Model intellectual

Every scientific discipline has its characteristic set of problems and systematic methods for obtaining their solution— that is, its paradigm. Chemical engineering is no exception. Since its birth in the last centmy, its fundamental intellectual model has undergone a series of dramatic changes. 

Computational chemistry is the culmination (to date) of the view that chemistry is best understood as the manifestation of the behavior of atoms and molecules, and that these are real entities rather than merely convenient intellectual models [4]. It is... 

In computational chemistry we take the view that we are simulating the behaviour of real physical entities, albeit with the aid of intellectual models and that as our models improve they reflect more accurately the behavior of atoms and molecules in the real world. 

Computational chemistry is the culmination (to date) of the view that chemistry is best understood as the manifestation of the behavior of atoms and molecules, and that these are real entities rather than merely convenient intellectual models [1]. It is a detailed physical and mathematical affirmation of a trend that hitherto found its boldest expression in the structural formulas of organic chemistry [2], and it is the unequivocal negation of the till recently trendy assertion [3] that science is a kind of game played with paradigms [4]. 

It is important to remember that bonding is always accompanied by a reduction in energy, and that all bonding theory is but an intellectual model, a mental scaffolding on an atomic scale, on which to hang our ideas in hopes of giving a self-consistent explanation of observed reality, see also Molecular Orbital Theory Valence Bond Theory. 

A company s cost accoimting system often results in an organization-wide "intellectual model" that is used by the company s management despite the fact that every one of the organization s managers knows the model is not true. 

Armed with the accurate cost information provided by a spreadsheet model based on its new "intellectual model," PlumbCo was able to make numerous changes that not only improved its own profitability, but also reduced costs to its customers. 

To effectively support management, a company s costing practices must provide an accurate cost model of its operations. It must also provide tools for supporting the variety of decisions for which cost information is an important input. Most importantly, it must promote an "intellectual model" in the minds of all members of management that helps them understand the true economic implications of every decision they make. 

The intellectual models for such classification were summarized and compared first. These start with the explicit or implicit definition of the basic or unit reaction to be classified, excluding schemes of sequential reactions even though in the laboratory several reactions, however defined, may occur in one operation. 

The role of oceanic physical chemistry and biochemistry in the enhanced greenhouse future is still uncertain. We have discussed the mechanisms generating a number of potential feedbacks, both positive and negative in their impact. However, new interactions are constantly being discovered in nature, and model representation of them is a rapidly evolving science. At present what we can say is that this is a young field of much intellectual and practical promise. 

Over the next few years, a conflnence of intellectual advances, technological challenges, and economic driving forces will shape a new model of what chemical engineering is and what chemical engineers do (Table 2.2). 

Johnstone (2000) emphasises the importance of beginning with the macro and symbolic levels (Fig. 8.3) because both comers of the triangle are vistrahsable and can be made concrete with models (p. 12). The strb-micro level, by far the most difficult (Nelson, 2002), is described by the atomic theory of matter, in terms of particles such as electrorrs, atoms and molecules. It is commorrly referred to as the molecular level. Johnstone (2000) describes this level simirltaneorrsly as the strength and weakness of the subject of cherrristry it provides strength through the intellectual basis for chemical explanatiorrs, but it also presents a weakness when novice students try to learn and rmderstand it. 

The concise Oxford dictionary of current English defines a model as a simplified. . . description of a system etc., to assist calculations and predictions. One can apply this definition in its wider sense to any intellectual activity (or its product) that tries to make out the components of a system and to predict the outcome of their interaction. Thus, to think is to model (beware, though, that the reverse is not necessarily true). 

The other limitation is intellectual - the appropriate interpretation of model predictions and integration into the decision-making process [106, 107]. 

Because simple lattice models take no account of local directional preferences, they fail to model these important local restraints on protein structure. Instead, they rely almost entirely on long-range interactions to encode the most stable conformation(s) (Dill et al., 1995). Thus the ability of lattice models to reproduce protein-like behavior must be called into question. And though their simplicity makes them intellectually attractive, their use in teaching and modeling protein-like behavior must be qualified with a caveat that local directional preferences have been ignored. 

Availability of Physical Properties Data and Model Parameters. We have found that the development of a data base for physical properties and other model parameters is as time consuming, and intellectually demanding, as the development of the model itself. One will be surprised to know, for example, that vapor pressure data at around 25°C for many commonly used solvents are non-existent. 

How do we know or decide what terms to put in the spin Hamiltonian This is a question of rather far-reaching importance because, since we look at our biomolecular systems through the framework of the spin Hamiltonian, our initial choice very much determines the quality limits of our final results. In other branches of spectroscopy this is sometimes referred to as a sporting activity. We are guided (one would hope) by a fine balance of intellectual inspection, (bio)chemical intuition, and practical considerations. In a more hypochondriacal vein, one could also call this the Achilles heel of the spectroscopy a wrong choice of the model (the spin Hamiltonian) will not lead to an accurate description of nature represented by the paramagnetic biomolecule. 

Models that are too complicated for the analytical methods of statistical mechanics can often be explored by computer simulations. In a certain sense these are a theoretician s experiment One can devise a model for a certain system, and then investigate with the aid of the computer its consequences. By varying the system parameters, or modifying features of the model, one can gain insight into the structure or dynamics of the system, which one could not obtain by other means. While computer simulations are not as intellectually satisfying as explicit calculations, they are often the only way to test a model. Sometimes they are also used to check the validity of approximations made in analytical calculations. 

Notwithstanding the intellectual challenges posed by the subject, the main impetus behind the development of computational models for turbulent reacting flows has been the increasing awareness of the impact of such flows on the environment. For example, incomplete combustion of hydrocarbons in internal combustion engines is a major source of air pollution. Likewise, in the chemical process and pharmaceutical industries, inadequate control of product yields and selectivities can produce a host of undesirable byproducts. Even if such byproducts could all be successfully separated out and treated so that they are not released into the environment, the economic cost of doing so is often prohibitive. Hence, there is an ever-increasing incentive to improve industrial processes and devices in order for them to remain competitive in the marketplace. 

At least three approaches have been proposed to solve for the mean pressure field that avoid the noise problem. The first approach is to extract the mean pressure field from a simultaneous consistent39 Reynolds-stress model solved using a standard CFD solver.40 While this approach does alleviate the noise problem, it is intellectually unsatisfying since it leads to a redundancy in the velocity model.41 The second approach seeks to overcome the noise problem by computing the so-called particle-pressure field in an equivalent, but superior, manner (Delarue and Pope 1997). Moreover, this approach leads to a truly... 

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