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Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine. However, it remains a daunting task to predict the nonlinear and transient in-cylinder flow motion because they are highly complex which change both in space and time. Recently, machine learning methods have demonstrated great promises to infer relatively simple temporal flow field development. This paper aims to feature a physics-guided machine learning approach to realize high accuracy and generalization prediction for complex swirl-induced flow field motions.

To achieve high-fidelity time-series prediction of unsteady engine flow fields, this work features an automated machine learning framework with the following objectives: (1) The spatiotemporal physical constraint of the flow field structure is transferred to machine learning structure. (2) The ML inputs and targets are efficiently designed that ensure high model convergence with limited sets of experiments. (3) The prediction results are optimized by ensemble learning mechanism within the automated machine learning framework.

The proposed data-driven framework is proven effective in different time periods and different extent of unsteadiness of the flow dynamics, and the predicted flow fields are highly similar to the target field under various complex flow patterns. Among the described framework designs, the utilization of spatial flow field structure is the featured improvement to the time-series flow field prediction process.

The proposed flow field prediction framework could be generalized to different crank angle periods, cycles and swirl ratio conditions, which could greatly promote real-time flow control and reduce experiments on in-cylinder flow field measurement and diagnostics.

The motion vector estimation algorithm is very widely used in many image process applications, such as the image stabilization and object tracking algorithms. The conventional searching algorithm, based on the block matching manipulation, is used to estimate the motion vectors in conventional image processing algorithms. During the block matching manipulation, the violent motion will result in greater amount of computation. However, too large amount of calculation will reduce the effectiveness of the motion vector estimation algorithm. This paper aims to present a novel searching method to estimate the motion vectors effectively.

This paper presents a novel searching method to estimate the motion vectors for high-resolution image sequences. The searching strategy of this algorithm includes three steps: the larger area searching, the adaptive directional searching and the small area searching.

The achievement of this paper is to develop a motion vector searching strategy to improve the computation efficiency. Compared with the conventional motion vector searching algorithms, the novel motion vector searching algorithm can reduce the motion matching manipulation effectively by 50 per cent.

This paper presents a novel searching strategy to estimate the motion vectors effectively. From the experimental results, the novel motion vector searching algorithm can reduce the motion matching manipulation effectively, compared with the conventional motion vector searching algorithms.

Two efficient computational procedures are presented for generating the global approximation vectors used in conjunction with the reduction methods for the large‐deflection non‐linear analysis of symmetric structures with unsymmetric boundary conditions. Both procedures are based on restructuring the governing equations for each of the unsymmetric global approximation vectors to delineate the different contributions to the symmetric and antisymmetric components of this vector. In the first procedure the unsymmetric global approximation vectors are approximated by linear combinations of symmetric and antisymmetric modes, which are generated by using the finite element method. The amplitudes of these modes are computed by using the classical Rayleigh‐Ritz technique. The second procedure is based on using a preconditioned conjugate gradient (PCG) technique for generating the global approximation vectors, and selecting the preconditioning matrix to be the matrix associated with the symmetric response. In both procedures the size of the analysis model used in generating the global approximation vectors is identical to that of the corresponding structure with symmetric boundary conditions. The similarities between the two procedures are identified, and their effectiveness is demonstrated by means of two numerical examples of large‐deflection, non‐linear static problems of shells.

The advantages of a direct superposition of the Ritz vector in dynamic response analysis (developed by Wilson, Yuan, and Dickens in 1982 and termed the WYD method) are that: no iteration is involved; the method is at least four times faster than the subspace iteration method; and fewer Ritz vectors are necessary for the mode superposition of dynamic response analysis than exact eigenvectors are used. The major purpose of this paper is to illustrate that the WYD method can also be used as a general approximate algorithm to calculate eigenvalues and eigenvectors. The WYD and Lanczos algorithms are very similar and a formula that relates the two is given in this paper. Although the exact algebraic value of only a single eigenvector of a multi‐eigenvalue can be calculated using either the WYD or Lanczos methods, an artificial round‐off is presented that can be used to solve the eigenvalue problem. A method of estimating the error introduced by the WYD method is also developed. A dynamic substructuring technique, based on the WYD method, and which assumes that the connectivities on the interfaces among the substructures need not be considered is also presented.

The Lanczos vectors and the Ritz vectors have been used for computing the dynamic response of linear structures. Although the procedures of using these two sets of vectors appear similar to the procedure of using the eigenvectors to find an approximate solution, the fundamental mechanisms of the three are different. We compare the three sets of vectors in detail to show some of the important differences in the hope that this comparison will be helpful to the use of the Lanczos vectors or the Ritz vectors for computing dynamic responses.

The aim of the paper is to conduct an analytical study of the two new methods of the permanent magnet synchronous motor torque and flux direct control with the predictive non‐linear torque and flux controller.

The method is based on the prediction of the torque and flux error vector in order to minimize the torque ripple and ensure the constant switching frequency.

The proposed methods ensure the torque and flux error vector minimization, reduction of the torque ripples, and constant switching frequency without deterioration of the dynamic properties of the standard direct torque control (DTC).

An innovative predictive DTC method is presented. The correctness of the analysis and main assumptions, as well as the expected final results have been verified in simulation.

Rotational‐translational addition theorems for the vector spheroidal wave functions Ma(i)mn(h; ξ, η, φ) and Na(i)mn(h; ξ, η, φ), i = 1,2,3,4, are derived from those for the corresponding scalar spheroidal wave functions ψ(i)mn(h; ξ, η, φ). A vector spheroidal wave function defined in one spheroidal coordinate system (h; ξ, η, φ) is expressed in terms of a series of vector spheroidal wave functions defined in another spheroidal coordinate system (h′; ξ′, η′, φ′), which is rotated and translated with respect to the first one. These theorems allow a rigorous treatment of boundary value problems relative to time‐harmonic vector field waves in the presence of a system of spheroids with arbitrary orientations. As a special case, general rotational‐translational addition theorems for vector spherical wave functions are also presented.

This chapter reviews factors responsible for climate change, impacts of the change on animal health, zoonotic diseases, and their linkage with One-Health program.

This chapter is based on the available literature related to climate change and its effect on animal health and production from different points. The causes and change forcers of climate change, direct and indirect effects of the change on animal health management, host–pathogen–vector interaction, and zoonotic diseases are included. Inter-linkage between climate change and One-Health program are also assessed.

Beside natural causes of climatic change, greenhouse gases are increasing due to human activities, causing global climate changes which have direct and indirect animal health and production performance impacts. The direct impacts are increased ambient temperature, floods, and droughts, while the indirect are reduced availability of water and food. The change and effect also promote diseases spread, increase survival and availability of the pathogen and its intermediate vector host, responsible for distribution and prevalence of tremendous zoonotic, infectious, and vector-borne diseases. The adverse effect on the biodiversity, distribution of animals and micro flora, genetic makeup of microbials which may lead to emerging and re-emerging disease and their outbreaks make the strong linkage between climate change and One-Health.

Global climate change is receiving increasing international attention where international organizations are increasing their focus on tackling the health impacts. Thus, there is a need for parallel mitigation of climate change and animal diseases in a global form.

Most research on climate change is limited to environmental protection, however this chapter provides a nexus between climate change, animal health, livestock production, and the One-Health program for better livelihood.