Computer Solutions for a Holistic Design

Engineering needs to evolve into an approach that considers everyday design decisions more holistically—with less focus on capacity, and more on performance. And with the advent of computer-driven applications, a range of calculation methodologies can be applied on a given project, each appropriate for certain projects or stages of design.


Engineering needs to evolve into an approach that considers everyday design decisions more holistically—with less focus on capacity, and more on performance. And with the advent of computer-driven applications, a range of calculation methodologies can be applied on a given project, each appropriate for certain projects or stages of design.

Developing solutions in terms of a single point of operation—such as design load/capacity—does nothing to address the optimization over time that performance evaluations require. Design load perspectives are a drastic simplification of a complex engineering challenge.

However, the design of engineered building systems has a long history of using simplified approaches, a history that springs from several facts:

  • Engineering evaluations must consider a combination of complex variable interactions.

  • There is often limited and inadequate scientific understanding of many of these interactions.

  • There were limited analytical tools to assess these interactions.

  • Economic considerations limited the options for comprehensive analysis.

Life becomes more complex

Some 30 years ago, a complex combination of variables became even more so. Driven by the need to minimize energy costs, engineers increasingly began to use computers as a means to optimize designs. Up until that time, there were limited incentives for computers as labor-savings devices, because the computer costs were significant, and most M/E/P engineering problems and design solutions had been concerned more with having enough to meet the load. System concepts were inherently energy- and resource-intensive; components were of minimal efficiency and controls technologies offered little to optimize the operation of building systems.

Also, complex systems have emerged relatively recently. Air conditioning as an engineering profession dealing with artificial environments is only about 70 years old. And the intricacies of power distribution concepts really only materialized in the last 25 years, as technology, especially machines with electronics, began to dominate building power design requirements in all environments, not just industrial settings.

Concurrent with the evolving complexities of building functions, the systems and controls technologies for M/E/P systems have advanced with increases in efficiency, responsiveness and flexibility for more dynamic operation and optimized control under many variable parameters.

However, despite these advancements in building equipment and systems, much of the engineering of M/E/P systems continues to focus on design load concepts that ignore the need to optimize selection and design approaches across all modes of operation.

More than necessary, building systems engineers allowed their profession to remain defined in minimalist terms. They had evolved a conservative attitude based on the rationale that the worst failure of an engineer was not having enough capacity to handle the load at design conditions.

Implications of technology

Computers that facilitated the performance of many repetitious calculations have offered real improvement in how engineers can address complex problems. They are made for number crunching, especially when they are applied to a series of calculations that require incrementally small adjustments using a very standardized set of input data.

The set of variables is predetermined, and the engineer has only to define the parameters that influence each variable. But what soon becomes apparent is that the real limitation of computers is the software—and the specific methodologies applied by each program.

The advent of personal computers and spreadsheets has ushered in an era in which engineers can easily customize their own calculations, and can apply experience and a series of "rules of thumb" to quickly, easily and accurately determine the design condition and corresponding equipment.

But therein lies a trap. The dependency on the increased accuracy of computers can lead to false confidence and security. This, in turn, can disguise the risks in errors of input, of judgment or of interpretation of results. The computer will consistently calculate the same answer for a given set of inputs—a great improvement over the potential error of manual calculations—but it doesn't make the answer more accurate or more appropriate. One can't get improved accuracy of output without two basic ingredients: improved accuracy of input and more precise—and probably complex—definition of the underlying functions or interactions that determine the calculation.

If the inputs are based on experience, prediction, assumption or some other approximate basis, the accuracy of the result is necessarily limited by the input. Further, if the inputs are assembled by different people with different understandings of the consequences of those inputs, the results will similarly be different in their outcome.

The discrepancies in results that derive from differences in experience are not necessarily bad, nor do they necessarily represent potential errors. They may merely reflect the differences in how engineers apply the quantitative answers to their engineering decisions about everything from equipment sizes to design concepts and layouts.

But this begs the question: How many "right" answers can there be to a given problem in building services engineering? The answer is that, in reality, problems are so complex that there are inevitably several possible solutions. As long as the solution avoids any excessively weak links in the concept, and the system can and is operated effectively by building operators, the overall system can be one of the best.

Levels of Complexity

With so many different analytical, computerized approaches to building systems design available, one must consider all the implications of selecting particular tools—and the basic differences among them. What, for example, are the implications of selecting a more or less sophisticated approach at a particular stage of the design process?

Six basic elements to consider—and the questions to ask—when evaluating an analytical approach include the following:

  • Input. What kinds and how much input data are required? Is the data quantitative or qualitative? Will further development of the analysis as the design progresses require simply more data or different kinds of data? What is the availability of the input data? Can it come from a single measurement or is trending or measurement over time required? How safely can data from other projects or existing conditions be used?

  • Calculations. What kinds of calculations are required? Are they simple one-time linear methods or do they require multiple calculations? Is a regressive or iterative technique necessary? Have the methods been developed into algorithms? Do they require sequential, but dependent calculation? Can they be easily implemented by engineered systems designers without complicated programming by using a spreadsheet approach?

  • Answers. What types of answers are provided? Are they quantitative or qualitative? Are they numerical or graphical?

  • Accuracy. What is the accuracy of the method and results? Are the answers sufficiently accurate to allow correct decisions to be made? Are they approximate, precise or somewhere in between? Can the expected accuracy be predicted? How easily can the answer be independently verified? Are there automatic error checking provisions? What is the risk that input mistakes will go undetected?

  • Analysis. What type of analysis or interpretation of the results is required? Are the answers straightforward and self-evident? Do they require interpretation? How important is it to define and describe the context and assumptions when communicating the results to clients? Do the results require the development of a value system to prioritize and compare results and pick a solution?

  • Costs. What are the costs in using the method? Is the calculation methodology available for purchase and at what licensing costs? Are the licensing costs one-time, annual or per use? Should the calculation methodology be developed in-house? What are the requirements and costs of validating the methodology and results? What time is required to run the analysis including inputs and interpretation? What additional costs are there associated with checking and verifying results and decisions?

Classes of calculation

There are four classes of calculations with progressively greater complexity and consequently more input, accuracy and sophistication—and more costs:

  • Rule of thumb is a simple parametric expressions of public or personal experience. It usually represents reliable macro indications of scale and concept. It can be manually applied or used in spreadsheets where multiple functions are applied simultaneously.

  • Generalized calculations involve only a few variables and typically have fairly linear relationships with limited compound effects.

  • Detailed calculations involve many more inputs and variables and may have non-linear as well as linear relationships. They will generally include more compound effects, possibly including non-steady state factors that vary independently or unpredictably over time.

  • Complex simulations involve most, if not all known variables affecting an actual condition of building dynamics. The inputs and results are very complex and typically entail either significant knowledge or assumptions about building functions and system operation. Few, if any variables are ignored. Any assumptions made must be tested and validated either through field data or by evaluating various what-if options to quantify the impact of assumptions.

In those cases where the analytical solutions depend upon circumstances that are either unpredictable or beyond the user or operator control—for example, weather conditions—the analytical approach must incorporate a statistical approach. It must quantify the results with a likelihood of occurrence so that design decisions can be given a context. Incorporating this type of variable requires defining a frequency distribution of differing conditions. By characterizing building and system use and operation with dynamics, more realistic system and component loads and performance can be assessed.

For example, computational fluid dynamics modeling of airflows within and around buildings give users and operators the ability to visualize complex interactions that occur in buildings. The movement of air, variations of temperature and humidity, and the migration and removal of contaminants can all be revealed.

Many engineering assumptions and calculations presume rather idealistic, and in most cases, extreme circumstances of system loads and operations. The more engineers perceive how imperfectly and dynamically their buildings and systems perform, the better equipped they will be to develop concepts and sizes that are more appropriate to real-world operation.

Computers aren't enough

Incorporating analytical tools that consider operations and energy use, in addition to design capacities, throughout the design process is a significant step toward design that is based on performance. But focus on performance-based decisions also comes from clearly communicating with owners about the implications of the various operating conditions.

Addressing the performance and not just capacity will require more sophisticated analytical approaches. Engineers must recognize the differences in sophistication of their analytical tools, and develop and apply them based on the project and the stage of design.

Analytical Elements

Input. What kind and how much?

Calculations. What kind are required?

Answers. What type are provided?

Accuracy. Are answers sufficiently accurate?

Analysis. What type is required?

Costs. What does the method of analysis cost?

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