High Number Of Iterations Research Articles

Cost optimization in structural design for reinforced concrete frames using Jaya algorithm

This study analyzes and optimizes the structural design of three-dimensional (3D) reinforced concrete framestructures to minimize the amount of two main materials, including concrete and steel reinforcement, used inreinforced concrete frames. Jaya algorithm was developed based on evolutionary algorithms to build the structural optimization model. The objective function (minimum material cost) with variables being column andbeam cross-sections was set up. In which, the constraints related to the maximum internal force and displacement being in the structural members satisfy limited strength and serviceability requirements. A subroutine written in Python was used to connect the optimization process to the structural analysis software, ETABS. The 3D reinforced concrete frame of a three-story building was selected for cost and design optimization analysis. In which, the optimization problem was solved in cases of three different maximum numbers of iterations, including 20, 35, and 50. In each iteration level, a setting of five independent runs was performed. The subroutine proved to be fast, robust, and convenient manner for solving optimization problems in designing 3D reinforced concrete frames. In which, the best optimal cost might be obtained after twenty iterations and the better convergence behavior occurred at higher numbers of iterations. The study results showed that, for this reinforced concrete frame, applying optimal design led to a materials cost reduction (success rate) by 33.67% compared to that of the conventional design. This success rate would fluctuate according to different nations’ design codes.

Hand drill shape optimization using Open CASCADE library and the steepest descent method

In this article, compliance optimization with the steepest descent method of the hand drill bits shapes for metal drilling is presented. The analysis of stress, displacements and compliance of the solid with random shape can be performed using the finite element method. In the case of a high number of optimization iterations, each analysis needs automatic modifications of geometry, mesh and boundary conditions. The Open CASCADE library can be used in the fast and automatic construction of modified models. It allows for fast reanalysis for each derivative of the objective function in the optimization process. The motivation of this research is to fill the gap in the literature on drilling technology. Most of the contemporary research is devoted to the oil and gas industry, while optimization of the hand drill bits used in metal drilling is rare. Compliance optimization allows us to find the shape which guarantees a greater stiffness for the specified loading conditions and a given amount of material. Although the obtained compliance was low, further experimental research would be needed to apply new solutions in drilling practice. This will involve the construction of the drill bit tips and the development of a heat treatment process

Integrating Null Controllability and Model-Based Safety Assessment for Enhanced Reliability in Drone Design

The increasing use of drones for safety-critical applications, particularly beyond visual lines of sight and over densely populated areas, necessitates safer and more reliable designs. To address this need, this paper introduces a novel methodology integrating Null Controllability with the Model-Based Safety Assessment (MBSA) framework AltaRica 3.0 to optimize propulsor configurations and system architectures. The main advancement of this method lies in the automation of reliability modeling and the integration of controllability assessment, eliminating restrictions on the types of propulsor configurations and system architectures that can be evaluated and significantly reducing the effort required for each design iteration. Through a hexarotor drone case study, the proposed method enabled a high number of design iterations, efficiently exploring various aspects of the design problem simultaneously, such as configuration, system architecture, and controllability hypothesis, which is not possible with state-of-the-art techniques. This approach demonstrated significant reliability improvements by implementing and optimizing redundancies, reducing the probability of loss of control by up to 99%. The case study also highlighted the increasing difficulty of enhancing reliability with each iteration and confirmed that it is unnecessary to consider more than two simultaneous failures for design optimization. A comparison of reliability figures with previous studies highlights the crucial role of system architecture in effectively enhancing drone design reliability. This work advances the field by providing an effective multidisciplinary modeling framework for drone design, enhancing reliability in safety-critical applications.

Robust Multiphase-Split Calculations Based on Improved Successive Substitution Schemes

Summary Successful large-scale compositional reservoir simulations require robust and efficient phase-split calculations. In recent years, there has been progress in three-phase-split calculations. However, there may be convergence issues when the number of equilibrium phases increases to four. Part of the problem is from the poor initial guesses. In phase-split computations, the results from stability provide good initial guesses. Successive substitution (SS) is a key step in phase-split calculations. The method, if efficient, can provide good initial guesses for the final step, the Newton method that has a rapid rate of convergence. In this contribution, we present a robust algorithm with high efficiency and robustness in phase-split calculations in two, three, and four phases. We find that a key step is the SS. The convergence may even be very slow away from the critical point and phase boundaries. A modified SS is used which may reduce the number of iterations many times. In the course of this investigation, we observe some regions often inside the phase envelopes (far from the phase boundary or critical points) with a very high number of SS iterations. The adoption of the improved SS iterations leads to a significant speedup of the multiphase-split computations. In some mixtures, the average reduction is more than 70%.

SHuffled Ant Lion Optimization approach with an exponentially weighted random walk strategy

Ant Lion Optimization (ALO) method is one of the population-based nature-inspired optimization algorithms which mimics the hunting strategy of antlions. ALO is successfully employed for solving many complicated optimization problems. However, it is reported in the literature that the original ALO has some limitations such as the requirement of high number of iterations and possibility of trapping to local optimum solutions, especially for complex or large-scale problems. For this purpose, the SHuffled Ant Lion Optimization (SHALO) approach is proposed by conducting two improvements in the original ALO. Performance of the proposed SHALO approach is evaluated by solving some unconstrained and constrained problems for different conditions. Furthermore, the identified results are statistically compared with the ones obtained by using the original ALO, two improved ALOs which are the self-adaptive ALO (saALO) and the exponentially weighted ALO (EALO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) approaches. Identified results indicated that the proposed SHALO approach significantly improves the solution accuracy with a mean success rate of 76% in terms of finding the global or near-global optimum solutions and provides better results than ALO (22%), saALO (25%), EALO (14%), GA (28%), and PSO (49%) approaches for the same conditions.

Conflict-free electric vehicle routing problem: an improved compositional algorithm

AbstractThe Conflict-Free Electric Vehicle Routing Problem (CF-EVRP) is a combinatorial optimization problem of designing routes for vehicles to execute tasks such that a cost function, typically the number of vehicles or the total travelled distance, is minimized. The CF-EVRP involves constraints such as time windows on the tasks’ execution, limited operating range of the vehicles, and limited capacity on the number of vehicles that a road segment can simultaneously accommodate. In previous work, the compositional algorithm ComSat was introduced to solve the CF-EVRP by breaking it down into sub-problems and iteratively solve them to build an overall solution. Though ComSat showed good performance in general, some problem instances took significant time to solve due to the high number of iterations required to find solutions for two sub-problems, namely the Routing Problem, and the Paths Changing Problem. This paper addresses the bottlenecks of ComSat and presents new formulations for both sub-problems in order to reduce the number of iterations required to find feasible solutions to the CF-EVRP. Experiments on sets of benchmark instances show the effectiveness of the presented improvements.

Augmented least squares, a powerful chemometric approach for the spectroscopic analysis of the antiretroviral therapy abacavir, lamivudine and dolutegravir in their ternary mixture

Augmented least squares models such as concentration residual augmented classical least squares (CRACLS) and spectral residual augmented classical least squares (SRACLS) are powerful chemometric approaches that can be applied for spectroscopic analysis of many pharmaceutical compounds. Herein, both CRACLS and SRACL have been employed for UV spectral analysis of three antiretroviral therapy namely abacavir (ACV), lamivudine (LMV) and dolutegravir (DTG) in their ternary mixture. A partial factorial design has been utilized for calibration set construction then both CRACLS and SRACLS models have been optimized regarding the number of iterations and principal components, respectively, using a leave-one-out cross-validation procedure. It was found that a higher number of iterations and principal components were required for modelling the minor component DTG indicating more augmentation procedures to improve the models’ accuracy. Validation of the proposed models was performed using external validation set of 13 mixtures and different validation parameters have been evaluated regarding models’ predictive abilities. Both models showed excellent performance for analyzing ACV and LMV with relative root mean square error of prediction (RRMSEP) below 2 %. However, higher RRMSEP values around 5 % were observed for the minor component DTG suggesting that these models should be utilized with caution when analyzing minor components in mixtures. Furthermore, the suggested models have been applied for analyzing ACV, LMV and DTG in their pharmaceutical formulation and excellent agreement was observed between the suggested models and the reported chromatographic method posing these models as powerful chemometric approaches for quality control analysis of many pharmaceutical compounds.

Visualization of small brain nuclei with a high-spatial resolution, clinically available whole-body PET scanner

ObjectiveTo verify the visibility of physiological 18F-fluorodeoxyglucose (18F-FDG) uptake in nuclei in and around the brainstem by a whole-body (WB) silicon photomultiplier positron emission tomography (SiPM-PET) scanner with point-spread function (PSF) reconstruction using various iteration numbers.MethodsTen healthy subjects (5 men, 5 women; mean age, 56.0 ± 5.0 years) who underwent 18F-FDG PET/CT using a WB SiPM-PET scanner and magnetic resonance imaging (MRI) of the brain including a spin-echo three-dimensional sampling perfection with application-optimized contrasts using different flip angle evolutions fluid-attenuated inversion recovery (3D-FLAIR) and a 3D-T1 magnetization-prepared rapid gradient-echo (T1-MPRAGE) images were enrolled. Each acquired PET image was reconstructed using ordered-subset expectation maximization (OSEM) with iteration numbers of 4, 16, 64, and 256 (subset 5 fixed) + time-of-flight (TOF) + PSF. The reconstructed PET images and 3D-FLAIR images for each subject were registered to individual T1-MPRAGE volumes using normalized mutual information criteria. For each MR-coregistered individual PET image, the pattern of FDG uptake in the inferior olivary nuclei (ION), dentate nuclei (DN), midbrain raphe nuclei (MRN), inferior colliculi (IC), mammillary bodies (MB), red nuclei (RN), subthalamic nuclei (STN), lateral geniculate nuclei (LGN), medial geniculate nuclei (MGN), and superior colliculi (SC) was visually classified into the following three categories: good, clearly distinguishable FDG accumulation; fair, obscure contour of FDG accumulation; poor, FDG accumulation indistinguishable from surrounding uptake.ResultsAmong individual 18F-FDG PET images with OSEM iterations of 4, 16, 64, and 256 + TOF + PSF, the iteration numbers that showed the best visibility in each structure were as follows: ION, MRN, LGN, MGN, and SC, iteration 64; DN, iteration 16; IC, iterations 16, 64, and 256; MB, iterations 64 and 256; and RN and STN, iterations 16 and 64, respectively. Of the four iterations, the 18F-FDG PET image of iteration 64 visualized FDG accumulation in small structures in and around the brainstem most clearly (good, 98 structures; fair, 2 structures).ConclusionsA clinically available WB SiPM-PET scanner is useful for visualizing physiological FDG uptake in small brain nuclei, using a sufficiently high number of iterations for OSEM with TOF and PSF reconstructions.

On the relevance of descriptor fidelity in microstructure reconstruction

AbstractA common strategy for reducing the computational effort of descriptor‐based microstructure reconstruction in the Yeong–Torquato algorithm lies in restricting the choice of descriptors to an efficiently computable subset. As an alternative, the number of iterations can be reduced by gradient‐based optimization as in differentiable microstructure characterization and reconstruction (DMCR). This allows for, but does not require, the use of a set of informative, high‐dimensional and computationally expensive descriptors that would be unfeasible for a high number of iterations. For this reason, the present work investigates the role of descriptor fidelity on microstructure reconstruction results. More precisely, spatial two‐ and three‐point correlations as well as the lineal path function are computed on 2D planes as well as on 1D lines. These descriptors are used for reconstruction with the Yeong–Torquato and DMCR algorithm and the results are compared throughout various microstructures, respectively.

An improved long short term memory network for intrusion detection.

Over the years, intrusion detection system has played a crucial role in network security by discovering attacks from network traffics and generating an alarm signal to be sent to the security team. Machine learning methods, e.g., Support Vector Machine, K Nearest Neighbour, have been used in building intrusion detection systems but such systems still suffer from low accuracy and high false alarm rate. Deep learning models (e.g., Long Short-Term Memory, LSTM) have been employed in designing intrusion detection systems to address this issue. However, LSTM needs a high number of iterations to achieve high performance. In this paper, a novel, and improved version of the Long Short-Term Memory (ILSTM) algorithm was proposed. The ILSTM is based on the novel integration of the chaotic butterfly optimization algorithm (CBOA) and particle swarm optimization (PSO) to improve the accuracy of the LSTM algorithm. The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. The performance of ILSTM and the intrusion detection system were evaluated using two public datasets (NSL-KDD dataset and LITNET-2020) under nine performance metrics. The results showed that the proposed ILSTM algorithm outperformed the original LSTM and other related deep-learning algorithms regarding accuracy and precision. The ILSTM achieved an accuracy of 93.09% and a precision of 96.86% while LSTM gave an accuracy of 82.74% and a precision of 76.49%. Also, the ILSTM performed better than LSTM in both datasets. In addition, the statistical analysis showed that ILSTM is more statistically significant than LSTM. Further, the proposed ISTLM gave better results of multiclassification of intrusion types such as DoS, Prob, and U2R attacks.

Prediction strategy for the forming and stuffing of non-circular tube

The manufacturing of exhaust filter systems for cars typically comprises the forming of a tube and the later stuffing of the formed product with a ceramic monolith and an elastomer mat for filtration. It is shown, that for oval tubes the stuffing process leads to inhomogeneous deformation of the final product wherefore it must be considered when designing the tools for the forming step. The high number of iterations for the tool design can hence be reduced. Additionally, under certain conditions, the necessity of simulating the forming process is shown to no longer be given. The reason for this is that during the stuffing step, no plastic deformation is introduced in the tubes. Consequently, the forming history, including local strain and residual stress distributions, do not need to be explicitly considered. A simplified methodology is presented based on these findings, which can find the required cross-section geometry of the tube entering the stuffing process. The goal of the previous forming process therefore is to produce exactly that geometry, which can be done using conventional forming process planning approaches.

Deep pre-trained FWI: where supervised learning meets the physics-informed neural networks

SUMMARYFull-waveform inversion (FWI) is the current standard method to determine final and detailed model parameters to be used in the seismic imaging process. However, FWI is an ill-posed problem that easily achieves a local minimum, leading the model solution in the wrong direction. Recently, some works proposed integrating FWI with Convolutional Neural Networks (CNN). In this case, the CNN weights are updated following the FWI gradient, defining the process as a Physics-Informed Neural Network (PINN). FWI integrated with CNN has an important advantage. The CNN stabilizes the inversion, acting like a regularizer, avoiding local minima-related problems and sparing an initial velocity model in some cases. However, such a process, especially when not requiring an initial model, is computationally expensive due to the high number of iterations required until the convergence. In this work, we propose an approach which relies on combining supervised learning and physics-informed by using a previously trained CNN to start the DL-FWI inversion. Loading the pre-trained weights configures transfer learning. The pre-trained CNN is obtained using a supervised approach based on training with a reduced and simple data set to capture the main velocity trend at the initial FWI iterations. The proposed training process is different from the initial works on the area which obtained the velocity model from the shots in supervised learning tasks and that required a large amount of labelled data to ensure reasonable model predictions. We investigated in our approach two CNN architectures, obtaining more robust results and a reduced number of parameters when using a modified U-Net. The method was probed over three benchmark models, showing consistently that the pre-training phase reduces the process’s uncertainties and accelerates the model convergence using minimal prior information. Besides, the final scores of the iterative process are better than the examples without transfer learning. Thus, transfer learning solved one main limitation of the previous PINN approaches: the unfeasible number of iterations when not using an initial model. Moreover, we tested the method using data with low-frequency band limitations, since the lack of low frequencies is a common issue within real seismic data. The inversion converges to reasonable results probing the method’s robustness with restricted frequency content.

Hybrid small-signal model parameter extraction for GaN HEMT based on QGA

ABSTRACT A novel algorithm is proposed for optimal extraction of GaN HEMT small-signal model parameters. The proposed Quantum Genetic Algorithm (QGA) exploits the superposition, entanglement and interference of quantum states, which solves the problems of high number of iterations and slow convergence when obtaining optimal solutions using Genetic Algorithms (GA). Meanwhile, it is solved that the Particle Swarm Optimisation (PSO) algorithm produces premature convergence and easily falls into the local optimum solution. In order to avoid the influence of distributed parasitic effects in large size devices under high-frequency conditions, a suitable frequency range is determined and combined with direct extraction techniques to determine the range of parameter values. The model parameter values are optimised step by step using QGA. In order to verify the superiority of QGA, QGA and PSO algorithms are both used to optimise GaN HEMT small-signal model parameters. By comparing the modelled S-parameter effects of the QGA and the PSO algorithm, it can be found that the QGA has better consistency with the measured data.

Managing Geotechnical Uncertainty With Simulation Models: An Introduction

In a standard deterministic analysis discrete scenarios are considered, and a moderately conservative “characteristic” value is used as a design basis. However, fixed or exact values in a real-world geotechnical site seldom occurs. Deterministic approaches may not explicitly consider the ground uncertainty. Simulations using various probabilities provides for this uncertainty as each parameter input is treated as a random variable within certain measured ranges or ability to evaluate. Monte Carlo (MC) sampling is a traditional technique for generating random numbers to sample from a probability distribution. When low probability events occur, a small number of MC iterations might not sample sufficient quantities of these outcomes for inclusion in the simulation model. Latin Hypercube (LH) sampling uses stratification of the input probability distributions, to overcome the limitations of Monte Carlo sampling. The simulation results show low probability outcomes are included in the sampling for the simulation model. At a high number of simulation iterations both provide similar outputs, but at low simulation iterations the LH is more reliable. However, both the MC and LH sampling suffer from impractical values at low or high probability events when the normal probability density function (PDF) is adopted. The normal PDF is commonly used in statistical modelling. Non-normal PDFs often represent the best fit PDF when a goodness of fit test is carried out. The errors associated with using the common normal PDF are shown with the above-mentioned simulation models. This best fit PDF applies whether simulation models as described above is used or even with simple “what if” sensitivity models in traditional analysis.

An Improved RANSAC Outlier Rejection Method for UAV-Derived Point Cloud

A common problem with matching algorithms, in photogrammetry and computer vision, is the imperfection of finding all correct corresponding points, so-called inliers, and, thus, resulting in incorrect or mismatched points, so-called outliers. Many algorithms, including the well-known randomized random sample consensus (RANSAC)-based matching, have been developed focusing on the reduction of outliers. RANSAC-based methods, however, have limitations such as increased false positive rates of outliers, and, consequently resulting in fewer inliers, an unnecessary high number of iterations, and high computational time. Such deficiencies possibly result from the random sampling process, the presence of noise, and incorrect assumptions of the initial values. This paper proposes a modified version of RANSAC-based methods, called Empowered Locally Iterative SAmple Consensus (ELISAC). ELISAC improves RANSAC by utilizing three basic modifications individually or in combination. These three modifications are (a) to increase the stability and number of inliers using two Locally Iterative Least Squares (LILS) loops (Basic LILS and Aggregated-LILS), based on the new inliers in each loop, (b) to improve the convergence rate and consequently reduce the number of iterations using a similarity termination criterion, and (c) to remove any possible outliers at the end of the processing loop and increase the reliability of results using a post-processing procedure. In order to validate our proposed method, a comprehensive experimental analysis has been done on two datasets. The first dataset contains the commonly-used computer vision image pairs on which the state-of-the-art RANSAC-based methods have been evaluated. The second dataset image pairs were captured by a drone over a forested area with various rotations, scales, and baselines (from short to wide). The results show that ELISAC finds more inliers with a faster speed (lower computational time) and lower error (outlier) rates compared to M-estimator SAmple Consensus (MSAC). This makes ELISAC an effective approach for image matching and, consequently, for 3D information extraction of very high and super high-resolution imagery acquired by space-borne, airborne, or UAV sensors. In particular, for applications such as forest 3D modeling and tree height estimations where standard matching algorithms are problematic due to spectral and textural similarity of objects (e.g., trees) on image pairs, ELISAC can significantly outperform the standard matching algorithms.

Numerical Simulation of Stresses in Functionally Graded HCS-MgO Cylinder Using Iterative Technique and Finite Element Method.

In this study, a thick hollow axisymmetric functionally graded (FG) cylinder is investigated for steady-state elastic stresses using an iteration technique and the finite element method. Here, we have considered a functionally graded cylinder tailored with the material property, namely, Young’s modulus, varying in an exponential form from the inner to outer radius of the cylinder. A mathematical formulation for stress analysis of functionally graded cylinder under internal and external pressure conditions is developed using constitutive relations for stress–strain, strain–displacement relations and the equation of equilibrium. The effect of the in-homogeneity parameter on radial displacement, radial and tangential stresses in a functionally graded cylinder made up of a High Carbon Steel (HCS) metal matrix, reinforced with Magnesium Oxide (MgO) ceramic is analyzed. The iterative method implemented is fast and converges to the solution which can be further improved by considering a higher number of iterations. This is depicted graphically by using radial displacement and stresses in a pressurized functionally graded cylinder obtained for the first two iterations. An iterative solution for non-FGM (or homogeneous material) is validated using the finite element method. The mechanical responses of the functionally graded cylinder obtained from the iterative method and the finite element method are then compared and found to be in good agreement. Results are presented in graphical and tabular form along with their interpretations.

Error matching elimination based on a local affine algorithm

Aiming at the shortcomings of orb algorithm using the traditional RANSAC (random sampling consistency) algorithm to eliminate mismatches, such as the high number of iterations when the sample size is large, and the proportion of error points also affects the fitting accuracy of the traditional algorithm, a rough and fine method to eliminate mismatches is proposed; The threshold screening method is used to select the optimal threshold and preliminarily eliminate a large number of wrong matching pairs to achieve the effect of rough elimination; Then the sub-window method is used to calculate the local affine matching under all windows, reduce the number of error points, and increase the correctness of the fitting model of RANSAC algorithm. The experimental results are compared with the traditional algorithm, and the matching accuracy is improved by 6% and combined with orb-slam2.

Learning spectral initialization for phase retrieval via deep neural networks.

Phase retrieval (PR) arises from the lack of phase information in the measures recorded by optical sensors. Phase masks that modulate the optical field and reduce ambiguities in the PR problem by producing redundancy in coded diffraction patterns (CDPs) have been included in these diffractive optical systems. Several algorithms have been developed to solve the PR problem from CDPs. Also, deep neural networks (DNNs) are used for solving inverse problems in computational imaging by considering physical constraints in propagation models. However, traditional algorithms based on non-convex formulation include an initialization stage that requires a high number of iterations to properly estimate the optical field. This work proposes an end-to-end (E2E) approach for addressing the PR problem, which jointly learns the spectral initialization and network parameters. Mainly, the proposed deep network approach contains an optical layer that simulates the propagation model in diffractive optical systems, an initialization layer that approximates the underlying optical field from CDPs, and a double branch DNN that improves the obtained initial guess by separately recovering phase and amplitude information. Simulation results show that the proposed E2E approach for PR requires fewer snapshots and iterations than the state of the art.

Leveraging Conflicting Constraints in Solving Vehicle Routing Problems

The Conflict-Free Electric Vehicle Routing Problem (CF-EVRP) is a combinatorial optimization problem of designing routes for vehicles to visit customers such that a cost function, typically the number of vehicles or the total travelled distance, is minimized. The CF-EVRP involves constraints such as time windows on the delivery to the customers, limited operating range of the vehicles, and limited capacity on the number of vehicles that a road segment can simultaneously accommodate. In previous work, the compositional algorithm ComSat was introduced and that solves the CF-EVRP by breaking it down into sub-problems and iteratively solve them to build an overall solution. Though ComSat showed good performance in general, some problems took significant time to solve due to the high number of iterations required to find solutions that satisfy the road segments’ capacity constraints. The bottleneck is the Path Changing Problem, i.e., the sub-problem of finding a new set of shortest paths to connect a subset of the customers, disregarding previously found shortest paths. This paper presents an improved version of the PathsChanger function to solve the Path Changing Problem that exploits the unsatisfiable core, i.e., information on which constraints conflict, to guide the search for feasible solutions. Experiments show faster convergence to feasible solutions compared to the previous version of PathsChanger.

A Study on Anti-Shock Performance of Marine Diesel Engine Based on Multi-Body Dynamics and Elastohydrodynamic Lubrication

Diesel engine anti-shock performance is important for navy ships. The calculation method is a fast and economic way compared to underwater explosion trial in this field. Researchers of diesel engine anti-shock performance mainly use the spring damping model to simulate the main bearings of a diesel engine. The elastohydrodynamic lubrication method has been continuously used in the main bearings of diesel engines in normal working conditions. This research aims at using the elastohydrodynamic lubrication method in the main bearings of the diesel engine in external shock conditions. The main bearing elastohydrodynamic lubrication and diesel engine multi-body dynamics analysis is based on AVL EXCITE Power Unite software. The external shock is equivalent to the interference on the elastohydrodynamic lubrication calculation. Whether the elastohydrodynamic lubrication algorithm can complete the calculation under interference is the key to the study. By adopting a very small calculation step size, a high number of iterations, and increasing the stiffness of the thrust bearing, the elastohydrodynamic lubrication algorithm can be successfully completed under the external impact environment. The calculation results of the accelerations on engine block feet have a similar trend as the experiment results. Diesel engines with and without shock absorbers in external shock conditions are calculated. This calculation model can also be used for diesel engine dynamics calculations and main bearing lubrication calculations under normal working conditions.