posted 04 June 2001 01:17 PM              

My first uncertainty calculation is pressure drop which of coarse is related to air flow, diameter of the tube, temperature etc. Would the partial derivative approach alone be OK -- or do I have to run an experiment in addition to performing the calculation.
Also, am I right to assume that once I determine all the uncertainties using the partial derivative approach, that I also consider the uncertainty of the standard that I'm using?

I'm desperate and confused-- so please help.

posted 06 June 2001 09:06 AM               

I would like to suggest some things as an action plan. You probably have already done some of this, but just in case ...

Research and Study:

• Get a copy of ANSI/NCSL Z540-2, the US version of the ISO Guide to the Expression of Uncertainty in Measurement.
  
• Get a good reference book on measurement uncertainty or error analysis.
  
• Search the Cove Forums for earlier message threads on this topic. A couple of suggestions are https://elsmar.com/ubb/Forum4/HTML/000047.html and https://elsmar.com/ubb/Forum4/HTML/000217.html
  
• Look at the tutorial on Measurement Systems Analysis on this forum. (https://elsmar.com/level2/cal_q1.html)
  
• Look at the NIST reference, http://physics.nist.gov/cuu/index.html
  

Arm yourself with Tools:
In particular, look at the FREEware measurement uncertainty calculator by Chris Grachanen. you can download it from Cal Lab Magazine, http://www.callabmag.com/freeware.html

Apply the Principles, but Start Simple:

• Get the low hanging fruit first. Start with what you already know or can easily find out, and get that documented first. (Example: the accuracy specifications of your measuring instruments.)
  
• I often continue with a simple sensitivity analysis of the major paramters that I can measure or control. (For example, if a measuring instrument's uncertainty changes by 5 ppm per degree C, calculate it out at a few measurement points to see how much effect it has.)
  
• Along the way, document your processes for determining various factors.
  
• Make sure you have every uncertainty factor properly characterized as Type A or Type B. Type A is factors that you measure yourself and analyze statistically. (Example: hourly values from your temperature/humidity datalogger.) Type B is numbers you get from other sources. (Example: the accuracy specification of your anemometer.)
  
• Before going to the work of setting up a designed experiment run, ask yourself if the factor is really a significant contributor to your measurement result, to the point where it makes economic sense to reduce it. If it is a significant portion and you think you can economically reduce the variation, then DOE may be justified. If the amount of variation is a trivial part of the result it may not be worth the effort.
  

Does this help some?

------------------
Graeme C. Payne
ASQ Certified Quality Engineer
[email protected]