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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Stress characterization for friction-stir-welded electric vehicle battery trays with application of neutron diffraction

The battery tray is an essential component that protects and controls battery-cell temperatures in electric and plug-in hybrid vehicles. The functional stress limit of the battery tray heavily depends on the residual stress acquired from the manufacturing process. Consequently, exceeding the stress limit of the battery tray during operation could compromise the battery-cell banks and may risk the vehicle's safety. Hence, understanding residual stress formation is vital for design and safety concerns. Here, in the current study, AA 6061-plates were friction stir welded to an A365 high-pressure die-cast battery tray to create sealed coolant channels in the battery tray. However, this multi-material lap friction stir weld introduces residual stress into the battery tray, resulting in distortion. This distortion was mitigated using burnishing or coining operations, though straightening the battery tray had initially unknown effects on the residual stress. Therefore, neutron diffraction was utilized to characterize residual stresses after straightening. The results indicate that the friction stir welding (FSW) operation generated residual stresses exceeding the yield strength of the material, consequently deforming the battery tray by ±3 mm from the pre-weld geometry. The burnishing operation reduced the residual stresses below the material's yield strength while restoring the tray to within ±0.75 mm of the pre-weld geometry. Similarly, the coining operation restored the battery tray to within ±0.75 mm of the pre-weld geometry, however, increasing the number of locations where the residual stress exceeds the yield strength of the material.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Monitoring and prediction of porosity in laser powder bed fusion using physics-informed meltpool signatures and machine learning

In this work we accomplished the monitoring and prediction of porosity in laser powder bed fusion (LPBF) additive manufacturing process. This objective was realized by extracting physics-informed meltpool signatures from an in-situ dual-wavelength imaging pyrometer, and subsequently, analyzing these signatures via computationally tractable machine learning approaches. Porosity in LPBF occurs despite extensive optimization of processing conditions due to stochastic causes. Hence, it is essential to continually monitor the process with in-situ sensors for detecting and mitigating incipient pore formation. In this work a tall cuboid-shaped part (10 mm × 10 mm × 137 mm, material ATI 718Plus) was built with controlled porosity by varying laser power and scanning speed. This test caused various types of porosity, such as lack-of-fusion and keyhole formation, with varying degrees of severity in the part. The meltpool was continuously monitored using a dual-wavelength imaging pyrometer installed in the machine. Physically intuitive process signatures, such as meltpool length, temperature distribution, and ejecta (spatter) characteristics, were extracted from the meltpool images. Subsequently, relatively simple machine learning models, e.g., K-Nearest Neighbors, were trained to predict both the severity and type of porosity as a function of these physics-informed meltpool signatures. These models resulted in a prediction accuracy exceeding 95% (statistical F1-score). The same analysis was carried out with a complex, black-box deep learning convolutional neural network which directly used the meltpool images instead of physics-informed features. The convolutional neural network produced a comparable F1-score in the range of 89–97%. Finally, these results demonstrate that using pragmatic, physics-informed meltpool signatures within a simple machine learning model is as effective for flaw prediction in LPBF as using a complex and computationally demanding black-box deep learning model.

36 MATERIALS SCIENCE↗
DOE OSTI2026

X-ray observation of individual Ti-6Al-4V spherical powder particle impact in an in-situ operando laser directed energy deposition system

Understanding the particle capture mechanism in the laser directed energy deposition additive manufacturing process provides a foundation for improving productivity and reducing material loss. Capturing this phenomenon is challenging due to its highly transient time interval. To analyze the impact behavior of spherical Ti-6Al-4V powder, a custom laser directed energy deposition system was designed to control the deposition of individual particles. Synchrotron X-ray imaging at 24 kHz monitored the impact dynamics of individual powder particles. A multi-physics model based on the Smoothed-Particle Hydrodynamics scheme supported the analysis by providing the temperature and velocity fields of the molten pool. Results revealed that in laser directed energy deposition, surface tension forces dominated the powder–molten pool interaction. Hydrophobic Ti-6Al-4V powder particles had an equilibrium contact angle of 121 degrees with their molten state. Furthermore, Ti-6Al-4V powder particles required a high impact velocity (6 to 12 m/s) to transition from oscillation to submergence behavior. Lastly, oscillating powder particles that impacted near the location of the laser beam exhibited faster melting, highlighting the contribution of the laser beam to the melting mechanism. This work lays the foundation for investigations of powder particle impact in laser directed deposition and supports the validation of numerical models of powder–molten pool interactions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems

Here we present a data-driven method for monitoring machine status in manufacturing processes. Audio and vibration data from precision machining are used for inference in two operating scenarios: (a) variable machine health states (anomaly detection); and (b) settings of machine operation (state estimation). Audio and vibration signals are first processed through Fast Fourier Transform and Principal Component Analysis to extract transformed and informative features. These features are then used in the training of classification and regression models for machine state monitoring. Specifically, three classifiers (K-nearest neighbors, convolutional neural networks and support vector machines) and two regressors (support vector regression and neural network regression) were explored, in terms of their accuracy in machine state prediction. It is shown that the audio and vibration signals are sufficiently rich in information about the machine that 100% state classification accuracy could be accomplished. Data fusion was also explored, showing overall superior accuracy of data-driven regression models.

42 ENGINEERING↗
DOE OSTI2022

Efficient high-fidelity TRISO statistical failure analysis using Bison: Applications to AGR-2 irradiation testing

The ability of tri-structural isotropic (TRISO) fuel to contain fission products is largely dictated by the quality of the manufacturing process, since most of the fission product release is expected to occur due to coating layer failure in a small number of particles containing defects. The Bison fuel performance code has capabilities to predict failure in individual particles, accounting for the presence of defects, and to apply statistical analysis methods to compute the probability of failure in a set of fuel particles. Bison has recently undergone significant development both to improve its physical representations of fuel particle behavior and to improve the efficiency of its statistical failure calculations. Physical model improvements include new capabilities to account for the pressure generated by fission gases on inner pyrolytic carbon (IPyC) crack surfaces and to use local material coordinate orientation to accurately incorporate the anisotropy in the material properties in aspherical particles. To improve statistical modeling efficiency, a direct integration approach which involves directly integrating the failure probability function associated with statistically varying parameters has been developed. The direct integration approach is much more efficient than the Monte Carlo (MC) schemes commonly employed, and allows Bison to directly run high-dimensional fuel performance models, which improves the accuracy of failure probability calculations. Finally, a set of benchmark problems is considered here to compare the MC and direct integration approaches, and a statistical failure analysis of compacts in the Advanced Gas Reactor (AGR)-2 experiments is performed using the direct integration approach.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Lithium substituted poly(amic acid) as a water-soluble anode binder for high-temperature pre-lithiation

Multifunctional binders hold great promise in advanced electrode designs for both fundamental research and practical utilization of lithium-ion batteries (LIBs). The reactions between Si/SiO x -dominated anodes with lithium are expected to be exothermic in principle, while the thermal tolerance along with the volume change makes high-temperature binders attractive for large scale roll-to-roll manufacturing. For instance, if a high temperature binder is also water soluble, it can be compatible with the current graphite-based anode manufacturing process. In this work, we present a water-soluble poly(amic acid)-based binder, which can withstand high temperature for industrial pre-lithiation process and effectively hold active materials together during repeated charge and discharge cycles. This lithium substituted poly(amic acid) binder (denoted as Li-Pa) can serve as a drop-in replacement for environmentally friendly electrode fabrication in large scale by providing aqueous solubility, exceptional thermal stability and mechanical flexibility.

25 ENERGY STORAGE↗
DOE OSTI2023

The impacts of membrane pinholes on PEM water electrolysis

Polymer electrolyte membrane (PEM) water electrolysis is a promising technology to efficiently produce green hydrogen. Irregularities in membrane electrode assembly (MEA) component materials caused by manufacturing, processing, handling, or operation can lead to performance loss and failure. One example is the presence of pinholes in the membrane, which can cause increased gas transport (crossover) and lead to electrical shorting. This work investigates the effects of intentionally introduced pinholes with various sizes up to 350μm in diameter. The presence of the pinholes resulted in minimal impacts on the cell voltage (±30 mV at 4Acm -2 ). However, the pinholes significantly increased H 2 crossover, which decreased hydrogen production efficiency and increased the minimum current density for safe operation. The impacts of the pinhole on the H 2 crossover and efficiency are more severe for open pinhole features than tear-like pinhole features. In conclusion, this research demonstrates that even small pinholes cannot be disregarded from a safety, efficiency, and operating strategy perspective.

08 HYDROGEN↗
DOE OSTI2022

Closed-loop control of meltpool temperature in directed energy deposition

The objective of this work is to mitigate flaw formation in powder and laser-based directed energy deposition (DED) additive manufacturing process through close-loop control of the meltpool temperature. In this work, the meltpool temperature was controlled by modulating the laser power based on feedback signals from a coaxial two-wavelength imaging pyrometer. The utility of closed-loop control in DED is demonstrated in the context of practically inspired trapezoid-shaped stainlesssteel parts (SS 316L). We demonstrate that parts built under closed-loop control have reduced variation in porosity and uniform microstructure compared to parts built under open-loop conditions. For example, post-process characterization showed that closed-loop processed parts had a volume percent porosity ranging from 0.036% to 0.043%. In comparison, open-loop processed parts had a larger variation in volume percent porosity ranging from 0.032% to 0.068%. Further, parts built with closed-loop processing depicted consistent dendritic microstructure. By contrast, parts built with open-loop processing showed microstructure heterogeneity with the presence of both dendritic and planar grains, which in turn translated to large variation in microhardness

36 MATERIALS SCIENCE↗
DOE OSTI2021

Thin-film composite membranes based on hyperbranched poly(ethylene oxide) for CO 2 /N 2 separation

Cross-linked amorphous poly(ethylene oxide) (XLPEO) is one of the leading membrane materials for post-combustion CO 2 capture. For example, XLPEO prepared from poly(ethylene glycol) methyl ether acrylate (PEGMEA) exhibited CO 2 permeability of 570 Barrer and CO 2 /N 2 selectivity of 41 at 35 °C. However, these XLPEOs cannot be dissolved in coating solutions, making it impossible to be fabricated into thin-film composite (TFC) membranes using state-of-the-art manufacturing processes. In this study, we synthesized high molecular weight yet soluble HPEO via atom transfer radical polymerization (ATRP). These polymers were thoroughly characterized and compared with XLPEO, including thermal transitions, free volumes, and pure-gas sorption and permeation properties. A polymer with the best combination of CO 2 permeability (540 Barrer) and CO 2 /N 2 selectivity (43) was fabricated into defect-free TFC membranes with a thickness as thin as 506 ± 44 nm. When challenged with simulated flue gas containing water vapor at 35 °C for over 100 h, the membrane shows stable CO 2 permeance of 850 GPU and CO 2 /N 2 selectivity of 37, comparable to the leading commercial membranes for carbon capture.

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

Surface engineering of polymeric membranes with metal oxides for improved fouling resistance and superior oil-water separation

Rapid industrial growth has increased the need for efficient membranes to separate oil-water emulsions. Polyvinylidene fluoride (PVDF) membranes, although widely used due to their chemical inertness and favorable mechanical properties, suffer from fouling due to their intrinsic hydrophobicity. Modifying these membranes after fabrication offers a practical solution as it easily fits into existing large-scale manufacturing processes. Atomic layer deposition (ALD), an atomically-precise vapor phase surface modification technique, can create ultrathin metal oxide layers that greatly improve membrane hydrophilicity without significantly affecting the original pore size. However, PVDF's lack of reactive chemical moieties makes ALD challenging. Here, in this study, we present a simple alkali treatment that greatly enhances ALD nucleation and growth on PVDF membranes. This treatment imparts exceptional oil-water emulsion separation capabilities and antifouling behavior in PVDF membranes after just a few ALD cycles, surpassing the performance of PVDF membranes coated with hundreds of ALD cycles. This dramatic reduction in the number of ALD cycles required could enable cost-effective modification of commercial PVDF membranes at scale using spatial, roll-to-roll ALD. These modified membranes outperform reported modified PVDF membranes, with >99 % permeance recovery and <1 % irreversible loss of permeance and >98 % oil rejection from oil-water emulsions over 100 h continuous operation, making them promising for advanced water purification technologies.

Atomic layer deposition↗
DOE OSTI2025

Assessment of the grindability of robocast silicon carbide

The demand for high-performance lightweight optics has driven interest in silicon carbide (SiC) due to its exceptional thermal stability, hardness, and strength-to-weight ratio. This study investigates the potential of robocasting, an additive manufacturing process, as a viable method for producing lightweighted SiC components for optical applications. Four samples with varied starting powder phases (α and β) and sintering conditions were fabricated and evaluated. Post-sintering surface and form were assessed using coherence scanning interferometry (CSI) and coordinate measuring machine (CMM) techniques. A three-stage grinding process was applied to each sample, with surface roughness assessed at each stage. Results demonstrate that samples with predominantly α-phase SiC and smaller particle sizes achieved superior surface finish, particularly sample D2-α-2135 °C, which displayed the lowest post-grinding Sq value of 0.178 µm. The analysis also indicated no significant print-through effect from the lightweighting structure, or print artifacts, at this stage of grinding. However, β-phase samples showed poorer grindability, increased surface roughness, and pitting. These findings suggest that phase composition and particle size are critical for achieving the desired surface quality in robocast SiC optics. Future work will incorporate additional samples and finer grinding wheels to refine surface quality further, supporting the development of SiC for high-precision optical applications.

Barrett, Taylor [ORNL]↗
DOE OSTI2024

High-temperature lean Cu alloys with Cr-to-Nb atomic ratio of 2

Two Cu-Cr-Nb alloys, denoted as alloy 1 (comprising Cu-0.89 at% Cr-0.42 at% Nb) and alloy 2 (comprising Cu-1.84 at% Cr-0.99 at% Nb), were produced through a series of manufacturing processes including vacuum induction melting, melt spinning, consolidation, brazing, and baking, with both alloys aimed at achieving a nominal Cr-to-Nb atomic ratio of 2. Microstructural characterization using transmission electron microscopy and X-ray diffraction identified the cubic C15 Laves-phase Cr 2 Nb as the dominant precipitate in both alloys, cross-validated by thermodynamic calculations and atomistic simulation-based density functional theory (DFT). Besides cubic C15 Cr 2 Nb, hexagonal C14-phase Cr 2 Nb and α-BiF3 cubic structured Cr 3 Nb were also observed in the alloys, including a coherent interface formed between the Cr 3 Nb precipitate and the Cu matrix. The hardness of the alloys increases, and the electrical conductivity decreases with increasing alloying addition content; two practical equations described the trends. Further DFT simulations revealed that the electrical conductivity (conductance) of the Cu/Cr 2 Nb interface is an order of magnitude higher than the intrinsic Cu high-angle grain boundaries.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Slug-flow synthesis of NCMA: Effect of substitution of cobalt with aluminum on the electrochemical performance of Ni-rich cathode for lithium-ion battery

Nickle-rich Li[Ni 1-x-y Co x Mn y ]O 2 (x, y≤0.1) (NCM) layered materials are known as promising cathode materials for next-generation lithium-ion batteries and electric vehicles owing to their high-reversible capacity and operating voltage of up to 3.6 vs Li/Li + . However, issues, such as irreversible phase transition, cation mixing, microcrack formation, thermal and structural stability of the material prevent its widespread adoption. Although, cation doping is a well-known technique to enhance the electrochemical performance of the NCM-based cathode material, the performance of the material is very sensitive to the doping amount. In this study, three Al-doped quaternary Ni-rich cathode materials Li[Ni 0.85 Co (0.1-x) Mn 0.05 Al x ]O 2 (where, x=0–0.04) (NCMA) are synthesized through three-phase slug-flow based continuous manufacturing process followed by high temperature calcination to study the effect of Aldoping on the performance of the cathode material while reducing Co. The slug flow-based production platform has several advantages, like particle size uniformity, high production rate, and homogeneity in elemental distribution. It is found that with an increase in Al content, the specific capacity decreases but the cyclic stability and rate capability increases. Furthermore, optimum Al-doping not only compensates for the adverse effect of low Co by decreasing the extent of cation mixing, but it also minimizes the electrode polarization and cracking of the particles.

25 ENERGY STORAGE↗
DOE OSTI2020

Ultra-efficient polymer binder for silicon anode in high-capacity lithium-ion batteries

As a highly promising anode material for high-capacity lithium-ion batteries (LIBs), the low electronic conductivity and large volume variation of silicon (Si) make the slurry-coating Si based electrode requiring high content of “inert” materials and suffering rapid capacity fading. In this work, a polyimine, synthesized via one-step condensation reaction, has been demonstrated as an ultra-efficient polymer binder that can resolve the above issues. The polyimine binder containing Si electrode delivers superior electrochemical performance: a delithiation specific capacity of 804.4 mAh g –1 with capacity retention of 82.4% after 1000 cycles at the current density of 2 A g –1 . The high efficiency of polyimine binder for Si electrode has also been demonstrated with ultrahigh weight ratio of “active” material to “inert” material (R A/I ). The electrode with 95 wt% of Si (95Si/Polyimine, R A/I = 19) reveals a reversible delithiation capacity of 2114 mAh g –1 (capacity retention ~ 80.4%) over 200 cycles at the current density of 400 mA g –1 . Even at the high current density of 2 A g –1 , a delithiation capacity of 1087.8 mAh g –1 after 500 cycles can be obtained. Molecular simulations and atomic force microscopy (AFM) indentation are utilized to investigate the ultra-efficiency of polyimine binder. With simple manufacturing process and ultra-efficient binder performance, the designed polyimine binder will be definitely meaningful in achieving low-cost and high-capacity LIBs with prolonged cycle life.

25 ENERGY STORAGE↗
DOE OSTI2023

TRISO fuel performance analysis: Uncertainty quantification toward optimization

Tri-structural isotropic (TRISO) fuel particles are a fuel form being considered for potential use in next-generation nuclear reactors (i.e., high-temperature gas-cooled reactors). Though the TRISO fuel manufacturing process has continually advanced in recent years, particle comparisons still reveal statistical variations and uncertainties in terms of geometric configurations and material properties. Given that the physical processes ongoing in TRISO fuel particles during reactor operation are highly correlated with each other, a small degree of uncertainty in one model may lead to significant uncertainty in another. This makes appropriate uncertainty quantification of TRISO fuel particles essential. However, one may wonder about the extent to which the current version of TRISO particles has been optimized, and whether any room remains for further improvements. This paper quantifies TRISO fuel performance model uncertainties that stem from geometric and material data. For this analysis, the BISON code was used, and the Advanced Gas Reactor (AGR)-2 experiment served as a reference case. A total of 10 5 calculations was performed for the uncertainty and optimization analysis, altering the geometric and material data within their uncertainty range. Lastly, the optimization potential of TRISO particles is evaluated from a fuel performance perspective.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

A visco-plastic constitutive model for accurate densification and shape predictions in powder metallurgy hot isostatic pressing

Powder metallurgy hot isostatic pressing (PM-HIP) is an advanced manufacturing process that produces near net shape parts with high material utilization and uniform microstructures. Despite being used frequently to produce small-scale components, the application of PM-HIP to large-scale components is limited due to inadequate understanding of its complex mechanisms that cause unpredictable post-HIP shape distortions. A computational model can provide necessary information about the intermediate and final stages of the HIP process that can help understand it better and make accurate predictions. Generally, two types of computational models are employed for PM-HIP of metal powders, namely, plastic and visco-plastic models. Between these, the plastic model is preferred due to its cheaper calibration approach requiring less experimental data. However, the plastic model sometimes produces incorrect predictions when slight variations of the HIP conditions are encountered in practical situations. Therefore, this work presents a visco-plastic model that addresses these limitations of the plastic model. A novel modified calibration approach is employed for the visco-plastic model that utilizes less experimental data than existing approaches. With the new approach, the data requirement is same for both plastic and visco-plastic models. This also enables a quantitative comparison of plastic and visco-plastic models, which have been only qualitatively compared in the past. When calibrated with the same experimental data, both the models are found to produce similar results. In conclusion, the calibrated visco-plastic model is applied to several complex geometries, and the predictions are found to be in good agreement with experimental observations.

Hot isostatic pressing↗
DOE OSTI2026

A fast and robust computational modeling approach for density and shape predictions in powder metallurgy hot isostatic pressing

Powder metallurgy hot isostatic pressing (PM-HIP) is an advanced manufacturing process that produces near-net-shape parts with high material utilization and uniform microstructures. PM-HIP is frequently used for producing small-scale parts with complicated geometries and is potentially economical for producing large-scale parts. However, excessive post-HIP shape distortions can reduce its effectiveness and economic advantage, especially for larger parts. A PM-HIP computational model can predict and help mitigate these distortions. However, due to complex deformation mechanisms and thermo-mechanical coupling present in PM-HIP processes, these non-linear computational models sometimes become numerically unstable. The numerical instabilities in these models can lead to very slow convergence or no convergence at all, which often translates to slow and unreliable models. These limitations are more pronounced in large models with complicated geometries. Hence, in this work, an alternative modeling approach is presented that improves numerical stability and computational performance. The presented approach achieves these improvements through approximating the fully coupled thermo-mechanical PM-HIP model as a decoupled model and adding inertial damping to the model’s mechanical part. In conclusion, a comparison with the fully coupled model indicated a slight dip in prediction accuracy (<5% error) but significant improvements in numerical stability (>20 times larger time step size) and computational performance (5-10 times speed-up with less computational resource usage) when using the presented approach.

Hot isostatic pressing↗
DOE OSTI2021

Autonomous nondestructive evaluation of resistance spot welded joints

The application of non-destructive evaluation approaches has attracted strong interests in modern automotive industries. Here, we present an autonomous deep-computing framework to analyze raw videos from infrared systems and to predict weld nugget shape and size with unprecedented accuracy and speed. In a comprehensive training and testing experiment with 90 videos (seven sets of welding material stack-ups), a new method was developed to assemble sufficient datasets for neural network training. Our framework successfully predicts all the nugget shapes with F1 scores that range from 0.84 to 0.92. The total training time on Nvidia DGX station takes less than 10 min for each set of welding material stack-up. The real inference time of an individual dataset (with 30 video frames) takes about 0.005 s. The procedure and methods developed in the study can be applied to other image-based weld property prediction, as well as other manufacturing processes. Furthermore, our well-trained neural networks take limited memory resources (2.3 MB) and are suitable for embedded microprocessors for in-situ welding quality control as edge computing within an intelligent welding framework.

42 ENGINEERING↗