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At least 19 records

Understanding and Modeling Pooled Rideshare Acceptance: Influential Factors, Preferred User Experiences, and Implications

This dissertation explores factors influencing pooled rideshare (PR) adoption to provide actionable insights for transportation network companies (TNCs) and policymakers. PR allows travelers to share rides with unknown passengers, offering benefits such as cost reduction and congestion relief. However, adoption remains limited due to safety concerns, privacy issues, and trust in rideshare platforms. A national U.S. survey with 5,385 respondents examined transportation preferences and barriers to PR adoption. Exploratory and confirmatory factor analyses identified five key factors influencing PR consideration—safety, service experience, privacy, traffic/environment, and time/cost. Second factor analyses examined ways to optimize PR experiences, revealing four factors—comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. Privacy concerns, for instance, using regression analysis, were found to reduce the likelihood of PR adoption by 77%, and convenience had the potential to increase it by 156%. The Pooled Rideshare Acceptance Model (PRAM), based on the Technology Acceptance Model, assessed the impact of these factors using the Structural Equation Model (SEM). Privacy, safety, trust, and convenience had a large effect (Cohen's f2 > 0.35) on PR acceptance, while multigroup analyses (PRAMMA) explored 16 demographic variables such as gender, generation, and income, emphasizing the need for tailored strategies. Based on all the statistical analysis and workshops using descriptive statistics, 95 actionable recommendations were made from the riders' perspective. Findings highlight the importance of customized services, user experience improvements, and policy interventions to enhance PR adoption. This dissertation provides a roadmap for future research and policy development, ensuring evidence-based, practical strategies to improve PR services in the U.S. and beyond.

Gangadharaiah, Rakesh↗

Data and scripts associated with “Riverine dissolved organic matter transformations increase with watershed area, water residence time, and Damköhler numbers in nested watersheds” (v2)

This data package is associated with the publication “Riverine dissolved organic matter transformations increase with watershed area, water residence time, and Damköhler numbers in nested watersheds” submitted to Biogeochemistry by Ryan et al., 2024 (DOI: https://doi.org/10.1007/s10533-024-01169-5). This study aims to investigate fundamental and transferable drivers of dissolved organic matter (DOM) diversity across five nested watersheds within the contiguous United States. DOM diversity was explored using ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The samples and the unprocessed FTICR-MS data used in this study are publicly available on the Environmental System Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE) data repository (see DOIs below). The data for the Willamette, Gunnison, Connecticut, and Deschutes basins were collected as part of a collaboration between the Watershed Rules of Life (WROL) project and Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS). The data for the Yakima River basin (YRB) was collected by the PNNL River Corridor SFA. The raw, unprocessed FTICR-MS data with additional (meta)data can be found at doi:10.15485/1895159 for WROL samples and doi:10.15485/1898912 for YRB samples. This data package contains the processed data used in the associated manuscript. This package also contains ancillary geospatial, hydrological, and geochemical information that supports the interpretation of the FTICR-MS data within Ryan et al., 2024. This data package is associated with the GitHub repository found at https://github.com/WHONDRS-Hub/rcsfa-RC4-WROL-YRB_DOM_Diversity. This data package was originally published August 2024. It was updated January 2025 (modified files). See the change history in the readme more details. At the directory level, the data package is comprised of three folders: (1) data, (2) output, and (3) src; and five additional files including the data dictionary (file ending in "_dd.csv”) and file-level metadata (file ending in “_flmd.csv”). The “src” folder contains the scripts used to process the FTICR data, conduct the analyses, and produce the manuscript figures. The inputs for these scripts are in the “data” folder and the returned outputs in the “output” folder. Inputs include temporal and spatial metadata associated with the sampling efforts, processed FTICR data, and total and normalized putative biochemical transformations per sample. Outputs include cleaned and combined data presented as tables, descriptive statistics, and plots. The file-level metadata file lists all files contained in this data package and descriptions for each. The data dictionary describes the units and definitions for each tabular data column or row header.

54 ENVIRONMENTAL SCIENCES↗

Assessment of BQ-9000 Biodiesel Properties for 2023

This is the seventh in a series of reports documenting the quality of biodiesel from U.S.- and Canadian-based producers that participate in the BQ-9000 program, the biodiesel industry’s voluntary quality assurance program. Participants provided monthly data on critical quality parameters for calendar year 2023 with quality data provided to a team of experts, who removed any identifying company information and provided anonymized data to the National Renewable Energy Laboratory (NREL) for statistical analysis. New for 2023, data on kinematic viscosity, sulfated ash, distillation temperature, carbon residue, and cetane were collected, as well as individual levels of sodium, potassium, calcium, and magnesium. Critical quality parameters analyzed are listed in Table ES-1 with descriptive statistics.

09 BIOMASS FUELS↗

Evaluation of probability density function descriptions for three-component Rayleigh–Taylor mixing

Results from simulations of a three-component Rayleigh–Taylor (RT) mixing problem are presented. These simulations are conducted in heavy–light–heavy and heavy–intermediate–light configurations, and each of these configurations are further considered in high- and low-Reynolds-number regimes. This results in RT-unstable flow with one or both interfaces initially unstable, permitting the influence of problem configuration on the statistical description of three-component RT-driven mixing to be considered. Mass fraction covariances are observed to undergo a sign change through the mixing layer in all four configurations considered. This appears to be unique to the multi-component case and represents another way in which multi-component RT mixing differs from the two-component case. Qualitative and quantitative comparisons of joint and marginal probability density function (PDF) descriptions of species concentration are made. Three-, five-, and six-parameter model PDFs are compared against simulation data to assess how accurately they describe the mixing, and it is found that three-component mixing requires at least a five-parameter model PDF to accurately describe the mixing. Notably, the marginal distributions of three-component mixing do not appear to conform to a beta distribution, representing a departure from the classical two-component RT case. In conclusion, statistical neutrality also appears to influence the optimal choice of model PDF, which is found to be a function of problem configuration.

Large-eddy simulation↗

Optimal binning of correlated measurements

Experimental measurements are commonly represented on a discrete grid, requiring a balance between granularity and statistical noise. Two strategies have traditionally been used to improve such representations: selecting an appropriate bin width to control discretization error and applying kernel-based smoothing to suppress fluctuations. Despite their shared goal, these approaches have largely developed independently, without a unified statistical description of how discretization and correlation jointly determine measurement precision. Here, we extend the discussion of optimal interval averaging to a correlation-aware setting by Gaussian process regression, which explicitly accounts for correlations among neighboring bins. Starting from first principles, we derive the mean-squared error of discretized measurements and obtain closed-form asymptotic expressions for the optimal bin width and correlation length. When recast in reduced variables, the theory reveals distinct universal scaling laws governing the error in the correlation-free and correlation-controlled regimes. Characterized by intrinsically smooth intensity profiles and counting-based statistics, neutron scattering measurements are well suited for demonstrating the enhanced error contraction enabled by inter-bin correlations. We show that such improvement is achievable over the experimentally accessible Q-range and across multiple instruments and material systems. These results show that explicitly accounting for correlations systematically reshapes the limits of precision in discretized, noise-limited measurements. More broadly, the framework provides a transferable statistical foundation for optimizing data representation, inference, and experimental design across the physical and data sciences.

Tung, Chi-Huan [ORNL] (ORCID:0000000221972074)↗

Hypertriton puzzle in relativistic heavy-ion collisions

The yields of hadrons and light nuclei in relativistic collisions of heavy-nuclei at a center of mass energy of $\sqrt{s_{NN}}$ = 2.6 TeV can be described remarkably well by a thermal distribution of an ideal gas of hadrons and light nuclei interacting only via the decay of resonances. Given the particularly small binding energy of hypertritons relative to the temperature describing the yields (about 156 MeV), one might naturally expect hypertrions to dissociate in medium, making the agreement of hypertriton yields with thermal predictions highly puzzling. The puzzle is compounded by the fact that small binding energy is associated with the large size of the hypertriton. This size is on a similar scale to the overall size of the fireball and much larger than the length scale over which temperatures in the fireball vary over phenomenologically relevant amounts. Here, this paper quantifies the tension this effect causes and shows that it is sufficiently large to render the thermal model inconsistent: its natural assumptions are in conflict with its outputs. The possibility that hypertritons are formed at freeze out as compact objects, quark droplets, that subsequently evolve into hypertritons is considered as a way to resolve the puzzle. It is noted that beyond making the assumption that compact quark droplets form, additional detailed dynamical assumptions which have not been justified are needed to make the thermal model work. The issue of why, despite these issues, the hypertriton is well described by a simple statistical description at freeze out is unresolved. Resolving the hypertriton puzzle is important as it may clarify whether the phenomenological success of the simple thermal model for yields accurately reflects the simple picture of the underlying physics on which it is based.

hydrodynamic models↗

Chain trajectories, domain shapes, and terminal boundaries in block copolymers

The packing geometry of macromolecules in complex mesophases is of key importance to self-organization in synthetic and biological soft materials. While approximate or heuristic models rely on often-untested assumptions about how flexible molecules “fit in” to distinct locations of complex assemblies, physical assemblies derive from ensembles of fluctuating conformations, obscuring the connection between mesophase geometry and the underlying arrangements. Here, we present an approach to extract and analyze features of molecular packing in diblock block copolymer (BCP) melts, a prototypical soft matter system, based on the statistical description of chain conformations in self-consistent field (SCF) theory. We show how average BCP chain trajectories in ordered morphologies can be analyzed from the SCF-derived orientational order parameter of chain segments. We use these extracted trajectories to analyze the features of local packing geometry, including chain bending and tilt, as well as the terminal boundaries that delineate distinct domains in ordered BCP morphologies. In conclusion, we illustrate this analysis by focusing on measurable features of packing frustration in 2D (columnar) and 3D (spherical and bicontinuous) morphologies, notably establishing an explicit link between chain conformations in complex morphologies and their medial geometry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Turbulence-Resolving Simulations of Hurricane Laura (2020): Insights Into Extreme Winds and Eyewall Turbulence

Extreme weather events pose significant risks to coastal and offshore energy infrastructure. In this work, we investigate the structure of mean winds and turbulence near the surface ( z < 300 m) that occurred during Hurricane Laura in 2020 on the US Gulf Coast. To this end, we perform turbulence-resolving simulations (..delta..x = 33.33 m) that encompass the entire eyewall of Hurricane Laura by introducing a novel method that we will refer to as Moving-Mesoscale to Static-LES Integrated Coupling (MOSAIC). The simulation results are validated against surface, boundary-layer, and mid-tropospheric observations. Our analysis of the extreme wind conditions near the surface indicates that the mean wind and turbulence profiles vary greatly at and near the eyewall, resulting in extreme values of mean wind speed (U > 50 m * s-1) and turbulence kinetic energy (k ~ 50 m2 * s-2) at altitudes above 50 m . To investigate in detail the nature of turbulence within the eyewall of the storm, we conduct higher-resolution simulations (..delta..x = 11.11 m) of a portion of the hurricane. We provide a comprehensive statistical description of turbulence in the eyewall region, highlighting the need to employ ..delta..x ~ 10 m grid spacing to capture the integral length-scales near the surface, second- and higher-order central moments (i.e., skewness and kurtosis), and spectral coherence in the flow accurately. These numerical simulations provide the most detailed description of mean winds and turbulent conditions within the eyewall of a historical tropical cyclone to date, illustrating how high-resolution simulations can enhance limited turbulence measurements in hurricanes and giving insight into the extreme conditions threatening offshore and coastal infrastructure.

17 WIND ENERGY↗

Choosing the Right Tool: A Comparative Study of Wetland Assessment Approaches

There are over 700 aquatic ecological assessment approaches across the globe that meet specific institutional goals. However, in many cases, multiple assessment tools are designed to meet the same management need, resulting in a confusing array of overlapping options. Here, we look at six riverine wetland assessments currently in use in Montana, USA, and ask which tool (1) best captures the condition across a disturbance gradient and (2) has the most utility to meet the regulatory or management needs. We used descriptive statistics to compare wetland assessments (n = 18) across a disturbance gradient determined by a landscape development intensity. Factor analysis showed that many of the tools had internal metrics that did not correspond well with overall results, hindering the tool’s ability to act as designed. We surveyed regional wetland managers (n = 56) to determine the extent of their use of each of the six tools and how well they trusted the information the assessment tool provided. We found that the Montana Wetland Assessment Methodology best measured the range of disturbance and had the highest utility to meet Clean Water Act (CWA§ 404) needs. Montana Department of Environmental Quality was best for the CWA§ 303(d) & 305(b) needs. The US Natural Resources Conservation Service’s Riparian Assessment Tool was the third most used by managers but was the tool that had the least ability to distinguish across a disturbance, followed by the US Bureau of Land Management’s Proper Functioning Condition.

54 ENVIRONMENTAL SCIENCES↗

Characterizing peak electricity demand for U.S. households: an assessment of end-use loads and demand factors

Understanding household peak electricity demand is critical to evaluate the technical need for electrical infrastructure upgrades. This study characterizes peak loads for existing and new equipment using metered data from a convenience sample of 11,940 U.S. dwellings from four sources, including 911 from two sources with end-use metering. After standardized data cleaning and labeling, we derived descriptive statistics for key metrics, such as maximum demand and demand factors, and developed predictive models relating 60- to 15-min demand for the National Electrical Code (NEC). Mean 15-min maximum demand was 9.7 kW (median 9.0 kW; IQR 7.0–11.5 kW, 95% CI 9.6–9.8 kW), indicating spare capacity in 98% of homes with hypothetical 100 A panels. Maximum demand increased with floor area and number of high-demand loads. Dwelling maximum demand was driven by higher-power, longer-duration heating appliances and vehicle charging, while most user-operated appliances contributed little. Demand factors are used to account for how most devices contribute less than their rated power to maximum demand. Existing load mean demand factors (28%; median 10%; IQR 0–58%; CI 28–29%) were higher than those for new loads (21%; median 7%; IQR 0–35%; CI 20–21%), because new loads changed the timing and magnitude of maximum demand. New high-demand loads had higher than average demand factors (40–60%). Whole dwelling demand factors support the NEC's 40% assumption, but they challenge its conservative 100% treatment of new HVAC. We propose a data-driven 50% demand factor for new equipment, which would align with metered data, improve affordability, and modernize electrical codes.

Appliances↗

Multiscale simulation of spatially correlated microstructure via a latent space representation

When deformation gradients act on the scale of the microstructure of a part due to geometry and loading, spatial correlations and finite-size effects in simulation cells cannot be neglected. We propose a multiscale method that accounts for these effects using a variational autoencoder to encode the structure–property map of the stochastic volume elements making up the statistical description of the part. In this paradigm the autoencoder can be used to directly encode the microstructure or, alternatively, its latent space can be sampled to provide likely realizations. Furthermore, we demonstrate the method on three examples using the common additively manufactured material AlSi10Mg in: (a) a comparison with direct numerical simulation of the part microstructure, (b) a push forward of microstructural uncertainty to performance quantities of interest, and (c) a simulation of functional gradation of a part with stochastic microstructure.

Elastoplasticity↗

Nonlinear Topological Photonics: Capturing Nonlinear Dynamics and Optical Thermodynamics

Combining multiple optical resonators or engineering dispersion of complex media has provided an effective method for demonstrating topological physics controlling photons in unprecedented ways such as unidirectional light propagation and spatially localized modes between an interface or on a corner. Further, adding nonlinear responses to those topological photonic systems has enabled achieving diverse phases of photons in both space and time, allowing for more functionalities in photonic devices that provide a new playground for studying dynamic features of nonlinear topological systems. However, most methods for describing nonlinear topological photonic systems rely on linear topological theories, making it challenging to accurately characterize the topology of nonlinear systems. Thus, substantial efforts have focused on rigorously describing nonlinear topological phases and developing effective tools to analyze nonlinear topological effects. Meanwhile, coupled multimode optical waveguides with nonlinear dynamic responses provide an excellent platform for the statistical description of photons, opening a new paradigm called “optical thermodynamics”. This review will introduce the basic concepts of nonlinear topological photonics and the recent development of theoretical approaches focusing on data-driven approaches for creating phase diagrams as well as the spectral localizer framework and the pseudospectrum method for understanding optical nonlinearities in topological systems. In addition, the new concept of optical thermodynamics will be introduced with some recent theoretical works.

Topological photonics↗

Transient hydroperoxyalkyl intermediates (•QOOH) in isopentane oxidation. II. Isomer-resolved unimolecular dynamics

Transient carbon-centered hydroperoxyalkyl intermediates (•QOOH) in isopentane oxidation are characterized by their time- and energy-resolved unimolecular dissociation dynamics to hydroxyl (OH) and cyclic ether products. Two distinct •QOOH isomers are examined with radical sites at a primary carbon of one of the methyl groups (β-Me) or a secondary carbon (β-Et) of the ethyl group. Energy-dependent unimolecular rates are obtained from the time-dependent appearance of OH products for the two isomers and compared with statistical microcanonical rates computed using RRKM theory, including heavy-atom tunneling, based on high-level theoretical calculations. A benchmark-corrected approach is utilized to compute high-accuracy stationary-point energies, most importantly, transition-state barriers, for the •QOOH_Me and •QOOH_Et isomers in isopentane oxidation, building on higher-level reference calculations for the oxidation of ethane (C 2 H 5 O 2 ) and propane (C 3 H 7 O 2 ), respectively. Here, the measured rates are compared with RRKM calculations incorporating the benchmark-corrected transition-state parameters, a vibrationally adiabatic multidimensional hindered-rotor treatment of key torsions, and quantum tunneling. Agreement between experiment and theory validates the statistical description and shows faster decay for •QOOH_Et due to its lower barrier. Both β-QOOH isomers decay almost exclusively to OH + cyclic ether products under the conditions studied.

Oxidation processes↗

Accelerated Depth Computation for Surface Boxplots with Deep Learning

Functional depth is a well-known technique used to derive descriptive statistics (e.g., median, quartiles, and outliers) for 1D data. Surface boxplots extend this concept to ensembles of images, helping scientists and users identify representative and outlier images. However, the computational time for surface boxplots increases cubically with the number of ensemble members, making it impractical for integration into visualization tools. In this paper, we propose a deep-learning solution for efficient depth prediction and computation of surface boxplots for time-varying ensemble data. Our deep learning framework accurately predicts member depths in a surface boxplot, achieving average speedups of 6X on a CPU and 15X on a GPU for the 2D Red Sea dataset with 50 ensemble members compared to the traditional depth computation algorithm. Our approach achieves at least a 99% level of rank preservation, with order flipping occurring only at pairs with extremely similar depth values that pose no statistical differences. This local flipping does not significantly impact the overall depth order of the ensemble members.

Han, Mengjiao↗

The relationship between stress, anxiety and eating behavior among Chinese students: a cross-sectional study

Background The expansion of higher education and the growing number of college students have led to increased awareness of mental health issues such as stress, anxiety, and eating disorders. In China, the educational system and cultural expectations contribute to the stress experienced by college students. This study aims to clarify the role of anxiety as a mediator in the relationship between stress and eating behaviors among Chinese college students. Methods This study utilized data from the 2021 Psychology and Behavior Investigation of Chinese Residents, which included 1,672 college students under the age of 25. The analysis methods comprised descriptive statistics, t -tests, Pearson correlation analyses, and mediation effect analysis. Results The findings indicate that Chinese college students experience high levels of stress, with long-term stress slightly exceeding short-term stress. Both types of stress were positively correlated with increased anxiety and the adoption of unhealthy eating behaviors. Anxiety was identified as a significant mediator, accounting for 28.3% of the relationship between long-term stress and eating behavior (95% CI = 0.058–0.183). The mediation effect of short-term stress on eating behavior through anxiety was also significant, explaining 61.4% of the total effect (95% CI = 0.185–0.327). Conclusion The study underscores the importance of stress management and mental health services for college students. It recommends a comprehensive approach to reducing external pressures, managing anxiety, and promoting healthy eating behaviors among college students. Suggestions include expanding employment opportunities, providing career guidance, enhancing campus and societal support for holistic development, strengthening mental health services, leveraging artificial intelligence technologies, educating on healthy lifestyles, and implementing targeted health promotion programs.

Chai, Yulin↗

COMPUTATIONAL MODELING OF IGNITION AND PREMIXED FLAME PROPAGATION INITIATED BY A PRE-CHAMBER TURBULENT JET

Addressing the pressing need for reduced carbon emissions, Turbulent Jet Ignition (TJI) emerges as a promising technology for ultra-lean combustion, offering enhanced thermal efficiencies and minimized cyclic variability in spark-ignited engines. To facilitate rapid testing and integration of this technology, a robust computational modeling framework is crucial. This study delves into the predictive capabilities of computational models for main-chamber ignition and premixed flame propagation using a single-cycle TJI rig measured by Biswas et al. (Applied Thermal Engineering, vol 106, 2016). Employing an open-source compressible flow simulation solver with Large Eddy Simulation (LES) for turbulence modeling, the investigation integrates the conventional Laminar Finite Rate Chemistry (LFRC) model alongside the transported Probability Density Method (PDF) for turbulence-chemistry interaction. A fully-consistent Eulerian Monte-Carlo Fields (EMCF) method is utilized to approximate the transported PDF, while Interaction by Exchange with Mean is employed to close micro-mixing terms in stochastic differential equations. A reduced chemical reaction mechanism with 21 species and 84 reactions (DRM-19) is used for solving chemical kinetics, and a double Gaussian energy deposition model is used to approximate the spark ignition in the pre-chamber. An unstructured O-grid mesh with 0.3 million cells in the prechamber and 1 million cells in the main chamber is employed. Results are divided into two phases: pre-chamber initialization and full TJI simulations. Validation of the predicted pre-chamber flame propagation and the lean ignition in the main-chamber is carried out by using available experimental data. Under quiescent conditions, both the LFRC and transported PDF methods largely underestimate the flame speed and subsequent pressure growth in the pre-chamber. A linear momentum forcing technique is applied to investigate the impact of initial turbulence in the pre-chamber, demonstrating a notable influence on flame propagation. Fine-tuning of the forcing coefficient reproduces the sudden pressure growth observed in the experiment. The experimentally validated pre-chamber simulation serves as the initial condition for the full TJI simulations. It is found that the LFRC model fails to predict lean-ignition in the main-chamber, resulting in a misfiring event. Incorporation of turbulence-chemistry interaction using the transported PDF method substantially improves the prediction of the ignition event in the main-chamber, achieving fair qualitative agreement and quantitative validation of combustion parameters within ±10% of the reported experimental data. The rich simulation results consisting of a full set of statistical description of the thermo-chemical states enable us to gain deep insights into the ignition mechanisms in the main chamber, which is limited when done experimentally. A novel dual ignition phenomenon is revealed in the TJI rig for the first time. Initially, a primary ignition kernel is formed at a downstream location which eventually detaches from the main jet. As the jet momentum decreases, a secondary ignition event follows, this time at a more upstream location which eventually combines with the primary ignition kernel to form a single connected flame front. Investigation of these ignition sequences in chemical composition space reveal distinct differences between the two. The primary ignition event in the main-chamber is followed by a large concentration of active radicals from the pre-chamber jet, accelerating the chain-branching steps, characterizing what has been referred to as flame ignition. In contrast, the secondary ignition occurs in the absence of active radicals in the pre-chamber jet, hence characterized as jet ignition. Further analysis of the effect of pre-chamber jet characteristics on lean ignition in the main-chamber is conducted by setting up cases with different initial pressure ratios (por) between the two chambers, a non-dimensional parameter, ranging from 1.2 to 3.2. As the initial pressure ratio increases, jet momentum increases, with dual ignition observed in cases above por= 2.2. Case with por= 3.2 lead to misfiring. The effect of ignition sequence on global combustion characteristics of TJI is analyzed. Dual ignition events lead to non-monotonicity in combustion characteristics such as global reaction progress variable, flame penetration, and global heat release rate. In dual ignition events, although the rate of fuel consumption and global heat release rate is initially lower, the secondary ignition leads to a sudden increase in flame surface area, resulting in a sudden jump and promoting the overall performance of the TJI system.

42 ENGINEERING↗

HIGH-FIDELITY SIMULATION OF SOOT FORMATION AND THERMAL RADIATION IN A LABORATORY-SCALE RICH-QUENCH-LEAN BURNER

High-fidelity simulations of a swirl-stabilized turbulent spray flame in a laboratory-scale aero-combustor have been performed to evaluate the predictability of state-of-the-art models in capturing soot formation. The simulations employ a complex chemical mechanism developed for Jet-A with PAH chemistry, coupled with the Hybrid Method of Moments (HMOM) soot model, and a Lagrangian dilute spray model for the fuel injection. Two simulations are performed to compare the results when thermal radiation is neglected or included in the solution with a mean spectral model. Modeling closures for the soot differential diffusion effects in mixture fraction space, as well as turbulence-radiation interaction are also evaluated using the data generated by the simulations. Given the degree of complexity of the simulation, the results showed good agreement with experimental measurements of the spatial distribution of the soot volume fraction ensemble average. A closer agreement with the experiment is observed when thermal radiation is included in the solution. Thermal radiation is observed to reduce the flame temperature and increase the flame intermittency, denoted by the increase in the temperature standard deviation in mixture fraction space. The reduction in temperature also leads to a reduction in PAH production and soot volume fraction. Turbulence is observed to have different effects on radiative emission depending on the mixture fraction. Turbulent scalar fluctuations significantly enhance radiative emission in fuel lean mixtures and can also play a role for fuel rich conditions. The statistical description of the turbulence-radiation interaction, previously proposed in the literature, was observed to correctly reproduce the high-fidelity results. Model coefficients were provided for swirl-stabilized flames. The soot differential diffusion model, previously proposed in the literature, based on the residual between the exact term and its model approximation, was also evaluated. The residual correction term further improved the agreement with exact differential diffusion term evaluated with the high-fidelity simulation data in mixture fraction space. The results suggest that the effective turbulent Lewis number can be equal to unity in simulations of turbulent non-premixed recirculating flames.

Soriano, Bruno [Sandia National Laboratories (SNL)↗

Grid Reliability Statistics [SWR-25-45]

This codebase houses a suite of statistical and descriptive analyses of NERC GADS data of interest to grid modelers and planners. This repository is envisioned to house a collection of statistical and descriptive summaries of NERC GADS data, particularly summaries that on their own might be insufficient to warrant publication. In addition, it is intended to disseminate results rather than to enable reproduction as GADS is non-public.

Murphy, Sinnott [National Renewable Energy Laborat↗