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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Chabazite Zeolite Membranes for Krypton/Xenon Separation: Control of Membrane Properties and Process Modeling

Nuclear energy is considered to be a clean and economical means of carbon-free energy. As part of the overall process for recycling used nuclear fuel (UNF), it is necessary to develop technologies to separate the off-gases formed during the fission of uranium and plutonium in nuclear power reactors. The off-gas stream is generated during the shearing and chopping, voloxidation, and acid dissolution steps, and would have a flow rate of ∼2500 L/min assuming a typical 800 tons heavy metal/yr reprocessing facility. Kr separation from Xe is one of the steps in a proposed UNF off-gas processing flowsheet. The Kr/Xe mixture is approximately 10/90 by volume and hence the separation of Kr would reduce the volume of radioactive {sup 85}Kr waste by a factor of 10. Kr/Xe separation is challenging since they are both chemically inert species and physically very similar (0.036 nm size difference). There is no existing process for this separation that is suitable for use in UNF recycling since conventional cryogenic distillation requires highly intensive power and large system volume. Membrane-based separation using radiation-stable materials such as zeolites could be an attractive separation method, if membranes with sufficient Kr flux and selectivity can be fabricated. Zeolite membranes offer an opportunity to exploit the size difference of Kr (kinetic diameter 0.36 nm) and Xe (0.396 nm) to achieve preferential permeation of the minority component (Kr) from the mixture, leading to an efficient separation with very low energy requirements. An ideal membrane process would permeate a stream of pure Kr for storage and also produce pure Xe which has commercial value (> $8000/kg at 99+% purity). SAPO-34 is a silico-alumino-phosphate zeolite molecular sieve with CHA framework topology and containing uniform micropores (0.38 nm), intermediate in size between the kinetic diameters of Kr and Xe. It was hypothesized that SAPO-34 membranes can separate krypton and xenon by diffusivity differences. Previously we fabricated SAPO-34 membranes and showed that they are capable of separating Kr from Xe. Our previous work also indicated that there is much potential for engineering the separation properties of these membranes with a view to improving permeance and selectivity and evaluating the expected process performance. The main objectives of the present study are: 1) development of SAPO-34 membranes with enhanced membrane performance via thickness reduction and ion exchange, and 2) process modeling for technic-economic analysis of zeolite membrane systems for Kr/Xe separation.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Bounding the Chemistry Conditions in the HM Solvent Extraction Process Criticality Safety Analyses

An approach new to the H-Canyon facility criticality safety analysis has been performed using highly detailed models of the H-Modified (HM) process mixer settlers. The HM process uses tributyl phosphate (TBP) in organic n-paraffin to extract and purify uranium from used nuclear fuel. This occurs in two cycles of counter-current solvent extraction. The aqueous stream is a weak to moderate nitric acid and the feed stock is an aqueous stream. The first Cycle, consisting of two 16-stage banks (A and B-Banks) and one 12-stage bank (C-Bank), is a three step process that separates uranium from the fuel matrix, fission products, and most of the transuranic actinide content. The first Cycle aqueous product is concentrated, adjusted for acidity and fed to the second Cycle. The second Cycle, consisting of one 16-stage bank (D-Bank) and one 12-stage bank (E-Bank), is a two-step process that further purifies the uranium and removes to a great extent remaining actinides and fission products Each stage of the mixer settler banks is modeled as three unique compositions: the mixer as a combined composition of aqueous and organic components and the settler one aqueous and one organic component. This work addresses what is believed to be normal, credible abnormal, and beyond credible abnormal conditions for a particular postulated series of process parameter upsets. The level of detail and the source for the stage-wise compositions are both new and unique to criticality safety analyses of the HM process. Initial work was presented in Ref. 1 and has since been refined. A predictive chemistry model for flowsheet development has been used to solve the solvent extraction equations for the prescribed conditions and the output of that model was then transformed into compositions for each mixture in each stage. The remaining question upon which to achieve consensus is if the approach was appropriately conservative by concluding the chemistry is appropriately bounded. Computational modeling for criticality calculations was performed using KENO-VI in the SCALE 6.1 code package. Computational modeling of the chemistry was performed using the SEPHIS Modification 4 code.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Potential Integration Between Residual Biogenic Process Resources and Greener Hydrogen Production from Steam Reformers

Biomass conversion processes have varying efficiencies towards specific products like liquid fuels; process inefficiencies result in byproducts such as off-gases, heat, and solid residues such as char. The efficient use of these byproducts is key towards getting the maximum sustainability benefits from valuable biomass resources. For example, there are various utility product options that can utilize heat and off-gases from biomass pyrolysis processes; they include process heat and steam, hydrogen, fuel gas, and electricity. Further, there is potential for the use of the off-gases to supplement natural gas feed into steam reformers for hydrogen production. This presentation highlights results from previous analyses on tradeoffs based on utility byproduct choices (https://doi.org/10.1039/D3SE00745F); maximizing hydrogen production from off-gases is one potential winning strategy. This leads to the question regarding the utilization of these off-gases in existing steam reformers and the process impacts from feeding off-gases. Process modeling of a steam reformer system (https://doi.org/10.1002/adsu.20230021) quantifies those impacts and shows how much off-gas substitution is possible within the limits of an existing design with such an integration strategy.

biogenic gases↗
DOE OSTI2024

Process Optimization of Carbon Electrode Materials Manufacturing by Experimental Study and Machine Learning Techniques

Electrospun carbon fibers from coal have been investigated as electrodes for batteries and supercapacitors. Despite the excellent properties of coal-derived carbon fibers (CCNF) for energy storage devices, there still lacks systematic understanding on how various process parameters affect final electrode performances, which poses challenges to scale from pilot to high volume manufacturing. The goals of this project are twofold. First, we focuse on process optimization for converting a new precursor from powder river basin (PRB) coal, referred to as coal-based polyurethane (CPU) to CCNF using electrospinning. Second, different machine learning techniques will be examined using experimental data from this work and open literature. Specifically, for CPU the following process parameters need to be characterized and optimized in order to produce CCNFs with desirable mechanical integrity and physiochemical properties: precursor composition and viscosity, operating voltage and distance, oxidation and carbonization temperature and duration. Consequently, physiochemical properties of the fibers were characterized to correlate these process parameters with desirable electrochemical performance. Given the complex nature of the fiber production process, ML models are assessed for their ability to capture the nonlinear relationship between process parameters and the electrochemical properties in applications including supercapacitors. As such, we applied various machine learning techniques, to determine which technique produces a model that best predicts device function.

Cincotta, Robert E.F.↗
DOE OSTI2025

Leveraging FPGA Advantages for Quicker Data Processing for LBNF

The Long Baseline Neutrino Facility (LBNF) will deliver a 2.4 MW muon neutrino beam from Fermilab to the Deep Underground Neutrino Experiment (DUNE), requiring unprecedented precision in beamline alignment to achieve DUNE's neutrino oscillation measurement goals. Vertical misalignments of beamline components as small as 0.5 mm can contribute 6-7\% uncertainty in predicted neutrino flux, necessitating sub-0.1 mm alignment monitoring capabilities. The Horn Location Sensor (HLS) system employs frequency sweep interferometry (FSI) in a distributed hydrostatic leveling network to achieve the required precision under harsh radiation conditions up to 5000 kRad/year. Traditional FSI implementations suffer from laser sweep nonlinearities that degrade resolution and require computationally intensive post-processing corrections using gas reference cells. This work presents a real-time FPGA-based implementation of the HLS data acquisition and processing system using a sweep tracker interferometer for dynamic sweep linearization. The system utilizes a PYNQ-Z2 FPGA with programmable logic implementing parallel 16k-point FFT processing across four channels, synchronized by the sweep tracker signal to eliminate post-processing requirements. Spectral performance testing demonstrates significant improvements in peak sharpness compared to traditional fixed-frequency digitization. The FPGA implementation enables real-time displacement monitoring with processing speeds orders of magnitude faster than software-based approaches, essential for the operational requirements of LBNF's eventual distributed sensor network. This advancement in real-time FSI processing directly supports DUNE's precision neutrino physics program by providing the rapid feedback necessary for maintaining stringent beamline alignment tolerances during high-power beam operations.

Rossel, Jacob↗
DOE OSTI2018

Process synthesis using block superstructure with automated flowsheet generation and optimization

An alternative method for chemical process synthesis using a block–based superstructure representation is proposed. The block–based superstructure is a collection of blocks arranged in a two–dimensional grid. The assignment of different equipment on blocks and the determination of their connectivity are performed using a mixed–integer nonlinear formulation for automated flowsheet generation and optimization–based process synthesis. Based on the special structure of the block representation, an efficient strategy is proposed to generate and successively refine feasible and optimized process flowsheets. Our approach is demonstrated using two process synthesis case studies adapted from the literature and one new process synthesis problem for methanol production from biogas.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Hierarchical Gaussian process-based Bayesian optimization for materials discovery in high entropy alloy spaces

Bayesian optimization (BO) is a powerful and data-efficient method for iterative materials discovery and design, particularly valuable when prior knowledge is limited, underlying functional relationships are complex or unknown, and the cost of querying the materials space is significant. Traditional BO methodologies typically utilize conventional Gaussian Processes (cGPs) to model the relationships between material inputs and properties, as well as correlations within the input space. However, cGP-BO approaches often fall short in multi-objective optimization scenarios, where they are unable to fully exploit correlations between distinct material properties. Leveraging these correlations can significantly enhance the discovery process, as information about one property can inform and improve predictions about others. Here, this study addresses this limitation by employing advanced kernel structures to capture and model multi-dimensional property correlations through multi-task (MTGPs) or deep Gaussian Processes (DGPs), thus accelerating the discovery process. We demonstrate the effectiveness of MTGP-BO and DGP-BO in rapidly and robustly solving complex materials design challenges that occur within the context of complex multi-objective optimization over FCC FeCrNiCoCu high entropy alloy (HEA) spaces, where traditional cGP-BO approaches fail. Furthermore, we highlight how the differential costs associated with querying various material properties can be strategically leveraged to make the materials discovery process more cost-efficient.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A reaction–diffusion model for grayscale digital light processing 3D printing

We report that digital light processing (DLP) 3D printing is an additive manufacturing process that utilizes light patterns to photopolymerize a liquid resin into a solid. Due to the accuracy of modern digital micromirror devices (DMD) and recent advances in resin chemistry, it is now possible to create functionally graded structures using different light intensity values, also known as grayscale DLP (g-DLP). Different intensities of light lead to differences in the polymer crosslinking density after curing, which ultimately produces a part with gradients of material properties. However, g-DLP is a complicated process. First, the DLP printing is a highly coupled chemical and physical process that involves light propagation, chemical reactions, species diffusion, heat transfer, volume shrinkage, and changes in mechanical behaviors of the curing resin. Second, in g-DLP, light gradients create strong in plane gradients of chemical species concentrations in the curing liquid resin due to the strong dependence of light intensity on the rate of monomer crosslinking. Furthermore, light gradients through the depth create concentration gradients due to the degree of cure dependent light absorption and the use of photoabsorbers. These complex physical features of the printing process must be understood in order to properly control printing parameters such as light exposure time, printing speed, and grayscale variations to achieve accurate mechanical properties. In this paper, a photopolymerization reaction–diffusion model is developed and used in conjunction with experiments to investigate the coupled effects of light propagation, chemical reaction rates, and species diffusion during g-DLP 3D printing. The model is implemented numerically utilizing the finite difference method and simulation results are compared to experimental findings of simple printed structures. The agreement between experimental and model predictions of simple quantities of interest, such as geometric feature sizes, shows that the model can capture the overcure due to free-radical and other species diffusion during printing when grayscale patterns are employed. This model lays the groundwork for future extensions that can incorporate more complex coupled physics such as heat transfer, volume shrinkage, and material property evolution, which are critically important in utilizing g-DLP 3D printing for the fabrication of high-performance parts which excellent geometric and material property tolerances.

36 MATERIALS SCIENCE↗
DOE OSTI2023

In-situ measurement and control of the tool-workpiece interface temperature during friction stir processing of 304/304L stainless steel

Friction stir processing of 304/304L stainless steel is an area of interest for repair of spent nuclear fuel canisters. To enable repair without canister deflection or fracture, it is necessary to define the limitations for the depth of the processing zone which will be critically dependent on the thermal history of the material. Here, this work provides the first direct measurement of the interface temperature for friction stir processing of steel. The measured temperature is in the range of 850–1050 °C which is 20–30 % higher than previously reported values. Notably, the peak temperature occurs at the pin tip which is attributed to a higher sticking fraction and subsequently higher strain rate near the tool axis of rotation. Furthermore, a clear correlation between the temperature, grain size, and hardness is established within a single weld nugget and across different processing conditions. In the future, this data may lead to improved process modelling through direct validation and allow for property optimization through direct microstructure control.

304 stainless steel↗
DOE OSTI2025

Formation trajectories of solution-processed perovskite thin films from mixed solvents

The engineering of mixed-solvent formulations and their evaporation conditions are key to reproducible perovskite coatings for high-performance photovoltaics. Here, we report a lumped-parameter evaporation model to predict the evolution of a perovskite ink liquid film over time (solvent ratio, solute concentration, and film thickness). The drying-rate model is validated via in situ film-thickness measurements, and the predicted transient liquid film state is mapped as a process path. These methods allow for the prediction of process sensitivity to local environmental factors and the understanding and visualization of a broader processing parameter space enabled through the coupling of process and ink engineering. Process maps are applied to create a new framework for scalable perovskite coating development with a goal of improving the reproducibility and transferability of perovskite fabrication. This approach is demonstrated with blade-coated FA 0.83 Cs 0.17 PbI 3 photovoltaic devices, improving the photovoltaic conversion efficiency from 17.5% ± 1.7% to 20.3% ± 0.6%.

14 SOLAR ENERGY↗
DOE OSTI2026

Proton-rich Production of Lanthanides: The vi Process

The astrophysical origin of the lanthanides is an open question in nuclear astrophysics. Besides the widely studied s, i, and r processes in moderately to strongly neutron-rich environments, an intriguing alternative site for lanthanide production could in fact be robustly proton-rich matter outflows from core-collapse supernovae under specific conditions—in particular, high-entropy winds with enhanced neutrino luminosity and fast dynamical timescales. In this environment, excess protons present after charged-particle reactions have ceased can continue to be converted to neutrons by (anti)neutrino interactions, producing a neutron-capture reaction flow up to A ∼ 200. This scenario, christened the νi process in a recent paper, has previously been discussed as a possibility. Here, we examine the prospects for the νi process through the lenses of stellar abundance patterns, bolometric light curves, and galactic chemical evolution models, with a particular focus on hypernovae as candidate sites. We identify specific lanthanide signatures for which the νi process can provide a credible supplement to the r/i processes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Modeling and Simulation of Compression Molding Process for Sheet Molding Compound (SMC) of Chopped Carbon Fiber Composites

Compression molded SMC composed of chopped carbon fiber and resin polymer which balances the mechanical performance and manufacturing cost presents a promising solution for vehicle lightweight strategy. However, the performance of the SMC molded parts highly depends on the compression molding process and local microstructure, which greatly increases the cost for the part level performance testing and elongates the design cycle. ICME (Integrated Computational Material Engineering) approaches are thus necessary tools to reduce the number of experiments required during part design and speed up the deployment of the SMC materials. As the fundamental stage of the ICME workflow, commercial software packages for SMC compression molding exist yet remain not fully validated especially for chopped fiber systems. In this study, SMC plaques are prepared through compression molding process. The corresponding simulation models are built in Autodesk Moldflow with the same part geometry and processing conditions as in the molding tests. The output variables of the compression molding simulations, including press force history and fiber orientation of the part, are compared with experimental data. Influence of the processing conditions to the fiber orientation of the SMC plaque is also discussed. It is found that generally Autodesk Moldflow can achieve a good simulation of the compression molding process for chopped carbon fiber SMC, yet quantitative discrepancies still remain between predicted variables and experimental results.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Evaluation of Material Balance Approaches for Hanford Direct-Feed Low Activity Waste Processing - 20022

The Hanford Site has accumulated millions of gallons of tank waste from reprocessing spent fuel to recover plutonium, uranium, cesium, and strontium. The supernatant from the accumulated tank waste will be treated using a Direct Feed Low-Activity Waste approach. The supernatant will be treated to remove solids and cesium in the Tank-Side Cesium Removal process in the Hanford tank farm, then vitrified in a semi-batch process in the Hanford Waste Treatment and Immobilization Plant (WTP). Currently, each batch of feed is sampled at three locations prior to being fed to the melter: the feed qualification tank in the Hanford tank farm as well as the concentrate receipt vessel (CRV) and melter feed preparation vessel (MFPV) in the WTP. The feed qualification sample is taken from a large batch of accumulated feed, only two to three samples are expected each year. Approximately 275 samples from the CRVs and 1100 samples from the MFPV are expected each year. An evaluation was performed to determine if a material balance based on the feed qualification sample could replace most of the sampling in the CRVs and MFPVs. The evaluation consisted of three elements: (1) determination of the practicality of using a material balance to estimate the stream composition of the CRV and MFPV contents, (2) evaluation of whether the material balance could be automated using the existing process control system, and (3) determination of the uncertainty in glass composition using the material balance approach. It is assumed that periodic sampling at the CRV and MFPV would be performed periodically to re-baseline the material balance, evaluations are in progress to determine the frequency of this periodic sampling. Process sample locations downstream of the melter were reviewed as well, but the partitioning of semi-volatile species in the melter was determined to preclude extending the material balance approach past the melter. It was determined that replacement of the CRV sample location was feasible and did not increase process uncertainty or significantly impact waste loading. Replacement of the MFPV sample was also determined to be feasible, but that measurement of the glass former chemical addition may be needed prior to addition of these chemicals to the MFPV. This measurement could be performed by an in situ laser-induced breakdown spectroscopy (LIBS) system. Limited tests were performed to evaluate LIBS for direct measurements of the low-activity waste melter feed. The use of a material balance would eliminate over 1200 samples each year if only 10% of the CRV and MFPV batches are sampled and could likely allow the WTP laboratory to operate on days only versus 24/7 operation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Pulsed Electric Field Processing of Apples

Pulsed electric field (PEF) processing is a nonthermal technology that has the potential to improve the efficiency of apple processing, particularly for firm cultivars that are growing in popularity. This study evaluated the effects of PEF pretreatment on the processability and product quality of Cosmic Crisp apples, a modern cultivar known for its firmness. Apples were treated with PEF and assessed for tissue softening, mechanical energy requirements, particle size reduction, browning tendencies, and juice yield. PEF treatment significantly softened the apple tissue, as demonstrated by reduced compression forces in Instron testing, and this softening translated to decreased grinding energy during applesauce production. There were no changes to the particle size distribution of the resulting applesauce. PEF treatment also altered browning behavior and produced higher juice yields following mechanical disruption, suggesting enhanced membrane permeability and mass transfer. Together, these results show that PEF pretreatment can reduce mechanical energy demands and increase extractability without reducing product quality in firm apple varieties. These outcomes highlight the potential of PEF to increase the value recovered from processing-grade apples and to support more efficient and sustainable apple processing operations.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
DOE OSTI2019

Potential synergies of drop-in biofuel production with further co-processing at oil refineries

Drop-in biofuels have been defined as functionally equivalent to petroleum-based transportation fuels and are fully compatible with the existing petroleum infrastructure. They will be essential in sectors such as aviation if we are to achieve emission reduction and climate mitigation goals. Currently, 'conventional' drop-in biofuels, which are primarily based on upgrading of lipids / oleochemicals, are the only significant source of commercial volumes of drop-in biofuels. However, the necessary increased, future volumes will likely come from 'advanced' drop-in biofuels based on biomass feedstocks such as forest and agriculture residues. Biocrudes / bio-oils produced from lignocellulosic feedstocks using thermochemical technologies such as gasification, pyrolysis, and hydrothermal liquefaction need to be further upgraded to drop-in biofuels. However, advanced drop-in biofuels have been slow to reach commercial maturity due to significant technical challenges, high capital costs, and the challenge of generally lower oil prices. It is likely that the co-processing of drop-in biofuels with conventional petroleum refining could considerably reduce capital costs. Initially, co-processing is likely to be established through the upgrading of conventional / oleochemical feedstocks (lipids). Lipids are readily available in large volumes (global production in 2017 was ~185 million metric tonnes) and can be more easily integrated into oil-refinery processes. In contrast, lignocellulose-derived biocrudes / bio-oils are not yet available in significant volumes and are more complex to co-process in a refinery. The likely strategies for co-processing of oleochemicals (lipids) and bio-oil and biocrude feedstocks based on different insertion points within the refinery infrastructure are discussed.

09 BIOMASS FUELS↗
DOE OSTI2025

Bayesian D‐Optimal Designs for Gaussian Process Surrogate Models

Computer experiments often employ space-filling strategies to create surrogate models with strong predictive performance. The impact of model parameter estimation for Gaussian process surrogates, however, is often overlooked. Obtaining a better initial estimate of the covariance lengthscale parameter, θ, can greatly improve the resulting Gaussian process fit through more effective sequential acquisitions during active learning. In this work, we propose a novel initial design maximizing the Bayesian D-optimality criterion of the Gaussian process lengthscale parameter. Previously published results have shown the emphasis on lengthscale estimation to be promising, but relied on an empirically driven design creation process. Our Bayesian D-optimal designs are rooted in information theory and lead to more informative sequential acquisitions by improving lengthscale estimation. In many cases, these gains eventually result in better surrogates than those seeded with space-filling initial designs. Furthermore, Bayesian D-optimal designs can be tailored to either isotropic or anisotropic covariance structures, and the Bayesian framework enables the inclusion of prior knowledge in the design process, offering greater flexibility and adaptability. Through several simulation studies, we demonstrate the advantages of Bayesian D-optimal designs in terms of both lengthscale estimation accuracy and predictive performance during active learning.

Bayesian experimental design↗
DOE OSTI2019

Max-Compound Cox Processes. I

Extreme values are considered in samples with random size that have a mixed Poisson distribution that is generated by a doubly stochastic Poisson process. Some inequalities are proved relating the distributions and moments of extrema with those of the leading process (the mixing distribution). Limit theorems are proved for the distributions of max-compound Cox processes, and limit distributions are described. An important particular case of the negative binomial distribution of a sample size corresponding to the case where the Cox process is led by a gamma Lévy process is considered, explaining a possible genesis of tempered asymptotic models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Friction Hydro-Pillar Processing of a High Carbon Steel: Joint Structure and Properties

A coupled experimental and theoretical study is reported here on friction hydro-pillar processing of AISI 4140 steel, which is a novel solid-state joining technique to repair and fill crack holes in thick-walled components by an external stud. The stud is rotated and forced to fill a crack hole by plastic flow. During the process, frictional heating occurs along the interface of the stud and the wall of crack hole leading to thermal softening of the stud that eases its plastic deformation. The effect of the stud force, its rotational speed and the total processing time on the rate of heat generation and resulting transient temperature field is therefore examined to correlate the processing variables with the joint structure and properties in a systematic and quantitative manner, which is currently scarce in the published literature. The results show that a gentler stud force rate and greater processing time can promote proper filling of the crack hole and facilitate a defect-free joint between the stud and original component.

36 MATERIALS SCIENCE↗