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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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Processing parameters and machine log data for the MCPC LDRD Agile investment is collected material samples processed. This dataset captures the selected processing parameters, machine logs captured during material processing, and descriptions of how characterization samples were extracted from processed plates of material. The collect characterization data is captured in other datasets.
Research into the processability of NiTiHf high-temperature shape memory alloys (HTSMAs) via laser powder bed fusion (LPBF) is limited; nevertheless, these alloys show promise for applications in extreme environments. This study aims to address this limitation by investigating the printability of four NiTiHf alloys with varying Hf content (1, 2, 15, and 20 at. %) to assess their suitability for LPBF applications. Solidification cracking is one of the main limiting factors in LPBF processes, which occurs during the final stage of solidification. To investigate the effect of alloy composition on printability, this study focuses on this defect via a combination of computational modeling and experimental validation. To this end, solidification cracking susceptibility is calculated as Kou’s index and Scheil–Gulliver model, implemented in Thermo-Calc/2022a software. An innovative powder-free experimental method through laser remelting was conducted on bare NiTiHf ingots to validate the parameter impacts of the LPBF process. The result is the processability window with no cracking likelihood under diverse LPBF conditions, including laser power and scan speed. This comprehensive investigation enhances our understanding of the processability challenges and opportunities for NiTiHf HTSMAs in advanced engineering applications.
Observations of GW170817 strongly suggest that binary neutron star (BNS) mergers produce rapid neutron-capture nucleosynthesis ( r -process) elements. However, it remains an open question whether these mergers can account for all the r -process element enrichment in the Milky Way’s history. Here, we constrain the contributions of the BNS channel using astrophysical neutron star observations. The rate and mass distributions are constrained by LIGO/Virgo/Kagra through the latest catalog GWTC-3, the neutron star equation of state by gravitational-wave, radio, and X-ray observations, and the delay time distribution by short gamma-ray burst (GRB) host galaxy associations. We present a Bayesian framework to consistently combine these observations with abundance information to quantify the contribution and uncertainties of single and multiple astrophysical enrichment sources, and obtain a distribution of per-event BNS r -process element yields consistent with geophysical and astrophysical abundance constraints. We then adopt a Galactic chemical evolution model assuming an instantaneous and fixed amount of Fe enrichment from core-collapse supernovae, and show that BNS-only enrichment scenarios remain inconsistent with the observed r-process abundance trend of disk stars in the Galaxy even with the uncertainties in BNS merger observations. Using stellar abundance observations instead of the short GRB constraints, we can infer a shorter BNS delay time distribution with power-law index α ≤ −2.0 and minimum delay time ${t}_{{\rm{\min }}}\leqslant 40$ Myr at 90% confidence, consistent with detailed Galactic chemical evolution models. Such delay times are in tension with those predicted by standard BNS formation models. Alternatively, we confirm that a two-channel scenario, in which the second channel tracks the star formation history without significant delay, can account for both Galactic stellar and short GRB observations. We estimate that 45%–90% of the r -process abundance in the Milky Way today would have been produced by this star formation-tracking channel, rather than BNS mergers with significant delay times.
Analysis conducted over the last few years has improved our understanding of how industrial process heat is used in the United States. These improvements are important for characterizing the possibilities for industrial decarbonization. However, this analysis has largely been conducted from technical perspective and has remained disconnected from the social processes that underlie how industrial firms may adopt and implement technologies to decarbonize their process heating operations. This presentation introduces the concept of generic and configurational technology systems, and outlines the how the incorporation of user requirements and local contexts are essential for successful implementation of configurational systems. Insights drawn from semi-structured interviews with representatives of industrial firms are used to support the hypothesis that industrial process heat technologies are configurational. The potential implications for strategies to decarbonize process heat are then discussed.
Co-processing of biogenic feedstocks within the existing petroleum infrastructure represents an opportunity to incorporate large volumes of bio-carbon into transportation fuels. However, upgrading intermediate liquids from biomass and waste to final products efficiently and economically, while minimizing impacts to existing refinery infrastructure, remains a notable barrier to commercialization. The co-processing project, Bio-oil Co-processing with Refinery Streams, aimed to generate foundational knowledge for processing renewable intermediates in petroleum refineries and offer co-processing strategies, as well as develop new methods for biogenic carbon tracking and measurement.
Acid mine drainage (AMD) has been a challenge for mine operators to address and has had significant environmental impacts when there is a failure to address it. Fortunately, there is a regulation in the US that requires the treatment of AMD before water can be discharged into the environment. AMD is generated when sulfide minerals are exposed to water and air from mining. The acidic water then leaches metals from the surrounding rock, creating the potential for environmental contamination of this acidic water containing dissolved metals. AMD can contain high concentrations of metals like iron, aluminum, and manganese and also have been shown to contain trace concentrations of critical minerals, including rare earth elements (REEs). This environmental waste stream is now being researched as a potential source for REEs. There is a patented process for processing and extracting REEs from AMD which produces rare earth oxide preconcentrate (REOP) from AMD treatment precipitates and a final stage mixed rare earth oxide (MREO) product. This body of research examines a selective leaching process for each of these products and determines the activation energy associated with the leaching processes. Acid leaching with HCl was examined to selectively leach REEs from REOP while contaminant metals remained in the solid residue. The maximum leaching recovery of the REEs from the REOP was approximately 90% and an activation energy of 6.4 kJ/mol at pH 3.0. Ammonium chloride leaching was examined to selectively remove contaminant metals from a MREO product. The ammonium chloride process successfully leached major contaminant metals in excess of 85% recovery and had a maximum activation energy of 40.0 kJ/mol.
According to the International Atomic Energy Agency in 2024, global nuclear energy capacity is projected to increase by 2.5 times by 2050 in their high-case scenario. Research on the laboratory scale of innovative processes within nuclear facilities is ongoing to improve nuclear fuel cycle capabilities. Vibroacoustic process monitoring of these techniques has potential to inform operators regarding process specific metrics, predictive maintenance, and anomalous event detection. Additionally, this type of monitoring has the potential to aid in nuclear safeguards. Challenges regarding vibroacoustic monitoring in these environments include high temperature, high radiation, limited access, and shielded equipment limiting sensor type and placement. Microphones, accelerometers, and temperature sensors were deployed both inside and near these environments during operation to determine environment driven limitations, sound attenuation of containment enclosures, process specific metrics, and future potential of vibroacoustic monitoring in these environments. Overall, this work highlights the benefits and limitations of vibroacoustic process monitoring in nuclear research facilities.
Abstract This research significantly enhances the applicability of thermoplastic olefins (TPOs) in the automotive industry using supercritical N 2 as a physical foaming agent, effectively addressing the limitations of traditional chemical agents. It merges experimental results with simulations to establish detailed material‐process‐microstructure‐performance (MP2) relationships, targeting 5–20% weight reductions. This innovative approach labeled digital lifecycle (DLC) helps accurately predict tensile, flexural, and impact properties based on the foam microstructure, along with experimentally demonstrating improved paintability. The study combines process simulations with finite element models to develop a comprehensive digital model for accurately predicting mechanical properties. Our findings demonstrate a strong correlation between simulated and experimental data, with about a 5% error across various weight reduction targets, marking significant improvements over existing analytical models. This research highlights the efficacy of physical foaming agents in TPO enhancement and emphasizes the importance of integrating experimental and simulation methods to capture the underlying foaming mechanism to establish material‐process‐microstructure‐performance (MP2) relationships. Highlights Establishes a material‐process‐microstructure‐performance (MP2) for TPO foams Sustainably produces TPO foams using supercritical (ScF) N 2 with 20% lightweighting Shows enhanced paintability for TPO foam improved surface aesthetics Digital lifecycle (DLC) that predicts both foam microstructure and properties DLC maps process effects & microstructure onto FEA mesh for precise prediction
Abstract Manufacturing plastic parts with secondary feedstocks has risen to the forefront of importance in recent years. However, the variation in molecular weight and rheology of secondary feedstock can lead to inconsistent part quality. This work evaluates the effectiveness of a novel closed‐loop adaptive process control system that adjusts nozzle pressure in response to in‐mold pressure data. Five different recycled polypropylene blends, with a broad distribution of flow properties, were evaluated to determine the effectiveness of the control system at reducing processing variation. The experimental results show that the process control strategy reduced the variation within the mold, as seen by in‐mold pressure curves and calculated in‐mold viscosity values. Additionally, the parameters that control the automated process adjustments were investigated, showing the importance of optimization. The analysis of the correlation between in‐mold rheology and mechanical properties showed a slight variation in the mechanical properties and parts weight with a coefficient of variation of under 5%. Overall, the results demonstrate the ability of pressure‐controlled molding and automated viscosity adjustment to reduce the variability when molding a secondary feedstock. Highlights Pressure‐controlled injection molding of recycled polypropylene. Automated closed‐loop adaptive process control methodology. Methodology resulted in a reduction in pressure variation during molding. Changes in mechanical properties and in‐mold viscosity were investigated. Results show the potential of pressure‐controlled molding at reducing variation.
Abstract Polypropylene (PP) is one of the most widely used plastics, yet its recycling remains limited, with less than 1% of solid waste PP being reprocessed. Mechanical recycling through extrusion is the most practical method, but inconsistent reprocessing conditions introduce variability in material properties. While temperature, screw speed, and residence time influence the thermomechanical stress applied during reprocessing, there are no standardized guidelines for optimizing these parameters. This study examines how these factors shape the properties of recycled PP, using conditions designed to mimic post‐industrial recycled (PIR) scrap. Residence time was measured using colorimetric tracking and correlated with molecular weight, viscosity, and mechanical properties over multiple extrusion cycles. Data‐driven modeling, including response surface methodology, support vector machines, and artificial neural networks, identified processing temperature as the dominant factor in material degradation, followed by residence time. Mechanical properties remained stable, while viscosity decreased predictably with increasing residence time. By linking reprocessing conditions to property evolution, this study provides a method to optimize processing parameters and reduce variability in recycled PP. These findings help manufacturers improve process control, making recycled PP more predictable for reuse in manufacturing. Highlights Study of PIR‐quality PP without additives or compatibilizers. Residence time analysis shows processing temperature drives PP property changes. Mark‐Houwink enables quick molecular weight checks for quality control. Models predict mechanical and rheological shifts in reprocessing. Optimized processing parameters minimize property degradation in recycling.
Due to the extensive production of shrimp in captivity, waste generation has increased significantly and has become an environmental problem. The recovery of biomolecules can be an important way to mitigate the environmental problems associated with processing in this sector. In this sense, the present work aimed to evaluate a biorefinery approach for valuing shrimp farming waste to obtain astaxanthin, chitin and chitosan. Stoichiometric segmentation was used as a tool to identify the process steps, whose information on process variables (fresh water consumption, flow rate and reaction conditions) was adapted from the literature. A biorefinery coupled to a shrimp processing plant was proposed for the immediate use of highly perishable biomass, to guarantee the quality of the extracted products and reduce storage and transportation costs. In practice, the biomass treatment sequence adopted (demineralization followed by deproteinization) eliminates the depigmentation step, as the chitin obtained has a lighter tone. The proposed route was evaluated for four scenarios based on two indicators: gross economic potential (EGP) and metrics for inspection of sales and reagents (MISR). The results indicate that economic viability is achieved only for the production of chitosan, resulting in a gross revenue of US$832.50/cycle and a MISR value > 1. The sale of astaxanthin promotes an increase of US$3.73/cycle in the EGP, considered too low for the inclusion of another stage in the process.
Metal-based additive manufacturing processes, such as powder bed fusion with electron beam (PBF-EB) process, also referred to as electron beam melting (EBM), can produce high-density parts with minimal residual stresses due to the uniform and coherent preheating of the powder bed. However, understanding and controlling the multiple stages of preheating is required to enable the production of high-quality, consistent parts of various materials. This work presents a large-scale, multi-layer, three-dimensional numerical analysis focused on studying the preheating stages for predicting thermal history during the PBF-EB process. The model follows a continuous multi-stage cyclic process, that incorporates all the main stages of the PBF-EB process for 316 L stainless steel. This includes the gradual deposition of a new powder layer, the first and second preheating levels of the powder bed, and the energy deposition during melting (excluding the actual melt-pool behavior simulation). The model employs an adaptive time-scaling approach that automatically adjusts the energy deposition for each solution time-increment. This allows for localized changes in time-resolution over an otherwise computationally expensive multi-layer procedure. The material property variations are also taken into account, with an emphasis on the subtle irreversible changes in powder effective thermal conductivity after the two requisite preheating stages of the powder bed. This effect is studied using simplified conductivity models from the literature for partially sintered powder, validated by a dedicated experiment and numerical simulation. The large-scale model is then used to estimate the actual temperatures during first and second preheating levels for 316 L steel, which is not yet fully supported commercially for PBF-EB. Model predictions are corroborated by experiments, using and analyzing IR images, taken at the completion of each layer by the machine’s built-in infrared camera. The current model also incorporates a qualitative assessment for the effects of conductivity change during pre-heating, as well as evaluates the applicability of the time-scaling approach.
Ammonium diuranate (ADU) is commonly encountered in the nuclear fuel cycle; however, previous investigations have shown that ADU is a complex mixture of distinct compounds. Moreover, production parameters are known to heavily influence the composition of the resulting ADU. Here, we examine four samples of ADU prepared at Oak Ridge National Laboratory (ORNL), and one sample of ADU made at Pacific Northwest National Laboratory (PNNL), with the goal of further characterizing and elucidating the effect of processing parameters such as stir rate, strike direction, and temperature on material composition. Process parameters during ADU precipitation at ORNL and PNNL were well documented, and we relate process variables to optical vibrational spectroscopic signatures observed using Raman and infrared (IR) spectroscopy. In addition, powder X-ray diffraction (PXRD) reveals differences in the solid-phase composition of ADU precipitates, but we find that the primary phase is similar to the uranyl oxyhydroxyhydrate mineral metaschoepite. Despite the significant phase contributions of a metaschoepite-like phase, spectroscopic evidence of both nitrate and ammonium are observed for all samples. To gain a more holistic understanding of spectroscopic features of process parameters in ADU, principal component analysis (PCA) is employed and results in observable signatures that relate to the stir rate used during synthesis. These results provide further information about the process-dependence of ADU precipitate composition.
Electrofuels (e-fuels) are synthetic fuels produced from carbon dioxide (CO 2 ) and electricity for blending with or replacing petroleum fuels. Nuclear energy is an attractive energy feedstock for e-fuel production because of its low environmental footprint and its ability to provide steady heat and power essential for e-fuels production. We modeled and evaluated the cost and environmental footprint of e-fuels production in the distillate range for three nuclear power scales, 100, 500, and 1000 MWe, through methanol and olefins intermediates leveraging commercial or high technology readiness level (TRL) processes. Compared to the commonly studied e-fuels from Fischer Tropsch process that has a distillate yield of <70% with the rest being low value naphtha, the proposed process via methanol intermediate increases the product selectivity with distillate yield of 96% and only 4% naphtha. The modeled process has a carbon conversion ratio of 98%, and a process energy efficiency of 56% relative to the total equivalent nuclear electricity input. The e-fuel plant economics and GHG emissions were estimated by considering CO 2 collected from ethanol plants adjacent to nuclear power plants. The estimated minimum fuel selling prices (MFSP) of e-fuel is in the range of $5.7-$9.1/gal depending on e-fuel plant scale, electricity cost, and CO 2 transportation distance. The corresponding e-fuels life cycle GHG emissions is estimated in the range of 5-6 gCO 2 e/MJ of liquid fuel using the R&D Greenhouse gases, Regulated Emissions, and Energy use in Technologies (R&D GREET) model.
Ferritic-Martensitic steel welds typically require post weld heat treatment (PWHT) to restore toughness and high temperature performance. This off-line thermal process reduces disparities between weld and base metal, but can cause distortion, cracking, or simply be impractical due to assembly size and joint non-uniformity. Here we show integrated welding and thermal processing applied to modified 9Cr-1Mo (Grade 91) steel, favored for advanced power generation applications, performed in real time through the addition of a secondary heat source near the primary weld head. Optimal integrated processing reduces weld fusion and heat affected zone hardness by 125 HV, approaching performance of conventional 730 °C, 60 min PWHT processing. Microstructures and mechanical performance are compared for mechanized GTAW welds, with equivalent lifetimes noted in cross-weld creep rupture tests up to 234 MPa at 550 °C, and up to 104 MPa at 650 °C. The integrated process was validated on a Grade 91 pressure vessel with multipass cold wire feed GTAW. After 550 °C, 71.4 bar thermomechanical cyclic testing, the maximum weld hardness is <350 HV.
This paper introduces a novel thermo-mechanical process to modify the local mechanical properties of 6xxx aluminum (Al-Mg-Si-Cu) alloy sheets. In this process, two pairs of rollers travel along the length of a sheet while locally bending and unbending it so that the final shape and thickness of the sheet remains unchanged. Room temperature bending/unbending (B/U) produces a deformation gradient through thickness and local hardening in both T4 and T6 sheets (with 41% and 18% greater Vickers hardness, respectively). High temperature bending/unbending performed at ~ 500°C via induction heating, produces significant improvements in local formability as measured by bend testing. Formability levels equivalent to the as-received T4 temper are achieved within the processed zones of T6 sheets with bend angles of ~ 150°, without disturbing the T6 temper in the remainder of the sheet. Finite element simulations and characterization explained the mechanical property improvements and the heterogenous microstructure with weakened Cube texture. Due to its similarity with the roller hemming process, and its compact dimensions, the apparatus developed for B/U has the potential for seamless integration with roller hemming robots to selectively process high strength aluminum sheet materials within mass-production settings.
Cyberphysical attacks on the digital backbone of Additive Manufacturing (AM) can compromise the printed part’s functionality. They can alter features in the digital geometry to introduce geometric defects (e.g., missing fillets) or alter process parameters to create local defects (e.g., voids). Addressing the downtime, waste, and quality deterioration associated with existing solutions requires operational resilience, i.e., rapid elimination or disruption of defect formation (to retain part function) without production stoppage or part disposal (to retain yield). This need is unmet due to the inherently unpredictable nature of attack-induced alterations, lack of access to the original geometric model for identification of altered geometric features, and in-process imposition of unknown process dynamics via attack-driven alteration of real-time-uncontrolled (or exogenous) parameters. This work establishes the above-mentioned operational resilience for the first time by creating two Digital Twins (DT). The Geometric DT (Geo-DT) is based on a unique physical-field-driven soft sensor and topology optimization method. The Process Digital Twin (Pro-DT) combines local defect quantification with a novel Reinforcement Learning formulation and training method. The importance of these methodological advances and the scalability of our approach are examined on a real AM testbed. It is shown that Geo-DT can correct geometric defects without access to the original digital geometry or explicit knowledge of attack-altered geometric features. Further, Pro-DT can accelerate real-time disruption of local defects despite attack-driven imposition of unknown process dynamics. We discuss how our framework goes beyond the contemporary focus on pre-attack security and in-attack detection towards resilience for AM and beyond.
Carbon-based perovskite solar cells (C-PSCs) present a low-cost route to efficient photovoltaic technology. Gas quenching is an essential process commonly used for solvent extraction in scalable module fabrication using solution processes, but film quality remains highly sensitive to the localized environmental processing temperature, especially during intermediate phase transitions before perovskite film high temperature annealing. In this study, we introduce a cold air quench strategy to precisely control the local crystallization temperature that simultaneously promotes uniform crystallization and facilitates strain relaxation in fully air-processed C-PSCs. It is found that implementing dry air at 10 °C as the quenching gas yields a champion power conversion efficiency (PCE) of 20.52 %, with preferential (110) orientation and reduced, homogenized in-plane strain. This newly developed technique of a cost-effective low temperature air quenching process also enhances carrier lifetime, reduces interface recombination, and improves charge extraction. Furthermore, this work presents cold air quenching as a scalable, economic method to control perovskite crystallization and strain in C-PSC fabrication, advancing the industrial viability of high-performance perovskite modules.