ARMY AL&T
WHAT IS LINEAR EXTRAPOLATION?
During mobility-performance testing, there are several key temperatures that govern the outcome. First, the vehicle performance specifica- tion lists an upper requirement of ambient temperature in the test environment for powertrain cooling. Most commonly, this is 120 degrees as this represents the extremes of desert warfare. Second, the vehi- cle powertrain has critical limits for each of its fluid temperatures (i.e., coolant, engine oil, trans- mission fluid, intake manifold air, etc.). The manufacturer assigns these critical-limit temperatures as a not-to-exceed boundary before damage or failure ensues, and the test fails. Lastly, ambient temper- ature—the air temperature during the test—significantly impacts the measured performance. While ambi- ent temperature at time of testing should be the same as the vehi- cle’s upper requirement for ambient
temperature, that presents a prob- lem. Ambient temperatures above 110 degrees rarely occur except as the peak temperature of the day within the United States. So, even though places like Death Valley and Yuma Proving Ground can touch 120 degrees, they can do that only for part of the year—June through August—and so the windows of opportunity to test at the temper- ature necessary are very small. Contrast that with the Army’s envi- ronmental chambers that can do it any day.
As an example, assume testers measure the ambient tempera- ture as 90 degrees and engine oil temperature as 250. We need to know the fluid temperature in hot desert conditions, so we subtract 90 degrees from 120, then add that extra 30 degrees to the fluid temperature of 250. That’s linear
extrapolation. For every increase of 1 degree in the ambient temper- ature, the fluid temperature can be expected to rise by the same amount. Therefore, in this test, linear extrapolation would predict the engine oil temperature as 280 degrees. If the vehicle had a critical limit for engine oil temperature at 275 degrees, it would fail the test.
Under the right conditions, linear extrapolation of ambient tempera- ture has been a useful tool when attempting to predict the perfor- mance of mechanically controlled powertrains. Because there are so many more variables with electron- ically controlled powertrains, linear extrapolation isn’t any longer a valid approach to the Army’s needs because we can’t see into proprie- tary algorithms and don’t know the variables the algorithms are using for control.
wheel speed, engine speed, coolant temper- ature, transmission oil temperature, engine oil pressure, intake manifold air tempera- ture and many other parameters.
To understand the major components of electronically controlled powertrains, think of the controller as the brain and the sensor network as the nervous system. Te sensor network is constantly relaying information to the controllers about the vehicle’s status and health. Te controllers then make decisions on behalf of the driver to deliver the best available performance. Without the brain and nerve system of the algorithm and the controller and
sensor network, mechanically controlled powertrains are unaware of their health status, unable to provide warning infor- mation to the driver (check engine light) or initiate self-protecting countermea- sures to avoid catastrophic failure, such as overheating and blowing the engine head gasket.
Interpretation of the data from feedback sensors by the controller is handled by a series of proprietary algorithms owned by the manufacturer. Since this information is not typically made available to the U.S. military, it is crucial to test the vehicle in representative conditions and observe the
response to ensure the vehicle meets the requirement.
NO SUBSTITUTE FOR PROOF Even when information is available from the manufacturer regarding its control algorithms, the importance of testing in representative conditions is not dimin- ished. Manufacturers do not always know the decisions their controller will make in all circumstances. Manufactur- ers use software modeling tools to predict the controller’s decision, but these soft- ware models also require validation in order to be fully trusted when operat- ing at the extremes of their design intent.
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