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NASA NTRSDate not supplied

Celestial Mapping System for Lunar Surface Mapping and Analytics

Celestial Mapping System (CMS) is a software platform to generate virtual 3D globe for celestial bodies within our solar system. Various layers are built on top of the virtual globe to provide visualization of high resolution imagery, enable precise measurements, build analytical capabilities and broad range of functionalities to assist planetary scientists and mission planners. CMS is built using OpenJDK 11 and will run on a wide variety of platforms such as Linux, Windows, OSX, etc. It has a thick client with less overhead to access hardware resources. This allows features such as terrain profiling and distance calculations to be performed on the client and on the fly. The present focus of CMS is on developing lunar mapping tool kits to provide features such as - 3D first person view with zoom and navigational capabilities, realistic terrain visualization based on LRO data, measurement tools, Apollo landing site annotations, stereoscopic view, elevation profiles, line of sight analysis and many more. The application is developed to provide situational and domain awareness on Lunar surface, planning capabilities for equipment placements and traverse path optimization. As data becomes available, CMS has the capabilities to integrate data sets that change dynamically in real-time, which will be useful for monitoring satellites and remotely-sensed data on Lunar surface. CMS utilizes NASA WorldWind Java library and OpenGL to achieve high-performance rendering of data and measurements, and also adheres to OGC standards. CMS supports importing synthetic features in a variety of 3D, 2D, vector and raster formats. Nomenclature is pulled from USGS Moon IAU2000 database, and lunar parameters are based of the standardized IAU2000 Moon ellipsoid. GDAL (Geospatial Data Abstraction Library) was used to modify and test the accuracy of datasets before integrating into the application. Our high-resolution global elevation model was compared with the LRO LOLA DEM elevation values and tested to ensure accuracy. Celestial Mapping System has several potential use cases for NASA including subsurface lava tubes visualization and analysis, soil analysis, resource visualization and representation on 3D globe.

GIS system↗
NASA NTRSDate not supplied

Spacecraft Line-Of-Sight Jitter Mitigation and Management Lessons Learned and Engineering Best Practices

Predicting, managing, controlling, and testing spacecraft line-of-sight (LoS) jitter caused by micro-vibrations due to onboard internal disturbance sources is a formidable multidisciplinary engineering task. It is especially challenging for those missions hosting high-performance, vibration-sensitive optical sensor payloads with stringent pointing stability requirements. Both NASA and ESA are planning technically aggressive spaceflight missions that include ultra-high-performance optical payloads with delicate, highly vibration-sensitive scientific and observational instruments. The GN&C community of practice will need to leverage and build upon their collective experiences and lessons learned to better address future micro-vibration challenges. To identify lessons learned and best engineering practices the NASA Engineering & Safety Center (NESC) sponsored a two-day Spacecraft LoS Jitter Workshop in late 2019. The workshop’s goal was to provide a multidisciplinary forum to elicit deeper understanding of the issues related to solving the spacecraft LoS jitter/micro-vibration problem. A primary objective was to identify and share best practices, rules of thumb, and options for jitter-related activities. Representatives from NASA, JPL, ESA, along with NASA’s industrial partners, independent consultant subject matter experts, and members of academia participated in the workshop. This paper will describe the motivation for the NASA Spacecraft LoS Jitter Workshop and summarize the identified findings, observations, and recommendations.

Lessons Learned;↗
NASA NTRSDate not supplied

SatCORPS Global Cloud Composite (GCC): the Design and Delivery of A High Quality, High Resolution, Global Cloud Product Available in Near-Real Time

The NASA Satellite ClOud and Radiation Property retrieval System (SatCORPS) supports the development of an analysis ready and cloud-optimized data transformation pipeline and geospatial service enablement of a global cloud composite (GCC) product derived from global geostationary satellite imagery. This geospatial service will be available at high temporal and spatial resolution via the SatCORPS web mapping application for visualization and analysis as well as direct ingestion to common geospatial software and custom programming. The resulting global cloud composite products from the processing pipeline can then be geospatially-service enabled as ArcGIS Image Services and Open Geospatial Consortium (OGC) Web Mapping/Coverage Services for visualization and analysis via a web mapping application and common geospatial software. Near real time global observations are created through the composition of five geostationary satellites that provides modelling and forecasting communities with the capability to provide high quality and timely information to start the projection process. The Global Cloud Composite product combines information from geostationary satellites, GOES-16, GOES-17, Himawari-8, Meteosat-11 and Meteosat-9 to create a single global composite netcdf file and images using the different products within the netcdf file. The SatCORPS team, though our Global Cloud Composite (GCC) product and web-based visualization tools including Geographical Information System (GIS) services provide near real time global cloud product information to both automated processes and traditional web users that is timely and high quality derived from geostationary satellites. The Global Cloud Composite product takes advantage of the scalable processing resources provided by the AWS batch service to provide new composites every thirty minutes. Because information from each of the low earth orbiting satellites is available on schedules tuned to the specific satellite, the processing algorithm temporally composites the final dataset as each satellite’s information becomes available. The SatCORPS team has leveraged our experience using Amazon Web Services (AWS) to build a low latency high availability tool that allows end users both human and automated to acquire high quality and high-resolution Geostationary Earth Orbiting (GEO) information at zero cost to the end user. This presentation will describe how we architected and implemented the service as well as lessons learned based on our experiences both developing and operating the system. The lessons learned include how we integrated multiple services including Amazon Batch, Amazon S3 and Amazon Lambda service to create a low cost but high-performance processing system that is capable of identifying and processing the most appropriate satellite overpass information into global cloud composites. We will also describe our web-based tools including our Geographic Information System that can be used for visualization and analysis. The products from the processing can be geospatially-service enabled as ArcGIS Image Services and Open Geospatial Consortium (OGC) Web Mapping/Coverage Services for visualization and analysis via a web mapping application and common geospatial software. The SatCORPS Global Composite Cloud product provides sophisticated global composited cloud research products with very low latency that we see that as filling a rapidly growing need in the research and modelling community with no up-front nor ongoing costs associated with downloading or using the information.

AWS AMCE SMCE GCC SATCORPS GLOBAL CLOUD COMPOSITE ↗
NASA NTRSDate not supplied

NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science

As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.

Neha Khadka↗
DOE OSTI2015

New Whole-House Solutions Case Study: Northwest Energy Efficient Manufactured Housing Program High-Performance Test Homes - Pacific Northwest

This project represents the third phase of a multi-year effort to develop and bring to market a High Performance Manufactured Home (HPMH). In this project, the Northwest Energy Efficient Manufactured Housing Program worked with Building America Partnership for Improved Residential Construction and Bonneville Power Administration to help four factory homebuilders build prototype zero energy ready manufactured homes, resulting in what is expected to be a 30% savings relative to the Building America Benchmark. (The actual % savings varies depending on choice of heating equipment and climate zone). Previous phases of this project created a HPMH specification and prototyped individual measures from the package to obtain engineering approvals and develop preliminary factory construction processes. This case study describes the project team's work during 2014 to build prototype homes to the HPMH specifications and to monitor the homes for energy performance and durability. Monitoring is expected to continue into 2016.

BA-PIRC↗
DOE OSTI2017

DOE Zero Energy Ready Home Case Study: Alliance Green Builders, Casa Aguila

Alliance Green Builders built this 3,129-ft2 home in the hills above Ramona, California, to the high-performance criteria of the DOE Zero Energy Ready Home (ZERH) program. The home should perform far better than net zero thanks to a super-efficient building shell, a wind turbine, three suntracking solar photovoltaic arrays, and solar thermal water heating.

Building America↗
DOE OSTI2017

DOE Zero Energy Ready Home Case Study: Greenhill Contracting, Inc., Hickory Ridge

Greenhill Contracting built this 3,912-ft2 house in Gardiner, New York, to the high-performance criteria of the DOE Zero Energy Ready Home (ZERH) program. A highly efficient air-source heat pump heats and cools the home’s interior, while the roof-mounted photovoltaic system offsets electricity usage to cut energy bills to nearly zero. Many months the home owners see a credit on their utility bill.

2017 Housing Innovation Award↗
DOE OSTI2017

Towards Common Components and a Standard Model for Workflow Systems

The role of scalable high-performance workflows and flexible workflow management systems that can support multiple simulations will continue to increase in importance. For example, with the end of Dennard scaling, there is a need to substitute a single long running simulation with multiple repeats of shorter simulations, or concurrent replicas. Further, many scientific problems involve ensembles of simulations in order to solve a higher-level problem or produce statistically meaningful results. However most supercomputing software development and performance enhancements have focused on optimizing single- simulation performance. On the other hand, there is a strong inconsistency in the definition and practice of workflows and workflow management systems. This inconsistency often centers around the difference between several different types of workfiows, including modeling and simulation, grid, uncertainty quantification, and purely conceptual workflows. This work explores this phenomenon by examining the different types of workflows and workflow management systems, reviewing the perspective of a large supercomputing facility, examining the common features and problems of workflow management systems, and finally presenting a proposed solution based on the concept of common building blocks. The implications of the continuing proliferation of workflow management systems and the lack of interoperability between these systems are discussed from a practical perspective. In doing so, we have begun an investigation of the design and implementation of open workflow systems for supercomputers based upon common components.

Billings, Jay Jay↗
DOE OSTI2019

Ga2O3 Packaging and Thermal Management Challenges and Opportunities

This project addresses the early-stage research and development to design and build packages that enable practical utilization of Ga2O3 semiconductor devices in power electronic circuits. The project will encompass electrical, thermal, thermomechanical and reliability aspects in the package design. As compared to silicon (Si), SiC or GaN-based packaging, using a Ga2O3 device will require significant research and innovations in the attachment of the oxide device to substrates, attachment of the substrate to the baseplate/heat exchanger, appropriate electrical interconnections, as well as the type and location of the thermal management solutions. A thorough understanding and characterization of the thermal transport properties of the Ga2O3 device layer is also essential to understand and quantify the package performance. Addressing all these aspects will result in a high-performance, low-cost and reliable package.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2019

A Knowledge Graph Approach for the Secondary Use of Cancer Registry Data

Population-based central cancer registries collect valuable structured and unstructured data primarily for cancer surveillance and research, enhancing insights into clinical features associated with cancer occurrence, cancer treatment, and cancer outcomes to guide interventions which reduce the cancer burden. Cancer registries primarily collect data on (1) cancer type (case or tumor); (2) patient demographics such as age, gender, and residential address at time of diagnosis; (3) planned first course of treatment; and (4) date of last contact, vital status, and cause of death. Cancer registry data is dynamic, structured data, which is extracted from many unstructured sources such as electronic healthcare records, and consolidated for reporting and other purposes. While available advanced analytic tools such as SEER*Stat have the ability to build SAS queries, we, however, explore an innovative knowledge graph approach to organizing cancer registry data for advanced analytics and visualization, which has unique advantages over approaches of existing tools. This innovative knowledge graph approach semantically enriches the data and easily enables linkage with third-party data, which can better explain variation in outcomes. We have developed a prototype knowledge graph based on data from the Louisiana Tumor Registry and other publicly available datasets including Behavioral Risk Factor Surveillance System, Clinical Trials, DBpedia, GeoNames, Rural-Urban Continuum Codes, and Semantic MEDLINE. The resource description framework (RDF) data model was selected to represent our knowledge graph, which contains more than 25 billion triples and is ~4TB in storage size. To exhibit the benefits of the knowledge graph approach, we used scenario specific queries, which find the relationships between cancer treatment sequences and outcomes. To illustrate its ease of use in iterative analysis, the knowledge graph was linked to external datasets for performing complex queries across multiple datasets. In addition, we used knowledge graphs to identify data discrepancies and to handle schema changes. Finally, we visualized the knowledge graph to discover data patterns. Our results demonstrate this graph-based solution enables cancer researchers to execute complex queries and more easily perform iterative analyses to improve understanding of cancer registry data. In the future, we would like to use high-performance computing (HPC) resources for faster-generating hypotheses with clinical potential from our knowledge graph.

Hasan, S M Shamimul↗
DOE OSTI2018

TSPR: The Total System Performance Ratio as a Metric for HVAC System Efficiency

North American energy codes do not typically discriminate between high-performing and poorly-performing HVAC system types, thereby perpetuating the installation of systems with high energy consumption and carbon emissions. The problem is compounded in the US by federal preemption rules, prohibiting state and local energy codes from mandating more efficient equipment than is defined in federal standards. The lack of a total system efficiency metric for HVAC effectively blocks an eventual transition to a performance-based code structure. To surmount this problem, the City of Seattle and Pacific Northwest National Laboratory have developed the Total System Performance Ratio (TSPR) as a proposed amendment to the 2018 Washington State Energy Code. The TSPR is a ratio that compares the annual space heating and cooling load of a building to the annual energy consumed by the building’s entire HVAC system. In the TSPR, target ratios for several building types are calculated in relation to those of the HVAC systems judged to be the most appropriate for each building type. Installation of the lowest-performing HVAC system types will be severely constrained, while exceptional design flexibility will still be provided. With financial support from an international nonprofit, a calculation software tool has been developed as a module of the US DOE Asset Score tool, using simplified building energy modeling. Taken together with whole-system evaluation options for lighting and building envelope systems, a whole-system HVAC performance evaluation would create a path for a transition away from prescriptive-based and towards performance-based codes.

Total System Performance Ratio, Performanced Based↗
DOE OSTI2019

Seminar 13 - Advanced Methods for Grid Integration of High-Performance Residential Communities

High-performance residential communities such as net-zero energy (NZE) and NZE-ready communities are emerging across the United States as many cities and states have committed to achieve ambitious energy efficiency and renewable energy goals between now and 2050. The achievement of these goals will transform the nation's communities into the holistic paradigms consisting of pervasive solar photovoltaics (PVs) and other renewable distributed energy resources, energy storage and flexible loads. However, integrating residential communities with high PV penetration may bring many challenges to the grid. For example, during many hours of the year, electric power is flowing out of PV-enabled homes and back into the grid. When this happens simultaneously in many nearby homes, distribution feeder voltage can be raised significantly. Intermittent solar power can also cause voltage flickers, leading to potential damages on both distribution feeder and home appliances. Existing solutions, such as PV curtailment, battery storage, and direct load control, are not able to address all the challenges in a cost-effective way. Better understanding is needed on how to deploy rooftop PV, how to predict building loads, and how to coordinate the homes in a community to enhance grid reliability. This seminar consists of four integral presentations that cover different aspects of a solution to these challenges. First, we will present an automated method to identify the best surfaces to deploy PV and size PV according to the PV penetration goals in a community. Second, we will present a streamlined way to create gray-box building models that are suitable for large-scale building-grid co-simulation. Next, we will present a data-driven method to estimate the end-use energy consumption to better coordinate the behind-the-meter resources to reduce utility cost, provide grid services, and improve renewable energy integration. Lastly, we will present a hierarchical control system that integrates the building models and the estimated non-dispatchable loads into the decision-making process to enhance the reliability and resilience of the residential community. Simulation results from an NZE-ready community in Fort Collins, Colorado will be presented in all four presentations and the guidelines on better grid integration of residential communities will be provided.

14 SOLAR ENERGY↗
DOE OSTI2020

Reconstruction of Charged Particle Tracks in Realistic Detector Geometry Using a Vectorized and Parallelized Kalman Filter Algorithm

One of the most computationally challenging problems expected for the High-Luminosity Large Hadron Collider (HL-LHC) is finding and fitting particle tracks during event reconstruction. Algorithms used at the LHC today rely on Kalman filtering, which builds physical trajectories incrementally while incorporating material e ects and error estimation. Recognizing the need for faster computational throughput, we have adapted Kalman-filterbased methods for highly parallel, many-core SIMD and SIMT architectures that are now prevalent in high-performance hardware. Previously we observed significant parallel speedups, with physics performance comparable to CMS standard tracking, on Intel Xeon, Intel Xeon Phi, and (to a limited extent) NVIDIA GPUs. While early tests were based on artificial events occurring inside an idealized barrel detector, we showed subsequently that our mkFit software builds tracks successfully from complex simulated events (including detector pileup) occurring inside a geometrically accurate representation of the CMS-2017 tracker. Here, we report on advances in both the computational and physics performance of mkFit, as well as progress toward integration with CMS production software. Recently we have improved the overall eciency of the algorithm by preserving short track candidates at a relatively early stage rather than attempting to extend them over many layers. Moreover, mkFit formerly produced an excess of duplicate tracks; these are now explicitly removed in an additional processing step. We demonstrate that with these enhancements, mkFit becomes a suitable choice for the first iteration of CMS tracking, and eventually for later iterations as well. We plan to test this capability in the CMS High Level Trigger during Run 3 of the LHC, with an ultimate goal of using it in both the CMS HLT and oine reconstruction for the HL-LHC CMS tracker.

Cerati, Giuseppe↗
DOE OSTI2021

An HPC-Based Hydrothermal Finite Element Simulator for Modeling Underground Response to Community-Scale Geothermal Energy Production

Geothermal heat, as renewable energy, shows great advantage with respect to its environmental impact due to its significantly lower CO2 emissions than conventional fossil fuel. Open and closed-loop geothermal heat pumps, which utilize shallow geothermal systems, are an efficient technology for cooling and heating buildings, especially in urban areas. Integrated use of geothermal energy technologies for district heating, cooling, and thermal energy storage can be applied to optimize the subsurface for communities to provide them with multiple sustainable energy and community resilience benefits. The utilization of the subsurface resources may lead to a variation in the underground environment, which might further impact local environmental conditions. However, very few simulators can handle such a highly complex set of coupled computations on a regional or city scale. We have developed high-performance computing (HPC) based hydrothermal finite element (FE) simulator that can simulate the subsurface and its hydrothermal conditions at a scale of tens of km. The HPC simulator enables us to investigate the subsurface thermal and hydrologic response to the built underground environment (such as basements and subways) at the community scale. In this study, a coupled hydrothermal simulator is developed based on the open-source finite element library deal.II. The HPC simulator was validated by comparing the results of a benchmark case study against COMSOL Multiphysics, in which Aquifer Thermal Energy Storage (ATES) is modeled and a process of heat injection into ATES is simulated. The use of an energy pile system at the Treasure Island redevelopment site (San Francisco, CA, USA) was selected as a case study to demonstrate the HPC capability of the developed simulator. The simulator is capable of modeling multiple city-scale geothermal scenarios in a reasonable amount of time.

Sun, Xiang↗
DOE OSTI2022

Work Smarter, Not Harder: Improving Energy Efficiency and Safety through Smarter Ventilation: Preprint

Ventilation is a key component to maintaining healthy, safe indoor air quality. Especially important in laboratories, ventilation is the first line of defense against airborne hazards produced during research activities. Though a vital component, laboratory ventilation systems are often victim to ineffective operation, posing a risk to the most important asset - the researchers. Furthermore, system inefficiencies can lead to up to 50% wasted energy. To improve both energy efficiency and safety in laboratories, we present the Smart Labs Toolkit - a resource developed by the U.S. Department of Energy Federal Energy Management Program and the International Institute for Sustainable Laboratories that guides laboratory stakeholders through a straight-forward, holistic approach to achieve dynamic, high-performance laboratories. Smart Labs enable safe and efficient world class science to occur in laboratories through high-performance methods. A Smart Labs program employs a combination of physical, administrative, and management techniques to assess, optimize, and manage high performance laboratories. We will focus on a central component of the Smart Labs approach - the Laboratory Ventilation Risk Assessment, a systematic process for identifying risk due to airborne hazards to inform the operation of dynamic ventilation that optimizes safety and efficiency. Case studies of organizations who have successfully implemented Smart Labs ventilation management programs will also be shared. In learning ventilation strategies successful in critical laboratory environments, learn the tools and resources needed to successfully manage energy in any building through smarter, safer ventilation.

buildings↗
DOE OSTI2024

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits and proof-of-principle error-correction on a single logical qubit. Nevertheless, despite significant progress and excitement, the path toward a full-stack scalable technology is largely unknown. There are significant outstanding quantum hardware, fabrication, software architecture, and algorithmic challenges that are either unresolved or overlooked. These issues could seriously undermine the arrival of utility-scale quantum computers for the foreseeable future. Here, we provide a comprehensive review of these scaling challenges. We show how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits, employing system engineering approaches, and performing distributed quantum computation within heterogeneous high-performance computing infrastructures. These opportunities for research and development could unlock certain promising applications, in particular, efficient quantum simulation/learning of quantum data generated by natural or engineered quantum systems. To estimate the true cost of such promises, we provide a detailed resource and sensitivity analysis for classically hard quantum chemistry calculations on surface-code error-corrected quantum computers given current, target, and desired hardware specifications based on superconducting qubits, accounting for a realistic distribution of errors. Furthermore, we argue that, to tackle industry-scale classical optimization and machine learning problems in a cost-effective manner, heterogeneous quantum-probabilistic computing with custom-designed accelerators should be considered as a complementary path toward scalability.

Mohseni, Masoud↗
DOE OSTI2022

Fundamental Investigations into the Properties and Performance of Advanced Materials

Intelligent engineering designs require an accurate understanding of material behavior, since any uncertainties or gaps in knowledge must be counterbalanced with heightened factors of safety, leading to overdesign. Therefore, building better structures and pushing the performance of new components requires an improved understanding of the thermomechanical response of advanced materials under service conditions. This dissertation provides fundamental investigations of several advanced materials: thermoset polymers, a common matrix material for fiber-reinforced composites and nanocomposites; aluminum alloy 7075-T6 (AA7075-T6), a high-performance aerospace material; and ceramic matrix composites (CMCs), an advanced composite for extreme-temperature applications. To understand matrix interactions with various interfaces and nanoinclusions at their fundamental scale, the properties of thermoset polymers are studied at the atomistic scale. An improved proximity-based molecular dynamics (MD) technique for modeling the crosslinking of thermoset polymers is carefully established, enabling realistic curing simulations through its ability to dynamically and probabilistically perform complex topology transformations. The proximity-based MD curing methodology is then used to explore damage initiation and the local anisotropic evolution of mechanical properties in thermoset polymers under uniaxial tension with an emphasis on changes in stiffness through a series of tensile loading, unloading, and reloading experiments. Aluminum alloys in aerospace applications often require a fatigue life of over 109 cycles, which is well over ii the number of cycles that can be practically tested using conventional fatigue testing equipment. In order to study these high-life regimes, a detailed ultrasonic cycle fatigue study is presented for AA7075-T6 under fully reversed tension-compression loading. The geometric sensitivity, frequency effects, size effects, surface roughness effects, and the corresponding failure mechanisms for ultrasonic fatigue across different fatigue regimes are investigated. Finally, because CMCs are utilized in extreme environments, oxidation plays an important role in their degradation. A multiphysics modeling methodology is thus developed to address the complex coupling between oxidation, mechanical stress, and oxygen diffusion in heterogeneous carbon fiber-reinforced CMC microstructures.

Schichtel, Jacob J.↗
DOE OSTI2024

Interfacial Analysis of Blister Formation in U-10Mo Mini-Plates

A thorough investigation of potential failure mechanisms builds confidence in the performance of monolithic U-10wt%Mo (U-10Mo) fuel plates. The lowenriched uranium (LEU) fuel system is currently undergoing qualification as a high U-density fuel that can be used to convert United States high-performance research reactors from high-enriched uranium (HEU) operation. This will require establishing fuel operational limits such that fission products and a coolable geometry are retained, even during off-normal reactor operation conditions [1]. Such off-normal conditions can subject the fuel plates to temperatures where the internal pressure of precipitated fission gasses result in a permanent deformed, raised area of the cladding, referred to as a blister. These blisters can reduce the local coolability of a fuel plate and can even close coolant channels of a fuel assembly, so blistered plates are considered failed regardless of whether the fuel is truly breached. Historically, a marginto- failure is established through out-of-pile blister threshold testing of irradiated fuel plates. This is accomplished by incrementally heating irradiated fuel plates until blisters are observed. This reveals the temperature threshold that would have resulted in a blister if a plate experienced them at those irradiation conditions [2]. While the blister testing itself reveals the temperatures at which the cladding mechanical integrity was exceeded, a more thorough investigation of the interface evolution in proximity to formed blisters may reveal mechanisms as to the blister formation and retention of fission gases. Previous studies have explored potential underlying mechanisms in historical plates that may have been close to blistering [3]; however, this work is the first exploration of blister tested irradiated plates, fabricated by a commercial vendor—another requirement for qualification of the fuel system [1]. The Mini-plate 1 (MP-1) experiment was the first in a series of irradiation and post-irradiation examination (PIE) campaigns to qualify the U-10Mo monolithic fuel system. It consisted of commercially fabricated 25.4×101.5 mm Al-clad mini-plates with a monolithic U-10Mo foil coated in a Zr diffusion barrier. The primary MP-1 PIE campaign was previously completed. Among the suit of examinations was a blister testing campaign, where plates were incrementally annealed in 25°C increments until blisters were observed or a maximum temperature of 550°C was reached [4]. The previously blistered plates from this campaign were revisited in this work.

25228↗