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At least 235 records · Page 13

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NASA NTRS2013

Thermal Hydraulics Design and Analysis Methodology for a Solid-Core Nuclear Thermal Rocket Engine Thrust Chamber

Nuclear thermal propulsion is a leading candidate for in-space propulsion for human Mars missions. This chapter describes a thermal hydraulics design and analysis methodology developed at the NASA Marshall Space Flight Center, in support of the nuclear thermal propulsion development effort. The objective of this campaign is to bridge the design methods in the Rover/NERVA era, with a modern computational fluid dynamics and heat transfer methodology, to predict thermal, fluid, and hydrogen environments of a hypothetical solid-core, nuclear thermal engine the Small Engine, designed in the 1960s. The computational methodology is based on an unstructured-grid, pressure-based, all speeds, chemically reacting, computational fluid dynamics and heat transfer platform, while formulations of flow and heat transfer through porous and solid media were implemented to describe those of hydrogen flow channels inside the solid24 core. Design analyses of a single flow element and the entire solid-core thrust chamber of the Small Engine were performed and the results are presented herein

Wang, Ten-See↗

NASA NTRS2024

Considerations for Thrust Vector Control (TVC) In-Flight Load Predictions

This presentation communicates an overview of a methodology developed by the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) Thrust Vector Control (TVC) Systems Integration & Components Branch (MSFC-ER63). The methodology is based on NASA heritage TVC design practices and is designed to analyze the summation of static and quasi-static environments acting on a gimbaled rocket engine/motor system. The analysis determines the maximum overall expected induced torque acting on the rocket engine/motor system about the gimbal center of rotation and assumes a mathematical 3-Dimensional (3D) 2-Degree-of-Freedom (DOF) rigid-body pendulum model over a predefined range of angular motion. Additionally, it calculates the associated reaction force acting along a TVC actuator line-of-action (LOA) to maintain thrust vector holding operations. This methodology is particularly useful for determining maximum overall design loads for TVC actuator design-space shaping and requirements determination.

Thrust Vector Control↗

DOE OSTI2021

From multivariate to functional data analysis: Fundamentals, recent developments, and emerging areas

Functional data analysis (FDA), which is a branch of statistics on modeling infinite dimensional random vectors resided in functional spaces, has become a major research area for Journal of Multivariate Analysis. We review some fundamental concepts of FDA, their origins and connections from multivariate analysis, and some of its recent developments, including multi-level functional data analysis, high-dimensional functional regression, and dependent functional data analysis. Here, we also discuss the impact of these new methodology developments on genetics, plant science, wearable device data analysis, image data analysis, and business analytics. Two real data examples are provided to motivate our discussions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Machine Learning-Based Anomaly Detection for PMT Data Quality Monitoring in the SBN and DUNE

Maintaining high-quality detector data is essential for achieving the scientific objectives of the Short-Baseline Neutrino (SBN) Program at Fermilab. Current data quality monitoring (DQM) procedures rely primarily on threshold-based metrics and manual inspection of detector monitoring plots, making the detection of subtle or gradually developing anomalies both time-consuming and dependent on expert interpretation. This project developed and evaluated a machine-learning workflow for automatically identifying anomalous photomultiplier tube (PMT) channels in the Short-Baseline Near Detector (SBND) using optical-hit amplitude data. A Python-based analysis program was developed to process ROOT files, extract statistical features describing individual PMT amplitude distributions, and generate feature vectors for anomaly detection. These features were used to train an Isolation Forest model using data representing normal detector operation. The trained model was subsequently applied to independent detector runs to identify channels exhibiting statistically unusual behavior relative to the learned reference response. To support expert interpretation, the workflow generated complementary diagnostic products, including anomaly score distributions, normalized amplitude comparisons, decision-tree visualizations, and principal component analysis (PCA) projections. This project demonstrated the feasibility of integrating unsupervised machine learning into detector data-quality monitoring and developed a complete workflow for automated PMT performance assessment to aid expert-driven review. Beyond its technical contributions, the VFP appointment fostered a research collaboration between Aurora University and Fermilab and provided direct workforce development benefits by training the visiting faculty member in detector-scale machine-learning methods that are now being incorporated into undergraduate coursework and research. The methodology developed here provides a foundation for future applications to ProtoDUNE and other liquid argon time projection chamber (LArTPC) detectors, contributing to ongoing efforts to improve detector reliability, reduce manual monitoring requirements, and enable scalable data quality monitoring for future large-scale neutrino experiments, including the Deep Underground Neutrino Experiment (DUNE).

Colón Santana, Juan A. [Unlisted, US, IL]↗

NASA NTRS1987

Approaches to the verification of rule-based expert systems

Expert systems are a highly useful spinoff of artificial intelligence research. One major stumbling block to extended use of expert systems is the lack of well-defined verification and validation (V and V) methodologies. Since expert systems are computer programs, the definitions of verification and validation from conventional software are applicable. The primary difficulty with expert systems is the use of development methodologies which do not support effective V and V. If proper techniques are used to document requirements, V and V of rule-based expert systems is possible, and may be easier than with conventional code. For NASA applications, the flight technique panels used in previous programs should provide an excellent way to verify the rules used in expert systems. There are, however, some inherent differences in expert systems that will affect V and V considerations.

Culbert, Chris↗

NASA NTRS1994

Vibration Isolation Technology (VIT) ATD Project

A fundamental advantage for performing material processing and fluid physics experiments in an orbital environment is the reduction in gravity driven phenomena. However, experience with manned spacecraft such as the Space Transportation System (STS) has demonstrated a dynamic acceleration environment far from being characterized as a 'microgravity' platform. Vibrations and transient disturbances from crew motions, thruster firings, rotating machinery etc. can have detrimental effects on many proposed microgravity science experiments. These same disturbances are also to be expected on the future space station. The Microgravity Science and Applications Division (MSAD) of the Office of Life and Microgravity Sciences and Applications (OLMSA), NASA Headquarters recognized the need for addressing this fundamental issue. As a result an Advanced Technology Development (ATD) project was initiated in the area of Vibration Isolation Technology (VIT) to develop methodologies for meeting future microgravity science needs. The objective of the Vibration Isolation Technology ATD project was to provide technology for the isolation of microgravity science experiments by developing methods to maintain a predictable, well defined, well characterized, and reproducible low-gravity environment, consistent with the needs of the microgravity science community. Included implicitly in this objective was the goal of advising the science community and hardware developers of the fundamental need to address the importance of maintaining, and how to maintain, a microgravity environment. This document will summarize the accomplishments of the VIT ATD which is now completed. There were three specific thrusts involved in the ATD effort. An analytical effort was performed at the Marshall Space Flight Center to define the sensitivity of selected experiments to residual and dynamic accelerations. This effort was redirected about half way through the ATD focusing specifically on the sensitivity of protein crystals to a realistic orbital environment. The other two thrusts of the ATD were performed at the Lewis Research Center. The first was to develop technology in the area of reactionless mechanisms and robotics to support the eventual development of robotics for servicing microgravity science experiments. This activity was completed in 1990. The second was to develop vibration isolation and damping technology providing protection for sensitive science experiments. In conjunction with the this activity, two workshops were held. The results of these were summarized and are included in this report.

Lubomski, Joseph F.↗

NASA NTRS2018

An Engineering Approach to Analyze Damage Initiation Modes in Tapered Composite Structures

In this work an engineering approach is demonstrated for analyzing damage initiation modes in tapered composite structures. The analysis methodology includes simulation of the non-linear static response of tapered composite structures under static tension loads to predict the location of interfacial delamination initiation. Furthermore, the developed methodology provides a strength-based criterion to assess whether damage initiation will occur in the inter-laminar delamination or intra-laminar matrix cracking mode. Based on the results of the analysis, a tapered composite structure is fabricated and tested under displacement-controlled quasi-static tension loading. The damage initiation location captured experimentally is compared with the analysis towards achieving preliminary qualitative validation. The linear stiffness of the tapered composite structure is predicted within 15% of the experimental average thereby achieving preliminary quantitative validation.

Rao, Prabhakar M.↗

NASA NTRSDate not supplied

An Engineering Approach to Analyze Damage Initiation Modes in Tapered Composite Structures

Prabhakar M Rao↗

DOE OSTI2023

Microstructure development in a cold worked SRF Nb sheet undergoing 700°C-900°C/3h heat treatments.

Bulk Nb for TESLA shaped SRF cavities is a mature technology, with high gradient (>35 MV/m) and high Q cavities (>1010, ~2K) routinely fabricated. Significant advances are in order to push Q?s to 1011 (~2K), and involve modifications to the sub-surface Nb layers by impurity doping (N, O). In order to achieve the lowest surface resistance any trapped flux needs to be expelled for cavities to reach high Q?s. There is clear evidence that cavities fabricated from polycrystalline sheets meeting current specifications require higher temperatures beyond 800oC leads to better flux expulsion, and hence improves Q. Recently, cavities fabricated with a non-traditional Nb sheet with initial cold work due to cold rolling expelled flux better after 800°C/3h heat treatment than cavities fabricated using fine-grain polycrystalline Nb sheets. Here, we analyze the microstructure development in Nb from the vendor supplied cold work non-annealed sheet that was fabricated into an SRF cavity as a function of heat treatment building upon the methodology development to analyze microstructure being developed by the FSU-MSU-UT-Austin- JLAB collaboration. The results indicate correlation between full recrystallization and better flux expulsion.

Balachandran, S.↗

DOE OSTI2024

Photovoltaic Analysis and Response Support (PARS) Platform for Solar Situational Awareness and Resiliency Services

The project's primary objective is to develop a digital-twin based Photovoltaic (PV) Analysis and Response Support (PARS) platform, which aims to provide real-time situational awareness and optimal response plans. This platform is designed to enhance the performance of hybrid PV systems, making them competitive with or even superior to conventional generation resources. The PARS platform enabled the project team to develop and evaluate an extensive suite of grid support functionalities for the hybrid PV systems to enhance grid performance, across key areas including visibility, dispatchability, security, resilience, and reliability. Given the global push toward achieving 100% clean energy by 2035, there is a significant increase in the integration of inverter-based resources (IBRs) throughout the energy grid. Effectively managing the inherent variability and uncertainty associated with IBRs is crucial for ensuring cost-effectiveness, reliability, and security in both the main grid and islanded microgrids. Constrained to a limited array of IEEE test systems or standard feeder models, traditional IBR modeling struggles to assimilate new field data, accurately reflect system dynamics, and adapt to the evolving energy landscape. In our project, we embraced a Digital Twin (DT) strategy for crafting the PARS platform. A digital twin acts as a precise virtual counterpart of a physical system, built on historical data and continuously honed with real-time insights. This enables the high-fidelity DT to accurately mirror current system operations and forecast future scenarios. Consequently, the PARS platform becomes an ideal environment for testing and refining monitoring, control, power, and energy management algorithms designed to boost hybrid PV system performance. The defining feature of the PARS platform, distinguishing it from other advanced simulation tools, is its exceptional adaptability. This is achieved by employing actual network topologies and utilizing real-time field data for fine-tuning and calibration, ensuring a close emulation of real-world conditions. The project deliverables include: 1) High-fidelity IBR models and tools for real-time parameterization, utilizing real-time field measurements to refine IBR models for enhanced accuracy and performance; 2) Grid-forming and Grid-following capabilities to deliver resilience services, including blackstart, voltage and frequency support, cold-load pick-up, power reserves, and three-phase load balancing across grid-connected and microgrid settings; 3) Machine learning-based forecasting tools and methods for generating synthetic data and topologies, creating diverse and realistic simulation environments for evaluating varied operational scenarios; 4) Advanced microgrid power and energy management algorithms for optimizing the integration and operation of PV, storage, and demand response resources within both feeder and community scales. The power grid data sets are provided by four utility companies in North Carolina and the New York Power Administration. Acting as industry advisors, our industry partners communicated stakeholder needs and regulatory standards to the research teams, aiding technology transfer by incorporating the developed methodologies into their daily operations. This collaboration ensures that the PARS platform, functioning as a power system digital twin, enhances our understanding of IBR dynamic behaviors and enables the development and evaluation of IBR control functions that match or exceed the capabilities of conventional synchronous generators.

14 SOLAR ENERGY↗

NASA NTRS2021

Predicting the Functional State of Protein Kinases Using Interpretable Graph Neural Networks

Kinases are a family of proteins that function as molecular switches, regulating several essential cellular activities such as cell proliferation. Dysfunctional kinases are implicated in several types of cancers and hence they are actively pursued as drug targets. Given the vast number of complex kinase structures that are available in the protein data bank (PDB), there is a necessity to develop methodologies that can identify structurally important moieties of the kinases in an automated fashion, for such techniques can be instrumental in identifying novel drug targets. In this work, we develop a graph neural network (GNN) based deep learning framework for classifying the functionally active and inactive states of a large set of eukaryotic protein kinases, making use of their 3D structure from the PDB. We show that GNN based machine learning models can classify protein states with an accuracy greater than 97%. We further use the GNN models to automatically identify regions of the kinases that are important for its function. For this purpose, Gradient-weighted Class Activation Mapping (Grad-CAM) was implemented on the protein graphs. Remarkably, Grad-CAM consistently identifies the highly conserved DFG motif as the most important part of the protein across the entire kinome, without any prior input. Other regions of the hydrophobic core such as the HRD motif were also identified by the interpretable GNN framework, consistent with the literature. We discuss the significance of each of these regions in detail.

Ashwin Ravichandran↗

DOE OSTI2021

Modeling of Thermal Decomposition of TATB-Based Explosive for Safety Analysis

We investigate and model the cook-off behavior of LX-17 to understand the response of explosive systems in abnormal thermal environments. Decomposition has been explored via conventional ODTX (One-Dimensional Time-to-eXplosion), PODTX (ODTX with pressure-measurement), TGA (Thermo-Gravimetric Analysis), and DSC (Differential Scanning Calorimetry) experiments under isothermal and ramped temperature profiles. The data were used to fit reaction rate parameters for proposed schemes in an ALE3D computational model. This model includes chemical reactions, thermo- and hydro-dynamics, and material properties, including thermal expansion, compressibility, and strength. These parameterizations were carried out utilizing a Python evolutionary optimization method on LLNL’s high-performance computing clusters. Additional experiments are being developed to further characterize and monitor decomposition intermediates to improve the model. Once experimentally validated, this model will be scalable to several applications involving LX-17. Furthermore, the optimization methodology developed herein should be applicable to other high explosive materials.

Chemistry - Chemical explosives↗

NASA NTRS2012

An Ontology for State Analysis: Formalizing the Mapping to SysML

State Analysis is a methodology developed over the last decade for architecting, designing and documenting complex control systems. Although it was originally conceived for designing robotic spacecraft, recent applications include the design of control systems for large ground-based telescopes. The European Southern Observatory (ESO) began a project to design the European Extremely Large Telescope (E-ELT), which will require coordinated control of over a thousand articulated mirror segments. The designers are using State Analysis as a methodology and the Systems Modeling Language (SysML) as a modeling and documentation language in this task. To effectively apply the State Analysis methodology in this context it became necessary to provide ontological definitions of the concepts and relations in State Analysis and greater flexibility through a mapping of State Analysis into a practical extension of SysML. The ontology provides the formal basis for verifying compliance with State Analysis semantics including architectural constraints. The SysML extension provides the practical basis for applying the State Analysis methodology with SysML tools. This paper will discuss the method used to develop these formalisms (the ontology), the formalisms themselves, the mapping to SysML and approach to using these formalisms to specify a control system and enforce architectural constraints in a SysML model.

Wagner, David A.↗

NASA NTRS1996

Cyclic Load Effects on Long Term Behavior of Polymer Matrix Composites

A methodology to compute the fatigue life for different ratios, r, of applied stress to the laminate strength based on first ply failure criteria combined with thermal cyclic loads has been developed and demonstrated. Degradation effects resulting from long term environmental exposure and thermo-mechanical cyclic loads are considered in the simulation process. A unified time-stress dependent multi-factor interaction equation model developed at NASA Lewis Research Center has been used to account for the degradation of material properties caused by cyclic and aging loads. Effect of variation in the thermal cyclic load amplitude on a quasi-symmetric graphite/epoxy laminate has been studied with respect to the impending failure modes. The results show that, for the laminate under consideration, the fatigue life under combined mechanical and low thermal amplitude cyclic loads is higher than that due to mechanical loads only. However, as the thermal amplitude increases, the life also decreases. The failure mode changes from tensile under mechanical loads only to the compressive and shear at high mechanical and thermal loads. Also, implementation of the developed methodology in the design process has been discussed.

Shah, A. R.↗

NASA NTRS2000

Coupling Satellite and Ground-Based Instruments to Map Climate Forcing by Anthropogenic Aerosols

Climate forcing by anthropogenic aerosols is a significant but highly uncertain factor in global climate change. Only satellites can offer the global coverage essential to reducing this uncertainty; however, satellite measurements must be coupled with correlative, in situ measurements both to constrain the aerosol optical properties required in satellite retrieval algorithms and to provide chemical identification of aerosol sources. This grant funded the first two years of a three-year project which seeks to develop methodologies for combining spaceborne lidar with in-situ aerosol data sets to improve estimates of direct aerosol climate forcing. Progress under this two-year grant consisted in the development and deployment of a new in-situ capability for measuring aerosol 180' backscatter and the extinction-to-backscatter ratio. This new measurement capacity allows definitive lidar/in-situ comparisons and improves our ability to interpret lidar data in terms of climatically relevant quantities such as the extinction coefficient and optical depth. Measurements were made along the coast of Washington State, in Central Illinois, over the Indian Ocean, and in the Central Pacific. Thus, this research, combined with previous measurements by others, is rapidly building toward a global data set of extinction-to-backscatter ratio for key aerosol types. Such information will be critical to interpreting lidar data from the upcoming PICASSO-CENA, or P-C, satellite mission. Another aspect of this project is to investigate innovative ways to couple the lidar-satellite signal with targeted in-situ measurements toward a direct determination of aerosol forcing. This aspect is progressing in collaboration with NASA Langley's P-C lidar simulator and radiative transfer modeling by the University of Lille, France.

Charlson, Robert J.↗

NASA NTRS2000

An Approach to Experimental Design for the Computer Analysis of Complex Phenomenon

The ability to make credible system assessments, predictions and design decisions related to engineered systems and other complex phenomenon is key to a successful program for many large-scale investigations in government and industry. Recently, many of these large-scale analyses have turned to computational simulation to provide much of the required information. Addressing specific goals in the computer analysis of these complex phenomenon is often accomplished through the use of performance measures that are based on system response models. The response models are constructed using computer-generated responses together with physical test results where possible. They are often based on probabilistically defined inputs and generally require estimation of a set of response modeling parameters. As a consequence, the performance measures are themselves distributed quantities reflecting these variabilities and uncertainties. Uncertainty in the values of the performance measures leads to uncertainties in predicted performance and can cloud the decisions required of the analysis. A specific goal of this research has been to develop methodology that will reduce this uncertainty in an analysis environment where limited resources and system complexity together restrict the number of simulations that can be performed. An approach has been developed that is based on evaluation of the potential information provided for each "intelligently selected" candidate set of computer runs. Each candidate is evaluated by partitioning the performance measure uncertainty into two components - one component that could be explained through the additional computational simulation runs and a second that would remain uncertain. The portion explained is estimated using a probabilistic evaluation of likely results for the additional computational analyses based on what is currently known about the system. The set of runs indicating the largest potential reduction in uncertainty is then selected and the computational simulations are performed. Examples are provided to demonstrate this approach on small scale problems. These examples give encouraging results. Directions for further research are indicated.

Rutherford, Brian↗

NASA NTRS1984

Biocatalyzed processes for production of commodity chemicals: Assessment of future research advances for N-butanol production

This report is a summary of assessments by Chem Systems Inc. and a further evaluation of the impacts of research advances on energy efficiency and the potential for future industrial production of acetone-butanol-ethanol (ABE) solvents and other products by biocatalyzed processes. Brief discussions of each of the assessments made by CSI, followed by estimates of minimum projected energy consumption and costs for production of solvents by ABE biocatalyzed processes are included. These assessments and further advances discussed in this report show that substantial decreases in energy consumption and costs are possible on the basis of specific research advances; therefore, it appears that a biocatalyzed process for ABE can be developed that will be competitive with conventional petrochemical processes for production of n-butanol and acetone. (In this work, the ABE process was selected and utilized only as an example for methodology development; other possible bioprocesses for production of commodity chemicals are not intended to be excluded.) It has been estimated that process energy consumption can be decreased by 50%, with a corresponding cost reduction of 15-30% (in comparison with a conventional petrochemical process) by increasing microorganism tolerance to n-butanol and efficient recovery of product solvents from the vapor phase.

Ingham, J. D.↗

NASA NTRS2010

Sorbent, Sublimation, and Icing Modeling Methods: Experimental Validation and Application to an Integrated MTSA Subassembly Thermal Model

This paper details the validation of modeling methods for the three core components of a Metabolic heat regenerated Temperature Swing Adsorption (MTSA) subassembly, developed for use in a Portable Life Support System (PLSS). The first core component in the subassembly is a sorbent bed, used to capture and reject metabolically produced carbon dioxide (CO2). The sorbent bed performance can be augmented with a temperature swing driven by a liquid CO2 (LCO2) sublimation heat exchanger (SHX) for cooling the sorbent bed, and a condensing, icing heat exchanger (CIHX) for warming the sorbent bed. As part of the overall MTSA effort, scaled design validation test articles for each of these three components have been independently tested in laboratory conditions. Previously described modeling methodologies developed for implementation in Thermal Desktop and SINDA/FLUINT are reviewed and updated, their application in test article models outlined, and the results of those model correlations relayed. Assessment of the applicability of each modeling methodology to the challenge of simulating the response of the test articles and their extensibility to a full scale integrated subassembly model is given. The independent verified and validated modeling methods are applied to the development of a MTSA subassembly prototype model and predictions of the subassembly performance are given. These models and modeling methodologies capture simulation of several challenging and novel physical phenomena in the Thermal Desktop and SINDA/FLUINT software suite. Novel methodologies include CO2 adsorption front tracking and associated thermal response in the sorbent bed, heat transfer associated with sublimation of entrained solid CO2 in the SHX, and water mass transfer in the form of ice as low as 210 K in the CIHX.

Bower, Chad↗