Application of Mathematical Models for Different Electroslag Remelting Processes

2017 ◽  
Vol 36 (4) ◽  
pp. 411-426
Author(s):  
Zhou Hua Jiang ◽  
Jia Yu ◽  
Fu Bin Liu ◽  
Xu Chen ◽  
Xin Geng
Keyword(s):  
Mathematical Models ◽  
Numerical Models ◽  
Electroslag Remelting ◽  
Scale Model ◽  
Multi Scale ◽  
Chemical Refining ◽  
Technology Process ◽  
Temperature Environment ◽  
The Relationship ◽  
Special Steels

AbstractThe electroslag remelting (ESR) process has been effectively applied to produce high grade special steels and super alloys based on the controllable solidification and chemical refining process. Due to the difficulties of precise measurements in a high temperature environment and the excessive expenses, mathematical models have been more and more attractive in terms of investigating the transport phenomena in ESR process. In this paper, the numerical models for different ESR processes made by our lab in last decade have been introduced. The first topic deals with traditional ESR process predicting the relationship between operating parameters and metallurgical parameters of interest. The second topic is concerning the new ESR technology process including ESR with current-conductive mould (CCM), ESR hollow ingot technology, electroslag casting with liquid metal(ESC LM), and so on. Finally, the numerical simulation of solidification microstructure with multi-scale model is presented, which reveals the formation mechanism of microstructure.

A Multi-Scale Modeling Method Based on Big Data

2015 ◽  
Vol 734 ◽  
pp. 447-450 ◽  
Author(s):  
Ji Wei Liu
Keyword(s):  
Big Data ◽  
Network Models ◽  
Modeling Method ◽  
Scale Model ◽  
Neural Network Models ◽  
Multi Scale ◽  
Scale Modeling ◽  
Input And Output ◽  
Multi Scale Modeling ◽  
The Relationship

A multi-scale modeling method based on big data was proposed to establish neural network models for complex plant. Wavelet transform was used to decompose input and output parameters into different scales. The relationship between these parameters were researched in every scale. Then models in each scale were established and added together to form a multi-scale model. A model of coal mill current in power plant was established using the multi-scale modeling method based on big data. The result shows that, the method is effective.

scholarly journals A multi-scale flow model for blood regulation in a realistic vascular system

2020 ◽  
Author(s):  
Ulin Nuha Abdul Qohar ◽  
Antonella Zanna Munthe-Kaas ◽  
Jan Martin Nordbotten ◽  
Erik Andreas Hanson
Keyword(s):  
Blood Flow ◽  
Blood Circulation ◽  
Numerical Models ◽  
Scale Model ◽  
Fluid Exchange ◽  
Desktop Computer ◽  
Multi Scale ◽  
Proposed Model ◽  
Porous Media Model ◽  
Capillary Bed

Abstract In the last decade, numerical models have been an increasingly important tool in medical science both for the fundamental understanding of the physiology of the human body as well as for diagnostics and personalized medicine. In this paper, a multi-scale model is developed for blood flow and regulation in a full vascular structure of an organ. We couple a 1D vascular graph model to represent blood flow in larger vessels and a porous media model to describe flow in smaller vessels and capillary bed. The vascular model is based on Poiseuille’s law, with pressure correction by elasticity and pressure drop estimation at vessels junctions. The porous capillary bed is modeled as a two compartments domain (arterial and venal) and Darcy’s law. The fluid exchange between the arterial and venal capillary bed compartments is defined as blood perfusion. The numerical experiments show that the proposed model for blood circulation: 1) is closely dependent on the structure and parameters of both the vascular vessels and of the capillary bed, and 2) it provides a realistic blood circulation in the organ. The advantage of the proposed model is that it is complex enough to capture the underlying physiology reliably, yet highly flexible as it offers the possibility of incorporating various local effects. Furthermore, the numerical implementation of the model is straightforward and allows for simulations on a regular desktop computer.

scholarly journals Formulation and Stability Analysis of a Multi-Scale Modeling Approach for Simulation of Elastic Wave Propagation

2021 ◽  
pp. 2141010
Author(s):  
Siddhesh Raorane ◽  
Tadeusz Uhl ◽  
Pawel Packo
Keyword(s):  
Wave Propagation ◽  
Stability Analysis ◽  
Elastic Wave ◽  
Numerical Models ◽  
Practical Interest ◽  
Elastic Wave Propagation ◽  
Scale Model ◽  
Perfect Agreement ◽  
Multi Scale ◽  
Analytical Formulae

In this work, we report on the formulation and detailed stability analysis of a dynamic multi-scale scheme involving two different local computational strategies for modeling of elastic wave propagation. The coupled model involves the Local Interaction Simulation Approach and Cellular Automata for Elastodynamics, however the presented analysis approach is general and applies to other numerical techniques. This scheme is capable of coupling two numerical models with possibly dissimilar spatial discretization lengths and material properties, hence it is appealing for a multi-scale and/or multi-resolution analysis. The method developed in this paper employs an interface force–displacement coupling to yield the multi-scale model equations. It is shown that the governing equations contain a self-coupling term that affects the stability of the system, as it contributes to additional stiffness at the interface. Stability analysis is presented in terms of rotations of two vectors in [Formula: see text] space, where each vector represents individual model’s stability. Three model configurations of practical interest were investigated, analytical formulae derived and used to analyze stability. These analytical formulae were compared against results from numerical simulations and perfect agreement was observed.

An Object-Oriented Analysis of Complex Numerical Models

2014 ◽  
Vol 611-612 ◽  
pp. 1356-1363 ◽  
Author(s):  
Piotr Macioł ◽  
Romain Bureau ◽  
Christof Sommitsch
Keyword(s):  
Numerical Models ◽  
Object Oriented ◽  
Scale Model ◽  
Internal Variables ◽  
Object Oriented Design ◽  
Multi Scale ◽  
Advantages And Disadvantages ◽  
Single Scale ◽  
Scale Models ◽  
Modelling Methodology

Modelling the behaviour of metal alloys during their thermo-mechanical processing relies on the physical and mathematical description of numerous phenomena occurring in several space scales and evolving on different characteristic times. Although it is possible to develop complicated multi-scale models, it is often simpler to simulate each phenomenon separately in a single-scale model and link all the models together in a global structure responsible for their good interaction. Such a structure is relatively difficult to design. Both efficiency and flexibility must be well balanced, keeping in mind the character of scientific computing. In that context, the Agile Multiscale Modelling Methodology (AM3) has been developed in order to support the object-oriented designing of complex numerical models [. In this paper, the application of the AM3 for designing a model of the metal alloy behaviour is presented. The basis and some consequences of the application of the Object-Oriented design of a sub-models structure are investigated. The object-oriented (OO) design of a 3 internal variables model of the dislocations evolution is presented and compared to the procedural one. The main advantages and disadvantages of the OO design of numerical models are pointed out.

Multi-scale characterization and modelling of damage evolution in nuclear Gilsocarbon graphite

2015 ◽  
Vol 1809 ◽  
pp. 1-6 ◽  
Author(s):  
Dong Liu ◽  
Peter Heard ◽  
Branko Šavija ◽  
Gillian Smith ◽  
Erik Schlangen ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Mechanical Testing ◽  
Numerical Models ◽  
Ion Beam ◽  
Microstructural Characterization ◽  
Scale Model ◽  
Small Scale ◽  
Length Scale ◽  
Multi Scale ◽  
Scale Characterization

ABSTRACTIn the present work, the microstructure and mechanical properties of Gilsocarbon graphite have been characterized over a range of length-scales. Optical imaging, combined with 3D X-ray computed tomography and 3D high-resolution tomography based on focus ion beam milling has been adopted for microstructural characterization. A range of small-scale mechanical testing approaches are applied including an in situ micro-cantilever technique based in a Dualbeam workstation. It was found that pores ranging in size from nanometers to tens of micrometers in diameter are present which modify the deformation and fracture characteristics of the material. This multi-scale mechanical testing approach revealed the significant change of mechanical properties, for example flexural strength, of this graphite over the length-scale from a micrometer to tens of centimeters. Such differences emphasize why input parameters to numerical models have to be undertaken at the appropriate length-scale to allow predictions of the deformation, fracture and the stochastic features of the strength of the graphite with the required confidence. Finally, the results from a multi-scale model demonstrated that these data derived from the micro-scale tests can be extrapolated, with high confidence, to large components with realistic dimensions.

Three-dimensional Reconstruction of Microscopic Image Based on Multi-ST Algorithm

2020 ◽  
Vol 64 (2) ◽  
pp. 20506-1-20506-7
Author(s):  
Min Zhu ◽  
Rongfu Zhang ◽  
Pei Ma ◽  
Xuedian Zhang ◽  
Qi Guo
Keyword(s):  
3D Reconstruction ◽  
Three Dimensional ◽  
Scale Model ◽  
Microscopic Image ◽  
Multi Scale ◽  
Gaussian Pyramid ◽  
Cost Aggregation ◽  
Dimensional Reconstruction ◽  
Image Pairs ◽  
Accurate Matching

Abstract Three-dimensional (3D) reconstruction is extensively used in microscopic applications. Reducing excessive error points and achieving accurate matching of weak texture regions have been the classical challenges for 3D microscopic vision. A Multi-ST algorithm was proposed to improve matching accuracy. The process is performed in two main stages: scaled microscopic images and regularized cost aggregation. First, microscopic image pairs with different scales were extracted according to the Gaussian pyramid criterion. Second, a novel cost aggregation approach based on the regularized multi-scale model was implemented into all scales to obtain the final cost. To evaluate the performances of the proposed Multi-ST algorithm and compare different algorithms, seven groups of images from the Middlebury dataset and four groups of experimental images obtained by a binocular microscopic system were analyzed. Disparity maps and reconstruction maps generated by the proposed approach contained more information and fewer outliers or artifacts. Furthermore, 3D reconstruction of the plug gauges using the Multi-ST algorithm showed that the error was less than 0.025 mm.

Identifying Physico-Chemical Laws from the Robotically Collected Data

2019 ◽  
Author(s):  
Liwei Cao ◽  
Danilo Russo ◽  
Vassilios S. Vassiliadis ◽  
Alexei Lapkin
Keyword(s):  
Experimental Data ◽  
Numerical Models ◽  
Predictor Variable ◽  
Physical Models ◽  
Training Data ◽  
Mixed Integer ◽  
Physico Chemical ◽  
Data Points ◽  
Future Work ◽  
The Relationship

<p>A mixed-integer nonlinear programming (MINLP) formulation for symbolic regression was proposed to identify physical models from noisy experimental data. The formulation was tested using numerical models and was found to be more efficient than the previous literature example with respect to the number of predictor variables and training data points. The globally optimal search was extended to identify physical models and to cope with noise in the experimental data predictor variable. The methodology was coupled with the collection of experimental data in an automated fashion, and was proven to be successful in identifying the correct physical models describing the relationship between the shear stress and shear rate for both Newtonian and non-Newtonian fluids, and simple kinetic laws of reactions. Future work will focus on addressing the limitations of the formulation presented in this work, by extending it to be able to address larger complex physical models.</p><p><br></p>

Effects of parametric uncertainty on multi-scale model predictions of shock response of a pressed energetic material

2019 ◽  
Vol 125 (23) ◽  
pp. 235104 ◽  
Author(s):  
Sangyup Lee ◽  
Oishik Sen ◽  
Nirmal Kumar Rai ◽  
Nicholas J. Gaul ◽  
K. K. Choi ◽  
...  
Keyword(s):  
Energetic Material ◽  
Parametric Uncertainty ◽  
Scale Model ◽  
Multi Scale ◽  
Model Predictions ◽  
Shock Response

scholarly journals The Winter Habitat Selection of Red Deer (Cervus elaphus) Based on a Multi-Scale Model

Animals ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 2454
Author(s):  
Yue Sun ◽  
Yanze Yu ◽  
Jinhao Guo ◽  
Minghai Zhang
Keyword(s):  
Habitat Selection ◽  
Environmental Factors ◽  
Cervus Elaphus ◽  
Red Deer ◽  
Scale Model ◽  
Winter Habitat ◽  
Multi Scale ◽  
Optimal Scale ◽  
Scale Models ◽  
Selection Of

Single-scale frameworks are often used to analyze the habitat selections of species. Research on habitat selection can be significantly improved using multi-scale models that enable greater in-depth analyses of the scale dependence between species and specific environmental factors. In this study, the winter habitat selection of red deer in the Gogostaihanwula Nature Reserve, Inner Mongolia, was studied using a multi-scale model. Each selected covariate was included in multi-scale models at their “characteristic scale”, and we used an all subsets approach and model selection framework to assess habitat selection. The results showed that: (1) Univariate logistic regression analysis showed that the response scale of red deer to environmental factors was different among different covariate. The optimal scale of the single covariate was 800–3200 m, slope (SLP), altitude (ELE), and ratio of deciduous broad-leaved forests were 800 m in large scale, except that the farmland ratio was 200 m in fine scale. The optimal scale of road density and grassland ratio is both 1600 m, and the optimal scale of net forest production capacity is 3200 m; (2) distance to forest edges, distance to cement roads, distance to villages, altitude, distance to all road, and slope of the region were the most important factors affecting winter habitat selection. The outcomes of this study indicate that future studies on the effectiveness of habitat selections will benefit from multi-scale models. In addition to increasing interpretive and predictive capabilities, multi-scale habitat selection models enhance our understanding of how species respond to their environments and contribute to the formulation of effective conservation and management strategies for ungulata.

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