Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “computer model calibration”

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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Using Leaf Chlorophyll to Parameterize Light-Use-Efficiency Within a Thermal-Based Carbon, Water and Energy Exchange Model

Chlorophylls absorb photosynthetically active radiation and thus function as vital pigments for photosynthesis, which makes leaf chlorophyll content (C(sub ab) useful for monitoring vegetation productivity and an important indicator of the overall plant physiological condition. This study investigates the utility of integrating remotely sensed estimates of C(sub ab) into a thermal-based Two-Source Energy Balance (TSEB) model that estimates land-surface CO2 and energy fluxes using an analytical, light-use-efficiency (LUE) based model of canopy resistance. The LUE model component computes canopy-scale carbon assimilation and transpiration fluxes and incorporates LUE modifications from a nominal (species-dependent) value (LUE(sub n)) in response to short term variations in environmental conditions, However LUE(sub n) may need adjustment on a daily timescale to accommodate changes in plant phenology, physiological condition and nutrient status. Day to day variations in LUE(sub n) were assessed for a heterogeneous corn crop field in Maryland, U,S.A. through model calibration with eddy covariance CO2 flux tower observations. The optimized daily LUE(sub n) values were then compared to estimates of C(sub ab) integrated from gridded maps of chlorophyll content weighted over the tower flux source area. The time continuous maps of daily C(sub ab) over the study field were generated by focusing in-situ measurements with retrievals generated with an integrated radiative transfer modeling tool (accurate to within +/-10%) using at-sensor radiances in green, red and near-infrared wavelengths acquired with an aircraft imaging system. The resultant daily changes in C(sub ab) within the tower flux source area generally correlated well with corresponding changes in daily calibrated LUE(sub n) derived from the tower flux data, and hourly water, energy and carbon flux estimation accuracies from TSEB were significantly improved when using C(sub ab) for delineating spatio-temporal variations in LUE(sub n). The results demonstrate the synergy between thermal infrared and shortwave reflective wavebands in producing valuable remote sensing data for operational monitoring of carbon and water fluxes.

Houlborg, Rasmus↗
NASA NTRS2016

Evaluation of Load Analysis Methods for NASAs GIII Adaptive Compliant Trailing Edge Project

The Air Force Research Laboratory (AFRL), NASA Armstrong Flight Research Center (AFRC), and FlexSys Inc. (Ann Arbor, Michigan) have collaborated to flight test the Adaptive Compliant Trailing Edge (ACTE) flaps. These flaps were installed on a Gulfstream Aerospace Corporation (GAC) GIII aircraft and tested at AFRC at various deflection angles over a range of flight conditions. External aerodynamic and inertial load analyses were conducted with the intention to ensure that the change in wing loads due to the deployed ACTE flap did not overload the existing baseline GIII wing box structure. The objective of this paper was to substantiate the analysis tools used for predicting wing loads at AFRC. Computational fluid dynamics (CFD) models and distributed mass inertial models were developed for predicting the loads on the wing. The analysis tools included TRANAIR (full potential) and CMARC (panel) models. Aerodynamic pressure data from the analysis codes were validated against static pressure port data collected in-flight. Combined results from the CFD predictions and the inertial load analysis were used to predict the normal force, bending moment, and torque loads on the wing. Wing loads obtained from calibrated strain gages installed on the wing were used for substantiation of the load prediction tools. The load predictions exhibited good agreement compared to the flight load results obtained from calibrated strain gage measurements.

Aerodynamic Loads↗
DOE OSTI2024

CFD simulations of Molten Salt Fast Reactor core cavity flows

Computational Fluid Dynamics (CFD) has become increasingly important in the research and development of advanced nuclear reactors. Here, in the current study, extensive CFD simulations were conducted for the coolant flow in Molten Salt Fast Reactor (MSFR) core models using the state-of-the-art spectral element flow solver Nek5000 and multiscale coarse-mesh thermal-hydraulic software Pronghorn. The underlying motivation is to seek an in-depth understanding of how the internal velocity distribution can be influenced by the MSFR core cavity shape, the Reynolds number, turbulence modeling options and the inlet boundary conditions. The CFD techniques involved in this investigation range from coarse-mesh CFD, RANS modeling, to the high-fidelity LES calculations. Specifically, a series of RANS simulations were performed for the 2-D axisymmetric core model and 3-D wedge domains to study the flow distribution inside the MSFR core. It is observed that a proper representation of the MSFR inlet channel duct is important for the prediction of internal flow distribution. It is also showcased here how researchers can leverage the Nek5000 CFD results to calibrate more efficient coarse-mesh CFD tools, like Pronghorn, for the actual MSFR design needs. Moreover, this paper highlights a 3-D LES model for an entire MSFR core using the spectral element method and demonstrates the feasibility of this modeling approach. The readiness and potential limitations of the RANS approach are examined with respect to the high-fidelity LES simulations. The present investigation lays a solid foundation as we are leveraging the high-fidelity CFD capabilities to inform MSFR design efforts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Evaluation of Models and Measurements to Estimate Solar Radiation for 1-Axis Tracking Modules at NREL’s SRRL

Solar radiation reaching photovoltaic (PV) modules on a 1-axis tracking system can be measured by reference cells or thermopiles. The former is often biased due to the reflection of solar radiation by the glass cover of the PV. The uncertainty can be moderated by applying a correction factor, as a function of solar incident angle and the refractive index of the glass, to the measurement. On the other hand, solar radiation on the inclined PV panels can be computed by transposition models using global horizontal irradiance (GHI) observations from thermopiles. This study examines the models and measurements to estimate solar radiation for 1-axis tracking modules at the National Renewable Energy Laboratory’s (NREL’s) Solar Radiation Research Laboratory (SRRL). The 1-minute plane-of-array (POA) irradiances from 2019 are computed using the observed GHIs and a transposition model developed by Perez et al. The POA irradiances are compared with the observation by an IMT reference cell and a Kipp & Zonen CM Pyranometer 22 (CMP22) thermopile. For the SRRL’s 1-axis tracking system with an annual solar energy of 2323.9 kWh/m 2 , the POA irradiance is overestimated by ~70 kWh/m 2 using the transposition model. This bias is reduced by more than 50% using the IMT measurements calibrated by a correction factor for a PV surface of antireflection coated glass.

POA irradiance↗
DOE OSTI2025

Implementation of disruptive designs for gas turbine components using direct energy deposition additive manufacturing

This research aims to develop a framework for establishing the correlation between in-situ monitoring data, process parameters, and microstructure evolution in blown-powder laser-directed energy deposition (DED) additive manufacturing (AM). To achieve this, a comprehensive manufacturing framework has been developed, spanning from in-situ data acquisition, melt-pool simulation, microstructure modeling, and statistical microstructure quantification. A machine learning-based surrogate model is constructed to predict melt pool geometry directly from in-situ coaxial camera data. The surrogate model is trained using outputs from a high-fidelity melt pool simulation, which provides accurate melt pool dimension data under varying process conditions. The predicted melt pool geometry is then used as input to a microstructure model to predict microstructural features. To rigorously compare and analyze microstructures, the project introduces statistical metrics that quantify differences based on key features such as morphology and texture. Microstructures are represented using advanced statistical descriptors including angular chord length distribution, two-point spatial statistics, orientation distribution function, and global spherical harmonic. These representations are used to compute four distinct “dissimilarity scores” that quantitatively capture differences in texture and morphology. This framework is demonstrated to enable automated calibration of simulation parameters by minimizing discrepancies between simulated and target microstructures. The technology developed in this project enables direct correlation between in-situ monitoring data and resulting microstructure, paving the way for adaptive microstructure control in metal AM. This capability strengthens the connection between process parameters and final material properties, facilitating more precise and reliable material design.

36 MATERIALS SCIENCE↗
DOE OSTI2021

General Relativistic Neutrino-driven Turbulence in One-dimensional Core-collapse Supernovae

Abstract Convection and turbulence in core-collapse supernovae (CCSNe) are inherently three-dimensional (3D) in nature. However, 3D simulations of CCSNe are computationally demanding. Thus, it is valuable to modify simulations in spherical symmetry to incorporate 3D effects using some parametric model. In this paper, we report on the formulation and implementation of general relativistic neutrino-driven turbulent convection in the spherically symmetric core-collapse supernova code GR1D . This is based upon the recently proposed method of Simulated Turbulence in Reduced Dimensionality (STIR) in Newtonian simulations from Couch et al. (2020). When the parameters of this model are calibrated to 3D simulations, we find that our general relativistic formulation of STIR requires larger turbulent eddies to achieve a shock evolution similar to the original STIR model. We also find that general relativity may alter the correspondence between progenitor mass and successful versus failed explosions.

Astronomy & Astrophysics↗
NASA NTRS1982

Applicaton of radiative transfer theory to microwave transmission medium calibrations

Precise determinations of the transmission medium loss and noise temperature contribution which are important to the performance characterization of low noise microwave receiving systems and thermal noise standards are discussed. Tropospheric loss is frequently inferred from microwave radiometer noise temperature measurements. Interpretation of these measurements requires an inversion of the radiative transfer integral equation. This is inconvenient even with computer techniques. Solutions of a rapidly convergent power series of the radiative transfer equations are presented. This solution is applicable to a low loss medium with either uniform or nonuniform loss distributions. A four layer atmosphere model is investigated to demonstrate the accuracy of the solution relative to the model. Applications include thermal noise standards and single- and dual-frequency water radiometers.

Stelzried, C. T.↗
NASA NTRS1989

Gravity field determination and error assessment techniques

Linear estimation theory, along with a new technique to compute relative data weights, was applied to the determination of the Earth's geopotential field and other geophysical model parameters using a combination of satellite ground-based tracking data, satellite altimetry data, and the surface gravimetry data. The relative data weights for the inhomogeneous data sets are estimated simultaneously with the gravity field and other geophysical and orbit parameters in a least squares approach to produce the University of Texas gravity field models. New techniques to perform calibration of the formal covariance matrix for the geopotential solution were developed to obtain a reliable gravity field error estimate. Different techniques, which include orbit residual analysis, surface gravity anomaly residual analysis, subset gravity solution comparisons and consider covariance analysis, were applied to investigate the reliability of the calibration.

Yuan, D. N.↗
NASA NTRS2019

A Multidisciplinary Modeling Approach of Plant Gas Exchange in Reduced Gravity Environments

In-situ food production is a necessary step for human exploration of the solar system and requires a deep understanding of plant growth in reduced gravity environments. In particular, the lack of buoyancy-driven convection changes the gas exchange at the leaf surface, which decreases photosynthesis and transpiration rates, and ultimately biomass production. To understand the intricate relations between physical, chemical, and biochemical processes, the following methodology combines the development of a mechanistic model of plant growth in reduced gravity environments, computational fluid dynamics (CFD) simulations, and experiments in different time frames.The model presented here is a coupled mass and energy balance using the single round leaf assumption, including gravity as an entry parameter, and the leaf surface temperature as an output variable. Measures of the leaf surface temperature using infra-red cameras allow for a computation of the transpiration rate. This approach was followed to design a parabolic flight experiment, which performed 7 flights, and enabled data collection for model validation in different gravity and ventilation settings on a short time frame. Current measures of carbon assimilation and transpiration rate at the leaf and canopy level using an infra-red gas analyzer (Li-6800) in 1g lab conditions on several species will enable a validation on longer time frames and further calibration of the model. CFD studies both on the parabolic flight and on the lab experimental set-up allow the precise assessment of ventilation above the canopy and plants' leaves.Ultimately, this work will provide recommendations for the design of future plant growth hardware, especially on the lowest adequate ventilation for optimal plant growth in reduced gravity environments, as well as assessing biomass and oxygen production rates on planetary surfaces and space stations. This work was funded by CNES, CNRS, Clermont Auvergne Metropole, and NASA Space Biology through NASA postdoctoral program / USRA.

Poulet, Lucie↗
DOE OSTI2023

Core Model Proposal #373: Add Exogenous Shutdown Decider

This core model proposal adds an exogenous shutdown decider that can scale the output of a vintaged technology in years after its initial operating period. The output can be scaled above or below the technology vintage's original output. This proposal also includes code changes to disable the behavior of the s-curve shutdown decider and profit shutdown decider in calibration periods. The proposal does not include any changes to data to utilize the new exogenous shutdown decider (it only introduces the capability). It should be noted that the exogenous shutdown decider and s-curve shutdown decider serve similar functions and generally should not be used together; however, the exogenous shutdown decider and profit shutdown decider reflect different dynamics and can be used in combination.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Bridging the gap between experiments and simulations using machine learning

The physics of inertial confinement fusion is rich and complex. Simulation codes that are used to design experiments are computationally expensive and lack the predictive capability required for extensive parameter exploration in search of a high-performing design for laser direct drive. In this work we use deep learning to build a fast emulator of experiments. To facilitate the development of the deep-learning model, an autoencoder is used to reduce the dimensionality of the input space. Two deep learning models are developed. One model is trained on a vast array of simulation data and is subsequently calibrated to expensive and limited experimental data using a technique known as “transfer learning.” The other model is trained on a statistical model and is subsequently calibrated using experimental data. A comparative study of the two predictive models is carried out. The models potentially reproduce key experimental observables with high accuracy and unprecedented inference times relative to those achieved with simulation codes. These models facilitate rapid exploration of a high dimensional input parameter space.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRSDate not supplied

Progress on Quantitative Infrared Thermography at the NASA Langley Aerothermodynamic Laboratory

A quantitative infrared thermography technique is being developed for accurate and high-resolution measurements of surface temperature and heat flux in the hypersonic wind tunnels at NASA Langley Research Center. A hemisphere test campaign was carried out in the Langley Aerothermodynamic Laboratory 20-Inch Mach 6 Air Tunnel to assess the current performance of the technique and to identify remaining challenges. Measurements were obtained for three different model materials over a range of freestream Reynolds numbers. The raw infrared images were converted to temperature via a radiometric calibration of the camera sensor. Corrections were made for losses due to test article emissivity and window transmissivity. A preliminary uncertainty analysis yielded an estimated accuracy within 4 K at moderate viewing angles, similar to that of a standard thermocouple. Surface heat flux was obtained from the measured surface temperatures via a finite-difference solution of the one-dimensional heat equation that modeled thermal variation of the material properties. Comparisons of the measured heating to both computational and theoretical predictions demonstrated agreement within the expected uncertainty of the technique. The heat transfer measurement near the stagnation point was found to be highly sensitive to the test article emissivity, which establishes the need for improved characterization of the wind tunnel model materials for high temperature applications.

infrared thermography↗
NASA NTRS1994

Pre- and postperihelon abundances of gas and dust in comet Halley

Photometrically calibrated spectra of comet P/Halley (1986 III) were recorded between 1985 September 12-1986 June 10 using the Ohio State University Image Dissector Scanner on the Perkins 72 inch telescope at the Lowell Observatory. Column densities of CN, C3, CH, C2, and NH2 were calculated from measured fluxes in these spectra, and molecular scale lengths were deduced from the radial distribution of CN, C3, C2, and NH2. Production rates were computed using the new scale lengths and a Haser model analysis. Continuum emission at 4260 A was used to derive gas-to-dust ratios. The data indicate than comet Halley was approximately 2-5 times more abundant in gas and dust at postperihelion than preperihelion. On 1986 June 8 we observed the onset of a cometary ourburst which appeared very strong in dust production. The gas-to-dust ratios appeared to be subject to changes as a result of short-term outbursts but otherwise did not exhibit any systematic dependence on heliocentric distance. Reflectivity gradients of the continuum were also measured from the spectra. While most of the continua were red, blue continua were also observed which may be correlated with dust outbursts.

Womack, Maria↗
DOE OSTI2020

Progress on the National Solar Radiation Data Base (NSRDB): A new DNI computation

This study introduces a new technique to compute direct normal irradiance (DNI) for improving the National Solar Radiation Data Base (NSRDB). A finite-surface integration algorithm is developed to compute solar radiation in differential solid angles and efficiently infer its contribution to a surface perpendicular to the solar direction. A lookup table of cloud bi-directional transmittance distribution function (BTDF) is developed by use of the discrete ordinates radiative transfer (DISORT) model for possible solar and observing directions and various cloud optical and microphysical properties. In each solar incident direction, DNI is given by the cloud BTDFs from approximately 200 differential solid angles. The simulated DNI is calibrated and evaluated using surface observations at the National Renewable Energy Laboratory's (NREL's) Solar Energy Research Laboratory (SRRL) and the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) facility.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Inverse Methods - Users Manual 5.6

The inverse methods team provides a set of tools for solving inverse problems in structural dynamics and thermal physics, and also sensor placement optimization via Optimal Experimental Design (OED). These methods are used for designing experiments, model calibration, and verfication/validation analysis of weapons systems. This document provides a user's guide to the input for the three apps that are supported for these methods. Details of input specifications, output options, and optimization parameters are included.

42 ENGINEERING↗
DOE OSTI2022

Comprehensive compartmental model and calibration algorithm for the study of clinical implications of the population-level spread of COVID-19: a study protocol

The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions. We propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model's predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A consistent dataset for the net income distribution for 190 countries and aggregated to 32 geographical regions from 1958 to 2015

Abstract. Data on income distributions within and across countries are becoming increasingly important for informing analysis of income inequality and understanding the distributional consequences of climate change. While datasets on income distribution collected from household surveys are available for multiple countries, these datasets often do not represent the same concept of inequality (or income concept) and therefore make comparisons across countries, over time and across datasets difficult. Here, we present a consistent dataset of income distributions across 190 countries from 1958 to 2015 measured in terms of net income. We complement the observed values in this dataset with values imputed from a summary measure of the income distribution, specifically the Gini coefficient. For the imputation, we use a recently developed nonparametric principal-component-based approach that shows an excellent fit to data on income distributions compared to other approaches. We also present another version of this dataset aggregated from the country level to 32 geographical regions. Our dataset is developed for the purpose of calibrating models such as integrated human–Earth system models with detailed data on income distributions. This dataset will enable more robust analysis of income distribution at multiple scales. The latest version of our data are available on Zenodo: https://doi.org/10.5281/zenodo.7093997 (Narayan et al., 2022b).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Model certainty in cellular network-driven processes with missing data

Mathematical models are often used to explore network-driven cellular processes from a systems perspective. However, a dearth of quantitative data suitable for model calibration leads to models with parameter unidentifiability and questionable predictive power. Here we introduce a combined Bayesian and Machine Learning Measurement Model approach to explore how quantitative and non-quantitative data constrain models of apoptosis execution within a missing data context. We find model prediction accuracy and certainty strongly depend on rigorous data-driven formulations of the measurement, and the size and make-up of the datasets. For instance, two orders of magnitude more ordinal (e.g., immunoblot) data are necessary to achieve accuracy comparable to quantitative (e.g., fluorescence) data for calibration of an apoptosis execution model. Notably, ordinal and nominal (e.g., cell fate observations) non-quantitative data synergize to reduce model uncertainty and improve accuracy. Finally, we demonstrate the potential of a data-driven Measurement Model approach to identify model features that could lead to informative experimental measurements and improve model predictive power.

59 BASIC BIOLOGICAL SCIENCES↗