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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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At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Kinetic model for quartz and spinel dissolution during melting of high-level-waste glass batch

The dissolution of quartz particles and the growth and dissolution of crystalline phases during the conversion of batch to glass potentially affects both the glass melting process and product quality. Crystals of spinel exiting the cold cap to molten glass below can be troublesome during the vitrification of iron-containing high-level wastes. To estimate the distribution of quartz and spinel fractions within the cold cap, we used kinetic models that relate fractions of these phases to temperature and heating rate. Fitting the model equations to data showed that the heating rate, apart from affecting quartz and spinel behavior directly, also affects them indirectly via concurrent processes, such as the formation and motion of bubbles. Because of these indirect effects, it was necessary to allow one kinetic parameter (the pre-exponential factor) to vary with the heating rate. The resulting kinetic equations are sufficiently simple for the detailed modeling of batch-to-glass conversion as it occurs in glass melters. The estimated fractions and sizes of quartz and spinel particles as they leave the cold cap, determined in this study, will provide the source terms needed for modeling the behavior of these solid particles within the flow of molten glass in the melter.

Batch melting, Cold cap, Quartz, Spinel, Waste gla↗
DOE OSTI2022

Automatic Calibration of a Geomechanical Model from Sparse Data for Estimating Stress in Deep Geological Formations

Summary In this study, we demonstrate geomechanical modeling with fully automatic parameter calibration to estimate the full geomechanical stress fields of a prospective US carbon dioxide (CO2) storage site, based on sparse measurement data. The goal is to compute full stress tensor field estimates (principal stresses and orientations) that are maximally compatible with observations within the constraints of the model assumptions, thereby extending pointwise, incomplete partial stress measurement to a simulated full formation stress field, as well as a rough assessment of the associated error. We use the Perch site, located in Otsego County, Michigan, USA, as our case study. The input data consist of partial stress tensor information inferred from in-situ borehole tests, geophysical well logs, and processing of seismic data. A static earth model (SEM) of the site was developed, and geomechanical simulation functionality of the open-source MATLAB Reservoir Simulation Toolbox (MRST) was used to model the stress field. Adjoint-based nonlinear optimization was used to adjust boundary conditions and material properties to calibrate simulated results of observations. Results were interpreted through a Bayesian framework. The focus of this paper is to demonstrate how the fully automatic calibration procedure works and discuss the results obtained; it does not attempt a detailed analysis of the stress field in the context of the proposed CO2 storage initiatives. Our work is part of a larger effort to noninvasively determine in-situ stresses in deep formations considered for CO2 storage. Guided by previously published research on geomechanical model calibration, our work presents a novel calibration approach supporting a potentially large number of linear or nonlinear calibration parameters to produce results optimally agreeing with available measurements and thus extend partial pointwise estimates to full tensor fields compatible with the physics of the site.

Engineering↗
DOE OSTI2014

Surface daytime net radiation estimation using artificial neural networks

Net all-wave surface radiation (R n ) is one of the most important fundamental parameters in various applications. However, conventional R n measurements are difficult to collect because of the high cost and ongoing maintenance of recording instruments. Therefore, various empirical R n estimation models have been developed. This study presents the results of two artificial neural network (ANN) models (general regression neural networks (GRNN) and Neuroet) to estimate R n globally from multi-source data, including remotely sensed products, surface measurements, and meteorological reanalysis products. R n estimates provided by the two ANNs were tested against in-situ radiation measurements obtained from 251 global sites between 1991–2010 both in global mode (all data were used to fit the models) and in conditional mode (the data were divided into four subsets and the models were fitted separately). Based on the results obtained from extensive experiments, it has been proved that the two ANNs were superior to linear-based empirical models in both global and conditional modes and that the GRNN performed better and was more stable than Neuroet. The GRNN estimates had a determination coefficient (R 2 ) of 0.92, a root mean square error (RMSE) of 34.27 W·m –2 , and a bias of –0.61 W·m –2 in global mode based on the validation dataset. In conclusion, ANN methods are a potentially powerful tool for global R n estimation.

14 SOLAR ENERGY↗
DOE OSTI2025

A machine learning framework for accurate and robust analysis of radiation detector pulses

The microscopic properties of atomic nuclei are used to study various scientific questions. They are essential for understanding the fundamental forces of nature and the chemical evolution of the universe. Detecting decay radiation from radioactive nuclei makes it possible to probe these fundamental nuclear properties. Detector waveform traces may contain additional information about the radiation. Generally, advanced signal processing techniques are needed to extract this additional information, often involving fitting the waveform with model response functions using non-linear least-squares optimization with second-order gradient methods. While this is a powerful technique, it is also computationally expensive, leading to slow processing time, which scales with the volume of data. To address this problem, we have developed a machine learning (ML) approach that infers the characteristics of traces from a model detector response function. In particular, we are interested in classifying whether a single recorded trace consists of one or two pulse constituents and estimating the pulse parameters. Furthermore, our proposed ML method can precisely extract the pulses’ parameters, such as energy and timing information, and accurately classify the pulse multiplicity of a trace. Unlike non-learning-based approaches, our ML approach uses neural networks that are significantly faster at inference, as they do not require any optimization during this stage.

Curve fitting↗
DOE OSTI2016

SU-G-JeP3-04: Estimating 4D CBCT from Prior Information and Extremely Limited Angle Projections Using Structural PCA and Weighted Free-Form Deformation

Purpose: To investigate the feasibility of using structure-based principal component analysis (PCA) motion-modeling and weighted free-form deformation to estimate on-board 4D-CBCT using prior information and extremely limited angle projections for potential 4D target verification of lung radiotherapy. Methods: A technique for lung 4D-CBCT reconstruction has been previously developed using a deformation field map (DFM)-based strategy. In the previous method, each phase of the 4D-CBCT was generated by deforming a prior CT volume. The DFM was solved by a motion-model extracted by global PCA and a free-form deformation (GMM-FD) technique, using data fidelity constraint and the deformation energy minimization. In this study, a new structural-PCA method was developed to build a structural motion-model (SMM) by accounting for potential relative motion pattern changes between different anatomical structures from simulation to treatment. The motion model extracted from planning 4DCT was divided into two structures: tumor and body excluding tumor, and the parameters of both structures were optimized together. Weighted free-form deformation (WFD) was employed afterwards to introduce flexibility in adjusting the weightings of different structures in the data fidelity constraint based on clinical interests. XCAT (computerized patient model) simulation with a 30 mm diameter lesion was simulated with various anatomical and respirational changes from planning 4D-CT to onboard volume. The estimation accuracy was evaluated by the Volume-Percent-Difference (VPD)/Center-of-Mass-Shift (COMS) between lesions in the estimated and “ground-truth” on board 4D-CBCT. Results: Among 6 different XCAT scenarios corresponding to respirational and anatomical changes from planning CT to on-board using single 30° on-board projections, the VPD/COMS for SMM-WFD was reduced to 10.64±3.04%/1.20±0.45mm from 21.72±9.24%/1.80±0.53mm for GMM-FD. Using 15° orthogonal projections, the VPD/COMS was further reduced to 1.91±0.86%/0.31±0.42mm based on SMM-WFD. Conclusion: Compared to GMM-FD technique, the SMM-WFD technique can substantially improve the 4D-CBCT estimation accuracy using extremely small scan angles to provide ultra-fast 4D verification. This work was supported by the National Institutes of Health under Grant No. R01-CA184173 and a research grant from Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Linear relationships for modeling CO 2 absorption in aqueous alkanolamine solutions in a thermodynamically consistent way

A thermodynamically consistent model for the carbon dioxide (CO 2 ) absorption in aqueous alkanolamine system is of great importance in the research and development of a CO 2 capture process. To facilitate the development of thermodynamic models, linear Gibbs free energy, enthalpy, and heat capacity relationships using well-known amines as reference are used to correlate the standard reference state properties of ionic species with those of molecular species in the electrolyte system, which has been approved to provide a reliable and consistent way to estimate required parameters when there is minimal or no appropriate experimental data available. Here, the proposed relationships have been applied to the development of an electrolyte nonrandom two liquid (NRTL) activity coefficient model for CO 2 absorption in aqueous 1-amino-2-propanol (A2P) solution, as an example to demonstration the methodology. With limited vapor–liquid equilibrium data and other thermodynamic properties, the parameters in the electrolyte NRTL model are identified with good accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Modeling coupled sorption and transformation of 17β-estradiol–17-sulfate in soil–water systems

Animal manure is the primary source of exogenous free estrogens in the environment, which are known endocrine-disrupting chemicals to disorder the reproduction system of organisms. Conjugated estrogens can act as precursors to free estrogens, which may increase the total estrogenicity in the environment. In this study, a comprehensive model was used to simultaneously simulate the coupled sorption and transformation of a sulfate estrogen conjugate, 17 beta-estradiol-17-sulfate (E2-17S), in various soil-water systems (non-sterile/sterile; topsoil/subsoil). The simulated processes included multiple transformation pathways (i.e. hydroxylation, hydrolysis, and oxidation) and mass transfer between the aqueous, reversibly sorbed, and irreversibly sorbed phases of all soils for E2-17S and its metabolites. The conceptual model was conceived based on a series of linear sorption and first-order transformation expressions. The model was inversely solved Using finite difference to estimate process parameters. A global optimization method was applied for the inverse analysis along with variable model restrictions to estimate 36 parameters. The model provided a satisfactory simultaneous fit (R-adj(2) = 0.93 and d = 0.87) of all the experimental data and reliable parameter estimates. This modeling study improved the understanding on fate and transport of estrogen conjugates under various soil-water conditions.

17β-Estradiol–17-sulfate↗
DOE OSTI2016

Short-Term Distribution System State Forecast Based on Optimal Synchrophasor Sensor Placement and Extreme Learning Machine

This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster. Specifically, considering the sensor installation cost and measurement error, an OSSP algorithm is proposed to reduce the number of synchrophasor sensor and keep the whole distribution system numerically and topologically observable. Then, the weighted least square (WLS) based system state estimator is used to produce the training data for the proposed forecaster. Traditionally, the artificial neural network (ANN) and support vector regression (SVR) are widely used in forecasting due to their nonlinear modeling capabilities. However, the ANN contains heavy computation load and the best parameters for SVR are difficult to obtain. In this paper, the ELM, which overcomes these drawbacks, is used to forecast the future system states with the historical system states. The proposed approach is effective and accurate based on the testing results.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Identification Uncertainty in Inverse Material Model Parameter Determination: A Sensitivity‐Based Decision Process for Load Path Selection

This research proposes a sensitivity-based framework for selecting the optimal prescribed loading path for a biaxial cruciform specimen. Optimality here is determined by the direction and magnitude of the prescribed displacement that minimizes the influence of random noise on the material model parameter identification. Using simulated experimental data based on finite element simulation, in this work, we identify the material model parameters of a Ludwik hardening model and plane stress implementation of the Hill-48 yield criterion using finite element model updating (FEMU). Our analysis reveals that the identification (or estimator) uncertainty of model parameters depends on the displacement boundary conditions (i.e., loading sequence) and the ground-truth value of the individual parameters. Optimal experimental design (OED) criteria based on the Fisher information matrix were investigated to mitigate indecision in the choice of optimal load path when the identification uncertainty of different material model parameters optimized at different load paths. The determinant of the Fisher information matrix was chosen here as the more useful metric due to its ability to capture uncertainty of the most influential material model parameters. The proposed framework demonstrates potential for real-time automated load step selection using scalar criteria derived prior to mechanical loading. The framework can be generalized to other geometries, boundary conditions and material models, allowing this procedure to be utilized for different experimental configurations and materials.

Fayad, Samuel S. [University of Illinois at Urbana↗
DOE OSTI2014

Implications of improved Higgs mass calculations for supersymmetric models

We discuss the allowed parameter spaces of supersymmetric scenarios in light of improved Higgs mass predictions provided by FeynHiggs 2.10.0. The Higgs mass predictions combine Feynman-diagrammatic results with a resummation of leading and subleading logarithmic corrections from the stop/top sector, which yield a significant improvement in the region of large stop masses. Scans in the pMSSM parameter space show that, for given values of the soft supersymmetry-breaking parameters, the new logarithmic contributions beyond the two-loop order implemented in FeynHiggs tend to give larger values of the light CP-even Higgs mass, M h , in the region of large stop masses than previous predictions that were based on a fixedorder Feynman-diagrammatic result, though the differences are generally consistent with the previous estimates of theoretical uncertainties. We re-analyse the parameter spaces of the CMSSM, NUHM1 and NUHM2, taking into account also the constraints from CMS and LHCb measurements of BR(B s → μ + μ - )and ATLAS searches forE/T events using 20/fb of LHC data at 8 TeV. Within the CMSSM, the Higgs mass constraint disfavours tan β 10, though not in the NUHM1 or NUHM2.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Far-field thermal imaging below diffraction limit

Non-uniform self-heating and temperature hotspots are major concerns compromising the performance and reliability of submicron electronic and optoelectronic devices. At deep submicron scales where effects such as contact-related artifacts and diffraction limits accurate measurements of temperature hotspots, non-contact thermal characterization can be extremely valuable. In this work, we use a Bayesian optimization framework with generalized Gaussian Markov random field (GGMRF) prior model to obtain accurate full-field temperature distribution of self-heated metal interconnects from their thermoreflectance thermal images (TRI) with spatial resolution 2.5 times below Rayleigh limit for 530nm illumination. Finite element simulations along with TRI experimental data were used to characterize the point spread function of the optical imaging system. In addition, unlike iterative reconstruction algorithms that use ad hoc regularization parameters in their prior models to obtain the best quality image, we used numerical experiments and finite element modeling to estimate the regularization parameter for solving a real experimental inverse problem.

42 ENGINEERING↗
DOE OSTI2021

Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study

Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d -1 (95% CI: 1.06 to 1.27 d -1 ), 0.777 d -1 (0.716 to 0.838 d -1 ), and 0.450 d -1 (0.378 to 0.522 d -1 ) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Role of copper in nitrous oxide accumulation in wetland soils from Illinois and South Carolina and stream sediments from Tennessee

Denitrification is microbially-mediated through enzymes containing metal cofactors. Laboratory studies of pure cultures have highlighted that the availability of copper (Cu), required for the multicopper enzyme nitrous oxide reductase, can limit nitrous oxide (N2O) reduction. However, in natural aquatic systems, such as wetlands and hyporheic zones in stream beds, the role of Cu in controlling denitrification remains incompletely understood. In this study, we collected soils and sediments from three natural environments -- riparian wetlands, marsh wetlands, and a stream -- to investigate their nitrogen species transformation activity at background Cu levels and different supplemented Cu loadings. All of the systems contained solid-phase associated Cu below or around geological levels (40–280 nmol g-1) and exhibited low dissolved Cu (3–50 nM), which made them appropriate sites for evaluating the effect of limited Cu availability on denitrification.The dataset contains the variation in the nutrient (nitrate (NO3-), nitrite (NO2-), and ammonium (NH4+)) and N2O concentrations during the incubation experiments to evaluate the effect of Cu. We have also reported the variation in metal concentrations (Cu, iron(Fe) and manganese (Mn)) and dissolved organic carbon (DOC) concentrations during the incubations. The parameters obtained using a kinetic model to quantitatively report the effect of Cu on nitrogen species conversion in natural aquatic systems are also added in the dataset. The dataset also contains the labile concentration of Cu estimated using a speciation model in MINTEQ. All data are provided in text-based CSV format with header sections indicating the data contained in each file and the corresponding units.Our study suggests that high concentrations of N2O accumulated in all microcosms lacking Cu amendment except for one stream sediment sample. With Cu added to provide dissolved concentrations at trace levels (10–300 nM), the reduction rate of N2O to N2 in the wetland soils and stream sediments was enhanced. A kinetic model could account for the trends in nitrogen species by combining the reactions for microbial reduction of NO3- to NO2-/N2O/N2 and abiotic reduction of NO2 to nitrogen (N2_. The model revealed that the rate of N2O to N2 conversion increased significantly in the presence of Cu. For riparian wetland soils and stream sediments, the kinetic model also suggested that overall denitrification is driven by abiotic reduction of NO2- in the presence of inorganic electron donors. This study demonstrated that natural aquatic systems containing Cu at concentrations less than or equal to crustal abundances may display incomplete reduction of N2O to N2 that would cause N2O accumulation and release to the atmosphere.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

TRIPOLI-4 criticality calculations for MOX fuelled SNEAK 7A and 7B fast critical assemblies

A prototype Generation IV fast neutron reactor is under design and development in France. The MOX fuel will be introduced into this self-generating core in order to demonstrate low net plutonium production. To support the TRIPOLI-4 Monte Carlo transport code in criticality calculations of fast reactors, the effective delayed neutron fraction {beta}eff estimation and the Probability Tables (PT) option to treat the unresolved resonance region of cross-sections are two essentials. In this study, TRIPOLI-4 calculations have been made using current nuclear data libraries JEFF-3.1.1 and ENDF/B-VII.0 to benchmark the reactor physics parameters of the MOX fuelled SNEAK 7A and 7B fast critical assemblies. TRIPOLI-4 calculated K{sub eff} and {beta}eff of the homogeneous R-Z models and the 3D multi-cell models have been validated against the measured ones. The impact of the PT option on K{sub eff} is 340 {+-} 10 pcm for SNEAK 7A core and 410 {+-} 12 pcm for 7B. Four-group spectra and energy spectral indices, f8/f5, f9/f5, and c8/f5 in the two SNEAK cores have also been calculated with the TRIPOLI-4 mesh tally. Calculated spectrum-hardening index f8/f5 is 0.0418 for SNEAK 7A and 0.0315 for 7B. From this study the SNEAK 3D models have been verified for the next revision of IRPhE (International Handbook of Evaluated Reactor Physics Benchmark Experiments). (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Dark Energy Survey Year 3 results: calibration of lens sample redshift distributions using clustering redshifts with BOSS/eBOSS

ABSTRACT We present clustering redshift measurements for Dark Energy Survey (DES) lens sample galaxies used in weak gravitational lensing and galaxy clustering studies. To perform these measurements, we cross-correlate with spectroscopic galaxies from the Baryon Acoustic Oscillation Survey (BOSS) and its extension, eBOSS. We validate our methodology in simulations, including a new technique to calibrate systematic errors that result from the galaxy clustering bias, and we find that our method is generally unbiased in calibrating the mean redshift. We apply our method to the data, and estimate the redshift distribution for 11 different photometrically selected bins. We find general agreement between clustering redshift and photometric redshift estimates, with differences on the inferred mean redshift found to be below |Δz| = 0.01 in most of the bins. We also test a method to calibrate a width parameter for redshift distributions, which we found necessary to use for some of our samples. Our typical uncertainties on the mean redshift ranged from 0.003 to 0.008, while our uncertainties on the width ranged from 4 to 9 per cent. We discuss how these results calibrate the photometric redshift distributions used in companion papers for DES Year 3 results.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Petroleum Refinery Jobs and Economic Development Impact (JEDI) Model User Reference Guide

The Jobs and Economic Development Impact (JEDI) models, developed through the National Renewable Energy Laboratory (NREL), are user-friendly tools utilized to estimate the economic impacts at the local level of constructing and operating fuel and power generation projects for a range of conventional and renewable energy technologies. The JEDI Petroleum Refinery Model User Reference Guide was developed to assist users in employing and understanding the model. This guide provides information on the model's underlying methodology, as well as the parameters and references used to develop the cost data utilized in the model. This guide also provides basic instruction on model add-in features, operation of the model, and a discussion of how the results should be interpreted. Based on project-specific inputs from the user, the model estimates job creation, earning and output (total economic activity) for a given petroleum refinery. This includes the direct, indirect and induced economic impacts to the local economy associated with the refinery's construction and operation phases. Project cost and job data used in the model are derived from the most current cost estimations available. Local direct and indirect economic impacts are estimated using economic multipliers derived from IMPLAN software. By determining the regional economic impacts and job creation for a proposed refinery, the JEDI Petroleum Refinery model can be used to field questions about the added value refineries may bring to the local community.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

An Efficient Bayesian Method for Advancing the Application of Deep Learning in Earth Science

Despite their wide and successful applications, deep learning (DL) models are prone to overfitting for small training datasets, produce a poor predictive performance for uncertain data, and provide point estimations without any indication of the accuracy and credibility. These limitations of the deterministic DL models hinder their effective application in Earth system science where the labelled data are sparse, noisy and incomplete with large uncertainty and where the predictive uncertainty quantification is needed for scientific understanding and policy decision making. Integration of Bayesian inference into DL models adds an estimate of uncertainty and regularization in the predictions. However, traditional Bayesian methods are computationally unaffordable and inflexible for high-dimensional problems, which limits their application to DL systems that typically have millions of model parameters. In this effort, we propose an efficient and general-purpose Bayesian inference method to advance DL model optimization and uncertainty quantification, so as to facilitate the adoption of DL in Earth sciences. In a demonstration, we integrate the proposed Bayesian method with a feedforward neural network (NN) to build a fast-to-evaluate surrogate of the complex Energy Exascale Earth System Land Model for efficient modeling. The formulated Bayesian NN, using a small number of training data, produces an accurate prediction with high credibility, whereas with the same small training size, the deterministic NN cannot yield a reasonable estimation and does not provide confidence information. The proposed Bayesian method is computationally efficient and flexible, capable of integration with diverse network variants such as convolutional NNs and recurrent NNs to advance the application of DL in Earth sciences.

Lu, Dan↗
DOE OSTI2025

Simplified Model and Approach to Transform Infrared Surface Temperature to Film Effectiveness in a Conjugate Heat Transfer Experiment

In the pursuit of more efficient gas turbines, film cooling is a critical technology. This article describes a simplified engineering model based on a one-dimensional thermal resistance network. The model is used to relate film-cooling effectiveness and heat transfer augmentation to local overall cooling effectiveness in a conjugate flat plate experiment. Here, this article presents experimental proof-of-concept data to demonstrate the potential for this model. In contrast to previous approaches, neither the wall heat flux nor the adiabatic wall temperature is required to estimate the local film-cooling performance parameters. The model predicts surface temperatures that are within the experimental uncertainties over the range for which the model is trained and to within five percent when the model is extrapolated to higher coolant channel Reynolds numbers. This article is relevant to conjugate test rigs that can measure the hot-surface temperature distribution with and without film cooling. This information may also be relevant to designers as a method to approximate surface temperatures or used as an approximate heat transfer model for optimization studies.

advanced turbines↗