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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 report presents an analysis of results of wind-tunnel tests conducted at the D.V.L. Values were determined for the effectiveness of all the controls at various angles of attack. The autorotation was studied by subjecting the rotating model to an air blast.
The dispersion of the measurements that contributed to the previously defined space absolute radiometric reference (SARR) is investigated by objective statistical analysis. The estimated standard deviation with which the reference is known is 0.22 W/sq m, corresponding to 0.016 percent of its mean value. Several updates are made in the SARR referenced total solar irradiance data series, which was previously obtained from November 1978 until December 1993. The shift in 1898-1990 of the NIMBUS 7 instrument identified by Lee in 1995 is investigated and taken into account, resulting in new values for the NIMBUS 7 measurements before 1990 and in a new SARR adjustment coefficient for the active cavity radiometer irradiance monitoring (ACRIM) 1 instrument. The data series is extended to the present by adding level 1 data of DIARAD/VIRGO on board SOHO. A preliminary SARR coefficient for level 1 DIARAD/VIRGO was obtained by comparison with SARR referenced ACRIM 2 data.
Microwave algorithms simplify complex physical processes in order to estimate geophysical parameters such as snow cover and snow depth. The microwave radiances received at the satellite sensor and expressed as brightness temperatures are a composite of contributions from the Earth's surface, the Earth's atmosphere and from space. Owing to the coarse resolution inherent to passive microwave sensors, each pixel value represents a mixture of contributions from different surface types including deep snow, shallow snow, forests and open areas. Algorithms are generated in order to resolve these mixtures. The accuracy of the retrieved information is affected by uncertainties in the assumptions used in the radiative transfer equation (Steffen et al., 1992). One such uncertainty in the Chang et al., (1987) snow algorithm is that the snow grain radius is 0.3 mm for all layers of the snowpack and for all physiographic regions. However, this is not usually the case. The influence of larger grain sizes appears to be of more importance for deeper snowpacks in the interior of Eurasia. Based on this consideration and the effects of forests, a revised SMMR snow algorithm produces more realistic snow mass values. The purpose of this study is to present results of the revised algorithm (referred to for the remainder of this paper as the GSFC 94 snow algorithm) which incorporates differences in both fractional forest cover and snow grain size. Results from the GSFC 94 algorithm will be compared to the original Chang et al. (1987) algorithm and to climatological snow depth data as well.
Quantifying spacecraft charging risks and associated hazards for the International Space Station (ISS) requires a plasma environment specification for the natural variability of ionospheric temperature (Te) and density (Ne). Empirical ionospheric specification and forecast models such as the International Reference Ionosphere (IRI) model typically only provide long term (seasonal) mean Te and Ne values for the low Earth orbit environment. This paper describes a statistical analysis of historical ionospheric low Earth orbit plasma measurements from the AE-C, AE-D, and DE-2 satellites used to derive a model of deviations of observed data values from IRI-2001 estimates of Ne, Te parameters for each data point to provide a statistical basis for modeling the deviations of the plasma environment from the IRI model output. Application of the deviation model with the IRI-2001 output yields a method for estimating extreme environments for the ISS spacecraft charging analysis.
Figures of Merit (FoMs) and the FoM software provide a method for quantitatively evaluating the quality of a regolith simulant by comparing the simulant to a reference material. FoMs may be used for comparing a simulant to actual regolith material, specification by stating the value a simulant s FoMs must attain to be suitable for a given application and comparing simulants from different vendors or production runs. FoMs may even be used to compare different simulants to each other. A single FoM is conceptually an algorithm that computes a single number for quantifying the similarity or difference of a single characteristic of a simulant material and a reference material and provides a clear measure of how well a simulant and reference material match or compare. FoMs have been constructed to lie between zero and 1, with zero indicating a poor or no match and 1 indicating a perfect match. FoMs are defined for modal composition, particle size distribution, particle shape distribution, (aspect ratio and angularity), and density. This TM covers the mathematics, use, installation, and licensing for the existing FoM code in detail.
Prediction of Worldwide Energy Resources (POWER) project provides irradiance dataset to support renewable energy, building energy efficiency and agricultural needs. These datasets are derived from Global Energy and Water Cycle Experiment Surface Radiation Budget (GEWEX SRB) and Clouds and the Earth’s Radiant Energy System (CERES SYN1Deg). A systematic bias has been reported between these two datasets for the years with overlapping observations. For obtaining a consistent climate data record spanning the entire time record of observations, it is crucial to understand and remove the bias in the irradiance dataset. Inconsistency in solar radiation data can lead to inaccurate conclusions about solar energy potential and obscure real trends in solar radiation patterns that would impact energy availability assessments. In this study, we adapt quantile mapping approach to remove the systematic bias and to improve reliability of shortwave and longwave irradiance data. We present a validation of the bias corrected data against ground truth. For each 1° latitude and 1° longitude grid box across the globe, we match the CDFs of the reference dataset (CERES SYN1Deg) to that of the SRB dataset, thereby, adjusting the irradiance values to match the empirical distribution of two different measurements. The performance of quantile mapping is evaluated by using the metrics such as Mean Absolute Deviation (MAD). The results indicate that the quantile mapping significantly improves the accuracy and reliability of solar irradiance dataset especially for the weather conditions associated with high cloud cover and extreme irradiance values. The initial range of MAD for the studied sites for daily data was 4 to 19 Wm-2. After correction these reduced to 3 to 7 Wm-2. The findings from this study have important implications for solar energy system design, agricultural planning, and climate modeling community. Reducing the inconsistency and biases in solar irradiance dataset can enable better planning and operation of solar energy systems, leading to increased efficiency and cost-effectiveness. Additionally, this work also contributes to the statistical post-processing techniques in the renewable energy domain and highlights the potential of historical and near-real-time NASA POWER dataset as a valuable resource for solar energy research and applications.
The unusually deep and extended solar minimum of cycle 2324 made it very difficult to predict the solar indices 1 or 2 years into the future. Most of the predictions were proven wrong by the actual observed indices. IRI gets its solar, magnetic, and ionospheric indices from an indices file that is updated twice a year. In recent years, due to the unusual solar minimum, predictions had to be corrected downward with every new indices update. In this paper we analyse how much the uncertainties in the predictability of solar activity indices affect the IRI outcome and how the IRI values calculated with predicted and observed indices compared to the actual measurements.Monthly median values of F2 layer critical frequency (foF2) derived from the ionosonde measurements at the mid-latitude ionospheric station Juliusruh were compared with the International Reference Ionosphere (IRI-2007) model predictions. The analysis found that IRIprovides reliable results that compare well with actual measurements, when the definite (observed and adjusted) indices of solar activityare used, while IRI values based on earlier predictions of these indices noticeably overestimated the measurements during the solar minimum.One of the principal objectives of this paper is to direct attention of IRI users to update their solar activity indices files regularly.Use of an older index file can lead to serious IRI overestimations of F-region electron density during the recent extended solar minimum.
Space Policy Directive-1 has led to NASA partnerships with commercial entities on procurement which includes the development of the Human Landing System (HLS) [1]. With the goal of delivering human crew to the lunar surface by 2024, system uncertainties become an important obstacle to the maturation of multiple new, driving technologies and mission concepts of the HLS program. As unmitigated uncertainties have previously led to failed development programs, these risks and their impacts must be understood and handled to ensure program success [2]. Sources of uncertainty include novel engine designs and configurations, increased reliance on cryogenic fluid management(CFM), and refueling technologies—which propagate as high-level performance metrics such as overall propellant mass and engine performance. Also, the occurrence of operational uncertainties—e.g. launch conditions or need to abort during the mission—can cause cascading effects on the rest of the mission that are difficult to definitively quantify, and are outside the scope of control. These concrete examples and other occurrences can be categorized as either epistemic or aleatory uncertainties.Epistemic uncertainty arises due to a lack of knowledge and can be alleviated with design and program maturation. Aleatory uncertainty is due to the inherent randomness of the system and cannot be directly reduced, unlike epistemic uncertainty. Robust design and probabilistic methods can compensate for aleatory effects. A taxonomy of uncertainty is referred to for this work [3]. In this paper, a probabilistic methodology to handle uncertainties has been demonstrated on a three-element HLS concept [1, 4], which allows tracking of current best estimates of the concept and assessment of concept design robustness against uncertainties. A sample case has been completed for this abstract, and an expansion on the methodology will be included in the final paper. This methodology has two key parts: first, the creation of a dynamic architecture model of a three-element HLS concept; and second, its use with surrogate modeling and range estimating techniques to capture and propagate uncertainties. This abstract will cover the basics of the approach used, and further details and justifications will be in the final paper.The mission profile associated with this three-element concept (Fig 1) was modeled as a set of mission events that facilitated mass changes, idles, or spacecraft maneuvers. The mission profile scope starts with each element’s NRHO orbit insertion and aggregation and ends at post-sortie rendezvous with Orion. More detail on the mission profile will be in the final paper. The DYnamic Rocket EQuation Tool (DYREQT), a space systems synthesis and sizing framework used by NASA, was used as the physics framework to model the HLS architecture for applying the probabilistic methodology [5, 6]. Specifically, a parametric representation of the lander, ascent, and transfer elements and the mission profile of each element was established, with vehicle and mission parameters available as inputs to allow for a dynamic model. Each vehicle stage was modeled with high-level performance metrics, using Isp and propellant mass fraction (PMF) to remain parametric. For the probabilistic analysis, uncertainties of interest within the HLS concept were enumerated and represented as parameters within the DYREQT model as inputs for vehicle stages or mission profile events. These parameters were frozen at their nominal values for the purposes of baselining architecture performance and sizing the vehicle appropriately based on reference documentation [1]. Range estimating—a probabilistic method that combines Monte Carlo sampling, focus on critical parameters, and heuristics to assess risk and opportunities—is traditionally used with Mass Equipment Lists (MELs), but has been adapted with operational parameters as well as vehicle parameters in theDYREQT model to capture mission uncertainty alongside vehicle uncertainty [7, 3]. This method was selected due to its application and insight on a system from a bottom-up perspective, independence from historical rules of thumb, and ability to generate sensitivities based on design decisions and uncertainties. As a sample case for the abstract, the boiloff rates of the vehicle elements and the loiter times during the mission (simulating launch time variations and changing window of opportunities) were used with range estimating to provide preliminary results. To perform the range estimation portion of this methodology (depicted in Fig. 3, further details in final paper), the DYREQT model was sampled using a Design of Experiments (DoE) to efficiently explore the architecture design space with respect to the sample set of uncertainty parameters; 5,000 cases via Latin Hypercube Sampling were computed on the DYREQT architecture model. Then, the results were used to create surrogate models, multivariate regressions that can visualize hypercube trends in the design space, of the architecture with respect to the uncertainty parameters. Range estimating was applied to the surrogates instead of the actual models, which saves computational expense due to the bulk of cases needed for the Monte Carlo simulation as part of range estimating. Uncertainty parameters were sampled independently from triangular distributions using the DoE ranges as ‘min’ and ‘max’, and the nominal value as ‘most likely’. Based engineering intuition, some uncertainty parameters are correlated—e.g. if the main propellant has a high boil-off rate, the oxidizer should follow suit as both are related to CFM technology.While a Monte Carlo simulation samples all inputs as independent, the results would show model correlations; thus, it is efficient to sample the inputs as correlated. Using a correlation matrix constructed for the uncertainty parameters, previously independent samples were transformed to perform a Correlated Monte Carlo. A table for the DoE ranges and probability distribution parameters is shown in Table 1, and more details on Correlated Monte Carlo Simulations will be discussed in the final paper. The model’s resulting DoE showed that multivariate polynomial equations fit via least squares method captured its behavior accurately for the sample case. For the Correlated Monte Carlo Simulation, a positive correlation between fuel and oxidizer boiloff rates was used as a demonstration. 10,000 cases were computed with the surrogates and the launched masses for each vehicle element was collated. The results can be displayed in a probability density function (PDF), showing the impact of the uncertainty parameters chosen. Integrating the PDFs will yield a cumulative distribution function (CDF) that shows the cumulative probability of a given value on the x-axis. For the sample case, the elements’ launch mass margin was calculated and represented in as CDFs, as a demonstrated representation of figures of merit for the HLS concept. For the lander and ascent elements, the NRHO mass insertion limit is 16t; the transfer element has a limit of 30t [1]. It can be seen with Figure 2 that this probabilistic methodology can provide insight into mass margin with respect to the uncertainties being modeled. Currently, the results show that the lander (descent) vehicle element has the most restrictive design space; it is the only element to show a 10% probability of negative margin. Further analysis on the Monte Carlo results will show sensitivities for driving constraints and parameters for architecture feasibility, which can lead to establishing potential mission rules.The combination of range estimating with a parametric architecture model for HLS demonstrated the capability of this probabilistic methodology in a sample case. As the HLS development progresses, this methodology has the potential for keeping current best estimates of architecture performance for awarded concepts due to the flexibility in DYREQT’s modeling framework and its parametric nature. Concept maturation and increased epistemic knowledge can be injected into the model probabilistic modeling, and thus continue to track probability of mission success.
Powerlaws have long been used to describe the spectral dependence of aerosol extinction, and the wavelength exponent of the aerosol extinction powerlaw is commonly referred to as the Angstrom exponent. The Angstrom exponent is often used as a qualitative indicator of aerosol particle size, with values greater than two indicating small particles associated with combustion byproducts, and values less than one indicating large particles like sea salt and dust. In this study, we investigate the relationship between the Angstrom exponent and the mode parameters of bimodal aerosol size distributions using Mie theory calculations and Aerosol Robotic Network (AERONET) retrievals. We find that Angstrom exponents based upon seven wavelengths (0.34, 0.38, 0.44, 0.5, 0.67, 0.87, and 1.02 micrometers) are sensitive to the volume fraction of aerosols with radii less then 0.6 micrometers, but not to the fine mode effective radius. The Angstrom exponent is also known to vary with wavelength, which is commonly referred to as curvature; we show how the spectral curvature can provide additional information about aerosol size distributions for intermediate values of the Angstrom exponent. Curvature also has a significant effect on the conclusions that can be drawn about two-wavelength Angstrom exponents; long wavelengths (0.67, 0.87 micrometers) are sensitive to fine mode volume fraction of aerosols but not fine mode effective radius, while short wavelengths (0.38, 0.44 micrometers) are sensitive to the fine mode effective radius but not the fine mode volume fraction.
Accurate determination of the electronic properties of correlated oxides remains a significant challenge for computational theory. Traditional Hubbard-corrected density functional theory (DFT+U) frequently encounters limitations in precisely capturing electron correlation, particularly in predicting band gaps. We introduce a systematic methodology to enhance the accuracy of diffusion Monte Carlo (DMC) simulations for both ground and excited states, focusing on LiCoO 2 as a case study. By employing a selected configuration interaction (sCI) approach, we demonstrate the capability to optimize wavefunctions beyond the constraints of single-reference DFT+U trial wavefunctions. Here, we show that the sCI framework enables accurate prediction of band gaps in LiCoO 2 , closely aligning with experimental values and substantially improving traditional computational methods. The study uncovers a nuanced mixed state of t 2g and e g orbitals at the band edges that is not captured by conventional single-reference methods, further elucidating the limitations of PBE+U in describing d-d excitations. Our findings advocate for the adoption of beyond-DFT methodologies, such as sCI, to capture the essential physics of excited-state wavefunctions in strongly correlated materials. The improved accuracy in band gap predictions and the ability to generate more reliable trial wavefunctions for DMC calculations underscore the potential of this approach for broader applications in the study of correlated oxides. This work not only provides a pathway for more accurate simulations of electronic structures in complex materials but also suggests a framework for future investigations of the excited states of other challenging systems.
The purpose of this guide is intended to be a quick reference for a Project-Control Account Manager (P-CAM) or technical manager empowered with a project’s cost, schedule, and technical responsibilities of a control account(s) when Earned Value Management (EVM) is required. The overall objective is to support the P-CAM in performing their responsibilities as they relate to EVM. In addition, the reference guide describes at a summary level how the scope, schedule, and budget of a project integrate for optimal planning and control of prime contracts and in-house projects. Because NASA implements a diverse and unique portfolio of projects, those projects have traditionally created project-specific systems to manage planning and performance analysis. However, establishment and implementation of a project management system that is common across all centers and mission directorates will facilitate the adoption of best business practices. In addition, the application of timely and predictive analysis, as well as, providing all stakeholders with greater insight into project performance will enhance opportunities for project success. For more detailed information, refer to the NASA EVM System Description; Integrated Baseline Review (IBR) Handbook; EVM Implementation Handbook; Schedule Management Handbook; Work Breakdown Structure (WBS) Handbook; and other agency/industry documentation. In addition, you may contact your local center’s EVM Focal Points. All this information and more can be found at and/or the NASA Engineering Network located at https://nen.nasa.gov/web/pm.
Global Reference Atmospheric Model (GRAM-99) is an engineering-level model of the Earth's atmosphere. It provides both mean values and perturbations for density, temperature, pressure, and winds, as well as monthly- and geographically-varying trace constituent concentrations. From 0-27 km, thermodynamics and winds are based on National Oceanic and Atmospheric Administration Global Upper Air Climatic Atlas (GUACA) climatology. Above 120 km, GRAM is based on the NASA Marshall Engineering Thermosphere (MET) model. In the intervening altitude region, GRAM is based on Middle Atmosphere Program (MAP) climatology that also forms the basis of the 1986 COSPAR Intemationa1 Reference Atmosphere (CIRA). MAP data in GRAM are augmented by a specially-derived longitude variation climatology. Atmospheric composition is represented in GRAM by concentrations of both major and minor species. Above 120 km, MET provides concentration values for N2, O2, Ar, O, He, and H. Below 120 km, species represented also include H2O, O3, N2O, CO, CH, and CO2. Water vapor in GRAM is based on a combination of GUACA, Air Force Geophysics Laboratory (AFGL), and NASA Langley Research Center climatologies. Other constituents below 120 km are based on a combination of AFGL and h4AP/CIRA climatologies. This report presents results of comparisons between GRAM Constituent concentrations and those provided by the Naval Research Laboratory (NRL) climatology of Summers (NRL,/MR/7641-93-7416, 1993). GRAM and NRL concentrations were compared for seven species (CH4, CO, CO2, H2O, N2O, O2, and O3) for months January, April, July, and October, over height range 0-115 km, and latitudes -90deg to + 90deg at 10deg increments. Average GRAM-NRL correlations range from 0.878 (for CO) to 0.975 (for O3), with an average over all seven species of 0.936 (standard deviation 0.049).
Back in 2018, the Fortran committee began planning new features for what is now expected to be Fortran 2023, "generic programming" was at the top of the list of requests from the user community. Here, generic programming refers to the ability to specify algorithms that are parameterized in terms of deferred _types_ (as opposed to values). Concrete algorithms are subsequently generated when the generic algorithms are "instantiated" with specific types. After significant consideration the language committee decided that the necessary changes to Fortran to support generic programming would be too large and complex to complete within a single release cycle of the standard. Instead, a new "generics" subgroup was formed with myself as the lead, and we were charged with developing generic programming features for the subsequent release _after_ the F2023, internally referred to as Fortran 202Y. I.e, the new subgroup was provided the luxury of developing the new capabilities across two full release cycles of the standard. This talk will describe the current approach to generics being pursued by the subgroup as well as the current status halfway through the development cycle. I will conclude with some motivating examples implemented with notional syntax.
The objective of this project is to define torque values for bolts and screws when loading is not a concern. Fasteners require a certain torque to fulfill its function and prevent failure. NASA Glenn Research Center did not have a set of fastener torque tables for non-critical applications without loads, usually referring to hand-tight or wrench-tight torqueing. The project is based on two formulas, torque and pullout load. Torque values are calculated giving way to preliminary data tables. Testing is done to various bolts and metal plates, torqueing them until the point of failure. Around 640 torque tables were developed for UNC, UNF, and M fasteners. Different lengths of thread engagement were analyzed for the 5 most common materials used at GRC. The tables were put together in an Excel spreadsheet and then formatted into a Word document. The plan is to later convert this to an official technical publication or memorandum.
The carbon monoxide (CO) reference scale created by the National Oceanic and Atmospheric Administration/Climate Monitoring and Diagnostics Laboratory (NOAA/CMDL) is used to quantify measurements of CO in the atmosphere, calibrate standards of other laboratories and to otherwise provide reference gases to the community measuring atmospheric CO. This reference scale was created based upon a set of primary standards prepared by gravimetric methods at CMDL and has been propagated to a set of working standards. In this paper we compare CO mixing ratios assigned to the working standards by three approaches: (1) calibration against the original gravimetric standards, (2) calibration using only working standards as the reference gas, and (3) calibration against three new gravimetric standards prepared to CMDL. The agreement between these values was typically better than 1%. The calibration histories of CMDL working standards are reviewed with respect to expected rates of CO change in the atmosphere. Using a Monte Carlo approach to simulate the effect of drifting standards on calculated mixing ratios, we conclude that the error solely associated with the maintenance of standards will limit the ability to detect small CO changes in the atmosphere. We also report results of intercalibration experiments conducted between CMDL and the Diode Laser Sensor Group (DACOM) at the NASA Langley Research Center (Hampton, Virginia), and CMDL and the Fraunhofer-Institut (Garmisch-Partenkirchen, Germany). Each laboratory calibrated several working standards for CO using their reference gases, and these results were compared to calibrations conducted by CMDL. The intercomparison of eight standards (CO concentrations between approximately 100 and approximately 165 ppb) by CMDL and NASA agreed to better than +/- 2%. The calibration of six standards (CO concentrations between approximately 50 and approximately 210 ppb) by CMDL and the Fraunhofer-Institut agreed to within +/- 2% for four standards, and to within +/- 5% for all six standards.
A questionnaire has been developed and used extensively as an aid to the appointment office in determining what department a patient should be referred to and what time for special consultations should be reserved. It has helped to maintain a balanced case load and informs the physician in advance about the patients he will see. By coordinating appointments so that the patient can visit two or more of the specialty departments in one day rather than having to return a number of times to see different doctors, it has increased efficiency. The questionnaire screens for important symptoms or trouble spots to which the computer is programmed to assign scoring values which in turn point out the clinic division or section to which the patient should initially be referred.
Accurate references and documentation are of upmost importance, especially with the current workforce makeup. Tribal knowledge is likely to be lost with turnover. Using a questioning attitude is a great way to find gaps in existing analysis and “normal practices.” Most of the references reviewed are from the 1970’s, with the most recent authored in 1994. Many of these references have been revised and/or superseded in the years since. Though safe, many of the values are likely overconservative, as improvements with modeling and additional critical experiments have been performed in the years since. It would be prudent to reevaluate, clarify, and simplify the values used in H-Canyon.
The GE Research team proposed to advance the TRL level of process lubricated gas bearings from TRL3 based on the DE-EE0007109 program to TRL5 addressing a critical risk item that radial gas bearings can provide satisfactory rotordynamic performance in supporting a representative rotor for sCO2 Power Cycles. The ability to apply process lubricated gas bearings can provide cost savings with reduction of the associated lube oil systems and an efficiency benefit worth 2% efficiency for the turbomachinery configuration first presented in DE-EE005804. The process lubricated gas bearings are enablers for advanced hermetically sealed configurations that have the potential for significant advancement in sCO2 Power Cycles for CSP applications including the 2% efficiency increase referred to above in addition to OPEX savings with no need to refill the CO2 valued at 60k USD/yr and cost reductions of 50 USD/kWe all leading to a potential 0.95¢/kWh reduction positively impacting CSP goals.