Mathematical Constraints in Multiscale Subgrid-Scale Modeling of Nonlinear Systems

To shed light on the subgrid-seale （SGS） modeling methodology of nonlinear systems such as the Navier-Stokes turbulence, we define the concepts of assumption and restriction in the modeling procedure, which are shown by generalized derivation of three general mathematical constraints for different combinations of restrictions. These constraints are verified numerically in a one-dimensional nonlinear advection equation. This study is expected to inspire future research on the SGS modeling methodology of nonlinear systems.

Cosmic Constraints to the wCDM Model from Strong Gravitational Lensing

We study the cosmic constraint to the wCDM （cold dark matter with a constant equation of state w） model via 118 strong gravitational lensing systems which are compiled from SLA CS, BELLS, LSD and SL2S surveys, where the ratio between two angular diameter distances Dobs =DA（Zl, Zs ） / D A （ O, Zs ） is taken as a cosmic observable. To obtain this ratio, we adopt two strong tensing models： one is the singular isothermal sphere model （SIS） and the other one is the power-law density profile （PLP） model. Via the Markov chain Monte Carlo method, the posterior distribution of the cosmological model parameters space is obtained. The results show that the cosmological model parameters are not sensitive to the parameterized forms of the power-law index γ. Furthermore, the PLP model gives a relatively tighter constraint to the cosmological parameters than that of the SIS model. The predicted value of Ωm = 0.31＋0.44 -0.24 by the SIS model is compatible with that obtained by P1anck2015： Ωm = 0.313 ± 0.013. However, the value of Ωm =0.15＋0.13 -0.11 based on the PLP model is smaller and has 1.25σ tension with that obtained by Planck2015.

Energy Conditions and Constraints on the Generalized Non-Local Gravity Model

We study and derive the energy conditions in generalized non-local gravity, which is the modified theory of general relativity obtained by adding a term m2n-2R□-nRto the Einstein-Hilbert action. Moreover, to obtain some insight on the meaning of the energy conditions, we illustrate the evolutions of four energy conditions with the model parameter ε for different n. By analysis we give the constraints on the model parameters ε.

Rheological Behaviour for Polymer Melts and Concentrated Solutions Part Ⅰ:A New Multiple Reptation Model to Predict the Nonlinear Visco-elasticity with Nagai Chain Constraints in Entangled Polymer Melts

An approach of stochastically statistical mechanics and a unified molecular theory of nonlinear viscoelasticity with constraints of Nagai chain entanglement for polymer melts have been proposed. A multimode model structure for a single polymer chain with n tail segments and N reversible entanglement sites on the test polymer chain is developed. Based on the above model structure and the mechanism of molecular flow by the dynamical reorganization of entanglement sites, the probability distribution function of the end-to-end vectr for a single polymer chain at entangled state and the viscoelastic free energy of deformation for polymer melts are calculated by using the method of the stochastically statistical mechanics. The four types of stress-strain relation and the memory function are derived from this thery. The above theoretical relations are verified by the experimentaf data for various polymer melts. These relations are found to be in good agreement with the experimental results

New model reference adaptive control with input constraints

A new scheme of adaptive control is proposed for a class of linear time-invariant（ LTI） dynamical systems,especially in aerospace,with matched parametric uncertainties and input constraints. Based on a typical and conventional direct model reference adaptive control scheme,various modifications have been employed to achieve the goal. ＂C omposite model reference adaptive control＂of higher performance is seam-lessly combined with ＂positive μ-mod＂,which consequently results in a smooth tracking trajectory despite of the input constraints. In addition,bounded-gain forgetting is utilized to facilitate faster convergence of parameter estimates. The stability of the closed-loop systemcan be guaranteed by using Lyapunov theory.The merits and effectiveness of the proposed method are illustrated by a numerical example of the longitudinal dynamical systems of a fixed-wing airplane.

Model-constrained and data-driven double-supervision acoustic impedance inversion

Seismic impedance inversion is an important technique for structure identification and reservoir prediction.Model-based and data-driven impedance inversion are the commonly used inversion methods.In practice,the geophysical inversion problem is essentially an ill-posedness problem,which means that there are many solutions corresponding to the same seismic data.Therefore,regularization schemes,which can provide stable and unique inversion results to some extent,have been introduced into the objective function as constrain terms.Among them,given a low-frequency initial impedance model is the most commonly used regularization method,which can provide a smooth and stable solution.However,this model-based inversion method relies heavily on the initial model and the inversion result is band limited to the effective frequency bandwidth of seismic data,which cannot effectively improve the seismic vertical resolution and is difficult to be applied to complex structural regions.Therefore,we propose a data-driven approach for high-resolution impedance inversion based on the bidirectional long short-term memory recurrent neural network,which regards seismic data as time-series rather than image-like patches.Compared with the model-based inversion method,the data-driven approach provides higher resolution inversion results,which demonstrates the effectiveness of the data-driven method for recovering the high-frequency components.However,judging from the inversion results for characterization the spatial distribution of thin-layer sands,the accuracy of high-frequency components is difficult to guarantee.Therefore,we add the model constraint to the objective function to overcome the shortages of relying only on the data-driven schemes.First,constructing the supervisor1 based on the bidirectional long short-term memory recurrent neural network,which provides the predicted impedance with higher resolution.Then,convolution constraint as supervisor2 is introduced into the objective function to guarantee the reliability and accuracy of the inversion results,which makes the synthetic seismic data obtained from the inversion result consistent with the input data.Finally,we test the proposed scheme based on the synthetic and field seismic data.Compared to model-based and purely data-driven impedance inversion methods,the proposed approach provides more accurate and reliable inversion results while with higher vertical resolution and better spatial continuity.The inversion results accurately characterize the spatial distribution relationship of thin sands.The model tests demonstrate that the model-constrained and data-driven impedance inversion scheme can effectively improve the thin-layer structure characterization based on the seismic data.Moreover,tests on the oil field data indicate the practicality and adaptability of the proposed method.

Multiproduct and multistage integrated production planning model and algorithm based on an available production capacity network

This research attempts to devise a multistage and multiproduct short-term integrative production plan that can dynamically change based on the order priority and virtual occupancy for application in steel plants. Considering factors such as the delivery time, varietal compatibility between different products, production capacity of variety per hour, minimum or maximum batch size, and transfer time, we propose an available production capacity network with varietal compatibility and virtual occupancy for enhancing production plan implementation and quick adjustment in the case of dynamic production changes. Here available means the remaining production capacity after virtual occupancy.To quickly build an available production capacity network and increase the speed of algorithm solving, constraint selection and cutting methods with order priority were used for model solving. Finally, the genetic algorithm improved with local search was used to optimize the proposed production plan and significantly reduce the order delay rate. The validity of the proposed model and algorithm was numerically verified by simulating actual production practices. The simulation results demonstrate that the model and improved algorithm result in an effective production plan.

Optimization of Line Configuration and Balancing for Flexible Machining Lines

Line configuration and balancing is to select the type of line and allot a given set of operations as well as machines to a sequence of workstations to realize high-efficiency production. Most of the current researches for machining line configuration and balancing problems are related to dedicated transfer lines with dedicated machine workstations. With growing trends towards great product variety and fluctuations in market demand, dedicated transfer lines are being replaced with flexible machining line composed of identical CNC machines. This paper deals with the line configuration and balancing problem for flexible machining lines. The objective is to assign operations to workstations and find the sequence of execution, specify the number of machines in each workstation while minimizing the line cycle time and total number of machines. This problem is subject to precedence, clustering, accessibility and capacity constraints among the features, operations, setups and workstations. The mathematical model and heuristic algorithm based on feature group strategy and polychromatic sets theory are presented to find an optimal solution. The feature group strategy and polychromatic sets theory are used to establish constraint model. A heuristic operations sequencing and assignment algorithm is given. An industrial case study is carried out, and multiple optimal solutions in different line configurations are obtained. The case studying results show that the solutions with shorter cycle time and higher line balancing rate demonstrate the feasibility and effectiveness of the proposed algorithm. This research proposes a heuristic line configuration and balancing algorithm based on feature group strategy and polychromatic sets theory which is able to provide better solutions while achieving an improvement in computing time.

Study on soybean potential productivity and food security in China under the influence of COVID-19 outbreak

The COVID-19 outbreak that became a global pandemic in early 2020 is starting to affect agricultural supply chains and leading to a rapid rise in global food prices.As many grain exporting countries announced a ban on grain exports,food security issues in China have attracted a significant international attention.Based on the Suitability Distribution Model and Soybean-Cereal Constraint Model,we explored the relationship between soybean production potential and food security.We calculated that the soybean potential planting area in China is 164.3 million ha.If the outbreak prevents China from importing soybeans,soybean planting area will need to be increased by 6.9 times to satisfy the demands.In the meantime,cereal self-sufficiency rate will drop to 63.4%,which will greatly affect food security.Each additional unit of soybean production will reduce 3.9 units of cereal production,and 1%increase in the self-sufficiency rate of soybean will result in a 0.63%drop in the self-sufficiency rate of cereal.Without sacrificing the self-sufficiency rate of cereal,the self-sufficiency rate of soybean is limited to 42%.Consequently,China will still need to import more than 68%of the current import volume of soybean.Although in the short term,the outbreak will not affect food security in China,as soybean imports decrease,insufficient supply of soybeans will affect people’s quality of life.To prevent the impact of the COVID-19 outbreak,China should increase soybean stocks and strengthen international cooperation.In the long term,increasing the self-sufficiency rate is a fundamental solution to solving soybean import dependency.The key to increasing soybean cultivation is by making soybean cultivation profitable and by building a sustainable soybean planting chain.

ELECTROWEAK MODEL AND CONSTRAINTS ON NEW PHYSICS

Revised November 2013 by J. Erler （U. Mexico） and A. Freit＆s （Pittsburgh U.）.10.1 Introduction 10.2 Renormalization and radiative corrections

SELF

On the research of assembly modeling of mechanical products,current CAD systems can only support the design Process of component-to-assembly. It is difficult to realize the design process of assembly-to -component.The theory of self-organizing assembly modeling based on relational constraints is proposed, which implements the product design of assembly-to-component commencing with conceptual design and supporting abstract design and step-nice refinement design.

Dynamic airspace sectorization via improved genetic algorithm

This paper deals with dynamic airspace sectorization （DAS） problem by an improved genetic algorithm （iGA）. A graph model is first constructed that represents the airspace static structure. Then the DAS problem is formulated as a graph-partitioning problem to balance the sector workload under the premise of ensuring safety. In the iGA, multiple populations and hybrid coding are applied to determine the optimal sector number and airspace sectorization. The sector constraints are well satisfied by the improved genetic operators and protect zones. This method is validated by being applied to the airspace of North China in terms of three indexes, which are sector balancing index, coordination workload index and sector average flight time index. The improvement is obvious, as the sector balancing index is reduced by 16.5 %, the coordination workload index is reduced by 11.2 %, and the sector average flight time index is increased by 11.4 % during the peak-hour traffic.

PREDICTIVE APPROACH TO FAILURE OF COMPOSITE LAMINATES WITH EQUIVALENT CONSTRAINT MODEL

This work established a new analytical model based upon the equivalent constraint model （ECM） to constitute an available predictive approach for analyzing the ultimate strength and simulating the stress/strain response of general symmetric laminates subjected to combined loading, by taking into account the effect of matrix cracking. The ECM was adopted to mainly predict the in-plane stiffness reduction of the damaged laminate. Basic consideration that progressive matrix cracking provokes a re-distribution of the stress fields on each lamina within laminates, which greatly deteriorates the stress distributed in the primary load-bearing lamina and leads to the final failure of the laminates, is introduced for the construction of the failure criterion. The effects of lamina properties, lay-up configurations and loading conditions on the behaviors of the laminates were examined in this paper. A comparison of numerical results obtained from the established model and other existed models and published experimental data was presented for different material systems. The theory predictions demonstrated great match with the experimental observations investigated in this study.

Hierarchical Geometric Constraint Model for Parametric Feature Based Modeling

A new geometric constraint model is described, which is hierarchical and suitable for parametric feature based modeling. In this model, different levels of geometric information are represented to support various stages of a design process. An efficient approach to parametric featu-re based modeling is also presented, adopting the high level geometric constraint model. The low level geometric model such as B-reps can be derived automatically from the high level geometric constraint model, enabling designers to perform their task of detailed design.

The Interaction between Control and Computing Theories:New Approaches

Day by day, networked control system（NCS） methods have been promoted for distributed closed-loop control systems.Interestingly, the integration of control and computing theories enhanced the development of networked control systems through remote control for wide applications employing the internet. Two further directions to networked control technology are LeaderFollower systems and model predictive control systems. Cloud control system is looked at an extension of networked control systems（NCS） using internet of things（IOT） methodologies. In this paper, a comprehensive literature survey of the new technology of control systems application performed on cloud computing is presented.