P. Macioł

An Object-Oriented Analysis of Complex Numerical Models

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.

Knowledge based system for runtime controlling of multiscale model of ion-exchange solvent extraction

The hereby paper concerns the issue of solution of runtime controlling of multiscale model of ion-exchange solvent extraction. It is based on cooperation of a framework applying Agile Multiscale Modeling Methodology (AM3), and the REBIT Knowledge Based System. Ion-exchange solvent extraction has been shortly introduced. Design assumptions of AM3 and theoretical basis of REBIT have been described. Designed workflows and rules for simple laminar/ turbulent flow and extraction processes have been shown.

Agile Multiscale Modelling of the Thermo-Mechanical Processing of an Aluminium Alloy

The multiscale modelling of the behaviour of metal alloys during processing is often limited by the computing power required to run them. The Agile Multiscale Methodology was conceived to enhance the designing and controlling of complex multiscale models through an automatic run-time adaptation of its constitutive sub-models. This methodology is used to simulate the behaviour of an 6082 aluminium alloy during its thermomechanical treatment. The macroscopic deformation, the work-hardening and the state of precipitation are computed in different modules, allowing the coupling of several software solutions (DEFORMTM2D and

Substituting of a Thermodynamic Simulation with a Metamodel in the Scope of Multiscale Modelling

A typical multiscale simulation consists of numerous fine scale models, usually one for each computational point of a coarse scale model. One of possible ways of limiting computing power requirements is replacing fine scale models with some simplified and speeded up ersatz ones. In this paper, the authors attempt to develop a metamodel, replacing direct thermodynamic computations of precipitation kinetic with an advanced approximating model. MatCalc simulator has been used for thermodynamic modelling of precipitation kinetic. Typical heat treatment of P91 steel grade was examined. Selected variables were chosen to be modelled with approximating models. Several attempts with various approximation variants (interpolation algorithms and Artificial Neural Networks) have been investigated and its comparison is included in the paper.

Ontology Dedicated to Knowledge-Driven Optimization for ICME Approach

Development of new materials, products and technologies with the ICME approach requires challenging computations, controlled by optimization algorithms. A computational time might be decreased with a “knowledge-driven optimization”—an optimization process is controlled not only by a numerical algorithm, but also by a Knowledge Based System. That requires development of a common language, able to cover communication between numerical models without sophisticated translators. There are several formalisms of knowledge representation, but the most common ones are based on First Order Logic (FOL) and Description Logic (DL). None of them meets all the requirements of knowledge management in ICME processes. We present an approach to development of an environment for knowledge management, combining DL and FOL. An exemplary multiscale problem is described, as well as an OWL2 based ontology and rules controlling an optimization process.

A Multi-agent Approach to Computational Optimization of Metal Forming Processes

In the paper we present a new original approach to a complex multi-phase process optimization problem that relies on dividing the optimization task into partial tasks related to the implementation of individual phases. To implement this concept, we propose a multi-agent approach. Its practical realization is shown on the example of manufacturing process for auto body parts made of the Advanced High Strength Steels (AHSS). Although it is a rather simple process consisting of only two phases, we assumed, however, that the results obtained will allow to extend further research to more complex problems. We present the operating principles of a multi-agent model, the flow of the messages between agents, and the architecture of the system. To ensure the proper speed of the whole system a simple and flexible multi-agent framework called Eve was used to develop a prototype of the system. Research performed, as well as preliminary tests have shown that a multi-agent approach can be successfully applied to reduce complexity of the whole optimization processes. Due to splitting a single, complex optimization process into several, partially independent optimization processes, delegating them to autonomous agents, and application of a knowledge-based reasoning system, significant advantage could be observed over to the solutions described so far in the literature.

Metamodelling with artificial neural networks by using high performance computing infrastructures

Three dimensional modelling of sophisticated metal forming processes are usually very time consuming. One of the approaches allowing to shorten computing time is creation of metamodel, which is much simpler version of the model and usually prepared by using one of the black box methods. Although the metamodel is fast, its creation is very demanding, while it requires many executions of the model to obtain input and output data relations. This crucial step of metamodel creation can be accelerated through distribution of calculations on many computing nodes of High Performance Computing (HPC) infrastructures. This paper presents approach to metamodelling with Artificial Neural Network (ANN), which besides obtaining data, requires another demanding step of training. The way of ANN training with different configurations by using HPC infrastructures is also described in details. Afterwards, the results, obtained for case study analysing on crankshaft curvature after forging, are presented.

Experimental Investigations And Numerical Modelling of 210CR12 Steel in Semi-Solid State

Experimental investigation, including hot compression and simple closed die filling was performed. Temperature range of tests was between 1225 °C and 1320 °C. Temperature selection was adequate with liquid fraction between 20 and 60%, which is typical for thixoforming processes. In the die filling test, steel dies with ceramic layer was used (highly refractory air‐setting mortar JM 3300 manufactured by Thermal Ceramics). Experiments were carried out on the Gleeble 3800 physical simulator with MCU unit. In the paper, methodology of experimental investigation is described. Dependency of forming forces on temperature and forming velocities is analysed. Obtained results are discussed. The second part of the paper concerns numerical modelling of semi‐solid forming. Numerical models for both sets of test were developed. Structural and Computational Fluid Dynamics models are compared. Initial works in microstructural modelling of 210CR12 steel behaviour are described. Lattice Boltzman Method model for thixotropi...

Rheological Models of Blood: Sensitivity Analysis and Benchmark Simulations

Modeling of blood flow with respect to rheological parameters of the blood is the objective of this paper. Casson type equation was selected as a blood model and the blood flow was analyzed based on Backward Facing Step benchmark. The simulations were performed using ADINA‐CFD finite element code. Three output parameters were selected, which characterize the accuracy of flow simulation. Sensitivity analysis of the results with Morris Design method was performed to identify rheological parameters and the model output, which control the blood flow to significant extent. The paper is the part of the work on identification of parameters controlling process of clotting.