Engineering Design Environments

1. Breathe simplicity, ---to make it useable, learnable, viewable, scalable, and maintainable.

2. Model without design prejudice! ---to think anew, "outside the box."

3. Make the variables massively aware and dynamic, ---to trend realistically.

4. Model the entire state-space, ---to discover model inconsistency.

5. Experience the entire state-space, ---to discover optimized situations.

Definitions and Descriptions

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Explanation of the Goals of the IDEAs Paradigm ---

1. Breathe simplicity into all aspects of the model:
   algorithms, platforms, applications, programming-languages, documentation, and user-interfaces  ---to make all aspects useable, viewable, learnable, scalable, and maintainable.

               Consider the the growth of processor speed and data storage capacity.
   Remember the "old days" when processors were relatively slow and a limited model using efficient algorithms was a necessity? Remember how important it was to estimate program "run-time" and "memory requirements"? Although still important for many special complex problems, many, many main-stream engineering designs and simulations run and scale nearly without user-regard for processor speed and memory capacity. Consequently, the IDEAs Paradigm prefers to implement something simple many times rather than something complex once. IDEAs says to take a complex algorithm and break it up into simpler parts. Although this may require more processing and memory, it helps to make the model code more transparent, understandable, learnable, scalable, documentable, and maintainable with the smallest amount of programming knowledge or user-training. Making the model usable to the widest possible user-base with a minimum amount of required training is the prime directive of the IDEAs paradigm.

               Consider the algorithms.
   IDEAs prefers to implement three simple types of straight-forward algorithms: (1) empirical (based on fitting to existing data), (2) extrapolation (based on projections of existing data), and (3) known physical laws or best theory.
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               Consider the platform, applications, and programming-languages.
   IDEAs prefers the simplest platform (personal computer), applications (EXCEL, ACCESS,...), and programming- languages (VisualBasic, Macro, EXCEL,...) that can get the job done. Nearly everyone knows how to use EXCEL, and VisualBasic is probably the easiest of the formal programming languages, and is also integrated into the EXCEL product. IDEAs says it's o.k. to integrate 20 multi-megabyte spreadsheets, using standard cell relationships, integrated VisualBasic and ACCESS database, and output to other programs as necessary.
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               Consider the documentation and the user-interface.
   IDEAs prefers the simplest method for documentation and user-interface. The EXCEL Workbook is an excellent user-interface, providing the standard cell input/output with integrated VisualBasic and Macro-Language programming environment. Many integrated graphical routines are also integrated. EXCEL provides excellent parameter awareness-and-feedback tracing, revision documentation, and on-cell documentation (cell notes).
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2. Build a model without conventional-design prejudice.
               Are you familiar with the adages, "don't reinvent the wheel" and "if it isn't broke, don't fix it"? We live in a time of increasingly comptetitive business environments, with ever smaller business cycle time, and ever more rapidly evolving technologies. We can use these new technologies to "reinvent a better wheel." We must think anew; we must think "outside-the-box." Leave the prejudice of conventional thinking behind.

               Some models may be unknowingly designed to support, or produce, a result that has already been proposed by alternative, or ad hoc means. There is a natural bias to confirm one's assumed hypothesis. IDEAs tries to avoid this natural prejudice, or bias, by emphasizing the importance of the relationships between variables and parameters without regard to the bigger picture that the total model will eventually paint. IDEAs demands massive linking of variables, forming interelationships as they should exist in nature. IDEAs attempts to remove prejudice and bias from the model-development process.
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               Prejudice also refers to making statements based upon single-point, or few-point, design studies rather than on a thorough review of the entire design "state space." Often simplistic models are generated that only have the capability and flexibility to yield single or few-point designs. A model that works only over a limited subset of the possibilities is suspect. Statements and conclusions made from a few suspect points can be very risky. IDEAs prohibits development of new design principles until the model has been verified over the entire design "state-space" up to the boundary conditions. This helps to assure that the model is valid and consistent (because a bad or incomplete model nearly always explodes or presents wierd results near the boundary conditions), and provides a view of the entire state-space, which can be very important when seeking situations of optimized performance in massively linked multi-multi-variable models.
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3. Make the variables massively aware and dynamic.
               Make the variables massively aware and dynamic, so that changes in one variable cause all or most other variables to automatically respond according to physics and engineering principles, to improve the flexibility of the model. Aware in the sense that changes in one parameter will affect all or most other parameters. Dynamic in the sense that feedback between parameters is the norm. By focusing on the relationships between variables, we can make the model more realistic, more lifelike, and simultaneously help to avoid introduction of bias into the model. Massive linking also helps to make the model much more flexible, allowing the user to immerse themself within the model.
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4. Model the entire state-space, test it up to its boundary-conditions.
               To aid discovery of model inconsistency, and to check for model validity, benchmark the multivariable model over its entire state-space, particulary at or very near the boundary conditions set by physics and engineering principles. Early versions of a model, when driven toward a boundary condition, usually announce the existence of an invalid or inconsistent model by falling apart (crashing or exploding). A model that works at or near its boundary conditions is a strong indication that the model is self-consistent, and more-likely to be valid. Thus, boundary-condition operation is a quality control check.
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5. Experience the entire state-space.
               Experience (visualize, sense, surf) the entire state-space, to identify new features, principles, and situations of optimized performance. It's not easy to draw comprehensive trends from a few points on a graph, and even two-dimensional parametric plots, or slices, of the ignition space can overlook important new solutions provided by the model. Methods for viewing three- and four-dimensional state-spaces include spreadsheets, contour plots and virtual reality. IDEAs imbeds the User within the state-space, allows the user to surf in all directions up to the boundary conditions. In this way, the User can seek situations of optimized performance, discover new trends and features in the solution state-space, and define new design principles leading to the next stage of model development or engineering design integration.
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