Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “calculator”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Vibrational and rotational excitation of CO in comets. Part 1: Non-equilibrium calculations. Part 2: Results of the calculation for standard bright comet, comet Iras-Araki-Alcock and comet Halley

The vibrational and rotational excitation of the CO molecule in cometary comae were investigated using a model which includes IR vibrational pumping by the solar flux, vibrational and rotational radiative decay, and collisional coupling among rotational states. Steady state was not assumed in solving the rate equations. The evolution of a shell of CO gas was monitored as it expanded from the nucleus into the outer coma. Collisional effects were treated using a kinetic temperature profile derived from theoretical work on the coma energy balance. The kinetic temperature was assumed to be extremely cold in the inner coma; this has significant consequences for the CO excitation. If optical depth effects are ignored, only low J transitions will be significantly excited in comets observed at high spatial resolution. Ground-based observations of CO co-vibrational and rotational transitions will be extremely difficult due to lack of sensitivity and/or terrestrial absorption. However, CO should be detectable from a large comet with favorable observing geometry if the CO is a parent molecule present at the 10% level (or greater) relative to H2O. Observations using cooled, spaceborne instruments should be capable of detecting CO emission from even moderately bright comets.

Chin, G.↗
NASA NTRS2009

Identification of Trends into Dose Calculations for Astronauts through Performing Sensitivity Analysis on Calculational Models Used by the Radiation Health Office

The Radiation Health Office (RHO) determines each astronaut s cancer risk by using models to associate the amount of radiation dose that astronauts receive from spaceflight missions. The baryon transport codes (BRYNTRN), high charge (Z) and energy transport codes (HZETRN), and computer risk models are used to determine the effective dose received by astronauts in Low Earth orbit (LEO). This code uses an approximation of the Boltzman transport formula. The purpose of the project is to run this code for various International Space Station (ISS) flight parameters in order to gain a better understanding of how this code responds to different scenarios. The project will determine how variations in one set of parameters such as, the point of the solar cycle and altitude can affect the radiation exposure of astronauts during ISS missions. This project will benefit NASA by improving mission dosimetry.

Adams, Thomas↗
NASA NTRS2002

Aeroelastic Calculations of Quiet High- Speed Fan Performed

An advanced high-speed fan was recently designed under a cooperative effort between the NASA Glenn Research Center and Honeywell Engines & Systems. The principal design goals were to improve performance and to reduce fan noise at takeoff. Scale models of the Quiet High-Speed Fan were tested for operability, performance, and acoustics. During testing, the fan showed significantly improved noise characteristics, but a self-excited aeroelastic vibration known as flutter was encountered in the operating range. Flutter calculations were carried out for the Quiet High-Speed Fan using a three-dimensional, unsteady aerodynamic, Reynolds-averaged Navier-Stokes turbomachinery code named "TURBO." The TURBO code can accurately model the viscous flow effects that can play an important role in various aeroelastic problems such as flutter with flow separation, flutter at high loading conditions near the stall line (stall flutter), and flutter in the presence of shock and boundary-layer interaction. Initially, calculations were performed with no blade vibrations. These calculations were at a constant rotational speed and a varying mass flow rate. The mass flow rate was varied by changing the backpressure at the exit boundary of the computational domain. These initial steady calculations were followed by aeroelastic calculations in which the blades were prescribed to vibrate harmonically in a natural mode, at a natural frequency, and with a fixed interblade phase angle between adjacent blades. The AE-prep preprocessor was used to interpolate the in-vacuum mode shapes from the structural dynamics mesh onto the computational fluid dynamics mesh and to smoothly propagate the grid deformations from the blade surface to the interior points of the grid. The aeroelastic calculations provided the unsteady aerodynamic forces on the blade surface due to blade vibrations. These forces were vector multiplied with the structural dynamic mode shape to calculate the work done on the blade during one vibration period, then this result was converted to an aerodynamic damping. Flutter occurs when the aerodynamic damping becomes negative, if structural damping is ignored. The results of these aeroelastic calculations are summarized in this plot of aerodynamic damping versus mass flow rate at a constant rotational speed. As the backpressure is increased, the mass flow rate through the fan decreases and the fan operating point moves towards the stall line. The aeroelastic calculations showed that the aerodynamic damping decreases as the stall line is approached, as observed during testing. In addition, the aeroelastic calculations with the TURBO code correctly predicted the aeroelastic parameters: the most unstable vibration mode and interblade phase angle, as observed during testing. The Quiet High-Speed Fan demonstrated significant noise reductions during testing, but flutter imposed limits on its operating range. The accurate calculation of the aeroelastic characteristics using the TURBO code is a significant step toward eliminating flutter from the operating range and toward realizing the benefits of reduced fan noise. The aeroelastic calculations described here were performed under a grant by University of Toledo researchers in collaboration with Glenn's researchers.

Bakhle, Milind A.↗
DOE OSTI2023

One-bond 13 C– 13 C spin-coupling constants in saccharides: a comparison of experimental and calculated values by density functional theory using solid-state 13 C NMR and X-ray crystallography

Methyl aldohexopyranosides were 13 C-labeled at contiguous carbons, crystallized, and studied by single-crystal X-ray crystallography and solid-state 13 C nuclear magnetic resonance (NMR) spectroscopy to examine the degree to which density functional theory (DFT) can calculate one-bond 13C–13C spin-coupling constants ( 1 J CC ) in saccharides with sufficient accuracy to permit their use in MA'AT analysis, a newly-reported hybrid DFT/NMR method that provides probability distributions of molecular torsion angles in solution (Zhang et al., J. Phys. Chem. B, 2017, 121, 3042–3058; Meredith et al., J. Chem. Inf. Model., 2022, 62, 3135–3141). Experimental 1 J CC values in crystalline samples of the doubly 13 C-labeled compounds were measured by solid-state 13 C NMR and compared to those calculated from five different DFT models: (1) 1 J CC values calculated from single structures identical to those observed in crystalline samples by X-ray crystallography (all atom refinement); (2) 1 J CC values calculated from the single structures in (1) but after Hirshfeld atom refinement (HAR); (3) 1 J CC values calculated from the single structures in (1) after DFT-optimization of hydrogen atoms only; and (4 and 5) 1 J CC values calculated in rotamers of torsion angle θ 2 (C1–C2–O2–O2H) or ω(C4–C5–C6–O6) from which either specific or generalized parameterized equations were obtained and used to calculate 1 J CC values in the specific θ 2 or ω rotamers observed in crystalline samples. Good qualitative agreement was observed between calculated 1 J CC values and those measured by solid-state 13 C NMR regardless of the DFT model, but in no cases were calculated 1 J CC values quantitative, differing (over-estimated) on average by 4–5% from experimental values. These findings, and those reported recently from solution NMR studies (Tetrault et al., J. Phys. Chem. B 2022, 126, 9506–9515), indicate that improvements in DFT calculations are needed before calculated 1 J CC values can be used directly as reliable constraints in MA'AT analyses of saccharides in solution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Sensitivity Calculations for Systems with Polyethylene Reflector Materials Using CLUTCH

The SCALE 6.2.4 code package contains four sequences for calculating $k_{eff}$ sensitivity coefficients. Two of these sequences use deterministic transport solvers: a one-dimensional (1D) capability based on XSDRN, and a two-dimensional (2D) capability based on NEWT. These sequences are restricted to the multigroup (MG) treatment of neutron energy. The three-dimensional (3D) sequences use the KENO V.a or KENO-VI Monte Carlo transport codes and can be used to calculate sensitivity coefficients with either MG or continuous-energy (CE) transport. The 3D sensitivities are ultimately reported in an MG structure, regardless of the method used in the transport calculations. If desired, the sensitivity coefficients can be reported with very fine energy resolution from a CE calculation, but they are calculated only in the MG library structure in the MG mode. CE TSUNAMI methods are available in SCALE starting in SCALE version 6.2. Sensitivity coefficients were generated using the 3D sequences as part of the generation of the SCALE 6.2.2 Validation Report; difficulties encountered when using the CLUTCH method for thick, fissionable-material reflectors were discussed and investigated as documented in a previous paper. This paper discusses the difficulties encountered in generating accurate sensitivity coefficients using the CLUTCH technique for polyethylene reflectors for two fast spectrum benchmarks. Direct perturbation (DP) calculations were performed to confirm the accuracy of the total sensitivity coefficient for important isotopes with large sensitivities in the system. Discrepancies were detected for CLUTCH-calculated sensitivity coefficients in the reflector of a critical experiment with a radial polyethylene reflector. A simple polyethylene-reflected plutonium sphere was then used to further investigate the discrepancy. Calculations performed using the iterated fission probability (IFP) method generated accurate sensitivity coefficients in both cases. The results of this study emphasize the need to confirm CLUTCH sensitivity results with DP calculations. IFP calculations are generally less efficient but more reliable than CLUTCH calculations. Improvements to the CLUTCH methodology that retain the greater efficiency but address identified difficulties are therefore potentially useful to analysts.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

How does uncertainty of soil organic carbon stock affect the calculation of carbon budgets and soil carbon credits for croplands in the U.S. Midwest?

Cropland carbon budget depicts the amount of carbon flowing in and out of agroecosystems and the changes in carbon stocks of soil and living biomass during the same period. Soil carbon credit is the additional change in soil carbon stock under certain farming practices compared with the business-as-usual practices. Accurately calculating cropland carbon budget and soil carbon credit is critical to assessing climate change mitigation potential in agroecosystems. The calculation of cropland carbon budget and soil carbon credit is sensitive to local soil and climatic conditions, especially initial soil organic carbon (SOC) stock, which is determined by both SOC concentration (SOC%) and bulk density (Bulk_Density). SOC stock data are either from soil sampling or gridded public survey data. In agroecosystem models, SOC stock data are a key model input for quantifying cropland carbon budget and soil carbon credit. However, various types and degrees of uncertainties exist in SOC stock datasets, which propagate to the quantification of SOC stock change. In particular, a large discrepancy is found in two widely used SOC stock datasets — Rapid Carbon Assessment dataset (RaCA) and Gridded Soil Survey Geographic Database (gSSURGO) — in the U.S. Midwest, with a relative difference (quantified using Normalized Root Mean Square Error, NRMSE) of 48.0% for 0–30 cm SOC stock between the two datasets. It remains largely unclear how uncertainty in SOC stocks affects the calculation of cropland carbon budget and soil carbon credit. To address this question, we used a well-validated process-based agroecosystem model, ecosys, to assess the impacts of SOC stock uncertainty on carbon budget and soil carbon credit calculation in the U.S. Midwestern corn-soybean rotation systems. Our results reveal the following findings: (1) A sizable discrepancy exists in simulated cropland carbon budget between using gSSURGO and using RaCA for their SOC% and Bulk_Density as model inputs, with a Pearson correlation coefficient (r) of only 0.4 for simulated change of SOC stock (ΔSOC) using these two different soil datasets. (2) Simulated cropland carbon budget components were more sensitive to initial SOC% than to Bulk_Density. For example, the upper and lower quartiles of multi-year averaged ΔSOC were –29.8 and 4.8 gC/m 2 /year for the selected counties respectively, with an uncertainty of 13.7 and 0.7 gC/m 2 /year induced by uncertainties in initial SOC% and Bulk_Density, respectively. (3) Both simulated ΔSOC and its uncertainty were negatively correlated with initial SOC%, whereas ΔSOC was negatively correlated with air temperature, and ΔSOC uncertainty was positively correlated with air temperature. (4) The uncertainty of calculated soil carbon credits was much smaller compared with the uncertainty of calculated absolute carbon budgets assuming the same SOC stock uncertainty level in the inputs. Specifically, in our assessment comparing planting cover crops vs no cover crop, the uncertainty of calculated soil carbon credits induced by initial SOC% uncertainty was less than 4% (relative to the quantified value of the soil carbon credits) for 90% of the cases. Our analysis highlights that high accuracy measurement of SOC% as inputs is needed for the calculation of cropland carbon budgets; however, soil carbon credit quantification is much less sensitive to the initial SOC% inputs, and the current publicly available soil datasets (e.g., gSSURGO) are largely suitable for the calculation of soil

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Validation of Light Water Reactor Ex-Core Calculations with VERA

The Consortium for Advanced Simulation of Light Water Reactors (CASL) Virtual Environment for Reactor Applications (VERA) is a reactor simulation software. It offers unique capabilities by combining high-fidelity in-core radiation transport with temperature feedback by using MPACT (a deterministic neutron transport code) and COBRA-TF (a thermal-hydraulic code) with follow-on, fixed-source transport calculations using the Shift Monte Carlo code to calculate ex-core quantities of interest. In these coupled calculations, MPACT provides Shift with the fission source for follow-on ex-core calculations. These ex-core simulations can be set up to calculate detector responses, as well as the flux and fluence in ex-core regions of interest, such as the reactor pressure vessel, nozzle, and irradiated capsules. A Watts Bar Nuclear Plant Unit 1 (WBN1) ex-core model was developed, as described in this paper, and this model was used to perform coupon calculations. The results for the coupon flux calculations show close agreement with the reference values for cycle 1 produced by the two-dimensional Discrete Ordinates Transport (DORT) code and presented in a BWXT Services Inc. report. However, differences in the results (10%) seen in cycles 2 and 3 and the reasons for these differences are discussed in this paper. The VERA WBN1 model was also used to perform a vessel fluence calculation for cycle 1. Additionally, a collaboration between CASL and Duke Energy led to the first code-to-code validation of VERA for reactor ex-core applications that used a model for the Shearon Harris reactor. Results from this collaboration show excellent agreement between VERA and the Monte Carlo N-Particle Transport Code for the detector response calculations. The work performed under this collaboration is also detailed in this paper.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Decay Dose Shielding Analysis with Hybrid Unstructured Mesh/Constructive Solid Geometry Monte Carlo Calculation and ADVANTG Acceleration

Here, the target segments of the Oak Ridge National Laboratory Second Target Station (STS) neutron production facility become highly activated due to spallation reactions or nuclei transmutation by primary protons and emitted neutrons. Once the target segments are removed from their location within the core vessel, decay dose rates must be accurately quantified to determine the shielding configurations of remote-handling tools and transport casks and to aid in planning maintenance activities. For this analysis, we utilized a hybrid unstructured mesh (UM)/constructive solid geometry approach for calculating spallation products and neutron fluxes, activation calculations using the AARE package that includes the CINDER2008 activation code to calculate the decay photon source at different cooling times, and the ADVANTG code to accelerate the final decay photon transport calculation. Both Type 316 stainless steel (SS-316) and lead were investigated as candidates for shielding materials. The decay photon transport calculation through the thick SS-316 or lead shields exhibited between 25 and 30 orders-of-magnitude attenuations in the radial direction, depending on the shield. Such a difficult shielding calculation required advanced variance reduction. ADVANTG has some missing features, which limits its usability in spallation neutron source applications. It does not support volumetric sources created for MCNP6.2 UM capability. An approximate source was created for this problem. Not only was this approximate source needed for running the ADVANTG calculation to generate the weight windows, but also it was essential to develop source biasing (SB) parameters that were crucial for dramatically accelerating the decay photon transport in this problem. With this approximate source, the analysis was completed in a very reasonable computational time, and the design of the STS remote-handling equipment was finalized. This paper compares the efficiency of Monte Carlo simulations with different weight window and SB parameters calculated using different approximate ADVANTG calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1997

Global Pattern of Potential Evaporation Calculated from the Penman-Monteith Equation Using Satellite and Assimilated Data

Potential evaporation (E(0)) has been found to be useful in many practical applications and in research for setting a reference level for actual evaporation. All previous estimates of regional or global E(0) are based upon empirical formulae using climatologic meteorologic measurements at isolated stations (i.e., point data). However, the Penman-Monteith equation provides a physically based approach for computing E(0), and by comparing 20 different methods of estimating E(0), Jensen et al. (1990) showed that the Penman-Monteith equation provides the most accurate estimate of monthly E(0) from well-watered grass or alfalfa. In the present study, monthly total E(0) for 24 months (January 1987 to December 1988) was calculated from the Penman-Monteith equation, with prescribed albedo of 0.23 and surface resistance of 70 s/m, which are considered to be representative of actively growing well-watered grass covering the ground. These calculations have been done using spatially representative data derived from satellite observations and data assimilation results. Satellite observations were used to obtain solar radiation, fractional cloud cover, air temperature, and vapor pressure, while four-dimensional data assimilation results were used to calculate the aerodynamic resistance. Meteorologic data derived from satellite observations were compared with the surface measurements to provide a measure of accuracy. The accuracy of the calculated E(0) values was assessed by comparing with lysimeter observations for evaporation from well-watered grass at 35 widely distributed locations, while recognizing that the period of present calculations was not concurrent with the lysimeter measurements and the spatial scales of these measurements and calculations are vastly different. These comparisons suggest that the error in the calculated E(0) values may not be exceeded, on average, 20% for any month or location, but are more likely to be about 15%. These uncertainties are difficult to quantify for mountainous areas or locations close to extensive water bodies. The difference between the calculated and observed E(0) is about 5% when all month and locations were considered. Errors are expected to be less than 15% for averages of E(0) over large areas or several months. Further comparisons with lysimeter observations could provide a better appraisal of the calculated values. Global pattern of E(0) was presented, together with zonal average values.

Choudhury, Bhaskar J.↗
DOE OSTI2020

VENTSAR V3.0: A Python GUI for Estimating Contaminant Concentrations on or Near Buildings Due to Building Effects and Plume Rise and For Calculating Inhalation and Plume Shine Doses

VENTSAR: Originated as VENTAX (Smith and Weber 1983) on the IBM Mainframe at the Savannah River Site (SRS) as a Fortran program. Updated to VENTSAR XL V1.0 (Simpkins 1997) as a spreadsheet version using macros created in Microsoft Excel. Later updated to VENTSAR XL V2.0 (Dixon 2018) as a spreadsheet executing a modified version of the original VENTAX Fortran code. Estimates contaminant concentrations on or near a building from a release at a nearby location. Calculates concentrations for a given meteorological exceedance probability or for a given stability and wind speed combination. Can model a single building with or without a penthouse on top or a ground location from either a stack or ground release. Plume rise can be considered. Contaminant releases can be chemical or radioactive with downwind concentrations determined at user-specified distances. Wind passing over and around buildings creates a complicated dispersion pattern. Air-intake vents may be located on building roofs or near the ground downwind of a release source. An estimation of pollutant concentrations on or near a structure is important in determining expected pollutant levels. Meteorological data are selected based on a specific area of the site. Fortran-based VENTAX able to make fast, complex mathematical calculations. Not user-friendly VENTSAR XL V1.0 no longer supported due to obsolete Excel macros. VENTSAR XL V2.0 an interim solution using an Excel spreadsheet to interface with the modified VENTAX Fortran code. Requires user to have Microsoft Excel. Not an intuitive interface for someone unfamiliar with VENTSAR. Does not perform dose calculations. VENTSAR V3.0 goal to combine the strengths of previous versions of VENTSAR into a single, powerful yet user-friendly program with a Graphical User Interface (GUI). Not dependent upon a specific program or plug-ins. Self-contained package to be used on any computer running Microsoft Windows. User-friendly, intuitive GUI created in Python for parameter input. Executes same modified VENTAX Fortran code as VENTSAR XL V2.0. Outputs formatted text file of completed calculations for easy review and dissemination. Performs inhalation and plume shine dose calculations for up to 11 user-selected nuclides from a nuclide dose factor library of almost 500 nuclides. 12 test cases were created for verification of V1.0 and V2.0. Same test cases run in V3.0 to verify correct performance. V3.0 produced similar results to V1.0. Confirmed GUI did not alter calculations in any way. Comparisons of the test case results confirm the V3.0 GUI does not influence the VENTSAR calculations. Simply passes same input parameters to VENTAX Fortran code for execution. Provides end user an easy-to-use, intuitive tool to quickly make building effect and plume rise calculations, as well as, inhalation and plume shine dose calculations. No experience with or working knowledge of Fortran, Excel, command line, or macros required. Self-contained package allows VENTSAR be deployed to any Windows computer without the need for specific programs or plug-ins.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Technical Background and Validation Report on the Residential Water Inhalation Risk Calculator Presented in the Risk Assessment Information System

Indoor air quality (IAQ) is critical for human health. Poor IAQ is linked to respiratory issues, cardiovascular diseases, and cancer. Indoor pollutants are emitted by typical household items such as cleaning products, personal care items, building materials, and tap water - an understudied volatile organic compound (VOC) source. This document presents the Residential Water Inhalation Risk Calculator (RWIRC), which estimates daily VOC exposure concentrations from various household water uses, such as showering and dishwashing, to assess exposure risks for the most vulnerable occupant. Integrated into the Risk Assessment Information System (RAIS) and sponsored by the US Department of Energy (DOE), the calculator divides a house into three compartments: shower, bathroom, and other spaces, accounting for daily water usage patterns and calculating VOC concentrations. Exposure data generated using the calculator can assist health assessors in estimating excess lifetime cancer risk (ELCR) and hazard index (HI) from VOC inhalation. Unlike traditional exposure models that utilize Andelman’s constant, the RWIRC continuously assesses variability in VOC concentrations and environmental conditions using differential equations to track VOC concentrations and air exchange between compartments. The calculator also provides unique volatilization fractions for each chemical and appliance, enhancing accuracy of the exposure concentration estimation. The RWIRC is accessible online and allows users to customize parameters (i.e., number of bathrooms, water temperature, and exhaust fan conditions) and input VOC characteristics (i.e., tap water and ambient air concentrations). This document provides a step-by-step guide on implementing the calculator. It also provides comparisons with the ATSDR-SHOWER calculator, using eight VOCs with varying physicochemical properties to reveal differences in algorithms and output concentrations. Simulations also assess how bathroom door positions and exhaust fan usage affect VOC exposure. The calculator results can enhance EPA risk screening levels for inhalation exposure to VOCs from tap water, offering a sophisticated tool for assessing inhalation risks and improving public health protection.

54 ENVIRONMENTAL SCIENCES↗