Skid Run 2010 02 11


Initial Observations:

Time Series Plot

Gas Composition

Sample Time 3:05 PM 3:26 PM

4:09

PM

3:50

PM

         
Fuel Softwood Chips Hardwood Pellets Hardwood Pellets Hardwood Pellets          
Type Gas Gas Gas Exhaust Proto-typical syngas        
CO2 [%] 8.2 8.4 5.9 13.1          
CO 15.3 25.1 15.5 0          
H2 8.9 13.5 11.2 0          
CH4 1.6 3.9 3.0 0          
N2 54.3 47.7 48.6 84.7          

Notes:

The precision of the CO measurement can't measure CO concentrations at PPM levels (e.g. in exhaust). According to this Iowa State FAQ, CO levels in car exhaust before a catalytic convertor reaches temperature can excede 8%.

 

Determination of Time Constants

The pyro-coil and muffler show obvious simple dynamics. See the wikipedia page on time constants in modelling of thermal systems.

Engine On (warming) (datalog time from 3360 to 4800 s)

Temperature

A

(amplitude)

tau

(time const.)

V_0

(init. temp.)

A*tau

(final temp.)

Muffler 3.161 196.961 -12.759 623
Pyrocoil In 2.578 221.591 53.020 571
Pyrocoil Out 0.6434 518.1744 18.9044 333

Engine Off (cooling) (datalog time from 5760 to 7320 s) 

Temperature

A

(amplitude)

tau

(time const.)

V_0

(init. temp.)

A*tau

(final temp.)

Muffler 0.0005132 1415 582 0.73
Pyrocoil In 0.1698 499.5 376.5 85
Pyrocoil Out 0.1373 616.3 262.2 85

Using the same naming conventions as wikipedia, where:

V(t) is the temperature over time

V_0 is the initial temperature [°C]

tau is the time constant (63% of the change occurs over this time) [in seconds]

A*tau is the final temperature (V_infinity) [°C]

 

Comments:

 

R code:

> t <- seq(0,7320-5760)

> m <- nls(T6_muffler[5760:7320] ~ V_0*exp(-t/tau) + A*tau*(1-exp(-t/tau)) ,start=list(A=3,tau=100,V_0=600))

> m