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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Influence of Diffuse and Ground-Reflected Irradiance on the Spectral Modeling of Solar Reference Cells

Thermal Energy Storage (TES) is a key component for solar thermal applications to bridge the gap between the demand for thermal energy and the supply of solar energy, whose availability depends on the time of day and season. Thus, cost-effective packed-bed thermal containers filled with a solid storage medium have been proposed for high-temperature sensible heat storage as materials are abundant and relatively cheap. Thus, it is necessary to investigate their performance and temperature profiles during the charge-discharge cycle. Several models are available for this purpose. Typically, the more detailed a model, the greater the computational effort required to solve it, and hence a time-efficient model is needed to prevent excessively long computation times for long-term analysis. At the more basic level, the common Hughes E-NTU model and the less realistic simplified Infinite-NTU model are very important for their less time and computational effort. In this paper, the appropriateness of employing the Infinite-NTU model was evaluated to investigate the performance of a typical and scalable rock-bed TES as a case study. The results presented provide a methodology to quickly test the validity of the model and predict the temperature profile for the case under study. Accordingly, such simple charge-discharge cycle thermal performance predictions are important to plan, design, and rapidly deploy a reliable and economical solar thermal system for the supply of valuable heat to high-temperature demanding applications of power generation and industrial processes as part of a rapid shift towards non-polluting renewable energy. Keywords: Solar Thermal, TES, Packed-bed, NTU model, Temperature profile

PV modeling↗
DOE OSTI2021

Revealing causes of macroscale heterogeneity in lithium ion pouch cells via synchrotron X-ray diffraction

Heterogeneous battery performance is a critical issue for maximization of cell lifetime capacity and safety. Using high energy synchrotron X-ray diffraction, the influence of charge rate, voltage limit, uneven stack pressure, and gas generation on the lithium transport properties was quantified in single-layer graphite/LiNi 0.5 Mn 0.3 Co 0.2 O 2 pouch cells. A freshly formatted cell tracked in operando during initial fast charge cycles indicated variable position-dependent performances, while lateral mapping showed a significant fast charge (6C) heterogeneity compared to slow charge (C/2). Pressure effects were non-dominant compared to charge rate. Maps of previously aged and rested cells indicate that lateral heterogeneity slowly equilibrates at rest, but regenerates upon further cycling at fast charge rate. Furthermore, an unformatted cell was mapped at charge and discharge during its first formation cycle to analyze the effect of byproduct gases on the heterogeneous lithium transport. Gas was observed as randomly interspersed “bubbles” which locally hindered lithium intercalation and caused significant heterogeneity. Electrode architectures and charging protocols that promote homogeneous intercalation are critical for predictable high-performance and long-life batteries.

25 ENERGY STORAGE↗
DOE OSTI2022

Comparing and contrasting In-Vial and full-scale systems for sparging volatile analytes

Here, in-vial sparging was demonstrated as an effective, practical alternative to a full-scale sparging system for supporting the analysis of volatile constituents. Using elemental mercury (Hg 0 ) and toluene as representative purgeable analytes, the mass removal for various sparge configurations was measured and a reduced order model was developed and validated. In the primary experiments, Hg 0 in the sparge gas was trapped on activated carbon or gold, thermally desorbed, and quantified using atomic absorption or atomic fluorescence spectroscopy. Toluene experiments using the same in-vial sparge apparatus and sparge parameters were performed to demonstrate the applicability of the reduced order model to a broad range of compounds. Toluene removal was tracked by measuring the remaining toluene in sparged aliquots using Ultraviolet-visible (UV–Vis) spectroscopy. For the sparging, flow rates varied from 25 to 75 mL/min for periods from 0 to 30 min. Sparge performance, mass removal as a function of time, and sparge gas volume were measured for both in-vial and full-scale systems. A model based on dimensionless Henry's Law coefficient, normalized sparge gas volume, and fractional extent of equilibrium matched the experimental data for both compounds and provides a practical tool for future applications. For the conditions tested in this study, the calibrated model indicated that the sparge gas in the in-vial system reached approximately 33% of its equilibrium value before exiting the water surface, while a full-scale system reached approximately 100%. The tests validated the quality, reproducibility, and predictability of sparging performance for both full scale and in-vial sparge systems. Related factors such as waste generation, worker risk, and labor were also assessed. Full scale sparge systems provide the advantage of lower detection levels due to larger sample volume, while the in-vial sparge systems provide advantages for most other factors; including automatability, reducing secondary wastes, lessening the need to clean and check the sparge apparatus, and lowering labor and costs. The data and associated reduced order model support continued development and deployment of in-vial sparge platforms as a practical option for analysis of purgeable analytes such as volatile organic compounds and volatile metals/organometallics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Measurement Acceptance Region (MAR) Assessment Results Based On Sludge Batch 10 Projections from December 2021, February 2022 and August 2022

The Defense Waste Processing Facility (DWPF) is currently preparing to initiate processing of Sludge Batch 10 (SB10), which is comprised of material from Tanks 11H, 13H, 15H, and 26F, Alternate Feed Stock-2 and Sodium Reactor Experiment material from H-Canyon. Frit 473 (8B 2 O 3 -8Li 2 O-5Na 2 O-79SiO 2 , in weight percent) was recommended for sludge-only (SO) and coupled processing with the Salt Waste Processing Facility (SWPF) based on previous assessments of SB10 projections with the DWPF Product Composition Control System (PCCS) glass property models and their associated Measurement Acceptance Region (MAR) constraints. Due to the lower processing rate of Sludge Batch 9 (SB9), the heel in Tank 40 is anticipated to be greater than 40 inches at the projected start of SB10 processing to meet the upcoming Accelerated Basin Deinventory addition dates in Tank 51 for Sludge Batch 11. In December 2021 and February 2022, Savannah River Remediation System Planning provided updated SB10 Tank 40 blend projections based on heels of 74 inches, 84 inches, 94 inches and 103.4 inches. Savannah River Mission Completion (SRMC) subsequently pursued Wash Cycle Y to further reduce the total sulfur in the sludge batch and increase processing flexibility at DWPF. In August 2022, SRMC System Planning provided an updated SB10 Tank 40 blend projection with a 76 inch-heel representing Decant Y2 and 60 kilogallons of bearing water inleakage that is anticipated during the Tank 51 to Tank 40 transfer. The objectives of this task were to: • Determine the impact on the operating windows for SO and coupled processing • Determine whether any composition gaps exist between the already completed SB10 variability study and the reprojected SB10 glass composition region • Compare the SB10 reprojected glass composition region to the DWPF PCCS model development and validation ranges to ensure that compositional gaps do not exist between the data sets. This report documents the results of these evaluations. Calculation-based frit assessments were performed using the DWPF PCCS glass property models and their associated MAR constraints. Evaluated parameters for coupled processing included the following transfer volumes per Sludge Receipt and Adjustment Tank batch: 5700 gallons of Tank 40 sludge, 2400-4500 gallons of the SWPF monosodium titanate (MST) and sludge solids stream, and 15,000 gallons of strip effluent. Based on these MAR assessment results, Frit 473 remains viable for SB10 processing. A target waste loading (WL) of 36% is possible for SO operation and single strike (0.4 g/L MST) coupled processing up to 600 mg/L of SB9 insoluble sludge solids at a nominal transfer volume of ~2800 gallons. Increasing the single strike transfer volume to 4500 gallons may reduce the maximum WL below 40%. Operating windows are 12 percentage points for SO processing and 14-16 percentage points for coupled processing. Frit 625 allows for a target WL of 36% and is acceptable for use during the SB9 to SB10 transition to deplete remaining inventory as needed. Operating windows are 8 percentage points for SO processing and 12-14 percentage points for coupled processing. Predictive PCCS evaluations performed at DWPF will provide insight into batch-specific acceptability at desired WLs for compositions having expected oxide ratios during processing versus the extreme vertices (corner points) evaluated in this study. The reprojected SB10 glass composition region generally overlaps the previously evaluated SB10 variability study composition region. Thus, the minor composition shift of these updated SB10 projections indicates that no additional glasses are necessary to demonstrate acceptability relative to the chemical durability of the Environmental Assessment benchmark glass and predictability using the current PCCS models for durability. Based on a comparison of the PCCS model development and validation data to the reprojected SB10 glass composition region, the viscosity and liquidus temperature models will reliably predict SB10 compositions. No additional glasses are necessary to demonstrate predictability of these models.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

An obscured quasar census with the 4MOST IR AGN survey: design, predicted properties, and scientific goals

ABSTRACT We present the 4MOST (4-metre Multi-Object Spectroscopic Telescope) infrared (IR) AGN survey, the first large-scale optical spectroscopic survey characterizing mid-infrared (MIR) selected obscured active galactic nuclei (AGNs). The survey targets $\approx 212\,000$ obscured IR AGN candidates over $\approx 10\,000 \rm \: deg^2$ down to a magnitude limit of $r_{\rm AB}=22.8 \, \rm mag$ and will be $\approx 100 \times$ larger than any existing obscured IR AGN spectroscopic sample. We select the targets using an MIR colour criterion applied to the unWISE catalogue from the WISE (Wide-field Infrared Survey Explorer) all-sky survey, and then apply a $r-W2\ge 5.9 \rm \: mag$ cut; we demonstrate that this selection will mostly identify sources obscured by $N_{\rm H}>10^{22} \rm \: cm^{-2}$. The survey complements the 4MOST X-ray survey, which will follow up $\sim 1\,\rm M$ eROSITA (extended ROentgen Survey with an Imaging Telescope Array)-selected (typically unobscured) AGN. We perform simulations to predict the quality of the spectra that we will obtain and validate our MIR–optical colour-selection method using X-ray spectral constraints and UV-to-far-IR spectral energy distribution (SED) modelling in four well-observed deep-sky fields. We find that: (1) $\approx 80-87{{\ \rm per\ cent}}$ of the WISE-selected targets are AGN down to $r_{\rm AB}=22.1-22.8 \: \rm mag$ of which $\approx 70{{\ \rm per\ cent}}$ are obscured by $N_{\rm H}>10^{22} \: \rm cm^{-2}$, and (2) $\approx 80{{\ \rm per\ cent}}$ of the 4MOST IR AGN sample will remain undetected by the deepest eROSITA observations due to extreme absorption. Our SED-fitting results show that the 4MOST IR AGN survey will primarily identify obscured AGN and quasars ($\approx 55{{\ \rm per\ cent}}$ of the sample is expected to have $L_{\rm AGN,IR}>10^{45} \rm \: erg \: s^{-1}$) residing in massive galaxies ($M_{\star }\approx 10^{10}-10^{12} \rm \: M_{\odot }$) at $z\approx 0.5-3.5$ with $\approx 33{{\ \rm per\ cent}}$ expected to be hosted by starburst galaxies.

Andonie, Carolina (ORCID:0000000255804298)↗
DOE OSTI2022

Deep learning-based spatio-temporal estimate of greenhouse gas emissions using satellite data

Accurate estimation of greenhouse gases (GHGs) emissions is very important for developing mitigation strategies to climate change by controlling and reducing GHG emissions. This project aims to develop multiple deep learning approaches to estimate anthropogenic greenhouse gas emissions using multiple types of satellite data. NO2 concentration is chosen as an example of GHGs to evaluate the proposed approach. Two sentinel satellites (sentinel-2 and sentinel-5P) provide multiscale observations of GHGs from 10-60m resolution (sentinel-2) to ~kilometer scale resolution (sentinel-5P). Among multiple deep learning (DL) architectures evaluated, two best DL models demonstrate that key features of spatio-temporal satellite data and additional information (e.g., observation times and/or coordinates of ground stations) can be extracted using convolutional neural networks and feed forward neural networks, respectively. In particular, irregular time series data from different NO 2 observation stations limit the flexibility of long short-term memory architecture, requiring zero-padding to fill in missing data. However, deep neural operator (DNO) architecture can stack time-series data as input, providing the flexibility of input structure without zero-padding. As a result, the DNO outperformed other deep learning architectures to account for time-varying features. Overall, temporal patterns with smooth seasonal variations were predicted very well, while frequent fluctuation patterns were not predicted well. In addition, uncertainty quantification using conformal inference method is performed to account for prediction ranges. Overall, this research will lead to a new groundwork for estimating greenhouse gas concentrations using multiple satellite data to enhance our capability of tracking the cause of climate change and developing mitigation strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Cooling dynamics of two titanium alloys during laser powder bed fusion probed with in situ X-ray imaging and diffraction

Metal parts produced by laser powder bed fusion (LPBF) additive manufacturing exhibit characteristic microstructures comparable to those observed in laser welding. The primary cause of this characteristic microstructure is rapid, localized heating and cooling cycles, which result in extreme thermal gradients where material solidification is followed by fast cooling in the solid state. The final microstructure and mechanical performance are also influenced by pore formation caused by melt pool fluid dynamics. Here, we use high speed, in situ X-ray diffraction to probe the kinetics of cooling and solid-solid phase transitions after laser melting in two aerospace titanium alloys: Ti-6Al-4V, an α + β alloy; and Ti-5Al-5V-5Mo-3Cr, a near-β alloy. We complement these diffraction studies with in situ X-ray imaging to probe melt pool dynamics and pore formation. From these two complementary probes, we quantify pore formation during melting and the subsequent microstructural evolution as the material rapidly cools after solidification. These results are critical for understanding defect formation and residual stress development in different titanium alloys under LPBF conditions and can help inform process models to predict final part performance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Measurement-driven large-eddy simulations of a diurnal cycle during a wake-steering field campaign

Abstract. High-fidelity flow modeling with data assimilation enables accurate representation of the wind farm operating environment under realistic, nonstationary atmospheric conditions. Capturing the temporal evolution of the turbulent atmospheric boundary layer is critical to understanding the behavior of wind turbines under operating conditions with simultaneously varying inflow and control inputs. This paper has three parts: the identification of a case study during a field evaluation of wake steering; the development of a tailored mesoscale-to-microscale coupling strategy that resolved local flow conditions within a large-eddy simulation (LES), using observations that did not completely capture the wind and temperature fields throughout the simulation domain; and the application of this coupling strategy to validate high-fidelity aeroelastic predictions of turbine performance and wake interactions with and without wake steering. The case study spans 4.5 h after midnight local time, during which wake steering was toggled on and off five times, achieving yaw offset angles ranging from 0 to 17°. To resolve nonstationary nighttime conditions that exhibited shear instabilities, the turbulence field was evolved starting from the diurnal cycle of the previous day. These background conditions were then used to drive wind farm simulations with two different models: an LES with actuator disk turbines and a steady-state engineering wake model. Subsequent analysis identified two representative periods during which the up- and downstream turbines were most nearly aligned with the mean wind direction and had observed yaw offsets of 0 and 15°. Both periods corresponded to partial waking on the downstream turbine, which had errors in the LES-predicted power of 4 % and 6 %, with and without wake steering. The LES was also able to capture conditions during which an upstream turbine wake induced a speedup at a downstream turbine and increased power production by up to 13 %.

17 WIND ENERGY↗
DOE OSTI2025

Selection of high-redshift Lyman-Break Galaxies from broadband and wide photometric surveys

In this paper, we investigate the possibility of selecting high-redshift Lyman-Break Galaxies (LBG) using current and future broadband wide photometric surveys, such as the Ultraviolet Near Infrared Optical Northern Survey (UNIONS) or the Vera C. Rubin Legacy Survey of Space and Time (LSST), using a Random Forest algorithm. This work is conducted in the context of future large-scale structure spectroscopic surveys like DESI-II, the next phase of the Dark Energy Spectroscopic Instrument (DESI), which will start around 2029.We use deep imaging data from the Hyper Suprime Camera (HSC) and the Canada-France-Hawaii Telescope Large Area U-band Deep Survey (CLAUDS) on the COSMOS and XMM-LSS fields. To predict the selection performance of LBGs with image quality similar to UNIONS, we degrade the u,g,r,i and z bands to UNIONS depth.The Random Forest algorithm is trained with the u,g,r,i and z bands to classify LBGs in the 2.5 < z < 3.5 range.We find that fixing a target density budget of 1,100 deg$^{-2}$, the Random Forest approach gives a density of z > 2 targets of 873 deg$^{-2}$, and a density of 493 deg$^{-2}$ of confirmed LBGs after spectroscopic confirmation with DESI. This UNIONS-like selection was tested in a dedicated spectroscopic observation campaign of 1,000 targets with DESI on the COSMOS field, providing a safe spectroscopic sample with a mean redshift of 3. This sample is used to derive forecasts for DESI-II, assuming a sky coverage of 5,000 deg$^{2}$. We predict uncertainties on Alcock-Paczynski parameters α$_{⊥}$ and α$_{∥}$ to be 0.7% and 1% for 2.6 < z < 3.2, resulting in a potential 2% measurement of the dark energy fraction at high redshift. Additionally, we estimate the uncertainty in local non-Gaussianity and predict σ$_{fNL}$ ≈ 7, which would be comparable to the current best precision achieved by Planck. The latter forecast suggests that achieving the precision required to place stringent constraints on inflationary models (σ$_{fNL}$ ≈ 1) using spectroscopic galaxy surveys necessitates the development of a next-generation (Stage V) spectroscopic survey.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Post-irradiation examination of legacy high burnup fuel to support safety testing

Safety/transient testing to evaluate performance under off-normal conditions is an essential pillar for both the development of Accident Tolerant Fuels (ATF) and the optimization of fuel operation economics beyond current discharge burnups. Among other factors, the successful interpretation of the transient testing results relies upon the knowledge of the initial conditions of the test, including the characteristics of the fuel system under scrutiny. When testing pre-irradiated material, the assumptions that the fuel and the cladding still have the same properties as in the pre-irradiation stage is obviously wrong and could affect the results of the test. This is particularly true the more burnup accumulates in the fuel rod and irradiation progresses. The knowledge of the initial microstructure of both fuel and cladding allows a clearer interpretation of the subsequent transient testing results, provides validation of the physical phenomena underlying the model predictions and eliminates the uncertainties related to the limited knowledge of the sample status before the test. One example is the phenomenon of fine fragmentation that occurs in Light Water reactor (LWR) fuel. During a Loss of Coolant Accident (LOCA) or Reactivity Initiated Transient (RIA) the fuel can severely fragment. During LOCA, high burnup fuel tend to finely fragment, which has raised safety concerns due to the increased likelihood of dispersal of such small particles once the cladding has burst and due to the increased fission gas release. Therefore, efforts have been devoted to the assessment of a pulverization threshold that could determine the conditions under which fine fragmentation is predominant. However, the lack of information regarding the initial conditions of the fuel, and the connections between those conditions and the pre-transient irradiation history, have hindered the development of a fully mechanistic fragmentation and pulverization criterion. The empirical relationships rely on conservative estimations, due to the lack of information on critical material properties and characteristics. More generally, experimental evidence of the irradiation-induced modifications at microstructural scale are necessary to determine the behavior of the material at the macroscopic scale, with the latter being the one of technological interest. Significant progress has been made in the last two decades in the developments of analytical materials science techniques that can be applied to highly radioactive materials, such as high burnup fuels. The availability of new techniques and the improvement of existing ones has enabled investigations previously not possible that can deepen the understanding of the fuel characteristics and properties at high burnup. The better knowledge of material behavior and irradiation-induced phenomena could help the prediction of its performance. In this context, the scope of the present work is to apply a wide portfolio of advanced characterization techniques to determine properties that are relevant for safety and performance. The results are interpreted in the context of engineering scale post-irradiation examinations and available information on the irradiation conditions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Selection of high-redshift Lyman-Break Galaxies from broadband and wide photometric surveys

Here, in this paper, we investigate the possibility of selecting high-redshift Lyman-Break Galaxies (LBG) using current and future broadband wide photometric surveys, such as the Ultraviolet Near Infrared Optical Northern Survey (UNIONS) or the Vera C. Rubin Legacy Survey of Space and Time (LSST), using a Random Forest algorithm. This work is conducted in the context of future large-scale structure spectroscopic surveys like DESI-II, the next phase of the Dark Energy Spectroscopic Instrument (DESI), which will start around 2029. We use deep imaging data from the Hyper Suprime Camera (HSC) and the Canada-France-Hawaii Telescope Large Area U-band Deep Survey (CLAUDS) on the COSMOS and XMM-LSS fields. To predict the selection performance of LBGs with image quality similar to UNIONS, we degrade the u,g,r,i and z bands to UNIONS depth. The Random Forest algorithm is trained with the u,g,r,i and z bands to classify LBGs in the 2.5 < z < 3.5 range. We find that fixing a target density budget of 1,100 deg -2 , the Random Forest approach gives a density of z > 2 targets of 873 deg -2 , and a density of 493 deg -2 of confirmed LBGs after spectroscopic confirmation with DESI. This UNIONS-like selection was tested in a dedicated spectroscopic observation campaign of 1,000 targets with DESI on the COSMOS field, providing a safe spectroscopic sample with a mean redshift of 3. This sample is used to derive forecasts for DESI-II, assuming a sky coverage of 5,000 deg 2 . We predict uncertainties on Alcock-Paczynski parameters α ⊥ and α ∥ to be 0.7% and 1% for 2.6 < z < 3.2, resulting in a potential 2% measurement of the dark energy fraction at high redshift. Additionally, we estimate the uncertainty in local non-Gaussianity and predict σ fNL ≈ 7, which would be comparable to the current best precision achieved by Planck. The latter forecast suggests that achieving the precision required to place stringent constraints on inflationary models (σ fNL ≈ 1) using spectroscopic galaxy surveys necessitates the development of a next-generation (Stage V) spectroscopic survey.

cosmological parameters from LSS↗
DOE OSTI2025

Development and evaluation of a new 4DEnVar-based weakly coupled ocean data assimilation system in E3SMv2

The development, implementation, and evaluation of a new weakly coupled ocean data assimilation (WCODA) system for the fully coupled Energy Exascale Earth System Model version 2 (E3SMv2) utilizing the four-dimensional ensemble variational (4DEnVar) method are presented in this study. The 4DEnVar method, based on the dimension-reduced projection four-dimensional variational (DRP-4DVar) approach, replaces the adjoint model with the ensemble technique, thereby reducing computational demands. Monthly mean ocean temperature and salinity data from the EN4.2.1 reanalysis are integrated into the ocean component of E3SMv2 from 1950 to 2021 with the goal of providing realistic initial conditions for decadal predictions and predictability studies. The performance of the WCODA system is assessed using various metrics, including the reduction rate of the cost function, root mean square error (RMSE) differences, correlation differences, and model biases. Results indicate that the WCODA system effectively assimilates the reanalysis data into the climate model, consistently achieving negative reduction rates of the cost function and notable improvements in RMSE and correlation across various ocean layers and regions. Significant enhancements are observed in the upper ocean layers across the majority of global ocean regions, particularly in the north Atlantic, north Pacific, and Indian Ocean. Model biases in sea surface temperature and salinity are also substantially reduced. For sea surface temperature, cold biases in the north Pacific and north Atlantic are diminished by about 1–2 °C, and warm biases in the Southern Ocean are corrected by approximately 1.5–2.5 °C. In terms of salinity, improvements are observed with bias reductions of about 0.5–1 psu in the north Atlantic and north Pacific and up to 1.5 psu in parts of the Southern Ocean. The ultimate goal of the WCODA system is to advance the predictive capabilities of E3SM for subseasonal to decadal climate predictions, thereby supporting research on strategic energy-sector policies and planning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

An indirect approach to optimize the reaction rates of thermal NO formation for diesel engines

With stringent emission regulations, it has become more important for modern diesel engine manufacturers to accurately predict engine-out nitrogen oxide (NO x ) emissions across a wide range of operating conditions. Thermal NO is the major source of engine-out NO x in modern diesel engines. For thermal NO formation, several earlier studies have recommended the forward and reverse reaction rate coefficients of the rate-limiting reaction (O + N 2 ⇌ NO + N). However, due to deficiencies in sub-models and inadequacies of reduced chemical mechanisms to represent diesel combustion, these recommended values more often than not need to be adjusted in reduced order combustion models to accurately predict engine-out NO x . Hence, in this work a systematic and computationally efficient approach has been proposed to streamline the process of determining the optimum reaction rate coefficients. Here, to develop the optimization approach, four different production diesel engines with different operating conditions in terms of speed, load, and exhaust-gas recirculation have been considered. Numerical simulations have been performed using a detailed zero-dimensional velocity-composition-frequency transported probability density function (0D-VCF-tPDF) model that uses hundreds of notional particles to capture in-cylinder stratification. Four different combinations of hydrocarbon and NO x chemical mechanisms were used to represent chemistry. It was found that for the rate-limiting reaction, the pre-exponent factors (A f1 , A r1 ) and activation energies (E A,f1 , E A,r1 ) of the forward and reverse reaction rates follow a linear band in A f1 - E A,f1 and A r1 - E A,r1 space where predicted engine-out NO x match the measured values closely. By encompassing such bands from different engines and considering constraints on activation energies, a reduced search domain of pre-exponent factors and activation energies was constructed that is expected to be applicable to any diesel engine. Eventually, computationally efficient three-line and one-line search approaches were proposed to determine the optimum values of the pre-exponent factors and activation energies that led to a minimum error between measured and predicted engine-out NO x . Finally, these three-line and one-line NO x optimization approaches were applied to a fifth production diesel engine for which the 0D-VCF-tPDF model showed a very good predictive performance in terms of predicting peak pressure, 50% burn rate, and engine-out NO x when compared to measured and 3D-CFD values.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Training data selection for event classification in a highly variable environment

A problem of interest for nuclear nonproliferation is monitoring activities at nuclear facilities, where proliferation events may only take place a few times and often under variable conditions. Machine learning has revolutionized data analytics by enabling the use of measurable signatures to generate predictive models of facility operations. However, traditional methods for training these models require large, reliable data sets with labeled observations, a challenge for nonproliferation. Highly variable conditions further complicate this as events from training data may have occurred in conditions quite different from the event of interest. Our hypothesis is that when events occur in a highly variable environment, careful training data selection for each test event could outperform the standard approach of using all available training data. We developed a method to optimize training data selection for the given test event and applied it to predicting the power level of the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. In this study, the reactor startup exhibits variability between occurrences due to natural variability in environmental conditions and operational procedures. Using a combination of analysis techniques, a similitude assessment was performed on data collected from HFIR to isolate clusters that were optimal for training a predictive model. Concepts such as dynamic time warping and Jaccard similarity were used in conjunction with clustering analysis. In order to validate this approach, the model was trained on every combination of unique training events and the predictive performance was compared to the performance using a subset of the training data selected by isolated clusters found through the similitude assessment.

Iyer, A↗
DOE OSTI2022

Nomogram for Predicting Postoperative Portal Venous Systemic Thrombosis in Patients with Cirrhosis Undergoing Splenectomy and Esophagogastric Devascularization

Objectives. The aim of the study is to develop a nomogram for predicting postoperative portal venous systemic thrombosis (PVST) in patients with cirrhosis undergoing splenectomy and esophagogastric devascularization. Methods. In total, 195 eligible patients were included. Demographic characteristics were collected, and the results of perioperative routine laboratory investigations and ultrasound examinations were also recorded. Blood cell morphological traits, including the red cell volume distribution width (RDW), mean platelet volume, and platelet distribution width, were identified. Univariate and multivariate logistic regressions were implemented for risk factor filtration, and an integrated nomogram was generated and then validated using the bootstrap method. Results. A color Doppler abdominal ultrasound examination on a postoperative day (POD) 7 (38.97%) revealed that 76 patients had PVST. The results of the multivariate logistic regression suggested that a higher RDW on POD3 (RDW3) (odds ratio (OR): 1.188, 95% confidence interval (CI): 1.073–1.326), wider portal vein diameter (OR: 1.387, 95% CI: 1.203–1.642), history of variceal hemorrhage (OR: 3.407, 95% CI: 1.670–7.220), and longer spleen length (OR: 1.015, 95% CI: 1.001–1.029) were independent risk parameters for postoperative PVST. Moreover, the nomogram integrating these four parameters exhibited considerable capability in PVST forecasting. The nomogram’s receiver operating characteristic curve reached 0.83 and achieved a sensitivity and specificity of 0.711 and 0.848, respectively, at its cutoff. The nomogram’s calibration curve demonstrated that it was well calibrated. Conclusion. The nomogram exhibited excellent performance in PVST prediction and might assist surgeons in identifying vulnerable patients and administering timely prophylaxis.

Chen, Miao↗
DOE OSTI2022

Numerical simulation of falling film sensible heat transfer over round horizontal tubes

Numerical simulations are performed to explore the heat transfer characteristics of falling films over horizontal round tubes with uniform heat flux imposed for a range of Reynolds numbers spanning the droplet, jet, and sheet regimes. Simulations results agree well with available experimental measurements. The study analyzes the local as well as average heat transfer behavior under the different flow modes. The numerical results show that the local Nusselt number (Nu) distribution depends on the flow features in each mode and varies substantially in all directions for the respective mode. In the droplet mode, the Nu value varies significantly as the droplet impinges and the remnant liquid-bridge retracts (peak instantaneous Nu near 6), followed by wave propagation over the tube surface with peak Nu around 0.25. For the jet modes, the local maximum in the heat transfer occurs off-center to the impingement location with magnitudes of peak Nu = 3.1 for the inline jet mode and Nu = 2.7 for the staggered jet mode, while on the rest of the tube surface, it has an inverse relation with the liquid film thickness. Substantial variations in the heat transfer value are also recorded in the middle of the two impinging jets with Nu = 0.95 in the inline jet mode where the neighboring jets do not interact, and Nu = 0.60 in the crest region of the staggered jet mode where the neighboring jets interact with each other. In the sheet mode, the Nu was seen to depend on the thickness of the liquid waves traversing over the tube surface. Lower Nu values were recorded beneath the crest location of the liquid waves, which increases (1.4–11.6% depending on circumferential location) abruptly in magnitude at the advancing fronts of the waves. The temperature distribution in the liquid film in each of the modes was examined to evaluate the mechanism of heat transfer process. This study also compares the local heat transfer coefficient distribution with the analytical heat transfer models derived to predict heat transfer performance over horizontal tube surfaces.

42 ENGINEERING↗
DOE OSTI2023

INL Report: Multiphysics Modeling and Simulation of Deimos, an Advanced Reactor Experiment

When designing a novel reactor, nuclear experiments are essential to validate the predictive capability of modeling/simulation tools and to justify the investment of a full-scale prototype. Being able to accurately predict temperature reactivity coefficients is of high importance to reactor designers as it impacts the safety and performance of the system. Deimos is a proposed graphite-moderated, beryllium-reflected, high assay low-enriched uranium (HALEU) tri-structural isotropic (TRISO) fueled experiment for NCERC. Deimos is a valuable experiment for validating the predictive performance of modeling and simulation tools since it comprises a relatively thermal energy spectrum with 84% thermal neutrons causing fission, 15% epithermal neutrons causing fission, and 1% fast neutrons causing fission. Additionally, by electrically heating the experiment, reactivity can be validated at various temperatures. Deimos will serve as a testbed for future advanced reactor concepts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Emerging hierarchical dislocation structures: Insights from scanning electron microscopy-electron backscatter diffraction in situ tensile testing and multifractal analysis

Understanding the evolution of dislocation structures during plastic deformation is critical for predicting the mechanical performance of metallic materials. In this work, we applied in situ scanning electron microscopy/electron backscatter diffraction tensile testing combined with multifractal (MF) analysis to assess deformation-induced dislocation structure evolution in solution-annealed 304 L stainless steel, both in its as-received and neutron-irradiated states (5.4 displacements per atom). The analysis of kernel average misorientation patterns revealed the formation of hierarchical dislocation arrangements that exhibit clear MF scaling behavior. Despite pronounced visual differences between nonirradiated and irradiated specimens—most notably, the appearance of dislocation channels after irradiation—the singularity spectra suggest that both conditions give rise to similar underlying hierarchical structures. MF analysis provides a quantitative measure of the spatial complexity and self-organization of dislocation patterns, highlighting the accelerated emergence and evolution of the dislocation structures in irradiated polycrystalline materials, as well as the limitation of their spatial extent. The findings indicate that irradiation not only modifies microstructure but also alters correlation-driven dislocation organization. More generally, they demonstrate that MF analysis is a powerful tool for probing mesoscale deformation mechanisms.

Dislocation structures↗