Mei C., Zhou J., Peng X. Simulation and Optimization of Furnaces and Kilns for Nonferrous Metallurgical Engineering
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Hologram Simulation of the
FKNME
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In this chapter, the concept “hologram simulation” is introduced and
elaborated. The multi-field coupling and equation-solving techniques are
also discussed as application examples of the hologram simulation strategy.
3.1
Concept and Characteristics of Hologram Simulation
The “hologram simulation” is a numerical simulation strategy aimed at the
optimization of model development and simulation results analysis for the
purpose of the best understanding of the engineering problems that are studied.
The term “hologram simulation” is inspired by the hologram theory and
information theory.
The word Āhologram” originates from the Greek words “holos gramma”,
which means “whole message”. The theory of holography was introduced by
the Hungarian scientist D. Gabor in 1948, but it had no application until the
emergence of the high intensity laser technology in the early 1960s. The
principle of the holography is to record both amplitude and phase information
of the light waves reflected by the objects, and to generate images with
three-dimensional lifelike effects. In contrast, normal photography technique
only records the amplitude of the light waves, which only carries the
information about the intensity of the lights. In short, hologram technique
enables more comprehensive and precise observation of the objects by
Chi Mei and Zhuo Chen
presenting more information from the objects.
The information theory and its recent extensive developments are the other
source of this technique. Shannon published his statistics-based grammatical
information theory in 1948. As a rigorously deductive system, Shannon’s
work is one of the important theoretical foundations which strongly backed
the development of the modern communication technologies. However, the
development in psychology and artificial intelligence areas in the past
decades has made the grammatical information theory handicapped in
dealing with the complex systems. In these cases, information is required to
convey message about not only the forms (grammar information) of the
objects but also the meanings (semantic information) and the functions
(pragmatic information). As a result, new theories were raised. To be
differentiated from the classic grammar information concept, the new
concept is called “the comprehensive information”.
The above-mentioned ideas developed from the holography theory and the
information theory are equally applicable when carrying out numerical
simulations of the engineering processes, including those involving furnaces
and kilns for nonferrous metallurgy. A successful model should not be merely a
mathematic approach to snapshoot a superficial, localized and stationary
picture flatly reflecting the physical processes, but it should be rather more a
powerful tool to reveal the comprehensive information, namely the
fundamental, full-scaled and dynamic aspects describing the hologram of the
processes. The modeling strategy that aims to obtaining “the comprehensive
information” of the objects is called “the hologram simulation”. In contrast to
the usual simulation strategy that is single parameter or single task orientated,
the hologram simulation highlights more efforts in the following phases and
aspects (Mei et al., 1999).
a) When physical models are established, the fundamental integrity and
coherency should be emphasized in studying the physical system. Approaches
intending to separate or isolate the processes should be avoided as much as
possible.
b) When mathematical are established, the inter-dependency or interactive
nature among the physical processes should be always primarily considered. The
governing equation set should be able to reflect as much as possible all of the
important processes and their interactions. Mathematical simplifications that may
result in cutting off the interactive links between physical processes should be
minimized.
c) During post-processing and analyzing the simulation results, obtaining
3 Hologram Simulation of the FKNME
comprehensive information is recommended to reflect all aspects of the objects
instead of narrowly focus on one or two of the immediately relevant points.
Take the study of the temperature field of the aluminum electrolytic cells for
example. A hologram simulation means that not only the fluid flow and
temperature fields should be investigated, but also the electric field, magnetic
filed, current field, electromagnetic force field and the freeze profile of the cells
should be analyzed.
d) As to the scope of the simulations, attention should be paid to the
entire domain but not just to obtaining localized information at a few
controlling points or mean values in an area. The logic behind is that
localized information or space-averaged information can be considered
reliable only after the overall validity of the total information has been
verified.
e) Efforts to obtain dynamic or quasi-dynamic information of the main
processes are encouraged. In fact, most processes that are considered as
steady-stated at the macro-scale level could be time-dependent at the micro-scale
level or when coupled with other processes. Based on this point of view, the
overall reliability of the modeling would be best verified through examining at the
dynamic or quasi-dynamic level.
In short, the hologram simulation underlines higher integration and
consistency regarding the model development process, and requires more
comprehensive and detailed coverage regarding information revealing the
objects being investigated.
3.2
Mathematical Models of Hologram Simulation
As an information technology, hologram simulation is a continuous process in
a relative sense that constantly approaches the truth and higher accuracy. Up to
now, the state of the art of hologram simulation of the FKNME is still far from
perfect. Most models are based on either “grey box” models, namely a mix of
theoretical and empirical approaches, or hybrid models which combines “grey
box” models and artificial intelligence models (refer to expert system, fussy
analysis, artificial neural network, etc.) (Mei et al., 1996). For the convenience
of model development, it is helpful to classify various engineering furnaces
and kilns into groups by their thermal processes and operating mechanisms.
Table 3.1 shows a possible way of classification, with their mathematical
models listed in Table 3.2.
Chi Mei and Zhuo Chen