CMES-Computer Modeling in Engineering & Sciences

About the journal

This journal publishes original research papers of reasonable permanent value, in the areas of computational mechanics, computational physics, computational chemistry, and computational biology, pertinent to solids, fluids, gases, biomaterials, and other continua. Various length scales (quantum, nano, micro, meso, and macro), and various time scales ( picoseconds to hours) are of interest. Papers which deal with multi-physics problems, as well as those which deal with the interfaces of mechanics, chemistry, and biology, are particularly encouraged. New computational approaches, and more efficient algorithms, which eventually make near-real-time computations possible, are welcome. Original papers dealing with new methods such as meshless methods, and mesh-reduction methods are sought.

Indexing and Abstracting

Science Citation Index (Web of Science): 2018 Impact Factor 0.796; Current Contents: Engineering, Computing & Technology; Scopus Citescore (Impact per Publication 2018): 1.02; SNIP (Source Normalized Impact per Paper 2018): 0.498; RG Journal Impact (average over last three years); Engineering Index (Compendex); Applied Mechanics Reviews; Cambridge Scientific Abstracts: Aerospace and High Technology, Materials Sciences & Engineering, and Computer & Information Systems Abstracts Database; CompuMath Citation Index; INSPEC Databases; Mathematical Reviews; MathSci Net; Mechanics; Science Alert; Science Navigator; Zentralblatt fur Mathematik; Portico, etc...

  • Parallelized Implementation of the Finite Particle Method for Explicit Dynamics in GPU
  • Abstract As a novel kind of particle method for explicit dynamics, the finite particle method (FPM) does not require the formation or solution of global matrices, and the evaluations of the element equivalent forces and particle displacements are decoupled in nature, thus making this method suitable for parallelization. The FPM also requires an acceleration strategy to overcome the heavy computational burden of its explicit framework for time-dependent dynamic analysis. To this end, a GPU-accelerated parallel strategy for the FPM is proposed in this paper. By taking advantage of the independence of each step of the FPM workflow, a generic parallelized computational framework for multiple types of analysis is established. Using the Compute Unified Device Architecture (CUDA), the GPU implementations of the main tasks of the FPM, such as evaluating and assembling the element equivalent forces and solving the kinematic equations for particles, are elaborated through careful thread management and memory optimization. Performance tests show that speedup ratios of 8, 25 and 48 are achieved for beams, hexahedral solids and triangular shells, respectively. For examples consisting of explicit dynamic analyses of shells and solids, comparisons with Abaqus using 1 to 8 CPU cores validate the accuracy of the results and demonstrate a maximum speed improvement of a factor of 11.2.
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  • Solving the Optimal Control Problems of Nonlinear Duffing Oscillators By Using an Iterative Shape Functions Method
  • Abstract In the optimal control problem of nonlinear dynamical system, the Hamiltonian formulation is useful and powerful to solve an optimal control force. However, the resulting Euler-Lagrange equations are not easy to solve, when the performance index is complicated, because one may encounter a two-point boundary value problem of nonlinear differential algebraic equations. To be a numerical method, it is hard to exactly preserve all the specified conditions, which might deteriorate the accuracy of numerical solution. With this in mind, we develop a novel algorithm to find the solution of the optimal control problem of nonlinear Duffing oscillator, which can exactly satisfy all the required conditions for the minimality of the performance index. A new idea of shape functions method (SFM) is introduced, from which we can transform the optimal control problems to the initial value problems for the new variables, whose initial values are given arbitrarily, and meanwhile the terminal values are determined iteratively. Numerical examples confirm the high-performance of the iterative algorithms based on the SFM, which are convergence fast, and also provide very accurate solutions. The new algorithm is robust, even large noise is imposed on the input data.
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  • Dynamic Analysis of Stochastic Friction Systems Using the Generalized Cell Mapping Method
  • Abstract Friction systems are a kind of typical non-linear dynamical systems in the actual engineering and often generate abundant dynamics phenomena. Because of non-smooth characteristics, it is difficult to handle these systems by conventional analysis methods directly. At the same time, random perturbation often affects friction systems and makes these systems more complicated. In this context, we investigate the steady-state stochastic responses and stochastic P-bifurcation of friction systems under random excitations in this paper. And in order to retain the non-smooth of friction system, the generalized cell mapping (GCM) method is first used to the original stochastic friction systems without any approximate transformation. To verify the accuracy and validate the applicability of the suggested approach, we present two classical nonlinear friction systems, i.e., Coulomb force model and Dahl force model as examples. Meanwhile, this method is in good agreement with the Monte Carlo simulation method and the computation time is greatly reduced. In addition, further discussion finds that the adjustable parameters can induce the stochastic P-bifurcation in the two examples, respectively. The stochastic P-bifurcation phenomena indicate that the stability of the friction system changes very sensitively with the parameters. Research of responses analysis and stochastic P-bifurcation has certain significances for the reliability and stability analysis of practical engineering.
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  • Hybrid Passive/Active Vibration Control of a Loosely Connected Spacecraft System
  • Abstract In this paper, a hybrid passive/active vibration (HPAV) controller of a loosely connected spacecraft consisting of a servicing satellite, a target and an X-shape structure isolator is first proposed to suppress vibrations of the system when subjected to the impulsive external excitations during the on-orbit missions. The passive dynamic response of the combined system can be adjusted appropriately to achieve the desired vibration isolation performance by tuning the structural parameters of the bio-inspired X-shape structure. Moreover, the adaptive control design through dynamic scaling technique is selected as the active component to maintain high vibration isolation performance in the presence of parameter uncertainties such as mass of the satellite platform, the damping and rotation friction coefficients of the X-shape structure. Compared with the pure passive system and the traditional spring-mass-damper (SMD) isolator, the HPAV strategy witnesses lower transmissibility, smaller vibration amplitude and higher convergence rate when subjected to the post-capture impact. Numerical simulations demonstrate the feasibility and validity of the proposed hybrid control scheme in suppressing vibrations of the free-floating spacecraft.
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  • Adaptive Quasi Fixed-Time Orbit Control Around Asteroid with Performance Guarantees
  • Abstract This paper investigates a novel quasi fixed-time orbit tracking control method for spacecraft around an asteroid in the presence of uncertain dynamics and unknown uncertainties. To quantitatively characterize the transient and steady-state responses of orbit tracking error system, a continuous performance function is devised via using a quartic polynomial. Then, integrating backstepping control technique and barrier Lyapunov function leads to a quasi fixed-time convergent orbit tracking controller without using any fractional state information and symbolic functions. Finally, two groups of illustrative examples are employed to test the effectiveness of the proposed orbit control method.
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  • A Robust Roll Stabilization Controller with Aerodynamic Disturbance and Actuator Failure Consideration
  • Abstract Combining adaptive theory with an advanced second-order sliding mode control algorithm, a roll stabilization controller with aerodynamic disturbance and actuator failure consideration for spinning flight vehicles is proposed in this paper. The presented controller is summarized as an “observer-controller” system. More specifically, an adaptive second-order sliding mode observer is presented to select the proper design parameters and estimate the knowledge of aerodynamic disturbance and actuator failure, while the proposed roll stabilization control scheme can drive both roll angle and rotation rate smoothly converge to the desired value. Theoretical analysis and numerical simulation results demonstrate the effectiveness of the proposed controller.
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  • Reentry Attitude Tracking Control for Hypersonic Vehicle with Reaction Control Systems via Improved Model Predictive Control Approach
  • Abstract This paper studies the reentry attitude tracking control problem for hypersonic vehicles (HSV) equipped with reaction control systems (RCS) and aerodynamic surfaces. The attitude dynamical model of the hypersonic vehicles is established, and the simplified longitudinal and lateral dynamic models are obtained, respectively. Then, the compound control allocation strategy is provided and the model predictive controller is designed for the pitch channel. Furthermore, considering the complicated jet interaction effect of HSV during RCS is working, an improved model predictive control approach is presented by introducing the online parameter estimation of the jet interaction coefficient for dealing with the uncertainty and disturbance. Moreover, considering the strong coupling effect between the yaw channel and roll channel, a coupled model predictive controller is designed by introducing the feedback of sideslip angle into the roll control channel to eliminate the coupling effect. Finally, the comparison simulations using the classical control method, MPC and IMPC approach are given to demonstrate the effectiveness and efficiency of the presented IMPC scheme.
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  • A Novel Two-Level Optimization Strategy for Multi-Debris Active Removal Mission in LEO
  • Abstract Recent studies of the space debris environment in Low Earth Orbit (LEO) have shown that the critical density of space debris has been reached in certain regions. The Active Debris Removal (ADR) mission, to mitigate the space debris density and stabilize the space debris environment, has been considered as a most effective method. In this paper, a novel two-level optimization strategy for multi-debris removal mission in LEO is proposed, which includes the low-level and high-level optimization process. To improve the overall performance of the multi-debris active removal mission and obtain multiple Pareto-optimal solutions, the ADR mission is seen as a Time-Dependant Traveling Salesman Problem (TDTSP) with two objective functions to minimize the total mission duration and the total propellant consumption. The problem includes the sequence optimization to determine the sequence of removal of space debris and the transferring optimization to define the orbital maneuvers. Two optimization models for the two-level optimization strategy are built in solving the multi-debris removal mission, and the optimal Pareto solution is successfully obtained by using the non-dominated sorting genetic algorithm II (NSGA-II). Two test cases are presented, which show that the low level optimization strategy can successfully obtain the optimal sequences and the initial solution of the ADR mission and the high level optimization strategy can efficiently and robustly find the feasible optimal solution for long duration perturbed rendezvous problem.
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  • Observability Analysis in Parameters Estimation of an Uncooperative Space Target
  • Abstract To study the parameter estimating effects of a free-floating tumbling space target, the extended Kalman filter (EKF) scheme is utilized with different high-nonlinear translational and rotational coupled kinematic & dynamic models on the LIDAR measurements. Applying the aforementioned models and measurements results in the situation where one single state can be estimated differently with varying accuracies since the EKFs based on different models have different observabilities. In the proposed EKFs, the traditional quaternions based kinematics and dynamics and the dual vector quaternions (DVQ) based kinematics and dynamics are used for the modeling of the relative motions between a chaser satellite and an uncooperative target. In the non-contact estimating scenarios, only highly nonlinear relative attitude and range measurements: the grapple fixture on the target measured from the chaser satellite via vision-based sensors, can be used. By evaluating the results of the EKFs, the observability properties of each EKF are studied analytically and numerically with the the Observability Gramian matrices (OG) and the standard deviations for every estimated parameters. The analysis of observability perform intensive studies and reveal the intrinsic factors that affect the accuracy and stability of the parameters estimation of an uncooperative space target. Finally, the analytical and numerical results show the optimal composition of the kinematic & dynamic models and measurements.
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  • Study on Forced Straight-Line Guidance for the Final Translation Phase of Spacecraft Rendezvous
  • Abstract Aimed at the problem of final translation of space rendezvous for the applications such as docking, inspection and tracking, optimal straight-line guidance algorithm based on pulse/continuous low-thrust in the context of Clohessy-Wiltshire dynamics is proposed. Two modes of guidance strategy: varying-speed and fixed-speed approaching scheme for V-bar and R-bar approach by using constant/finite low-thrust propulsion respectively are studied, and the corresponding fuel-optimal conditions are obtained. Numerical simulation is conducted to verify and test the proposed algorithms. The results show that there is generally no different between the fuel consumptions by using the two different approaching modes for V-bar case. However, the conclusion for R-bar case is different that the using of continuous low-thrust cost more fuel and transfer time.
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  • Analytic Initial Relative Orbit Solution for Angles-Only Space Rendezvous Using Hybrid Dynamics Method
  • Abstract A closed-form solution to the angles-only initial relative orbit determination (IROD) problem for space rendezvous with non-cooperated target is developed, where a method of hybrid dynamics with the concept of virtual formation is introduced to analytically solve the problem. Emphasis is placed on developing the solution based on hybrid dynamics (i.e., Clohessy-Wiltshire equations and two-body dynamics), obtaining formation geometries that produce relative orbit state observability, and deriving the approximate analytic error covariance for the IROD solution. A standard Monte Carlo simulation system based on two-body dynamics is used to verify the feasibility and evaluate the performance proposed algorithms. The sensitivity of the solution accuracy to the formation geometry, observation numbers is presented and discussed.
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  • Agile Satellite Mission Planning via Task Clustering and Double-Layer Tabu Algorithm
  • Abstract Satellite observation schedule is investigated in this paper. A mission planning algorithm of task clustering is proposed to improve the observation efficiency of agile satellite. The newly developed method can make the satellite observe more targets and therefore save observation resources. First, for the densely distributed target points, a pre-processing scheme based on task clustering is proposed. The target points are clustered according to the distance condition. Second, the local observation path is generated by Tabu algorithm in the inner layer of cluster regions. Third, considering the scatter and cluster sets, the global observation path is obtained by adopting Tabu algorithm in the outer layer. Simulation results show that the algorithm can effectively reduce the task planning time of large-scale point targets while ensuring the optimal solution quality.
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  • The Frequency Selection of SH0 Waves for Total Transmission and Its Application in the Damage Detection of Aircrafts
  • Abstract Based on wave interference, a methodology to realize the total transmission phenomenon of SH0 waves is proposed in this paper. After a systematical theoretical investigation, an exact frequency of a flat plate consisting of another medium with finite length, is obtained, which is furthermore exemplified by the finite element method. This frequency is the same as the classical Fabry-Perot condition and dependent on the thickness of the material. It has been revealed that an SH0 wave, with its wavelength equal to twice of the length of another medium, can totally transmit across the medium without reflection. Especially when the impedance changes in a specific range, the energy of transmitted waves can keep in a high level, which is frequency-independent. Not limited by a flat plate, the Fabry-Perot condition is also suitable for a scraggy plate when the thickness variation is relatively small. Finally, using the transfer matrix method, the wave propagation in a plate with multiple layers is quantitatively investigated, and the frequency analysis for total transmission is carried out. The methodology, as well as the design scheme proposed, is achievable and artificially controllable, which opens a new prospect for the wave control and final applications in aeronautics and astronautics.
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  • Wind Power Forecasting Methods Based on Deep Learning: A Survey
  • Abstract Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics.
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  • Fusion of Medical Images in Wavelet Domain: A Hybrid Implementation
  • Abstract This paper presents a low intricate, profoundly energy effective MRI Images combination intended for remote visual sensor frameworks which leads to improved understanding and implementation of treatment; especially for radiology. This is done by combining the original picture which leads to a significant reduction in the computation time and frequency. The proposed technique conquers the calculation and energy impediment of low power tools and is examined as far as picture quality and energy is concerned. Reenactments are performed utilizing MATLAB 2018a, to quantify the resultant vitality investment funds and the reproduction results show that the proposed calculation is very quick and devours just around 1% of vitality decomposition by the hybrid combination plans. Likewise, the effortlessness of our proposed strategy makes it increasingly suitable for continuous applications.
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  • A Hybrid Encryption Algorithm for Security Enhancement of Wireless Sensor Networks: A Supervisory Approach to Pipelines
  • Abstract Transmission pipelines are vulnerable to various accidents and acts of vandalism. Therefore, a reliable monitoring system is needed to secure the transmission pipelines. A wireless sensor network is a wireless network consisting of distributed devices distributed at various distances, which monitors the physical and environmental conditions using sensors. Wireless sensor networks have many uses, including the built-in sensor on the outside of the pipeline or installed to support bridge structures, robotics, healthcare, environmental monitoring, etc. Wireless Sensor networks could be used to monitor the temperature, pressure, leak detection and sabotage of transmission lines. Wireless sensor networks are vulnerable to various attacks. Cryptographic algorithms have a good role in information security for wireless sensor networks. Now, various types of cryptographic algorithms provide security in networks, but there are still some problems. In this research, to improve the power of these algorithms, a new hybrid encryption algorithm for monitoring energy transmission lines and increasing the security of wireless sensor networks is proposed. The proposed hybrid encryption algorithm provides the security and timely transmission of data in wireless sensor networks to monitor the transmission pipelines. The proposed algorithm fulfills three principles of cryptography: integrity, confidentiality and authentication. The details of the algorithm and basic concepts are presented in such a way that the algorithm can be operational.
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  • Conceptual Modular Design of Auto Body Frame Based on Hybrid Optimization Method
  • Abstract This article presents a systematic research methodology of modular design for conceptual auto body frame by hybrid optimization method. A modified graph-based decomposition optimization algorithm is utilized to generate an optimal BIW assembly topo model composed of “potential modules”. The consistency constraint function in collaborative optimization is extended to maximize the commonality of modules and minimize the performance loss of all car types in the same product family simultaneously. A novel screening method is employed to select both “basic structures” and “reinforcement” modules based on the dimension optimization of the manufacturing elements and the optimal assembly mode; this allows for a more exhaustive modular platform design in contrast with existing methods. The proposed methodology is applied to a case study for the modular design of three conceptual auto body types in the same platform to validate its feasibility and effectiveness.
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  • FSPAM: A Feature Construction Method to Identifying Cell Populations in ScRNA-seq Data
  • Abstract The emergence of single-cell RNA-sequencing (scRNA-seq) technology has introduced new information about the structure of cells, diseases, and their associated biological factors. One of the main uses of scRNA-seq is identifying cell populations, which sometimes leads to the detection of rare cell populations. However, the new method is still in its infancy and with its advantages comes computational challenges that are just beginning to address. An important tool in the analysis is dimensionality reduction, which transforms high dimensional data into a meaningful reduced subspace. The technique allows noise removal, visualization and compression of high-dimensional data. This paper presents a new dimensionality reduction approach where, during an unsupervised multistage process, a feature set including high valuable markers is created which can facilitate the isolation of cell populations. Our proposed method, called fusion of the Spearman and Pearson affinity matrices (FSPAM), is based on a graph-based Gaussian kernel. Use of the graph theory can be effective to overcome the challenge of the nonlinear relations between cellular markers in scRNA-seq data. Furthermore, with a proper fusion of the Pearson and Spearman correlation coefficient criteria, it extracts a set of the most important features in a new space. In fact, the FSPAM aggregates the various aspects of cell-to-cell similarity derived from the Pearson and Spearman metrics, and reveals new aspects of cell-to-cell similarity, which can be used to extract new features. The results of the identification of cell populations via k-means++ clustering method based on the features extracted from the FSPAM and different datasets of scRNA-seq suggested that the proposed method, regardless of the characteristics that govern each dataset, enjoys greater accuracy and better quality compared to previous methods.
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  • Numerical Analysis of Non-Fourier Heat Transfer in a Solid Cylinder with Dual-Phase-Lag Phenomenon
  • Abstract In this study, transient non-Fourier heat transfer in a solid cylinder is analytically solved based on dual-phase-lag for constant axial heat flux condition. Governing equations for the model are expressed in two-dimensional cylindrical coordinates; the equations are nondimensionalized and exact solution for the equations is presented by using the separation of variable method. Results showed that the dual-phase-lag model requires less time to meet the steady temperature compared with single-phase-lag model. On the contrary, thermal wave diffusion speed for the dual-phase-lag model is greater than the single-phase-lag model. Also the effect of relaxation time in dual-phase-lag model has been taken on consideration.
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