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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Next Generation NASA Initiative for Space Geodesy

Space geodesy measurement requirements have become more and more stringent as our understanding of the physical processes and our modeling techniques have improved. In addition, current and future spacecraft will have ever-increasing measurement capability and will lead to increasingly sophisticated models of changes in the Earth system. Ground-based space geodesy networks with enhanced measurement capability will be essential to meeting these oncoming requirements and properly interpreting the sate1!ite data. These networks must be globally distributed and built for longevity, to provide the robust data necessary to generate improved models for proper interpretation ofthe observed geophysical signals. These requirements have been articulated by the Global Geodetic Observing System (GGOS). The NASA Space Geodesy Project (SGP) is developing a prototype core site as the basis for a next generation Space Geodetic Network (SGN) that would be NASA's contribution to a global network designed to produce the higher quality data required to maintain the Terrestrial Reference Frame and provide information essential for fully realizing the measurement potential of the current and coming generation of Earth Observing spacecraft. Each of the sites in the SGN would include co-located, state of-the-art systems from all four space geodetic observing techniques (GNSS, SLR, VLBI, and DORIS). The prototype core site is being developed at NASA's Geophysical and Astronomical Observatory at Goddard Space Flight Center. The project commenced in 2011 and is scheduled for completion in late 2013. In January 2012, two multiconstellation GNSS receivers, GODS and GODN, were established at the prototype site as part of the local geodetic network. Development and testing are also underway on the next generation SLR and VLBI systems along with a modern DORIS station. An automated survey system is being developed to measure inter-technique vector ties, and network design studies are being performed to define the appropriate number and distribution of these next generation space geodetic core sites that are required to achieve the driving ITRF requirements. We present the status of this prototype next generation space geodetic core site, results from the analysis of data from the established geodetic stations, and results from the ongoing network design studies.

Merkowitz, S. M.↗
NASA NTRS2009

PCC Framework for Program-Generators

In this paper, we propose a proof-carrying code framework for program-generators. The enabling technique is abstract parsing, a static string analysis technique, which is used as a component for generating and validating certificates. Our framework provides an efficient solution for certifying program-generators whose safety properties are expressed in terms of the grammar representing the generated program. The fixed-point solution of the analysis is generated and attached with the program-generator on the code producer side. The consumer receives the code with a fixed-point solution and validates that the received fixed point is indeed a fixed point of the received code. This validation can be done in a single pass.

Kong, Soonho↗
NASA NTRS2019

Heat Analysis Manager (HAM), a Thermal Desktop API Based Heat Map Generation Software

Thermal engineers often create custom heat maps to analyze their thermal model. However, generating a heat map is difficult because thermal simulation only readily provide attributes of simulated nodes such as temperature, capacitance, heat generation, and a network of conductances. Heat flow values are a derived quantity from the nodal attributes, and the data processing and management of heat flow between nodes quickly become difficult for large models. Deriving a network of heat flow values requires vast amount of calculations and data handling, heat map generation process generally suffers from processing speed, loss of accuracy, and/or presentation of data in a useful format. Heat Analysis Manager (HAM) is a Thermal Desktop (TD) based free multi-purpose tool developed to aid thermal engineers in analyzing their thermal model, including a heat map generation functionality. HAM’s heat map generator retains accuracy and fast processing speed by utilizing TD’s application programming interface (API) and built-in TD’s “Qflow from Results.” Furthermore, HAM’s heat map output is presented in an easily customizable format in Excel, allowing users to create various custom visual heat maps. A full description of how HAM utilizes TD’s API to create a customizable heat map is provided. A simple model demonstration is included along with step-by-step procedures on creating custom heat maps. HAM’s heat map result has been verified against TD’s and other heat map generation software, and verification methods are also included.

Thermal desktop↗
NASA NTRS2018

An Algorithm for Trajectory Generation in Redundant Manipulators with Joint Transmission Accommodation

Trajectory generation for manipulators involves generating incremental updates of joint variables to achieve a desired end–effector motion. The Jacobian matrix maps incremental joint motion to incremental end–effector motion in a linear fashion and is typically used in manipulator trajectory generation algorithms. In the case of a redundant manipulator, the Jacobian matrix is not square or invertible and therefore algorithms based on pseudoinverses and their variations are commonly used for trajectory generation. These methods either are computationally not efficient or do not utilize all the joints in motion generation and therefore do not completely exploit the redundancy of the manipulator. The method presented in this paper is a simple method that maximizes transmission of all joint variables onto a desired end–effector motion trajectory. The method is based on aligning the null–space of an augmented Jacobian matrix with the path of the desired end–effector motion, from which a linear combination of joints that projects fully onto the desired end–effector trajectory is obtained. In this manner, all joints of the redundant manipulator are used to generate the end–effector trajectory accommodating the ability of each joint in terms of its motion transmission.

Ravani, Bahram↗
NASA NTRSDate not supplied

Implementation of a Hybrid Edge Node-Centroid Node Approach for the Generation of Reduced Thermal Models

Reduced thermal models are often required for delivery to organizations that manage observatory or launch models at the highest levels of assembly. However, the effort to generate reduced models, and verify against their detailed counterparts, is a challenge that has not yet been conclusively solved. Higher level organizations often place a limit on the number of nodes for delivered models with the assumption that smaller models generally result in less computation time. However, the burden of producing and verifying the accuracy of the reduced models is placed primarily on the lower-level organizations, which in turn consumes resources needed to produce these models. Limiting the total number of allowable nodes may also prevent users from taking full advantage of software capabilities that allow for faster generation of models, such as finite elements, which generally require more nodes than older centroid based models. A methodology using Thermal Desktop was described in 2010 which used: (1) finite elements and edge nodes for a conduction matrix, (2) centroid nodes for capacitance and radiative computations, and (3) the super network feature to produce a conduction matrix based only on the centroid nodes. At that time, the methodology was clear, but the implementation would have had to be done manually; however, with the inclusion of the OpenTD Application Programming Interface, this methodology can now be implemented programmatically and for the first time, be a viable approach for the generation of reduced models. The approach was implemented and developed at the NASA Goddard Space Flight Center (GSFC) for the Capture, Containment, and Return System (CCRS) payload on the Earth Return Orbiter (ERO) as part of the Mars Sample Return mission, resulting in the TCYEE tool. ERO features a spacecraft bus provided by Airbus through the European Space Agency with node limitations on the delivered CCRS model provided by GSFC. TCYEE was used to generate the reduced model for this delivery and the predictions compared favorably to the detailed model currently in use for the thermal performance evaluation. Furthermore, TCYEE is being explored for potential use on the Roman Space Telescope (RST) for the generation of reduced models for delivery to the launch provider, which also has node limit requirements on the RST observatory model for use in launch simulation analyses. This paper describes the methodology, its implementation, and compares the performance of reduced models generated by TCYEE to their detailed counterparts.

Thermal Desktop↗
NASA NTRSDate not supplied

Implementation of a Hybrid Edge Node-Centroid Node Approach for the Generation of Reduced Thermal Models

Reduced thermal models are often required for delivery to organizations that manage observatory or launch models at the highest levels of assembly. However, the effort to generate reduced models, and verify against their detailed counterparts, is a challenge that has not yet been conclusively solved. Higher level organizations often place a limit on the number of nodes for delivered models with the assumption that smaller models generally result in less computation time. However, the burden of producing and verifying the accuracy of the reduced models is placed primarily on the lower-level organizations, which in turn consumes resources needed to produce these models. Limiting the total number of allowable nodes may also prevent users from taking full advantage of software capabilities that allow for faster generation of models, such as finite elements, which generally require more nodes than older centroid based models. A methodology using Thermal Desktop was described in 2010 which used: (1) finite elements and edge nodes for a conduction matrix, (2) centroid nodes for capacitance and radiative computations, and (3) the super network feature to produce a conduction matrix based only on the centroid nodes. At that time, the methodology was clear, but the implementation would have had to be done manually; however, with the inclusion of the OpenTD API, this methodology can now be implemented programmatically and for the first time, be a viable approach for the generation of reduced models. The approach was implemented and developed at the NASA Goddard Space Flight Center (GSFC) for the Capture, Containment, and Return System (CCRS) payload on the Earth Return Orbiter (ERO) as part of the Mars Sample Return (MSR) mission, resulting in the TCYEE tool. ERO features a spacecraft bus provided by Airbus through the European Space Agency (ESA) with node limitations on the delivered CCRS model provided by GSFC. TCYEE was used to generate the reduced model for this delivery and the predictions compared favorably to the detailed model currently in use for the thermal performance evaluation. Furthermore, TCYEE is being explored for potential use on the Roman Space Telescope (RST) for the generation of reduced models for delivery to the launch provider, which also has node limit requirements on the RST observatory model for use in launch simulation analyses. This paper describes the methodology, its implementation, and compares the performance of reduced models generated by TCYEE to their detailed counterparts.

Hume L. Peabody↗
NASA NTRSDate not supplied

Implementation of a Hybrid Edge Node-Centroid Node Approach for the Generation of Reduced Thermal Models

Reduced thermal models are often required for delivery to organizations that manage observatory or launch models at the highest levels of assembly. However, the effort to generate reduced models, and verify against their detailed counterparts, is a challenge that has not yet been conclusively solved. Higher level organizations often place a limit on the number of nodes for delivered models with the assumption that smaller models generally result in less computation time. However, the burden of producing and verifying the accuracy of the reduced models is placed primarily on the lower-level organizations, which in turn consumes resources needed to produce these models. Limiting the total number of allowable nodes may also prevent users from taking full advantage of software capabilities that allow for faster generation of models, such as finite elements, which generally require more nodes than older centroid based models. A methodology using Thermal Desktop was described in 2010 which used: (1) finite elements and edge nodes for a conduction matrix, (2) centroid nodes for capacitance and radiative computations, and (3) the super network feature to produce a conduction matrix based only on the centroid nodes. At that time, the methodology was clear, but the implementation would have had to be done manually; however, with the inclusion of the OpenTD API, this methodology can now be implemented programmatically and for the first time, be a viable approach for the generation of reduced models. The approach was implemented and developed at the NASA Goddard Space Flight Center (GSFC) for the Capture, Containment, and Return System (CCRS) payload on the Earth Return Orbiter (ERO) as part of the Mars Sample Return (MSR) mission, resulting in the TCYEE tool. ERO features a spacecraft bus provided by Airbus through the European Space Agency (ESA) with node limitations on the delivered CCRS model provided by GSFC. TCYEE was used to generate the reduced model for this delivery and the predictions compared favorably to the detailed model currently in use for the thermal performance evaluation. Furthermore, TCYEE is being explored for potential use on the Roman Space Telescope (RST) for the generation of reduced models for delivery to the launch provider, which also has node limit requirements on the RST observatory model for use in launch simulation analyses. This paper describes the methodology, its implementation, and compares the performance of reduced models generated by TCYEE to their detailed counterparts.

Thermal desktop↗
NASA NTRSDate not supplied

Advances in Automation of Overset Structured Volume Mesh Generation and Domain Connectivity

Automation of overset structured surface mesh generation has recently been accomplished by the creation of face, edge, and node meshes based on Boundary Representation solids as the geometry input. The current work continues the automation effort in overset volume mesh generation and domain connectivity based on the auto-generated surface meshes. All near-body curvilinear volume meshes are automatically generated using hyperbolic methods. Automation of this step is enabled by appropriate surface grid point distribution, and selection of boundary-splay and smoothing parameters based on concave and convex surface features. The off-body domain is covered by two automatically generated grid systems. The first contains a single Cartesian mesh with a uniform core enclosing all near-body volume meshes and stretched layers that extend to the far field, while the second consists of a set of small stretched Cartesian grids covering pockets of off-body orphan points. With high quality mesh overlap mostly guaranteed by the surface meshing scheme, orphan points that need to be covered by the second Cartesian mesh system are located away from the fine grid spacing region near the wall. Using line-segment and ray-pierce tests against the surface grids, hole-cutting is accomplished on both near and off-body volume grids resulting in appropriate clearances from the wall. The complete mesh generation automation process is demonstrated on five test cases where flow solutions are also computed and compared with solutions obtained using other methods.

TTT↗
DOE OSTI2020

Induced Markov chain for wind farm generation forecasting

Systems and methods for forecasting power generation in a wind farm are disclosed. The systems and methods utilize an induced Markov chain model to generate a forecast of power generation of the wind farm. The forecast is at least one of a point forecast or a distributional forecast. Additionally, the systems and methods modify at least one of: (i) a generation of electricity at a power plant coupled to a common power grid as the wind farm; or (ii) a distribution of electricity in the common power grid based on the forecast of power generation of the wind farm. In an exemplary approach, utilizing the induced Markov chain model to generate the forecast may include determining a series of time adjacent power output measurements based on historical wind power measurements and calculating a time series of difference values based on the series of time adjacent power output measurements.

17 WIND ENERGY↗
DOE OSTI2021

Differential neutron spectrum generator and related methods and systems

A neutron spectrum generator is disclosed herein including a neutron source, a scatterer positioned in a direct path between the neutron source and a neutron detector, and a material shell configured to have at least one non-uniform characteristic selected from the group consisting of a material, a thickness, a length, an angle, a layer, and combinations thereof to generate a specific spectrum at the neutron detector that is different than the spectrum of the neutron source. A related method includes measuring a first response generated by a first material shell of a neutron spectrum generator interacting with a neutron source, replacing the first material shell with a second material shell, measuring a second response generated by a second material shell of a neutron spectrum generator interacting with the neutron source, and determining a total fission response by determining a difference between the first response and the second response.

Scates, Wade↗
DOE OSTI2022

Generative attribute optimization

A generative attribute optimization (“GAO”) system facilitates understanding of effects of changes of attribute values of an object on a characteristic of the object and automatically identifying attribute values to achieve a desired result for the characteristic. The GAO system trains a generator (encoder and decoder) using an attribute generative adversarial network. The GAO model includes the trained generator and a separately trained predictor model. The GAO model inputs an input image and modified attribute values and employs the encoder and the decoder to generate a modified image that is the input image modified based on the modified attribute values. The GAO model then employs the predictor model to that inputs the modified image and generate a prediction of a characteristic of the modified image. The GAO system may employ an optimizer to modify the attribute values until an objective based on the desired result is achieved.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

SIDIS-RC EvGen: A Monte-Carlo event generator of semi-inclusive deep inelastic scattering with the lowest-order QED radiative corrections

SIDIS-RC EvGen is a C++ standalone Monte-Carlo event generator for studies of semi-inclusive deep inelastic scattering (SIDIS) processes at medium to high lepton beam energies. In particular, the generator contains binary and library components for generating SIDIS events and calculating cross sections for unpolarized or longitudinally polarized beam and unpolarized, longitudinally or transversely polarized target. The structure of the generator incorporates transverse momentum-dependent parton distribution and fragmentation functions, whereby we obtain multi-dimensional binned simulation results, which will facilitate the extraction of important information about the three-dimensional nucleon structure from SIDIS measurements. In order to build this software, we have used recent elaborate QED calculations of the lowest-order radiative effects, applied to the leading order Born cross section in SIDIS. Here, in this paper, we provide details on the theoretical formalism as well as the construction and operation of SIDIS-RC EvGen, e.g., how we handle the event generation process and perform multi-dimensional integration. We also provide example programs, flowcharts, and numerical results on azimuthal transverse single-spin asymmetries.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Evolutionary Architecture Search for Generative Adversarial Networks Based on Weight Sharing

Generative adversarial networks (GANs) are a powerful generative technique but frequently face challenges with training stability. Network architecture plays a significant role in determining the final output of GANs, but designing a fine architecture demands extensive domain expertise. This article aims to address this issue by searching for high-performance generator’s architectures through neural architecture search (NAS). The proposed approach, called evolutionary weight sharing GANs (EWSGAN), is based on weight sharing and comprises two steps. First, a supernet of the generator is trained using weight sharing. Second, a multiobjective evolutionary algorithm (MOEA) is employed to identify optimal subnets from the supernet. These subnets inherit weights directly from the supernet for fitness assessment. Two strategies are used to stabilize the training of the generator supernet: 1) a fair single-path sampling strategy and 2) a discarding strategy. Experimental results indicate that the architecture searched by our method achieved a new state-of-the-art among NAS–GAN methods with a Fréchet inception distance (FID) of 9.09 and an inception score (IS) of 8.99 on the CIFAR-10 dataset. Finally, it also demonstrates competitive performance on the STL-10 dataset, achieving FID of 21.89 and IS of 10.51.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Operating Wind Turbine as Synchronous Generator: Modeling and Power-Hardware-in-the-Loop Demonstration

Grid-forming (GFM) control of Type 3 and Type 4 wind turbine generators (WTGs) has attracted substantial attention in power systems research; however, the limited overcurrent capability of power electronics converters continues to deteriorate the grid strength of the evolving power systems. Synchronous wind, also referred to as a Type 5 WTG, offers a unique GFM solution to address grid integration and grid strength issues by keeping the grid largely synchronous at very high integration levels of renewable generation. A Type 5 WTG interfaces with the electric grid via a synchronous generator driven by a variable speed hydraulic torque converter; hence, the wind rotor operates in variable-speed mode for maximum power generation, and the generator shaft remains synchronous to the grid. This paper develops and tests a high-fidelity model of a Type 5 WTG in a power-hardware-in-the-loop testing environment, and it presents its operation characteristics under different grid contingencies. The power-hardware-in-the-loop demonstration shows that a Type 5 WTG inherently behaves as a GFM unit and can obtain similar performance in terms of power response, wind rotor dynamics, and stability enhancement compared to a Type 3 WTG in GFM control mode. Furthermore, the paper provides further insight into how Type 5 WTGs can support the smooth transition to power systems with high integration levels of inverter-based resources.

17 - WIND ENERGY↗
DOE OSTI2020

Generation and evaluation of synthetic patient data

Background: Machine learning (ML) has made a significant impact in medicine and cancer research; however, its impact in these areas has been undeniably slower and more limited than in other application domains. A major reason for this has been the lack of availability of patient data to the broader ML research community, in large part due to patient privacy protection concerns. High-quality, realistic, synthetic datasets can be leveraged to accelerate methodological developments in medicine. By and large, medical data is high dimensional and often categorical. These characteristics pose multiple modeling challenges. Methods: In this paper, we evaluate three classes of synthetic data generation approaches; probabilistic models, classification-based imputation models, and generative adversarial neural networks. Metrics for evaluating the quality of the generated synthetic datasets are presented and discussed. Results: While the results and discussions are broadly applicable to medical data, for demonstration purposes we generate synthetic datasets for cancer based on the publicly available cancer registry data from the Surveillance Epidemiology and End Results (SEER) program. Specifically, our cohort consists of breast, respiratory, and non-solid cancer cases diagnosed between 2010 and 2015, which includes over 360,000 individual cases. Conclusions: We discuss the trade-offs of the different methods and metrics, providing guidance on considerations for the generation and usage of medical synthetic data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Using Generative AI to implement the discrepancy checker for a Nearly Autonomous Management and Control System for Advanced Reactors

Developments related to generative artificial intelligence (AI) have brought a major breakthrough in AI. These developments are rapidly accelerating developments in different science and engineering applications. Nearly Autonomous Management and Control (NAMAC) system provides recommendations to the operator for maintaining the safety and performance of the reactor. The discrepancy checker (DC) is an important component of the NAMAC) system, whose goal is to determine if the plant is moving towards the expected system state after the control actions are injected. In this work, we explore generative AI methods, particularly, a generative pretrained transformer (GPT) for implementing the DC function in NAMAC. The GPT-based DC aims to alert the operator in situations outside NAMAC’s scope and act as a chatbot the operator can use to retrieve relevant information. This study involves two versions of GPT developed by OpenAI: GPT-3.5 and GPT-4. These GPTs are trained on huge amounts of undisclosed general domain datasets. We explored two methods to adapt GPTs for DC implementation in NAMAC: fine-tuning and retrieval augmented generation. A small knowledge base (information file) that encompasses rules for DC implementation and some general information related to NAMAC has been created to support DC implementation using GPT. In this work, the GPT-based DC implementations have been tested for their reasoning abilities, comprehension, information retrieval, and extraction abilities. It should be noted that this paper only presents a preliminary study to test the feasibility of DC implementation using generative AI technology. Given the potential risks and severe consequences associated with nuclear reactor applications, combined with the black-box nature of AI, extensive offline and online testing and reliability analyses of GPT-based DCs are needed for further developing such capabilities.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Diffusion Probabilistic Modeling for Video Generation

Denoising diffusion probabilistic models are a promising new class of generative models that mark a milestone in high-quality image generation. This paper showcases their ability to sequentially generate video, surpassing prior methods in perceptual and probabilistic forecasting metrics. We propose an autoregressive, end-to-end optimized video diffusion model inspired by recent advances in neural video compression. The model successively generates future frames by correcting a deterministic next-frame prediction using a stochastic residual generated by an inverse diffusion process. We compare this approach against six baselines on four datasets involving natural and simulation-based videos. We find significant improvements in terms of perceptual quality and probabilistic frame forecasting ability for all datasets.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

Advanced Stirling Radioisotope Generator Engineering Unit 2 Anomaly Investigation

The Advanced Stirling Radioisotope Generator (ASRG) Engineering Unit 2 (EU2) is the highest fidelity electrically heated Stirling radioisotope generator built to date. NASA Glenn Research Center completed the assembly of the ASRG EU2 in September 2014 using hardware from the now cancelled ASRG flight development project. The ASRG EU2 integrated the first pair of Sunpower's Advanced Stirling Convertors (ASC−E3 #1 and #2) in an aluminum generator housing with Lockheed Martin's (LM's) Engineering Development Unit (EDU) 4 controller. After just 179 hr of EU2 generator operation, the first power fluctuation occurred on ASC−E3 #1. The first power fluctuation occurred 175 hr later on ASC−E3 #2. Over time, the power fluctuations became more frequent on both convertors and larger in magnitude. Eventually the EU2 was shut down in January 2015. An anomaly investigation was chartered to determine root cause of the power fluctuations and other anomalous observations. A team with members from Glenn, Sunpower, and LM conducted a thorough investigation of the EU2 anomalies. Findings from the EU2 disassembly identified proximate causes of the anomalous observations. Discussion of the team's assessment of the primary possible failure theories, root cause, and conclusions is provided. Recommendations are made for future Stirling generator development to address the findings from the anomaly investigation. Additional findings from the investigation are also discussed.

Stirling convertors↗