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NASA NTRS2018

The Ocean Carbon States Database: A Proof-of-Concept Application of Cluster Analysis in the Ocean Carbon Cycle

In this paper, we present a database of the basic regimes of the carbon cycle in the ocean, the 'ocean carbon states', as obtained using a data mining/pattern recognition technique in observation-based as well as model data. The goal of this study is to establish a new data analysis methodology, test it and assess its utility in providing more insights into the regional and temporal variability of the marine carbon cycle. This is important as advanced data mining techniques are becoming widely used in climate and Earth sciences and in particular in studies of the global carbon cycle, where the interaction of physical and biogeochemical drivers confounds our ability to accurately describe, understand, and predict CO2 concentrations and their changes in the major planetary carbon reservoirs. In this proof-of-concept study, we focus on using well-understood data that are based on observations, as well as model results from the NASA Goddard Institute for Space Studies (GISS) climate model. Our analysis shows that ocean carbon states are associated with the subtropical-subpolar gyre during the colder months of the year and the tropics during the warmer season in the North Atlantic basin. Conversely, in the Southern Ocean, the ocean carbon states can be associated with the subtropical and Antarctic convergence zones in the warmer season and the coastal Antarctic divergence zone in the colder season. With respect to model evaluation, we find that the GISS model reproduces the cold and warm season regimes more skillfully in the North Atlantic than in the Southern Ocean and matches the observed seasonality better than the spatial distribution of the regimes. Finally, the ocean carbon states provide useful information in the model error attribution. Model air-sea CO2 flux biases in the North Atlantic stem from wind speed and salinity biases in the subpolar region and nutrient and wind speed biases in the subtropics and tropics. Nutrient biases are shown to be most important in the Southern Ocean flux bias.

carbon cycle↗

NASA NTRSDate not supplied

System Safety Analysis of Complex NASA Systems with Model-Based Engineering (REV B)

The emergence of model-based engineering is transforming design and analysis methodologies. A recognized benefit of model-based engineering is the existence of a “single source of truth” about the system that becomes the authoritative source of data and information for designers, analysts, and developers. This promotes consistency and efficiency as the design emerges and can be used to further optimize the design. Integrating System Safety Engineers to the “single source of truth” will ensure that the outputs of their assessments and analyses are relevant to the design as it evolves. Use of an integrated system model enables near immediate evaluation of a design change as well as development of operational processes for risk assessment and communication. Such models can enable efficient and timely analysis of system hazards (e.g., hazard fault tree analysis and procedure simulations) and produce complete, accurate, and more consistent products (e.g., hazard reports and safety requirement evaluations). Therefore, an agency-sponsored team at Goddard Space Flight Center (GSFC) recently completed a System Safety Study of modeling and testing capabilities as part of a Model-Based Safety and Mission Assurance Initiative (MBSMAI). Using an existing model developed for reliability analyses, GSFC modeling and system safety experts performed system safety analysis/modeling and produced safety products. The team evaluated model-based feasibility to support System Safety Engineering, developed safety analysis modeling processes, and identified tool capability advancement/development needs. These study results indicate model-based engineering is valid and useable for System Safety Engineering for NASA if adequate modeling processes and environment are established.

NASA↗

NASA NTRSDate not supplied

System Safety Analysis of Complex NASA Systems with Model-Based Engineering

The emergence of model-based engineering is transforming design and analysis methodologies [5]. A recognized benefit of model-based engineering is the existence of a “single source of truth” about the system that becomes the authoritative source of data and information for designers, analysts, and developers. This promotes consistency and efficiency as the design emerges and can be used to further optimize the design. Integrating System Safety Engineers to the “single source of truth” will ensure that the outputs of their assessments and analyses are relevant to the design as it evolves. Use of an integrated system model enables near immediate evaluation of a design change as well as development of operational processes for risk assessment and communication. Such models can enable efficient and timely analysis of system hazards (e.g., hazard fault tree analysis and procedure simulations) and produce complete, accurate, and more consistent products (e.g., hazard reports and safety requirement evaluations). Therefore, an agency-sponsored team at Goddard Space Flight Center (GSFC) recently completed a System Safety Study of modeling and testing capabilities as part of a Model-Based Safety and Mission Assurance Initiative (MBSMAI). Using an existing model developed for reliability analyses [1], GSFC modeling and system safety experts performed system safety analysis/modeling and produced safety products. The team evaluated model-based feasibility to support System Safety Engineering, developed safety analysis modeling processes, and identified tool capability advancement/development needs. These study results indicate model-based engineering is valid and useable for System

NASA↗

NASA NTRSDate not supplied

System Safety Analysis of Complex NASA Systems with Model Based Engineering

Model Based Engineering↗

NASA NTRS2007

Reinforced Carbon-Carbon Subcomponent Flat Plate Impact Testing for Space Shuttle Orbiter Return to Flight

Following the tragedy of the Space Shuttle Columbia on February 1, 2003, a major effort commenced to develop a better understanding of debris impacts and their effect on the Space Shuttle subsystems. An initiative to develop and validate physics-based computer models to predict damage from such impacts was a fundamental component of this effort. To develop the models it was necessary to physically characterize Reinforced Carbon-Carbon (RCC) and various debris materials which could potentially shed on ascent and impact the Orbiter RCC leading edges. The validated models enabled the launch system community to use the impact analysis software LS DYNA to predict damage by potential and actual impact events on the Orbiter leading edge and nose cap thermal protection systems. Validation of the material models was done through a three-level approach: fundamental tests to obtain independent static and dynamic material model properties of materials of interest, sub-component impact tests to provide highly controlled impact test data for the correlation and validation of the models, and full-scale impact tests to establish the final level of confidence for the analysis methodology. This paper discusses the second level subcomponent test program in detail and its application to the LS DYNA model validation process. The level two testing consisted of over one hundred impact tests in the NASA Glenn Research Center Ballistic Impact Lab on 6 by 6 in. and 6 by 12 in. flat plates of RCC and evaluated three types of debris projectiles: BX 265 External Tank foam, ice, and PDL 1034 External Tank foam. These impact tests helped determine the level of damage generated in the RCC flat plates by each projectile. The information obtained from this testing validated the LS DYNA damage prediction models and provided a certain level of confidence to begin performing analysis for full-size RCC test articles for returning NASA to flight with STS 114 and beyond.

Melis, Matthew E.↗

NASA NTRS2021

Bowtie Analysis of the Effects of Unmanned Aircraft on Air Traffic Control

Within the aviation domain, there is a growing industry demand to develop and integrate remotely piloted operations into the National Airspace System. However, it is not yet well understood how the integration of unmanned aircraft with impact air traffic control, and specifically, the air traffic controllers who are at the sharp end of this safety critical system. This research presented in this paper aimed to begin to address this gap in understanding by identifying and exploring potential hazards associated with introducing Unmanned Aircraft into the national airspace system, and identify possible mitigations to reduce identified risks. A bowtie risk analysis methodology was used to identify and analyze hazards. A focus-group format discussion was conducted with nine subject matter experts as participants. Findings identified five areas of potential risk, each associated with multiple hazards. Mitigations for each hazard are reported. Findings have essential implications for the safe and efficient integration of unmanned aircraft into the national airspace.

unmanned aerial vehicles↗

NASA NTRSDate not supplied

Materials Informatics at NASA GRC: Machine Learning Surrogate Modeling, Data Management, and Integrated Toolsets for Establishing/Maintaining the Digital Thread

Integrated Computational Materials Engineering (ICME) has recently received widespread attention due to its promises in reducing dependence on physical testing for engineering design by relying on simulation, reducing both time and cost to market for various applications. ICME however requires validated multiscale material models, which heavily depend on available test data with full material and test pedigree, including material processing, test and measurement equipment, raw data collection, and analysis methodology and results that is findable and usable, along with integrated, efficient toolsets for effectively passing information across various length and time scales across such models. At the NASA Glenn Research Center under the Transformational Tools and Technologies Project, significant recent efforts have been directed towards establishing the required cyberinfrastructure to enable optimized ICME processes and the design of “fit-for-purpose” materials to achieve the goals outlined in the NASA Vision 2040 report. Such efforts include development of multiscale physics-based material models, which can be used to train highly efficient surrogate machine learning models, development of best practices and infrastructure for effective, traceable materials information management, and development of toolsets that integrate with physics-based codes, machine learning models, and an information management system to enable high throughput of materials data collection and analysis, establishment of digital twins and the digital thread, and automation of the ICME design process for material optimization.

Machine Learning↗

DOE OSTI2023

Assessing Low-Temperature Geothermal Play Types: Relevant Data and Play Fairway Analysis Methods

This data catalog contains information on low temperature geothermal play types. The U.S. Department of Energy (DOE) Geothermal Technologies Office (GTO) supports the Geothermal Heating and Cooling Geospatial Datasets and Analysis project, conducted by the National Renewable Energy Laboratory (NREL). This project is part of a broader effort to demonstrate the multifaceted value of integrating geothermal power and geothermal heating and cooling technologies into national decarbonization strategies and community energy plans. There is a need to establish baseline low-temperature geothermal resource data sets and evaluate methods for deploying these technologies. This project aims to reduce exploration risk of low temperature geothermal systems by collecting baseline datasets that can be used for Play Fairway Analysis methodologies. This data catalog contains links to publicly available datasets from different sources that can be relevant for the low temperature geothermal systems. This submission contains data catalogs for Alaska, Hawaii, and the Conterminous United States, as well as a technical report on the methods used to classify and asses the geothermal play types.

15 GEOTHERMAL ENERGY↗

NASA NTRS2007

Finite Element Simulation of Solid Rocket Booster Separation Motors During Motor Firing

One of the toughest challenges facing Solid Rocket Booster (SRB) engineers is to ensure that any design changes made to the Shuttle-Derived Booster Separation Motors (BSM) for future space exploration vehicles is able to withstand the increasingly hostile motor firing environment without cracking its critical component - the graphite throat. This paper presents a critical analysis methodology and techniques for assessing effects of BSM design changes with great accuracy and precision. For current Space Shuttle operation, the motor firing occurs at SRB separation - approximately 125 seconds after Shuttle launch at an altitude of about 28 miles. The motor operation event lasts about two seconds, however, the surface temperature of the graphite throat increases approximately 3400 F in less than one second with a corresponding increase in surface pressure of approximately 2200 pounds per square inch (psi) in less than one-tenth of a second. To capture this process fully and accurately, a two-phase sequentially coupled thermal-mechanical finite element approach was developed. This method allows the time- and location-dependent pressure fields to interact with the spatial-temporal thermal fields throughout the operation. The material properties of graphite throat are orthotropic and temperature-dependent. The analysis involves preload and multiple body contacts.

Yu. Weiping↗

NASA NTRSDate not supplied

Time-Accurate Computational Fluid Dynamics Analysis of a Supersonic Turbine to Assess Structural Mode Excitation

Gas turbines can experience significant unsteady loading during operation which can cause damage such as blade cracking or bearing wear. While computational fluid dynamics (CFD) modeling of full three-dimensional gas turbine flow to verify meanline design performance is standard practice within the industry, the use of time-accurate CFD makes it possible to assess the unsteady loading which can be especially problematic at operating conditions where unsteady fluid loading aligns with structural modes. The Fluid Dynamics branch (ER42) at NASA Marshall Space Flight Center has developed a time-accurate CFD analysis methodology using the unstructured, density-based, Loci-CHEM solver which is capable of simulating the relative motion required for turbomachinery applications through sliding interface meshes. The highly-parallel unstructured solver allows for full three-dimensional, time-accurate simulation of turbines including upstream and downstream components such as manifolds, nozzles, and stators, any of which could excite a turbine structural mode. Recently, Loci-CHEM was used to analyze the FASTRAC supersonic turbine to assess the loading on the rotor. Overall performance and time-averaged loading on the rotor was computed, including the axial force over the entire rotor disk which is required for force balance calculations on the turbopump. The FASTRAC turbine has supersonic nozzles upstream of the rotor that result in significant unsteady loading on the rotor blades at multiples of the nozzle pass frequency. The unsteady blade pressure loading was decomposed into the frequency domain for direct use in structural forced response analysis. Utilizing this type of CFD analysis coupled with structural analysis has proven to be a valuable tool in identifying structural modes that are being excited by unsteady fluid loading.

M.V. Fischels↗

NASA NTRSDate not supplied

Time-Accurate Computational Fluid Dynamics Analysis of a Supersonic Turbine to Assess Structural Mode Excitation

M V Fischels↗

DOE OSTI2026

Beam-beam backgrounds for the Cool Copper Collider

In this paper, we present a comprehensive characterization of beam-beam backgrounds for the Cool Copper Collider (C 3 ), a proposed linear e + e - collider designed for precision Higgs studies at center-of-mass energies of 250 and 550 GeV. Using a simulation pipeline based on the Key4hep framework, we evaluate incoherent pair production and hadron photoproduction backgrounds through the SiD detector for baseline, power-efficiency, and high-luminosity C 3 operating scenarios. The occupancy induced by the beam-beam background is evaluated for each scenario, validating the compatibility of the existing SiD detector design with operations at C 3 without substantial modifications. Furthermore, at the same time, the modular simulation framework and analysis methodology presented in this paper offer a versatile toolkit for background studies in future collider proposals, contributing to a common platform for different machine designs.

Analysis and statistical methods↗

NASA NTRS2010

Scaled CMOS Technology Reliability Users Guide

The desire to assess the reliability of emerging scaled microelectronics technologies through faster reliability trials and more accurate acceleration models is the precursor for further research and experimentation in this relevant field. The effect of semiconductor scaling on microelectronics product reliability is an important aspect to the high reliability application user. From the perspective of a customer or user, who in many cases must deal with very limited, if any, manufacturer's reliability data to assess the product for a highly-reliable application, product-level testing is critical in the characterization and reliability assessment of advanced nanometer semiconductor scaling effects on microelectronics reliability. A methodology on how to accomplish this and techniques for deriving the expected product-level reliability on commercial memory products are provided.Competing mechanism theory and the multiple failure mechanism model are applied to the experimental results of scaled SDRAM products. Accelerated stress testing at multiple conditions is applied at the product level of several scaled memory products to assess the performance degradation and product reliability. Acceleration models are derived for each case. For several scaled SDRAM products, retention time degradation is studied and two distinct soft error populations are observed with each technology generation: early breakdown, characterized by randomly distributed weak bits with Weibull slope (beta)=1, and a main population breakdown with an increasing failure rate. Retention time soft error rates are calculated and a multiple failure mechanism acceleration model with parameters is derived for each technology. Defect densities are calculated and reflect a decreasing trend in the percentage of random defective bits for each successive product generation. A normalized soft error failure rate of the memory data retention time in FIT/Gb and FIT/cm2 for several scaled SDRAM generations is presented revealing a power relationship. General models describing the soft error rates across scaled product generations are presented. The analysis methodology may be applied to other scaled microelectronic products and their key parameters.

Microelectronics Reliability↗

DOE OSTI2021

Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery

Understanding the interactions among agricultural processes, soil, and plants is necessary for optimizing crop yield and productivity. This study focuses on developing effective monitoring and analysis methodologies that estimate key soil and plant properties. These methodologies include data acquisition and processing approaches that use unmanned aerial vehicles (UAVs) and surface geophysical techniques. In particular, we applied these approaches to a soybean farm in Arkansas to characterize the soil–plant coupled spatial and temporal heterogeneity, as well as to identify key environmental factors that influence plant growth and yield. UAV-based multitemporal acquisition of high-resolution RGB (red–green–blue) imagery and direct measurements were used to monitor plant height and photosynthetic activity. We present an algorithm that efficiently exploits the high-resolution UAV images to estimate plant spatial abundance and plant vigor throughout the growing season. Such plant characterization is extremely important for the identification of anomalous areas, providing easily interpretable information that can be used to guide near-real-time farming decisions. Additionally, high-resolution multitemporal surface geophysical measurements of apparent soil electrical conductivity were used to estimate the spatial heterogeneity of soil texture. By integrating the multiscale multitype soil and plant datasets, we identified the spatiotemporal co-variance between soil properties and plant development and yield. Our novel approach for early season monitoring of plant spatial abundance identified areas of low productivity controlled by soil clay content, while temporal analysis of geophysical data showed the impact of soil moisture and irrigation practice (controlled by topography) on plant dynamics. Our study demonstrates the effective coupling of UAV data products with geophysical data to extract critical information for farm management.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Measurement Uncertainty in One-Of-A-Kind Event Data Analysis

A golden standard in science is to repeat an experiment a statistically significant number of times, recording data using the same set of detectors and the same data analysis methodology. In such case experimental error includes both the range of true values generated by repetitions of the experiment, and measurement uncertainty caused by the detector. They are independent. It is a huge and too frequently used simplification, to assume that one can measure multiple repetitions of an identical experiment, resulting in identical true experimental value. Repetitions, as similar is it is experimentally achievable, have unavoidable built-in differences resulting in a range of the true values rather than in a single value. When modern, very sensitive and well calibrated measurement systems are used, this range is not negligible, and sometimes dominates over the measurement uncertainty. Range of true values depends on built-in differences in physics of the experiment. Stochastic physical processes result typically in a broader range of true values than non-stochastic processes do. Measurement uncertainty depends on a measurement method (properties of the detector not of the experiment). Modern measurement methods, including digital ones, frequently make the measurement uncertainty very small. When data from one–of –a kind experiment are analyzed, only the measurement uncertainty is reported. It provides no information about the range of true experimental values, neither about reliability of a reported data point. Reliability of a data point is in general independent from its measurement uncertainty. However, in practice reliable measurement methods frequently have high measurement uncertainty, while low reliability methods are applied to limit measurement uncertainty. Comparison of reliable data with high measurement uncertainty to not so reliable data measured with low uncertainty is discussed – in different scenarios different data analysis methods are applicable. Methods for data analysis from an experiment repeated statistically significant number of times are very well developed. They do not require a detailed expertise in physics of an experiment, nor in the properties of the measurement system used, and meaning of the reported uncertainty is well understood in any scientific community. It all changes when data from one-of-a-kind experiment is analyzed. Analyst’s expertise is required both in the physics of the experiment and in all aspects of the measurement system, all possible malfunctions. Data users must remember that only measurement uncertainty is reported from any one-of-a-kind experiment. Theory with simulations may provide estimation of expected built-in differences in the experiment, and by this of expected range of true values for a given experiment; yet measurement uncertainty can never be used in place of the range of true experimental values.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Uncertainty about the Uncertainty [Slides]

A golden standard in science is to repeat an experiment a statistically significant number of times, recording data using the same set of detectors and the same data analysis methodology. In such case experimental error includes both the range of true values generated by repetitions of the experiment, and measurement uncertainty caused by the detector. They are independent. It is a huge and too frequently used simplification, to assume that one can measure multiple repetitions of an identical experiment, resulting in identical true experimental value. Repetitions, as similar is it is experimentally achievable result in a range of the true values rather than in a single value. When modern, very sensitive and well calibrated measurement systems are used, this range is not negligible, and sometimes dominates, in comparison to the measurement uncertainty. Range of true values depends on the physics of the experiment, while measurement uncertainty depends on the measurement method (properties of the detector not of the experiment). When data from one–of–a kind experiment are analyzed, only the measurement uncertainty is reported. It gives no information about the range, in which the true values of experiment would spread if the experiment was repeated. A frequently used approximation, that if a physical quantity is measured as a function of time, only measurement of this quantity, produces uncertainty is also in some real experiments fare to strong. Example: In reaction history time measurement uncertainty propagated to alpha dominated under certain conditions over the flux measurement uncertainty propagated to alpha. Reliability of a data point is in general independent from its measurement uncertainty. However, in practice reliable measurement methods frequently have high measurement uncertainty, while low reliability methods are applied to limit measurement uncertainty. Comparison of reliable data with high measurement uncertainty to not so reliable data measured with low uncertainty is discussed – in different scenarios different data analysis methods are applicable.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Reducing Exposures to Airborne Particles Through Improved Filtration: A High-Level Modeling Analysis

Indoor airborne particulates are well-known health hazards and filtration is one common method of reducing exposures. Based on our previously developed Regional Shelter Analysis methodology and parameters that characterize the existing US building stock, we perform a high-level assessment of the potential benefits of upgrading existing filters in furnace and in heating, ventilation, and air conditioning systems using off-the-shelf filters. We use three metrics to assess the improvement: Building Transmission Factor (a measure of protection against outdoor airborne particles), Indoor Normalized Time and Space Integrated Air Concentration (a measure of indoor exposure to indoor-origin airborne particles), and Building Exit Fraction (fraction of indoor airborne particles that are released to the outdoor atmosphere). We also discuss the potential reduction in regional exposures due to particles exiting the building and exposing downwind building occupants. Our modeling indicates that while buildings provide their occupants passive protection against airborne particulate hazards, including but not limited to PM 2.5 , PM 10 , and wildfire smoke; improving particle filtration efficiency may further improve this protection. The degree of improvement varies with particle size and building type. Of the building types studied, apartments are predicted to benefit most, with greater than a factor of 2 improvement (≥50% reduction in exposures) for 1 µm particle exposures when using MERV 7 to 12 rated filters. Non-residential buildings were notably less responsive to improved filtration but had the highest Building Exit Fractions with 30% to 40% of indoor airborne particles released to the outdoor atmosphere (apartment buildings only released 6% to 9%). Improvements predicted for single family homes were intermediate between apartments and non-residential buildings. Improvements in the Regional Exposure metric are larger, ranging from a factor of 2.5x to 10x for residences (when using MERV 7 to 12 rated filters) and up to 25x for large apartments with MERV 14 or 15 rated filters. The results of our modeling analysis are broadly consistent with the limited experimental data and modeling results available in the literature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Modeling the Effect of Surface Platinum–Tin Alloys on Propane Dehydrogenation on Platinum–Tin Catalysts

Uncertainty analysis, reported experimental literature data, and density functional theory were synthesized to model the effect of surface tin coverage on platinum-based catalysts for nonoxidative propane dehydrogenation to propylene. Here, this study tests four different platinum–tin skin surface models as potential catalytic sites, Pt 3 Sn/Pt(100), PtSn/Pt(100), Pt 3 Sn/Pt(111), and Pt 2 Sn/Pt(211), and compares them to the corresponding pure Pt surface sites using an uncertainty analysis methodology that uses BEEF-vdW with its ensembles (BMwE) to generate the uncertainty for the energies of the intermediates and transition states. One experimental data set with two experimental observations, selectivity to propylene and turnover frequency of propylene, was used as a calibration data set to evaluate the impact of the experimental data on informing the models. This study finds that the prior model for Pt 3 Sn/Pt(100) is the most active and Pt 2 Sn/Pt(211) is the most selective toward propylene. Active sites on the (100) facet have the highest probability of being responsible for C 1 and C 2 product formations (C–C bond cleavage). Increasing the Sn coverage on the (100) surface facet to a PtSn/Pt(100) active site leads to a significantly reduced rate and might explain the experimentally observed higher selectivity of Sn-doped catalysts relative to pure Pt catalysts. Next, this study finds that for all surfaces, except PtSn/Pt(100), the rate-controlling steps are the initial dehydrogenation steps alongside some partially rate-controlling second dehydrogenation steps. For PtSn/Pt(100), only the initial terminal dehydrogenation step to CH 3 CH 2 CH 2 * and second dehydrogenation steps are rate-controlling. Next, the calibrated models for all surfaces were found to be selective toward propylene production and model the reported turnover frequency successfully. Nevertheless, Pt 2 Sn/Pt(211) emerges as the active site with some (minor) evidence as the main active site based on Jeffreys’ scale interpretation of Bayes factors. This observation agrees with prior studies that also found step sites to be most likely the most relevant active sites for pure Pt catalysts. Overall, the results indicate that tin, in addition to affecting the binding strength of the adsorbed species, prevents deeper dehydrogenation (reducing coking) and cracking reactions through increasing activation barriers for unwanted side reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗