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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Nondestructive Evaluation of Concrete: Elastic Property Imaging Through Full Waveform Inversion

Concrete is a major construction material worldwide and plays a crucial role in the nuclear industry. The elastic properties of concrete are prone to change and degrade while in service, as it is often subjected to extreme operational and environmental conditions. An accurate evaluation of concrete's elastic properties is thus essential to ensure structural integrity and safety. This is especially true for concrete in nuclear power plants, where irradiation effects significantly impact concrete mechanical properties. There are various methods to assess these properties, with ultrasound-based techniques showing high potential due to their nondestructive nature, cost-effectiveness, and safety. While several nondestructive evaluation methods exist, most rely on idealizations such as assuming homogeneous material and plane wavefronts. In this work, we address these issues by introducing an ultrasound-based nondestructive method aimed at reconstructing spatially varying images of concrete mechanical properties. By accurately modeling wave physics, including scattering and reflection, we overcome several of the aforementioned idealizations and aim to utilize the full waveform for imaging material properties through depth, resulting in more reliable images. Full waveform inversion (FWI) was first introduced by geophysicists to reconstruct subsurface elastic property images. The goal is to minimize the difference between simulated and recorded wavefield signals, often through gradient-based optimization algorithms. While FWI is primarily conducted using the acoustic approximation of the wave equation, few works focus on elastic FWI, where the goal is to reconstruct images of not only the pressure wave speed but also the shear wave speed and density (or their equivalents). This work explores the potential of using elastic FWI to predict concrete mechanical properties as an initial effort for a more accurate monitoring of concrete conditions in service. Reconstructing images of different elastic parameters enables more specificity and accurate condition diagnosis. This paper will detail this approach and provide examples demonstrating the effectiveness of elastic FWI in reconstructing comprehensive maps of concrete mechanical properties.

42 - ENGINEERING↗
DOE OSTI2025

Nondestructive Evaluation of Concrete: Elastic Property Imaging Through Full-Waveform Inversion

Concrete is a vital material in construction—especially in the nuclear industry, where it is used in critical structures such as containment vessels. Over time, concrete can degrade due to harsh operational and environmental conditions, necessitating that its elastic properties be accurately evaluated to ensure structural integrity and safety. Traditional nondestructive evaluation methods such as ultrasound-based techniques often rely on simplifying assumptions that may not hold true for concrete. This paper presents an advanced ultrasound-based method that uses elastic full-waveform inversion (EFWI) to create detailed images of concrete’s mechanical properties. By accurately modeling wave behaviors such as scattering and reflection, we aim to overcome the limitations of conventional ultrasonic-based methods. In this work, the imaging problem involved reconstructing the various elastic properties of a heterogenous concrete block with three steel rebars embedded in it. The ultrasonic measurements were synthetically generated from multiple sources and receivers, and the reconstruction process was performed using a gradient-based optimization algorithm. Our approach leveraged EFWI to reconstruct high-resolution images of the pressure wave speed, shear wave speed, and density. Multiple misfit functions—including L2-norm, cross-correlation (CC), and L1-norm—combined with total variation (TV) regularization and parameter constraints using a Sigmoid function—were explored for the reconstruction. The results demonstrated that using the L1-norm misfit function in conjunction with TV regularization and Sigmoid constraints significantly improved the reconstruction quality in comparison to traditional methods. This approach provided clearer images with fewer artifacts and better captured background heterogeneity. Our findings highlight that, when properly designed, EFWI carries great potential for providing comprehensive, more accurate, and more reliable assessments of concrete conditions, as is crucial for the maintenance and safety of nuclear power plant structures.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Learning interpretable surface elasticity properties from bulk properties via neural network equation learners

Surface elasticity is central to understanding the mechanics and stability of surfaces and interfaces. It is characterized by quantities such as surface tension, residual surface stress, and surface stiffness. However their analytical expressions are typically difficult to derive from atomistic data, and depend strongly on modeling choices. This work presents a neural network-based equation learner which combines customized activation functions and connection-based pruning to discover parsimonious, closed-form equations for surface elasticity from atomistic simulations. Applying the method to seven face-centered cubic (FCC) metals, our equation learner uncovers interpretable equations that describe both low-Miller index and high-Miller index surface properties, capturing long-tail property distributions accurately. The discovered expressions are decoupled into two components: a universal, geometry-driven orientation function, and material-specific baseline coefficients. We find that lower-order properties such as surface tension are fundamentally geometry dependent, while higher-order properties such as surface stress and elasticity show more complex geometry and material dependence. We also relate material dependent coefficients to bulk properties, forming a clear map from bulk material properties to surface elasticity. Overall, this approach demonstrates that interpretable neurosymbolic machine learning can bridge the gap between atomistic simulations and physical laws, enabling the discovery of generalizable structure–property relationships for materials science phenomena such as surface elasticity.

Equation learning↗
DOE OSTI2026

Leveraging Polymorphism in YbCuBi to Map Transport and Elastic Properties

AMX Zintl compounds with the hexagonal ZrBeSi structure have gained significant attention for their remarkable vacancy tolerance and low thermal conductivity. Their 2D honeycomb sublattice, composed of M–X covalent bonds, is believed to contribute to high anharmonicity and unusual thermal transport properties. In this study, we explore the temperature-dependent polymorphism of YbCuBi as a model system to investigate the relationship between the structure and elastic and thermal transport properties in AMX Zintls. YbCuBi undergoes a structural transition from the “flat” Cu–Bi layers in the ZrBeSi structure to corrugated layers in the LiGaGe structure below 410 K, resulting in a distortion of its centrosymmetric structure. To probe the effects of this crystallographic transition, we employ inelastic neutron scattering and temperature-dependent resonant ultrasound spectroscopy. These experimental findings, coupled with first-principles calculations and thermal conductivity measurements, allow us to elucidate a direct relationship between corrugation of the honeycomb lattice and the observed changes in elastic and thermal transport properties. These insights can be extended to other Zintl phases with similar structure types, providing a platform for the rational design of functional materials with tailored thermal properties.

Chemical structure↗
DOE OSTI2025

Elastic properties of the W 0.75 R e 0.25 alloy at high pressure up to 183 GPa

The high pressure equation of state for the W 0.75 R e 0.25 alloy is experimentally determined up to 183 GPa with synchrotron angle-dispersive powder x-ray diffraction in the diamond-anvil cell and to ∼925 GPa with density-functional theory. W-Re alloys are used in many industrial high-temperature applications and as a confining gasket material in high-pressure diamond-anvil cell research. The inclusion of 25 wt. % Re achieves the highest performance in terms of strength and ductility while also maintaining the body-centered-cubic (bcc) crystal structure, yet to date there has been no investigation into its elastic behavior at high pressure. We present the experimentally and theoretically determined volumetric and elastic pressure response and systematically compare these results to other W-Re alloys, finding that the bulk modulus of W-Re alloys varies nonlinearly with Re content and W 0.75 Re 0.25 becomes less incompressible than W at 85 GPa. Published by the American Physical Society 2025

Alloys↗
DOE OSTI2025

Materials property changes in ETU-10 graphite due to neutron irradiation at elevated temperatures

Graphite grade ETU-10, from IBIDEN Co., Ltd. Has been irradiated in the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL). The irradiation program was developed to provide a preliminary study the irradiation-induced property changes to the dimensions/volume, elastic properties, strength, electrical resistivity, coefficient of thermal expansion, and thermal diffusivity/conductivity over a range of temperatures and neutron exposures that may be relevant for future nuclear reactors. The irradiation envelope covers a range of irradiation temperatures (300°C–900 °C) and fluences (up to 40 × 10 25 n/m 2 [E > 0.1 MeV] or ~30 dpa) that would be relevant for advanced nuclear reactors. Further, the dimensional change was observed to be anisotropic for an isotropic graphite, the specimen dimensions, volume, Young's modulus, shear modulus, and strength all displayed a parabolic fluence dependence, the electrical resistivity had a rapid rise followed by a decrease and a later increase, at high fluence the mean coefficient of thermal expansions was similar for all irradiation temperatures, and thermal conductivity rapidly decreased followed by a continued loss.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Data-Driven Tailoring Optimization of Thermoset Polymers Using Ultrasonics and Machine Learning

Thermoset polymers are highly demanded for their structural robustness, thermal stability, and chemical resistance. Tailoring the properties of these polymers for high-performance applications is often preferred to designing brand-new polymers. However, the traditional destructive techniques used to characterize their properties as a function of manufacturing parameters are expensive and time-consuming. A novel non-destructive, data-driven method leveraging ultrasonics and machine learning techniques to tailor the properties of thermosets as a function of the manufacturing parameters is demonstrated. Thermoset epoxy samples with varying curing temperatures (15–40 °C) and curing agent amounts (±40%) were manufactured and tested. Their curing kinetics were monitored by determining the sound speed in the material in real time, while the longitudinal modulus of the samples was determined post-cure. Machine learning models were developed using a k-nearest neighbors algorithm. These models were implemented to predict the curing and final elastic properties using the manufacturing parameters, i.e., stoichiometry and curing temperature, and vice versa. Understanding and modeling how these parameters affect the cure kinetics and final properties will allow for efficient and reliable optimization of thermoset tailoring and manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Anisotropic structure and optical engineering of strontium titanate and zirconia responding to sequential hydrogen and helium irradiation

Unraveling the correlation between strain engineering with anisotropic optical properties via nanoscale defects gradually evolved into a strategy for fundamental studies and technological applications, yet it remains understudied in functional complex oxides compared to metals and semiconductors. Here, the methodology of strain engineering for the determination of the lattice parameters, parallel/normal to the sample surface, in the individual layers of single-crystalline superlattices is derived, which is based on analysis of high-angle X-ray diffraction measurements in combination with diffraction reciprocal space mapping. With modeling that takes into account the effect of elastic properties, two elastically anisotropic materials, SrTiO 3 and ZrO 2 , have been compared in terms of defect-induced elastic strain caused by individual and sequential H + and He 2+ irradiation. The anisotropic lattice swelling with corresponding refractive index, and obstructive behavior of elastic strain recovery are demonstrated in SrTiO 3 , while approaching strain behaviors accompanied by isotropic refractive index distribution in both orientations are confirmed in ZrO 2 . Under sequential He 2+ /H + irradiation, pre-existing He-vacancy complexes acted as vacancy traps, preferentially capturing H + clusters to induce lattice distortion and enhance absorption. Nanohardness increments (ΔH) calculated by the DBH model matched nanoindentation results, confirming that sequential irradiation generated higher indentation yield stress than individual irradiations.

Anisotropic expansion↗
NASA NTRS2011

Thermal Cycling and Isothermal Deformation Response of Polycrystalline NiTi: Simulations vs. Experiment

A recent microstructure-based FEM model that couples crystal-based plasticity, the B2<-> MB190 phase transformation and anisotropic elasticity at the grain scale is calibrated to recent data for polycrystalline NiTi (49.9 at.% Ni). Inputs include anisotropic elastic properties, texture and differential scanning calorimetry data, as well as a subset of recent isothermal deformation and load-biased thermal cycling data. The model is assessed against additional experimental data. Several experimental trends are captured - in particular, the transformation strain during thermal cycling monotonically increases and reaches a peak with increasing bias stress. This is achieved, in part, by modifying the martensite hardening matrix proposed by Patoor et al. [Patoor E, Eberhardt A, Berveiller M. J Phys IV 1996;6:277]. Some experimental trends are underestimated - in particular, the ratcheting of macrostrain during thermal cycling. This may reflect a model limitation that transformation-plasticity coupling is captured on a coarse (grain) scale but not on a fine (martensitic plate) scale.

Phase Transformations↗
DOE OSTI2026

Ultrasound Characterization of Plastic Bonded Explosives With Varied Binder Content and Density

Advanced explosive formulations and methods of production require rapid characterization for both processes and materials. Here, we present results for ultrasound characterization of a plastic bonded explosive (PBX) formulation as a function of binder weight percent and pressing density using through-transmission sound speed measurements. The explosive in this study was composed of 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) combined with the fluoropolymer binder Kynar Ultraflex B. Binder content ranged from 2.6 wt.% to 11.5 wt.% with associated explosive formulation densities from 1.69 g/cc to 1.89 g/cc. We find linear and nonlinear changes in longitudinal and shear sound speeds, peak frequencies, and measured elastic properties for the different PBX formulations. These measured changes can differentiate the tested formulations, highlighting the potential for ultrasound as a nondestructive characterization method for newly-formulated PBXs. We also demonstrate through-transmission measurements using different coupling media and present results from an ultrasound technique called resonant ultrasound spectroscopy (RUS). Both transmission and RUS approaches may allow for rapid and even automated characterization of PBX components suitable for high-throughput manufacturing operations.

Engineering↗
DOE OSTI2025

Developing Machine Learning Interatomic Potential for Fe-Cr-Ni Alloys

Accurate prediction of creep and fatigue behavior of stainless steel at elevated temperatures in hydrogen environment requires fundamental understanding of alloy-hydrogen interaction at cross-scale including bulk lattice and key defects such as vacancies, grain boundaries, surfaces, stacking faults, dislocations, and precipitates. This project aims to predict creep behavior of 347H stainless steel with H using machine learning interatomic potentials based on first-principles density functional theory simulations. The Moment Tensor Potentials platform is adopted for this work since it demonstrates a fine balance between model accuracy and computational efficiency. The potential is well trained based on large amount of high-fidelity density functional theory calculations. The validation is carried out by comparing various important properties including short range order, coefficient of thermal expansion, elastic properties, stacking fault energy, grain boundary energy, and surface energy. This work lays the foundation for reliable atomistic simulation of high temperature hydrogen attack of stainless steel.

density functional theory (DFT)↗
DOE OSTI2026

Microscopic Dynamics Controls Coupling and Cluster Formation in Brush Particle Solids

Thermodynamics-based models predict the structure of polymer-grafted nanoparticles (PGNs) as well as their assembly behavior based on geometric parameters such as particle size, degree of polymerization, and density of grafted chains. The role of microscopic polymer dynamics, such as the mobility of repeat units in the melt state, in the evolution of the structure and properties remains unknown. Brillouin light spectroscopy (BLS), due to its capability to concurrently discern the local and global elastic properties of PGN assemblies, enables the probing of microscopic processes, such as brush interdigitation, sensitive to the annealing of the assembly. For poly(methyl methacrylate) (PMMA)-grafted silica (SiO 2 ) PGNs in the dry powder state and annealed above the glass transition temperature, BLS revealed fully reversible local elasticity, indicative of limited interdigitation between adjacent PGNs. This contrasts with polystyrene (PS)−SiO 2 analogs that displayed ready (and irreversible) fusion of brush layers during annealing. The retardation of brush interdigitation in PMMA-grafted systems is surprising, given the similar thermomechanical properties of both polymers, and is rationalized as the consequence of higher friction between PMMA repeats compared to PS. Microscopic dynamics thus has a profound impact on the kinetic path of structure (and property) evolution and thus should be considered during the processing of PGNs into functional hybrid materials.

chemical structure↗
DOE OSTI2025

Compression Response of Silicone-Based Composites with Integrated Multifunctional Fillers

Polydimethylsiloxane (PDMS) is known for its exceptional mechanical properties, chemical stability, and flexibility. Recent advancements have focused on developing functional PDMS composites by integrating various functional fillers, including polymers, ceramics, and metals, for advanced applications such as electronics, medical devices, and aerospace. Consequently, there is a growing need to investigate PDMS composites to achieve higher filler loadings offering enhanced mechanical performance. This study addresses this need by utilizing the high molecular weight (MW) PDMS resin we have developed, offering its high elongation capacity of up to >6500%. We incorporated boron (B), hollow glass microballoons (HGMs), and tungsten-coated hollow glass microballoons (WHGMs) into the developed high MW PDMS. The resulting composites demonstrated excellent elastic properties and significant compression resilience (35–80%) and elastic modulus (1.28–10.15 MPa) at high filler loadings (~60 vol.%). Specifically, B/PDMS composites achieved up to 67.6 vol.% of B, HGM/PDMS composites held up to 68.6 vol.% of HGM, and WHGM/PDMS composites incorporated up to 54.0 vol.% of WHGM. These findings highlight the potential of high MW PDMS for developing high-performance PDMS composites suitable for advanced applications such as aerospace, automotive, and medical devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Rippled metamaterials with scale-dependent tailorable elasticity

Thermally induced ripples are intrinsic features of nanometer-thick films, atomically thin materials, and cell membranes, significantly affecting their elastic properties. Despite decades of theoretical studies on the mechanics of suspended thermalized sheets, controversy still exists over the impact of these ripples, with conflicting predictions about whether elasticity is scale-dependent or scale-independent. Experimental progress has been hindered so far by the inability to have a platform capable of fully isolating and characterizing the effects of ripples. This knowledge gap limits the fundamental understanding of thin materials and their practical applications. Here, we show that thermal-like static ripples shape thin films into a class of metamaterials with scale-dependent, customizable elasticity. Utilizing a scalable semiconductor manufacturing process, we engineered nanometer-thick films with precisely controlled frozen random ripples, resembling snapshots of thermally fluctuating membranes. Resonant frequency measurements of rippled cantilevers reveal that random ripples effectively renormalize and enhance the average bending rigidity and sample-to-sample variations in a scale-dependent manner, consistent with recent theoretical estimations. The predictive power of the theoretical model, combined with the scalability of the fabrication process, was further exploited to create kirigami architectures with tailored bending rigidity and mechanical metamaterials with delayed buckling instability.

Applied Physical Sciences↗
DOE OSTI2025

Mapping the structural–mechanical landscape of amorphous carbon with ReaxFF molecular dynamics

We use ReaxFF molecular dynamics (MD) to investigate the relationship between structural and mechanical properties in bulk and nanostructured amorphous carbon (a-C). The liquid-quench MD method is used to generate isotropic bulk samples with mass densities ranging from 0.96 to 3.29 g/cm3. Structural analysis identifies two types of structures with distinct short- and medium-range order: lower-density sp2-dominated a-C, which is characterized by a bimodal ring-size distribution, and higher-density sp3-dominated tetrahedral amorphous carbon (ta-C), exhibiting a unimodal ring-size distribution. Stress–strain MD simulations and analysis reveal how an atomistic structure impacts elastic properties and post-yield atomic rearrangements. All stretched structures demonstrate elastic isotropy and plasticity driven by a ring-size expansion mechanism reflected in changes in ring statistics. The plastic region is substantially larger in ta-C than in a-C due to the post-yield shift from sp3 to sp2 C dominant bonding. In both a-C and ta-C, ultimate failure occurs when a reactive crack, traversed by long sp chains, forms and propagates predominantly perpendicular to the direction of the applied strain. Oxygen infiltration into the fractured region significantly reduces stress resistance, primarily through the early rupture of long sp chains. MD simulations and analysis are extended to a-C slabs, a-C nanotubes, and partially a-C nanotubes. The latter nanostructure highlights the differences between the elastically isotropic a-C walls, which develop circumferential cracking, and the crystalline walls, which tear along crystallographic directions. These results provide a strong foundation for further computational characterization of a-C materials.

Dernov, A. (ORCID:0009000004220973)↗
DOE OSTI2026

A new interatomic potential for mixed Mg-Al-Ga-In spinels

While density functional theory (DFT) has become the de facto approach for accurate simulation of materials at the atomic scale, there are many aspects of materials that are simply out of reach of DFT methods. In particular, finite temperature properties such as diffusivities, the structure and properties of grain boundaries and interfaces, and the study of defect properties in complex alloys are computationally challenging for DFT methods. Recently, a new class of spinels in which three cations order over two sublattices was discovered. In order to predict the properties of these types of structures, classical potentials are a must. Here, in this work, we derive a new classical potential for Mg-bearing spinels in which the B cations are Al, Ga, and/or In. The potential does well in describing the DFT energetics of various spinel structures as a function of chemistry and inversion. In particular, it reproduces the thermodynamically favorable MgAlGaO 4 structure while correctly predicting that neither MgAlInO 4 nor MgGaInO 4 are stable. Further, it reproduces physical trends in elastic properties as compared against experiment.

36 MATERIALS SCIENCE↗