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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A Novel Surface Restoration Method for Hydropower Steel Components: Plasma Gouging and Inconel Cold Spray Coating for Material Repair

A novel surface restoration approach combining plasma gouging with subsequent cold spray deposition is demonstrated in this study. Plasma gouging effectively removes the damaged steel surface, leaving grooves and forming a plasma gouging affected zone up to ~300?µm thick. Within this zone, a hardened martensitic layer with a peak hardness of ~370?HV—almost twice that of the base substrate (~200?HV)—is produced. Following plasma gouging, cold spray deposition is applied to rebuild the surface. Experimental results reveal that cold spray coatings deposited at low energy suffer from delamination, whereas high energy cold spray deposits yield a well-bonded coating. Microstructural analysis further shows that the martensitic layer formed by plasma gouging undergoes dynamic recrystallization during cold spray, reducing the hardness to 317?HV. These findings demonstrate the feasibility of this hybrid method for effective surface restoration of damaged steel components.

Wang, Tianhao↗
DOE OSTI2021

Radiation Effects Related to Repaired BWR Core Shrouds

The material properties of the core shroud and supporting tie rod components are important factors for Boiling Water Reactor (BWR) life extension considerations. Given the variability of nickel and boron content found in 304 stainless steel shroud plates, and XM-19 and Inconel X-750 tie rod materials, this paper combines the latest spectral information in the vicinity of the core shroud to calculate hydrogen and helium gas production limits as well as atomic displacement damage spanning 80 full-power years (FPY) using the SPECTER code. Results indicate that, whereas boron has little effect on the atomic displacement damage, helium production from boron can be important and must be included in weldability assessments. Since the boron concentration is uncertain, helium generation is calculated assuming a composition range based on specifications and reported measurements. The results show that helium production caused by transmutations in the 304 SS shroud material are dominated by the concentration of boron and will have a significant impact on weldability after only a few years of service. For tie rods, the susceptibility to irradiation-assisted stress corrosion cracking (IASCC) has been assessed by determining helium production in Inconel X-750 IASCC specimens irradiated in the Advanced Test Reactor (ATR) reactor and correlating with the helium produced in a BWR. As a tie rod material in a high thermal neutron flux environment, Inconel X-750 is more likely to be IASCC sensitive during service in a BWR due to helium than the XM-19 stainless steel alloy because of a greater nickel content and boron concentration. Weldability of the core shroud plate material, IASCC of the tie rod materials, and the gamma photon contribution to radiation damage are discussed.

radiation damage in materials, BWR↗
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗
DOE OSTI2021

Structural evolution of a DNA repair self-resistance mechanism targeting genotoxic secondary metabolites

Abstract Microbes produce a broad spectrum of antibiotic natural products, including many DNA-damaging genotoxins. Among the most potent of these are DNA alkylating agents in the spirocyclopropylcyclohexadienone (SCPCHD) family, which includes the duocarmycins, CC-1065, gilvusmycin, and yatakemycin. The yatakemycin biosynthesis cluster in Streptomyces sp. TP-A0356 contains an AlkD-related DNA glycosylase, YtkR2, that serves as a self-resistance mechanism against yatakemycin toxicity. We previously reported that AlkD, which is not present in an SCPCHD producer, provides only limited resistance against yatakemycin. We now show that YtkR2 and C10R5, a previously uncharacterized homolog found in the CC-1065 biosynthetic gene cluster of Streptomyces zelensis , confer far greater resistance against their respective SCPCHD natural products. We identify a structural basis for substrate specificity across gene clusters and show a correlation between in vivo resistance and in vitro enzymatic activity indicating that reduced product affinity—not enhanced substrate recognition—is the evolutionary outcome of selective pressure to provide self-resistance against yatakemycin and CC-1065.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

How well do self-interaction corrections repair the overestimation of static polarizabilities in density functional calculations?

Herein, we examine the effect of removing self-interaction error (SIE) on the calculation of molecular polarizabilities in the local spin density (LSDA) and generalized gradient approximations (GGA). To this end, we utilize a database of 132 molecules taken from a recent benchmark study [Hait and Head-Gordon, Phys. Chem. Chem. Phys. , 2018, 20 , 19800] to assess the influence of SIE on polarizabilities by comparing results with accurate reference data. Our results confirm that the general overestimation of molecular polarizabilities by these density functional approximations can be attributed to SIE. However, removing SIE using the Perdew–Zunger self-interaction-correction (PZ-SIC) method, implemented using the Fermi–Löwdin Orbital SIC approach, leads to an underestimation of molecular polarizabilities, showing that PZ-SIC overcorrects when combined with LSDA or GGA. Application of a recently proposed locally scaled SIC [Zope, et al., J. Chem. Phys. , 2019, 151 , 214108] is found to provide more accurate polarizabilities. We attribute this to the ability of the local scaling scheme to selectively correct for SIE in the regions of space where the correction is needed most.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗