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All sources NASA NTRS DOE OSTI

DOE OSTI2022

Evaluation of bifacial module technologies with combined–accelerated stress testing

In view of the increasing interest and market share of bifacial cells and modules, suitable substrates such as glass and transparent backsheets along with ethylene vinyl acetate (EVA) and polyolefin elastomer (POE) encapsulants were examined in combined-accelerated stress testing (C-AST) to evaluate and compare degradation modes. Testing with both monofacial and bifacial cells, we found glass-glass modules with monofacial cells led to greater grid finger breakage than those with polymeric backsheets. Furthermore, this is attributed to previous X-ray topography and modeling work showing higher stress in cells and interconnections in glass-glass modules than glass-backsheet modules. Consistent with the objectives of C-AST, which stresses modules at levels corresponding to the limits seen in the natural environment, we observed the UV-fluorescence signatures of modules tested in C-AST (considering the degradation associated with developing chromophores, moisture penetration and photobleaching effects) to be like those in fielded modules, more so than other chamber stress testing implemented for comparison. We found light-induced degradation (LID) in module types with regenerated (inactive) cells with C-AST, suggesting the possibility of LID destabilization in some field conditions. We could also distinguish potential-induced degradation (PID) on the back of the bifacial passivated emitter and rear cells (PERC) in C-AST. Confirming with ex-situ tests, we found polarization-type PID most prevalent in glass-glass modules with EVA as would be anticipated considering the greater leakage current through such module encapsulation. Unlike PID tests performed in the dark, which can lead to false positive PID test results, field-representative illumination is experienced by the modules on the front and back side while –1200 V system voltage is applied in C-AST, supporting the conclusion that this module type with glass-glass construction would be susceptible to PID in the field.

14 SOLAR ENERGY↗

DOE OSTI2022

Total Mercury Analysis of Radioactive Waste Containing Multiple Mercury Species

In this work, a direct mercury analyzer (DMA) was used to analyze total mercury in radioactive waste samples containing methylmercury and other forms of mercury including Hg o (elemental) together with inorganic ionic mercury species and complexes. These samples were also analyzed using Cold Vapor Atomic Absorption Spectroscopy (CVAAS) and/or Inductively Coupled Plasma Mass Spectroscopy (ICMS). Comparative statistical evaluation of the results from spike solutions/simulants, liquid radioactive waste samples, and interlaboratory performance test samples demonstrated that the various methods generated accurate and/or equivalent total mercury data and equivalent precision (2σ ± < 20%). Triplicate total mercury analysis of an exemplar radioactive waste resulted in an average value of 54.1 mg/L (as Hg) using CVAAS, 55.1 mg/L using ICPMS and 56.0 mg/L using DMA. A primary advantage of the DMA in a radioactive environment is avoiding multi-step, labor-intensive, time-consuming and waste-producing sample preparation protocols needed for CVAAS and ICPMS. DMA was determined to be the preferred method for measuring total mercury in the Savannah River National Laboratory (SRNL) radioanalytical laboratory based on analytical performance combined with ease of use in a radiological containment unit.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2023

Biomarkers of oxidative stress, diet and exercise distinguish soldiers selected and non-selected for special forces training

The metabolomic profiles of Soldiers entering the U.S. Special Forces Assessment and Selection course (SFAS) have not been evaluated. To compare pre-SFAS blood metabolomes of Soldiers selected during SFAS versus those not selected, and explore the relationships between the metabolome, physical performance, and diet quality. Fasted blood samples and food frequency questionnaires were collected from 761 Soldiers prior to entering SFAS to assess metabolomic profiles and diet quality, respectively. Physical performance was assessed throughout SFAS. Between-group differences (False Discovery Rate < 0.05) in 108 metabolites were detected. Selected candidates had higher levels of compounds within xenobiotic, pentose phosphate, and corticosteroid metabolic pathways, while non-selected candidates had higher levels of compounds potentially indicative of oxidative stress (i.e., sphingomyelins, acylcarnitines, glutathione, amino acids). Multiple compounds higher in non-selected versus selected candidates included: 1-carboxyethylphenylalanine; 4-hydroxy-nonenal-glutathione; α-hydroxyisocaproate; hexanoylcarnitine; sphingomyelin and were associated with lower diet quality and worse physical performance. Candidates selected during SFAS had higher pre-SFAS levels of circulating metabolites that were associated with resistance to oxidative stress, higher physical performance and higher diet quality. In contrast, non-selected candidates had higher levels of metabolites potentially indicating elevated oxidative stress. These findings indicate that Soldiers who were selected for continued Special Forces training enter the SFAS course with metabolites associated with healthier diets and better physical performance. Additionally, the non-selected candidates had higher levels of metabolites that may indicate elevated oxidative stress, which could result from poor nutrition, non-functional overreaching/overtraining, or incomplete recovery from previous physical activity.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

The Influence of Residual Stress on Fatigue Crack Growth Rates in Stainless Steel Processed by Different Additive Manufacturing Methods

The properties and microstructure of Type 304L stainless steel produced by two additive manufacturing (AM) methods—directed energy deposition (DED) and powder bed fusion (PBF)—are evaluated and compared. Localized heating and steep temperature gradients of AM processes lead to significant residual stress and distinctive microstructures, which may be process-specific and influence mechanical behavior. Test data show that materials produced by DED and PDF have small differences in tensile strengths but clear differences in residual stress and microstructural features. Measured fatigue crack growth rates (FCGRs) for cracks propagating parallel to and perpendicular to the build directions differ between the two AM materials. To separate the influences of residual stress and microstructure, K-control test procedures with decreasing and constant stress intensity factor ranges are used to measure FCGRs in the near-threshold regime (crack growth rates ≤ 1 × 10 −8 m/cycle). Residual stress is quantified by the residual stress intensity factor, K res , measured by the online crack compliance method. Correcting the FCGR data for differences in K res brings results for specimens of the two AM materials into agreement with each other and with results for wrought specimens, when the latter are corrected for crack closure. Differences in microstructure and tensile strength have an insignificant influence on FCGRs in these tests.

additive manufacturing↗

DOE OSTI2021

Machine-Learning-based Algorithms for Automated Image Segmentation Techniques of Transmission X-ray Microscopy (TXM)

Four state-of-the-art Deep Learning-based Convolutional Neural Networks (DCNN) were applied to automate the semantic segmentation of a 3D Transmission x-ray Microscopy (TXM) nanotomography image data. The standard U-Net architecture as baseline along with UNet++, PSPNet, and DeepLab v3+ networks were trained to segment the microstructural features of an AA7075 micropillar. A workflow was established to evaluate and compare the DCNN prediction dataset with the manually segmented features using the Intersection of Union (IoU) scores, time of training, confusion matrix, and visual assessment. Comparing all model segmentation accuracy metrics, it was found that using pre-trained models as a backbone along with appropriate training encoder-decoder architecture of the Unet++ can robustly handle large volumes of x-ray radiographic images in a reasonable amount of time. This opens a new window for handling accurate and efficient image segmentation of in situ time-dependent 4D x-ray microscopy experimental datasets.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Direct Fabrication of Vertically Stacked Double Barrier Tunnel Junctions Based on Graphene and h-BN

Direct manufacturing of two-dimensional material-based double barrier (DB) tunnel junctions, based on a lithography-free approach was developed. Graphene/h-BN/Graphene/h-BN/Graphene devices were deposited on Si/SiO2 substrates by employing a plasma enhanced chemical vapor deposition technique in a sequential manner. DB tunneling junctions with varying barrier widths (by varying the thickness of the second graphene layer) were studied. Samples were characterized using Raman, Atomic Force Microscopy and X-ray photoemission spectroscopy. The I–V characteristics of tunneling current showed resonant tunneling behavior at room temperature with a negative differential conductance. The behavior could be explained with quantum mechanical double barrier tunneling model in which analytic solutions to Schrödinger’s equation were obtained in each region of the system. Resonances in transmission probability coefficient for varying barrier widths were evaluated and compared with the experimental results.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Temperature-dependent mechanical properties and crystal plasticity parameters for additively manufactured Haynes-214 alloy: Experiments and numerical modeling

Our experimental mechanical testing data demonstrated that the additively manufactured (AM) laser powder bed fusion (L-PBF) Haynes-214 alloy exhibits non-linear mechanical properties as the temperature rises from ambient to 870 °C. Crystal plasticity (CP) simulations provide an effective approach to gaining deeper insights into microstructure-property linkages under thermomechanical loading. This method can reduce the need for costly high-temperature mechanical testing while accounting for the effects of crystallographic texture and grain morphology on the mechanical behavior of AM materials. However, calibrating a CP model is time-consuming because individual simulations are computationally expensive and hundreds (or more) of iterations over parameter sets may be required. To address this issue, we have designed a machine learning-differential evolution (ML-DE) CP framework that can accurately interpolate the tensile properties of AM L-PBF Haynes-214 alloy across a wide temperature range from ambient to 870 °C, with minimal reliance on experimental data. The framework uses electron backscatter diffraction (EBSD) measurements to generate statistically equivalent microstructural volume elements to serve as inputs to the CP modeling framework. Stress–strain curves were generated from 1000 CP simulations, which serve as the training data set for the three ML regression algorithms explored: linear, extra-trees, and multi-layer perceptron. These three regression models were independently evaluated to compare their efficiency and identify the most suitable algorithm for the given problem. Results revealed that the extra-trees ML regressor outperforms the other models in both qualitative and quantitative aspects with an R 2 of 0.98. Subsequently, the differential evolution optimization approach is employed to calibrate the ML-based CP material parameters with experimental results obtained at various temperatures. Finally, temperature-dependent CP material parameters are formulated. The effectiveness and efficiency of the designed framework are validated through comparison with experimental results, demonstrating a high degree of agreement. These calibrated parametric constitutive equations enable further use of the CP model to study the deformation behavior of this alloy under a wide range of thermo-mechanical loading conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Californium-252 production at the High Flux Isotope Reactor - I: Validation study using campaign data

This paper presents a series of 252 Cf production validation and code-to-code comparison studies performed based on data from the production campaigns at the High Flux Isotope Reactor (HFIR). These studies support efforts to convert HFIR from using highly enriched uranium (HEU) fuel to low-enriched uranium (LEU) fuel. HFIR must maintain its world-class performance and missions following this conversion, and because 252 Cf is a vital neutron-emitting radioisotope used for a variety of high-impact applications (e.g., reactor startup, cancer treatment), the ability to efficiently produce 252 Cf must be preserved. In this work, the HFIRCON, Shift, ORIGEN, and TCOMP codes were deployed, and several sets of data libraries were investigated to better understand the calculation codes and the data biases. As-loaded target composition data, as-run irradiation history data, and post-irradiation measurements from recent multi-cycle irradiation campaigns of the HEU core were used to validate and determine methodology biases. Further, the findings demonstrated a good agreement, with results falling within 3 standard deviations of measurements. This paper lays the ground work for the second paper, which evaluates and compares 252 Cf production and safety metrics with the HEU core and a proposed LEU core.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2025

Differentiable hybrid neural network approach for enhancing reactor dynamics simulations

Reactor dynamics simulations provide essential insights into the time-dependent behavior of nuclear reactors under various operating conditions. However, high-fidelity simulations can be computationally intensive, requiring significant computational resources. Here, to address this challenge, this study employs a differentiable hybrid model that utilizes neural networks as a corrector to enhance the performance of a low-fidelity simulation, aligning its predictions with those of a high-fidelity simulation. Low-fidelity and high-fidelity simulations were obtained by adjusting the mesh size in the System Dynamics Analysis Tool. The differentiable hybrid model was trained in two approaches: time-step-wise and sequence-wise. It was then applied to simulate various transients in a molten salt reactor. Its performance was evaluated by comparing its responses to transients against those of the high-fidelity simulation. An additional approach was performed using a data-driven model to correct the low-fidelity simulation. In comparison, the differentiable hybrid model showed significant improvements in transient prediction, effectively addressing the limitations of the low-fidelity simulations. The results highlighted the robustness of the differentiable hybrid model in both training approaches. It delivered simulations that were at least 3.8 times faster than high-fidelity models. In the time-step-wise approach, it achieved at least a 39% improvement in accuracy. In the sequence-wise approach, it showed at least an 81% accuracy improvement over the full transient. This approach offers a promising path for improving computational efficiency without compromising accuracy in nuclear reactor simulations, making it suitable for real-time digital twin applications.

42 - ENGINEERING↗

DOE OSTI2024

A methodology for selection of solid desiccants in energy recovery ventilators

Controlling indoor humidity levels is essential for maintaining acceptable indoor air quality in buildings. The use of energy recovery ventilators (ERVs) is an energy-efficient way to regulate indoor air humidity. Fixed-bed regenerators and rotary wheels are widely used ERVs because of their high sensible and latent effectiveness. These ERVs are made of desiccant-coated substrates, which enable them to transfer moisture between the supply and exhaust air streams. However, the moisture transfer ability of ERVs depends on the physiochemical and sorption properties of desiccants. Extensive, full-scale experiments are required to determine the best desiccant material for these systems. This paper presents a simplified method of selecting suitable desiccant materials for ERVs. The methodology involves important characterization methods, literature correlations for performance prediction, and cost-effective testing methods prior to full-scale testing, and full-scale test methods are discussed in detail. Furthermore, the performance of a few newly derived materials is evaluated and compared with that of conventional desiccants such as silica gel and molecular sieves. The highest latent effectiveness was obtained for composite of super absorbent polymer (SAP) with potassium formate (SAP-HCO2K-50 %), all-polymer porous solid desiccant (APPSD) and metal organic framework (MOF)–MIL–101 (Cr), followed by activated carbon fibre felt (ACFF) Silica sol-LiCl30, SAP, silica gel, MOF–303, and molecular sieve. Researchers and manufacturers would benefit from the proposed methodology and presented data in developing new desiccant materials for ERV applications.

Energy recovery↗

DOE OSTI2020

On The Use of Sectional Techniques for the Solution of Depolymerization Population Balances: Results on a Discrete-Continuous Mesh

To study the discrete bond-breaking phenomena of depolymerization, the use of a fully continuous Population Balance Equation (PBE) is inadequate to embody all the inherent characteristics of the process, thus resulting in the need for a discrete-continuous mesh. In this work, the performance of the three most state-of-the-art sectional techniques, i.e. the fixed pivot technique (FPT), cell average technique (CAT) and finite volume scheme (FVS) in approximating discrete depolymerization using discrete-continuous PBEs was extensively compared and evaluated. The solutions from these three methods show different accuracy depending on the breakage mechanisms. For chain-end scission, the FPT and the CAT satisfactorily predict the population densities and moments whereas the FVS fails to predict the population densities but preserves the zeroth and the first moments. In the application of a discrete-continuous model, we identified a previously-not-reported issue of a precipitous drop in the number density at the boundary of discrete and continuous region specifically for chain-end scission. We successfully fixed this problem by employing the alterations proposed in this paper, to the particle allocation functions at the boundary points. For random scission, all three sectional techniques predict the population densities and moments to a high degree of accuracy, even at a very coarse mesh, through the use of our new stoichiometric kernel which is able to closely approximate the inherently discrete bond-breaking depolymerization process. The assessments in this present work intends to provide a clear-cut direction to efficient and economical modelling of depolymerization processes.

Ahamed, Firnaaz↗

DOE OSTI2020

Biological Valorization of Natural Gas for the Production of Lactic Acid: Techno-Economic Analysis and Life Cycle Assessment

Due to the availability of natural gas, shale gas, and flared gas, there is increased interest in converting methane in these gases into commodity chemicals via cost-effective and sustainable processes. Recently, we have demonstrated an efficient process concept converting methane to lactic acid (LA) by methanotrophic bacteria with high carbon flux, pivoting potentials for commercialization. Herein, we present a first-of-kind conceptual design and a techno-economic analysis (TEA) of a biorefinery process with calculated minimum selling prices. This study includes both sensitivity analysis and scenario analysis for key cost drivers and for projecting path forwards in research and development. The analyses reveal that the conversion efficiency, LA product titer, LA productivity, and gas flow rate have the most significant impacts on cost. The MSP varies from $5.83-$2.17/kg LA depending on these key process attributes. Further, a life cycle assessment of this process concept is evaluated and compared to understand the sustainable aspects. In this TEA study, the bio-upgrading process from natural gas to LA presents a potentially promising approach in terms of the feedstock versatility, economic viability, and environmental impact.

BIOMASS FUELS,NATURAL GAS↗

DOE OSTI2020

Accelerating geostatistical modeling using geostatistics-informed machine Learning

Ordinary Kriging (OK) is a popular geostatistical algorithm for spatial interpolation and estimation. The computational complexity of OK changes quadratically and cubically for memory and speed, respectively, given the number of data. Therefore, it is computationally intensive and also challenging to process a large set of data, especially in three-dimensional (3D) cases. This paper develops a geostatistics-informed machine learning (GIML) model to improve the efficiency of OK by reducing the number of points required to be estimated using OK. Specifically, only a very few of the unknown points are estimated by OK to get the weights and estimations, which are used as the training dataset. Moreover, the governing equations of OK are used to guide our proposed machine learning to better reproduce the spatial distributions. Our results show that the proposed GIML can reduce the computational time of OK by at least one order of magnitude. The effectiveness of the GIML is evaluated and compared using a 2D case. Furthermore, we demonstrate its efficiency and robustness by considering a different number of training samples on various 3D simulation grids.

58 GEOSCIENCES↗

DOE OSTI2025

Countercurrent flow characteristics of next generation solvent in novel 3D printed columns for carbon capture

Solvent based absorption for carbon dioxide capture in a packed column is being considered as an efficient technology for the decarbonization process of point source. Significant efforts are underway to improve the design of packings with the cutting edge 3D printing technology for efficient carbon capture. Accordingly, multiphase flow studies were conducted to assess the performance of novel 3D printed columns with various triply periodic minimal surface (TPMS) designs. The effects of solvent properties, liquid and gas loads on the performance of TPMS columns are extensively explored. Hydrodynamics of the potential water-lean solvents (EEMPA) as well as aqueous monoethanolamine (MEA) solvent for carbon capture are evaluated and compared. The interfacial area and liquid holdup increase with increasing liquid loads (q L ) for TPMS columns. Schwarz column consistently shows the highest liquid holdup value. The EEMPA exhibits higher values for the interfacial area (10–15 %) and liquid holdup (~4 %) in comparison to the MEA. TPMS columns exhibit the intermediate value of dry pressure drop between the random and the structure packed columns. Among selected TPMS columns, the gyroid packing shows the lowest pressure drop. The gas load has marginal impact on the interfacial area at lower value while a higher gas load leads to column’s flooding. Prior to the flooding, the interfacial area in TMPS packings rises with increased gas load at a fixed liquid load except Schwarz packing where interfacial area is incentive to the gas load. Additionally, the liquid holdup and wet pressure drop rise as gas load increases in TPMS columns. Overall, Fisher Koch packing is more susceptible to flood as compared to other TPMS packings. Flow regimes: loading and flooding are also delineated in the TPMS packings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Robustness of cement-based materials: From dosage variations to yield stress fluctuations

The idea behind the present work is to assess robustness of fresh cement-based materials (i.e. their ability to display the same properties when submitted to variations in the way they are produced) from a generic and analytical point of view. First, analytical models from literature relating components proportions and yield stress are combined into a multi-scale and physical approach and compared to our experimental yield stress measurements. In the second part, the derivation of these analytical relationships is used to assess robustness as a function of components proportions, which allows for the most sensitive variations to be mapped onto component dosage from a yield stress point of view. In the case of fluid concretes, this approach showcases the interest of working at slightly higher water content and slightly lower aggregates fraction along with benefits of viscosity modifying agents / plasticizers combination.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Unravelling chloride transport/microstructure relationships for blended-cement pastes with the mini-migration method

A chloride mini-migration method is proposed to estimate effective diffusion coefficients at the scale of the cement paste and to investigate mechanisms with complementary microstructure analyses on the same material. Blended-cement pastes with a wide range of properties were investigated in this study: systems at w/b = 0.3–0.5 including Portland cement, white Portland cement, slag-Portland cement, fly ash, glass powder, and/or limestone and calcined clay. Comparisons showed the relative and combined importance of three main parameters on the effective diffusion coefficient: the porosity, the pore connectivity parameter and the conductivity of the pore solution (low values of the latter two are generally key aspects of the high resistance of blended-cement systems against chloride ingress). Notably, a general correlation for all the investigated systems was established between the effective diffusion coefficient and the bulk conductivity (whereas no simple correlation was observed between the effective diffusion coefficient and the formation factor or the pore connectivity parameter).

36 MATERIALS SCIENCE↗

DOE OSTI2021

Performance of fly ash concrete with ferronickel slag fine aggregate against alkali-silica reaction and chloride diffusion

Ferronickel slag (FNS) is an industrial by-product of ferronickel alloy production at a high temperature which can be a promising potential to be used as fine aggregate to produce more sustainable concrete. In this study, the performance of concrete containing ferronickel slag sand and fly ash relating to alkali-silica reaction (ASR) and chloride contamination was investigated. ASR-induced expansion, chloride diffusion resistance, and chloride binding capacity of FNS concrete were determined through concrete prism tests (CPT), accelerated diffusion test, and bulk diffusion test. Thermogravimetric analysis (TGA) was conducted to measure the amount of Portlandite and Friedel's salt in concrete. Concrete with 50 wt% FNS sand as fine natural aggregate replacement and 25 wt% of cement replacement by fly ash showed a remarkable potential to be used not only as a low-carbon concrete with comparable mechanical properties to conventional concrete but also with a better performance against ASR and chloride contamination.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Assessment of mechanical, thermal properties and crystal shapes of monoclinic tricalcium silicate from atomistic simulations

The two most common polymorphs in industrial alite, M1 and M3, were characterized at the molecular scale. Different methods were employed and discussed to assess mechanical properties and specific heat of both polymorphs. The calculated homogenized elastic moduli and specific heat were found in good agreement with experimental measurements. A comparative analysis of spacial Youngs modulus reveal isotropic and anisotropic spacial distribution for M{sub 1} and M{sub 3} respectively. A more isotropic compressive strength is also reported for M{sub 1} when compared to M{sub 3} polymorph. Cleavage energies computation allowed to proposed equilibrium shapes for both polymorph, with significant differences. While the lowest cleavage energies were found along (100) and (001) for both polymorphs, the constructed M1 crystal possesses 3 independent facets, against seven for the M3 polymorph.

36 MATERIALS SCIENCE↗