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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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Gives a brief overview of some of the validation issues between a shareable benchmark and real-world algorithm use through our nanoindentation and maritime exemplars.
Experience gained in the development and integration of experiments for the OSS-1 payload to be carried on board the Space Shuttle for the characterization of the effects of the STS environment on future scientific missions is related. Following a review of mission objectives, including the demonstration of Shuttle research capabilities and the evaluation of new technology, the experiments and mission operations to be performed are outlined, and mission support requirements are indicated. The conduct of mission management by the Goddard Space Flight Center, which required interfacing with a large number of other agencies, centers, and investigators and contractors and an enormous amount of formal documentation, is then examined, with consideration given to the management of mechanical, electrical, and thermal systems development mock-up construction, EMI/EMC and acoustic testing, the formation of an integration and test team, and trade-offs between cost, schedule and performance. It is concluded that although work still remains to be done, significant progress has been made in ensuring that the STS is a viable system for space exploration.
The Space Shuttle orbiter's thermal protection system (TPS), designed mainly on the basis of wind tunnel test data, has successfully completed its design, development, and flight test program. The flight test data provide an exceptional opportunity to evaluate the use of wind tunnel test data for the design of TPS for reentry vehicles. Comparisons of flight test data against wind tunnel data used to design the orbiter's TPS were developed. These flight data, though still in the preliminary analysis phases, generally support the use of wind tunnel test data in the design of TPS for hypersonic reentry vehicles.
Vapor Compression Distillation (VCD) is the chosen technology for urine processing aboard the International Space Station (155). Development and life testing over the past several years have brought to the forefront problems and solutions for the VCD technology. Testing between 1992 and 1998 has been instrumental in developing estimates of hardware life and reliability. It has also helped improve the hardware design in ways that either correct existing problems or enhance the existing design of the hardware. The testing has increased the confidence in the VCD technology and reduced technical and programmatic risks. This paper summarizes the test results and changes that have been made to the VCD design.
This paper is based on experiences being gained through a project entitled "The Mississippi Community College Pilot Project". The project was labeled "pilot" because it is thought that lessons learned during the implementation of this project may aid similar endeavors in other states. The objective of the project is to provide curriculum enrichment and associated faculty enhancement through the use of earth observations data in biological and physical sciences courses. The premise underlying the objective is that information from earth observations from satellite and aircraft platforms provides an effective means of illustrating and explaining science topics/phenomena in a new and/or different perspective. It is also thought that the use of data acquired from space may also serve to captivate the students interest and/or inquisitiveness about the particular science issue.
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Abstract not provided.
Machine learning is becoming prevalent in high energy physics, with numerous applications in physics analyses and event reconstruction showing great improvements compared to traditional computing methods. This thesis studies three projects which each propose new avenues for machine learning applications within the high energy physics CMS experiment located at CERN. In the first project, a search for a dark matter signal called “emerging jets” is performed, using graph neural networks to greatly increase sensitivity to the signal’s signature within the data. The result of this dark matter search sets the most stringent exclusion limits to date on theoretical emerging jet models. Motivated by inefficiencies encountered when processing the emerging jet graph neural network at Fermi National Accelerator Laboratory’s computing centers, the second project re-optimizes the computing centers for machine learning inference. This re-optimization uses NVIDIA Triton Inference Servers to process users’ analysis code heterogeneously, therefore achieving high processing throughput and decreasing user time-to-insight. The last project focuses on an upgrade to the CMS experiment’s real-time event selection system which improves physics object reconstruction under harsh processing conditions. A boosted decision tree is used to quickly and efficiently quantify a reconstructed particle’s “track quality” in order to remove particle tracks reconstructed erroneously. In summary, this thesis will not only present examples of how high energy physics can greatly benefit by leveraging machine learning techniques for physics analysis and reconstruction, but will also provide guidance on how the field can prepare for the inevitable increase in machine learning applications.
The DEVELOP Program, part of NASA’s Applied Sciences’ Capacity Building Program, connects decision makers and early career individuals through rapid 10-week feasibility studies. These projects utilize Earth observation assets to address environmental issues and are conducted by small, interdisciplinary teams who work autonomously under the guidance of a cohort of science advisors. The teams tailor their research to create end products that support partner decision making processes. The connection to real-world decision making and direct communication with partner organizations drives meaningful experiences for DEVELOP participants that are directly applicable to future work in the geosciences. This poster will share DEVELOP’s model for offering meaningful experiences to its participants, address lessons learned over the past 20+ years, and highlight a variety of testimonials.
Lessons learned during the development of the NASA Systems Test and Operations Language (STOL) Intelligent Tutoring System (ITS), being developed at NASA Goddard Space Flight Center are presented. The purpose of the intelligent tutor is to train STOL users by adapting tutoring based on inferred student strengths and weaknesses. This system has been under development for over one year and numerous lessons learned have emerged. These observations are presented in three sections, as follows. The first section addresses the methodology employed in the development of the STOL ITS and briefly presents the ITS architecture. The second presents lessons learned, in the areas of: intelligent tutor development; documentation and reporting; cost and schedule control; and tools and shells effectiveness. The third section presents recommendations which may be considered by other ITS developers, addressing: access, use and selection of subject matter experts; steps involved in ITS development; use of ITS interface design prototypes as part of knowledge engineering; and tools and shells effectiveness.
Building automation and controls are becoming increasingly complex with the emergence of Grid Integrated Efficient Buildings (GEBs) as well as new highly efficient sequences of operation and data-driven control schemes. However, there remains a significant gap in hands-on training opportunities for building operators and technicians to gain practical experience with advanced control systems in a low-risk environment. This paper presents BOPTEST (Building Optimization Performance Test) as a suitable platform for workforce training in building controls and GEB technologies. BOPTEST provides a suite of standardized building simulation test cases with a REST API, real-time control interfaces through BACnet, semantic models connecting users to building data, and built-in calculation of control metrics and performance indicators. The platform enables trainees to interact with virtual buildings using industry-standard protocols while learning how to implement and innovate control strategies. The training platform is designed to offer a structured and interactive learning experience for building engineers, helping them effectively develop, learn, and retain skills in fault identification, troubleshooting, and correction. The workflow is divided into three main phases: 1) Setup, 2) Exercise, and 3) Review, each comprising specific activities performed by either the instructor or the student. Initial pilot training sessions have yielded positive feedback from instructors and participants and demonstrates that BOPTEST effectively fills an industry need for a low-risk training resource via simulation of real building control systems, allowing trainees to gain practical experience before working in the field. The platform's ability to provide immediate performance feedback while maintaining familiar industry interfaces makes it particularly suitable for workforce development programs. This work provides a replicable model for leveraging building simulation in control education and training.
Building automation and controls are becoming increasingly complex with the emergence of Grid Integrated Efficient Buildings (GEBs) as well as new highly efficient sequences of operation and data-driven control schemes. However, there remains a significant gap in hands-on training opportunities for building operators and technicians to gain practical experience with advanced control systems in a low-risk environment. This paper presents BOPTEST (Building Optimization Performance Test) as a suitable platform for workforce training in building controls and GEB technologies. BOPTEST provides a suite of standardized building simulation test cases with a REST API, real-time control interfaces through BACnet, semantic models connecting users to building data, and built-in calculation of control metrics and performance indicators. The platform enables trainees to interact with virtual buildings using industry-standard protocols while learning how to implement and innovate control strategies. The training platform is designed to offer a structured and interactive learning experience for building engineers, helping them effectively develop, learn, and retain skills in fault identification, troubleshooting, and correction. The workflow is divided into three main phases: 1) Setup, 2) Exercise, and 3) Review, each comprising specific activities performed by either the instructor or the student. Initial pilot training sessions have yielded positive feedback from instructors and participants and demonstrates that BOPTEST effectively fills an industry need for a low-risk training resource via simulation of real building control systems, allowing trainees to gain practical experience before working in the field. The platform's ability to provide immediate performance feedback while maintaining familiar industry interfaces makes it particularly suitable for workforce development programs. This work provides a replicable model for leveraging building simulation in control education and training.
Developing transferable machine learning models is trending in data-driven materials research. However, how to apply such models to a specific research domain remains unclear. Here, in this work, we choose high-entropy materials as a platform with a specialized data set containing 145,323 DFT-relaxed materials. This data set is used to explore the role of domain-specific knowledge in training effective models. Our tests with three representative graph neural network architectures indicate the model complexity has much smaller influence on performance than the data itself. Specifically, the consideration of low-energy atomic ordering, structures with diverse elemental coverage, and high-order interactions significantly influences the model performance. We also find that domain knowledge-driven sampling can greatly enhance unsupervised learning techniques. This research highlights that developing specialized data sets is more beneficial than further complicating deep learning architectures. Additionally, physics-inspired sampling algorithms are crucially needed for better machine learning models for a specific materials research domain.