber Education thru Open to All Accessible Pathways

Boise State University’s (BSU) Cyber Operations and Resilience CORe program was intentionally designed so that any student, especially non-traditional and non-technical students, with an interest in cybersecurity could have an education and training pathway to enter the cyber workforce. The CORe curriculum focuses on teaching students how to design, apply, and improve cybersecurity through the interaction of people, processes, and technology. CORe is a stackable curriculum with elective credit hours and options for various academic and industry certificates and certifications that enable students to customize their unique career pathway. The CORe program guides students to think about the system being managed, the risks presented, and the dynamic intersection of system elements when considering how to incorporate resilience frameworks in achieving a resilient system. By developing systems thinking, the students gain an understanding of the interdependencies interacting with the operational system. Further, the CORe program encourages students to integrate cybersecurity knowledge with models and frameworks found in other academic disciplines through a unifying systems approach. CORe is designed around the realities of today’s broad cyber landscape: that breaches will occur in any system over time and proactive design of resilience into systems to detect, respond, and recover in a timely and orderly manner is critical. Students are taught to think holistically about cybersecurity focusing on all system elements. CORe is not a traditional cybersecurity degree. CORe is distinguished by the non-traditional engineering, computer science approach to cybersecurity education with the singular focus on infusing resilience operations and transdisciplinary systems thinking principles throughout the curriculum.

99 GENERAL AND MISCELLANEOUS↗