[kubark42]

Originally Posted by jfitzpat

Sorry, I'm going to be terse and may miss some issues, I'm trying to help a plant get to rich burning LP engines the size of houses within the upcoming emissions compliance today because we're anticipating a lot of rain.

Think about that. 1. They calibrate measured to actual with a fudge factor. This covers a number of variables, including driver behavior. 2. They are measuring their results with fuel fillups. Are you certain that they are filling their stock tank systems at existing gas stations to the level of accuracy you are asserting?

Repeatability is repeatability. I’m not a fluids expert, but I do expect that the gas station pump is accurate to beyond 0.1%. Otherwise, they’re making 0.1% on every liter they sell (after all, they’d be stupid to do anything other than have their pumps be at the absolute limit of the law). So if people see +-0.1% on multiple fuel-ups, that’s a strong indicator that they’re on to something.

Then he probably does not work on the fluid dynamics side. I'd strongly recommend that you read some of the SAE research papers in this area. Or, just try an external, and more precisely controllable, tank feeding the conventional one and measure for yourself.

He does CFD models of gasoline fuel injector asymmetrical dispersion patterns at high pressure. (Good lord, the processing power he’s got to throw at this kind of thing. He’s the kind of guy who asks for a 64-core computer, and then runs a process on it for 2 weeks at a time.) But this is besides the point. I’ll explain why just after.

No. Look at the trend in efficiency over the last 3 decades. A big key is higher compression. This means that peak fuel delivery has to high, which makes operating injectors fast enough at idle speeds difficult. There are several technologies in use, but look at peak-hold injectors, which are expressly meant to address this problem.

Now look at modern control methods of peak-hold injectors, which include techniques like PWM. In PWM control, open and close times are conditionally varied - that is, they cannot be simply averaged out, they need to be measured and modeled continuously. This makes for a considerably more demanding measurement system and computations. More sophisticated device, multipled by every injector... It seems contrary to your basic assertion. Add the current trend for muliple injectors and even duel fuels, and it sees dead end.

By measuring current, instead of voltage, we can do the same on peak-hold as we do on saturation injectors. This complicates the sensor package a tiny bit, as now hall sensors or current shunts have to be used instead of simple voltage probes, but does not radically change the problem, nor the solution.

But again, this is getting lost in the details. The internal management system has far better ways of calculating fuel flow than I do. Our method demonstrates that with a certain number of inputs you can reconstruct the efficiency of the system. Whether or not this particular sensoring approach works in the future is not important. In the future we will have newer, better ways. You list one below.

But there are relatively low cost methods that do. For example, wideband UEGO measurement is becoming more common in modern vehicles. If you combine good lambda measurement with, say, MAF, you have volume of air and combustion ratio, so you know the volume of fuel burned.

No! That’s not measuring, that’s inferring. And if you’re not using a model based stochastic filter to do the inferring, you’re not getting the most out your data. Pick your poison-- EKF, UKF, sliding point observers, particle filters, information filters, etc…-- but definitely don’t go without.

In an abstract sense, the UEGO/MAF approach and the injector patency approach are identical, only the physical parameters change. My goal is to observe power_in, and both are valid approaches.

Even better, by the sound of it they are complimentary approaches.That is a perfect proof to my above point that “we’ll find a better way”. So this sounds like a promising line of attack for better models, and more importantly for diesels. Do you have a good reference?

Then you may be thinking about the wrong end of the problem. Look at your simplified model and consider the influence of torque. Now look at your instrumentation, it is all, generally speaking, at best accuracy at peak torque.

Now look at the torque curve and the weighting of your data predictor. You have the least error in your sample prediction and your calculation at peak torque.

On the flip side, forget the physics, math, and sensing and look at the data! Ultimately, you have to explain why your chart has the lowest error, essentially zero, at peak torque, but huge amounts of error elsewhere!

I can’t explain that, in part because I don’t believe that. My observations show a peak of 35% holistic system efficiency. That’s too big, by a heftier margin than I’m comfortable with. But I’m expecting that that is in part because I’m using manufacturer’s data for things such as air resistance and am overestimating tire rolling resistance by a substantial margin (by >25% according to what I saw today.)

Could you specify what it is exactly that is bothering you? For the life of me I can’t see the problem you describe.

If the cause of error is not understood and established, then there is no reason to presume that incremental improvement can occur. In other words, we can't assume it is a matter of better math. It could easily be the precision and limits of your underlying measurements, or even a flaw in your foundational models and premises. In science, everything is on the table until the data is explained and the results replicated.

That is a question of philosophy. Anyone who’s ever studied observers knows that EKF (Extended Kalman Filters for everyone else out there) cannot be shown to globally converge. There is no mathematical proof for it. Yet, in no physical case have they ever been show not to globally converge. So even at the upper echelons of the mathematical world, there is cutting edge research being done, based on something that can’t be proven in a global sense. Even mathematicians are willing to fudge on things and not explain details that aren’t important when the system “just works”.

Now, I’m not saying that this is an excuse nor a reason for every Tom, Dick, and Harry who’s too lazy to cross his t’s, dot his i’s, and expects his new-fangled system to run on imaginium, but I do think it’s a mistake not to go further in this case because we have some nagging unknowns.

Actually, you are not looking at total system efficiency, you are looking at a fairly narrow area of system efficiency, post drive train (but while ignoring many real world events)

As far as the V/P curve and slope, that is too big a question to properly answer here. I'd recommend starting with basic texts in engine design. But, in super brief, think of a single cyl in an engine. Mechancially, we have a constantly changing volume (pistone up, piston down). In combustion, we are creating additional pressure which also follows a curve (picture the flame front radiating from the ignition source while generating an envelope of gases).

If we could somehow create a perfect engine, plotting these two on a two axis graph, we would basically have a repeating rectangle. But chemistry and simle mechanics do not allow this, so we get a deflated and slighly twisted football (American). We do different things to try to draw portions closer to an ideal rectangle, like turbulence in the mix giving faster burns at higher RPMs, but, ultimately, the optimum point of peak pressure to occur is fixed, literally built into the cyl. So all engines have a fairly small peak efficiency island.

Ah, I see. You’re talking about the volume vs. pressure thermodynamic graphs. (Personally, I always preferred entropy vs. temperature, but I guess that just depends on who and what you work with.) Now that I know what you’re talking about in thermodynamic terms, I’m doubly unclear about why I should see the curve, nor which part of it I should expect to see in an efficiency map, esp. a curve-fitted one.

No, I'm saying that your model appears to be too simple to be of much use in actually predicting operational economy. Consider a seemingly simple question, why do people here hate driving in winter?

Simple, fuel economy is worse. But why?

There are actually multiple reasons, even the fuel composition is different, but let's look at just two, emissions and aircharge.

The effiency of a cat is very narrow, both in terms of gas composition and in terms of required exhaust temp. So a modern automobile runs, as much as possible, at lambda 1.0 (actually, the vehicles are closed loop to equivelency ratio, but we often talk about the reciprocal, lambda). This gives both peak EGT, and a cat friendly gas composition.

Now, it is winter, and the air charge entering the engine is denser, so it takes more fuel to reach the same stoichiometric ratio. Simply by virtue of air being dense, you have to burn more fuel at even the lightest loads to keep emissions systems operating.

If you are a small plane pilot, you love cooler denser air, because you are taking off and generally climbing at wide open throttle. That denser air means you climb faster (for multiple reasons, some more important than others, but a big one is the plane is probably normally aspirated and the density altitude is lower).

I am a small plane pilot, and I do love cold days, and cold, clear nights even better. (Just so long as there’s no ice accumulation due to radiative heat loss). But you comparison is unfair. You’re assuming that we claim that the summer efficiency map is the same as the winter efficiency map. Not so.

While we don’t even have enough data yet to show how the holistic efficiency map changes across the seasons (hurry up and get here, summer!), it’s a reasonable guess that it will only change the magnitudes of the efficiency curve, and not its general shape.

I'd be very happy to proved wrong, but you will need to do some experimentation and collect some data to do so!

Well, look at it this way. We have three possibilities:

1) Drivers follow the optimum trajectory through R_17 space and see a net fuel economy increase.
2) Drivers follow the optimum trajectory through R_17 space and see a net fuel economy decrease.
3) Drivers follow the optimum trajectory through R_17 space and see no net change in fuel economy.

Many experiments with manufacturer provided data have shown that #1 is the outcome. See for example the excellent results from E. Hellstrom in “Look-ahead control for heavy trucks to minimize trip time and fuel consumption”. The only question left is, then, “Can we provide a similar increase in system performance with an efficiency map observed from real-world driving?”

Dollars to donuts gets you that the answer is “yes”.

Note that the typical user will NOT be able to guess the optimal control trajectory. If there’s one thing I’ve learned about optimal control as a mechanical engineer, it’s to shut up when the math speaks. Optimal control defies all physical intuition, and it’s terribly frustrating because it is optimal. There is no room for debate with optimal results, there is only room for debate with the model compromises that led to that result. The scary thing is how terribly “wrong” most of what we do is. Well, not wrong, but sub-optimal.

***

Exercise for the reader: you are the driver of a car on a road that is horizontally straight, but that has exactly the shape of a sine curve in the vertical dimension. (Imagine a roller coaster) The car starts of on the righthand side of one of the waves, pointing uphill. You have one single gear, and an ideal clutch (i.e., a clutch that instantly engages without slipping). Your engine efficiency curve looks like a bell curve (i.e. there’s a certain speed toward the middle of the curve at which you have maximum efficiency). Whenever you disengage the clutch, the engine turns off. You have no air or rolling losses whatsoever. (In fact, no losses at all aside from those dictated by the efficiency curve.)

Your goal is to get to the top of the hill three hills to the right, all while using the least energy possible. What is your optimal strategy?

I’ll send a free logging board and an AVRDragon to the first person who gets it right before next week Friday.