Accurate Prediction Of Response Research Articles

Spatial and Temporal Tumor Heterogeneity in Gastric Cancer: Discordance of Predictive Biomarkers.

Gastric cancer (GC) is a highly heterogeneous disease that varies in both histological presentation and genetic characteristics. Recent advances in the treatment of metastatic and unresectable GC have made several biomarker tests essential for patient management. Predictive biomarkers such as human epidermal growth factor receptor 2 (HER2), programmed death-ligand 1 (PD-L1), mismatch-repair (MMR) proteins, claudin 18.2, and fibroblast growth factor receptor 2b (FGFR2b) are commonly evaluated using immunohistochemistry. However, the expression levels of these biomarkers may vary across different tumor areas, and the accuracy of biomarker diagnosis can be affected by sample quantity, sample location, and collection method. Therefore, tumor heterogeneity presents substantial challenges for accurate biomarker-based diagnosis and prediction of therapeutic responses. Tumor heterogeneity can be categorized into spatial heterogeneity, which refers to variations within the primary tumor (intra-tumoral) or between primary and metastatic sites, and temporal heterogeneity, which encompasses changes over time. This review addresses the tumor heterogeneity in predictive biomarker expression in GC, focusing on HER2, PD-L1, MMR, the Epstein-Barr virus, claudin 18.2, and FGFR2b.

A machine learning based prediction model for the impact mechanical response of composite laminates considering microstructure sensitive transverse properties

AbstractThe accurate and efficient prediction of impact mechanical response is crucial for safety design of composite structures. In this work, high‐fidelity representative volume elements (RVEs) with fiber, matrix and fiber/matrix interface are established, in which random fiber distributions are considered. A failure envelope under transverse loads is proposed based on computational micromechanical RVEs, and it is implemented by ABAQUS VUMAT subroutines to predict the mechanical response of composite laminates under impact loads. Based on a dataset from computational macromechanical finite element simulations, an artificial neural network model is established and trained. It is found that the random fiber distribution introduced a more obvious fluctuation to tension/compression strength than shear strength. The proposed failure criteria showed a better performance than Hashin and Tsai‐Wu criteria especially in combined compression and shear loads. An ANN model with 8 hidden layers can achieve the prediction with an acceptable coefficient of determination (R2) 0.98 and loss functions of mean absolute error (MAE) 71. For certain impact loading conditions, the well trained machine learning model predicted impact contact force history within 30 min, while the FEA costs about more than 75 min on the same computer. The prediction speed is increased by over 60% for certain impact loading conditions. It is hence shown that this method provides a potential alternative for evaluation of the impact resistance of composite structures.Highlights High‐fidelity computational micromechanics analysis based on representative volume elements are performed to uncover the complex relationship between microstructure and transverse strengths of composite laminates. The microstructure dependent transverse strength criterion shows high accuracy in combined compression and shear loads compared with Hashin and Tsai‐Wu criteria. A multi‐layer artificial neural network model is established and trained for the rapid prediction of impact force history for composite laminates. The rapid prediction method is achieved with a coefficient of determination of 0.98, and the prediction speed is increased by more than 60% for certain impact loading conditions.

Identification of gene signatures relevant to the efficacy of immune checkpoint inhibitors in non-small cell lung cancer.

Despite significant advancements in the treatment of non-small cell lung cancer (NSCLC) through immunotherapy, many patients still exhibit resistance to this approach. This study aims to identify the characteristics of individuals who can benefit from immunotherapy, especially immune checkpoint inhibitors (ICIs), and to investigate optimal strategies for patients who experience resistance to it. Data on gene expression patterns and clinical information from NSCLC patients who underwent immunotherapy were obtained from the Gene Expression Omnibus databases. A predictive signature for immunotherapy prognosis was developed using a training dataset and validated with validation datasets. Immune landscape and immunotherapy responsiveness analyses were conducted to assess the risk signature. Additionally, data from a study on immunotherapy were used to evaluate the correlation between MNX1 mutation and the effectiveness of ICIs, including clinical data and whole exome sequencing data. We identified 7 genes in NSCLC using RNA-seq data that were significantly associated with the efficacy of immunotherapy. Based on these genes, a risk signature was created to predict the efficacy of ICIs. Patients in the low-risk group had better outcomes compared to those in the high-risk group after receiving ICIs. Additionally, our analysis of the immune landscape revealed a significant association between the high-risk signature and an immunosuppressive state. We also discovered an unexpected role of tumor-specific MNX1 and HOXD1 in suppressing the immune response against cancer. Notably, NSCLC patients with MNX1 mutations experienced prolonged progression-free survival. Furthermore, we identified several medications that exhibited increased sensitivity in patients with high MNX1 expression, with topoisomerase inhibitors showing the highest level of sensitivity. This could be a potential strategy to improve the efficacy of ICIs. The risk signature has demonstrated its effectiveness in forecasting the prognosis of NSCLC treated with ICIs, enabling better patient stratification and more accurate prediction of immunotherapy response. Moreover, MNX1 and HOXD1 have been identified as key molecules related to immunotherapy resistance. Inhibition of these molecules, combined with current ICIs, offers novel strategies for the management of NSCLC patients.

Predicting multi-parametric dynamics of an externally forced oscillator using reservoir computing and minimal data

AbstractMechanical systems exhibit complex dynamical behavior from harmonic oscillations to chaotic motion. The dynamics undergo qualitative changes due to changes to internal system parameters like stiffness and changes to external forcing. Mapping out complete bifurcation diagrams numerically or experimentally is resource-consuming, or even infeasible. This study uses a data-driven approach to investigate how bifurcations can be learned from a few system response measurements. Particularly, the concept of reservoir computing (RC) is employed. As proof of concept, a minimal training dataset under the resource constraint problem of a Duffing oscillator with harmonic external forcing is provided as training data. Our results indicate that the RC not only learns to represent the system dynamics for the external forcing seen during training, but it also provides qualitatively accurate and robust system response predictions for completely unknown multi-parameter regimes outside the training data. Particularly, while being trained solely on regular period-2 cycle dynamics, the proposed framework correctly predicts higher-order periodic and even chaotic dynamics for out-of-distribution forcing signals.

Matrix structural analysis of beams on elastic supports: implementation in Python

Technological resources have made it possible to model natural phenomena and engineering problems more accurately, thus allowing better results. This article presents the computational implementation of a procedure for matrix structural analysis of beams. Both rigid and elastic supports were considered to represent the contact between the structural model and the external environment. A computational code was created in Python language. The matrix method allows a detailed analysis of the structural response. The developed code calculates stiffness matrices and force vectors of beam elements, which are assembled into a global stiffness matrix and a global force vector, and the equilibrium equation system is then solved. Results such as nodal displacements and slopes, support reactions, and internal forces are obtained. Different support conditions, namely fixed, hinged, and elastic supports, as well as different loading conditions, were investigated for beams. Results highlight that the presence of elastic supports can significantly affect the overall structural behavior of the system. By incorporating these effects into the analysis, more accurate predictions of structural response can be obtained, leading to safer and more economical solutions. The article provides a basis for further investigation of the behavior of complex structural systems. The use of Python language can be justified as it provides a versatile and efficient platform for conducting structural analysis and is suitable for future advances in the field of structural engineering.

Machine learning-aided selection of CPT-based transformation models using field monitoring data from a specific project

Transformation models have been widely used in geotechnical engineering to relate data from lab or field tests (e.g., cone penetration tests, CPT) to design parameters required in geotechnical analysis and design. Proper selection of transformation models is crucial but challenging for accurate prediction of geotechnical responses (e.g., reclamation-induced settlement) in practice. This study proposes a general machine learning framework that accommodates a wide variety of existing CPT-based transformation models and uses field monitoring data (e.g., settlement data observed from a specific project) to select suitable transformation models for improving prediction of spatiotemporally varying reclamation-induced settlement. The proposed approach takes advantage of sparse dictionary learning (SDL) and achieves prediction of settlement by a linear weighted sum of dictionary atoms that are constructed using outputs from finite element models (FEM) of reclamation-induced consolidation. Input parameters of the FEM models are determined using existing transformation models in literature. A transformation model database that relates multiple soil consolidation parameters with CPT data is also compiled for consolidation analysis and dictionary construction in SDL. The proposed approach is illustrated using a real reclamation project in Hong Kong. Results show that the proposed approach provides an effective and transparent vehicle to leverage existing abundant transformation models, identify appropriate transformation models using field monitoring data, and improve prediction of spatiotemporally varying reclamation-induced settlement, with greatly reduced prediction uncertainty. The transformation model selection and settlement prediction are also improved continuously as more field monitoring data are obtained.

Real-time prediction of structural responses in deep-water jacket platforms using Ada-STLNet: A comprehensive analysis with prototype monitoring data

Real-time prediction of the mechanical behaviors based on prototype monitoring data is crucial for ensuring the integrity and safety of deep-water jacket platforms. As complex nonlinear systems, these platforms’ response exhibit varying temporal trends, dynamic spatial correlations, and load dependencies. This makes accurate prediction of future axial force and bending moment responses a significant challenge. In this paper, a novel deep learning method called Adaptive Spatio-Temporal Load Network (Ada-STLNet) is proposed aiming to address the issue of multi-node axial force and bending moment response prediction of deep-water jacket platform. Our model utilizes an enhanced graph convolution (EGCN) and self-attention mechanism for efficient spatial correlation modeling in multi-node response, LSTM for long sequence temporal feature capture. It integrates spatial and temporal, along with load-aware modules to capture intricate patterns in structural responses. Additionally, a multi-gated coupling mechanism with two independent gating modules is designed to adaptively represent the complex dependencies of structural responses, ensuring model stability and robustness. Extensive experiments conducted on prototype structural monitoring data from a deep-water jacket platform in the South China Sea demonstrate that Ada-STLNet outperforms six traditional models, including HA, SVR, KNN, MLP, LSTM, and GRU, across various prediction steps. The proposed model exhibits superior accuracy, particularly in short-term predictions, achieving up to 62.80% improvement in MAE and 48.11% in RMSE for axial force predictions compared to the second-best model. Ablation studies confirm the effectiveness of each component within Ada-STLNet. This research highlights the potential of Ada-STLNet for reliable and accurate prediction of structural responses, contributing significantly to the field of marine engineering.

Training of a physics-based thermo-viscoplasticity model on big data for polypropylene

Research on data-driven constitutive models has demonstrated their outstanding ability to provide highly accurate predictions of the general stress-strain response after learning from data only. Here, we demonstrate that physics-based models can equally benefit from training procedures relying on big data. Specifically, we employ the thermo-mechanically coupled viscoplasticity model [Anand, L., Ames, N.M., Srivastava, V., Chester, S., 2009. A thermo-mechanically coupled theory for large deformations of amorphous polymers. Part 1: Formulation. International Journal of Plasticity] to describe the large deformation response of polypropylene. It combines both mechanism-based evolution equations and high mathematical flexibility. More than 100 constant velocity and strain rate jump experiments are performed on flat tensile specimens extracted from 3 mm thick isotactic polypropylene sheets. The exact cross-sectional area is measured with surround DIC, while an IR camera monitored the surface temperature field. The experiments typically reached true strains greater than 0.8 and cover temperatures and strain rates ranging from 25 to 85 °C and 10–4 to 100s-1, respectively. Training over 100,000 unique random combinations of experiments is performed to identify all model parameters. The effect of the training (and testing) subsets size and composition is carefully analyzed to ensure a high generalization ability. It is found that training based on 26 randomly-selected experiments leads to the most robust parameter estimates. The obtained model performs remarkably well on all our experiments (among which 70 % are unseen during training) with a root mean square error of less than 1.5 MPa. As a byproduct, we also found that there exists a subset of two specific experiments for training that lead to an equally accurate model for polypropylene.

Brain structural connectomic topology predicts medication response in youth with bipolar disorder: A randomized clinical trial

BackgroundResponse to pharmacotherapy varies considerably among youths with bipolar disorder (BD) and is poorly predicted by clinical or demographic features. It can take several weeks to determine whether medication for BD is clinically effective. Although neuroimaging biomarkers are promising predictors, few studies examined the predictive value of the brain connectomic topology. MethodsBD-I youth (N = 121) with no prior psychopharmacotherapy were randomized to 6-weeks of double-blind quetiapine or lithium. Structural magnetic resonance imaging (MRI) was performed before medication and at one week after medication initiation. Brain structural connectome was established from the MRI scans, and topological metrics were calculated for each patient. Deep learning-based prediction model was built using baseline and one-week connectome topology to predict medication response at week 6. ResultsBoth baseline topological metrics and one-week topological changes could predict treatment response with significant accuracy (73.8 % - 86.8 %). A longitudinally joint model combining baseline and one-week topology provided the highest accuracy (91.3 %). The transferability between models for quetiapine and lithium was relatively poor. In addition, predictions for the two drugs were driven by similar baseline but distinct one-week salient topological patterns. LimitationsIndependent replication is needed to validate our preliminary findings. ConclusionBrain structural connectomic topology at baseline and its acute changes within the first week enable accurate BD medication response prediction. The most contributive brain regions differed between prediction models for quetiapine and lithium after one week. These findings provide preliminary evidence for the development of neuroimaging-based biomarkers for guiding therapeutic interventions in youth with BD.

DeepDR: a deep learning library for drug response prediction.

Accurate drug response prediction is critical to advancing precision medicine and drug discovery. Recent advances in deep learning (DL) have shown promise in predicting drug response; however, the lack of convenient tools to support such modeling limits their widespread application. To address this, we introduce DeepDR, the first DL library specifically developed for drug response prediction. DeepDR simplifies the process by automating drug and cell featurization, model construction, training, and inference, all achievable with brief programming. The library incorporates three types of drug features along with nine drug encoders, four types of cell features along with nine cell encoders, and two fusion modules, enabling the implementation of up to 135 DL models for drug response prediction. We also explored benchmarking performance with DeepDR, and the optimal models are available on a user-friendly visual interface. DeepDR can be installed from PyPI (https://pypi.org/project/deepdr). The source code and experimental data are available on GitHub (https://github.com/user15632/DeepDR).

Bayesian Network Analysis: Assessing and Restoring Ecological Vulnerability in the Shaanxi Section of the Qinling-Daba Mountains Under Global Warming Influences

Human activities, especially industrial production and urbanization, have significantly affected vegetation cover, water resource cycles, climate change, and biodiversity in the Qinling-Daba Mountain region and its surrounding areas. These activities contribute to complex and lasting impacts on ecological vulnerability. The Qinling Mountain region exhibits a complex interaction with human activities. The current research on the ecological vulnerability of the Qinling Mountain region primarily focuses on spatial distribution and the driving factors. This study innovatively applies the VSD assessment and Bayesian networks to systematically evaluate and simulate the ecological vulnerability of the study area over the past 20 years, which indicates that the integration of the VSD model with the Bayesian network model enables the simulation of dynamic relationships and interactions among various factors within the study areas, providing a more accurate assessment and prediction of ecosystem responses to diverse changes from a dynamic perspective. The key findings are as follows. (1) Areas of potential and slight vulnerability are concentrated in the Qinling-Daba mountainous regions. Over the past 20 years, areas of extreme and high vulnerability have significantly decreased, while areas of potential vulnerability and slight vulnerability have increased. (2) The key factors impacting ecological vulnerability during this period included industrial water use, SO2 emissions, industrial wastewater, and ecological water use. (3) Areas primarily hindering the transition to potential vulnerability are concentrated in well-developed small urban regions within basins. Furthermore, natural factors like altitude and temperature, which cannot be artificially regulated, are the major impediments to future ecological restoration. Therefore, this paper recommends natural restoration strategies based on environmental protection and governance strategies that prioritize green development as complementary measures. The discoveries of the paper provide a novel analytical method for the study of ecological vulnerability in mountainous areas, offering valuable insights for enhancing the accuracy of ecological risk prediction, fostering the integration of interdisciplinary research, and optimizing environmental governance and protection strategies.

Drug Sensitivity Prediction Based on Multi-stage Multi-modal Drug Representation Learning.

Accurate prediction of anticancer drug responses is essential for developing personalized treatment plans in order to improve cancer patient survival rates and reduce healthcare costs. To this end, we propose a drug sensitivity prediction model based on multi-stage multi-modal drug representations (ModDRDSP) to reflect the properties of drugs more comprehensively, and to better model the complex interactions between cells and drugs. Specifically, we adopt the SMILES representation learning method based on the deep hierarchical bi-directional GRU network (DSBiGRU) and the molecular graph representation learning method based on the deep message-crossing network (DMCN) for the multi-modal information of drugs. Additionally, we integrate the multi-omics information of cell lines based on a convolutional neural network (CNN). Finally, we use an ensemble deep forest algorithm for the prediction of drug sensitivity. After validation, the ModDRDSP shows impressive performance which outperforms the four current industry-leading models. More importantly, ablation experiments demonstrate the validity of each module of the proposed model, and case studies show the good results of ModDRDSP for predicting drug sensitivity, further establishing the superiority of ModDRDSP in terms of performance.

Hybrid CFRP‐GFRP sheets for flexural strengthening of continuous RC beams: Experimentation and analytical modeling

AbstractContinuous reinforced concrete (RC) beams cast in place are commonly used in construction; however, there is a notable gap in the literature regarding the performance of continuous RC beams strengthened with fiber‐reinforced polymer (FRP) sheets. Strengthening RC beams with FRP sheets typically leads to reduced ductility and moment redistribution capacity due to the linear stress–strain behavior of FRP materials compared to non‐strengthened RC beams. Addressing this gap, this study explores the feasibility of enhancing the mechanical properties and ductility of strengthened elements through a hybrid approach, combining carbon‐fiber‐reinforced polymer (CFRP) and glass‐fiber‐reinforced polymer (GFRP) sheets. An experimental program was conducted, retrofitting two continuous two‐span RC beams (250 × 150 × 6000 mm) with hybrid CFRP‐GFRP (HCG) sheets. Concentrated loads were applied at the center of each span, and comprehensive data on strains in FRP sheets, longitudinal reinforcements, and crack propagation patterns were recorded and meticulously analyzed. The outcomes demonstrated that employing HCG sheets for strengthening RC continuous beams significantly improves ductility, load‐carrying capacity, and moment redistribution, surpassing the performance of beams strengthened with either CFRP or GFRP sheets. To ensure accurate predictions of the flexural response, an analytical model was developed and rigorously verified using the experimental results. The model takes into account the strain compatibility condition and provides insights into the behavior of continuous RC beams strengthened with CFRP, GFRP, and HCG sheets. This research contributes valuable knowledge to the understanding of FRP sheet strengthening techniques, emphasizing the efficacy of HCG sheets for achieving enhanced structural performance in continuous RC beams.

Pulse-type seismic response prediction for uncertain structures based on the attention mechanism and wavelet decomposition augmented decomposition learning

Accurate prediction of structural responses under seismic action is important for the performance assessment of structures. However, depending on the variability of the structure (e.g., the design information) or ground motion characteristics (e.g., the presence of pulses), the seismic response characteristics of the may be significantly different. To overcome this problem, this study proposes a novel structural feature attention mechanism and wavelet decomposition augmented decomposition learning method (SFA-WD-DL) for predicting the time-series of the structural response under pulse-type ground motions. Unlike previous studies, the proposed method can be applied to structures with different shapes. Innovatively, a structural feature attention mechanism is embedded within a neural network model, enabling the consideration of different structural parameters. In addition, a wavelet decomposition-based velocity pulse identification method is combined with decomposition learning, using decomposed pulses and high-frequency features as inputs to the neural network model. This process simplifies pulse feature recognition task of the model. To avoid an unbalanced distribution of ground motion features affecting model performance, a balanced sampling strategy for ground motion datasets combining ground motion clustering and category reorganization methods is proposed, which effectively improves the model robustness. The accuracy and applicability of the proposed method are validated using a dataset containing reinforced concrete frame structures of different shapes and a pulse-type ground motion dataset containing 148 records. In addition, the importance levels of the structural features are obtained through the weight distribution of the structural feature attention mechanism. This indicates that the proposed SFA-WD-DL method is easily interpretable and can extract key features affecting the structural response.

Experimental and Theoretical Analysis of Frequency- and Temperature-Dependent Characteristics in Viscoelastic Materials Using Prony Series

This study comprehensively investigates the frequency- and temperature-dependent viscoelastic properties of two elastomer materials, focusing on the comparison between experimental results and theoretical models derived from Prony series coefficients. Dynamic Mechanical Analysis (DMA) was performed across a broad temperature range of 0–100 °C and frequency range of 0.1–100 Hz to generate storage modulus and relaxation modulus data for both materials. Relaxation tests were conducted at 25 °C to further characterize the time-dependent behavior. Time–Temperature Superposition (TTS) was applied to the resultant shift factors used to fit both Williams–Landel–Ferry (WLF) and Arrhenius equations. Additionally, sinusoidal sweep tests were carried out at 0 °C, 25 °C, 50 °C, and 80 °C, with frequencies ranging from 1 Hz to 1000 Hz, to experimentally determine the natural frequencies of the elastomers. The findings demonstrate that Prony series coefficients derived from storage modulus data offer a more accurate prediction of the viscoelastic response and natural frequencies compared to those derived from relaxation modulus data. The storage modulus data closely match the experimentally observed natural frequencies, while the relaxation modulus data exhibit larger deviations, particularly at higher temperatures. The study also reveals temperature-dependent behavior, where increasing temperature reduces the stiffness of the materials, leading to lower natural frequencies. This comprehensive analysis highlights the importance of selecting appropriate modeling techniques and data sources, particularly when predicting dynamic responses under varying temperature and frequency conditions.

Modeling via peridynamics for damage and failure of hyperelastic composites

Modeling damage and failure behaviors of hyperelastic composites under large deformations is pivotal for advancing the design of cutting-edge elastomers used in biomedical engineering and soft robotics. However, existing methods struggle with capturing the non-linearities and singularities in the displacement field under such conditions. To address these difficulties, we propose a novel bond-based peridynamics (PD) framework with multiple advancements. First, we develop a PD bond strain model grounded in the nonlinear Piola-Kirchhoff stress-stretch relationship, precisely capturing hyperelasticity and ensuring full compliance with thermodynamic laws and kinematics in large deformation scenarios. Second, our framework employs a particle discretization technique that not only sidesteps the mesh distortion issues commonly encountered in grid-based methods subjected to large deformation but also significantly lowers the computational complexity due to the ease of numerical implementation of random inclusion distributions. Third, we propose, for the first time, a refined 3D hyperelastic model within the PD framework that enables a more comprehensive and accurate prediction of material responses to external loads, surpassing the limitations of conventional 2D simulations. Validation against experimental data demonstrates that our model accurately captures key physical phenomena in hyperelastic composites, such as spontaneous crack initiation and propagation, interface debonding, crack coalescence, and the formation of non-smooth crack surfaces. Crucially, this framework is versatile and adaptable to a wide range of engineered composite systems with different inclusions and matrices, making it a powerful tool for predicting and analyzing large deformation behaviors in various advanced applications.

Hysteretic Dynamic Modeling and Vibration Characteristics Analysis of Metal-Rubber Isolators

A novel hysteretic constitutive model (HCM) with four parameters was proposed to reproduce the nonlinear elasticity and dry friction characteristics of metal–rubber isolators (MRIs). The HCM accurately characterized the nonlinear hysteretic behavior due to its intrinsic relationship with the geometric properties of the hysteresis loop. A physics-based parameter estimation strategy was developed to provide reasonable initial iteration values. Three MRIs with different design stiffnesses were tested to investigate their hysteretic behavior under various excitation levels. The HCM accurately reproduced the entire experimental hysteresis loops using only a subset of the experimental data. This exhibited higher precision and scalability compared to the classical dynamic model. Friction damping forces were employed to distinguish the stick-slip states of the three MRIs. Additionally, the identified parameters were used to simulate the steady-state vibration response of a complex MRI system. The resulting frequency response functions (FRFs) agreed well with the experimental data. The proposed model can effectively capture the amplitude-dependent effects in MRI systems with various design stiffnesses, enabling more accurate predictions of vibration responses.

Fractional-Order Modeling and Stochastic Dynamics Analysis of a Nonlinear Rubbing Overhung Rotor System

To study the nonlinear dynamic behavior and system stability of a rubbing overhung rotor with viscoelastic and memory-effect damping and random uncertain parameters, this paper introduces a fractional-order modeling and stochastic dynamic analysis method for the nonlinear overhung rotor system with frictional impact faults. Firstly, the dynamic equations of the overhung rotor considering friction effect and fractional damping effect are established based on the transfer matrix method and fractional order derivative. Then, the time-domain response of the fractional-order dynamic equations is solved by combining the Runge–Kutta method and the continuous fractional expansion, and the steady-state response characteristics of different fractional damping are analyzed in the deterministic case. Finally, to analyze the response of the system under the effect of stochastic parameters, the sparse grid-based PCE metamodel of the system response is developed. Statistical moments, probability distributions, and sensitivity indices of the response of stochastic systems are revealed. The results of this paper provide a theoretical basis for efficient and accurate prediction of the stochastic response of nonlinear rubbing overhung rotor systems.

Test and simulation of high temperature resistant polyamide composite with single lap single bolt connection

The advancement of next-generation aerospace vehicles has presented new requirements and challenges for ensuring the structural integrity of aircraft components in extreme environments. Consequently, the utilization of high temperature resistant polyamide composite materials has become pivotal in the manufacturing of aerospace vehicle parts that operate under high temperatures (250 °C). As a critical connection technology for these materials, the mechanical behavior of bolted connection structures under high temperatures requires further investigation. In this paper, a combination of experimental and numerical simulation is used to investigate the load carrying capacity and failure mechanism of T700/BMP316 composite bolted joints at room temperature and 250 °C. The experimental results show that the ultimate load carrying capacity of the structure at 250 °C is only 13.1 % lower than that of the room temperature environment, indicating that the temperature softening effect of such composites is not significant. Scanning electron microscope (SEM) and computed tomography (CT) results indicate that the structural damage modes were the crushing of the hole edge fibers and matrix due to the extrusion by the bolts, as well as the interlaminar delamination damage. Temperature effects were taken into account for the composite principal structure and finite element modeling was performed using a combination of Pinho criterion and Cohesive model. Numerical simulations allow accurate prediction of the load-displacement response and damage pattern throughout the damage evolution phase. The high temperature test results and the developed finite element model involved in this study can support the design of new-generation aerospace vehicles.

Data-assisted non-intrusive model reduction for forced nonlinear finite elements models

AbstractSpectral submanifolds (SSMs) have emerged as accurate and predictive model reduction tools for dynamical systems defined either by equations or data sets. While finite-elements (FE) models belong to the equation-based class of problems, their implementations in commercial solvers do not generally provide information on the nonlinearities required for the analytical construction of SSMs. Here, we overcome this limitation by developing a data-driven construction of SSM-reduced models from a small number of unforced FE simulations. We then use these models to predict the forced response of the FE model without performing any costly forced simulation. This approach yields accurate forced response predictions even in the presence of internal resonances or quasi-periodic forcing, as we illustrate on several FE models. Our examples range from simple structures, such as beams and shells, to more complex geometries, such as a micro-resonator model containing more than a million degrees of freedom. In the latter case, our algorithm predicts accurate forced response curves in a small fraction of the time it takes to verify just a few points on those curves by simulating the full forced-response.