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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 OSTI2020

Quality Control of Additively Manufactured Metallic Structures with Machine Learning of Thermography Images

Additive manufacturing (AM) of high-strength metals is currently based on the laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Quality control (QC) requires nondestructive evaluation of actual AM structures. Flash thermography is a potentially promising QC technique because it is scalable to arbitrary structure size. However, the detection sensitivity of this method is limited by noise. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatial–temporal blind source separation and spatial–temporal sparse dictionary learning methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced by the LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. Finally, the ML methods with higher accuracy require a longer runtime. However, this runtime is sufficiently short to perform QC within a realistic time frame.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Effect of loading path on grain misorientation and geometrically necessary dislocation density in polycrystalline aluminum under reciprocating shear

Solid phase processing (SPP) is a promising alloy fabrication technique to produce fine and homogeneous grain structures for high-performance alloys. However, there is very limited modeling capability to understand and predict the grain refinement during SPP. In this work, the crystal plasticity theory was used to study elastic-plastic deformation in polycrystalline aluminums under large shear deformation. Two approaches, kernel averaged misorientation (KAM) and grain reference orientation deviation (GROD), were used to assess the grain misorientations. The geometrically necessary dislocation (GND) density was computed with the plastic strain rate. The deformation simulations were carried out under two loading conditions to investigate the effect of loading paths on the evolutions of grain misorientation and GND density. The results show that the regions with high misorientation and GND density first appear near grain boundaries. These regions then extend toward interior grains. The loading path affects dislocation system activation and dislocation recovery, hence dislocation evolution and misorientation. In conclusion, both two- and three-dimensional simulations showed that the spatial and temporal evolutions of GROD, KAM, and GND density in are closely correlated, which indicates they all can be used as criteria of grain refinement or recrystallization.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Polylactic acid (PLA)-based multifunctional and biodegradable nanocomposites and their applications

Polylactic acid (PLA)-based nanocomposites are emerging as multifunctional, biodegradable materials, offering sustainable alternatives to petroleum-based plastics. This review examines recent advancements in PLA nanocomposites, focusing on enhanced mechanical strength, thermal stability, and biodegradability achieved through nanofillers like metallic particles, carbon-based materials, and ceramics. Techniques such as in situ polymerization, melt mixing, and electrospinning enable application-specific improvements. PLA's limitations, including brittleness and low barrier properties, are addressed to support diverse applications: in packaging (e.g., extended shelf life), biomedicine (e.g., degradable implants), and electronics (e.g., flexible devices). Despite challenges with filler dispersion and thermal resistance, continued innovations expand PLA's potential across multiple industries, contributing to a sustainable, circular economy.

Biodegradable polymers↗
DOE OSTI2021

Using concentrating-solar-power plants as economic carbon-free capacity resources

Deep decarbonization of electricity systems raises concerns about the need for generating capacity that can maintain reliable energy supply. Given this concern, we explore the ability of concentrating-solar-power plants with thermal energy storage to provide a carbon-free source of such capacity. We develop an approach to assessing the capacity contribution of concentrating-solar-power plants that considers future systems. Such considerations are important, because of potential differences in net-load patterns compared to today (e.g., due to higher renewable-energy penetrations). Using historical data spanning an 18-year period, we demonstrate that concentrating-solar-power plants with thermal energy storage can provide this necessary capacity with little impact on their financial viability. We examine the impact of myopic decisions and imperfect foresight of future system conditions on the operation of concentrating-solar-power plants and the resultant impact on their capacity contributions. We find that imperfect foresight can have limited impacts on such a use of concentrating-solar-power plants, so long as proper forecasting techniques are developed and used. Overall, our work shows that concentrating-solar-power plants with thermal energy storage may have a role to play in delivering reliable electricity supply in a decarbonized energy system.

14 SOLAR ENERGY↗
DOE OSTI2020

Combining Mass Spectrometry of Picoliter Samples with a Multicompartment Electrodynamic Trap for Probing the Chemistry of Droplet Arrays

Single droplet levitation provides contactless access to the microphysical and chemical properties of micrometer-sized samples. Most applications of droplet levitation to chemical and biological systems use nondestructive optical techniques to probe droplet properties. To provide improved chemical specificity, we coupled a multicompartment quadrupole electrodynamic trap (QET) with single droplet mass spectrometry. Our QET continuously traps a monodisperse droplet population (tens to hundreds of droplets) and allows for the simultaneous sizing of a single droplet using its Mie scattering pattern. Single droplets are subsequently ejected into the ionization region of an ambient pressure inlet mass spectrometer. We optimized two complementary soft ionization techniques for picoliter aqueous droplets: (1) paper spray (PS) ionization and (2) thermal desorption glow discharge (TDGD) ionization. Both techniques detect oxygenated organic acids in single droplets, with signal-to-noise ratios >100 and detection limits on the order of 10 pg. Sensitivity and reproducibility across single droplets are driven by the droplet deposition location and spray stability in PS-MS and the ionization region humidity and analyte evaporation rate in TDGD-MS. Importantly, the analyte evaporation rate can control the TDGD-MS quantitative capability because high evaporation rates result in significant ion suppression. Additionally, this effect is mitigated by optimizing the vaporization temperature, droplet size range, and analyte volatility. We demonstrate quantitative and reproducible measurements with a droplet internal standard (<10% RSD) and compare the sensitivity of PS-MS and TDGD-MS. Finally, we demonstrate the application of QET-MS to the study of heterogeneous chemical kinetics with the reaction of gas phase O 3 and aqueous maleic acid droplets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Unveiling the Liquid Electrolyte Solvation Structure by Small Angle X-ray Scattering

The fundamental understanding of the liquid electrolyte (LE) solvation structure and electrode–electrolyte interface behavior will bring more in-depth thinking and revolutionary changes to the entire electrochemical energy storage field. Regardless of the variety of techniques available, the methods employed to investigate electrolyte solvation structures over a long-range are severely limited. Small-angle X-ray scattering (SAXS) is an ideal complement to Raman spectroscopy, Fourier transform infrared spectroscopy, or nuclear magnetic resonance, which offers a unique perspective from a larger scale on clusters or networks in electrolytes. Understanding the solvation structures from a few angstroms to hundreds of nanometers will undoubtedly lay a good foundation for studying the macroscopic transport properties, such as viscosity and ionic conductivity, of the LEs. Here, in this Perspective, we discuss the use of SAXS to investigate the solvation structures in different electrolyte systems, a prospect for the SAXS broader application in the electrolyte study, and some challenges that need to be solved in the SAXS application.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Role of Transition Metals in Metal–Organic Frameworks as Nanoporous Ion Emitters for Thermal Ionization Mass Spectrometry

Thermal ionization mass spectroscopy (TIMS) is a powerful analytical technique that allows for precise determination of isotopic ratios. Analysis on low abundance samples, however, can be limited by the ionization efficiency. Following an investigation into a new type of metal-organic hybrid material devised to promote the emission of analyte ions (nano-PIES) and reduce traditional sample loading problems, this work probes the impact that changing the metal in the material has on the ionization of Uranium-238. Being derived from metal-organic frameworks (MOFs), nano-PIEs inherit the tunability of the parent MOFs; the MOF-74 series has been well studied for probing the impact various framework metals (i.e., Mg, Mn, Co, Ni, Cu, Zn, and Cd) have on material properties, and thus, a series of nano-PIEs with different metals were derived from an isoreticular MOF-74 series. In conclusion, trends in ionization efficiency were studied as a function of ionization potential, volatility, and work function of the framework metals as well as the mechanism of ionization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Intrinsic 1${T}^{{\prime} }$ phase induced in atomically thin 2 H -MoTe 2 by a single terahertz pulse

The polymorphic transition from 2H to 1${T}^{{\prime} }$-MoTe 2 , which was thought to be induced by high-energy photon irradiation among many other means, has been intensely studied for its technological relevance in nanoscale transistors due to the remarkable improvement in electrical performance. However, it remains controversial whether a crystalline 1${T}^{{\prime} }$ phase is produced because optical signatures of this putative transition are found to be associated with the formation of tellurium clusters instead. Here we demonstrate the creation of an intrinsic 1${T}^{{\prime} }$ lattice after irradiating a mono- or few-layer 2H-MoTe 2 with a single field-enhanced terahertz pulse. Unlike optical pulses, the low terahertz photon energy limits possible structural damages. We further develop a single-shot terahertz-pump-second-harmonic-probe technique and reveal a transition out of the 2H-phase within 10 ns after photoexcitation. Our results not only provide important insights to resolve the long-standing debate over the light-induced polymorphic transition in MoTe 2 but also highlight the unique capability of strong-field terahertz pulses in manipulating quantum materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Analytical model for laser-induced transient grating measurements of thermal diffusivity in non-opaque materials

The thermal transport and elastic properties of materials are often measured using the laser-induced transient grating spectroscopy (TGS) technique. The analysis of the TGS signal usually involves fitting well-known expressions, derived assuming the limiting cases of opaque or transparent materials, to the measured data. In this paper, the system of thermoelastic equations is analytically solved for an isotropic homogeneous material assuming finite laser penetration depth, which is an important consideration when the penetration depth is on the order of the acoustic wavelength. The need to use such a solution is discussed and compared to the expression for opaque material. The solution is benchmarked against TGS signal measured on single-crystal silicon with {100} surface orientation and is found to significantly improve the accuracy of the calculated thermal diffusivity as compared to using the expression for opaque material.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Scattering-based structural inversion of soft materials via Kolmogorov–Arnold networks

Small-angle scattering techniques are indispensable tools for probing the structure of soft materials. However, traditional analytical models often face limitations in structural inversion for complex systems, primarily due to the absence of closed-form expressions of scattering functions. To address these challenges, we present a machine learning framework based on the Kolmogorov–Arnold Network (KAN) for directly extracting real-space structural information from scattering spectra in reciprocal space. This model-independent, data-driven approach provides a versatile solution for analyzing intricate configurations in soft matter. By applying the KAN to lyotropic lamellar phases and colloidal suspensions—two representative soft matter systems—we demonstrate its ability to accurately and efficiently resolve structural collectivity and complexity. Here, our findings highlight the transformative potential of machine learning in enhancing the quantitative analysis of soft materials, paving the way for robust structural inversion across diverse systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Two-dimensional correlation analysis for x-ray photoelectron spectroscopy

Abstract X-ray photoelectron spectroscopy (XPS) measures the binding energy of core-level electrons, which are well-localised to specific atomic sites in a molecular system, providing valuable information on the local chemical environment. The technique relies on measuring the photoelectron spectrum upon x-ray photoionisation, and the resolution is often limited by the bandwidth of the ionising x-ray pulse. This is particularly problematic for time-resolved XPS, where the desired time resolution enforces a fundamental lower limit on the bandwidth of the x-ray source. In this work, we report a novel correlation analysis which exploits the correlation between the x-ray and photoelectron spectra to improve the resolution of XPS measurements. We show that with this correlation-based spectral-domain ghost imaging method we can achieve sub-bandwidth resolution in XPS measurements. This analysis method enables XPS for sources with large bandwidth or spectral jitter, previously considered unfeasible for XPS measurements.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Fuel recycling and impurity characteristics in long-pulse H-mode plasmas with full metal and dynamically coated walls on EAST

We report the basic behaviors of fuel recycling and impurity accumulation during >100 s long-pulse H-mode plasma under full metal wall conditions on EAST. Significant fuel recycling and impurity rising, particularly from heavy impurities, have been observed when operating with a bare metal wall or a deteriorate real-time coated wall, which severely limits the duration of H-mode discharges. To address this issue, a novel dynamic wall coating technique combining feedforward and feedback controls has been successfully developed. The feedforward control presets the Li powder injection rate based on prior experimental observations, whereas the feedback control dynamically modulates the Li injection rate in response to real-time Li-II line emission measurements. Using this approach, a 605 s H-mode plasma has been achieved with fuel recycling and impurity level maintained stable. This result extends the previous record of a 403 s long-pulse H-mode plasma Gong et al (2024 Nucl. Fusion 64 112013) by over 200 s. It demonstrates the effectiveness of the dynamic powder injection technique in controlling fuel recycling and impurities accumulation, while prolonging plasma duration. These findings offer valuable insights into potential applications of other low-Z powder, such as boron, in ITER.

fuel recycling↗
DOE OSTI2023

ANN-based ground motion model for Turkey using stochastic simulation of earthquakes

SUMMARY Turkey is characterized by a high level of seismic activity attributed to its complex tectonic structure. The country has a dense network to record earthquake ground motions; however, to study previous earthquakes and to account for potential future ones, ground motion simulations are required. Ground motion simulation techniques offer an alternative means of generating region-specific time-series data for locations with limited seismic networks or regions with seismic data gaps, facilitating the study of potential catastrophic earthquakes. In this research, a local ground motion model (GMM) for Turkey is developed using region-specific simulated records, thus constructing a homogeneous data set. The simulations employ the stochastic finite-fault approach and utilize validated input-model parameters in distinct regions, namely Afyon, Erzincan, Duzce, Istanbul and Van. To overcome the limitations of linear regression-based models, artificial neural network is used to establish the form of equations and coefficients. The predictive input parameters encompass fault mechanism (FM), focal depth (FD), moment magnitude (Mw), Joyner and Boore distance (RJB) and average shear wave velocity in the top 30 m (Vs30). The data set comprises 7359 records with Mw ranging between 5.0 and 7.5 and RJB ranging from 0 to 272 km. The results are presented in terms of spectral ordinates within the period range of 0.03–2.0 s, as well as peak ground acceleration and peak ground velocity. The quantification of the GMM uncertainty is achieved through the analysis of residuals, enabling insights into inter- and intra-event uncertainties. The simulation results and the effectiveness of the model are verified by comparing the predicted values of ground motion parameters with the observed values recorded during previous events in the region. The results demonstrate the efficacy of the proposed model in simulating physical phenomena.

Karimzadeh, Shaghayegh (ORCID:0000000337531676)↗
DOE OSTI2020

Short-depth circuits for efficient expectation-value estimation

The evaluation of expectation values Tr[$\rho \mathcal{O}$] for some pure state $\rho$ and Hermitian operator $\mathcal{O}$ is of central importance in a variety of quantum algorithms. Near-optimal techniques have been developed in the past and require a number of measurements N approaching the Heisenberg limit N = O(1/ ε) as a function of target accuracy ε. The use of quantum phase estimation (QPE) requires, however, long circuit depths C = O(1/ ε) making its implementation difficult on near-term noisy devices. The more direct strategy of operator averaging is usually preferred as it can be performed using N = O(1/ε 2 ) measurements and no additional gates aside from those needed for the state preparation. In this work we use a simple but realistic model to describe the bound state of a neutron and a proton (the deuteron) to show that the latter strategy can require an overly large number of measurements in order to achieve a prefixed relative target accuracy ε r . Further, we propose to overcome this problem using a single step of QPE and classical postprocessing. This approach leads to a circuit depth C = O (ε μ ) (with μ ≥ 0) and to a number of measurements N = O (1/ε 2 + ν ) for 0 < ν ≤ 1 and a much smaller prefactor. We provide detailed descriptions of two implementations of our strategy for ν = 1 and ν ≈ 0.5 and derive appropriate conditions that a particular problem instance has to satisfy in order for our method to provide an advantage.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Nodeless Superconducting Gap in the Candidate Topological Superconductor Sn{sub 1-x}In{sub x}Te for x=0.7.

High-pressure synthesis techniques have allowed for the growth of Sn1-xInxTe samples beyond the ambient In-saturation limit of x = 0.5 (T-c similar to 4.5 K). In this study we present measurements of the temperature dependence of the London penetration depth Delta lambda(T) in this superconducting doped topological insulator for x = 0.7, where T-c(,onset) approximate to 5 K. The results indicate fully gapped BCS-like behavior, ruling out odd-parity A(2u), pairing; however, odd-parity A(1u) in pairing is still possible. Critical field values measured below 1 K and other superconducting parameters are also presented.

Smylie, M. P.↗
DOE OSTI2024

A High-Quality Workflow for Multi-Resolution Scientific Data Reduction and Visualization

Multi-resolution methods such as Adaptive Mesh Refinement (AMR) can enhance storage efficiency for HPC applications generating vast volumes of data. However, their applicability is limited and cannot be universally deployed across all applications. Furthermore, integrating lossy compression with multi-resolution techniques to further boost storage efficiency encounters significant barriers. To this end, we introduce an innovative workflow that facilitates high-quality multi-resolution data compression for both uniform and AMR simulations. Initially, to extend the usability of multi-resolution techniques, our workflow employs a compression-oriented Region of Interest (ROI) extraction method, transforming uniform data into a multi-resolution format. Subsequently, to bridge the gap between multi-resolution techniques and lossy compressors, we optimize three distinct compressors, ensuring their optimal performance on multi-resolution data. These optimizations can improve the compression ratio of SOTA approaches by up to 3.3× under the same data quality loss. Lastly, we incorporate an advanced uncertainty visualization method into our workflow to understand the potential impacts of lossy compression. Experimental evaluation demonstrates that our workflow achieves significant compression quality improvements.

Wang, Daoce↗
DOE OSTI2023

The endohyphal microbiome: current progress and challenges for scaling down integrative multi-omic microbiome research

Abstract As microbiome research has progressed, it has become clear that most, if not all, eukaryotic organisms are hosts to microbiomes composed of prokaryotes, other eukaryotes, and viruses. Fungi have only recently been considered holobionts with their own microbiomes, as filamentous fungi have been found to harbor bacteria (including cyanobacteria), mycoviruses, other fungi, and whole algal cells within their hyphae. Constituents of this complex endohyphal microbiome have been interrogated using multi-omic approaches. However, a lack of tools, techniques, and standardization for integrative multi-omics for small-scale microbiomes (e.g., intracellular microbiomes) has limited progress towards investigating and understanding the total diversity of the endohyphal microbiome and its functional impacts on fungal hosts. Understanding microbiome impacts on fungal hosts will advance explorations of how “microbiomes within microbiomes” affect broader microbial community dynamics and ecological functions. Progress to date as well as ongoing challenges of performing integrative multi-omics on the endohyphal microbiome is discussed herein. Addressing the challenges associated with the sample extraction, sample preparation, multi-omic data generation, and multi-omic data analysis and integration will help advance current knowledge of the endohyphal microbiome and provide a road map for shrinking microbiome investigations to smaller scales.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A Hybrid AI/ML and Computational Mechanics Based Approach for Time-Series State and Fatigue Life Estimation of Nuclear Reactor Components

Environmental fatigue modeling is a complex problem due to multiple failure modes and their intermixing. The failure modes are function of various underlying causes in addition to the corrosive effect of reactor coolant environment. Some of the major causes are time-dependence of material associated with cyclic loading, load sequence effect associated with random/variable amplitude loading, effect of strain amplitude and rates, effect of varying temperature (along both temporal and spatial directions) and the effect of mean strain and stress. The nonlinear intermixing of failure modes associated with above mentioned causing parameters makes the environmental fatigue modeling is a challenging task. Because of this challenge, fatigue is traditionally being modeled based on experimental data. However, test based empirical approach often requires hundreds of fatigue tests to model the above-mentioned intermixing failure causes even for a single material system. The problem is further exaggerated for reactor component made from multi-material systems such as made from both carbon and stainless-steel base metals and their similar and dissimilar metal welds. With the difficulty of conducting hundreds of fatigue tests to capture the above-mentioned intermixing failure causes, fatigue modeling approaches often depends on empirical models based on limited available test data such as available through ASME code and NUREG 6909. However, these limited test-data-based models may not be enough to accurately predict the life of reactor components. Accurate prediction of life of reactor component would become a necessity, particularly when the license of the reactors to be extended for long-term-operation (LTO) that is for well beyond its original design life of 40 years. The requirement of extending the license of reactor under LTO requires hundreds of fatigue tests to be conducted to understand the mechanism associated with the above-mentioned interdependent failure causes. However, conducting large number of fatigue tests is not a feasibility due to the cost involved. To address this issues Argonne National Laboratory (ANL) with the sponsorship of DOE Light Water Reactor Sustainability (LWRS) program trying to develop a hybrid predictive modeling approach. This is based on limited experiment-data, Artificial-intelligence (AI) – Machine-Learning (ML) - Deep-Learning (DL) based techniques and Multiphysics-computational-mechanics based modeling tools. The hybrid approach not-only can improve the accuracy of the existing stress analysis and fatigue modeling approach but also can reduce the over-dependency on test-based approach. Towards this goal following are some of the major contributions based on ANL’s FY-20 environmental fatigue modeling activities: 1) A cyclic plasticity material model database for 82/182 dissimilar metal weld, which can be readily shared with US nuclear industry and regulatory agency on request. 2) A well validated analytical modeling methodology to perform cycle-by-cycle stress prediction under both constant amplitude fatigue loading and variable amplitude fatigue loading (with load-sequence effect). 3) An AI/ML/DL based methodology to predict unmeasurable cyclic strain based on other available sensor signals. This type of approach can be used for estimating strain in real reactor components from other sensor readings. 4) An AI/ML based approach to improve the US capability on environmental fatigue testing. This is by improving ANL’s existing environmental fatigue testing capacity to conduct ASME required strain-controlled tests (by controlling strain amplitudes and its rate), while not measuring the strain (due to the difficulty of placing an extensometer in a narrow autoclave in a PWR-water-test system). 5) A simulation and experiment based probabilistic modeling methodology for time-series fatigue state and life estimation of reactor metal such as dissimilar metal weld.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗