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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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This data set shows the operation of the fuel cell inverter under grid-forming mode of operation, grid-following mode of operation and transition between the two modes.
The growing size and complexity of spacecraft power systems coupled with limited space/ground communications necessitate increasingly automated onboard control systems. Research in computer science, particularly artificial intelligence has developed methods and techniques for constructing man-machine systems with problem-solving expertise in limited domains which may contribute to the automation of power systems. Since these systems perform tasks which are typically performed by human experts they have become known as Expert Systems. A review of the current state of the art in expert systems technology is presented, and potential applications in power systems management are considered. It is concluded that expert systems appear to have significant potential for improving the productivity of operations personnel in aerospace applications, and in automating the control of many aerospace systems.
The NASA Marshall Space Flight Center proposes to implement computer-controlled fault injection into an electrical power system breadboard to study the reactions of the various control elements of this breadboard. Elements under study include the remote power controllers, the algorithms in the control computers, and the artificially intelligent control programs resident in this breadboard. To this end, a study of electrical power system faults is being performed to yield a list of the most common power system faults. The results of this study will be applied to a multichannel high-voltage DC spacecraft power system called the large autonomous spacecraft electrical power system (LASEPS) breadboard. The results of the power system fault study and the planned implementation of these faults into the LASEPS breadboard are described.
NASA/Marshall Space Flight Center proposes to implement fault injection into an electrical power system breadboard to study the reactions of the various control elements of this breadboard. Among the elements to be studied are the remote power controllers, the algorithms in the control computers, and the artificially intelligent control programs resident in this breadboard. To this end, a study of electrical power is being performed to yield a list of the most common power system faults. The results of this study are being applied to a multichannel high-voltage DC spacecraft power system called the Large Autonomous Spacecraft Electrical Power System Breadboard. Some of the reactions of the breadboard to some of the faults which have been encountered are presented along with the results of this study.
Analytical derivations of stress intensity factors (SIF's) of a multicracked plate can be complex and tedious. Recent advances, however, in intelligent application of symbolic computation can overcome these difficulties and provide the means to rigorously and efficiently analyze this class of problems. Here, the symbolic algorithm required to implement the methodology described in Part 1 is presented. The special problem-oriented symbolic functions to derive the fundamental kernels are described, and the associated automatically generated FORTRAN subroutines are given. As a result, a symbolic/FORTRAN package named SYMFRAC, capable of providing accurate SIF's at each crack tip, was developed and validated. Simple illustrative examples using SYMFRAC show the potential of the present approach for predicting the macrocrack propagation path due to existing microcracks in the vicinity of a macrocrack tip, when the influence of the microcrack's location, orientation, size, and interaction are taken into account.
An essential part of describing the damage state and predicting the damage growth in a multicracked plate is the accurate calculation of stress intensity factors (SIF's). Here, a methodology and rigorous solution formulation for SIF's of a multicracked plate, with fully interacting cracks, subjected to a far-field arbitrary stress state is presented. The fundamental perturbation problem is derived, and the steps needed to formulate the system of singular integral equations whose solution gives rise to the evaluation of the SIF's are identified. This analytical derivation and numerical solution are obtained by using intelligent application of symbolic computations and automatic FORTRAN generation capabilities (described in the second part of this paper). As a result, a symbolic/FORTRAN package, named SYMFRAC, that is capable of providing accurate SIF's at each crack tip was developed and validated.
Multilayer neural networks were successfully trained to classify segments of 12-channel electroencephalogram (EEG) data into one of five classes corresponding to five cognitive tasks performed by a subject. Independent component analysis (ICA) was used to segregate obvious artifact EEG components from other sources, and a frequency-band representation was used to represent the sources computed by ICA. Examples of results include an 85% accuracy rate on differentiation between two tasks, using a segment of EEG only 0.05 s long and a 95% accuracy rate using a 0.5-s-long segment.
Our planet has been endowed with a host of natural mechanisms to keep the environment and climate in balance. Humans are now facing the need to restore this balance that has been upset in the past years because of a growing population and resource demands. To steer dependency away from freshwater crops and decrease environmental damage from humanity s fuel and energy demands, it is necessary to take advantage of the natural adaptive biomass resources that are already in place. Using methods of Green Planet Architecture, based on compilations of current research and procedures, could lead to new forms of energy and fueling as well as new sources for food and feed. Green Planet Architecture involves climatic adaptive biomass; geospatial intelligence; agri- and aqua-culture life cycles; and soil, wetland, and shoreline restoration. Plants such as Salicornia, seashore mallow, castor, mangroves, and perhaps Moringa can be modified (natural, model-assisted, or genetically modified) to thrive in salt-water and brackish water or otherwise not arable conditions, making them potentially new crops that will not displace traditional farming. These fueling sources also have potential to be used in other rapid-growth industries, such as the aviation industry, that have incentive to move towards more sustainable fuel supplies. This paper highlights an example of how synergistic development of biomass resources and geospatial intelligence high-performance computing capabilities can be focused to resolve potential drought-famine problems. These techniques, provide a basis for future e-science-based discovery (and access) through technology that can be expanded to support global societal applications.
This planet has been endowed with a host of natural mechanisms to keep the environment and climate in balance. Humans are now facing the need to restore this balance that has been upset in the past years because of a growing population and resource demands. To steer dependency away from freshwater crops and decrease environmental damage from humanity s fuel and energy demands, it is necessary to take advantage of the natural adaptive biomass resources that are already in place. Using methods of "Green Planet Architecture," based on compilations of current research and procedures, could lead to new forms of energy and fueling as well as new sources for food and feed. Green Planet Architecture involves climatic adaptive biomass; geospatial intelligence; agri- and aqua-culture life cycles; and soil, wetland, and shoreline restoration. Plants such as Salicornia, seashore mallow, castor, mangroves, and perhaps Moringa can be modified (naturally, model-assisted, or genetically) to thrive in salt water and brackish water or otherwise not arable conditions, making them potentially new crops that will not displace traditional farming. These fueling sources also have potential to be used in other rapid-growth industries, such as the aviation industry, that have incentive to move towards more sustainable fuel supplies. This report highlights an example of how synergistic development of biomass resources and geospatial intelligence high-performance computing capabilities can be focused to resolve potential drought-famine problems. These techniques provide a basis for future e-science-based discovery (and access) through technology that can be expanded to support global societal applications.
Digital Engineering technologies are transforming long-stagnant development processes by applying the tremendous advancements in Information Technology (IT) to classical engineering tasks such as design, analysis, and fabrication of space-flight instrument structures. Generative Design leverages developments in Artificial Intelligence (AI) and Cloud computing to enable a paradigm shift in the design process, allowing the engineer to focus on defining the requirements and objectives of the design while AI generates optimized designs which comply with the input requirements. Digital Manufacturing allows these complex lightweight designs to be efficiently manufactured by directly fabricating from the resulting 3D models. The development of these two Digital Engineering technologies realizes significant mass savings while simultaneously reducing structure development time from months to days. This paper describes the development of the Evolved Structures process applying these technologies to spaceflight optical instrument structures including an example demonstrating greater than 10x reduction in development time/cost and greater than 3x improvement in structural performance.
NASA has a vast archive of engineering guidelines, standards, and best practices collected over decades. This encompasses a breadth of topics from rocketry and engineering standards to risk management and space-related health issues. This wealth of information, while invaluable to NASA engineers, staff, and the public, is too extensive for any individual to fully comprehend. To address this challenge, we have developed Atlas, a tool within NASA's Mission Cloud Platform that enables users to query these diverse sources effectively. Atlas allows users to ask natural language questions and receive answers grounded in factual information from source documents. The tool provides responses with direct quotations and links to original documents, ensuring transparency and accuracy. It can address a wide range of queries, from specific technical details like safe distances for rocket launches from lightning to broader topics such as crew health requirements for long-duration space missions, corrosion protection in low Earth orbit, and NASA's agreements with various entities. In developing Atlas, we encountered and overcame several technical challenges. Large Language Models often struggle with consistently providing accurate information, especially for highly specialized topics. We implemented strategies to prevent hallucinations and ensure the reliability of responses, even for complex questions on topics ranging from NASA Mission Classes to intricate rocket science concepts. Additionally, we addressed the challenges of delivering quick responses while maintaining cost-effectiveness. Our presentation will detail the innovative approaches we employed to optimize performance and efficiency, making Atlas a powerful and practical tool for accessing NASA's extensive knowledge base.
Quick and effective accident management is essential in any industry in order to limit and contain possible threats to both people and environment/assets. This is in particular relevant in the nuclear industry where accidents may have major impacts from an economic, health and societal point of view. As an example, the Fukushima Dai-ichi nuclear power plant accident highlighted the importance of the ability of plant operators and plant staff to react quickly and effectively in accident conditions. This particular event showed the importance of being able to: • Determine/estimate the actual status of the plant (i.e., diagnosis) when the monitoring system is corrupted or partially unavailable, and, • Forecast its future evolution (i.e., prognosis). In this paper we want to describe a research direction geared toward the development of a new set of advanced diagnosis and prognosis tools. We employ innovative data mining and machine learning techniques that are able to infer plant status and mimic the plant’s full temporal behavior in order to assist the reactor operators during an accident scenario.
Models of existing buildings are currently unreliable unless calibrated manually by a skilled professional. Autotune, as the name implies, automates this process by calibrating the model of an existing building to measured data, and is now available as open source software. This enables private businesses to incorporate Autotune into their products so that their customers can more effectively estimate cost savings of reduced energy consumption measures in existing buildings.
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With the introduction of smart devices and rise in distributed, networked energy systems, diligent focus on cybersecurity for an evolving grid is crucial. Watch this video to learn more about what NREL is doing to mitigate threats to today’s energy infrastructure and provide a pathway to a more secure and resilient energy future.
National Virtual Biotechnology Laboratory (NVBL) Symposium - Jim Brase, Deputy Associate Director for Computation, Lawrence Livermore National Laboratory, “Future S&T Needs: Artificial Intelligence/Machine Learning and Computing”
With access to large datasets, deep neural networks (DNN) have achieved human-level accuracy in image and speech recognition tasks. However, in chemistry, data is inherently small and fragmented. In this work, we develop an approach of integrating rule-based knowledge into the Chemception CNN model via transfer learning techniques. The resulting model, ChemNet, is a transferable and generalizable deep neural network for chemical property prediction that learns in a semi-supervised manner from large unlabeled chemical databases. When ChemNet is further fine-tuned on 3 smaller datasets to predict chemical properties that it was not originally trained on, we show that ChemNet exceeds the accuracy of existing Chemception CNN models and other contemporary DNN models that were trained using conventional supervised learning approaches. These results indicate that pre-training ChemNet on a large diverse chemical database while incorporating chemistry domain knowledge, enables the development of more generalizable deep neural networks for the prediction of novel chemical properties.
The ultimate goal of this study is to minimize (or eliminate) human intervention in the scanning and 3D CAD modeling process in the generation of 3D as-built model of nuclear facilities. This paper describes the application of the state-of-the-art deep learning technology in 3D point cloud data segmentation which is a part of the 3D modeling process. The baseline deep learning network chosen in this study is PointNet. We compare the deep learning approach with a conventional filtering approach, and show the feasibility and potential of deep learning technology. (authors)