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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Cleaned 5-Minute Resolution Air Quality and Meteorological Data from Nine TCEQ CAMS Sites in Houston, Texas (Nov 2021 – Oct 2022)

These data encompass 5-minute air monitoring and meteorological observations collected in the greater Houston, Texas metropolitan region, at nine (9) Continuous Ambient Monitoring Stations (CAMS) operated by the Texas Commission on Environmental Quality (TCEQ) between November 1, 2021 and October 31, 2022. The CAMS sites (CAMS 1, 8, 35, 45, 148, 403, 405, 410, and 1052) were chosen because their instrumentation includes measurements of PM2.5. These sites also provide continuous multi-parameter air-quality and meteorological measurements. Particulate matter (PM2.5, PM10) was sampled along with several trace gases, including ozone (O3), nitrogen oxides (NO, NO2, NOx), sulfur dioxide (SO2), and carbon monoxide (CO). The data set also contains standard surface meteorological parameters (temperature, humidity, pressure, wind speed, and wind direction). Several sites also include AutoGC-based measurements of volatile organic compounds (VOCs). Air monitoring instruments deployed at the selected sites comprise the following systems: BAM-1020 or TEOM (PM2.5), Thermo Scientific TEI 49i (O3), TEI 42i (NOx), and AutoGCs (VOCs). This data set is similar to the data included within the houairq5mX1.00 datastream, except for a few additional quality control steps. A systematic data cleaning and verification process was performed on the data set to ensure its quality and preparation for analysis. Removal of non-numeric status flags (e.g., [LIM], [QAS], [SPZ], [CAL], [PMA], [AQI], [SPN], [MAL]) was accomplished by employing rule-based string parsing to extract valid numerical values. Missing entries were set to -9999; however, invalid or anomalous values (e.g., 99999) were retained as originally reported by the TCEQ to preserve data provenance. The time sequence was verified for completeness, removal of duplicates, and uniformity at 5-minute intervals. Column labeling was standardized, and corresponding values were assessed for physical plausibility. All timestamps in the data set were reported in Coordinated Universal Time (UTC) as provided by the TCEQ. Further, the latitude and longitude coordinates were added for each CAMS site. A subset of the data (June 1–September 30, 2022) has been used in the following publication: Subba et al. 2025. “Implications of sea breeze circulations on boundary layer aerosols in the southern coastal Texas region.” EGUsphere 2025: 1–49, https://doi.org/10.5194/egusphere-2025-2659.

latitude↗
DOE OSTI2023

Bayesian sequential optimal experimental design for nonlinear models using policy gradient reinforcement learning

We present a mathematical framework and computational methods for optimally designing a finite sequence of experiments. This sequential optimal experimental design (sOED) problem is formulated as a finite-horizon partially observable Markov decision process (POMDP) under a Bayesian setting and with information-theoretic utilities. The formulation is general and may accommodate continuous random variables, non-Gaussian posteriors, and nonlinear forward models. The sOED design policy incorporates elements of feedback and lookahead simultaneously, and we show it to generalize the commonly-used batch and greedy design strategies. We solve for the sOED policy using the policy gradient (PG) method from reinforcement learning, and provide a derivation for the PG expression in the sOED context. Adopting an actor-critic approach, the policy and value functions are parameterized using deep neural networks and improved via PG estimates produced from simulated episodes of designs and observations. The new PG-sOED algorithm is first validated on a linear-Gaussian benchmark, and then compared against other design baselines on a sensor movement problem for contaminant source inversion in a convection-diffusion field. As a result, we provide explanation for the policy behaviors using knowledge of the underlying physical process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Development of simplified model for injection rate prediction of diesel injectors during transient and steady operation

Determining the injection rate of the diesel injector during the transient short injection operations is a critical matter to enhance the combustion process by improving air/fuel mixture formation. In this regard, numerous injection models have been suggested in the literature to predict the injection rate accurately. However, such algorithms have not precisely predicted the rate of injection (ROI) in transient short injection events. In addition, most of those models require many input parameters to characterize hydraulic and mechanical subsystems in the injector, which can make them inconvenient to tune for different injector specifications, in other words not suitable for universal usage. Therefore, our study aims to develop a simple injection model that can predict injection rate precisely in the short and long injection durations considering relatively fewer input parameters. The model traces the pressure variation inside the sac based on the bulk modulus theory to predict nozzle exit velocity, thus the injection rate of the injector. In the algorithm, the bulk modulus of fuel (E) and discharge coefficients of the nozzle hole and needle seat (C d,H and C d,N ) have been considered to vary rather than set constant during the injection process since such flow parameters would have a critical impact on the flow development at the transient stage, thus affecting the injection rate of diesel injectors. Here with this modelling strategy, the accuracy of the new model on ROI prediction was confirmed with acceptable model error for both short and long injection durations by validating against experimental results. To understand the effect of the new model scheme on ROI prediction, ROI results with the new model have been discussed by comparing it with the ROI results predicted with constant flow parameters.

42 ENGINEERING↗
DOE OSTI2019

Computational experiments to characterize bubble formation and movement in waste glass foam layer

Computer simulation experiments are a valuable complement to physical experimentation for systems where there is limited access to quantities of interest. The motivation for this work is to characterize the fluid dynamic and heat transfer processes that affect melt rate of waste slurry during vitrification, which can limit the throughput rate of the melter. Heat transfer to the cold cap from the molten glass below this layer is an important factor determining melt rate. In this study, computational fluid dynamics is used to elucidate the behavior of gas-filled cavities that form underneath the cold cap in a laboratory-scale waste glass melter. The cold cap consists of a reacting feed layer above a foam layer. The cavities are a result of coalesced gases from collapsing primary foam and air from forced convection bubblers. Bubbles in the foam layer move horizontally with the circulation pattern of the melt and escape around the edges of the cold cap to the plenum above. The effects of varying thermophysical properties and foam parameters based on the bounds of high-level waste glass formulations were explored. Using the DAKOTA toolkit, a Latin hypercube space-filling design was used to randomly distribute samples in bins to achieve good coverage over the parameter space with minimal points. Using the results from the set of simulations, a Gaussian process model with a linear trend was used to create response surfaces for several key quantities of interest, including temperatures, temperature gradients, velocities, and foam extent beneath the cold cap.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Estimation of process steady state with autoregressive models and Bayesian inference

To improve efficiency, separations engineers will typically design process circuits containing recirculating streams, which mix one or more of the process outputs with the feed material. Doing so can improve efficiency, but will cause a delay in the system reaching steady state conditions until the recirculating load mass flows stabilize. In testing separation circuits, engineers will often test a variety of factors and complete an analysis from sample results. Knowledge of if a process is at steady state, as well as the steady state conditions of a process, is essential for a valid techno-economic analysis. However, the definition of process steady state is often poorly defined, or does not include uncertainty quantification. If the performance of a process operating under two different sets of conditions are compared, an engineer who does not test for steady state or quantify steady state conditions risks producing a faulty analysis. In this work, a Bayesian statistical method for testing if all streams are at steady state is further motivated and then derived. Then after testing for steady state, the same model is used with a prior distribution that enforces a steady state assumption to estimate steady state conditions. Further, these methods were validated in a solvent extraction pilot plant where steady state conditions for all outflows were inferred with uncertainty quantification. Analysis is completed with functions available to the reader as part of the BayesMassBal (V 1.1.0) software package written in R.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

A Bayesian Approach for Statistical–Physical Bulk Parameterization of Rain Microphysics. Part I: Scheme Description

A novel framework is proposed for the bulk parameterization of rain microphysics: the Bayesian Observationally Constrained Statistical–Physical Scheme (BOSS). It is designed to facilitate direct constraint by observations using Bayesian inference. BOSS combines existing process-level microphysical knowledge with flexible process rate formulations and parameters constrained by observations within a Bayesian framework. Furthermore, using a raindrop size distribution (DSD) normalization method that relates DSD moments to one another via generalized power series, generalized multivariate power expressions are derived for the microphysical process rates as functions of a set of prognostic DSD moments. The scheme is flexible and can utilize any number and combination of prognostic moments and any number of terms in the process rate formulations. This means that both uncertainty in parameter values and structural uncertainty associated with the process rate formulations can be investigated systematically, which is not possible using traditional schemes. In this paper, BOSS is compared to two- and three-moment versions of a traditional bulk rain microphysics scheme (denoted as MORR). It is demonstrated that some process formulations in MORR are analytically equivalent to the generalized power expressions in BOSS using one or two terms, while others are not. BOSS is able to replicate the behavior of MORR in idealized one-dimensional rainshaft tests, but with a much more flexible and systematic design. Part II of this study describes the application of BOSS to derive rain microphysical process rates and posterior parameter distributions in Bayesian experiments using Markov chain Monte Carlo sampling constrained by synthetic observations.

58 GEOSCIENCES↗
DOE OSTI2020

Survey of Modeling and Simulation Techniques for Advanced Manufacturing Technologies Volume II – Predicting Material Performance from Material Microstructure

This report describes the current state of modeling and simulation techniques for predicting the properties of materials fabricated with advanced manufacturing techniques, given the initial microstructure of the material. The report includes a literature survey and a gap analysis outlining and prioritizing key issues in applying these modeling and simulation techniques to nuclear reactor structural materials. The discussion covers both physics-based and data-driven modeling techniques and includes a broad range of manufacturing techniques and materials that may have future nuclear applications. This report is the second in a two-part series, with the first report covering modeling and simulation methods for predicting the initial, as-manufactured structure of advanced manufacturing materials, given a description of the process. Both reports focus on a set of manufacturing technologies likely to be applied to reactor structural components. Taken together, the two reports provide a complete summary of the current state of processing-structure-properties models for advanced manufacturing as well as a survey of applications to reactor structural materials

42 ENGINEERING↗
DOE OSTI2024

HERO WEC 2024 Hydraulic Configuration Deployment Data

The following submission includes raw and processed data from the in water deployment of NREL's Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC), in the form of parquet files, TDMS files, CSV files, bag files and MATLAB workspaces. This dataset was collected in March 2024 at the Jennette's pier test site in North Carolina. This submission includes the following: - Data description document (HERO WEC FY24 Hydraulic Deployment Data Descriptions.doc) - This document includes detailed descriptions of the type of data and how it was processed and/or calculated. - Processed MATLAB workspace - The processed data is provided in the form of a single MATLAB workspace containing data from the full deployment. This workspace contains data from all sensors down sampled to 10 Hz along with all array Value Added Products (VAPs). - MATLAB visualization scripts - The MATLAB workspaces can be visualized using the file "HERO_WEC_2024_Hydraulic_Config_Data_Viewer.m/mlx". The user simply needs to download the processed MATLAB workspaces, specify the desired start and end times and run this file. Both the .m and .mlx file format has been provided depending on the user's preference. - Summary Data - The fully processed data was used to create a summary data set with averages and important calculations performed on 30-minute intervals to align with the intervals of wave resource data reported from nearby CDIP ocean observing buoys located 20km East of Jennette's pier and 40km Northeast of Jennette's pier. The wave resource data provided in this data set is to be used for reference only due the difference in water depth and proximity to shore between the Jennette's pier test site and the locations of the ocean observing buoys. This data is provided in the Summary Data zip folder, which includes this data set in the form of a MATLAB workspace, parquet file, and excel spreadsheet. - Processed Parquet File - The processed data is provided in the form of a single parquet file containing data from all HERO WEC sensors collected during the full deployment. Data in these files has been down sampled to 10 Hz and all array VAPs are included. - Interim Filtered Data - Raw data from each sensor group partitioned into 30-minute parquet files. These files are outputs from an intermediate stage of data processing and contain the raw data with no Quality Control (QC) or calculations performed in a format that is easier to use than the raw data. - Raw Data - Raw, unprocessed data from this deployment can be found in the Raw Data zip folder. This data is provided in the form of TDMS, CSV, and bag files in the original format output by the MODAQ system. - Python Data Processing Script - This links to an NREL public github repository containing the python script used to go from raw data to fully processed parquet files. Additional documentation on how to use this script is included in the github repository. This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Collapsar Gamma-Ray Bursts Grind Their Black Hole Spins to a Halt

Abstract The spin of a newly formed black hole (BH) at the center of a massive star evolves from its natal value due to two competing processes: accretion of gas angular momentum that increases the spin and extraction of BH angular momentum by outflows that decreases the spin. Ultimately, the final, equilibrium spin is set by a balance between both processes. In order for the BH to launch relativistic jets and power aγ-ray burst (GRB), the BH magnetic field needs to be dynamically important. Thus, we consider the case of a magnetically arrested disk (MAD) driving the spin evolution of the BH. By applying the semianalytic MAD BH spin evolution model of Lowell et al. to collapsars, we show that if the BH accretes ∼20% of its initial mass, its dimensionless spin inevitably reaches small values,a≲ 0.2. For such spins, and for mass accretion rates inferred from collapsar simulations, we show that our semianalytic model reproduces the energetics of typical GRB jets,L jet ∼ 10 50 erg s −1 . We show that our semianalytic model reproduces the nearly constant power of typical GRB jets. If the MAD onset is delayed, this allows powerful jets at the high end of the GRB luminosity distribution,L jet ∼ 10 52 erg s −1 , but the final spin remains low,a≲ 0.3. These results are consistent with the low spins inferred from gravitational wave detections of binary BH mergers. In a companion paper by Gottlieb et al., we use GRB observations to constrain the natal BH spin to bea≃ 0.2.

Astronomy & Astrophysics↗
DOE OSTI2023

E3SMv1 simulation output from sensitivity study on aerosol process coupling

This dataset contains annual mean and 6-hourly output data from a set of E3SMv1 simulations that demonstrates the impact of numerical process coupling on the simulated dust life cycle. The simulations are documented and analyzed in the attached manuscript (pdf file). Publishing the manuscript in a peer-reviewed journal will require making the model output publicly available. We plan to use DataHub for that. The data files to be uploaded are currently saved on Compy under /compyfs/wanh895/scidac_share/cflx_2023/

Wan, Hui↗
DOE OSTI2026

Predicting compatibility between ferredoxins and the Fe protein of nitrogenase using in silico protein modeling

Biological nitrogen fixation is the process by which certain bacteria and archaea use the enzyme nitrogenase to reduce atmospheric nitrogen into bioavailable ammonium. Engineering non‐nitrogen‐fixing organisms, like plants, to use nitrogenase could reduce dependency on synthetic fertilizer and mitigate the environmental impacts of industrial fertilizer production. However, nitrogenase activity requires delivery of reducing power by small electron carrying proteins known as ferredoxins and flavodoxins, and successfully engineering nitrogenase into new systems will require a mechanistic understanding of electron delivery by these proteins. Most organisms often have multiple ferredoxins, raising the question of which ferredoxin can support nitrogenase activity. The purpose of this study is to gain insight into how we can predict which ferredoxin is compatible with the Fe protein, the component of nitrogenase that interacts with ferredoxin or flavodoxin. Our in silico protein–protein docking simulations reveal that most ferredoxins and flavodoxins involved in nitrogen fixation have the shortest distance (≤10 Å) between their redox cofactor and the [4Fe‐4S] cluster of the Fe protein. We found shorter cofactor distance contributes to faster intermolecular electron tunneling rates. Bacterial ferredoxins that play a role in nitrogen fixation also exhibit more complementary interactions with the Fe protein than bacterial and plant ferredoxins not involved in this process. Heterologous expression of a set of ferredoxins from both nitrogen‐fixing and non‐nitrogen‐fixing bacteria in the diazotroph Rhodopseudomonas palustris supports our model‐derived prediction that shorter distances between the electron‐carrying cofactors favor nitrogenase compatibility. These findings offer a framework to predict and potentially enhance ferredoxin–nitrogenase compatibility, which will help to improve our ability to engineer nitrogen fixation into non‐nitrogen‐fixing organisms like plants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Understanding Reproducibility of Sputter‐Deposited Metastable Ferroelectric Wurtzite Al 0.6 Sc 0.4 N Films Using In Situ Optical Emission Spectrometry

High‐Sc Al 1– x Sc x N thin films are of tremendous interest because of their attractive piezoelectric and ferroelectric properties, but overall film quality and reproducibility are widely reported to suffer as x increases. In this study, structural and electrical properties of metastable Al 0.6 Sc 0.4 N films are connected with plasma changes during film growth, identified via glow discharge optical emission spectroscopy (GD‐OES), and linked to the target mode changes. This in situ GD‐OES technique uses changes in the N 2 (I) intensity, correlated with DC bias hysteresis behavior of a Al 0.6 Sc 0.4 target in metallic and poisoned modes, to identify films that subsequently exhibit unacceptable structural and electrical performance. Two representative samples deposited under identical conditions but possessing distinct properties related to phases present in the films are focused on. Films sputtered under a poisoned target mode produce pure wurtzite ferroelectric Al 0.6 Sc 0.4 N with a reversible 80 μC cm −1 polarization and 3.1 MV cm −1 coercive field. When identical chamber settings are used but the process starts in metallic mode, a mixed wurtzite/rocksalt film is deposited which exhibits nanometer‐scale changes to the film microstructure and a nonferroelectric response. These results illustrate the utility of optical emission spectroscopy for tracking target mode fluctuations when fabricating metastable materials such as high‐Sc Al 1– x Sc x N films.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Holomorphic modular bootstrap revisited

In this work we revisit the “holomorphic modular bootstrap”, i.e. the classification of rational conformal field theories via an analysis of the modular differential equations satisfied by their characters. By making use of the representation theory of PSL(2, $\mathbb{Z}_n$), we describe a method to classify allowed central charges and weights (c, h i ) for theories with any number of characters d. This allows us to avoid various bottlenecks encountered previously in the literature, and leads to a classification of consistent characters up to d = 5 whose modular differential equations are uniquely fixed in terms of (c, h i ). In the process, we identify the full set of constraints on the allowed values of the Wronskian index for fixed d ≤ 5.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

An empirical investigation of organic software product lines

Abstract Software product line engineering is a best practice for managing reuse in families of software systems that is increasingly being applied to novel and emerging domains. In this work we investigate the use of software product line engineering in one of these new domains, synthetic biology. In synthetic biology living organisms are programmed to perform new functions or improve existing functions. These programs are designed and constructed using small building blocks made out of DNA. We conjecture that there are families of products that consist of common and variable DNA parts, and we can leverage product line engineering to help synthetic biologists build, evolve, and reuse DNA parts. In this paper we perform an investigation of domain engineering that leverages an open-source repository of more than 45,000 reusable DNA parts. We show the feasibility of these new types of product line models by identifying features and related artifacts in up to 93.5% of products, and that there is indeed both commonality and variability. We then construct feature models for four commonly engineered functions leading to product lines ranging from 10 to 7.5 × 10 20 products. In a case study we demonstrate how we can use the feature models to help guide new experimentation in aspects of application engineering. Finally, in an empirical study we demonstrate the effectiveness and efficiency of automated reverse engineering on both complete and incomplete sets of products. In the process of these studies, we highlight key challenges and uncovered limitations of existing SPL techniques and tools which provide a roadmap for making SPL engineering applicable to new and emerging domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Formation of helium-bubble networks in tungsten

The nucleation and subsequent growth of helium bubbles in bulk tungsten is investigated using molecular dynamics simulations. By considering a setting that includes the diffusion process of helium clusters, we study their attachment to existing bubbles and their interaction with tungsten crowdion structures generated in the bubble growth process. Here, we find that incoming helium atoms, and especially small helium clusters, can become trapped in the crowdion structures, providing nucleation sites for new helium bubbles, and leading to a distributed network of bubbles rather than a single, growing bubble. The nature of this network depends on both the temperature and the implantation flux of helium. Our results indicate that the kinetic interaction of He with generated dislocations is a key factor dictating the evolution of bubble distributions in plasma-exposed tungsten.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Restoring ZSM-5 performance for catalytic fast pyrolysis of biomass: Effect of regeneration temperature

Zeolite catalysts used for biomass catalytic fast pyrolysis (CFP) deactivate rapidly, similar to a fluidized catalytic cracking (FCC) catalyst used in refining. To operate effectively when there is rapid deactivation, biomass CFP can take place in a riser FCC-style reactor in which the catalyst has a short contact time (seconds) with reactants before it is regenerated. The regeneration, therefore, has two major needs for effective operation: 1) heat balance, since the heat required for the CFP reactions is brought into the reactor by the hot catalyst and 2) relatively short (minutes) regeneration to restore the catalyst activity to be near its initial state. In order to understand effective conditions to regenerate zeolites used for CFP, a series of experiments were performed to determine the effect of regeneration temperature on the activity of ZSM-5 (SiO 2 /Al 2 O 3 = 30). After use for pine pyrolysis vapor upgrading, the catalyst was oxidized in 4% O 2 at temperatures between 500-700 °C and reevaluated for the upgrading of pine pyrolysis vapors to assess the extent of regeneration. Additional testing was performed using ethylene aromatization as a surrogate reaction to probe regeneration efficiency. Regeneration experiments were performed for either a fixed length of time (20 min) or until there was no further CO 2 measured in the effluent gas. Results from the ethylene aromatization reactions were shown to serve as an excellent surrogate for CFP reactivity and indicated that the use of model compound studies can effectively be used to understand reaction and regeneration processes from biomass CFP. Both sets of results indicate that a spent ZSM-5 used for biomass CFP could be fully regenerated at 650 degrees C and 700 °C within 20 min, whereas regeneration temperatures of 550 °C and 600 °C required longer regeneration temperatures and in the case of regeneration at 500 °C, there may be coke species that are not removed and the catalyst activity may never be fully restored. Characterization by pyridine diffuse reflectance infrared spectroscopy, thermogravimetric analysis coupled with infrared spectroscopy, and N 2 physisorption showed that higher regeneration temperatures are more effective for restoring Bronsted acid sites and catalyst mesoporosity by rapidly removing aromatic coke deposits. In conclusion, regeneration at 650 °C and 700 °C led to a slightly higher total porosity as compared to the pristine catalyst, which was attributed to the formation of additional mesoporosity from catalyst steaming.

09 BIOMASS FUELS↗
DOE OSTI2021

Microstructural basis for improved corrosion resistance of laser surface processed AZ31 Mg alloy

Despite their excellent strength to weight ratio, wider use of Mg alloys for light weighting applications is limited by their poor corrosion resistance, especially in chloride ion-containing environments. The present study shows improved corrosion resistance imparted by laser surface processing (LSP) of a commercial AZ31 (Mg-3Al-1Zn) alloy. Nanosecond laser processing at three different power settings was carried out on the surface of a 1 mm thick rolled AZ31 sheet. Corrosion testing based on ASTM B117 method and several other electrochemical tests indicates substantial enhancement of corrosion resistance in LSP-treated AZ31. The underlying reason behind improved corrosion resistance of LSP-AZ31 surface has been identified using a variety of characterization tools (SEM, TEM, XPS). Formation of a ~0.5 µm thick mixed metal (Mg, Al) oxide surface film together with refinement in the size and number density of Al-Mn intermetallic particles appear to be playing a major role towards improved corrosion resistance after LSP treatment of AZ31 alloy.

Jana, Saumyadeep↗
DOE OSTI2023

Phase imaging of irradiated foils at the OMEGA EP facility using phase-stepping X-ray Talbot–Lau deflectometry

Abstract Diagnosing the evolution of laser-generated high energy density (HED) systems is fundamental to develop a correct understanding of the behavior of matter under extreme conditions. Talbot–Lau interferometry constitutes a promising tool, since it permits simultaneous single-shot X-ray radiography and phase-contrast imaging of dense plasmas. We present the results of an experiment at OMEGA EP that aims to probe the ablation front of a laser-irradiated foil using a Talbot–Lau X-ray interferometer. A polystyrene (CH) foil was irradiated by a laser of 133 J, 1 ns and probed with 8 keV laser-produced backlighter radiation from Cu foils driven by a short-pulse laser (153 J, 11 ps). The ablation front interferograms were processed in combination with a set of reference images obtained ex situ using phase-stepping. We managed to obtain attenuation and phase-shift images of a laser-irradiated foil for electron densities above ${10}^{22}\;{\mathrm{cm}}^{-3}$ . These results showcase the capabilities of Talbot–Lau X-ray diagnostic methods to diagnose HED laser-generated plasmas through high-resolution imaging.

47 OTHER INSTRUMENTATION↗