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Results for “Finite Element Simulation”

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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Monolithic optoelectronic circuit design for on-chip terahertz applications

We demonstrate a monolithic coplanar stripline platform for on-chip terahertz (THz) generation, transmission, and detection, addressing key challenges of mode purity, bandwidth, and referencing. Capacitive coupling of the photoconductive generator switch enforces pure odd-mode propagation, increases THz field strength, and extends the operational frequency range, achieving 0.05–1.4 THz. Our architecture enables fully monolithic fabrication with amorphous silicon switches, in situ field referencing, and galvanic isolation between generation and detection. Finite-element simulations and experiments confirm that suppressing parasitic modes improves signal integrity, providing a robust platform for high-fidelity THz spectroscopy, ultrafast electronics, and nanoscale quantum materials research.

Capacitive coupling↗
DOE OSTI2023

Boosting Piezoelectricity by 3D Printing PVDF‐MoS 2 Composite as a Conformal and High‐Sensitivity Piezoelectric Sensor

Abstract Additively manufactured flexible and high‐performance piezoelectric devices are highly desirable for sensing and energy harvesting of 3D conformal structures. Herein, the study reports a significantly enhanced piezoelectricity in polyvinylidene fluoride (PVDF) achieved through the in situ dipole alignment of PVDF within PVDF‐2D molybdenum disulfide (2D MoS 2 ) composite by 3D printing. The shear stress‐induced dipole poling of PVDF and 2D MoS 2 alignment are harnessed during 3D printing to boost piezoelectricity without requiring a post‐poling process. The results show a remarkable, more than the eight‐fold increment in the piezoelectric coefficient ( d 33 ) for 3D printed PVDF‐8wt.% MoS 2 composite over cast neat PVDF. The underlying mechanism of piezoelectric property enhancement is attributed to the increased volume fraction of β phase in PVDF, filler fraction, heterogeneous strain distribution around PVDF‐MoS 2 interfaces, and strain transfer to the nanofillers as confirmed by microstructural analysis and finite element simulation. These results provide a promising route to design and fabricate high‐performance 3D piezoelectric devices via 3D printing for next‐generation sensors and mechanical–electronic conformal devices.

2D MoS2↗
DOE OSTI2024

Multilaminate Energy Storage Films from Entropy‐Driven Self‐Assembled Supramolecular Nanocomposites

Abstract Composite materials comprising polymers and inorganic nanoparticles (NPs) are promising for energy storage applications, though challenges in controlling NP dispersion often result in performance bottlenecks. Realizing nanocomposites with controlled NP locations and distributions within polymer microdomains is highly desirable for improving energy storage capabilities but is a persistent challenge, impeding the in‐depth understanding of the structure–performance relationship. In this study, a facile entropy‐driven self‐assembly approach is employed to fabricate block copolymer‐based supramolecular nanocomposite films with highly ordered lamellar structures, which are then used in electrostatic film capacitors. The oriented interfacial barriers and well‐distributed inorganic NPs within the self‐assembled multilaminate nanocomposites effectively suppress leakage current and mitigate the risk of breakdown, showing superior dielectric strength compared to their disordered counterparts. Consequently, the lamellar nanocomposite films with optimized composition exhibit high energy efficiency (>90% at 650 MV m −1 ), along with remarkable energy density and power density. Moreover, finite element simulations and statistical modeling have provided theoretical insights into the impact of the lamellar structure on electrical conduction, electric field distribution, and electrical tree propagation. This work marks a significant advancement in the design of organic–inorganic hybrids for energy storage, establishing a well‐defined correlation between microstructure and performance.

Li, He↗
DOE OSTI2021

Kinetic Limitations in Single‐Crystal High‐Nickel Cathodes

Abstract High‐nickel cathodes attract immense interest for use in lithium‐ion batteries to boost Li‐storage capacity while reducing cost. For overcoming the intergranular‐cracking issue in polycrystals, single‐crystals are considered an appealing alternative, but aggravating concerns on compromising the ionic transport and kinetic properties. We report here a quantitative assessment of redox reaction in single‐crystal LiNi 0.8 Mn 0.1 Co 0.1 O 2 using operando hard X‐ray microscopy/spectroscopy, revealing a strong dependence of redox kinetics on the state of charge (SOC). Specifically, the redox is sluggish at low SOC but increases rapidly as SOC increases, both in bulk electrodes and individual particles. The observation is corroborated by transport measurements and finite‐element simulation, indicating that the sluggish kinetics in single‐crystals is governed by ionic transport at low SOC and may be alleviated through synergistic interaction with polycrystals integrated into a same electrode.

Ge, Mingyuan↗
DOE OSTI2020

Kinetic Limitations in Single-Crystal High-Nickel Cathodes

High-nickel cathodes attract immense interest for use in lithium-ion batteries, to boost Li-storage capacity while reducing cost. For overcoming the intergranular-cracking issue in polycrystals, single-crystals are considered an appealing alternative, but aggravating concerns on compromising the electrochemical kinetics of electrodes. We report here a quantitative assessment of electrochemical reaction in single-crystal LiNi 0.8 Mn 0.1 Co 0.8 O 2 using operando hard X-ray microscopy/spectroscopy, revealing a strong dependence of redox kinetics on the state of charge (SOC) - being sluggish at low SOC while increasing rapidly as SOC increases both in the bulk electrode and individual particles. The observation is corroborated by transport measurements and finite-element simulation, indicating that the sluggish kinetics in single-crystals is governed by ionic transport at low SOC and may be alleviated through synergistic interaction with polycrystals integrated into a same electrode. Finally, with the elucidated mechanistic origin and alleviation solution of kinetic limitations in single-crystal high-Ni cathodes, this work constitutes a step forward in enabling their practical deployment.

25 ENERGY STORAGE↗
DOE OSTI2023

Modeling of the effectiveness of novel edge seal designs for fast, low‐Cap‐Ex manufacturing

Abstract In the manufacturing of photovoltaic (PV) modules, the lamination process can take up to 20 min to complete. In this work, new lamination processes are being developed, and have been prototyped, which hope to be able to cut this time down to as little as 30 s. This could provide significant savings in the cost of lamination equipment, floor space, and energy. PV modules are expected to have a lifespan exceeding 20 to 30 years. For moisture‐sensitive PV technologies, the edge seal between the two layers of glass can be the weakest point of its reliability. There is an inherent challenge when evaluating edge seal materials due to their low permeation rates. As part of Colorado State University's Photovoltaic Research and Development 2, work at the National Renewable Energy Laboratory has developed models to evaluate edge seal configurations in glass‐glass PV modules. Here, this new manufacturing process is evaluated for long‐term moisture durability. Different edge seal design options within glass–glass PV modules are explored. Most of these designs are targeting a superstrate on glass configuration, e.g CdTe, but some designs could be used on conventional crystalline Si cells. Using COMSOL finite element simulation software, we investigated the edge seal and interlayer design configurations containing silicone perimeter edge adhesive, desiccated polyisobutylene‐based edge seal, air, and polyolefin while integrating climate conditions equivalent to a hot and humid environment such as Miami, Florida. We found optimized configurations that will allow the module to prevent moisture ingress over 50 years minimizing the amount of time and material used while utilizing polymers that are easily dispensed.

14 SOLAR ENERGY↗
DOE OSTI2026

Input specific neural networks

Neural networks have emerged as powerful tools for mapping between inputs and outputs. However, their black-box nature limits the ability to encode or impose specific structural relationships between inputs and outputs. Many scientific and engineering problems, such as constitutive modeling in solid mechanics, require networks that can enforce convexity, monotonicity, or other structural constraints to ensure physical consistency. Here, we introduce the Input Specific Neural Network (ISNN), a new architecture that enables multiple, distinct constraints to be imposed on different input subsets for scalar-valued outputs. This framework unifies convex, monotone–convex, monotone, and arbitrary mappings within a single network for the first time. Two ISNN architectures with analytical first- and second-order derivatives are developed. We demonstrate the performance on synthetic toy problems, inverse problems in isotropic hyperelasticity, and finite element simulations. ISNNs achieve improved extrapolation behavior, require fewer invariant inputs than standard input convex networks for polyconvex potentials, and enable significant computational savings via manual differentiation. We also show how ISNNs can be used to learn structural relationships between inputs and outputs via a binary gating mechanism. Particularly, ISNNs are employed to model a homogenized anisotropic free energy potential in a decoupled multiscale setting. The network learns whether or not the potential should be modeled as polyconvex and retains only the relevant layers while using the minimum number of inputs. ISNNs provide a flexible foundation for embedding structural priors into neural networks, enhancing both interpretability and stability. They are broadly applicable across computational mechanics and other scientific domains requiring constrained functional relationships.

Jadoon, Asghar A. [Univ. of Texas, Austin, TX (Uni↗
DOE OSTI2025

Determining anisotropic slip system rate sensitivities of Ti-6Al-4V using high-energy X-ray diffraction microscopy

Variation of strain rate sensitivity among different families of slip systems in the hexagonal close-packed (α) phase of titanium (Ti) alloys has the potential to alter microscale load redistribution during both creep and dwell fatigue loading. However, existing literature contains conflicting reports regarding the degree of anisotropy present in the strain rate sensitives between α slip system families across Ti alloys. Here, we quantify the strain rate sensitivity of α slip system families in Ti-6Al-4V using high-energy X-ray diffraction microscopy (HEDM). We present a novel procedure in which we utilize the HEDM-measured grain-scale stress states during stress relaxation to determine the strain rate sensitivities of the basal $\langle a \rangle$ , prismatic $\langle a \rangle$, and first-order pyramidal $\langle c + a \rangle$ slip system families. In addition, we measure the strain rate sensitivity exponent of the macroscopic response and the α phase for comparison. We find rate sensitivities for the different slip system families to range from 0.02 to 0.04, which—while varied—are relatively isotropic in comparison with some values presented in the literature. We also demonstrate the effects of the measured anisotropic rate sensitivities using crystal plasticity finite element simulations.

Peterson, Kenneth M. [Pennsylvania State Univ., Un↗
DOE OSTI2020

Triaxial Constraint and Tensile Strength Enhancement in Brazed Joints

A brazed joint consists of a low-melting point and thin interlayer sandwiched between the high-melting-point base materials, in which the interlayer strength is typically lower than that of the base material. When this butt-joined composite is loaded uniaxially in the direction perpendicular to the plane of the brazing layer, the tensile strength is found to be much higher than that of the braze. This seems to violate the iso-stress condition in such a butt-joint serial configuration. Furthermore, the stress triaxiality has been usually ascribed, but without a quantitative rationalization, as being responsible for this tensile strength enhancement. Here a complete finite element simulation has been conducted to study the dependence of triaxiality and strength enhancement on geometric and material parameters. Two asymptotic limit solutions (based on Bridgman and Xia–Shih solutions, respectively) have been identified to understand the simulation results. The critical role of void evolution has been revealed when making a quantitative comparison to available experiments. In addition, ductility of the brazed joint, which has not been fully addressed in literature, is investigated by the Gurson–Tvergaard–Needleman model.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Particle size effects in metallic microparticle impact-bonding

In cold spray processing of metals, the critical velocity for particle bonding is dependent on both intrinsic material properties, such as density and (spall) strength, and extrinsic process parameters, such as the powder particle size. Here, we specifically isolate and investigate particle size effects on the critical velocity for bonding through laser- induced single particle impact experiments and finite element simulations. We also present a predictive framework to correlate particle size and critical velocity. We show that an increase in particle size leads to an increase in the temperature of the jet formed at the interface of the particle and the substrate. This increased temperature locally decreases the spall strength of the material which, in turn, decreases the critical velocity for larger particles.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multiscale mechanical design of the lightweight, stiff, and damage-tolerant cuttlebone: A computational study

Cuttlebone, the endoskeleton of cuttlefish, offers an intriguing biological structural model for designing low-density cellular ceramics with high stiffness and damage tolerance. Cuttlebone is highly porous (porosity ~93%) and lightweight (density less than 20% of seawater), constructed mainly by brittle aragonite (95 wt%), but capable of sustaining hydrostatic water pressures over 20 atmospheres and exhibits energy absorption capability under compression comparable to many metallic foams (~4.4 kJ/kg). Here, in this work, we computationally investigate how such remarkable mechanical efficiency is enabled by the multiscale structure of cuttlebone. Using the common cuttlefish, Sepia Officinalis, as a model system, we first conducted high-resolution synchrotron micro-computed tomography (µ-CT) and quantified the cuttlebone's multiscale geometry, including the 3D asymmetric shape of individual walls, the wall assembly patterns, and the long-range structural gradient of walls across the entire cuttlebone (ca. 38 chambers). The acquired 3D structural information enables systematic finite-element simulations, which further reveal the multiscale mechanical design of cuttlebone: at the wall level, wall asymmetry provides optimized energy absorption while maintaining high structural stiffness; at the chamber level, variation of walls (number, pattern, and waviness amplitude) contributes to progressive damage; at the entire skeletal level, the gradient of chamber heights tailors the local mechanical anisotropy of the cuttlebone for reduced stress concentration. Our results provide integrated insights into understanding the cuttlebone's multiscale mechanical design and provide useful knowledge for the designs of lightweight cellular ceramics.

36 MATERIALS SCIENCE↗
DOE OSTI2023

An assessment of the utility of multirate time integration for the modeling of laser powder bed fusion

Finite element simulation of the laser powder bed fusion process is made challenging by the inherently multiscale nature of the process. When using the typical global time stepping techniques, slowly-evolving regions of the domain receive the same numerical treatment as the regions with the highest temperature rates. The current work details the implementation and evaluation of an implicit multirate method which is able to advance different regions of the domain with distinct time steps, depending on their current solution rate. Previous work indicates that this representation of the temporal scales of the problem can lead to significant reductions in wall clock run time, and it is shown herein that multirate time integration, when used with a uniform process-scale mesh, can result in speedups of approximately 19, 42, and 87 times, for domains with edge lengths of approximately 1 mm, 2 mm, and 5 mm, respectively. When used in conjunction with $\ h$-refinement (limited to two levels of refinement), the resulting speedups (taken relative to the uniform mesh) are around 28, 85, and 665 times. The method is demonstrated to converge as indicated by the literature, and its use with an AM-Bench domain is demonstrated.

42 ENGINEERING↗
DOE OSTI2025

Energy absorption of architectured PrintCast interpenetrating composites in tension

Additively manufactured (AM) metal-metal composites consisting of PrintCasted 316 L austenitic stainless-steel lattice structures infiltrated with A356 casting alloy, have recently been developed for use in high energy absorption systems with potential applications ranging from static load bearing to dynamic blast containment structures. This system has a unique mechanical behavior as the volume fraction of lattice increases showing a transition from localized to de-localized failure and dramatic increase in energy absorption capability. In this work, PrintCast A356/316 L composite tensile specimens were produced with lattice volume fractions ranging from 20 % to 50 % to capture the range of this behavior. Finite element simulations support neutron diffraction measurements of stress state. Results illustrate that in tension, the reinforcement material is in tension while the matrix support material is in compression, information offering significant insight into the transition to de-localized failure. Moreover, the simulation results provide further insight into how interfacial bonding (or lack of bonding) affects the energy absorption capabilities of the PrintCast composites.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Effects of edge-seal design on the mechanical and thermal performance of vacuum-insulated glazing

Although vacuum-insulated glazing (VIG) has been proposed as a promising solution towards developing energy-efficient buildings, VIGs have not become popular in the market due to several technical challenges including the complexity of the fabrication process. In particular, the edge-seal is a key component that significantly affects the thermal insulation and mechanical performance, and the development of edge-seal with adequate thermal insulation, mechanical strength, and reasonable processing cost is essential to overcome such technical issues in VIG. For this purpose, effects of edge-seal design parameters on the VIG performance should be identified. In this research, we analyzed the edge-seal for thermal transport as well as structural stresses to study the effects, and then identified and evaluated the material mixes for the edge-seal requirements. Here, the finite element simulations showed the significance of VIG corner calculation on overall thermal transmittance and the importance of seal conductivity below 1 W/m.K. The experiments with the flexible seals with different ratios of fine glass powder demonstrated that the measured shear strength values for the seal with less than 30% glass powder were more than 10 times larger than the calculated shear stress values. Based on these simulation and experimental results, a flexible sealant was developed using a proprietary mix of ceramic materials that meets the requirements of the designed VIG edge-seal, including structural as well as thermal stress resistance and a low conductivity. Moreover, the sealant is self-curing under atmospheric conditions, and thus it does not require costly inline process of laser curing or oven baking.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties

Here we introduce a denoising diffusion algorithm to discover microstructures with nonlinear fine-tuned properties. Denoising diffusion probabilistic models are generative models that use diffusion-based dynamics to gradually denoise images and generate realistic synthetic samples. By learning the reverse of a Markov diffusion process, we design an artificial intelligence to efficiently manipulate the topology of microstructures to generate a massive number of prototypes that exhibit constitutive responses sufficiently close to designated nonlinear constitutive behaviors. To identify the subset of microcstructures with sufficiently precise fine-tuned properties, a convolutional neural network surrogate is trained to replace high-fidelity finite element simulations to filter out prototypes outside the admissible range. Results of this study indicate that the denoising diffusion process is capable of creating microstructures of fine-tuned nonlinear material properties within the latent space of the training data. More importantly, this denoising diffusion algorithm can be easily extended to incorporate additional topological and geometric modifications by introducing high-dimensional structures embedded in the latent space. Numerical experiments are conducted on the open-source mechanical MNIST data set (Lejeune, 2020). Consequently, this algorithm is not only capable of performing inverse design of nonlinear effective media, but also learns the nonlinear structure–property map to quantitatively understand the multiscale interplay among the geometry, topology, and their effective macroscopic properties.

42 ENGINEERING↗
DOE OSTI2024

Predicting non-linear stress–strain response of mesostructured cellular materials using supervised autoencoder

Recent breakthroughs in advanced manufacturing capabilities have made it possible to design and print sophisticated topologies of cellular structures using diverse engineering materials such as metals, polymers, and ceramics. In these architectured materials, it is often desirable to tailor the mechanical properties by altering the unit cell topology. This necessitates an in-depth understanding of how the topology of the unit cell structure affects the macroscopic behavior of the material in both the linear and the non-linear regimes encountered under large compression. Here, we have developed a machine learning (ML) approach capable of accelerating the prediction of the stress–strain response of a polymer-based cellular structure under uniaxial confined compression. As part of generating the training data for ML, 60,000 mesostructures were generated using a relatively novel approach based on cellular automata, and their corresponding stress–strain responses were obtained from the finite element simulations. Principal component analysis (PCA) was used to reduce the dimensionality of the stress–strain curves. With only 20 principal components, PCA captured 99.89% of the variance in the stress–strain curves while reducing the dimensionality by 5X. ML using supervised autoencoder was able to successfully speed up the prediction of the non-linear stress–strain response of a unit cell by up to 4600X. The proposed method can serve as an efficient data generation tool and a rapid means for predicting the structure–property relationship through accelerated forward modeling of cellular materials under compaction, in cases where the macroscopic stress–strain response is governed by the unit-cell topology.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Stochastic nonlinear analysis of unidirectional fiber composites using image-based microstructural uncertainty quantification

Here, we present a data-driven nonlinear uncertainty quantification and propagation framework to study the microstructure-induced stochastic performance of unidirectional (UD) carbon fiber reinforced polymer (CFRP) composites. The proposed approach integrates (1) microscopic image characterization, (2) stochastic microstructure reconstruction, and (3) efficient multiscale finite element simulations enabled by self-consistent clustering (SCA) analysis. To model the complex microstructural variability, the proposed UQ methods take the non-Gaussian uncertainty sources into account through a distribution-free sampling approach leveraging nonparametric and asymptotic statistical tools. A hierarchical conditional sampling strategy enables the simultaneous sampling of multiple sources of uncertainties. Our approach provides insights into the impact of microstructural variabilities, which are shown to have an increasing impact on the nonlinear responses of UD CFRP parts under progressive compression loading and ultimately on the failure rate over time. We discover that before CFRP parts start to fail, a characteristic time period emerges with distinctive uncertainty distributions specific to the microstructure variability and the probability of failure. Identifying the failure time period is crucial to the reliability prediction, which is an essential component of CFRP design.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Performance assessment of 3D printed multi-material energy absorber for automotive bumper: pedestrian lower extremity protection

Designing an energy absorber for automotive bumpers involves balancing low-speed and high-speed impacts to ensure safety, reduce repair costs, and meet regulatory standards. Here, this study explores a novel design using multi-material 3D printing and structural optimization to fabricate a lightweight and cost-efficient energy absorber. The design effectively dissipates energy in low-speed collisions and minimizes force transmission in high-speed pedestrain impacts, helping to meet both safety and performance requirements. The energy absorber design combines 20% carbon fiber-reinforced acrylonitrile butadiene styrene (CF-ABS) and thermoplastic polyurethane (TPU) for optimal stiffness and flexibility. It uses 3D-printed lattice structures optimized through finite element simulations to help meet both low-speed and high-speed impact requirements. Full-scale energy absorbers were 3D-printed using optimized CF-ABS/TPU blends and tested under high-speed impact using the Flexible Pedestrian Legform Impactor (Flex-PLI). For fair comparison, a baseline bumper with a traditional triangular lattice structure, also 3D-printed from the same CF-ABS/TPU materials, was similarly tested. Interestingly, both the optimized and baseline 3D-printed energy absorbers showed nearly identical performance, successfully meeting injury limits. Their performances were also benchmarked against an injection-molded energy absorber. While both 3D-printed and injection-molded designs met injury limits, the 3D-printed absorber exhibited a higher tibia bending moment, indicating an opportunity for further optimization. A Techno-Economic Analysis compared the costs of producing energy absorbers using traditional manufacturing and 3D printing. The analysis highlighted that 3D printing offers cost benefits for low to medium production volumes, with the total cost per energy absorber at ∼ $\$$74, compared to traditional methods that become economical beyond 2000 units.

Additive manufacturing↗