Airflow: Understanding Its Importance in Design

Design verification requires sophisticated models, the simulation they offer, and real-world validation of their results.

Sensors and sensing for real-world physical parameters form a large part of the electronics scene, but it’s easy to confuse them. A senior project leader I once worked with always reminded us that “sensors are easy, but sensing is hard,” yet it took me a while to grasp what he meant fully. It’s this: often, the difficulty is not the sensor itself but the challenge of placing it in a harsh or awkward setting.

For example, thousands of distinct types and sizes of temperature sensors are available, each tailored to a broad physical parameter of interest, such as temperature, and designed to match the many temperature ranges and unique installations where temperature measurement is needed. These sensors use techniques ranging from thermocouples to solid-state diodes and hot-wire anemometers to highly advanced optical principles.

Still, there’s a dilemma in many cases: many good sensors are available, but the specific application scenario makes it difficult to use one meaningfully or reasonably accurately. It’s not the sensor’s “fault” but the situation’s particulars.

One obvious example is measuring the available amount (mass) of liquid fuel in a spacecraft’s tank in orbit or traveling in the gravity-free void of space. While many available sensors and techniques exist for this, each has size, complexity, weight, and accuracy drawbacks, and is complicated by shifting conditions or acceleration during course corrections.

These sensor versus sensing issues don’t apply only to such esoteric, out-of-this-world situations. Consider the vital topic of measuring airflow in a car’s passenger cabin with two or more occupants. These days, you want to ensure good airflow when people are in close proximity for well-known reasons. If you need some insight into why you should be at least a little concerned, see the article from Physics Today [1], which appeared approximately a year before the COVID-19 pandemic.

There are questions such as how many windows to open in a moving car? Regardless of that number, which specific window(s) should you open, and by how much? This is a case where the likely intuitive answer of “open all the windows” may be wrong. Further, even if that is the best technical solution, it may be impractical or undesirable.

That’s just the start of the “windows” analysis. If you want to restrict the solution to just two windows, for example, which two do you open? Should it be the two front windows? To what extent are the answers a function of where the passenger or passengers are seated? Perhaps use the driver’s window and the rear-right window (diagonally across from the driver’s window), or maybe just the passenger and rear-left windows? What’s the impact of having the car’s vent setting in different positions? How do the air conditioner settings affect these decisions?

Many airflow sensor instruments, such as the Center 332 Hot-Wire Anemometer from Center Technology Corp (Figure 1), are available. This handheld unit with a separate extendable probe measures air velocity from 0 to 25 meters/second (equivalent to 0 to 5000 feet/minute) and airflow (volume) from 0 to 10⁶ cubic meters/minute (about 8.5 × 10⁸ cubic feet/minute), with an accuracy of ±3%.

Figure 1. The Center 332 Hot-Wire Anemometer from Center Technology provides measurement of air velocity and airflow volume over a wide range; its extended sensor minimizes disturbances to the airflow being measured. (Image: Center Technology)

Of course, single-point measurements using a single probe would be time-consuming. Perhaps a better approach would be to use a testbed with a wind tunnel, a car mockup, and some passive “dummies” representing the driver and passengers, with multiple sensors placed at strategic points.

However, it turns out that it is not an easy situation to instrument for various reasons. Low-speed airflow can be measured using wind vanes, impellers (rotary fans), air-pressure sensors, or hot-wire anemometers. However, instrumenting the car’s interior requires a fair number of sensors, wiring, calibration, and decisions about placement and orientation. It gets complicated fairly quickly, even if you switch to wireless sensors to reduce the wiring and setup.

Still, having a good sensor or instrument is only part of the solution. This was made clear in a pair of related articles — one in AAAS Science Advances [2] and the other in Physics Today [3] — that discuss the challenges related to assessing airflow in cars. The authors conclude that given the many variables of the arrangement and how and where you measure the airflow, it’s a problem that does not lend itself to real-world physical instrumentation as much as to modeling and simulation.

Good sensors need to be balanced by good models

I don’t have a problem relying on modeling and simulation, as modern tools are amazingly good. For the automobile airflow problem, there are no secondary effects, such as temperature changes that result in material contraction or expansion, to complicate the situation, as is the case in many other real-world situations. Here, it’s all about localized airflow volume and velocity as a function of car speed, passengers, and window positions.

However, nearly all such simulations have a potential problem: they are heavily dependent upon the fidelity of the underlying models. It’s hard to know how accurately you need to model the car’s surfaces and interior geometry for this project.

Not all researchers are committed solely to models and simulations. An interesting study of air-change rate (ACH) in cars from an academic journal [4] shows that the authors ran tests under various conditions using four different cars. The authors went well beyond basic airflow sensors and added an instrument-grade monitor to measure carbon monoxide (CO) concentrations and an optical-scattering monitor to measure respirable particle concentrations.

Will a slight variation in passenger cabin dimensions (after all, every car is slightly different) make a big difference in the results? Can you do a meaningful sensitivity analysis on how the simulation results will be affected by simplifications in the model (Figure 2)?

Figure 2. Airflow patterns in a car are complicated and dependent upon many factors, including cabin size and geometry, vehicle speed, occupancy, and the number and location of open windows. The image shows computer simulations of airflow patterns and aerosol concentrations inside a passenger car driven at 50 miles per hour with front-right and rear-left windows open. (Image: Varghese Mathai, via Physics Today)

After reviewing a large number of window, vent, A/C, and passenger combinations, it makes sense that the best approach is to use a good model followed by simulations. But it’s also nice to see that one modeled scenario was assessed and validated using a real car and numerous airflow sensors as a validity check and credibility verification.

It’s the same as when you complete a Spice-like circuit simulation: it’s useful and informative to see the performance characterized with such apparent precision and reams of data. Still, you’ll always feel more confident about those perspectives if a prototype in near-final configuration is also tested and comes within about 5% or 10% of the simulation results.

Conclusion

Don’t be fooled: sometimes, problems with sensing real-world parameters are due to the application scenario, not the sensors themselves. It’s essential to think carefully about what sensor arrangement is needed, how many sensors, their locations, their impact on the test, and other relevant factors. Even so, a 3D simulation may be a better option, provided — and it’s a big assumption — that you can develop a viable, credible, and verifiable model. It’s even better to faithfully model and simulate one scenario and verify it with a real-world physical model.