Dynamic Factor Correlations

The actual service life of the pavement is related to dynamic tire loads. To accurately calculate dynamic loads, a multibody dynamics model of a 6-axle heavy truck was developed. The difference between simulated and experimental RMS values of wheel centre force and acceleration was within 15%. Based on the validated dynamics model, the effects of ride quality index (RQI), vehicle speed, load capacity, and suspension damping coefficient on dynamic impact factor (DI) and dynamic load amplitude (DLA) were explored. When the vehicle was fully loaded, both DI and DLA increased with decreasing RQI, increasing vehicle speed, and increasing suspension damping coefficient. Among them, the effect of RQI and vehicle speed on DLA and DI was much greater than that of suspension damping factor. In addition, the dispersion of DI values for each axle increased as the vehicle mass decreased. Therefore, it is necessary to consider the combined effects of RQI, vehicle speed and load capacity when applying dynamic tire loads in pavement design or life estimation. The procedure in this paper facilitates the accurate calculation of dynamic loads for different pavement and vehicle parameters and is recommended to be used in vehicle-pavement coupled analysis.

Parallel implementation of efficient preconditioned linear solver for grid-based applications in chemical physics. II: QMR linear solver

Purpose. Development of a mathematical model of a heavy loaded gear transmission with a self-aligning drive gear; evaluation of the dynamic load on the gear transmission in the gear alignment process. Methodology. The calculation schematic and equations of the relative motion of the self-aligning drive gear are formed using the methods of rigid body dynamics. Analytical expressions for the gear self-alignment time, collision velocity during the alignment and dynamic load factor are obtained by integrating an ordinary differential equation. Methods of the linear theory for oscillations are used to determine the dynamic factor. Findings. The article investigates the state-of-art design and mathematical models of the self-aligning gear. An equation for the relative motion of the moving part of the gear has been formed using the methods of rigid body dynamics. It is shown that by using the proposed hypotheses, the movement of the gear can be reduced to rotation about the instantaneous axis. The influence of geometric and dynamic parameters of the ball mill drive on dynamic loads in the open gear transmission is investigated. The gear alignment speed dependences on the tooth mesh misalignment angle in the gear transmission and the inertial parameters of the gear have been obtained. The obtained dependencies were used to calculate the time and speed of the gear alignment in the open gear transmission of the ball mill 5.5 6.5 (central discharge ball mill). It is shown that in the real range of mesh misalignment angles and gear parameters, the time of the gear alignment is several orders of magnitude less than the time of teeth re-engagement. In the presence of the variable component of the mesh misalignment angle, the gear will constantly make a relative motion with strikes; depending on the current value of the mesh misalignment angle, the dynamic load on the gear transmission can be significant. It is shown that when assessing the efficacy of self-aligning gears, it is necessary to take into account a possible increase in dynamic loads. The dynamic factor and the load factor are calculated for the nominal value of the mesh misalignment angle in the open gear transmission of 5.5 6.5 ball mills. Originality. A mathematical model of the dynamics of a self-aligning gear transmission in heavy duty machine drives has been developed. A quantitative assessment of internal dynamic load factor in an open gear transmission of 5.5 6.5 ball mills has been carried out. Practical value. A method for determining the dynamic component of the load on a gear transmission containing a self-aligning drive gear has been developed.

Evaluation of dynamic vehicle axle loads on bridges with different surface conditions

The tiltrotor drive system is a complex and coupled system with different torsional dynamics compared to conventional rotorcraft. It is designed to guarantee safety and reliability and tested for the effects of its dynamics and loads on component sizing. The epicyclic gearing system, the final reduction stage in most aircraft gearboxes, is most impacted by such dynamics. The components of this subsystem must withstand all peak loads due to novel control strategies and torsional dynamics. In this paper, a gear contact analysis model is coupled with a validated torsional dynamics model of a tiltrotor interconnected drive system. The coupled model predicts component-level design loads in the interconnected drive system originating from its torsional dynamic behavior. An accurate gear macro and micro geometry is generated in the gear contact model which is used to derive the time varying mesh stiffnesses at the sub-component interfaces. The variable mesh stiffnesses are applied in the gear dynamic model to simulate the dynamic responses of the gear system under load. Finally, a dynamic overload factor relating the peak dynamic and static loads is analytically assessed, and it is shown to exceed conventional predictions in the case of an asymmetrically loaded interconnected drive system. This dynamic factor can be implemented to inform design decisions during the development of future multi-rotor VTOL aircrafts to mitigate component damage and, in turn, increase the safety and extend the lifespan of critical components.

A hybrid thrust magnetic bearing (HTMB) with permanent magnet creating bias flux is used to save energy. The eddy currents have a fundamental impact on the dynamic characteristics of the magnetic bearing system. Therefore, the dynamic factor models affected by the eddy currents rather than static model are required to insure reliable and effective operation. The dynamic factor models affected by the eddy currents for the HTMB with permanent magnets and subsidiary air gap are presented in this paper. The equivalent magnetic circuit models of the HTMB are given and derived to calculate the dynamic force-current factor and the dynamic force-displacement factor. Furthermore, the dynamic factors of the HTMB are also investigated by the transient nonlinear finite element method (FEM). The analytical results show that the two dynamic factors have the different frequency response characteristics, both in the equivalent magnetic circuit model and from the results of the nonlinear FEM. Furthermore, the distribution of flux density in the air gap at different frequency response is shown and compared to static flux by using FEM. The dynamic performance is determined by the dynamic force-current factor model due to the difference between the control flux path and the permanent magnet bias flux path. The analysis results calculated by the simplified analytical model agree well with the results calculated by transient FEM.

This paper addresses the channel estimation problem for three-dimensional (3-D) massive multiple-input multiple-output (MIMO) systems, where the base station (BS) is equipped with a two-dimensional uniform planar array (UPA) to serve a number of user equipments (UEs). To implement with low hardware complexity, the number of available radio-frequency (RF) chains at BS is constrained to be much smaller than the number of antennas. The theoretical analysis of sparse property for 3-D massive MIMO channel reveals that there exists two kinds of sparse structures in beam-domain channel vector, namely the common sparsity structure among sub-arrays of UPA and block sparsity structure per sub-array. Based on this property, a novel structured sparse Bayesian learning framework is proposed to estimate the channel between BS and UE reliably. Moreover, an adaptive spatial interpolation scheme is proposed to further reduce the number of required RF chains at BS while maintaining the estimation performance. The simulation results show that the proposed scheme provides stable estimation performance for a variety of scenarios with different numbers of RF chains, transmit signal-to-noise ratios, Rician factors, and angular spreads, and outperforms the reference schemes significantly.

This paper proposes a compressive sensing (CS) method for multi-channel electroencephalogram (EEG) signals in Wireless Body Area Network (WBAN) applications, where the battery life of sensors is limited. For the single EEG channel case, known as the single measurement vector (SMV) problem, the Block Sparse Bayesian Learning-BO (BSBL-BO) method has been shown to yield good results. This method exploits the block sparsity and the intra-correlation (i.e., the linear dependency) within the measurement vector of a single channel. For the multichannel case, known as the multi-measurement vector (MMV) problem, the Spatio-Temporal Sparse Bayesian Learning (STSBL-EM) method has been proposed. This method learns the joint correlation structure in the multichannel signals by whitening the model in the temporal and the spatial domains. Our proposed method represents the multi-channels signal data as a vector that is constructed in a specific way, so that it has a better block sparsity structure than the conventional representation obtained by stacking the measurement vectors of the different channels. To reconstruct the multichannel EEG signals, we modify the parameters of the BSBL-BO algorithm, so that it can exploit not only the linear but also the non-linear dependency structures in a vector. The modified BSBL-BO is then applied on the vector with the better sparsity structure. The proposed method is shown to significantly outperform existing SMV and also MMV methods. It also shows significant lower compression errors even at high compression ratios such as 10:1 on three different datasets.

A method to test and calibrate linear circuits is proposed. A tested circuit is partitioned at nodes where the voltage measurements are taken. Test equations are formulated on the basis of Kirchoff's current law equations at the partition points. This results in the Jacobian matrix with a sparse block structure. The test points selection and the element evaluation can be performed in parallel, reducing computation time and enhancing the test performance. >

A new computational scheme for solving the bound state configuration-space Faddeev equations is applied. The scheme is based on the spline-approximation and the adiabatic limit of Faddeev equations. An ordering of variables being in agreement with the limit was chosen. As a result the matrix of the eigenvalue problem has a sparse block structure. Calculations of the bound states of µpp, µdd, µtt mesic molecules and ¯pdd, ¯ptt antiprotonic ones, were performed. To check the method, calculations of the binding energies for such systems as the positronium ion Ps−,3H and3He were carried out. The results are compared with the best results of other authors.

The iterative solution of the large systems of equations that result from discontinuous Galerkin (DG) discretizations require the ability to carry out fast matrix-vector products. DG matrices have a sparse block structure with a constant number of non-zero equal-sized non-overlapping blocks per row. General-purpose sparse matrix-vector product algorithms are not designed to exploit the specic structure of the DG matrices and, as a consequence, result in sub-optimal performance. To address this issue, we propose a sparse matrix-vector product for DG discretizations based on a dense tensor contraction. A GPU implementation of the proposed algorithm for a hybridizable discontinuous Galerkin (HDG) method is tested on the NVIDIA GEFORCE GTX 285. The results show that the tensor contraction performs at about 20 to 25 GFLOP/s in double precision with a sustained eciency of more than 40% (60 GBytes/s) of the peak memory bandwidth (160 GBytes/s). Moreover, for HDG matrices in double precision, the proposed method is 2 times faster than the general sparse matrix-vector products provided by the GPU library CUSPARSE and about 30 times faster than MATLAB running on a CPU.

An efficient preconditioning algorithm is presented for solving linear systems for which the matrix exhibits a certain sparse block structure, such as PDEs in two or more dimensions. From the set of all matrices orthogonally similar to the original— subject to the constraint that blocks of the transformation are proportional to the identity— the most block-diagonally dominant member is determined. The diagonal blocks of this new matrix are then taken as the preconditioner. Constructing the preconditioner is computationally inexpensive, and fully parallelizable. Moreover, the sparsity pattern is preserved under the transformation, and for the scattering applications arising in molecular and chemical physics, it is shown that most of the computation need be performed only once for a large number of linear system solves. Results are summarized for two such systems, for which the total CPU effort was reduced by almost two orders of magnitude. Copyright © 2000 John Wiley & Sons, Ltd.

Parallel implementation of efficient preconditioned linear solver for grid-based applications in chemical physics. I: Block Jacobi diagonalization

Block preconditioning and domain decomposition methods. II

Evaluation of multivariate GARCH models in an optimal asset allocation framework