Numerical Analysis of PRISM-PY Calculations for Hard-and Soft-Core Generic Polymer Models

Generic polymer models capturing the chain connectivity and excluded-volume interactions between polymer segments can be classified, according to whether or not the 3D integral of the latter diverges, into hard- and soft-core models. Taking homogeneous systems of compressible homopolymer melts (or equivalently homopolymer solutions in an implicit, good solvent) in the continuum as an example, we recently compared the correlation effects on the structural and thermodynamic properties of the hard- and soft-core models given by the polymer reference interaction site model (PRISM) theory with the Percus-Yevick (PY) closure (Polymers 2023, 15, 1180). Here we analyzed in detail the numerical errors and behavior of the interchain pair correlation functions (PCFs) given by the PRISM-PY calculations of these models using an efficient numerical approach that we proposed. Our numerical approach has the least number of independent variables to be iteratively solved, analytically treats the discontinuities caused by the non-bonded pair potential (such as that of the hard spheres) and takes only the inverse Fourier transform of the interchain indirect PCF between polymer segments (which is continuous and decays towards 0 with increasing wavenumber much faster than both the interchain direct and total PCFs), and is essential for us to accurately solve the PRISM-PY theory for chain length N as large as 106. To capture the correlation-hole effect, the real-space cut-off in the PRISM calculations should be proportional to the square root of N.

Generic-Model-Based Description Scheme for MPEG-7

We propose a new description scheme for MPEG-7:Generic-model-based Description Scheme to describe contents of audio, video, text and other sorts of multimedia. It uses a generic model as the description frame, which provides a simple but useful object\|based structure. The main components of the description scheme are generic model, objects and object features. The proposed description scheme is illustrated and exemplified by Extensible Markup Language. It aims at clarity and flexibility to support MPEG\|7 applications such as query and edit. We demonstrate its feasibility and efficiency by presenting applications: Digital Broadcasting and Edit System (DEBS) and Non\|linear Edit System (NLES) that already used the generic structure or will greatly benefit from it.

A Generic Design Model for Evolutionary Algorithms

A generic design model for evolutionary algorithms is proposed in this paper. The model, which was described by UML in details, focuses on the key concepts and mechanisms in evolutionary algorithms. The model not only achieves separation of concerns and encapsulation of implementations by classification and abstraction of those concepts, it also has a flexible architecture due to the application of design patterns. As a result, the model is reusable, extendible, easy to understand, easy to use, and easy to test. A large number of experiments applying the model to solve many different problems adequately illustrate the generality and effec-tivity of the model.

Validation of Full-Converter Wind Power Plant Generic Model Based on Actual Fault Ride-Through Measurements

Modeling and validation of full power converter wind turbine models with field measurement data are rarely reported in papers. In this paper an aggregated generic dynamic model of the wind farm consisting of full power converter wind turbines is composed and the model validation based on actual field measurements is performed. The paper is based on the measurements obtained from the real short circuit test applied to connection point of observed wind farm. The presented approach for validating the composed model and fault ride-through （FRT） capability for the whole wind park is unique in overall practice and its significance and importance is described and analyzed.

Boiler-turbine control system design using continuous-time nonlinear model predictive control

A continuous-time nonlinear model predictive controller(NMPC) was designed for a boiler-turbine unit.The controller was designed by optimizing a receding-horizon performance index,with the nonlinear system approximated by its Taylor series expansion with a certain order,the magnitude saturation constraints on the inputs satisfied by increasing the predictive time,and the rate saturation conditions on the actuators satisfied by tuning the time constant of the reference trajectories in a reference governor.Simulation results showed that the controller can drive the drum pressure and output power of the nonlinear boiler-turbine unit to follow their respective reference trajectories throughout a varying operation range and keep the water level deviation within tolerances.Comparison of the NMPC scheme with the generic model control(GMC) scheme indicated that the responses are slower and there are more oscillations in the responses of the water level,fuel flow input and feed water flow input in the GMC scheme when the boiler-turbine unit is operating over a wide range.

Sensor Fault Tolerant Generic Model Control for Nonlinear Systems

A modified Strong Tracking Filter (STF) is used to develop a new approach to sensor fault tolerant control. Generic Model Control (GMC) is used to control the nonlinear process while the process runs normally because of its robust control performance. If a fault occurs in the sensor, a sensor bias vector is then introduced to the output equation of the process model. The sensor bias vector is estimated on line during every control period using the STF. The estimated sensor bias vector is used to develop a fault detection mechanism to supervise the sensors. When a sensor fault occurs, the conventional GMC is switched to a fault tolerant control scheme, which is, in essence, a state estimation and output prediction based GMC. The laboratory experimental results on a three tank system demonstrate the effectiveness of the proposed Sensor Fault Tolerant Generic Model Control (SFTGMC) approach.

Flow-aware synthesis: A generic motion model for video frame interpolation

A popular and challenging task in video research,frame interpolation aims to increase the frame rate of video.Most existing methods employ a fixed motion model,e.g.,linear,quadratic,or cubic,to estimate the intermediate warping field.However,such fixed motion models cannot well represent the complicated non-linear motions in the real world or rendered animations.Instead,we present an adaptive flow prediction module to better approximate the complex motions in video.Furthermore,interpolating just one intermediate frame between consecutive input frames may be insufficient for complicated non-linear motions.To enable multi-frame interpolation,we introduce the time as a control variable when interpolating frames between original ones in our generic adaptive flow prediction module.Qualitative and quantitative experimental results show that our method can produce high-quality results and outperforms the existing stateof-the-art methods on popular public datasets.

Learning Invariant Representation of Multiscale Hyperelastic Constitutive Law from Sparse Experimental Data

Constitutive modeling of heterogeneous hyperelastic materials is still a challenge due to their complex and variable microstructures.We propose a multiscale datadriven approach with a hierarchical learning strategy for the discovery of a generic physics-constrained anisotropic constitutive model for the heterogeneous hyperelastic materials.Based on the sparse multiscale experimental data,the constitutive artificial neural networks for hyperelastic component phases containing composite interfaces are established by the particle swarm optimization algorithm.A microscopic finite element coupled constitutive artificial neural networks solver is introduced to obtain the homogenized stress-stretch relation of heterogeneous materials with different microstructures.And a dense stress-stretch relation dataset is generated by training a neural network through the FE results.Further,a generic invariant representation of strain energy function(SEF)is proposed with a parameter set being implicitly expressed by artificial neural networks(SANN),which describes the hyperelastic properties of heterogeneous materials with different microstructures.A convexity constraint is imposed on the SEF to ensure that the multiscale constitutive model is physically relevant,and the ℓ_(1) regularization combined with thresholding is introduced to the loss function of SANN to improve the interpretability of this model.Finally,the multiscale model is hierarchically trained,cross-validated and tested using the experimental data of cord-rubber composite materials with different microstructures.The proposed multiscale model provides a convenient and general methodology for constitutive modeling of heterogeneous hyperelastic materials.