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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Interim Development of new Class B Code Case with Variable Design Lifetime and enhanced design rules to guard against cyclic structural failure mode

This report summarizes updates on the ongoing development of the new American Society of Mechanical Engineers Section III, Division 5, Class B rules to address the potential failure modes of Class B components. An initial design-by-analysis approach and supporting design rules are presented. The elastic-perfectly plastic (EPP) analysis approach has been adopted for primary stress limit and ratcheting check to prevent the component failure against the structural failure mode against primary load and strain accumulation due to cyclic loads. The creep-fatigue damage assessment uses explicitly defined elastic follow-up calculated from the stress concentration region of given component. Damage fraction calculation uses a novel coupled approach to capture the influence of the elastic follow-up and provide adequate conservatism for Class B components. The proposed Class B rules does not use the stress classification approach and uses the combined loads to assess component.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Self‐Healing System for Polydicyclopentadiene Thermosets

Self-healing offers promise for addressing structural failures, increasing lifespan, and improving durability in polymeric materials. Implementing self-healing in thermoset polymers faces significant manufacturing challenges, especially due to the elevated temperature requirements of thermoset processing. To introduce self-healing into structural thermosets, the self-healing system must be thermally stable and compatible with the thermoset chemistry. This article demonstrates a self-healing microcapsule-based system stable to frontal polymerization (FP), a rapid and energy-efficient manufacturing process with a self-propagating exothermic reaction (≈200 °C). A thermally latent Grubbs-type complex bearing two N-heterocyclic carbene ligands addresses limitations in conventional G2-based self-healing approaches. Under FP's elevated temperatures, the catalyst remains dormant until activated by a Cu(I) co-reagent, ensuring efficient polymerization of the dicyclopentadiene (DCPD) upon damage to the polyDCPD matrix. The two-part microcapsule system consists of one capsule containing the thermally latent Grubbs-type catalyst dissolved in the solvent, and another capsule containing a Cu(I) coagent blended with liquid DCPD monomer. Using the same chemistry for both matrix fabrication and healing results in strong interfaces as demonstrated by lap-shear tests. In an optimized system, the self-healing system restores the mechanical properties of the tough polyDCPD thermoset. Self-healing efficiencies greater than 90% via tapered double cantilever beam tests are observed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

STRUCTURAL MODELING TO SUPPORT POST-YIELD ACCEPTANCE CRITERIA FOR SPENT NUCLEAR FUEL CLADDING

Spent nuclear fuel (SNF) is evaluated for structural failure during storage and transportation scenarios. The U.S. Department of Energy’s Spent Fuel and Waste Science and Technology (SFWST) program has sponsored significant research in quantifying mechanical loads on SNF during storage and transportation scenarios using experimental and modeling methods. The SFWST program has also performed significant research on measuring the mechanical behavior of irradiated SNF as defueled cladding segments and cladding with fuel pellets to measure composite behavior. This paper considers some of the key material data from the Sibling Pin testing and uses structural modeling and analysis methods that have been informed by testing to consider post-yield acceptance criteria for SNF cladding structural analysis. Test data published by Oak Ridge National Laboratory (ORNL) and Pacific Northwest National Laboratory (PNNL) are the foundation for informing the material behavior of the models developed in this study. In particular, four-point bend (4PB) tests of fueled and defueled cladding segments provide significant information about the bending failure mode of SNF. ORNL’s 4PB test data is on fueled cladding segments, so the composite behavior of SNF is demonstrated. This paper describes PNNL’s coincident beam model that was developed to approximate the composite behavior of SNF. This paper also presents PNNL’s structural dynamic finite element models of a cask tip-over scenario, which is predicted to cause the strongest mechanical loads on SNF of all postulated storage and transportation scenarios. SNF bending loads predicted in the cask tip-over scenario and cladding acceptance criteria beyond yield are considered, with justification based on the Sibling Pin test data. ASME Boiler and Pressure Vessel code stress intensity limits are also considered. The ultimate goal of this work is to aid in the justification of structural acceptance criteria for SNF cladding beyond the cladding’s irradiated yield strength for use in structural analysis of all storage and transportation scenarios.

Klymyshyn, Nicholas A.↗
DOE OSTI2026

A Fast, Accurate Prediction for System-Wide Damage Due to Dynamic Wind Loading

The complex relationship between photovoltaic (PV) hardware configurations, overall system dynamics, and turbulent aerodynamic phenomena generates highly unsteady, non-uniform loads that can lead to damaging instabilities. These effects may result in glass breakage, cell cracking, and structural failures in frames and mounting systems, even under moderate wind conditions. Addressing industry concerns about premature system failures in field conditions deemed survivable, our research aims to develop a fast and accurate predictive model for system damage. This model integrates configurable hardware choices with advanced simulation tools to represent the overall system-specific dynamics effectively. Using this model, we predict responses under varying weather conditions and hardware setups, translating these predictions into pre-trained surrogate models capable of accurately identifying failure risks and rapidly testing new system hardening measures. In this presentation, we will showcase preliminary results in capturing system dynamics through our customizable library of PV hardware configurations. Additionally, we will highlight how these new tools build upon PVade's established wind load modeling capabilities and foster the development of advanced AI/ML surrogates for improving system robustness.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Anomaly Detection in Materials Digital Twins with Multiscale ICME for Additive Manufacturing

Detecting anomaly in fatigue and fracture experimental materials science is an interesting yet challenging topic. The reasons are threefold. First, the anomalous microstructure feature that gives rise to structural failure is small, sometimes in the order of 10 -7 of the interrogated volume. This, in turn, results in a highly imbalanced classification problem in machine learning (ML). Second, the consequence is high, in the sense that the test specimen is destructed in such case. Third, the convolution between microstructure stochasticity and the small probability of void nucleation, growth, and coalescence makes failure and fracture a hard-to-predict and challenging problem in materials science due to its irreproducibility, even experimentally. In this paper, we developed a materials digital twin and applied anomaly detection methods to detect voids and anomaly in additive manufacturing (AM). The materials digital twin is driven by two integrated computational materials engineering (ICME) models, which are kinetic Monte Carlo (kMC) and crystal plasticity finite element method (CPFEM). In conclusion, we demonstrated that by using anomaly detection, it is possible to detect voids and other defects in materials digital twin, which paves way for future research in integrating materials digital twin with its physical counterpart.

ICME↗
DOE OSTI2025

Delamination-informed lifecycle decisions: A dielectric and machine learning framework for composite sorting and recycling

Composite materials are widely used in aerospace, marine, and automotive sectors due to their high strength-to-weight ratio and durability. However, their long-term reliability can be compromised by damage accumulation. Specifically, delamination initiation serves as a precursor to structural failure, which is often difficult to detect during damage inspection. Identifying and sorting delamination initiation in samples not only increases operational safety while providing critical information for end-of-life decisions, which influences both the service life extension value and the efficiency of fiber extraction during recycling. This research addresses two challenges: (1) developing a nondestructive, ex-situ framework to sort composite materials based on damage severity, particularly delamination, and (2) understanding how damage in composites influences resin removal during pyrolysis. Both experimental work and finite element analysis were performed to predict critical stress levels that are associated with delamination onset. Based on these results, three loading levels 50 %, 75 %, and 90 % of maximum stress, were selected for controlled experiments, generating composite samples with varying extents of damage for machine learning model training. Microscopic imaging of these samples confirmed the damage progression from matrix cracking to delamination, validating the computational predictions. We explored supervised machine learning using dielectric measurements to classify damage states. Preliminary results show an artificial neural network can identify early delamination which is a potential precursor to failure, with 94.44 % accuracy on our dataset. A parallel investigation into the effect of damage severity on pyrolysis recycling showed that heavily delaminated samples required significantly less energy for comparable matrix removal than undamaged samples.

dielectric variables↗
DOE OSTI2024

Computational modeling of coupled mechanical damage and electrochemistry in ternary oxide composite electrodes

Performance degradation of ternary layered oxide cathodes largely originates from their loss of structural integrity in cyclic usage. Mechanical damage, such as intergranular fracture of the active particles, is not only a mechanical cleavage process but also interferes with electrochemical kinetics such as infiltration of liquid electrolyte, surface corrosion of the constituent primary particles, and may eventually isolate the primary grains from the electron conducting network. Here, in this work, we develop a computational framework that integrates electrochemistry of a LiNi x Mn y Co 1−x−y O 2 (NMC) composite cathode with mechanical damage of the active particles. To fully examine the intricate chemomechanical behavior of the electrode, we evaluate the effects of the anisotropic material properties, the influence of mechanical potential on Li transport, and the concurrent intergranular fracture and electrolyte penetration along the grain boundaries upon multiple cycles. Electrolyte infiltration benefits capacity retention but aggravates further mechanical damage by corrosion. Structural failure mostly occurs in the first charging due to the anisotropic mechanical strain between the primary grains, while the resulting damage remains stable in the later few cycles. The results are consistent with experimental observations and the integration of electrochemistry and mechanical failure enables a step further understanding of the complex mechanism of battery degradation.

Battery degradation↗
DOE OSTI2026

Robust dynamic operation of high temperature electrolysis solid oxide cells

Here, this study evaluates the durability of Ni/YSZ-supported SOECs under dynamic operating conditions relevant to real-world applications. Systematic tests were conducted to assess cell performance under steam cycling (3 to 75% humidified H 2 ), mode cycling between SOFC and SOEC operation, thermal cycling (150 to 750 °C at OCV, and 600 to 800 °C at 1.3V), and redox cycling (between 50% humidified H 2 and 50% humidified N 2 ). Steam cycling, mode cycling, and thermal cycling at OCV do not significantly accelerate performance degradation. Thermal cycling at 1.3V caused minimal damage within 600 to 800 °C. Full redox cycling (multi-hour oxidation holds) induced cell structural failure, while partial redox cycling (0.5 h holds) was tolerated. Extensive characterization revealed some material evolution, namely Sr and Co secondary phase formation, within the oxygen electrode due to La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3-δ instability, especially for steam cycling and mode cycling. Minimal changes were identified within the Ni-YSZ fuel electrodes. These findings provide critical insights into SOEC reliability under dynamic conditions, supporting their application in dynamic or intermittent energy systems.

08 HYDROGEN↗
DOE OSTI2024

Silicon ion radiation as a viable surrogate for emulating neutron radiation damage in silicates

Abstract Nuclear power plants are aging around the world, and a precise assessment of irradiation damage in their components is needed. One key component, concrete, and specifically the silicates in its aggregates, can undergo significant expansion upon neutron radiation, which can lead to cracking and, ultimately, structural failure. However, assessing and predicting the extent of damage via neutron radiation is challenging due to reasons such as residual radioactivity and, most importantly, the high time involved. Here, we evaluate whether ion radiation can be a viable surrogate. Specifically, by employing Si 2+ ion radiations and a comprehensive multi-modal imaging protocol, we report mineral-specific responses for key silicates such as quartz, albite, anorthite, and microcline. We find that 10 MeV Si 2+ ions result in mineral expansions that are remarkably comparable to neutron radiation equivalent expansions (R 2 = 0.86, RMSE = 1.29%), opening up pathways towards rapid assessment of silicates subject to irradiation.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Infrared thermography NDT for in-situ defect detection in sandwich composite panel manufacturing

Composite manufacturing presents numerous challenges, as defects can arise from various sources throughout the process. In sandwich composite structures, the integration of a foam core introduces additional complexity and increases the likelihood of defect formation like delamination. To mitigate these issues and reduce the risk of future structural failures, in-situ monitoring during manufacturing is essential. This study investigates infrared (IR) thermography as a non-destructive technique for detecting manufacturing defects in foam-core sandwich composite panels under thermally excited conditions representative of in-situ processing. A stationary FLIR A8590 IR camera (640 × 512 pixels, 30Hz, 17mm lens, 9 ft stand-off distance) was used to monitor prefabricated panels subjected to controlled external heating simulating compression molding and resin cure exotherm. Interlaminar delamination defects with characteristic sizes ranging from 0.25 × 0.25in² to 5 × 5in² produced measurable surface temperature depressions of approximately 4–10°C during transient cooling, exceeding the effective noise floor of the camera by more than two standard deviations. Thicker laminates exhibited prolonged defect detectability windows due to increased thermal diffusion time. In contrast, embedded Teflon inclusions generated weak thermal contrasts of ≤ 3°C, approaching the measurement noise floor, due to limited thermal property contrast with the surrounding glass fiber composite. These results establish quantitative detectability limits for stationary thermographic inspection of sandwich composite panels under manufacturing-representative thermal cycles.

Barakat, Abdallah [ORNL] (ORCID:0000000296141398)↗
DOE OSTI2024

A General Overview of Cathodic Protection in Reinforced Concrete

Corrosion of reinforcement steel in concrete is a prevalent issue in infrastructures worldwide, with the direct costs of repair estimated to be $1 trillion annually. Treatment options are complicated by the concrete’s role in the corrosion process. Ordinary, modern-day cement is so alkaline that it maintains a thick passive layer on the steel surface, reducing corrosion to inconsequential rates. However, environmental contaminants in the form of carbon dioxide or chlorides may cause a breakdown in the passive layer, exposing the steel surface to corrosion. Galvanic cells will be free to form due to potential differences along the rebar, and anode sites will create dissolved iron ions that cannot travel far through the still concrete. Thus, rust products will accumulate directly onto the steel/concrete interface. Iron oxides have a much higher volume relative to steel, and even a miniscule amount of rust can produce enough volumetric stress to cause cracking, spalling and delamination of the surrounding concrete, increasing the risk of structural failure. Safeguards and inhibiting technology exist to limit the chances of corrosion initiation by either species, but cracking of the concrete cover and gradual accumulation of contaminants means that corrosion is unavoidable in certain environments and will initiate, given enough time.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Solar Photovoltaics Resilient Fasteners Levelized Cost of Energy (LCOE) Tool

Solar photovoltaics (PV) module fasteners are one of the most common structural failure points on PV systems, particularly in high winds and coastal areas with ocean spray. Some fastener types have been shown to survive these conditions at higher rates than others. The fastener type, material, quantity, and placement all impact performance. Fasteners that fail less often typically have a higher upfront cost, but this investment can pay off in savings from less frequent torque audits (which reduces O&M costs), reduced system damage, and decreased system downtime. We developed an Excel-based tool to evaluate different module fasteners for a PV system - either a new or retrofit project - and compare differences in upfront and outyear costs to determine the expected life cycle costs and simple payback periods of different fastener options. The tool is site-specific, with inputs including system attributes (such as system size, location, price of power) and fastener attributes (such as design, washer type, nut type, use of locking hardware, materials, installation time, and torque audit requirements). A baseline fastener scenario can be compared to up to four proposed fastener scenarios. In addition to presenting expected life cycle cost implications of the different fastener options, the tool produces results showing the reductions in outyear costs needed to offset any initial cost premiums for more reliable fasteners across four categories: preventative O&M, avoided damage, reduced downtime, and reduced insurance premiums. These numbers can serve as decision aids for users when considering fastener options on new or existing projects. This poster will present the tool, methodology, and scenarios using example sites to highlight the tool capabilities and how it can inform different fastener decisions on different projects. Future work includes incorporating lifetime expected damage costs by embedding damage function curves that the authors are developing from field data.

14 SOLAR ENERGY↗
DOE OSTI2026

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2025 Prototype Manufacture Awardee: XFlow Energy

Vertical-axis wind turbines offer a compelling path to low-cost, reliable distributed energy that can compete with utility prices for businesses, landowners, and municipalities in rural areas. They generate power by using a main rotor shaft perpendicular to the ground to pick up wind from different directions without a yaw mechanism. However, previous commercialization attempts struggled to account for the unsteady aerodynamics of this design, leading to low power production and/or premature structural failure. To address these challenges, Competitiveness Improvement Project recipient XFlow Energy is building on work completed during three previous CIP funding awards to develop a 30-kW vertical-axis wind turbine that achieves both low costs and high energy output.

17 WIND ENERGY↗
DOE OSTI2025

Prediction of carbon nanostructure mechanical properties and the role of defects using machine learning

Graphene-based nanostructures hold immense potential as strong and lightweight materials, however, their mechanical properties such as modulus and strength are difficult to fully exploit due to challenges in atomic-scale engineering. This study presents a database of over 2,000 pristine and defective nanoscale CNT bundles and other graphitic assemblies, inspired by microscopy, with associated stress–strain curves from reactive molecular dynamics (MD) simulations using the reactive INTERFACE force field (IFF-R). These 3D structures, containing up to 80,000 atoms, enable detailed analyses of structure-stiffness-failure relationships. By leveraging the database and physics- and chemistry-informed machine learning (ML), accurate predictions of elastic moduli and tensile strength are demonstrated at speeds 1,000 to 10,000 times faster than efficient MD simulations. Hierarchical Graph Neural Networks with Spatial Information (HS-GNNs) are introduced, which integrate chemistry knowledge. HS-GNNs as well as extreme gradient boosted trees (XGBoost) achieve forecasts of mechanical properties of arbitrary carbon nanostructures with only 3 to 6% mean relative error. The reliability equals experimental accuracy and is up to 20 times higher than other ML methods. Predictions maintain 8 to 18% accuracy for large CNT bundles, CNT junctions, and carbon fiber cross-sections outside the training distribution. The physics- and chemistry-informed HS-GNN works remarkably well for data outside the training range while XGBoost works well with limited training data inside the training range. The carbon nanostructure database is designed for integration with multimodal experimental and simulation data, scalable beyond 100 nm size, and extendable to chemically similar compounds and broader property ranges. The ML approaches have potential for applications in structural materials, nanoelectronics, and carbon-based catalysts.

Winetrout, Jordan J.↗
DOE OSTI2026

Mechanics of pore array collapse and interaction in shock-compressed polymethyl methacrylate (PMMA)

Recent studies on dynamic pore collapse have revealed significant development of shear localization, which can lead to material failure in porous structures and hot spot generation in energetic materials. These findings have dramatically improved the understanding of failure mechanisms during pore collapse but also prompt further investigation of realistic porous materials. In particular, porous media consist of many pores and porous networks. Even in low-porosity materials, pores can form in close proximity during the manufacturing process, leading to the critical question of pore–pore interaction during collapse under dynamic loading conditions. This study investigates, via plate impact experiments coupled with high-speed internal digital image correlation and shadowgraphy techniques, the collapse of two pores in shock-compressed PMMA at stresses between 0.4 and 1 GPa. The results of these experiments provide new insights into shear localization in pore collapse, in addition to distinct interactions between pores. Shadowgraphy measurements reveal novel, direct visualization of shear band development and crack evolution from pore surfaces. Spacing between adiabatic shear bands is measured over a range of impact stresses and is predicted accurately by the Grady–Kipp model. Pore interactions are found to effect a transition in the impact stress threshold at which different failure mechanisms initiate and are also found to possibly influence preferential sites for shear cracking. Throughout the study, numerical and theoretical models are leveraged to understand shear localization behavior. The role of baroclinicity and wave interactions between the pores is used to elucidate interaction mechanisms between pores.

Lawlor, Barry P. [California Institute of Technolo↗
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 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↗