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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 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

GNSS Installation at the ARM Southern Great Plains (SGP) Atmospheric Observatory Field Campaign Report

GRUAN, the GCOS Reference Upper Air Network, is an international reference observing network of sites measuring essential climate variables above Earth's surface, designed to fill an important gap in the current global observing system. GRUAN’s objectives are to (1) provide long-term, high-quality climate records, (2) constrain and calibrate data from more spatially comprehensive global observing systems (including satellites and current radiosonde networks), and (3) fully characterize the properties of the atmospheric column.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

GNSS Installation at the Eastern North Atlantic (ENA) Atmospheric Observatory Field Campaign Report

GRUAN, the Global Climate Observing System (GCOS) Reference Upper Air Network, is an international reference observing network of sites measuring essential climate variables above Earth's surface, designed to fill an important gap in the current global observing system. GRUAN’s objectives are to (1) provide long-term, high-quality climate records, (2) constrain and calibrate data from more spatially comprehensive global observing systems (including satellites and current radiosonde networks), and (3) fully characterize the properties of the atmospheric column.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Effects of Cone Penetrometer Testing on Shallow Hydrogeology at a Contaminated Site

Penetration testing is a popular and instantaneous technique for subsurface mapping, contaminant tracking, and the determination of soil characteristics. While the small footprint and reproducibility of cone penetrometer testing makes it an ideal method for in-situ subsurface investigations at contaminated sites, the effects to local shallow groundwater wells and measurable influence on monitoring networks common at contaminated sites is unknown. Physical and geochemical parameters associated with cone penetrometer testing were measured from a transect of shallow groundwater monitoring wells adjacent to penetrometer testing. For wells screened above the depth of cone refusal, the physical advancement and retraction of the cone had a significant effect ( p < 0.01) on water level for several pushes within 10 meters of a monitoring well, and a measured increase in specific conductivity. No effect on geochemistry or water level was observed in continuous monitoring data from wells screened below the depth of cone refusal, but variability in specific conductivity from these wells during penetration testing was only a fraction of the natural variation measured during precipitation events. Continuous measurements of specific conductivity and water level demonstrated that the effects of penetration testing have limited spatial and temporal distributions with a null effect post-testing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

WRF-simulated low-level jets over Iowa: characterization and sensitivity studies

Abstract. Output from 6 months of high-resolution simulations with the Weather Research and Forecasting (WRF) model are analyzed to characterize local low-level jets (LLJs) over Iowa for winter and spring in the contemporary climate. Low-level jets affect rotor plane aerodynamic loading, turbine structural loading and turbine performance, and thus accurate characterization and identification are pertinent. Analyses using a detection algorithm wherein the wind speed above and below the jet maximum must be below 80 % of the jet wind speed within a vertical window of approximately 20–530 m a.g.l. (above ground level) indicate the presence of an LLJ in at least one of the 14 700 4 km×4 km grid cells over Iowa on 98 % of nights. Nocturnal LLJs are most frequently associated with stable stratification and low turbulent kinetic energy (TKE) and hence are more frequent during the winter months. The spatiotemporal mean LLJ maximum (jet core) wind speed is 9.55 m s−1, and the mean height is 182 m. Locations of high LLJ frequency and duration across the state are seasonally varying, with a mean duration of 3.5 h. The highest frequency occurs in the topographically complex northwest of the state in winter and in the flatter northeast of the state in spring. Sensitivity of LLJ characteristics to the (i) LLJ definition and (ii) vertical resolution at which the WRF output is sampled is examined. LLJ definitions commonly used in the literature are considered in the first sensitivity analysis. These sensitivity analyses indicate that LLJ characteristics are highly variable with definition. Use of different definitions identifies both different frequencies of LLJs and different LLJ events. Further, when the model output is down-sampled to lower vertical resolution, the mean jet core wind speed height decreases, but spatial distributions of regions of high frequency and duration are conserved. Implementation of a polynomial interpolation to extrapolate down-sampled output to full-resolution results in reduced sensitivity of LLJ characteristics to down-sampling.

17 WIND ENERGY↗
DOE OSTI2017

Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux ( H ), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed solar-induced fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H , and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on a triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimates of LE, H , and GPP from 2007 to 2015 at 1° × 1° spatial resolution and at monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from the FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analyzing WECANN retrievals across three extreme drought and heat wave events demonstrates the capability of the retrievals to capture the extent of these events. Uncertainty estimates of the retrievals are analyzed and the interannual variability in average global and regional fluxes shows the impact of distinct climatic events – such as the 2015 El Niño – on surface turbulent fluxes and GPP.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Numerical Prediction of CCV in a PFI Engine using a Parallel LES Approach

Cycle-to-cycle variability (CCV) is detrimental to IC engine operation and can lead to partial burn, misfire, and knock. Predicting CCV numerically is extremely challenging due to two key reasons. Firstly, high-fidelity methods such as large eddy simulation (LES) are required to accurately resolve the incylinder turbulent flowfield both spatially and temporally. Secondly, CCV is experienced over long timescales and hence the simulations need to be performed for hundreds of consecutive cycles. Ameen et al. (Int. J. Eng. Res., 2017) developed a parallel perturbation model (PPM) approach to dissociate this long time-scale problem into several shorter timescale problems. The strategy is to perform multiple single-cycle simulations in parallel by effectively perturbing the initial velocity field based on the intensity of the in-cylinder turbulence. This strategy was demonstrated for motored engine and it was shown that the mean and variance of the in-cylinder flowfield was captured reasonably well by this approach. In the present study, this PPM approach is extended to simulate the CCV in a fired port-fuel injected (PFI) SI engine. Two operating conditions are considered – a medium CCV operating case corresponding to 2500 rpm and 16 bar BMEP and a low CCV case corresponding to 4000 rpm and 12 bar BMEP. The predictions from this approach are also shown to be similar to the consecutive LES cycles. Both the consecutive and PPM LES cycles are observed to under-predict the variability in the early stage of combustion. The parallel approach slightly underpredicts the cyclic variability at all stages of combustion as compared to the consecutive LES cycles. However, it is shown that the parallel approach is able to predict the coefficient of variation (COV) of the in-cylinder pressure and burn rate related parameters with sufficient accuracy, and is also able to predict the qualitative trends in CCV with changing operating conditions. The convergence of the statistics predicted by the PPM approach with respect to the number of consecutive cycles required for each parallel simulation is also investigated. It is shown that this new approach is able to give accurate predictions of the CCV in fired engines in less than one-tenth of the time required for the conventional approach of simulating consecutive engine cycles.

Ameen, Muhsin M↗
DOE OSTI2024

Gradient-based surface nuclear magnetic resonance for groundwater investigation

In medical magnetic resonance imaging, spatial localization (imaging) is based upon the application of controlled magnetic field gradients on top of the main magnetic field to spatially modulate the frequency and/or phase of the nuclear magnetic resonance (NMR) signal across the volume of investigation. In this work, we have applied similar physical principles to produce controlled magnetic field gradients during surface NMR-based groundwater investigations. In this approach, a gradient pulse of variable amplitude or duration is applied immediately after the excitation pulse to cause predictable phase encoding of the NMR signal as a function of depth. This approach is also applicable to emerging surface NMR detection methods that use a prepolarization field with fast nonadiabatic turn-off to generate detectable NMR signals from the shallow subsurface. In this case, the gradient pulse is applied after terminating the prepolarization field and provides a heretofore unavailable means of localizing the NMR response as a function of depth. The application of gradients can also be combined with tip-angle-based modulation to yield higher imaging resolution than can be achieved through either gradient- or tip-angle-based imaging alone. We implemented this new gradient-based capability into a surface NMR gradient generation accessory that is compatible with the GMR-Flex instrument and developed surface NMR-specific forward modeling and linear inverse models. We validated the accuracy of this novel gradient-based sNMR technology using computer simulations, experiments using a small pool filled with a discrete layer of bulk water, and field experiments at well-characterized groundwater test sites along Ebey Island, WA, and Larned, KS. The gradient-based sNMR imaging observations were compared with high-resolution direct push NMR results observed at these sites. The results of computer simulations and field experiments indicate improvements in both the detection (signal-to-noise ratio) and spatial resolution of shallow subsurface water content using gradient-based surface NMR, compared with traditional surface NMR imaging methods.

Geochemistry & Geophysics↗
DOE OSTI2018

Parametric Sensitivity and Uncertainty Quantification in the Version 1 of E3SM Atmosphere Model Based on Short Perturbed Parameter Ensemble Simulations

Abstract The atmospheric component of Energy Exascale Earth System Model version 1 has included many new features in the physics parameterizations compared to its predecessors. Potential complex nonlinear interactions among the new features create a significant challenge for understanding the model behaviors and parameter tuning. Using the one‐at‐a‐time method, the benefit of tuning one parameter may offset the benefit of tuning another parameter, or improvement in one target variable may lead to degradation in another target variable. To better understand the Energy Exascale Earth System Model version 1 model behaviors and physics, we conducted a large number of short simulations (three days) in which 18 parameters carefully selected from parameterizations of deep convection, shallow convection, and cloud macrophysics and microphysics were perturbed simultaneously using the Latin hypercube sampling method. From the perturbed parameter ensemble simulations and use of different skill score functions, we identified the most sensitive parameters, quantified how the model responds to changes of the parameters for both global mean and spatial distribution, and estimated the maximum likelihood of model parameter space for a number of important fidelity metrics. Comparison of the parametric sensitivity using simulations of two different lengths suggests that perturbed parameter ensemble using short simulations has some bearing on understanding parametric sensitivity of longer simulations. Results from this analysis provide a more comprehensive picture of the Energy Exascale Earth System Model version 1 behavior. The difficulty in reducing biases in multiple variables simultaneously highlights the need of characterizing model structural uncertainty (so‐called embedded errors) to inform future development efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A gradient-based deep neural network model for simulating multiphase flow in porous media

We report simulation of multiphase flow in porous media is crucial for the effective management of subsurface energy and environment-related activities. The numerical simulators used for modeling such processes rely on spatial and temporal discretization of the governing mass and energy balance partial-differential equations (PDEs) into algebraic systems via finite-difference/volume/element methods. These simulators usually require dedicated software development and maintenance, and suffer low efficiency from a runtime and memory standpoint for problems with multi-scale heterogeneity, coupled-physics processes or fluids with complex phase behavior. Therefore, developing cost-effective, data-driven models can become a practical choice, and in this work, we choose deep learning approaches as they can handle high dimensional data and accurately predict state variables with strong nonlinearity. In this paper, we describe a gradient-based deep neural network (GDNN) constrained by the physics related to multiphase flow in porous media. We tackle the nonlinearity of flow in porous media induced by rock heterogeneity, fluid properties, and fluid-rock interactions by decomposing the nonlinear PDEs into a dictionary of elementary differential operators. We use a combination of operators to handle rock spatial heterogeneity and fluid flow by advection. Since the augmented differential operators are inherently related to the physics of fluid flow, we treat them as first principles prior knowledge to regularize the GDNN training. We use the example of pressure management at geologic CO 2 storage sites, where CO 2 is injected in saline aquifers and brine is produced, and apply GDNN to construct a predictive model that is trained with physics-based simulation data and emulates the physics process. We demonstrate that GDNN can effectively predict the nonlinear patterns of subsurface responses, including the temporal and spatial evolution of the pressure and saturation plumes. We also successfully extend the GDNN to convolutional neural network (CNN), namely gradient-based CNN (GCNN), and validate its capability to improve the prediction accuracy. GDNN has great potential to tackle challenging problems that are governed by highly nonlinear physics and enable the development of data-driven models with higher fidelity.

42 ENGINEERING↗
DOE OSTI2015

Incorporating an advanced aerosol activation parameterization into WRF-CAM5: Model evaluation and parameterization intercomparison

Aerosol activation into cloud droplets is an important process that governs aerosol indirect effects. The advanced treatment of aerosol activation by Fountoukis and Nenes (2005) and its recent updates, collectively called the FN series, have been incorporated into a newly developed regional coupled climate-air quality model based on the Weather Research and Forecasting model with the physics package of the Community Atmosphere Model version 5 (WRF-CAM5) to simulate aerosol-cloud interactions in both resolved and convective clouds. The model is applied to East Asia for two full years of 2005 and 2010. A comprehensive model evaluation is performed for model predictions of meteorological, radiative, and cloud variables, chemical concentrations, and column mass abundances against satellite data and surface observations from air quality monitoring sites across East Asia. The model performs overall well for major meteorological variables including near-surface temperature, specific humidity, wind speed, precipitation, cloud fraction, precipitable water, downward shortwave and longwave radiation, and column mass abundances of CO, SO 2 , NO 2 , HCHO, and O 3 in terms of both magnitudes and spatial distributions. Larger biases exist in the predictions of surface concentrations of CO and NO x at all sites and SO 2 , O 3 , PM 2.5 , and PM 10 concentrations at some sites, aerosol optical depth, cloud condensation nuclei over ocean, cloud droplet number concentration (CDNC), cloud liquid and ice water path, and cloud optical thickness. Compared with the default Abdul-Razzack Ghan (2000) parameterization, simulations with the FN series produce ~107–113% higher CDNC, with half of the difference attributable to the higher aerosol activation fraction by the FN series and the remaining half due to feedbacks in subsequent cloud microphysical processes. With the higher CDNC, the FN series are more skillful in simulating cloud water path, cloud optical thickness, downward shortwave radiation, shortwave cloud forcing, and precipitation. Here, the model evaluation identifies several areas of improvements including emissions and their vertical allocation as well as model formulations such as aerosol formation, cloud droplet nucleation, and ice nucleation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Influence of Soil Moisture on the Development of Organized Convective Systems in South America

Abstract This study investigates the role of soil moisture (SM) on the initiation and organization of convective systems using the convection‐permitting ICOsahedral Non‐hydrostatic (ICON) model. We conduct two sets of experiments: a Control experiment with interactive SM and a fixed SM experiment (FixedSM) with invariable SM conditions. We focus on two regions in South America: the Amazon and southeastern South America (SESA). Larger organized convective systems are associated with greater SM heterogeneity in both regions, though other large‐scale synoptic influences affect the robustness of this relationship in SESA. These results remain largely unaffected by disabling the effects of precipitation on SM in the FixedSM experiment, and complementary analyses using satellite‐based estimates of SM and precipitation support these findings. Spatial compositing of mesoscale environments in the Amazon shows the presence of well‐defined SM gradients, at a length scale of a few hundred kilometers, many hours before convective system detection. Larger SM gradients correspond to larger gradients in thermodynamic variables, particularly surface temperature and sensible heat flux, and are associated with larger convective systems. Overall, our findings suggest that surface heterogeneities such as SM gradients not only affect deep convection initiation, as previously suggested, but they can also encourage the growth and organization of convective systems into larger clusters, particularly in the absence of significant synoptic influences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Method for computationally efficient design of dielectric laser accelerator structures

Here, dielectric microstructures have generated much interest in recent years as a means of accelerating charged particles when powered by solid state lasers. The acceleration gradient (or particle energy gain per unit length) is an important figure of merit. To design structures with high acceleration gradients, we explore the adjoint variable method, a highly efficient technique used to compute the sensitivity of an objective with respect to a large number of parameters. With this formalism, the sensitivity of the acceleration gradient of a dielectric structure with respect to its entire spatial permittivity distribution is calculated by the use of only two full-field electromagnetic simulations, the original and ‘adjoint’. The adjoint simulation corresponds physically to the reciprocal situation of a point charge moving through the accelerator gap and radiating. Using this formalism, we perform numerical optimizations aimed at maximizing acceleration gradients, which generate fabricable structures of greatly improved performance in comparison to previously examined geometries.

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

Toward quantum plasmonic networks

Here, we demonstrate the transduction of macroscopic quantum entanglement by independent, distant plasmonic structures embedded in separate thin silver films. In particular, we show that the plasmon-mediated transmission through each film conserves spatially dependent, entangled quantum images, opening the door for the implementation of parallel quantum protocols, super-resolution imaging, and quantum plasmonic sensing geometries at the nanoscale level. The conservation of quantum information by the transduction process shows that continuous variable multi-mode entanglement is momentarily transferred from entangled beams of light to the space-like separated, completely independent plasmonic structures, thus providing a first important step toward establishing a multichannel quantum network across separate solid-state substrates.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Geospatial and Information Substitution and Anonymization Tool (GISA)

The Geospatial and Information Substitution and Anonymization Tool (GISA) incorporates techniques for obfuscating identifiable information from point data or documents, while simultaneously maintaining chosen variables to enable future use and meaningful analysis. This approach promotes collaboration and data sharing while also reducing the risk of exposure to sensitive information. GISA can be used in a number of different ways, including the anonymization of point spatial data, batch replacement/removal of user-specified terms from file names and from within file content, and aid with the selection and redaction of images and terms based on recommendations using natural language processing. Version 1 of the tool, published here, has updated functionality and enhanced capabilities to the beta version published in 2023. Please see User Documentation for further information on capabilities, as well as a guide for how to download and use the tool. If there are any feedback you would like to provide for the tool, please reach out with your feedback to edxsupport@netl.doe.gov. Disclaimer: This project was funded by the United States Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. The Geospatial and Information Substitution and Anonymization Tool (GISA) was developed jointly through the U.S. DOE Office of Fossil Energy and Carbon Management’s EDX4CCS Project, in part, from the Bipartisan Infrastructure Law.

Bipartisan Infrastructure Law↗
DOE OSTI2018

Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) Ice Nucleating Particle Measurements Field Campaign Report

Evidence exists that sea-spray-sourced ice nucleating particles (INPs) are organic in origin, resulting from biological and biochemical processes in seawater and the sea surface microlayer, and represent a distinctly different and variable, but oftentimes much less effective, INP population in comparison to long-range transported desert dust (DeMott et al. 2016; McCluskey et al. 2018a). Over the Southern Ocean (SO), far from land sources, these INPs may dominate in the marine boundary layer (McCluskey et al. 2018b). Thereby, marine INPs may affect cloud phase and lifetime in this region where pervasive supercooled and mixed-phase boundary-layer clouds appear underrepresented in climate models. This motivated collection of ice nucleating particle concentration measurements during the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility Measurements of Aerosols, Radiation and Clouds over the Southern Ocean (MARCUS) deployment of the second ARM Mobile Facility (AMF2) on the Aurora Australis vessel from early November, 2017 (Austral spring) to late March, 2018 (Austral fall). During this period, four voyages were made from Hobart, Tasmania to the Australian Antarctic Division bases at Casey, Davis, and Mawson, as well as a final resupply cruise to Macquarie Island, the sight of the related Macquarie Island Cloud and Radiation Experiment (MICRE) campaign. Aerosol filter collections of alternating 24- and 48-hour duration were made during all voyages. These filters were returned frozen for subsequent processing to determine variability in the temperature spectrum of the concentrations of INPs active via the immersion freezing mechanism and to conduct additional analyses to indicate the chemical and biological nature of INPs over the SO. The MARCUS data set provides the first spatial and seasonal picture of INP concentrations over the SO in more than four decades, complementing the full annual cycle of INP data we collected for a point location in MICRE.

Ice nucleation, aerosols, arctic processes, marine↗
DOE OSTI2022

The global decline in the sensitivity of vegetation productivity to precipitation from 2001 to 2018

The sensitivity of vegetation productivity to precipitation (S ppt ) is a key metric for understanding the variations in vegetation productivity under changing precipitation and predicting future changes in ecosystem functions. However, a comprehensive assessment of S ppt over all the global land is lacking. Here, we investigated spatial patterns and temporal changes of S ppt across the global land from 2001 to 2018 with multiple streams of satellite observations. We found consistent spatial patterns of S ppt with different satellite products: S ppt was highest in dry regions while low in humid regions. Grassland and shrubland showed the highest S ppt , and evergreen needle-leaf forest and wetland showed the lowest. Temporally, S ppt showed a generally declining trend over the past two decades (p < .05), yet with clear spatial heterogeneities. The decline in S ppt was especially noticeable in North America and Europe, likely due to the increase in precipitation. In central Russia and Australia, however, S ppt showed an increasing trend. Biome-wise, most ecosystem types exhibited significant decrease in S ppt , while grassland, evergreen broadleaf forest, and mixed forest showed slight increases or non-significant changes in S ppt . Our finding of the overall decline in S ppt implies a potential stabilization mechanism for ecosystem productivity under climate change. However, the revealed S ppt increase for some regions and ecosystem types, in particular global grasslands, suggests that grasslands might be increasingly vulnerable to climatic variability with continuing global climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Renewable Energy Potential (reV) Model: A Geospatial Platform for Technical Potential and Supply Curve Modeling

The Renewable Energy Potential (reV) model is a platform for detailed assessment of renewable energy (RE) resources and their geospatial intersection with grid infrastructure and land use characteristics. The reV model currently supports photovoltaic (PV), concentrating solar power (CSP) and land-based wind turbine technologies. Modules in the reV framework function at different spatial and temporal resolutions, allowing for assessment of resource potential, technical potential and supply curves at varying levels of detail. The platform runs on NREL's High Performance Computing system, providing scalable and efficient performance from a single location all the way up to continental scales, for a single year or decades of time series resource data. Coupled with NREL's System Advisor Model (SAM), reV supports resource assessment from 5-minute to hourly temporal resolution and provides for analysis of long-term (i.e., year-on-year) variability of RE generation (e.g., interannual variability and exceedance probabilities). Technical potential is measured as a function of resource potential and limitations put on developable land area defined by the user. For example, the user can limit development by land ownership, terrain, land use/cover, and urban areas, as well as custom inputs. Technology, grid interconnection and operation costs, based on the latest market data and future projections, are also embedded in the model. The supply curve module is a spatial sorting algorithm based on plant siting, grid interconnection cost, and regional competition, which provides a geographically discrete estimate of levelized cost of electricity (LCOE) and supply (i.e., capacity) for specific renewable technologies. The reV model currently provides broad coverage across North America, South and Central Asia, South America and South Africa to inform national- and international-scale analyses as well as regional infrastructure and deployment planning.

13 HYDRO ENERGY↗
DOE OSTI2021

Beyond Taylor’s hypothesis: a novel volumetric reconstruction of velocity and density fields for variable‑density and shear flows

Abstract This work presents a novel numerical procedure for reconstructing volumetric density and velocity fields from planar laser-induced fluorescence (PLIF) and stereoscopic particle image velocimetry (SPIV) data. This new method is theoretically and practically demonstrated to provide more accurate 3D vortical structures and density fields in high shear flows than reconstruction methods based on the mean convective velocity. While Taylor’s hypothesis of frozen turbulence is commonly applied by using the local mean streamwise velocity, the proposed algorithm uses the measured local instantaneous velocity for data convection. It consists of a step-by-step reconstruction based on a mixed Lagrangian–Eulerian solver that includes the 3D interpolation of scattered flow data and that relaxes the Taylor’s hypothesis by iterative enforcement of the incompressibility constraint on the velocity field. This methodology provides 3D fields with temporal resolution, spatial resolution, and accuracy comparable to that of real 3D snapshots, thus providing a practical alternative to tomographic measurements. The procedure is validated using numerical data of the constant-density channel flow available on the Johns Hopkins University Turbulence Database (JHTDB), showing the accurate reconstruction of the 3D velocity field. The algorithm is applied to an experimental dataset of PLIF and SPIV measurements of a variable-density jet flow, demonstrating its capability to provide 3D velocity and density fields that are more consistent with the Navier–Stokes equations compared to the mean flow convective method. Graphic abstract

42 ENGINEERING↗