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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 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Oscillation Amplitude Growth for a Decelerating Object with Constant Pitch Damping

The equations governing the deceleration and oscillation of a blunt body moving along a planar trajectory are re-expressed in the form of the Euler-Cauchy equation. An analytic solution of this equation describes the oscillation amplitude growth and frequency dilation with time for a statically stable decelerating body with constant pitch damping. The oscillation histories for several constant pitch damping values, predicted by the solution of the Euler-Cauchy equation are compared to POST six degree-of-freedom (6-DoF) trajectory simulations. The simulations use simplified aerodynamic coefficients matching the Euler-Cauchy approximations. Agreement between the model predictions and simulation results are excellent. Euler-Cauchy curves are also fit through nonlinear 6-DoF simulations and ballistic range data to identify static stability and pitch damping coefficients. The model os shown to closely fit through the data points and capture the behavior of the blunt body observed in simulation and experiment. The extracted coefficients are in reasonable agreement with higher fidelity, nonlinear parameter identification results. Finally, a nondimensional version of the Euler-Cauchy equation is presented and shown to be a simple and effective tool for designing dynamically scaled experiments for decelerating blunt capsule flight.

Schoenenberger, Mark↗
NASA NTRS2017

The Flight Optimization System Weights Estimation Method

FLOPS has been the primary aircraft synthesis software used by the Aeronautics Systems Analysis Branch at NASA Langley Research Center. It was created for rapid conceptual aircraft design and advanced technology impact assessments. FLOPS is a single computer program that includes weights estimation, aerodynamics estimation, engine cycle analysis, propulsion data scaling and interpolation, detailed mission performance analysis, takeoff and landing performance analysis, noise footprint estimation, and cost analysis. It is well known as a baseline and common denominator for aircraft design studies. FLOPS is capable of calibrating a model to known aircraft data, making it useful for new aircraft and modifications to existing aircraft. The weight estimation method in FLOPS is known to be of high fidelity for conventional tube with wing aircraft and a substantial amount of effort went into its development. This report serves as a comprehensive documentation of the FLOPS weight estimation method. The development process is presented with the weight estimation process.

Wells, Douglas P.↗
NASA NTRSDate not supplied

Reduced-Order Aerodynamic Modeling Based on CFD Frequency Responses from Multisine Inputs

A system identification analysis was performed to determine a reduced-order model (ROM) of a computational fluid dynamics (CFD) solver in support of linear aeroservoelastic model development and feedback control design. The approach was applied to the FUN3D code for the half-span wind tunnel test article used in the NASA-Boeing collaboration called the Integrated Adaptive Wing Technology Maturation (IAWTM) project. In a transonic flow condition, multiple inputs (11 structural mode displacements and 3 control surface deflections) were simultaneously excited with orthogonal phase-optimized multisines while multiple outputs (the corresponding 14 generalized aerodynamic forces) were recorded. From these recorded times series, the matrix of frequency responses was computed and subsequently fit using rational function approximations (RFAs). It was found that the entire (14 x 14) matrix of frequency responses could be determined from a single CFD run and that results generally followed trends predicted using other methods. Differences were attributed to the modeling fidelity and nonlinearities from structural mode and control surface interactions at higher reduced frequencies. More accurate fits of the RFAs to the frequency response data were obtained by making two CFD runs, one with only structural mode excitations and one with only control surface excitations, which reduced the degree of nonlinearity in the modeling data.

Aeroservoelasticity↗
NASA NTRSDate not supplied

Artemis 1 Radiation Modeling and Analysis Using Operational Methods

The successful completion of the Artemis 1 mission has provided NASA with a significant volume of space radiation dosimetry data that can be used to verify the accuracy of current operational SRAG dose computation models and methods for Beyond LEO missions. High-Fidelity CAD models of the Artemis 1 MPCV Orion were raytraced in 10,000 directions per sensor location, including the location of Artemis HERA sensors, Artemis RAMs (TLDs), and the crew active dosimetry (CADs). The full Artemis 1 mission was modelled using the as-flown trajectory, Ap9-Ae9 IRENE providing the VAB trapped proton and electron environment, and the Badhwar-O’Neill 2020 model providing the freespace GCR environment. IGRF 12 was used to compute cutoff rigidities for the GCR environment in LEO. 1DHZETRN was used to estimate point doses at each sensor position. Computed RAM doses were within 5% of measured values. The computed VAB transit and freespace GCR dose rates showed good qualitative agreement with measured HERA values. An estimate of normalized mean effective dose for a hypothetical male crew member on Artemis 1 using current standard operational methods was computed to be 22.3 mSv.

Artemis 1↗
DOE OSTI2024

Downscaling Synthetic Populations to Realistic Residential Locations

High-fidelity pattern of life (PoL) models require realistic origin points for predictive trip modeling. This paper develops and demonstrates a reproducible method using open data to match synthetic populations generated from census surveys to plausible residential locations (building footprints) based on housing attributes. This approach presents promise over extant methods based on housing density, particularly in small neighborhood areas with heterogeneous land-use.

Tuccillo, Joe↗
DOE OSTI2020

Extraction of mechanical properties of materials through deep learning from instrumented indentation

Instrumented indentation has been developed and widely utilized as one of the most versatile and practical means of extracting mechanical properties of materials. This method is particularly desirable for those applications where it is difficult to experimentally determine the mechanical properties using stress–strain data obtained from coupon specimens. Such applications include material processing and manufacturing of small and large engineering components and structures involving the following: three-dimensional (3D) printing, thin-film and multilayered structures, and integrated manufacturing of materials for coupled mechanical and functional properties. Here, we utilize the latest developments in neural networks, including a multifidelity approach whereby deep-learning algorithms are trained to extract elastoplastic properties of metals and alloys from instrumented indentation results using multiple datasets for desired levels of improved accuracy. We have established algorithms for solving inverse problems by recourse to single, dual, and multiple indentation and demonstrate that these algorithms significantly outperform traditional brute force computations and function-fitting methods. Moreover, we present several multifidelity approaches specifically for solving the inverse indentation problem which 1) significantly reduce the number of high-fidelity datasets required to achieve a given level of accuracy, 2) utilize known physical and scaling laws to improve training efficiency and accuracy, and 3) integrate simulation and experimental data for training disparate datasets to learn and minimize systematic errors. The predictive capabilities and advantages of these multifidelity methods have been assessed by direct comparisons with experimental results for indentation for different commercial alloys, including two wrought aluminum alloys and several 3D printed titanium alloys.

36 MATERIALS SCIENCE↗
NASA NTRS2014

High Performance EVA Glove Collaboration: Glove Injury Data Mining Effort

Human hands play a significant role during extravehicular activity (EVA) missions and Neutral Buoyancy Lab (NBL) training events, as they are needed for translating and performing tasks in the weightless environment. It is because of this high frequency usage that hand- and arm-related injuries and discomfort are known to occur during training in the NBL and while conducting EVAs. Hand-related injuries and discomforts have been occurring to crewmembers since the days of Apollo. While there have been numerous engineering changes to the glove design, hand-related issues still persist. The primary objectives of this study are therefore to: 1) document all known EVA glove-related injuries and the circumstances of these incidents, 2) determine likely risk factors, and 3) recommend ergonomic mitigations or design strategies that can be implemented in the current and future glove designs. METHODS: The investigator team conducted an initial set of literature reviews, data mining of Lifetime Surveillance of Astronaut Health (LSAH) databases, and data distribution analyses to understand the ergonomic issues related to glove-related injuries and discomforts. The investigation focused on the injuries and discomforts of U.S. crewmembers who had worn pressurized suits and experienced glove-related incidents during the 1980 to 2010 time frame, either during training or on-orbit EVA. In addition to data mining of the LSAH database, the other objective of the study was to find complimentary sources of information such as training experience, EVA experience, suit-related sizing data, and hand-arm anthropometric data to be tied to the injury data from LSAH. RESULTS: Past studies indicated that the hand was the most frequently injured part of the body during both EVA and NBL training. This study effort thus focused primarily on crew training data in the NBL between 2002 and 2010. Of the 87 recorded training incidents, 19 occurred to women and 68 to men. While crew ages ranged from thirties to fifties, the age category most affected was in the forties range. Incident rate calculations (incidents per 100 training runs) revealed that the 2002, 2003, and 2004 time periods registered the highest reported incident rate levels (3.4, 6.1, and 4.1 respectively) when compared to the following years (all ≤ 1.0). In addition to general hand-arm discomfort being the highest reported result from training, specific types of hand injuries or symptoms included erythema, fingernail delamination, abrasions, muscle soreness/fatigue, paresthesia, bruising, blanching, and edema. Specific body locations most affected by hand injuries included the metacarpophalangeal joints, fingernails, finger crotches, fingers in general, interphalangeal joints, and fingertips. Causes of injuries reported in the LSAH data were primarily attributed to the forces that the gloved hands were exposed to due to hand intensive tasks and/or poor glove sizing. DISCUSSION: Although the age data indicate that most injuries are reported by male crewmembers in their forties, that is also the dominant gender and age range of most EVA crew therefore it is not an unexpected finding. Age and gender analysis will continue as more details on the uninjured population is accrued. While there is a reasonable mechanism to link training quantity to injury, the results were inconsistent and point to the need for a consistent method of suit-related injury screening and documentation. For instance, the high-incident rate levels for the years 2002 to 2004 could be attributed to a comprehensive medical review of crewmembers post-NBL EVA training that occurred from July 19, 2002 to January 16, 2004. Furthermore, there could have been increased awareness from an investigation at the NBL. These investigations may have temporarily increased the fidelity of reported injuries and discomforts during these dates as compared to surrounding years, when injury signs and symptom were no longer actively being investigated but rather voluntarily reported. Data mining for possible mechanistic factors continues and includes more detailed training timelines, hand anthropometry, and suit sizing information. The limited published data looking at hand-arm anthropometry correlated hand-anthropometry metrics with injuries stemming from glove design and operation. Future work will include further evaluation of body sizing and fit in relation to hand injury incidents.

Reid, C. R.↗
DOE OSTI2020

Extraction of mechanical properties of materials through deep learning from instrumented indentation

Instrumented indentation has been developed and widely utilized as one of the most versatile and practical means of extracting mechanical properties of materials. This method is particularly desirable for those applications where it is difficult to experimentally determine the mechanical properties using stress–strain data obtained from coupon specimens. Such applications include material processing and manufacturing of small and large engineering components and structures involving the following: three dimensional (3D) printing, thin-film and multilayered structures, and integrated manufacturing of materials for coupled mechanical and functional properties. Here, we utilize the latest developments in neural networks, including a multifidelity approach whereby deep-learning algorithms are trained to extract elastoplastic properties of metals an d alloys from instrumented indentation results using multiple datasets for desired levels of improved accuracy. We have established algorithms for solving inverse problems by recourse to single, dual, and multiple indentation and demonstrate that these algorithms significantly outperform traditional brute force computations and function-fitting methods. Moreover, we present several multifidelity approaches specifically for solving the inverse indentation problem which 1) significantly reduce the number of high-fidelity datasets required to achieve a given level of accuracy, 2) utilize known physical and scaling laws to improve training efficiency and accuracy, and 3) integrate simulation and experimental data for training disparate datasets to learn and minimize systematic errors. The predictive capabilities and advantages of these multifidelity methods have been assessed by direct comparisons with experimental results for indentation for different commercial alloys, including two wrought aluminum alloys and several 3D printed titanium alloys.

3D printed materials, stress-strain behavior, mult↗
NASA NTRS2026

A Summary of Test and Analysis Results from a Second Lift+Cruise Full-Scale Drop Test

The realization of advanced air mobility markets is enabling new forms of transportation to take shape in the United States and around the world. Though currently in development, as these markets mature, new types of vertical take-off and landing (VTOL) vehicles have been undergoing development for use. There are many factors which must be addressed prior to these types of vehicles becoming viable alternative forms of transportation in these markets. These factors include incorporation into the existing airspaces, the logistics of operating in urban environments, along with numerous factors associated with safety and reliability. To address some of the safety aspects associated with the development of these new types of vehicles, NASA has been conducting research into the performance of an example electric VTOL (eVTOL) aircraft as a part of the Revolutionary Vertical Lift Technology (RVLT) project. Over the course of this research, many aspects including the development of energy absorbing components, the evaluation of seating systems, the development of advanced finite element material model systems and the acquisition of full-scale vehicle impact data were investigated. The report will discuss aspects related to the acquisition of full-scale vehicle data which occurred in the form of a full-scale impact test conducted in the Summer of 2025. This test was on a NASA designed Lift+Cruise composite cabin test article and represented a partial capstone in the entirety of previous eVTOL research conducted for the project. In this test, a variety of experiments were included in order to investigate the effect of a full-scale environment on the experiment results. In parallel, the development of a computational impact model to simulate the full-scale test will be discussed in this report. A model of the Lift+Cruise test article was developed utilizing data collected from previous sub- and full-scale test data and then simulated in the current test environment. The model development, its use in pre-test predictions, and its use in post-test correlation will all be presented. This report will present the test data acquired from the Lift+Cruise test and document several of the results obtained. One intended result is to determine the effect of a complex full-scale crash impact on the identification of occupant injury risk within seat and vehicle designs. A second intended result is to determine whether high-fidelity models can be used with some confidence in the prediction of test events and can allow for additional test cases to be simulated without the need of having to conduct additional tests. The overall goal of the test is to provide the community with data that can be used for design, development or certification efforts, along with providing data on what an example eVTOL crash incident could entail.

energy storage systems↗
DOE OSTI2021

Comparing Global Spray Combustion Characteristics and Local Shot-to-Shot Variations in a Reacting n -Heptane Spray

In support of efforts to develop improved models of turbulent spray behavior and combustion in diesel engines, experimental data and analysis must be obtained for guidance and validation. For Reynolds-averaged Navier–Stokes (RANS)-based Computational fluid dynamics (CFD) modeling approaches, representative ensemble average experimental results are important. For high-fidelity models such as large eddy simulations (LES)-based CFD, precise individual experimental results are desirable. However, making comparisons between a given experiment and LES is a challenge since local parameters cannot be directly compared. In this work, an optically accessible constant pressure flow rig (CPFR) is utilized to acquire diesel-like fuel injection and reaction behavior simultaneously with three optical diagnostic techniques: rainbow Schlieren deflectometry (RSD), OH* chemiluminescence (OH*), and two-color pyrometry (2CP). The CPFR allows a large number of repeated injection experiments to be performed for statistical analysis and convergence using ensemble-averaging techniques, while maintaining highly repeatable test conditions. Even for stable test conditions, variations in local turbulent fuel–air mixing introduce variability, which manifests as significant differences in OH* and 2CP results. Experimental measurements of characteristic parameters including liquid and vapor jet penetration, liftoff length, soot temperature and concentration, and turbulent flame speed, along with the shot-to-shot variability of each dataset, are presented and discussed. Finally, a statistical method is utilized to analyze the extent of this variability, and to identify superlative injections within the dataset for discussion and analysis of shot-to-shot variations.

42 ENGINEERING↗
DOE OSTI2025

Explaining word embeddings with perfect fidelity: a case study in predicting research impact

The best-performing approaches for scholarly document quality prediction are based on embedding models. In addition to their performance when used in classifiers, embedding models can also provide predictions even for words that were not contained in the labelled training data for the classification model, which is important in the context of the ever-evolving research terminology. Although model-agnostic explanation methods, such as Local interpretable model-agnostic explanations, can be applied to explain machine learning classifiers trained on embedding models, these produce results with questionable correspondence to the model. We introduce a new feature importance method, Self-Model Entities Rated (SMER), for logistic regression-based classification models trained on word embeddings. We show that SMER has theoretically perfect fidelity with the explained model, as the average of logits of SMER scores for individual words (SMER explanation) exactly corresponds to the logit of the prediction of the explained model. Quantitative and qualitative evaluation is performed through five diverse experiments conducted on 50,000 research articles (papers) from the CORD-19 corpus. In conclusion, through an AOPC curve analysis, we experimentally demonstrate that SMER produces better explanations than LIME, SHAP and global tree surrogates.

Coarse-grained models↗
NASA NTRS2008

Precision Measurement of Black Hole Binary Dynamics: Analyzing the LISA Data Stream

One of the richest potential sources of insight into fundamental physics that LISA will be capable of observing is the inspiral of supermassive black hole binaries (BHBs). However, the data analysis challenge presented by the LISA data stream is quite unlike the situation for present day gravitational wave detectors. In order to make the precision measurements necessary to achieve LISA's science goals, the BHB signal must be distinguished from a data stream that not only contains instrumental noise, but potentially thousands of other signals as well, so that the "background" we wish to separate out to focus on the BHB signal is likely to be highly nonstationary and nongaussian, as well as being of scientific interest in its own right. In addition, whereas the theoretical templates that we calculate in order to ultimately estimate the parameters can afford to be somewhat inaccurate and still be effective for present day and near future detectors, this is not the case for LISA, and extremely high fidelity of the theoretical templates for high signal-to-noise signals will be required to prevent theoretical errors from dominating the parameter estimates. NVe, will describe efforts in the community of LISA data analysts to address the challenges regarding the specific issue of BHB signals. These efforts include using a Markov Chain Monte Carlo approach with the freedom to model the BHB and the other signals present in the data stream simultaneously, rather than trying to remove other signals and risk biasing the remaining data. The Mock LISA Data Challenge is a community of LISA scientists who generate rounds of simulated LISA noise with increasingly difficult signal content, and invite the LISA data analysis community to exercise their methods, or develop new methods, in an attempt to extract the parameters for the signals embedded in the mock data. In addition to practical approaches such ,is this to assess the level of parameter accuracy, one can apply the Fisher matrix formalism to assess both the statistical errors from noise and the theoretical errors

McWilliams, Sean T.↗
DOE OSTI2025

Modular Hydronic Room Conditioning System (CRADA NFE-24-10120 Final Report)

This project presents the development and validation of high-fidelity numerical models for fin-tube heat exchangers to enable accurate performance prediction and informed design optimization. The modeling framework integrates detailed geometric specifications, thermophysical property data, and system-level constraints to simulate the heat exchanger’s behavior under a range of operating conditions. The model is calibrated using real-world product specifications and validated against experimental data collected from controlled cooling and heating tests. In cooling mode, the model captures the overall trends in capacity and outlet air temperature but tends to underpredict latent effects, especially at lower air flow rates. In heating mode, the simulation consistently overestimates both the thermal capacity and outlet air temperature, indicating the need for refinement in air-side heat transfer assumptions. Despite these deviations, the model provides a solid foundation for optimization, allowing key design variables to be tuned within physical and performance-based constraints. This research advances the ability to simulate, validate, and optimize fin-tube heat exchanger designs with greater confidence and efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2003

Improving the Science Observing Efficiency of the Chandra X-ray Observatory via Chandra Radiation

The Chandra X-ray Observatory (CXO), NASA' latest "Great Observatory", swas launched on July 23, 1999 and reached its final orbit on August 7, 1999. The CXO is in a highly elliptical orbit, approximately 140,000 km x 10,000 km, and has a period of approximately 63.5 hours (approx. 2.65 days). It transits the Earth's Van Allen belts once per orbit during which no science observations can be performed due to the high radiation environment. The Chandra X-ray Observatory Center currently uses the National Space Science Data Center's "near Earth" AP-8/AE-8 radiation belt model to predict the start and end times of passage through the radiation belts. Our earlier analysis demonstrated that our implementation of the AP-8/AE-8 model (a simple dipole model of the Earth's magnetic field) does not always give sufficiently accurate predictions of the start and end times of transit of the Van Allen belts. This led to a change in our operating procedure whereby we "padded" the start and end times of transit by 10 kiloseconds (ks) so that ACIS, the primary science instrument on-board Chandra, would not be exposed to the "fringes" of the Van Allen belts on ingress and egress for any given transit. This additional 20 ks per orbit during which Chandra is unable to perform science observations sums to approximately 3 Ms of "lost" science time per year and therefore reduces the science observing efficiency of the Observatory. To address the need for a higher fidelity radiation model appropriate for the Chandra orbit, the Chandra Radiation Model (CRM) was developed. The CRM is an ion model for the outer magnetosphere and is based on data from the EPIC/ICS instrument on-board the Geotail satellite as well as data from the CEPPAD/IPS instrument on-board the Polar satellite. With the production and implementation of the CRM, we present the results of a study designed to investigate the science observing time that may be recovered by using the CRM as a function of the additional low-energy proton exposure to ACIS.

Virani, Shanil↗
NASA NTRS2003

Improving the Science Observing Efficiency of the Chandra X-Ray Observatory via the Chandra Radiation Model

The Chandra X-ray Observatory (CXO), NASA's latest "Great Observatory", was launched on July 23, 1999 and reached its final orbit on August 7, 1999. The CXO is in a highly elliptical orbit, approximately 140,000 km x 10,000 km, and has a period of approximately 63.5 hours (N 2.65 days). It transits the Earth's Van Allen belts once per orbit during which no science observations can be performed due to the high radiation environment. The Chandra X-ray Observatory Center currently uses the National Space Science Data Center's "near Earth" AP-8/AE-8 radiation belt model to predict the start and end times of passage through the radiation belts. Our earlier analysis demonstrated that our implementation of the AP-8/AE-8 model (a simple dipole model of the Earth's magnetic field) does not always give sufficiently accurate predictions of the start and end times of transit of the Van Allen belts. This led to a change in our operating procedure whereby we "padded" the start and end times of transit by 10 kiloseconds (ks) so that ACIS, the primary science instrument on-board Chandra, would not be exposed to the "fringes" of the Van Allen belts on ingress and egress for any given transit. This additional 20 ks per orbit during which Chandra is unable to perform science observations sums to approximately 3 Ms of "lost" science time per year and therefore reduces the science observing efficiency of the Observatory. To address the need for a higher fidelity radiation model appropriate for the Chandra orbit, the Chandra Radiation Model (CRM) was developed. The CRM is an ion model for the outer magnetosphere and is based on data from the EPIC/ICS instrument on-board the Geotail satellite as well as data from the CEPPAD/IPS instrument on-board the Polar satellite. With the production and Implementation of the CRM, we present the results of a study designed to investigate the science observing time that may be recovered by using the CRM as a function of the additional low-energy proton exposure to ACIS.

Virani, Shanil↗
DOE OSTI2024

FORGE STRESS annual report

The project's goal is to combine high-fidelity numerical models and true-triaxial block fracturing tests at high temperatures to understand the relationship between in situ stress, thermal effects, wellbore orientations and hydraulic fracture patterns. The numerical models are calibrated against field data, such as well pressures and microseismic data, and employed to estimate the in situ stress at the FORGE site. Laboratory experiments investigate the complex physics driving hydraulic fracture nucleation in EGS, enhancing understanding of the role of parameters like temperature, well orientation, and stress. Additionally, they are employed to validate some of the numerical tools used in the project. The project will have a significant impact by: (1) improving the characterization of the in-situ stress field at FORGE; (2) demonstrating the use of high-fidelity modeling tools for EGS; (3) providing a unique set of high-temperature hydraulic fracturing results to identify key components for in-situ stress estimation, validate current theories, and propose new ones; (4) offering a validated set of numerical tools within an open-source simulation framework, GEOS, that will be available to any future user.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Building a DFT+U machine learning interatomic potential for uranium dioxide

Despite uranium dioxide (UO 2 ) being a widely used nuclear fuel, fuel performance models rely extensively on empirical correlations of material behavior, leveraging the historical operating experience of UO 2 . Mechanistic models that consider an atomistic understanding of the processes governing fuel performance (such as fission gas release and creep) will enable a better description of fuel behavior under non-prototypical conditions such as in new reactor concepts or for modified UO 2 fuel compositions. To this end, molecular dynamics simulation is a powerful tool for rapidly predicting physical properties of proposed fuel candidates. However, the reliability of these simulations depends largely on the accuracy of the atomic forces. Traditionally, these forces are computed using either a classical force field (FF) or density functional theory (DFT). While DFT is relatively accurate, the computational cost is burdensome, especially for f-electron elements, such as actinides. By contrast, classical FFs are computationally efficient but are less accurate. For these reasons, we report a new accurate machine learning interatomic potential (MLIP) for UO 2 that provides high-fidelity reproduction of DFT forces at a similar low cost to classical FFs. We employ an active learning approach that autonomously augments the DFT training data set to iteratively refine the MLIP. To further improve the quality of our predictions, we utilize transfer learning to retrain our MLIP to higher-accuracy DFT+U data. We validate our MLIPs by comparing predicted physical properties (e.g., thermal expansion and elastic properties) with those from existing classical FFs and DFT/DFT+U calculations, as well as with experimental data when available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Assessment of various tar and soot treatment methods and a priori analysis of the steady laminar flamelet model for use in coal combustion simulation

Soot formed in the fuel-rich regions of coal flames is known to have a significant impact on the radiative heat transfer characteristics of the flame. It is therefore important to account for the effect of soot radiation when simulating coal combustion systems. Several recent coal combustion studies have used acetylene as a surrogate for devolatilized tar and neglect soot formation altogether, while others have utilized empirical models for tar and soot or even chemical mechanisms which include tar and soot as species. In this work, we investigate several methods of treating coal-derived tar and soot and the impact each treatment has on predicted furnace temperature, particle mass loss, and other quantities of interest. Furthermore, we perform a priori comparisons of simulation results to calculations from a steady laminar flamelet model in order to assess the accuracy of a flamelet approach for each tar and soot treatment considered. Data used as a basis for the study is generated through One-Dimensional Turbulence (ODT) simulation of a coal combustion system in which high- fidelity models are used for gas phase chemistry and devolatilization, where char combustion is neglected.

97 MATHEMATICS AND COMPUTING↗