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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Unsupervised learning-enabled pulsed infrared thermographic microscopy of subsurface defects in stainless steel

Metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing method (AM) frequently contain microscopic porosity defects, with typical approximate size distribution from one to 100 microns. Presence of such defects could lead to premature failure of the structure. In principle, structural integrity assessment of LPBF metals can be accomplished with nondestructive evaluation (NDE). Pulsed infrared thermography (PIT) is a non-contact, one-sided NDE method that allows for imaging of internal defects in arbitrary size and shape metallic structures using heat transfer. PIT imaging is performed using compact instrumentation consisting of a flash lamp for deposition of a heat pulse, and a fast frame infrared (IR) camera for measuring surface temperature transients. However, limitations of imaging resolution with PIT include blurring due to heat diffusion, sensitivity limit of the IR camera. We demonstrate enhancement of PIT imaging capability with unsupervised learning (UL), which enables PIT microscopy of subsurface defects in high strength corrosion resistant stainless steel 316 alloy. PIT images were processed with UL spatial–temporal separation-based clustering segmentation (STSCS) algorithm, refined by morphology image processing methods to enhance visibility of defects. The STSCS algorithm starts with wavelet decomposition to spatially de-noise thermograms, followed by UL principal component analysis (PCA), fine-tuning optimization, and neural learning-based independent component analysis (ICA) algorithms to temporally compress de-noised thermograms. The compressed thermograms were further processed with UL-based graph thresholding K-means clustering algorithm for defects segmentation. The STSCS algorithm also includes online learning feature for efficient re-training of the model with new data. For this study, metallic specimens with calibrated microscopic flat bottom hole defects, with diameters in the range from 203 to 76 µm, were produced using electro discharge machining (EDM) drilling. While the raw thermograms do not show any material defects, using STSCS algorithm to process PIT images reveals defects as small as 101 µm in diameter. To the best of our knowledge, this is the smallest reported size of a sub-surface defect in a metal imaged with PIT, which demonstrates the PIT capability of detecting defects in the size range relevant to quality control requirements of LPBF-printed high-strength metals.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Modeling strategy for progressive failure prediction in lithium-ion batteries under mechanical abuse

The prediction for the internal failure of lithium-ion batteries (LIBs) under external mechanical abuse loading remains a challenge for safe design. This paper systematically studies the modeling approach for progressive failure simulation and short-circuit prediction. Hemispherical indentation experiments are conducted on a battery specimen containing 5 representative sandwich (RS) layers to examine the deformation and progressive failure behavior of battery components. Three different models, viz, a high-fidelity detailed model, an intermediate homogenized model and a fully homogenized model, are developed and employed to simulate the progressive failure in a multi-layer battery specimen. The simulation results obtained using the three models all correlate well with experimental phenomena, capturing the overall stress-strain response and ultimate failure of the battery structure. The numerical results can help us understand the failure behavior and the mechanism for mechanical abuse-induced short circuit in LIBs. The feasibility of the three models for use in progressive failure prediction is compared by analyzing the capability of the models in predicting the critical failure events, the computational efficiency of the models, and the feasibility to implement coupled mechanical-electrochemical-thermal simulations. The results of this work provide useful insights on practical choices for the modeling strategy and safety design of LIBs under mechanical abuse conditions.

25 ENERGY STORAGE↗
DOE OSTI2021

Artificial Reasoning System for Symptom-Based Conditional Failure Probability Estimation Using Bayesian Network

Advances in nuclear power technologies require enhanced capabilities for operator advice and autonomous control. One of the first tasks in the development of such capabilities is the formulation of symptom-based conditional failure probabilities for structures, systems, and components (SSCs) of interest, for which the primary goal is to aid plant personnel in deducing the probabilistic performance status of the monitored SSCs and in detecting impending faults/failure. The task of conditional failure probability estimation is a bidirectional inference problem and shall be logically tackled by the Bayesian network (BN) approach. As a knowledge-based artificial intelligence tool and a probabilistic graphical model, BN offers the capability of reasoning under uncertainty and graphical representation emulating the physical behavior of the target SSC. This paper provides a systematic overview of the BN technique and the software tools for handling implementation of BN models, along with the associated knowledge representation and reasoning paradigm. Both operational data and expert judgement can be readily incorporated into the knowledge base of a BN model. The challenges with data availability are highlighted, and the general approach to target SSC identification is presented. Our focus is upon failure-prone and risk-important balance of plant assets, especially cases having strong operator involvement. An exemplary case study on the failure of a motor-driven centrifugal pump is also conducted to demonstrate the usefulness and technical feasibility of the proposed artificial reasoning system using an expert system shell.

Zhao, Xingang↗
DOE OSTI2022

Revisiting Multi-Material Composite Structures with Homogenized Composite Properties

Composite structures inherently develop residual stresses during their curing process. Driven predominately by mismatched thermal strains between differing materials or ply orientations, but also affected by curing process phenomena like polymer shrinkage, these residual stresses can lead to failure within composite structures. There are several methods varying in complexity that can be used to model the development of residual stresses, all of which are capable of capturing sufficient detail to understand the residual stress state at the ply level. However, explicitly modeling all plies of a layup in a composite structure can be prohibitively expensive based on the number of plies, structure size, and required element size. The computational cost can be reduced through the homogenization of the composite layup without losing much fidelity of the overall response of the structure. The homogenization process reduces the many plies of a laminate to a single lamina that reduces complexity and increases the mesh size where a single element can span multiple plies. This report focuses on verification and validation efforts for a homogenization process using a suite of finite element simulations rather than an analytic solution derived from classical laminate theory. Initial verification using representative element volumes indicated there was minimal error in the homogenization process; however, this compounded to a small, but acceptable error in strip and split ring experimental composite structures. The error does under predict the residual stress state in the strip and split ring and should be accounted for when simulating composite structures with homogenized properties.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Enhancing fatigue life by ductile-transformable multicomponent B2 precipitates in a high-entropy alloy

Catastrophic accidents caused by fatigue failures often occur in engineering structures. Thus, a fundamental understanding of cyclic-deformation and fatigue-failure mechanisms is critical for the development of fatigue-resistant structural materials. Here we report a high-entropy alloy with enhanced fatigue life by ductile-transformable multicomponent B2 precipitates. Its cyclic-deformation mechanisms are revealed by real-time in-situ neutron diffraction, transmission-electron microscopy, crystal-plasticity modeling, and Monte-Carlo simulations. Multiple cyclic-deformation mechanisms, including dislocation slips, precipitation strengthening, deformation twinning, and reversible martensitic phase transformation, are observed in the studied high-entropy alloy. Its improved fatigue performance at low strain amplitudes, i.e., the high fatigue-crack-initiation resistance, is attributed to the high elasticity, plastic deformability, and martensitic transformation of the B2-strengthening phase. This study shows that fatigue-resistant alloys can be developed by incorporating strengthening ductile-transformable multicomponent intermetallic phases.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Failure of molecular dynamics to provide appropriate structures for quantum mechanical description of the aqueous chloride ion charge-transfer-to-solvent ultraviolet spectrum

The lowest band in the charge-transfer-to-solvent ultraviolet absorption spectrum of aqueous chloride ion is studied by experiment and computation. Interestingly, the experiments indicate that at concentrations up to at least 0.25 M, where calculations indicate ion pairing to be significant, there is no notable effect of ionic strength on the spectrum. The experimental spectra are fitted to aid comparison with computations. Classical molecular dynamic simulations are carried out on dilute aqueous Cl - , Na + , and NaCl, producing radial distribution functions in reasonable agreement with experiment and, for NaCl, clear evidence of ion pairing. Clusters are extracted from the simulations for quantum mechanical excited state calculations. Accurate ab initio coupled-cluster benchmark calculations on a small number of representative clusters are carried out and used to identify and validate an efficient protocol based on time-dependent density functional theory. The latter is used to carry out quantum mechanical calculations on thousands of clusters. The resulting computed spectrum is in excellent agreement with experiment for the peak position, with little influence from ion pairing, but is in qualitative disagreement on the width, being only about half as wide. It is concluded that simulation by classical molecular dynamics fails to provide an adequate variety of structures to explain the experimental CTTS spectrum of aqueous Cl - .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Large Area Detection of Microstructural Defects with Multi-Mode Ultrasonic Signals

Cyclic loading or other stresses can lead to development of cracks and crack growth in mechanical structures, leading to eventual failure. While ultrasound imaging can be used for non-destructive testing of such structures, conventional ultrasound techniques are often limited by crack size, density, and areal coverage. An effective characterization of real-world, large-area structures is required at an early damage stage to prevent catastrophic failure and predict remaining life. In this study, a new nonlinear ultrasonic testing (NUT) method is proposed for large-area monitoring of practical structures with arbitrary complexity by using multiple-mode guided-wave ultrasonic signals. The proposed guided-wave NUT technique requires single-element transducers, simple electronics, and a mixed time-frequency domain signal processing. As a proof-of-concept demonstration, numerical simulations and experiments are performed on an A36 carbon steel beam assembly with previously formed microstructural defects that cause nonlinearities in ultrasonic response. The quadratic dependence of the nonlinear wave excitation on the input ultrasonic signal amplitude is shown by numerical simulations, and such a nonlinear ultrasonic response is experimentally observed in the zone with a high density of microstructural defects.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Enhancing functionalities in carbon fiber composites by titanium dioxide nanoparticles

Carbon fiber composite's high specific strength makes it incredibly useful for structural applications. However, their low strain-to-failure can be problematic in structural applications that can potentially see high strain conditions or high fatigue cycles resulting in sudden, catastrophic failure. This is further complicated by damage manifesting within the composite thus not showing damage indicators on the surface. Therefore, monitoring the structural integrity and strain history of the composite in application by itself—while maintaining positive composite performance—is vital to ensure safe operation. In this study, a homogeneous dispersion of TiO 2 nanoparticles on carbon fiber is demonstrated to generate a piezoresistive carbon fiber polymer matrix composite with enhanced self-sensing capabilities. The nanoparticle-embedded composites also exhibited superior strength and damping potential compared to the nanoparticle-free composites. The apparent interlaminar shear strength increased by up to 15% and the damping loss factor increased by an average of 150% while the piezoresistive sensitivity increased by up to 180% with the addition of a small weight fraction of nanoparticles to the fiber surface. These results demonstrate an approach to simultaneously improve the sensing capabilities, damping behavior and mechanical strength of carbon fiber composites by simply adding nanoparticles in the fiber sizing using a commercially scalable deposition method.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Wind farm flow control: prospects and challenges

Abstract. Wind farm control has been a topic of research for more than two decades. It has been identified as a core component of grand challenges in wind energy science to support accelerated wind energy deployment and to transition to a clean and sustainable energy system for the 21st century. The prospect of collective control of wind turbines in an array, to increase energy extraction, reduce structural loads, improve the balance of systems, reduce operation and maintenance costs, etc. has inspired many researchers over the years to propose innovative ideas and solutions. However, practical demonstration and commercialization of some of the more advanced concepts has been limited by a wide range of challenges, which include the complex physics of turbulent flows in wind farms and the atmosphere, uncertainties related to predicting structural load and failure statistics, and the highly multi-disciplinary nature of the overall design optimization problem, among others. In the current work, we aim at providing a comprehensive overview of the state of the art and outstanding challenges, thus identifying the key research areas that could further enable commercial uptake and success of wind farm control solutions. To this end, we have structured the discussion on challenges and opportunities into four main areas: (1) insight in control flow physics, (2) algorithms and AI, (3) validation and industry implementation, and (4) integrating control with system design (co-design).

Meyers, Johan (ORCID:0000000228284397)↗
DOE OSTI2025

Maturing Rational Design Methodologies and Industry Consensus Engineering Standards: Critical Fastened Joints - Solar PV Industry

Critical structural joints can be seen throughout a solar array and are called upon to secure modules and keep racking assembled and able to resist large demands from winds and snow loads. In the relatively new and fast-growing solar PV industry, the important role these hardware assemblies (e.g. clips, clamps, bolts, nuts, washers) play is not well understood by product designers. Failures with critical structural joints are surprisingly common and point to the need for maturing the engineering and assembly of these joints. The wide variety of design concepts (Figure 2&2) demonstrate interesting and innovative ideas but are lacking the basics of fastener engineering seen in matured industries (e.g. transportation, buildings). Complicating the maturing process for critical structural joints is that they are one component in rack supporting structures that exhibits a systems behavior; each component will affect the other and play a key role in maintaining structural integrity. When wind loads the surface of a module, the underlying racking members deflect and twist which in turn imparts forces back into the joints and into the mounted modules. Often, these supporting rack structures exhibit high deflections and low natural frequencies which amplify the demands placed into the joints even in moderate winds. Current engineering practices and associated structural conventions view solar racking support structures as they would a high mass building that exhibit more static behaviors in wind events. Solar structures are unique from high mass buildings and require the development of solar specific industry engineering consensus standards.

14 SOLAR ENERGY↗
DOE OSTI2021

Influence of alkali-silica reaction on the shear capacity of reinforced concrete beams with minimum transverse reinforcement

A growing number of critical concrete infrastructure are affected by alkali-silica reaction (ASR) damage such as the Seabrook Nuclear Power Plant in New Hampshire, Parker arch-gravity dam in Arizona, and several highway bridges in California and Texas. ASR causes expansion and cracking and degrades the concrete mechanical properties. Despite a wealth of material level studies there is still limited large-scale experimental data regarding the effects of ASR on reinforced concrete (RC) members. Due to the brittle nature of the shear failure in RC structures, this study focuses on the shear response of full-scale ASR damaged RC beams with minimum shear reinforcement. Six RC beams were built with different levels of ASR susceptibility and conditioned in different environments during which continuous expansion monitoring was performed. The beams all contained reactive fine aggregate (sand) and two of them had additional alkali, 1.25% by weight of cement, to accelerate ASR. The highest expansion rate of the beams happened during the first 150 days and the expansions stayed constant after 240 days and 330 days for beams conditioned in outdoor and laboratory conditions, respectively, until the last measurement at 575 days. The highest expansion, 0.4%, was seen in the beams with additional alkali and conditioned outside with regular water spray. Out of the six beams, three were selected at different levels of ASR damage and two shear tests were performed on the minimally reinforced spans close to the ends of each beam. Results indicated that beams gain shear strength from ongoing cement hydration in the presence of moisture for ASR expansions less than 0.2%. Compared to one of the beams with 0.2% ASR expansion, one of the other samples with 0.4% expansion lost 6% of its shear strength, 25% of its shear stiffness, and showed about two times larger shear cracks and shear deformations at peak load. Finally, the shear reinforcement yielded at 20% less load in the beam with 0.4% expansion compared to the beam with 0.2% expansion.

42 ENGINEERING↗
DOE OSTI2026

Noise-aware optimization in nominally identical manufacturing and measuring systems for high-throughput parallel workflows

Device-to-device variability in experimental noise critically impacts reproducibility, especially in automated, high-throughput systems like additive manufacturing farms. While manageable in small labs, such variability can escalate into serious risks at larger scales, such as architectural 3D printing, where noise may cause structural or economic failures. This contribution presents a noise-aware decision-making algorithm that quantifies and models device-specific noise profiles to manage variability adaptively. It uses distributional analysis and pairwise divergence metrics with clustering to choose between single-device and robust multi-device Bayesian optimization strategies. Unlike conventional methods that assume homogeneous devices or enforce generic robustness, the proposed framework explicitly determines whether shared optimization across devices is appropriate based on the degree of inter-device noise heterogeneity. This enables improved performance, reproducibility, and efficiency. An experimental case study involving three nominally identical 3D printers (same brand, model, and close serial numbers) demonstrates reduced redundancy, lower resource usage, and improved reliability, along with improved convergence stability and solution quality through the selection of the appropriate optimization strategy based on the degree of inter-device noise heterogeneity. Overall, this framework establishes a general approach for precision- and resource-aware optimization in scalable, automated experimental platforms, demonstrated here on a representative multi-device 3D printing case study.

Schenk, Christina↗
DOE OSTI2021

Temperature and time effects of post-weld heat treatments on tensile properties and microstructure of Zircaloy-4

Opportunities exist to expand the application of Zircaloy-4 (Zry-4) into lower-temperature (<100°C) applications. Welded areas within these structures are potential failure points and post-weld heat treatments (PWHTs) are not yet standardized for application in this temperature range. Zry-4 tungsten inert gas (TIG) welds were given PWHTs from 450-900°C with hold times from 0.5-48 hours to investigate changes to tensile properties and microstructure. Treatment of 800°C for 1 hour was found to produce the most ductile material, measured by total elongation of the tensile bar (16.5%). Furthermore, both PWHT time and temperature were observed to increase total elongation, except in extreme (>800°C or >18hours) cases, but the effect of temperature was stronger. The second order cumulative annealing parameter shows a trend with tensile properties. Average grain sizes in the fusion zone and heat affected zone did not change under any of the PWHTs until the development of an undesirable blocky-alpha phase, which was only observed to form in the heat affected zones for hold times greater than 18hr at 800°C.

36 MATERIALS SCIENCE↗
DOE OSTI2024

On principles of emergent organization

After more than a century of concerted effort, physics still lacks basic principles of spontaneous organization. To appreciate why, we first state the problem, outline historical approaches, and survey the present state of the physics of self-organization. This frames the particular challenges arising from mathematical intractability and the resulting need for computational approaches, as well as those arising from a chronic failure to define structure. Then an overview of two modern mathematical formulations of organization—intrinsic computation and evolution operators—lays out a way to overcome these challenges. Together, the vantage point they afford shows how to account for the emergence of structured states via a statistical mechanics of systems arbitrarily far from equilibrium. The result is a constructive path forward to principles of organization that builds on mathematical identification of structure.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Understanding the Impact of Lignocellulosic Biomass Variability on the Size Reduction Process: A Review

The importance of size reduction to the commercial deployment of biomass utilization technologies cannot be overemphasized. Size reduction is necessary to create good flowing, easily digestible lignocellulosic biomass. However, inefficiencies and inconsistencies have not been well addressed for the process of size reduction. This has motivated quite a few research studies, some of which have reported the influence of process variables and pretreatment on the size reduction performance as well as the development of novel size reduction equipment and empirical mathematical models. In this Perspective, we present a systematic and critical evaluation of the state of lignocellulosic biomass size reduction research to provide insights for engineers and scientists for future development and investigation. We first briefly discuss the structural biology and failure mode of lignocellulosic biomass. Then, we present a comprehensive, data-driven picture of the interactions among lignocellulosic biomass attributes, size reduction process variables, pretreatment processes, and size reduction process performance (as defined by product characteristics and size reduction energy consumption). The sizing equipment tool wear issues are also summarized. Finally, we highlight some existing gaps and future research opportunities for lignocellulosic biomass size reduction to inform the development of newer technologies that could effectively produce high-value lignocellulosic biomass particles.

09 BIOMASS FUELS↗
DOE OSTI2024

Stress Field Dynamics and Fault Slip Potential in the Paradox Basin

Abstract The Paradox Basin, straddling Utah, Colorado, Arizona, and New Mexico is characterized by an intricate amalgamation of evaporites and clastic layers and is dominated by prominent salt walls and related subsurface structures. Our research offers a new examination of the stress distribution across the basin, deriving from continuous and discrete stress measurements conducted in boreholes in the region and focal mechanism analysis, emphasizing variations over salt structures. Integrating Coulomb failure criteria with probabilistic methods, we assess potential fault movements resulting from fluid pressure alterations. Our approach provides a comprehensive understanding of the Paradox Basin's state of stress, showing a continuous change of the maximum horizontal stress orientation from N‐S at the Wasatch Fault Zone to WNW‐ESE in the northern part of the Paradox Basin and to WSW‐ENE in the southern part of the basin. Further East, into the Colorado Plateau and the Uncompahgre Uplift, the S H max orientation becomes E‐W. Decoding stress orientation dynamics has enabled critical insights into fault slip potential, especially in the basin's northern region. The salt wall faults are less likely to slip, and the Paradox Formation's evaporite and clastic rock sequence can serve as a potential low seismic risk target for carbon storage and hydrocarbon extraction.

Geochemistry & Geophysics↗
DOE OSTI2026

Roadrunner

SAND2026-17073O Roadrunner software provides a comprehensive platform for simulating the mechanical behavior of crystalline materials under various loading conditions, allowing users to investigate the effects of dislocation slip hardening and damage evolution. Developed as a fork of the Multiphysics Object Oriented Simulation Environment (MOOSE) software from Idaho National Laboratory, Roadrunner is optimized for high-performance computing and can simulate large-scale problems, enabling researchers to explore complex scenarios. Its applications include material design and optimization in aerospace and automotive industries, investigation of failure mechanisms in structural materials, and development of predictive models for crystalline materials under various loading conditions. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Lim, Hojun [Sandia National Lab. (SNL-CA), Livermo↗
DOE OSTI2021

Assessment of Cladding-Cladding Delamination Failures in U-Mo Fuel Plates

Monolithic fuel is a candidate fuel form for the conversion of high-performance research reactors. This plate-type fuel consists of a high-density, U-Mo alloy-based fuel in a monolithic form that is sandwiched between zirconium diffusion barriers and encapsulated in an aluminum cladding. To qualify this fuel system, the program must demonstrate that the fuel meets the safety standards, has a predictable behavior, and performs well in a reactor. Although many plates have shown satisfactory thermo-mechanical performance in reactor, a small number of experimental plates exhibited failures, rendering performance of these plates unacceptable. Post-irradiation examinations of failed plates have revealed that various failures modes are present, including pillowing, blistering, buckling, warping, delamination, fracture, rupture, oxide spallation, etc. Previous studies have implied that such various operational and loading conditions could generate a complex stress-strain state, facilitating distinct failure modes that could impact the structural integrity. To understand such various failure modes, a comprehensive set of studies focusing on failure modes is being performed. As a part of these studies, this work studies the cladding-cladding separation failures that were observed in some of previous irradiation tests. To understand the root cause of cladding separation failures, selected plates from previous irradiation experiments were simulated under comparable operational conditions. Stress-strain state of the plates at the interface were examined. The results were comparatively evaluated to understand possible effects of stress state of the bond-line on delamination failures. This paper presents these findings, discusses such failure modes, and examines the influence of various factors on previously observed cladding delamination failures.

42 ENGINEERING↗