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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Tribal Revegetation Project Final Project Report: 92-Acre Area, Area 5 Radioactive Waste Management Complex, Nevada National Security Site, Nevada

Nuwu (Southern Paiute), Newe (Western Shoshone), and Nuumu (Owens Valley Paiute) are linguistically related, Numic-speaking peoples who are part of the broader Uto-Aztecan language group. Numic peoples view the land as a holistic, living, sentient being with feelings and purpose. The land is personified with human characteristics and it needs to be experienced to be understood through “learning by doing.” Numic peoples do not support ground disturbing activities within their ancestral lands, including activities tied to the storage of low-level radioactive waste or classified materials on the NNSS, which they view as culturally inappropriate. These deep-rooted ancestral connections are the impetus for reinforcing Numic responsibility for healing disturbed areas by integrating respect and patience with consistent Tribal interaction. Tribal Ecological Knowledge (TEK) is the science of describing Tribal approaches for understanding natural resources. Numic TEK is embedded in traditional teachings learned incrementally over time though experience and it evolves through lessons learned and responses to environmental changes over millennia. Therefore, TEK can broaden and enhance Western scientific knowledge associated with revegetation, especially in highly disturbed areas. The project blended TEK with Western scientific ecological methods to create a vegetative cover within test plots on the 92-Acre Area located at the Radioactive Waste Management Complex (RWMC) located in Area 5 on the Nevada National Security Site (NNSS). The vegetated test plots were systematically created for the Department of Energy (DOE) in tandem with the existing Federal Facilities Agreement and Consent Order (FFACO) with the Nevada Department of Environmental Protection (NDEP). Three previous contractor-lead attempts at revegetation, one targeting full cover revegetation and two targeting test plot revegetation, did not achieve the anticipated results at this location. When presented to the 16 American Indian Tribal nations and affiliated groups with cultural and historical ties to the NNSS, the group appointed a Tribal Revegetation Committee (TRC) that included six expert Tribal knowledge holders to collaborate with an ethnoecologist/cultural anthropologist and two biologists. Project design, planning, seed and outplant selection, spiritual land preparation, and methodology were guided by the TRC and an ethnoecologist/cultural anthropologist from Portland State University (PSU) and biologists from Desert Research Institute (DRI). Using TEK, the TRC recommended a specific seed mixture that contained nine native plant species and three species of outplants. The revegetation effort included preparing and planting thirty 10 m × 10 m (32.8 ft × 32.8 ft) seeded plots, twelve of which also included outplants; and eight 10 m × 100 m (32.8 ft × 328 ft) plots that only received outplants, all atop a waste cell cover. The TRC and the project team creatively adapted TEK with Western scientific methods so that the planned revegetation efforts could occur within the safety and security parameters of the RWMC. Test plots were subjected to one of five soil treatments with varying combinations of straw or mulch applications, soil amendment, and/or outplant planting and one of two watering treatments (watered or unwatered). Planting was divided into two events: one in the fall season and another during the subsequent spring season based on TEK and a corresponding recommendation from the TRC. Monitoring and spiritual management was conducted by the TRC to evaluate plant progress on a monthly basis (in conjunction with the biologist and anthropologist) during each respective growing season for a period of three years after planting. This approach allowed Tribal members the opportunity to conduct traditional blessings and other culturally appropriate activities to restore balance to the land in accordance with Tribal protocols. Following TEK-guided methods, successful plant establishment from seed stock and outplants was observed in many plots. Overall, plots planted in the spring, as recommended by the TRC, showed higher rates of outplant survival and native seedling emergence than those planted in the fall. This finding is significant because it is contrary to the original guidance and previous approaches provided for planting in this region. The TRC believes the frequent co-occurrence of native seedlings near surviving outplants indicates an important symbiotic relationship understood by Tribal communities and overlooked by others. Watered outplants displayed much higher survivability than unwatered plants, even after watering was reduced after the plants were established. Soil amendments and mulch created higher densities of native plants from seed. Many native seedlings showed significant delays in germination, which is considered a normal adaptation to desert climates. Some native plants did not germinate until the third growing year, whereas others germinated during the first growing year, which demonstrates the complexity of the desert environment. Evidence of native insects, reptiles, mammals, and birds, as well as native plants that were not part of the planted species, were noted and considered culturally significant. Despite the presence of non-native plants, native outplants continued to thrive and the incidence of native plant germination from seed increased over time. These successful revegetation results where previous efforts were unsuccessful reinforce the importance of integrating regionally appropriate, TEK-guided methodology with Western science to achieve positive results and the necessity of integrating Tribal involvement in all stages of the revegetation effort. Expanded approaches coupled with Tribal knowledge and tools from Western science addressed a complex problem tied to revegetating atop a low-level radioactive waste cell. The level of Tribal participation serves as a progressive model for building collaborative relationships and addressing ecological challenges on the NNSS.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Polymer Additive Manufacturing for Marine Renewable Energy Applications: Best Practices, Research Trends, and Current Challenges

Additive manufacturing (AM) is a rapidly growing technology space, not only for prototyping, but is also becoming more feasible at larger scales and increasing component quantities. There are a large variety of AM processes and materials available to users and effectively applying those processes and materials to a specific use case can be challenging. One specific area where AM could be particularly beneficial is marine renewable energy (MRE). Not only is MRE a relatively nascent industry with a near-term need for rapid deployments and prototype testing, but developers could also see long-term benefits from the broad variety of environmentally resistant materials available and the ability to manufacture complex geometries that AM technologies offer. Over the past 4 years, AM materials have played an increasing role in the Advanced Materials project; a multi-year, multi-laboratory research project funded by the U.S. Department of Energy's Water Power Technologies Office, with the main goal of reducing barriers to the adoption of complex materials in the MRE industry. The primary focus of this project is to develop test methods and generate datasets to understand the long-term performance of advanced materials in marine environmental and address specific material challenges as they arise. This report provides an extensive overview of the research that has been performed specific to AM polymers as part of the Advanced Materials project. The intention of this document is to provide recommendations of best practices with regards to material selection, mechanical test method development, and design practices, lessons learned along the way, current research trends, and ongoing challenges with regards to AM polymers in marine environments. In particular, this report focuses on several key aspects: Material and process selection, Environmental conditioning and subsequent degradation quantification through mechanical characterization, Composite reinforcements on AM polymer substrates, Adhesion of instrumentation for mechanical characterization and loads measurements, Protective coatings for preventing biofouling and water ingress, Other MRE case studies where AM has proved particularly useful. Ultimately, we hope that the test methods that have been developed, data generated, and lessons learned from this research will be valuable to the MRE community (researchers and developers alike), as well as other industries, and can be used as a reference point as the respective MRE and AM industries continue to grow and mature.

16 TIDAL AND WAVE POWER↗
NASA NTRS2006

Educational Experiences of Embry-Riddle Students through NASA Research Collaboration

NASA's educational. programs benefit students and faculty while increasing the overall productivity of the organization. The NASA Graduate Student Research Program (GSRP) awards fellowships for graduate study leading to both masters and doctoral degrees in several technical fields. GSRP participants have the option to utilize NASA Centers andlor university research facilities. In addition, GSRP students can serve as mentors for undergrad students to provide a truly unique learning experience. NASA's Cooperative Education Program allows undergraduate students the chance to gain "real-world" work experience in the field. It also gives NASA a no risk capability to evaluate the true performance of a prospective new hire without relying solely on a "paper resume" while providing the students with a greater hiring potential upon graduation, at NASA or elsewhere. University faculty can also benefit by participating in the NASA Faculty Fellowship Program (NFFP). This program gives the faculty an opportunity to work with NASA peers. The Mission Analysis Branch of the Expendable Launch Vehicles Division at NASA Kennedy Space Center has utilized these two programs with students from Embry-Riddle Aeronautical University (ERAU) to conduct research in modeling and developing a parameter estimation method for spacecraft fuel slosh using simple pendulum analogs. Simple pendulum models are used to understand complicated spacecraft fuel slosh behavior. A robust parameter estimation process will help to identif' the parameters that will predict the response fairly accurately during the initial stages of design. These programs provide students with a unique opportunity to work on "real-world" aerospace problems, like spacecraft fuel slosh,. This in turn reinforces their problem solving abilities and their communication skills such as interviewing, resume writing, technical writing, and presentation. Faculty benefits by applying what they have learned to the classroom. Through university collaborations with NASA and industry help students to acquire skills that are vital for their success upon entering the workforce.

Schlee, Keith↗
DOE OSTI2015

MO-DE-BRA-01: Enhancing Radiation Physics Instruction Through Gamification and E-Learning

Purpose: This project sought to “gamify” the instruction of radiation interaction physics concepts for technology students. Gamification applies game mechanics and user interactions in active learning contexts. In one part of this project, a self-guided eModule was developed for conceptual radiation interaction instruction. In a second part, a web-based game, Particle Launch (http://particle-launcher.ist.unomaha.edu), was created to challenge students to quickly apply radiation interaction concepts in a way that is stimulating and motivating. Methods: The eModule, focused on conceptual interaction physics, was designed in Adobe Captivate and incorporates animation, web videos, and assessment questions in order to generate student interest. Navigating the whole module takes 40 minutes for beginners. Assessments after three main sections are comprised of 3–4 questions randomly selected from a question pool. In collaboration with the University of Nebraska at Omaha’s College of Information Science and Technology, the Particle Launch game was created with the Unity gaming engine and designed with a game-play look and feel. The object of the game is to utilize different particles, energies, and directions to destroy a target given a limited number of resources and time to complete the task. A rewards system encourages accurate shots. Results: The eModule part of the project encourages a flipped classroom model in which class time is devoted to application of concepts rather than information-based lectures. Currently, eModule assessments are not tracked but this feature could be incorporated to encourage participation. Furthermore, in a class of five technology students, the game was found to be fun and engaging and had the effect of reinforcing basic concepts from the eModule. Conclusion: Gamification has significant potential to alter medical physics instruction. Game-play feedback is an important part of the learning process. Students found Particle Launch inviting and challenging and further research could help game design. This project was generously supported by the Office of the Vice-Chancellor for Academic Affairs and the University of Nebraska Medical Center.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

Remaining Useful Strength (RUS) Prediction of SiCf-SiCm Composite Materials Using Deep Learning and Acoustic Emission

Prognosis techniques for prediction of remaining useful life (RUL) are of crucial importance to the management of complex systems for they can lead to appropriate maintenance interventions and improvements in reliability. While various data-driven methods have been introduced to predict the remaining useful life (RUL) of machinery systems or batteries, no research has been reported on the remaining useful strength (RUS) prediction of silicon carbide fiber reinforced silicon carbide matrix (SiCf-SiCm) materials with pivotal role in its potential usage as a structural material in nuclear reactors and turbine engines. Knowledge of its degradation process is of the utmost importance to the manufacturers. For this purpose, two approaches based on the machine-learning techniques of random-forest (RF) and convolutional neural network (CNN) are proposed to predict the RUS of SiCf-SiCm using only acoustic emission (AE) signals generated during the material’s stress applying process. Experimental results show that the CNN models achieved better predictive performance than the RF models but the latter with expert-engineered features achieves better prediction for AE signals in the early stage of degradation. Additionally, our results demonstrate that both models can correctly predict the SiCf-SiCm RUS as evaluated by our robust testing method from which the best average root mean square error (RMSE) and Pearson correlation coefficient of 3.55 ksi units and 0.85 were obtained.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Concrete Structure Health Monitoring Using Vibro-acoustic Testing and Machine Learning

Assessment and management of aging concrete structures in nuclear power plants require a more systematic approach than simple reliance on margins of safety in existing codes. Research is required to deploy online monitoring capability to assess the health of concrete structures in nuclear power plants and perform predictive analytics based on heterogeneous measurements, in order to reduce the operation and maintenance costs. The research effort described in this report focuses on the health monitoring of concrete slabs containing reactive aggregates using the vibro-acoustic modulation (VAM) technique and predictive modeling. The research is developing an approach to enable detection, localization, and estimation of the extent of degradation in concrete slabs due to alkali-silica reaction (ASR). The project outcomes are generic and extendable to other degradation modes in concrete. A concrete slab without reinforcements was cast at Vanderbilt University and cured in aggressive conditions to accelerate ASR degradation. In this report, we develop a methodology for the automation of VAM testing, and the construction of machine learning models to facilitate damage localization in concrete structures. We investigate how machine learning models based on physics simulation data can be effectively utilized for damage localization (damage location and depth) and estimation in a concrete slab specimen. Our study indicates that these models could potentially support damage localization and estimation for NPP structures.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2012

Bridging the Gap Between Scientists and Classrooms: Scientist Engagement in the Expedition Earth and Beyond Program

Teachers in today s classrooms need to find creative ways to connect students with science, technology, engineering, mathematics (STEM) experts. These STEM experts can serve as role models and help students think about potential future STEM careers. They can also help reinforce academic knowledge and skills. The cost of transportation restricts teachers ability to take students on field trips exposing them to outside experts and unique learning environments. Additionally, arranging to bring in guest speakers to the classroom seems to happen infrequently, especially in schools in rural areas. The Expedition Earth and Beyond (EEAB) Program [1], facilitated by the Astromaterials Research and Exploration Science (ARES) Directorate Education Program at the NASA Johnson Space Center has created a way to enable teachers to connect their students with STEM experts virtually. These virtual connections not only help engage students with role models, but are also designed to help teachers address concepts and content standards they are required to teach. Through EEAB, scientists are able to actively engage with students across the nation in multiple ways. They can work with student teams as mentors, participate in virtual student team science presentations, or connect with students through Classroom Connection Distance Learning (DL) Events.

Graff, P. V.↗
DOE OSTI2024

Intricate short-range order in GeSn alloys revealed by atomistic simulations with highly accurate and efficient machine-learning potentials

GeSn alloys hold promise for silicon-compatible integrated applications in electronics, photonics, and topological quantum devices. However, understanding their intricate structures using density functional theory (DFT) calculations is hindered by spatiotemporal constraints. To overcome this limitation, we develop highly accurate and efficient machine-learning interatomic potentials based on a neuroevolution potential approach with farthest point sampling on a comprehensive DFT data set. The application of the developed machine-learning potential in large-scale atomistic simulations bridges the spatiotemporal gap between modeling and advanced characterizations, and facilitates the discovery of structural intricacies in GeSn alloys. Through extensive statistical sampling, we identify a type of short-range order (SRO) that is distinguished by both its structural signature and electronic band gap from the SRO structure previously predicted. Modeling based on a large simulation cell reveals the coexistence of nano SRO domains with various degrees of ordering, demonstrating a complex spatial heterogeneity of SRO structure. Finally, our study not only reinforces the significance of fine-level structural information in alloys, but it also constitutes an effective framework for exploring SRO in a broad range of complex alloys based on highly accurate and effective machine-learning potentials.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Drilling, Completion, and Data Collection Plans An Assessment of Geological Carbon Sequestration Options in the Illinois Basin: Phase III

Executive Summary The Illinois Basin – Decatur Project (IBDP) is managed by the Midwest Geological Sequestration Consortium (MGSC) and is led by the Illinois State Geological Survey (ISGS) at the University of Illinois. The project site is located on the Archer Daniels Midland Company (ADM) property in Decatur, Illinois, and is a fully integrated carbon capture and storage (CCS) project that uses CO₂ captured from the ethanol-producing fermentation process at the ADM corn-processing plant (Finley et. al., 2013). IBDP has a goal of injecting one million tonnes of CO₂ into the basal sands of the Mt. Simon Sandstone over a three-year period. This is a multifaceted project, and this report details the planning and results of the drilling, completions, well testing, log data acquisition, and the Health, Safety, and Environment (HSE) aspects of the project. Three deep wells were planned for the IBDP: • The injection well: Injection Well #1 (CCS1); • The monitoring well (both in-zone and above seal): Verification Well #1 (VW1); and • The geophone monitoring well: Geophysical Monitoring Well #1 (GM1). The detailed plans for these wells are attached to the appendices of this document. The wells were drilled successfully with little deviation from the original plans. The biggest change from the plan to execution was the need to adjust for larger-than-expected loss of circulation in the Potosi section of the Knox Formation. The completions reports also attached to this document detail the well constructions as they were actually built. Injectivity testing was carried out, and the perforating plans were adjusted based on the results. Additional perforations and acidizing were performed as a result of the injectivity testing. The testing plans are detailed in this report along with the actual testing results. The injectivity testing results were used in the modeling and simulation efforts. Detailed HSE plans were developed and implemented during the planning and execution phases of the project. The implementation included an HSE Bridging Document, which served to unify the HSE policies of the project partners and key subcontractors. The HSE plan and actual HSE results are presented in this document. There were no recordable HSE incidents during the project. A detailed logging program was developed based on project needs. The log data were acquired in accordance with the plan, and both the plan and log results are presented in this report. Log data were heavily utilized by the research staff, modelers, reservoir engineers, and for technical and permitting efforts. 5 Several key lessons were learned during the project: • Safety in operations and execution is paramount and is only achieved through proper planning and behavior control. The certainty of this was reinforced through implementation of this lesson and the resultant flawless HSE performance during the project. • Losses of drilling fluid circulation were larger than anticipated within the Potosi Formation. Circulation was only recovered through cementing the loss zones. • When possible, minimizing complexity in permit requirements and well designs is preferable. • The size of the wells were outside of the standard experience and expertise typical within the basin, and therefore required substantial planning and ramp-up of contractors and partners to meet project objectives. • With multiple stakeholders and research partners, establishing objectives and requirements early and adhering to change request procedures throughout the project are critical to manage competing data and sampling objectives that may be detrimental to overall progress. The well construction and completion operations were successfully executed, with all wells built in a manner that achieved excellent wellbore integrity. Log planning involved a number of stakeholders and technical specialists. Data collection from logging, coring, and testing was excellent. Time and effort spent with the associated contractors and suppliers to develop a well plan beyond normal scope proved highly successful, resulting in a well-construction and completion project that surpassed expectations. The world-class HSE results also demonstrate the commitment of all stakeholders in the project. The details follow in the body of this document

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Micrometer: Micromechanics transformer for predicting full field mechanical responses of heterogeneous materials

Predicting mechanical responses of heterogeneous materials across scales remains a significant challenge. Traditional computational methods often struggle with complex and multiscale nature of these materials, limiting their effectiveness in real-world applications. Here, in this paper, we introduce Micrometer, a vision transformer based deep learning model designed to predict full field mechanical responses of heterogeneous materials, bridging the gap between computer vision and solid mechanics problems. We show that Micrometer, trained on a large-scale high-resolution dataset of 2D fiber-reinforced composites, can achieve state-of-the-art performance in predicting microscale strain fields across a wide range of material properties and loading conditions. Our model demonstrates accuracy and computational efficiency in applications such as computational homogenization and multiscale modeling, reducing computational time by up to two orders of magnitude compared to conventional numerical solvers while maintaining less than 1 % errors in predicting macroscale stress fields. Furthermore, we showcase Micrometer’s adaptability through transfer learning experiments on new materials with limited data, highlighting its potential to tackle diverse scenarios in computational solid mechanics. These results represent a significant step towards AI-driven innovation in materials science, addressing the limitations of traditional numerical methods and paving the way for more efficient simulations of heterogeneous materials across various industrial applications.

Composite materials↗
NASA NTRS1995

Emergence of Relations and the Essence of Learning: A Review of Sidman's Equivalence Relations and Behavior: A Research Story

Sidman addresses two very important questions in Equivalence Relations and Behavior: A Research Story: What are the bases of behavioral competence? And how do units of learning become related? The book recounts the story of how an understanding of emergent relations and competencies was achieved through studies in his teaching-research program with mentally retarded subjects. Although children normally accrue vast networks of relations between stimuli and events, those with mental retardation typically do not. Consequently, by learning how to establish those networks, Sidman and his students contribute richly both to the cultivation of competencies by their subjects and, more generally, to an understanding of real-world human behavior. The basic equivalence paradigm affords the subject feedback and reinforcement for very specific choices during training, but the test is not for those choices! Rather, tests for equivalence look for new choices, ones seemingly quite foreign to the training regimen. The tests for equivalence relations entail presentations of stimuli that were the options for conditional choice during reinforced training. In tests of equivalence, correct choices are novel; hence, they have never been reinforced during training. The study of equivalence relations can encourage the emergence of new perspectives that are more symbiotic than competitive. In full acknowledgment of the important role and contributions made by those who identify themselves as experimental analysts of behavior, it is timely that rapprochements be worked toward, as indeed they are, to meld that perspective with others of our time. Both our research methods and our expectations about the nature of the learning process and the abilities of our subjects can delimit what they might learn and what we, in turn, learn about their learning. The text will be of great value for instruction at the upper-division and graduate levels. Its impact will be substantial, for it defines an important advance in our efforts to understand the richness of behavior in both humans and nonhuman animals. Although not presented to that end, the book might also serve to bridge communications with other groups of animal researchers whose interests lie more in a comparative or ethological framework.

Rumbaugh, Duane M.↗
DOE OSTI2023

Tyramine and its Amtyr1 receptor modulate attention in honey bees (Apis mellifera)

Animals must learn to ignore stimuli that are irrelevant to survival and attend to ones that enhance survival. When a stimulus regularly fails to be associated with an important consequence, subsequent excitatory learning about that stimulus can be delayed, which is a form of nonassociative conditioning called ‘latent inhibition’. Honey bees show latent inhibition toward an odor they have experienced without association with food reinforcement. Moreover, individual honey bees from the same colony differ in the degree to which they show latent inhibition, and these individual differences have a genetic basis. To investigate the mechanisms that underly individual differences in latent inhibition, we selected two honey bee lines for high and low latent inhibition, respectively. We crossed those lines and mapped a Quantitative Trait Locus for latent inhibition to a region of the genome that contains the tyramine receptor gene Amtyr1 [We use Amtyr1 to denote the gene and AmTYR1 the receptor throughout the text.]. We then show that disruption of Amtyr1 signaling either pharmacologically or through RNAi qualitatively changes the expression of latent inhibition but has little or slight effects on appetitive conditioning, and these results suggest that AmTYR1 modulates inhibitory processing in the CNS. Electrophysiological recordings from the brain during pharmacological blockade are consistent with a model that AmTYR1 indirectly regulates at inhibitory synapses in the CNS. Our results therefore identify a distinct Amtyr1 -based modulatory pathway for this type of nonassociative learning, and we propose a model for how Amtyr1 acts as a gain control to modulate hebbian plasticity at defined synapses in the CNS. We have shown elsewhere how this modulation also underlies potentially adaptive intracolonial learning differences among individuals that benefit colony survival. Finally, our neural model suggests a mechanism for the broad pleiotropy this gene has on several different behaviors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Statistical and Machine Learning Approaches to Analyzing Pipeline Incidents in the United States (2010–2024)

This study applies machine learning methods to analyze natural gas pipeline incidents in the United States using the Pipeline and Hazardous Materials Safety Administration (PHMSA) Gas Distribution Incident Dataset (2010–2024). The dataset includes over 600 variables describing incident characteristics, infrastructure attributes, and contributing factors associated with unintentional gas releases. The objective is to assess whether these features can reliably predict the underlying cause of pipeline failures. Multinomial logistic regression and Random Forest models were developed to classify incident causes, including excavation damage, corrosion, equipment failure, and natural forces. Results show that excavation damage is both the most frequent and most predictable cause, with models achieving strong performance for this category. However, when excavation damage is excluded, model accuracy declines significantly, with some models performing near random levels. Across all approaches, severe class imbalance and limited variability in key predictors constrain predictive performance. Pipeline age and diameter emerge as the most influential variables, but they provide insufficient discriminatory power to distinguish among less frequent failure types. These findings indicate that non-excavation-related incidents are rare, heterogeneous, and weakly represented in the dataset, limiting the effectiveness of machine learning classification. Overall, this study highlights the structural limitations of the PHMSA dataset for predictive modeling and underscores the need for improved data balance and feature enrichment. The results reinforce excavation damage prevention as the most impactful strategy for reducing pipeline incidents.

03 NATURAL GAS↗
NASA NTRS1992

Thermal conductance of two interface materials and their applications in space systems

The temperature control of spacecraft and instrument systems and subsystems requires heat transfer interface materials that possess good thermal and structural characteristics, among other properties, to respond to the vacuum environment of space. These materials must be easy to apply to, and remove from, the surfaces where they are applied, and must be able to withstand power dissipation extremes, and be used for different clamping configurations and pressures. Silicone based greases, used in the past, tend to migrate and to contaminate nearby surfaces. Bare metal to metal contact offers low thermal conductance and difficulties in estimating the actual heat transfer. Several polymeric materials containing different thermal conductive compounds and structural reinforcements were prepared to overcome grease and metal problems. Two polymeric materials were evaluated: Cho-Therm 1671 elastomer; and the CV-2946, a conductive RTV silicone. Tests were done to learn more about these products. Results indicate that the tightly bolted, torqued fixtures did not buckle or distort, and provided optimum thermal conductance. Fixtures simulating actual spacecraft configuration suffered bowing and separating.

Scialdone, J. J.↗
DOE OSTI2022

Nanoindentation mapping defects filtration for heterogeneous materials using generative adversarial networks

Advanced composite materials with multiple phases and heterogeneous microstructure necessitate spatial mapping characterization of elastic modulus to develop constitutive relations and overall mechanical response. Such modulus mapping can be obtained using the nanoindentation technique, where the indenter tip raster over the selected microstructure region. Typically, a surface preparation procedure is done in the specimens to ensure proper contact between the indenter tip and sample surface. However, a near-perfect surface finish is unachievable in heterogeneous materials, primarily with ceramic reinforcements, due to the differential material removal rate during polishing. Thus, the nanoindenter records localized erroneous measurements due to differences in surface roughness and corresponding force response. This study establishes a novel deep learning-based strategy to rectify incorrect experimental spatial measurements acquire during nanoindentation modulus mapping. Here, the integrated bicubic interpolation and generative adversarial networks (GANs) model was trained using 14 ceramic and 18 metallic data sets, each comprising 65,536 measurements. The developed algorithm was validated against experimental measurements on four unknown specimens. The standard deviation in measured elastic modulus reduces by ~50% in ceramics and ~72% in metallic samples. This computational framework proposes a novel approach to reducing uncertainty in materials’ properties using state-of-the-art computer vision techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Carbon Nanotube Chopped Fiber for Enhanced Properties in Additive Manufacturing

Nanocomp Technologies, Inc. is working with Oak Ridge National Laboratory to develop carbon nanotube (CNT) composite materials and evaluate their use in additive manufacturing (3D printing). The first phase demonstrated feasibility and improvements for carbon nanotube (CNT)- acrylonitrile butadiene styrene (ABS) composite filaments use in additive manufacturing, with potential future work centering on further improvements. By focusing the initial phase on standard processing methods (developed mainly for the incorporation of carbon fibers in ABS) and characterization techniques, a basis of knowledge for the incorporation of CNTs in ABS was learned. The ability to understand the various processing variables is critical to the successful development of these composites. From the degradation effects on ABS (caused by excessive temperatures), to the length of time the ABS is in the melt state, to the order of addition of constituents, and also to the many possible mixing approaches, a workable flow sequence that addresses each processing step is critical to the final material properties. Although this initial phase could not deal with each of these variables in-depth, a future study is recommended that will build on the lessons learned for this effort.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2016

MO-DE-BRA-02: SIMAC: A Simulation Tool for Teaching Linear Accelerator Physics

Purpose: The first goal of this work is to develop software that can simulate the physics of linear accelerators (linac). The second goal is to show that this simulation tool is effective in teaching linac physics to medical physicists and linac service engineers. Methods: Linacs were modeled using analytical expressions that can correctly describe the physical response of a linac to parameter changes in real time. These expressions were programmed with a graphical user interface in order to produce an environment similar to that of linac service mode. The software, “SIMAC”, has been used as a learning aid in a professional development course 3 times (2014 – 2016) as well as in a physics graduate program. Exercises were developed to supplement the didactic components of the courses consisting of activites designed to reinforce the concepts of beam loading; the effect of steering coil currents on beam symmetry; and the relationship between beam energy and flatness. Results: SIMAC was used to teach 35 professionals (medical physicists; regulators; service engineers; 1 week course) as well as 20 graduate students (1 month project). In the student evaluations, 85% of the students rated the effectiveness of SIMAC as very good or outstanding, and 70% rated the software as the most effective part of the courses. Exercise results were collected showing that 100% of the students were able to use the software correctly. In exercises involving gross changes to linac operating points (i.e. energy changes) the majority of students were able to correctly perform these beam adjustments. Conclusion: Software simulation(SIMAC), can be used to effectively teach linac physics. In short courses, students were able to correctly make gross parameter adjustments that typically require much longer training times using conventional training methods.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1999

Wilberforce Power Technology in Education Program

The Wilberforce Power Technology in Education Program is a multipart program. Three key parts of this program will be described. They are: (1) WISE-The Wilberforce Summer Intensive Experience. This annual offering is an educational program which is designed to provide both background reinforcement and a focus on study skills to give the participants a boost in their academic performance throughout their academic careers. It is offered to entering Wilberforce students. Those students who take advantage of WISE learn to improve important skills which enable them to work at higher levels in mathematics, science and engineering courses throughout their college careers, but most notably in the first year of college study. (2) Apply technology to reaming. This is being done in several ways including creating an electronic chemistry text with hypertext links to a glossary to help the students deal with the large new vocabulary required to describe and understand chemistry. It is also being done by converting lecture materials for the Biochemistry class to PowerPoint format. Technology is also being applied to learning by exploring simulation software of scientific instrumentation. (3) Wilberforce participation in collaborative research with NASA's John H. Glenn Research Center at Lewis Field. This research has focused on two areas in the past year. The first of these is the deposition of solar cell materials. A second area involves the development of polymeric materials for incorporation into thin film batteries.

Gordon, Edward M.↗