Design verification requires both sophisticated models and the simulation they offer, as well as 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 the two. A senior project leader I once worked with was always reminding us that “sensors are easy, but sensing is hard,” yet it took me a while to fully grasp what he meant. 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 not only to a broad physical parameter of interest, such as temperature, but which is also designed to match the many temperature ranges and unique installations where temperature measurement is needed. These sensors use techniques ranging from basics such as thermocouples to solid-state diodes and hot-wire anemometers all the way to extremely advanced optical principles.
Still, in many cases, there’s a dilemma: 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 rather the particulars of the situation.
One obvious example is measuring the available amount (mass) of liquid fuel in a tank of a spacecraft in orbit or traveling in the gravity-free void of space. While there are many available sensors and techniques for this, each has drawbacks in size, complexity, weight, and accuracy 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 important topic of measuring airflow in a car’s passenger cabin with two or more occupants. Especially 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 perhaps just the passenger window and the rear-left window? What’s the impact of having the car’s vent setting in different positions? How do the air conditioner settings affect these decisions?
There are many available airflow sensor instruments, such as the Center 332 Hot-Wire Anemometer from Center Technology Corp (Figure 1). 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%.
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 a variety of reasons. Low-speed airflow can be measured using sensors such as wind vanes, impellers (rotary fans), air-pressure sensors, or hot-wire anemometers. However, instrumenting the interior of the car requires a fair number of sensors along with their 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] — which 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 it does to modeling and simulation.
Good sensors need to be balanced by good models
I don’t have a problem with 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 a variety of 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 a little 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)?
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 an actual prototype in near-final configuration is also tested and comes to 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 important 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 when you can faithfully model and simulate one scenario and then verify it with a real-world physical model.
EE World related content
The hot wire anemometer, Part 1: Principles
The hot wire anemometer, Part 2: Implementation
How can you get 5 different pressure sensor ranges from a single sensor?
Dev board hosts air-flow-monitoring differential pressure sensor
How does a thermocouple work, and do I really need an ice bath (part 1 of 2)?
How does a thermocouple work, and do I really need an ice bath (part 1 of 2)?
Temperature Sensors: thermocouple vs. RTD vs. thermistor vs. semiconductor IC
External references
[1] Physics Today, “Disease transmission via drops and bubbles”[2] AAAS Science Advances, “Airflows inside passenger cars and implications for airborne disease transmission”
[3] Physics Today, “The air we breathe in a car”
[4] The Journal of Exposure Science & Experimental Epidemiology, “Air change rates of motor vehicles and in-vehicle pollutant concentrations from secondhand smoke”
