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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Ab initio density matrix renormalization group study of magnetic coupling in dinuclear iron and chromium complexes

The applicability of ab initio multireference wavefunction-based methods to the study of magnetic complexes has been restricted by the quickly rising active-space requirements of oligonuclear systems and dinuclear complexes with S > 1 spin centers. Ab initio density matrix renormalization group (DMRG) methods built upon an efficient parameterization of the correlation network enable the use of much larger active spaces, and therefore may offer a way forward. Here, we apply DMRG-CASSCF to the dinuclear complexes [Fe{sub 2}OCl{sub 6}]{sup 2−} and [Cr{sub 2}O(NH{sub 3}){sub 10}]{sup 4+}. After developing the methodology through systematic basis set and DMRG M testing, we explore the effects of extended active spaces that are beyond the limit of conventional methods. We find that DMRG-CASSCF with active spaces including the metal d orbitals, occupied bridging-ligand orbitals, and their virtual double shells already capture a major portion of the dynamic correlation effects, accurately reproducing the experimental magnetic coupling constant (J) of [Fe{sub 2}OCl{sub 6}]{sup 2−} with (16e,26o), and considerably improving the smaller active space results for [Cr{sub 2}O(NH{sub 3}){sub 10}]{sup 4+} with (12e,32o). For comparison, we perform conventional MRCI+Q calculations and find the J values to be consistent with those from DMRG-CASSCF. In contrast to previous studies, the higher spin states of the two systems show similar deviations from the Heisenberg spectrum, regardless of the computational method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

Architecture and design of third Qinshan nuclear power plant risk monitor

Risk monitor is a real-time analysis tool to determine the point-in-time risk based on actual plant configuration, which is an important application of PSA (Probabilistic Safety Assessment). In this study the status and development trend of risk monitor were investigated and a risk monitor named TQRM (Third Qinshan nuclear power plant Risk Monitor) was developed. The B/S architecture and the two key computing methods pre-solved and resolving PSA model method adopted in TQRM were introduced. The functions and technical features were also presented. Now TQRM has been on-line for more than one year and used in the operation and maintenance of TQNPP. The experience demonstrates that TQRM's results are accurate and real-time, the architecture is stable, and it could be extended and maintained conveniently for any other Risk-Informed Application. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Classification of computed thermal tomography images with deep learning convolutional neural network

Thermal tomography (TT) is a computational method for the reconstruction of depth profile of the internal material defects from Pulsed Infrared Thermography (PIT) nondestructive evaluation. Here, the PIT method consists of recording material surface temperature transients with a fast frame infrared camera, following thermal pulse deposition on the material surface with a flashlamp and heat diffusion into material bulk. TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of the subsurface internal defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to the heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify the size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. The performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shapes imported from scanning electron microscopy images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on the TT images of elliptical defects is capable of classifying the shape and orientation of irregular defects.

42 ENGINEERING↗
DOE OSTI2020

Determining Multi-Component Phase Diagrams with Desired Characteristics Using Active Learning

Herein, we demonstrate how to predict and experimentally validate phase diagrams for multi-component systems from a high-dimensional virtual space of all possible phase diagrams involving several elements based on small existing experimental data. The experimental data for bulk phases for known systems represents a sampling from this space, and screening the space allows multi-component phase diagrams with given design criteria to be built. This approach uses machine learning methods to predict phase diagrams and Bayesian experimental design to minimize experiments for refinement and validation, all within an active learning loop. The approach is proven by predicting and synthesizing the ferroelectric ceramic system (1-ω)(Ba 0.61 Ca 0.28 Sr 0.11 TiO 3 )-ω(BaTi 0.888 Zr 0.0616 Sn 0.0028 Hf 0.0476 O 3 ) with a relatively high transition temperature and triple point, as well as the NiTi-based pseudo-binary phase diagram (1-ω)(Ti 0.309 Ni 0.485 Hf 0.20 Zr 0.006 )-ω(Ti 0.309 Ni 0.485 Hf 0.07 Zr 0.068 Nb 0.068 ) designed for high transition temperature (ω ≤ 1). Each phase diagram is validated and optimized through only three new experiments. The complexity of these compounds is beyond the reach of today’s computational methods.

36 MATERIALS SCIENCE↗
DOE OSTI2018

An evaluation of point-insertion sequences for incremental Delaunay tessellations

Currently, incremental algorithms may be seen as the lowest-cost computational methods to generate Delaunay tessellations in several point distributions. In this work, eight point-insertion sequences in incremental algorithms for generating Delaunay tessellations are evaluated. More specifically, four point-insertion sequences in incremental algorithms for generating Delaunay tessellations are proposed: with orders given by the red–black tree with in-order and level-order traversals, spiral ordering, and H-indexing. These four incremental algorithms with such sequences are compared with four incremental algorithms with point-insertion orders given by the following sequences: the Hilbert and Lebesgue curves, cut-longest-edge kd-tree, and random order. Six 2-D and seven 3-D point distributions are tested, with sets ranging from 25,000 to 8,000,000 points. The results of computational and storage costs of these eight algorithms are analyzed. It follows that the incremental algorithm with a point-insertion sequence in the order given by the cut-longest-edge kd-tree shows the lowest computational and storage costs of the sequences tested.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

CareWELL: Multimodal Region Representation Learning with Spatial Contexts for Urban Health

Rapid urbanization affects living environments by intensifying exposure to air pollution, heat, noise, and urban dynamics, which together contribute to uneven health outcomes across neighborhoods. For instance, cardiovascular, respiratory, and mental health conditions are each influenced by distinct exposures such as air pollution, extreme temperatures, or limited access to green space. These heterogeneous patterns require understanding the characteristics of geographic regions in order to explain why urban health risks vary across urban areas. Recent work in self-supervised region representation learning provides a promising way to model such characteristics from multimodal geospatial data. However, existing methods face two major limitations: (i) they often depend on non-public datasets, limiting reproducibility and applicability, and (ii) their generic pretraining objectives overlook health-relevant determinants, including temporal variability in environmental exposures and inequalities in social conditions. To address these gaps, we propose Context-Aware Region rEpresentation with Weather, Environment, and Location Learning (CareWELL). CareWELL leverages large language models to encode seasonal variability in weather, employs contrastive learning to align geo-coordinate and weather representations, and introduces a context-aware objective that integrates socio-demographic factors while preserving spatial correlations. We evaluate CareWELL by predicting six urban health outcomes in Manhattan, New York City, and demonstrate that CareWELL consistently outperforms state-of-the-art baselines as well as a traditional spatial computing method. These results suggest the importance of context-aware pretraining objectives for learning health-relevant region representations.

Namgung, Min [ORNL]↗
DOE OSTI2017

OECD/NEA EGMPEBV Activities in Multi-Physics Verification and Validation

The Expert Group on Multi-Physics Experimental Data, Benchmarks and Validation (EGMPEBV) was established in 2014 by the Nuclear Energy Agency (NEA) at the Organization for Economic Co-operation and Development (OECD) to establish processes for certification of experimental data and development of benchmark models for validation of Multi-Physics (MP) Modeling and Simulation (M and S) computational systems. The aim of the group is to provide member countries with guidelines and recommendations for validating and improving their novel multi-physics simulations, and with an access to key experimental data. To be used to their full potential, these novel tools will require a more complex suite of validation experiments due to the multiple energy, spatial and temporal scales, as well as the number of physical phenomena being simulated. They will also require accurate measurements of all terms, including coupling terms representing the feedback effects, in situations where the appropriate experimental techniques and facilities might not exist today. However, the ability to conduct validation experiments has for certain applications progressed at a slower pace than for computational methods, or in some cases it has significantly regressed (through shutdown of facilities, or through retirement of experts). In addition, as modelling capabilities are reaching deeper levels in single physics phenomena and in coupled behaviors, appropriate experimental techniques may not yet exist. Validation of multiple physics models requires a wide variety of experimental data, which emphasizes the importance of maximizing the use of historically accumulated data to avoid the significant cost of performing similar experiments today. Moreover, the pedigree and quality of this data as it pertains to modern day simulation methods may be insufficient for rigorous validation of high fidelity modeling and simulation tools. Thus, these data can often exhibit limitations including a lack of measurement accuracy or a lack of knowledge of the measurement uncertainty in the experimental techniques; an absence of measurements for some critical parameters; an obscuring of relationships between individual physics phenomena owing to the output data resolution; or simply an inability to allow for a detailed reinterpretation of the experiments based on the documented information. The organization of the EGMPEBV relies on three Task Forces, focused on 1) Experimental Data Qualification and Benchmark Evaluation; 2) Methods and Standards, and 3) Specific Applications. Task Force 1 is focused on providing better and more accurate experimental datasets, which support validation of high-fidelity multi-physics modelling and simulation (M and S) tools. At the same time it has to consider the incompleteness of past experimental data sets (in terms of data, documentation, or uncertainties) and with the limited number of available multi-physics experimental facilities. Task Force 2 is designed to develop validation methods and guidelines, and uncertainty qualification for the new family of multi-physics, multi-scale codes in the context of emerging demands such as longer fuel cycles and power uprate. Task Force 3 is focused on applications of validation experiments and has the objective to organize multi-physics M and S benchmarks involving validation experiments.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2013

Density functional theory study of the interaction of vinyl radical, ethyne, and ethene with benzene, aimed to define an affordable computational level to investigate stability trends in large van der Waals complexes

Our purpose is to identify a computational level sufficiently dependable and affordable to assess trends in the interaction of a variety of radical or closed shell unsaturated hydro-carbons A adsorbed on soot platelet models B. These systems, of environmental interest, would unavoidably have rather large sizes, thus prompting to explore in this paper the performances of relatively low-level computational methods and compare them with higher-level reference results. To this end, the interaction of three complexes between non-polar species, vinyl radical, ethyne, or ethene (A) with benzene (B) is studied, since these species, involved themselves in growth processes of polycyclic aromatic hydrocarbons (PAHs) and soot particles, are small enough to allow high-level reference calculations of the interaction energy ΔE{sub AB}. Counterpoise-corrected interaction energies ΔE{sub AB} are used at all stages. (1) Density Functional Theory (DFT) unconstrained optimizations of the A−B complexes are carried out, using the B3LYP-D, ωB97X-D, and M06-2X functionals, with six basis sets: 6-31G(d), 6-311 (2d,p), and 6-311++G(3df,3pd); aug-cc-pVDZ and aug-cc-pVTZ; N07T. (2) Then, unconstrained optimizations by Møller-Plesset second order Perturbation Theory (MP2), with each basis set, allow subsequent single point Coupled Cluster Singles Doubles and perturbative estimate of the Triples energy computations with the same basis sets [CCSD(T)//MP2]. (3) Based on an additivity assumption of (i) the estimated MP2 energy at the complete basis set limit [E{sub MP2/CBS}] and (ii) the higher-order correlation energy effects in passing from MP2 to CCSD(T) at the aug-cc-pVTZ basis set, ΔE{sub CC-MP}, a CCSD(T)/CBS estimate is obtained and taken as a computational energy reference. At DFT, variations in ΔE{sub AB} with basis set are not large for the title molecules, and the three functionals perform rather satisfactorily even with rather small basis sets [6-31G(d) and N07T], exhibiting deviation from the computational reference of less than 1 kcal mol{sup −1}. The zero-point vibrational energy corrected estimates Δ(E{sub AB}+ZPE), obtained with the three functionals and the 6-31G(d) and N07T basis sets, are compared with experimental D{sub 0} measures, when available. In particular, this comparison is finally extended to the naphthalene and coronene dimers and to three π−π associations of different PAHs (R, made by 10, 16, or 24 C atoms) and P (80 C atoms)

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

SU-E-T-122: Anisotropic Analytical Algorithm (AAA) Vs. Acuros XB (AXB) in Stereotactic Treatment Planning

Purpose: To evaluate dosimetric differences between superposition beam model (AAA) and determinant photon transport solver (AXB) in lung SBRT and Cranial SRS dose computations. Methods: Ten Cranial SRS and ten Lung SBRT plans using Varian, AAA -11.0 were re-planned using Acuros -XB-11.0 with fixed MU. 6MV photon Beam model with HD120-MLC used for dose calculations. Four non-coplanar conformal arcs used to deliver 21Gy or 18Gy to SRS targets (0.4 to 6.2cc). 54Gy (3Fractions) or 50Gy (5Fractions) was planned for SBRT targets (7.3 to 13.9cc) using two VAMT non-coplanar arcs. Plan comparison parameters were dose to 1% PTV volume (D1), dose to 99% PTV volume( D99), Target mean (Dmean), Conformity index (ratio of prescription isodose volume to PTV), Homogeneity Index [ (D2%-D98%)/Dmean] and R50 (ratio of 50% of prescription isodose volume to PTV). OAR parameters were Brain volume receiving 12Gy dose (V12Gy) and maximum dose (D0.03) to Brainstem for SRS. For lung SBRT, maximum dose to Heart and Cord, Mean lung dose (MLD) and volume of lung receiving 20Gy (V20Gy) were computed. PTV parameters compared by percentage difference between AXB and AAA parameters. OAR parameters and HI compared by absolute difference between two calculations. For analysis, paired t-test performed over the parameters. Results: Compared to AAA, AXB SRS plans have on average 3.2% lower D99, 6.5% lower CI and 3cc less Brain-V12. However, AXB SBRT plans have higher D1, R50 and Dmean by 3.15%, 1.63% and 2.5%. For SRS and SBRT, AXB plans have average HI 2 % and 4.4% higher than AAA plans. In both techniques, all other parameters vary within 1% or 1Gy. In both sets only two parameters have P>0.05. Conclusion: Even though t-test results signify difference between AXB and AAA plans, dose differences in dose estimations by both algorithms are clinically insignificant.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Efficient core-excited state orbital perspective on calculating x-ray absorption transitions in determinant framework

X-ray absorption spectroscopy (XAS) is an explicit probe of the unoccupied electronic structure of materials and an invaluable tool for fingerprinting various electronic properties and phenomena. Computational methods capable of simulating and analyzing such spectra are therefore in high demand for complementing experimental results and for extracting valuable insights therefrom. In particular, a recently proposed first-principles approach titled many-body XAS (MBXAS), which approximates the final (initial) state as a Slater determinant constructed from Kohn-Sham (KS) orbitals optimized in the absence (presence) of the relevant core electron has shown promising prospects in evaluating the transition amplitudes. In this article, we show that the MBXAS approach can be rederived using a transition operator expressed entirely in the basis of core-excited state KS orbitals and that this reformulation offers substantial practical and conceptual advantages. In addition to circumventing previous issues of convergence with respect to the number of unoccupied ground-state orbitals, the aforementioned representation reduces the computational expense by rendering the calculation of such orbitals unnecessary altogether. Furthermore, the reformulated approach also provides a direct pathway for comparing the many-body approximation with the so-called single-particle treatment and indicates the relative importance in observed XAS intensity of the relaxation of the valence occupied subspace induced by the core excitation. Finally, using the core-excited state basis, we define auxiliary orbitals for x-ray absorption and demonstrate their utility in explaining the spectral intensity by contrasting them with single-particle approximations to the excited state.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Durable Mn-Based PGM-Free Catalysts for Polymer Electrolyte Membrane Fuel Cells

This proposed project aims to develop and evaluate novel manganese based, nitrogen-derived, PGM-free electrocatalysts (denoted as Mn-N-C) to fully address the membrane electrolyte assemblies (MEA)’s ionomer degradation issue resulting from iron. Four thrusts will be pursed in this proposed project. First, advanced first-principles computation methods will be employed to accelerate the rational catalyst design and synthesis. Second, an effective hydro-gel method will be used to maximize atomic Mn active sites embedded in carbon matrix. Next, state-of-the art methods in fuel cell companies will be used to fabricate MEAs containing the Mn-N-C catalysts. Finally, industry standards will be rigorously followed to evaluate fuel cell performance and durability of the Mn-N-C catalysts. With successful completion of the project, it is expected that the following outcomes will be achieved. (1) A set of MEAs containing the Mn-N-C catalysts and with active area large than 50 cm 2 for independent testing, (2) testing results demonstrating that the MEAs of Mn-N-C catalysts have mass activity of 0.044 A/cm 2 at 0.9 VIR-free and H 2 /air performance of 0.50 V at 1.0 A/cm 2 ; (3) fundamental understanding of the composition-structure-property relation of the PGM-free Mn- N-C catalysts, and (4) computational data, measurement data, and publications deposited into the database of ElectroCat Consortium.

08 HYDROGEN↗
DOE OSTI2015

Atomistic to continuum modeling of solidification microstructures

We summarize recent advances in modeling of solidification microstructures using computational methods that bridge atomistic to continuum scales. We first discuss progress in atomistic modeling of equilibrium and non-equilibrium solid–liquid interface properties influencing microstructure formation, as well as interface coalescence phenomena influencing the late stages of solidification. The latter is relevant in the context of hot tearing reviewed in the article by M. Rappaz in this issue. We then discuss progress to model microstructures on a continuum scale using phase-field methods. We focus on selected examples in which modeling of 3D cellular and dendritic microstructures has been directly linked to experimental observations. Finally, we discuss a recently introduced coarse-grained dendritic needle network approach to simulate the formation of well-developed dendritic microstructures. The approach reliably bridges the well-separated scales traditionally simulated by phase-field and grain structure models, hence opening new avenues for quantitative modeling of complex intra- and inter-grain dynamical interactions on a grain scale.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Optimization of Diode Laser System to Treat Benign Prostate Hyperplasia Final Report CRADA No. TSB-1154-95

Benign prostate hyperplasia (BPH) is a pervasive condition of enlargement of the male prostate gland which leads to several urinary difficulties ranging from hesitancy to incontinence to kidney dysfunction in severe cases. Currently the most common therapy is transurethral resection of the prostate (TURP) utilizing an electrosurgical device. Although TURP is largely successful, new BPH therapy methods are desired to reduce the cost and recovery time, improve the success rate, and reduce side effects. Recently, lasers have been introduced for this purpose. Indigo Medical Inc. is currently engaged in the development, testing, and preparation for sales of a new diode laser based BPH therapy system. The development is based on laboratory experiments, animal studies, and a limited FDA-approved clinical trial in the US and in other countries. The addition of sophisticated numerical modeling, of the sort that has been highly developed at Lawrence Livermore National Laboratory, can greatly aid in the design of the system and treatment protocol. The benefits to DOE include the maintenance and advancement of numerical modeling expertise in radiation-matter interactions of the sort essential for the stockpile stewardship, inertial confinement fusion, and advanced manufacturing, and the push on advanced scientific computational methods, ultimately in areas such as 3-D transport.

42 ENGINEERING↗
DOE OSTI2016

Correlative characterization of primary particles formed in as-cast Al-Mg alloy containing a high level of Sc

Primary particles formed in as-cast Al-5Mg-0.6Sc alloy and their role in microstructure and mechanical properties of the alloy were investigated using optical microscopy (OM), scanning electron microscopy (SEM), electron back-scatter diffraction (EBSD) and tensile testing. It was found that primary particles due to a close orientation to matrix could serve as the potent heterogeneous nucleation sites for α-Al during solidification and thus impose a remarkable grain refinement effect. Eutectic structure consisted of layer by layer of ‘Al{sub 3}Sc + α-Al + Al{sub 3}Sc + ⋯’ and cellular-dendritic substructure were simultaneously observed at the particles inside, indicating that these particles could be identified as the eutectics rather than individual Al{sub 3}Sc phase. A calculating method, based on EBSD results, was introduced for the spatial distribution of these particles in matrix. The results showed that these eutectic particles randomly distributed in matrix. In addition, the formation of primary eutectic particles significant improved the strength of the Al-Mg alloy in as-cast condition, which is ascribed to the structural evolution from coarse dendrites to prefect fine equiaxed grains. On the other hand, these large-sized particles due to the tendency to act as the microcrack sources could cause a harmful effect in the ductility of Al-Mg-Sc alloy. - Highlights: •Primary particles exhibit an ‘Al{sub 3}Sc + α-Al + Al{sub 3}Sc + ⋯’ multilayer feature with a cellular-dendritic mode of growth. •EBSD analyses the mechanism of grain refinement and the distribution of primary particles in α-Al matrix. •A computational method was presented to calculate the habit planes of primary particles.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Massively Parallel Bayesian Model Calibration and Uncertainty Quantification with Applications to Nuclear Fuels and Materials

The U.S. Department of Energy (DOE)’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel cycle systems. This program has been providing engineering-scale support for the development of BISON, a high-fidelity and high-resolution fuel performance tool. Fuel behavior in a nuclear reactor is governed by a complex network of mechanisms interacting with various other physics aspects in the reactor system. Any model developed to represent the fuel behavior will likely be idealized resulting in uncertainties in their predictions compared to the observed data. As such, this report was motivated by the need to identify the sources of uncertainties and quantify and propagate them through the fuel model outputs. Such quantification of uncertainties will establish a level of model trustworthiness, identify approaches to improve the model trustworthiness, and even guide optimal experiment design for maximal information gain. To accomplish the uncertainty quantification for computational models, this report has relied on the Bayesian framework which provides probabilistic treatment of models their inputs and outputs. The current state-of-the-art on performing Bayesian Uncertainty Quantification (UQ) for nuclear engineering models using High Performance Computing (HPC) resources have been reviewed. Implementation of capabilities for massively parallel Bayesian UQ in Multiphysics Object-Oriented Simulation Environment (MOOSE) is discussed. Several verification cases are discussed to verify the accuracy of the quantified uncertainties using the developed computational capabilities in MOOSE. Then, the problem of quantifying the uncertainties in TRI-Structural isOtropic (TRISO) fuel silver release is addressed. For the first time, the uncertainties arising from the TRISO Fission Gas Release (FGR) model due to model inadequacy and experimental noise are quantified. Also, the Bayesian capabilities are applied to the calibration of the MATPRO creep model, a widely used model in several fuel assessment cases. The impact of the prediction uncertainties in the MATPRO model on the fuel cladding behavior as part of the TRIBULATION assessment case (which is an integral effects case) is investigated. This report concludes with a discussion on the future work for the UQ for computational models.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

Validation of a 2.5D CFD model for cylindrical gas–solids fluidized beds

The 2.5D model recently proposed by Li et al. (Li, T., Benyahia, S., Dietiker, J., Musser, J., and Sun, X., 2015. A 2.5D computational method to simulate cylindrical fluidized beds. Chemical Engineering Science. 123, 236-246.) was validated for two cylindrical gas-solids bubbling fluidized bed systems. Different types of particles tested under various flow conditions were simulated using the traditional 2D model and the 2.5D model. Detailed comparison against the experimental measurements on solid concentration and velocity were conducted. Comparing to the traditional Cartesian 2D flow simulation, the 2.5D model yielded better agreement with the experimental data especially for the solid velocity prediction in the column wall region.

Numerical simulation↗
DOE OSTI2021

Experimental and computational analysis of bending fatigue failure in chopped carbon fiber chip reinforced composites

With a better balance among good mechanical performance, high freedom of design, and low material and manufacturing cost, chopped carbon fiber chip reinforced sheet molding compound (SMC) composites show great potential in different engineering applications. Here in this paper, bending fatigue behaviors of SMC composites considering the heterogeneous fiber orientation distributions have been thoroughly investigated utilizing both experimental and computational methods. First, four-point bending fatigue tests are performed with designed SMC composites, and the local modulus is adopted as a metric to represent the local fiber orientation of two opposing sides. Interestingly, SMC composites with and without large discrepancy in local modulus of opposing sides show different fatigue behaviors. Interrupted tests are conducted to explore the bending fatigue failure mechanism, and the damage processes of valid specimens are also closely examined. We find that the fatigue failure of SMC composites under four-point bending is governed by crack propagation instead of crack initiation. Because of this, the heterogeneous local fiber orientations of both sides of the specimen influence fatigue life. The microstructure of the lower side shows a direct influence while that of the upper side also exhibiting influence which becomes more prominent for high cycle fatigue cases. Furthermore, a hybrid micro–macro computational model is proposed to efficiently study the cyclic bending behavior of SMC composites. The region of interest is reconstructed with a modified random sequential absorption algorithm to conserve all the microstructural details including the heterogeneous fiber orientation, while the rest of the regions are modeled as homogenized macro-scale continua. Combined with a framework to capture the progressive fatigue damage under cyclic bending, the bending fatigue behaviors of SMC composites are accurately captured by the hybrid computational model comparing with our experimental analysis.

36 MATERIALS SCIENCE↗