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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Outcomes of HPC User Support using a Science Gateway AI Assistant

High Performance Computing (HPC) is a vital resource for nuclear energy research, facilitating advanced simulations and complex modeling of the quantification and qualification of advanced reactor technology. However, a common gap in knowledge exists around utilizing HPC systems, particularly for nuclear energy researchers unfamiliar with specific HPC systems. A researcher may be well-versed in using one HPC system and understanding its associated processes. Yet, they might struggle when faced with a different HPC system and its unique processes. HPC support staff play a crucial role in addressing these challenges by providing educational resources and assisting users. However, they also face the challenge of maintaining these systems and ensuring they run efficiently for all users, a responsibility that can be challenging to scale effectively with the increasing demand and expansion of HPC systems. This paper addresses this knowledge gap with an artificial intelligence (AI) assistant that offers on-demand, site-specific HPC support for researchers. Idaho National Laboratory (INL) has deployed an AI assistant that is intended to supplement expert HPC support staff and assist nuclear energy researchers. This paper reports on a four-and-a-half-month study evaluating the integration of an AI assistant within a science gateway, with the goal of enhancing existing HPC support.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Lawrence Livermore National Laboratory (LLNL) Laboratory Directed Research and Development (LDRD) Annual Report (FY 2023)

As Lawrence Livermore National Laboratory’s most significant resource for supporting internally directed research and development, the LDRD Program provides investments in cutting-edge science and technology that allow the Laboratory to attract and retain the world’s most talented scientists and engineers and enables them to expand the frontiers of knowledge and anticipate emerging national security challenges. In this annual report, we summarize how Lawrence Livermore National Laboratory (LLNL) uses LDRD investments to advance our knowledge in strategic science and technology domains, develop our world-class workforce, and foster innovation in key programmatic areas.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Survey and Gap Prioritization of U.S. Electric Vehicle Charge Management Deployments

The goal of this study was to survey and characterize the scope of current technical and programmatic knowledge pertaining to EV charge management technologies and practices in the US and relevant international jurisdictions. This characterization of existing field demonstrations and knowledge derived were used to determine gaps in the SCM demonstration landscape. Addressing these gaps through research and demonstration could increase confidence in the U.S. that load management and EV charge control could achieve overarching societal benefits. A survey of charge management deployments and input from stakeholders was completed to determine the state-of-the-art of smart charge management (SCM) where SCM is defined as controlling the amount of power exchanged between chargers and EVs to meet customers' charging needs while also responding to external power demand or pricing signals to provide load management, resilience, or other benefits to the customer and electric grid. The survey was the basis of the gap analysis in this report and determines which areas are well understood, with high confidence, and which areas need further investigation. Existing examples of EV charge management are characterized here to determine aspects that are ready for widespread deployment and have been demonstrated in the field. These include demonstration studies, pilots, programs, and EV-specific tariffs. In all, 110 examples of charge management were characterized. The data sources were public literature and utility filings as well as targeted interviews. In addition, 43 interviews with stakeholders were conducted with a consistent set of questions used in each interview. This study prioritized gaps in demonstrated SCM capabilities based on 1) Urgency of the particular use-case to offset traditional grid assets, 2) Impact, extensibility, and scaling of results across the entire spectrum of 3000+ utility service territories including projected technical and market potential for a given grid service, and 3) Value of federal funding in addressing the gap, including potential to leverage and/or add scope to existing field demonstrations funded by other non-federal funding mechanisms.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Actinides and Neutron Scattering at the Spallation Neutron Source and High Flux Isotope Reactor

The importance of actinides for science, technology, and medicine is widely recognized and does not require a lengthy introduction. However, the fundamental understanding of actinides and their chemical and physical properties lags behind the knowledge of most other elements in the periodic table. Most actinides (89 ≤ Z ≤ 103) are man-made and were produced in particle accelerators or nuclear reactors in the second half of the 20th century. Although trends in bonding and electronic structure are well characterized and documented across the periodic table, fundamental questions remain regarding the chemistry and physics of the actinides. This lack of knowledge is largely because most isotopes of actinides are intensely radioactive, which greatly increases the difficultly in handling them.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Final Technical Report: Center for Mechanistic Control of Unconventional Formations

The overarching mission of the Center for Mechanistic Control of Unconventional Formations (CMC-UF) was to garner cross-cutting, fundamental, geoscience knowledge to achieve mechanistic control over the strongly coupled nonequilibrium physical and geochemical processes in extreme geological environments including shale, mudstone, marls, and other tight rocks with nanoscale pores. Collectively, these are referred to as unconventional formations and they often play the role of seals for other subsurface storage formations. The fundamental knowledge garnered by CMC-UF enabled science-based management of US unconventional formations for subsurface storage of carbon dioxide and TWh quantities of renewable energy as hydrogen and/or compressed air over longer timeframes as well as for natural gas production, with reduced environmental impacts, in the short term.

58 GEOSCIENCES↗
DOE OSTI2024

CEERP SM3 Research, Monitoring, and Evaluation Workshop: Summary Report

The Columbia Estuary Ecosystem Restoration Program (CEERP) workshop, “CEERP Synthesis Memorandum (SM3): Research, Monitoring, and Evaluation,” was held in Portland, Oregon on June 24–25 2024. A diverse group of 26 domain experts with demonstrated knowledge and experience working in the Lower Columbia River Estuary (LCRE) participated. The workshop supported development of the forthcoming third Synthesis Memorandum for CEERP. The workshop furthered the collaborative understanding of the state of the science regarding the LCRE, helped to identify remaining knowledge gaps and uncertainties, and assisted in the prioritization of future restoration research and monitoring.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Proteomics Analysis of Human Contaminant Proteins

Complete characterization of unknowns via proteomics remains challenging. There exist regions of mass spectrometry-based proteomics data where empirical measurements are not attributed to peptides, and/or sequenced peptides from mass spectra are not attributed to any source. These uncharacterized regions are known as the “dark” proteome. Many proteomics tools rely on some a priori knowledge of sample composition; few tools allow for investigation of unknowns without relying on composition assumptions. Further, the potential low abundance of minor traces in these uncharacterized regions can make elucidation of the “dark” proteome challenging. Herein, we describe the development and evaluation of approaches to study the “dark” proteome and move towards an untargeted approach for more complete characterization, namely by studying minor human protein traces in non-human samples and combining that approach with non-human source organism identification without relying on assumptions. Human protein markers, in the form of genetically variant peptides, have been extensively examined in a variety of human matrices, including blood, plasma, and hair, but have yet to be investigated in non-human samples, such as cell cultures, as human contaminant traces. Genetically variant peptides are those that are found in proteins carrying single nucleotide polymorphisms. In this work, we aimed to (1) investigate the feasibility of detecting human contaminant genetically variant peptides (GVPs) in a diverse set of non-human organisms using public proteomics data and a computational pipeline, as well as to (2) develop a combined capability for untargeted source organism characterization and GVP detection. To our knowledge, this is the first report of applying these approaches towards a more complete proteomic characterization of unknowns. We successfully demonstrate the feasibility of broad human contaminant GVP detection in proteomics data, develop a better understanding of GVP detectability, characterize the sample-to-sample variability in GVP detection, and identify a core set of GVPs that can potentially be used as markers indicative of the human contaminant traces portion of the “dark” proteome. Further, we developed and evaluated a combined pipeline, MARLOWE-GVP, that enables both untargeted source organism characterization and GVP detection. We show high accuracy of correct source organism characterization and high degree of similarity of human contaminant GVP detection compared to the conventional approach. Success on both these efforts have allowed us to advance our understanding and characterization of the “dark” proteome.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Diaspora: Resilience-enabling services for science from HPC to edge

Scientific applications of interest to DOE must increasingly engage distributed resources (e.g., instruments, remote computers, data stores, edge devices) and deliver more stringent levels of service (e.g., uninterrupted processing of experiment data streams). In such systems, state is distributed and components can fail in many ways, often silently, making application resilience a major concern. Addressing the resilience needs of such applications requires methods for gaining knowledge of resources and applications and for translating that knowledge into action. We are working on addressing these needs in the context of multi-messenger astronomy, where detecting and responding to unusual transient events in multiple cosmic messengers (gravitational wave, electromagnetic, high- energy particles) from different instruments leads to a federated learning problem.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Statistically-driven Experimental Design to Improve Reference-free Quantification of Small Molecules by Liquid Chromatography-Mass Spectrometry

Non-targeted analysis of small molecules and metabolites in unknown, complex samples using liquid chromatography-tandem mass spectrometry remains challenging. One of the main bottlenecks is the extensive unannotated regions of metabolomics mass spectrometry data, resulting in knowledge gaps. Small molecule annotation in mass spectrometry data has conventionally relied on reference standards and libraries for compound identification and confirmation, which can constrain compound identification to those molecules already known, thus limiting the ability to discover new knowledge and new markers. Retention time prediction can facilitate and expedite unknown compound identification in non-targeted analysis of complex metabolomics samples. Additionally, accurate retention time predictions can also inform sample mixture design for LC-MS/MS analyses. However, current machine learning-based methods for retention time prediction are typically developed for specific chromatographic platforms and are not generalizable across scales. And while technologies and methods to improve reference-free metabolite identification for more comprehensive annotation of unknowns has received much attention, development of the same for quantitation without reference standards has been much more limited, despite its importance in toxicological, environmental, food safety, forensics, and clinical applications. We believe that a reference-free quantitation strategy that exploits mass spectrometry data already collected for reference-free identification can provide much more insight on unknowns, and move the metabolomics field for more complete unknowns characterization. As such, we pursue two efforts to improve upon current state-of-the-art methods in non-targeted analysis: (1) machine learning-based retention time prediction and (2) statistical design of experiments framework for reference-free quantitation. In this work, we develop and demonstrate (1) a generalizable retention time prediction capability across chromatographic conditions and scales, and (2) a statistical design-based framework for response factor contribution elucidation and reference-free quantitation. Evaluation of our retention time prediction model, PrediToR, showed approximately 24% improvement over current models, and we observed approximately 10X improvement in concentration estimation accuracy from our statistical design-based response factor model over a primarily ionization efficiency-based model. We expect that future efforts to improve upon these new capabilities will further advance non-targeted analysis of small molecules towards truly reference-free metabolomics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Investigation of Frozen Chemistry for Molten Salt Reactors (MSRs)

Understanding accident progression and the potential/conditions for fission product release from fuel is necessary to evaluate safety for any nuclear reactor system. Molten Salt Reactors (MSRs) under development need such analysis to support safety evaluations. Fission product chemistry specific to MSR concepts is a critical area that introduces distinct considerations relative to the current state-of-knowledge in reactor safety, primarily developed for water-moderated nuclear reactor systems. In Light Water Reactor (LWR) systems, it is necessary to capture the chemical interaction of fission products with the reactor evironment, containment and confinement systems. The overall effects at this point are relatively well understood for the purposes of performing safety evaluations. A key insight from LWR studies is that fission product chemical behavior can be reasonably captured by modeling approaches where the chemistry is "frozen". These modeling approaches assume that radionuclide reaction and speciation can be represented by chemical classes, each with characteristic transport behavior that is invariant under a broad range of thermochemical conditions. However, radionuclides can exhibit a range of behavior in the liquid salt-melt phase of the coolant used in MSRs. Radionuclides, salt, and the metal containment surfaces (i.e. pipes) can co-exist in dynamic equilibrium that could evolve with small system mass changes. A detailed investigation to the degree the equilibrium state can dynamically evolve with changes in the conditions of the molten salt mixture has not been previously conducted. It is currently not well understood where frozen chemistry assumptions are valid. Expanding the state-of-knowledge in this regard is relevant to better assessing the range of chemical effects that should be incorporated as part of MSR safety assessments. This investigation used the Oak Ridge Isotope GENeration (ORIGEN) module of the Standardized Computer-Analysis for Licensing Evaluation (SCALE) code to generate simulated radionuclide inventories for the MSR Experiment (MSRE) and then modeled reactor chemical speciation using the Molten Salt Thermodynamic Database – Thermochemical (MSTDB-TC) coupled with Thermochimica. The effect of composition variation during decay of fission product inventory in a molten salt over a period of 500 days prolonged post- at multiple temperatures was studied. Mass fractions for fluorine and berilium were varied in order to probe the effects of free fluorine control. Finally, speciation of fluoride reactors were showed by comparing MSRE readionuclide inventories with a FLiBe based molten salt breeder reactor (MSBR). The results showed that fission product mass change has little effect on phase mass changes and vapor pressures for fluoride species, but differ with varying carrier and fuel salt compositions. However, iodine species were found to have a vapor pressure not only dependent on temperature, but also the free fluorine potential, releasing iodine when the free fluorine potential is equal to the iodine inventory. This observation, however, arose under free fluorine potentials that are very unlikely to be realized in typical molten salt mixtures. Despite this observation, temperature was found to be the dominant parameter that drove phase change and fission product species vapor pressure. The results indicate that the current frozen chemistry approach is adequate for MSR analysis.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Hardware Fuzzing with An Emulator

Bugs in digital logic have led to some significant security vulnerabilities. Hardware bugs are particularly troublesome since they cannot be easily patched. Additionally, if the bug is in the root of trust, all trust built upon it can be vulnerable. Traditional testing either require a deep knowledge of the system, creative attack vectors and lots of human interaction. This is not scalable as there are very few engineers that can wear the hat of a designer, a verification engineer, and a cybersecurity expert. Hardware fuzzing is a relatively new research area in dynamic hardware testing. It has proven to be an effective method for discovering bugs, unexpected behaviors, and security vulnerabilities in software. While hardware fuzzing is new to the hardware domain, it has a strong track record in software testing. Fuzzing is a testing technique that randomly mutates the input data to uncover bugs or vulnerabilities in the design. It is especially good at finding corner cases that test engineers can not envision. Another advantage over other dynamic testing techniques is that, if done well, deep knowledge of the design is not required. Additionally, fuzzing scales well. If the system is set up correctly, it can run unsupervised for weeks if necessary. In this work, we propose using hardware fuzzing to improve the input vector generation for an information flow tracking tool. To get reasonable throughput of test vectors, an emulator is targeted as the execution platform. Efficient emulator execution has some specific requirements.

42 ENGINEERING↗
DOE OSTI2024

Basic Energy Sciences Roundtable: Foundational Science to Accelerate Nuclear Energy Innovation

Energy security, availability, and reliability are among the greatest challenges facing the nation and the planet. An abundant potential source of energy resides in the fundamental atomic building blocks of the universe in the form of nuclear fission and fusion reactions. In fact, energy from nuclear fission currently provides the majority of the world’s zero-carbon electricity, and future fusion energy systems offer great promise; carbon-free nuclear energy technologies can be key to the world’s decarbonized energy future. Although contemporary fission systems use well-established technologies to supply safe and efficient baseload power, they could be more fuel efficient and less costly. Moving beyond massive light-water fission reactors to a variety of advanced nuclear systems—which will vary in size and operate in extremes of temperature, corrosivity, and other parameters—will place stringent conditions on materials and chemical systems. New demands will be placed on the coolants and solvents, the materials, and the monitoring tools used in these reactors. Fusion-based nuclear energy will require superior materials to withstand extremely high temperatures, plasma exposure, radiation damage, and implanted gases. The advantages associated with these new fission and fusion technologies will be realized only through continued advancements in the fundamental science underpinning our knowledge of the physics and chemistry of nuclear systems gained via improved experimental and computational methods. In July 2022, the U.S. Department of Energy’s Office of Basic Energy Sciences—in coordination with the Offices of Nuclear Energy, Fusion Energy Sciences, and Advanced Scientific Computing Research—held a virtual roundtable titled “Foundational Science to Accelerate Nuclear Energy Innovation” to discuss the scientific and technical barriers for advanced nuclear energy systems. Five priority research opportunities were identified to address these scientific and technical challenges and to accelerate progress toward the realization of next-generation fusion and fission energy systems. The foundational science gaps inhibiting the advancement of nuclear energy technologies are identified and tackled in five priority research opportunities. These opportunities pave the way to accelerate the development and ultimately the adoption of new nuclear energy systems. They include the fundamental aspects of ion-electron interactions; novel properties of next-generation coolants and solvents; interfacial dynamics, not only in solids, but in other aspects of nuclear reactors; novel operando and in situ monitoring and sensing; and artificial intelligence to accelerate condensed phases discovery. Building on the foundation established by previous Basic Energy Sciences workshops, these opportunities encompass recent advances in fundamental knowledge and focus on the experimental and computational methods needed to resolve major technical challenges for nuclear energy technologies. Through developing fundamental scientific insight as well as pushing the frontiers of modeling complex systems and probing the operation of materials and chemical systems in extreme environments, research motivated by the priorities identified here will further develop the promise, potential, and utilization of nuclear energy for a clean energy future.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Controlling reversible phase transitions in rare-earth nickelates for novel memory devices

Resistive switching in correlated complex oxides is lucrative for emerging applications in neuromorphic computing, and densely scaled non-volatile memory. Electrical conductance of such complex oxides can be controllable switched across multiple orders of magnitude by either (a) electroforming a conduction channel (e.g., in tungsten oxide), or (b) inducing Mott-Hubbard transition (e.g., in rare-earth nickelates)– both via controlled migration of defects (such as oxygen vacancies) under applied bias. Nevertheless, the promise of such defect-driven electronic transitions are far from realized due to a lack of fundamental understanding of the atomic-scale processes that underlie migration and spatiotemporal evolution of oxygen vacancies over nano-to-mesoscopic length/timescales under applied electric field. In this project, we employ a synergistic integration of density functional theory (DFT) calculations, ab initio/classical molecular dynamics (AIMD/CMD) simulations, machine learning (ML), precision synthesis, and multi-modal X-ray imaging experiments to address this knowledge gap. Such an integrated approach offers to elucidate the correlations between subtle structural distortion and oxidation states; treat localized charge carriers; describe defect/ion transport in the presence of electric field; and, in turn, greatly advance the current understanding of microstructural evolution in complex oxides under applied bias. The fundamental knowledge gained from this work will enable precise control over hierarchical defect structures and unravel new routes to manipulate resistance states in complex oxides. This, in turn, will accelerate design of novel devices with desired set of neural functionalities, and high-speed densely-scaled resistive random access memory technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

National Fire Protection Association Spurs the Safe Adoption of EVs Through Education and Outreach

As electric vehicles (EV) and their charging infrastructure steadily increase in popularity across the U.S., growing pains still exist, impeding their true potential for growth. The barriers include a general lack of knowledge around EV systems, range anxiety, electrical workplace safety practices, code compliance and inspection for charging stations, maintenance garages and workers, insurance concerns, and the potential risks associated with damaged lithium-ion batteries (fires, re-ignition, water immersion, and stranded energy).NFPA partnered with SMEs, training development organizations, and Clean City Coalitions to create a series of common online trainings for each EV ecosystem sector, educating communities on the realities of EV proliferation, myths, facts, statistics, codes, and basic information for each sector to become versed in EV knowledge. We also constructed a workshop program for the many EV stakeholders who should be involved in setting up a strategic, cohesive outlook for the participating towns and cities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Interparticle Characterization of Mechanical Biomass Particle-Particle and Particle-Wall Interactions

The biomass materials industry faces significant challenges in managing material variability and its impact on storage and handling systems. Physical properties such as moisture content, particle size, and density fluctuate considerably, leading to operational issues like bridging and ratholing that disrupt material flow. These variations create a complex cascade effect throughout the process chain, affecting transportation, storage, and conversion processes. The economic consequences of this variability manifest in increased operational costs, maintenance requirements, and system downtime. Environmental factors further complicate the situation, as weather conditions and seasonal availability influence material properties and system performance. Engineers employ specialized equipment design, material characterization protocols, and pre-processing steps like size reduction and homogenization to address these challenges. A critical knowledge gap exists between continuous-level constitutive models and particle-scale behavior. This project developed a novel device to quantify interparticle mechanics between biomass particles, measuring friction and adhesion forces between particles and wall materials. The research focused on corn stover and southern pine forest residue, creating a comprehensive database of particle interactions. This breakthrough enables direct application in particle-based computational modeling, advancing the field's understanding of biomass handling characteristics and supporting the development of more reliable and efficient storage and handling systems. The project's outcomes contribute significantly to understanding biomass's mechanical and flow characteristics, particularly how variability at the particle level affects larger-scale handling operations. This knowledge is crucial for engineering feedstock supply systems that consistently meet quality and cost specifications for various conversion processes. The innovative experimental setup developed through this research represents a significant advancement in biomass characterization methodology. Providing precise measurements of particle-level interactions establishes a foundation for more accurate predictive modeling of bulk material behavior. This enhanced understanding of fundamental particle mechanics enables engineers to anticipate better and address handling challenges before they manifest in full-scale operations. This research opens new avenues for optimizing biomass handling systems through data-driven design approaches. The comprehensive database of particle interactions serves as a valuable resource for future research and development efforts, potentially leading to more efficient and cost-effective biomass processing solutions. This advancement in particle-level mechanics could revolutionize how biomass handling systems are designed and operated, contributing to more sustainable and reliable renewable energy production.

09 BIOMASS FUELS↗
DOE OSTI2024

A Data-Driven Approach to Real-World Degradation of Backsheets

The objectives of this project are as follows: • The population behavior of fielded modules in various conditions of use • Predictions of materials in specific climatic zones • Understanding of a module’s local environment in the field on its degradation It aims to understand how and what backsheet materials of photovoltaic modules degrade in the real world field. • Field Survey Protocol This project is started from the protocol, because all the data, information, domain knowledge are from experience of the real world field surveys, which is based on the protocol. The Protocol is explored from the experience of the field survey observations. With the increasing of the field surveys, it is refined for three versions, which are Task 1.0 (Section 3.1), Task 6.0 (Section 3.6), Task 10.0 (Section 3.10) respectively. It includes a document and a training video, which is able to direct other teams to follow the same procedure with the sites surveyed during this project. The documents include the detailed information but is not limited to the terminology definition, instruments SOP, preparation items for the surveys, form for the data collections, the order of the information collection. The final version of the protocol can be found at Appendix A, see Section 5. Additional, It can also be found at Open Science Framework (OSF), see Section 3.18 for detail. • Written Waiver Request Before staring the field surveys, the request of the waiver for international surveys is completed, because the limitation of climate zone in the United States, see Appendix B in Section 6 for the request documents. Unfortunately, only 1 international site from Taiwan, China can be finished, due to the COVID-19. • Field Survey According to the protocol we built in Section 3.1, 3.6 and 3.10, 41 sites have been surveyed across seven different climate zones (Cfa, Csa, Csb, BSk, Dfa, Dfb, Am). A variety of materials, including Polyethylene Naphthalate (PEN), Polyethylene Terephthalate (PET), Polyvinyl Fluoride (PVF), Polyvinylidene Fluoride (PVDF), Acrylic PVDF, Fluoroethylene Vinyl Ether (FEVE), and Glass, were identified. These sites are located in various states including California, South Carolina, New Mexico, Maryland, Ohio, Tennessee, Florida, Massachusetts, Illinois, Minnesota, Oregon, Colorado, and Taiwan, Republic of China. The ages of the sites ranged from 2 - 38 years in service and the field size varied from 1 MW - 25 MW. All requirements for the modeling have been satisfied. Some observations like ’Edge Effect’ for the rows and Junction box heating will also be a useful knowledge to build the model. Section 3.7 provides detailed information on the sites visited during this reporting period.

14 SOLAR ENERGY↗
DOE OSTI2025

Construction Methodology Transformation for the Benefit of Workforce Development

Construction is a key economic engine driving both national and global economies. While manual, onsite construction methods dominate the U.S. construction industry, a major shift towards offsite methods has been underway due to its efficiency, speed, and potential cost savings. The workforce necessary for offsite construction growth does not exist in its current form because of the focus on onsite methodologies and the lack of exposure to offsite building methods at all levels of a student’s learning journey. The growth of the U.S. construction industry and competitiveness in an increasingly global construction market over the coming decades can only be supported by a dramatic increase in the use of offsite methods, which requires ramping up workforce training for certain skillsets. The goal of Construction Methodology Transformation for the Benefit of Workforce Development was to understand the opportunities and barriers in both education and industry and to identify best practices for offering curriculum and training to educators, industry, and students that would support skills needed for careers in offsite construction. Our team proposed combining three offsite construction workforce development needs: content development, exposure and training, and job placement - under a single Platform model that would increase experience and career opportunities for students and help match them with potential industry members. Through our proposed solution we expected to see: developed offsite curriculum being utilized by educators and students; an increase in the identification of construction technology and offsite construction methods; an average increase in knowledge gain of at least 25% after participation in pilots; better equipped candidates who are prepared for jobs in offsite construction; and a beta workforce development platform that helps build more pathways for students looking for careers in offsite construction. The two pilots included almost 250 students and resulted in an average knowledge gain of 37 percent. Our research has identified areas of opportunity, for both education and industry to make collaborative training programs more efficient and successful. The chosen techniques for this program are extremely effective when both the school and factory have solid processes and cultures in place to accept students into training programs. This project serves as an important stepping stone to industrywide collaboration to move workforce development for offsite construction forward across the country. With continued collaboration programs like this can provide much needed early exposure and training in offsite construction and we can begin to fill important positions for the future of construction.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Predicting Selective Laser Printing Print Quality of Polymer Powders through Melt Flow Index

Selective Laser Sintering (SLS) uses a precisely controlled laser to fuse polymer powder to build complex 3D shapes. While SLS covers a wide application space, the processing knowledge of polymer powder is limited, restricting the number of commercial powders available. This study serves to expand the processing knowledge of polypropylene and polyethylene in parallel with a “mature” SLS feedstock, nylon, through melt flow index (MFI) characterization. Differential Scanning Calorimetry (DSC) was used to explore the sintering window of the polymers. Polyethylene and polypropylene exhibited a relatively narrow sintering window between 4 – 5 °C, whereas the sintering window of nylon was much wider, between 23 – 24 °C. This suggests that the print quality between polyethylene and polypropylene would be similar; however, X-ray computed tomography revealed a higher volume of print defects, voids, and de lamination in polyethylene than in polypropylene. MFI analysis provided additional insight into the difference in print quality, as the MFI of polyethylene was 7.16 g/10 min, 9.95 g/10 min for polypropylene, and 17.23 g/10 min for nylon. MFI is inversely correlated to melt viscosity, and a low melt viscosity is desired for proper coalescing between the layers and particles. These results suggest that MFI is a promising tool, in conjunction with traditional thermal analysis, for screening candidate powder feedstocks for SLS and optimization of print parameters for novel powders.

36 MATERIALS SCIENCE↗