SEARCH · Search NASA

Results for “Process settings”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 649 records · Page 36

All sources NASA NTRS DOE OSTI

DOE OSTI2018

Computing erasure metadata and data layout prior to storage using a processing platform

Techniques are provided for computing data and metadata layout prior to storage in a storage system using a processing platform. An exemplary processing platform comprises one or more of a compute node and a burst buffer appliance. The processing platform communicates with a plurality of the compute nodes over a network, wherein a plurality of applications executing on the plurality of compute nodes generate a plurality of data objects; computes erasure metadata for one or more of the data objects on at least one of the compute nodes; and provides the erasure metadata with the corresponding one or more data objects to a storage system. The processing platform optionally determines a full set of the data objects to be stored and queries the storage system to determine an anticipated layout of the full set of the data objects to be stored. The anticipated layout allows special handling, for example, for small files and large files that are identified based on predefined criteria.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2019

Using Correlations to Cs-137 to Reduce Uncertainty in SRS Tank Farm Tc-99 and I-129 Inventories - 19266

Performance Assessment (PA) modeling of the Saltstone Disposal Facility (SDF) shows that iodine-129 (I-129) dominates peaks of potential future doses, followed by contributions from technetium-99 (Tc-99). While these radionuclides are important from a PA perspective, they are relatively unimportant from a safety analysis or occupational exposure perspective. As such, the storage of Tc-99 and I-129 within liquid waste at Savannah River Site (SRS) have not been well tracked, resulting in considerable uncertainty in the estimated inventories of Tc-99 and I-129 used for PA modeling. Alternatively, cesium-137 (Cs-137) is considered a high-risk radionuclide from an occupational exposure perspective and is well characterized. Within the liquid waste at SRS, analyses have shown that concentrations of Cs-137 are closely correlated to both Tc-99 and I-129. These correlations and the abundance of Cs-137 data were leveraged to significantly reduce inventory uncertainty for waste destined to be disposed at the SDF. Previous attempts to mathematically define these correlations had two flaws: (1) the defined correlations failed to address the impact of the relatively short half-life of Cs-137 compared to the long half-lives of Tc-99 and I-129, and (2) the analyses relied on a small number of samples. To address the issue of the decay rate from the short half-life, all Cs-137 concentrations were decayed to a single future date. To improve the number of samples used to develop the correlations, a literature review was performed resulting in a larger data set. The data underwent a validation process, then the correlations were updated. The resulting correlations were used to improve inventory estimates for Tc-99 and I-129 for an ongoing revision to the SDF PA. (author)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Stepwise Dynamic Calibration of a Hydromechanical Simulation Using Time-Lapse Vertical Seismic Profile

This study aims to develop a methodology for calibrating subsurface stress changes through time-lapse Vertical Seismic Profiling (VSP) integration. The selected study site is the 13-10A injector well within the ongoing CO2-EOR operation of the Farnsworth Field Unit. The Time-lapse VSP dataset carries the combined effects of fluid substitution and mean effective stress changes, thereby providing a dataset amenable for the calibration of production and injection-induced stress changes. The concept is similar to calibrating a reservoir simulation model in that the process honor real field data to set up an inverse problem. The solution optimizes the independent and impactful geomechanical parameters that replicate the observed time-lapse seismic velocity changes. This stress calibration is enabled by 4D geomechanical modeling and the VSP Integration workflow. This calibration benefits from extensive geological, geophysical and geomechanical characterization through 3D seismic data, geophysical well logs, and core assessed as part of the 1D MEM conducted on the 13-10A subject well. These data are used to develop a site-specific rock physics model. The Biot Gassmann workflow combines rock physics and reservoir simulation outputs to determine the fluid substitution contribution to seismic velocity change. Additionally, modeled seismic velocity attributed to mean effective stress are determined from the geomechanical simulation outputs, and the stress-velocity relationship developed from the ultrasonic seismic velocity measurements on the extracted Morrow B core. A penalty function is then formed between the modeled seismic velocities and the observed time-lapse VSP dataset. Four independent and impactful geomechanical parameters have been determined. These are the bulk modulus and shear modulus for zero porosity and the shear and compressional seismic velocity to mean effective stress derivatives. The dataset of numerous coupled hydromechanical- geomechanical simulation realizations is built by combining variations of the four stated geomechanical parameters. A machine learning-assisted workflow comprised of an artificial neural network and a particle swarm optimizer are used to converge on the optimal geomechanical parameters. The successful execution of this workflow has affirmed the suitability of acoustic time-lapse measurements for 4D-VSP geomechanical stress calibration pending measurable stress sensitivities within the anticipated effective stress changes and the availability of suitable and reliable datasets for petroelastic modeling.

02 PETROLEUM↗

DOE OSTI2018

Asymptotic Equivalence of Probability Measures and Stochastic Processes

Let P{sub n} and Q{sub n} be two probability measures representing two different probabilistic models of some system (e.g., an n-particle equilibrium system, a set of random graphs with n vertices, or a stochastic process evolving over a time n) and let M{sub n} be a random variable representing a “macrostate” or “global observable” of that system. We provide sufficient conditions, based on the Radon–Nikodym derivative of P{sub n} and Q{sub n}, for the set of typical values of M{sub n} obtained relative to P{sub n} to be the same as the set of typical values obtained relative to Q{sub n} in the limit n→∞. This extends to general probability measures and stochastic processes the well-known thermodynamic-limit equivalence of the microcanonical and canonical ensembles, related mathematically to the asymptotic equivalence of conditional and exponentially-tilted measures. In this more general sense, two probability measures that are asymptotically equivalent predict the same typical or macroscopic properties of the system they are meant to model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

Method for optimization of huff-n-puff gas injection in shale reservoirs

Methods for optimization of liquid oil production by huff-n-puff in shale reservoirs to achieve an improved (and optimal) oil recovery factor. The process determines and utilizes the optimum huff and puff times, number of cycles and soaking time under practical operation and reservoir conditions. The huff time in the process is a period so long that the pressure near the wellbore reaches the set maximum injection pressure during the huff period. The puff time in the process is the time required for the pressure near the wellbore to reach the set minimum production pressure during the puff period. Soaking is typically not necessary during the huff-n-puff gas injection in shale oil reservoirs. The number of huff-n-puff cycles is determinable by the time in which the economic rate cut-off is reached.

04 OIL SHALES AND TAR SANDS↗

DOE OSTI2021

Method for optimization of huff-n-puff gas injections in hydrocarbon reservoirs

Sheng, James J.↗

DOE OSTI2023

Method for optimization of huff-n-puff gas injection in hydrocarbon reservoirs

Sheng, James J.↗

DOE OSTI2024

Data for Grogan et al. "Bringing Hydrologic Realism to Water Markets"

This data set provides model output and post-processing files required to reproduce the results, tables, and figures in the paper "Bringing Hydrologic Realism to Water Markets" by Grogan et al. (in review). Other input data used in this study includes: Lisk, M., Grogan, D., Zuidema, S., Caccese, R., Peklak, D., Zheng, J., Fisher-Vanden, K., Lammers, R., Olmstead, S., & Fowler, L. (2023). Harmonized Database of Western U.S. Water Rights (HarDWR) (Version v1) [Data set]. MSD-LIVE Data Repository. https://doi.org/10.57931/2205619 Two models were used in this study: (1) The University of New Hampshire Water Balance Model WBM, and (2) a Water Market Model. Market model code and model output post-processing code that make use of these data can be found here Model output files are: 1. WBM output files: scenario[x]_wbm_output.zip Where [x] is one of 1, 2, 2a, 3, and 3a Each zipped directory contains 7 gridded NetCDF files, each reporting the 10-year annual average value of a given variable, in units of average mm/day: File Name: wbm_indUseGross_yc.nc; Description: Water withdrawals by industry (part of the urban sector) File Name: wbm_domUseGross_yc.nc; Description: Water withdrawals by the domestic sector (part of the urban sector) File Name: wbm_irrigationGross_yc.nc; Description: Water withdrawals for agriculture File Name: wbm_irrigationExtra_yc.nc; Description: Water withdrawals from unsustainable groundwater for agriculture File Name: wbm_indUseEvap_yc.nc; Description: Consumptive water use by industry File Name: wbm_domUseEvap_yc.nc; Description: Consumptive water use by the domestic sector File Name: wbm_irrigationNet_yc.nc; Description: Consumptive water use by agriculture The file full_cell_area.nc gives the area of each grid cell in km2, which is used for converting water depth to water volume. 2. Water market model output & post processing output Folder: marketTrdSummaries/ Description: Files in this folder are used as input to code 1_WelfareCalculation_actual_trades.R. They summarize historical water right trade transactions in each state. File Name: welfare_gain_by_state_sector.csv; Description: Welfare gains by state and sector, as shown in Figure 3F. Used in code Figure3.R and produced (as a .xlsx file) by code 2_DemandCurves_simulated_trades.R File Name: welfare_data_actual.rdata; Description: welfare gains by WMA from actual historical trades, as shown in Figure 3A. This data is the output of code 1_WelfareCalculation_actual_trades.R File Name: welfare_summary_simulated.xlsx; Description: Welfare gains by state as simulated by the market model in Scenario 1. Produced by code 2_DemandCurves_simulated_trades.R, and used in code 4_WelfareCalculation.R. File Name: welfare_summary_cutoffs.xlsx; Description: Welfare gains by state as simulated by the market model in Scenario 2. Produced by code 3_DemandCurves_simulated_trades_cutoffs.R, and used in code 4_WelfareCalculation.R. File Name: welfare_summary_cutoffs_SGMS.xlsx; Description: Welfare gains by state as simulated by the market model in Scenario 2a. Produced by code 3_DemandCurves_simulated_trades_cutoffs.R, and used in code 4_WelfareCalculation.R. File Name: welfare_data_actual.rdata; Description: Spatial data, actual historical welfare gains by WMA as shown in Figure 3A. Produced by code 4_WelfareCalculation.R and used by code Figure3.R. File Name: welfare_data_simulated.rdata; Description: Spatial data, simulated Scenario 1 welfare gains by WMA as shown in Figure 3B. Produced by code 4_WelfareCalculation.R and used by code Figure3.R. File Name: welfare_data_simulated_cutoffs.rdata; Description: Spatial data, simulated Scenario 2 welfare gains by WMA. Produced by code 4_WelfareCalculation.R and used by code Figure3.R. File Name: welfare_data_simulated_cutoffs_SGMA.rdata; Description: Spatial data, simulated Scenario 2a welfare gains by WMA. Produced by code 4_WelfareCalculation.R and used by code Figure3.R. Additional files are provided for efficient reproduction of tables and figures. These include: File Name: wma_thresold_dates_Scenario2(a).csv; Description: Wet vs. paper right threshold dates for each WMA. Shown in Figure 2A,B. Produced and used by code calculate_thresolds_Figure2.R File Name: WWRTradeBounds (directory); Description: Trade boundary shapefile required to reproduce Figure 3A-D. Used in code Figure3.R File Name: welfare_region_totals.csv; Description: Welfare gains for the entire study region, as shown in Figure 3E. Used in code Figure3.R File Name: Welfare_gain_by_state_sector.csv; Description: Welfare gains by state and sector, as shown in Figure 3F. Used in code Figure3.R and produced (as a .xlsx file) by code 2_DemandCurves_simulated_trades.R File Name: WECC_MERIT_5min_v3b_mask.nc; Description: Gridded file that identified which land grid cells are in the WBM model domain, used for processing in code Figure4.py File Name: Table_1.csv; Description: All data in Table 1, reproducible from WBM output files using code table_1.R

Economics↗

DOE OSTI2024

Neural network emulation of spontaneous fission

Large-scale computations of fission properties are an important ingredient for nuclear reaction network calculations simulating rapid neutron-capture process (the 𝑟 process) nucleosynthesis. Due to the large number of fissioning nuclei potentially contributing to the 𝑟 process, a microscopic description of fission based on nuclear density functional theory (DFT) is computationally challenging. Here, we explore the use of neural networks (NNs) to construct DFT emulators capable of predicting potential energy surfaces and collective inertia tensors across the whole nuclear chart, starting from a minimal set of DFT calculations. We use constrained Hartree-Fock-Bogoliubov (HFB) calculations to predict the potential energy and collective inertia tensor in the axial quadrupole and octupole collective coordinates, for a set of nuclei in the 𝑟-process region. We then employ NNs to emulate the HFB energy and collective inertia tensor across the considered region of the nuclear chart. Least-action pathways characterizing spontaneous fission half-lives and fragment yields are then obtained by means of the nudged elastic band method. The potential energy predicted by NNs agrees with the DFT value to within a root-mean-square error of 500 keV, and the collective inertia components agree to within an order of magnitude. These results are largely independent of the NN architecture. The exit points on the outer turning line are found to be well emulated. For the spontaneous fission half-lives the NN emulation provides values that are found to agree with the DFT predictions within a factor of 10 3 across more than 70 orders of magnitude. Neural networks are able to emulate the potential energy and collective inertia well enough to reasonably predict physical observables. Future directions of study, such as the inclusion of additional collective degrees of freedom and active learning, will improve the predictive power of microscopic theory and further enable large-scale fission studies.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

A Bayesian Approach for Statistical–Physical Bulk Parameterization of Rain Microphysics. Part II: Idealized Markov Chain Monte Carlo Experiments

Observationally informed development of a new framework for bulk rain microphysics, the Bayesian Observationally Constrained Statistical–Physical Scheme (BOSS; described in Part I of this study), is demonstrated. This scheme’s development is motivated by large uncertainties in cloud and weather simulations associated with approximations and assumptions in existing microphysics schemes. Here, a proof-of-concept study is introduced using a Markov chain Monte Carlo sampling algorithm with BOSS to probabilistically estimate microphysical process rates and parameters directly from a set of synthetically generated rain observations. The framework utilized is an idealized steady-state one-dimensional column rainshaft model with specified column-top rain properties and a fixed thermodynamical profile. Different configurations of BOSS—flexibility being a key feature of this approach—are constrained via synthetic observations generated from a traditional three-moment bulk microphysics scheme. The ability to retrieve correct parameter values when the true parameter values are known is illustrated. For cases when there is no set of true parameter values, the accuracy of configurations of BOSS that have different levels of complexity is compared. It is found that addition of the sixth moment as a prognostic variable improves prediction of the third moment (proportional to bulk rain mass) and rain rate. In contrast, increasing process rate formulation complexity by adding more power terms has little benefit—a result that is explained using further-idealized experiments. BOSS rainshaft simulations are shown to well estimate the true process rates from constraint by bulk rain observations, with the further benefit of rigorously quantified uncertainty of these estimates.

58 GEOSCIENCES↗

DOE OSTI2021

Transport Upscaling under Flow Heterogeneity and Matrix-Diffusion in Three-Dimensional Discrete Fracture Networks

For this work, we investigate the combined effects of network scale flow variability and retention due to matrix-diffusion on the scaling behavior of transport through fractured media. Two of the principal mechanisms controlling the transport of solutes through fractured low-permeability media are broad distributions of flow velocities and retention times in the solid matrix. We study the relative impact of these two processes under different initial conditions using a set of three-dimensional discrete fracture network simulations. We use these simulations to develop and calibrate an upscaled continuous time random walk (CTRW) approach for advective transport based on an Ornstein-Uhlenbeck model for the particle velocities that accounts for the fracture-matrix coupling using a compound Poisson process. This CTRW model can be conditioned on the initial solute distribution and allows to observe late-time scaling behavior at distances beyond what is feasible to observe using high-fidelity direct numerical simulations. We determine that the initial distribution of particles leads to marked differences in the persistent long-term scale behavior in the solute travel time distributions, even those undergoing retention due to matrix diffusion through implementation and analysis of the model.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Materials data analytics for 9% Cr family steel

A materials data analytics (MDA) methodology was developed in this study to evaluate publicly available information on 9% Cr family steel and to handle nonlinear relationships and the sparsity in materials data for this alloy class. The overarching goal is to accelerate the design process as well as to reduce the time and expense associated with qualification testing of new alloys for fossil energy applications. Data entries in the analyzed data set for 82 iron–base alloy compositions, several processing parameters, and results of tensile mechanical tests selected for this study were arranged in 34 columns by 915 rows. While detailed microstructural information was not available, it is assumed that the compositional space for the 9 to 12% Cr steels is limited such that all data entries have a tempered martensitic microstructure during service. Establishing a hierarchy of first–order trends in the publicly available data requires the MDA to filter out the biases. Complexity of the phase transformations and microstructure evolution in the multicomponent alloys (using 21 chemical elements) with major influence on mechanical properties, leads to inefficiency in direct application of unbiased linear regression across the entire data space. To address the nonlinearity, analyses of tensile data were performed in composition–based clusters. Clusters corresponding to moderately frequent patterns and maximized information gain were further refined by using p–norm distance measures, matching the alloy classification groups adopted by industry. Lastly, the evolutionary method of propagating an ensemble of competing cluster–based models proved to be a viable option in dealing with scarce, multidimensional data.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Region and cloud regime dependence of parametric sensitivity in E3SM atmosphere model

Abstract The Department of Energy (DOE)’s Energy Exascale Earth System Model (E3SM), including its atmosphere model (EAM), has many relatively new features. In a previous study we conducted a systematic parametric sensitivity analysis for EAM based on short, perturbed parameter ensemble (PPE) simulations, mainly focusing on global mean climate features and metrics. While parameter values in global climate models are generally invariant in space and time, model response to parameters perturbation may vary by regions and climate regimes, which motivates the need to better understand the EAM model behaviors and physics at regional scale and process level. In this study, using the same set of PPE simulations and a similar sensitivity analysis framework, we identify parameters that cause largest sensitivities over different regions and compare model responses in fast atmospheric processes to the parameters across different cloud regimes for several important cloud-related fidelity metrics. We find that cloud forcing has opposite response to some parameters over mid-latitude vs. tropical land. We also analyze how the parametric sensitivity varies as stratocumulus transitions to shallow convection and to deep convection over ocean. Low cloud forcing and shortwave cloud forcing in the subtropical eastern Pacific are most sensitive to the parameters controlling the width of the probability density function (PDF) of the subgrid vertical velocity ( w’ ) ( gamma ) and the damping of the w’ skewness ( c8 ) near the coast but become more sensitive to the parameter affecting the damping of the w’ variance ( c1 ) further offshore. Detailed interpretation of the spatial dependence of parametric sensitivity is provided. We also investigate how the parametric sensitivity evolves with prediction duration. This study improves our process-level understanding of cloud physics and parameterization and provides insights for developing more advanced regime-aware parameterization schemes in global climate model.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Abridged spectral matrix inversion: parametric fitting of X-ray fluorescence spectra following integrative data reduction

Recent improvements in both X-ray detectors and readout speeds have led to a substantial increase in the volume of X-ray fluorescence data being produced at synchrotron facilities. This in turn results in increased challenges associated with processing and fitting such data, both temporally and computationally. Herein an abridging approach is described that both reduces and partially integrates X-ray fluorescence (XRF) data sets to obtain a fivefold total improvement in processing time with negligible decrease in quality of fitting. The approach is demonstrated using linear least-squares matrix inversion on XRF data with strongly overlapping fluorescent peaks. This approach is applicable to any type of linear algebra based fitting algorithm to fit spectra containing overlapping signals wherein the spectra also contain unimportant (non-characteristic) regions which add little (or no) weight to fitted values, e.g. energy regions in XRF spectra that contain little or no peak information.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2019

An Investigation of Hydrometeor Latent Cooling upon Convective Cold Pool Formation, Sustainment, and Properties

Downdrafts extending from convective clouds can produce cold pools that propagate outward, sometimes initiating new convection along their leading edges. Models operating at scales requiring convective parameterizations usually lack representation of this detail, and thus fail to predict this convective regeneration and longer episodes of convective activity. Developing such parameterizations requires an improved understanding of the physical drivers of cold pools, and detailed studies of the roles of all the contributing microphysical processes have been lacking. This study utilizes a set of 12 simulations conducted within a single convective environment, but with variability in the microphysical fields produced by varying parameters influencing warm-rain or ice processes. Time-integrated microphysical budgets quantify the contribution of each hydrometeor type to the total latent cooling occurring in the downdrafts that form and sustain the cold pool. The timing of the onset of the cold pool is earlier in cases with a stronger warm rain process, but both graupel and rain were equally as likely to be the dominant hydrometeor in the downdraft first forming the cold pool. Graupel sublimation is the dominant term in sustaining the cold pool in all simulations, but the evaporation of rain has the strongest correlation to the cold pool expansion rate, depth, and intensity. Reconciling the current results with past studies elucidates the importance of considering: graupel sublimation, the latent cooling only in downdrafts contributing to the cold pool, and latent cooling in those downdrafts at altitudes that may be significantly higher than the melting level.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Can liquid cloud microphysical processes be used for vertically pointing cloud radar calibration?

Cloud radars are unique instruments for observing cloud processes, but uncertainties in radar calibration have frequently limited data quality. Thus far, no single robust method exists for assessing the calibration of past cloud radar data sets. Here, we investigate whether observations of microphysical processes in liquid clouds such as the transition of cloud droplets to drizzle drops can be used to calibrate cloud radars. Specifically, we study the relationships between the radar reflectivity factor and three variables not affected by absolute radar calibration: the skewness of the radar Doppler spectrum ( γ ), the radar mean Doppler velocity ( W ), and the liquid water path (LWP). For each relation, we evaluate the potential for radar calibration. For γ and W , we use box model simulations to determine typical radar reflectivity values for reference points. We apply the new methods to observations at the Atmospheric Radiation Measurement (ARM) sites North Slope of Alaska (NSA) and Oliktok Point (OLI) in 2016 using two 35 GHz K a -band ARM Zenith Radars (KAZR). For periods with a sufficient number of liquid cloud observations, we find that liquid cloud processes are robust enough for cloud radar calibration, with the LWP-based method performing best. We estimate that, in 2016, the radar reflectivity at NSA was about 1±1  dB too low but stable. For OLI, we identify serious problems with maintaining an accurate calibration including a sudden decrease of 5 to 7 dB in June 2016.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long-term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C-cycle processes. Bayesian calibration was conducted using quality-controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass-shrub mixture, and grass-tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R 2 > 0.6, RMSE <390 g C m -2 ) relative to net ecosystem exchange of CO 2 (NEE) (R 2 > 0.4, RMSE <180 g C m -2 ). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R 2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long-term network-based monitoring of vegetation biomass, C fluxes, and SOC stocks.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Method to Extract System-Independent Material Properties From Dual-Energy X-Ray CT

In 2016, we published a method for processing dual-energy computed tomography (DECT) data called system-independent rho-e/Z-e (ρ e /Z e ) or SIRZ. Using data from multiple DECT systems and spectra, SIRZ estimated the electron density ρ e and effective atomic number Z e (based on published X-ray cross sections), for a set of known specimens. However, the decomposition process required complex spectral modeling of the DECT system, which made SIRZ difficult to implement and automate. This paper describes the subsequent work on “SIRZ-2” to simplify the spectral modeling, automate the process, and improve its range and versatility. The SIRZ-2 basis functions are more accurate for the X-ray energy range (<;200 keV) and materials that are common to DECT applications in security and nondestructive characterization. To evaluate the SIRZ-2 performance, eight well-characterized specimens (with Z e of 6-20) were scanned with multiple spectral pairs (up to 200 keV) on seven different DECT systems, including a commercial airport luggage scanner. For these varied tests, the average SIRZ-2 relative errors for ρ e estimates were four times lower (0.7% compared to 3.0% for SIRZ), while average Z e relative errors were comparably low for both methods (<;1.5%). Average precision errors were also low (<;2.3% for both). SIRZ-2 produces accurate, repeatable, and system-independent material property estimates of (ρ e , Z e ) that can be directly compared across DECT systems and over time.

42 ENGINEERING↗