Streamlined Water Distribution Systems, Engineering Design, and Optimization

Water and energy are scarce resources, the conservation of which is becoming increasingly important. Researchers at Wayne State University, lead by civil engineering department chair Carol Miller, are developing a computer-controlled approach for operating the Detroit water system. According to the Chicago Tribune, Miller hopes to reduce energy consumption for the system by shifting from manual control of system pumps, to automatic control. The water system is so large that these improvements stand to deliver significant energy savings. In addition, Miller estimates this will save ‘10 million tons of greenhouse gases and other pollutants per year’.

This is one of many engineering systems that can be viewed as a design optimization problem, and I would like to use water distribution system improvement as an example to explain what design optimization is.

In engineering design we have lots of decisions to make, decisions like what materials to use, the size of components in our system, or how parts of our system should work together. In mathematical optimization, we seek to minimize or maximize something by choosing the right values for some set of variables. Design optimization links engineering design with mathematical optimization in a way that helps us identify what design decisions will lead to the best possible engineering design.

How can we frame the operation of a water distribution system as a design optimization problem? We have three tasks to make this happen:

1. Identify Design Decisions: each design problem has some degree of design flexibility. That is, designers are free to make decisions about certain aspects of their system. In new systems that are being designed from the ground up, there is a lot more design freedom (more decisions to make). If a system must use some already developed components, then some design decisions are already made, reducing design freedom. In the case of the water distribution system, there is even less design freedom, since the physical system already exists. In any case, designers need to identify what aspects of a system they have control over; these aspects are the design variables. One set of specific values for the design variables represents one system design alternative. In the water distribution problem here, the design variables are quantities that define how each pump in the system should be controlled.
2. Specify Design Objective: We need to have some way of comparing design alternatives and evaluating which designs are better. A design objective, or objective function, is a system property that we can measure, and that reflects the usefulness of a particular design. The design objective drives the design process, and is a critical choice in product development. Whether or not design optimization is used formally, product designers choose a design objective, or at least set priorities for their product (influenced by the market segment they are targeting). For example, in automotive design, Porsche engineers have performance as a design objective, while Aptera engineers consider energy efficiency paramount. The resulting designs reflect the difference in design objective. In the water distribution system problem, we are seeking to minimize energy consumption.
3. List Design Constraints: Engineering design is full of tradeoffs; that is, if we seek to optimize one thing, something else is bound to get worse. We can’t simply focus on the design objective alone and expect to develop a usable system. In the automotive example, some constraints include safety, size, range, and cost. In addition, by choosing energy efficiency over performance for a design objective, Aptera engineers still need to meet some minimal performance constraints. Who would want to buy a car so slow that it’s not driveable in traffic, even if it could acheive 500 mpge? If we sought to minimize energy consumption in the water distribution system problem without considering any constraints, we might arrive at a solution that says we simply should never turn on any pumps. We need to impose a constraint to make this work: require that water delivery needs are met.

The design optimization approach to engineering design involves minimizing or maximizing some design objective, while meeting a set of constraints, by varing something you have control over. This way of presenting an engineering design problem is actually pretty natural. Some engineers may be using the design optimization process informally, even if they are not aware of it. Design can be viewed as the process of finding the set of design variable values that satisfies the design constraints and optimizes the design objective. To summarize the water distribution system design optimization problem, we are trying to find an automated pump control policy that minimizes energy consumption, while ensuring water delivery needs are met.

Now that we have presented our design problem as an optimization problem, how do we actually solve it? In some cases, engineers could build physical prototypes and use a trial and error approach to search for the optimal design. This could get very expensive. A little more sophisticated approach might employ systematic testing and statistical models. This still requires expensive physical protypes. Would this even be practical for the water distribution system needs? Would engineers be allowed to try out new (untested) pump control ideas, risking water delivery failures for such a large metropolitan area? It sounds like we need some way to test design alternatives without actually having to test them in real life. This is where physics-based modeling and computer simulations come into play. Researchers and engineers have developed computer models for all sorts of systems that allow designers to test out ideas in a virtual world. These models help predict how a system design will behave, without actually having to build it. The software and computers are far from free, and the models are not 100% accurate, but they are accurate enough to help make design decisions, and allow designers to test out far more design alternatives than are possible with physical prototypes. If you would like to learn more about computer modeling, you can read through an ongoing series of articles on modeling.

If a system can be modeled using a computer simulation, then engineers can use optimization algorithms to solve the design optimization problem described above. These algorithms are computer programs that very intelligently choose what designs to test (using computer simulations) so that we can find the optimal design quickly. Using design optimization can help engineers develop better products in shorter time periods. Using optimization to develop better water distribution systems has actually been going on for several years. A full issue of the journal Engineering Optimization was devoted to this topic (you can read an overview of the issue here). In many of these articles, the engineers have additional design flexibility; they are not just looking at changing how the system is operated, but also at how the physical system is designed.

Design optimization and modeling are topics that I will revisit. These are important tools that could be used to transform how engineering design is done, and enable engineers to create systems that use much less energy, while meeting or exceeding our performance expectations. It’s my hope that more engineers adopt design optimization and use it to improve sustainability and quality of life, and that more people can become aware of design and design optimization, their impact on how we live, and the role they can play in our shift to a sustainable path.