Measurement uncertainty fundamentals

Foundational vocabulary for bias, precision, and propagation, the layer beneath instrument-specific derivations such as the pneumatic probes overview, the 3-hole probe uncertainty note, and the narrative in experimental aerothermal measurement.

Reference Metrology GUM-aligned practice
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Start with your question

Why visitors arrive: You need metrology basics before reading instrument-specific derivations.

Your question: What is the difference between bias and precision, and how do I propagate uncertainty?

You may also be asking

  • Where is a worked probe example?
  • How does this appear in publications?
  • What checklist applies to experiments?

Where to go next

Uncertainty is part of the model

An experimental result without a stated uncertainty budget is not falsifiable. Before comparing CFD to data, transferring a calibration between tunnels, or publishing a loss coefficient, separate what is systematic (bias) from what scatters (precision), then propagate both through the algebra that converts raw readings into physical quantities.

Core vocabulary

Term Meaning Typical sources
Bias (systematic) Persistent offset in a measured value; repeatable if conditions unchanged Transducer calibration drift; misalignment; polynomial fit error; facility-specific flow non-uniformity
Precision (random) Scatter about the mean under ostensibly identical conditions Tunnel unsteadiness; electronic noise; sampling window; spatial averaging
Standard uncertainty Estimated standard deviation of a quantity, often denoted \(u(x)\) Type A from repeated observations; Type B from bounds, certificates, or models
Expanded uncertainty Interval about the estimate, typically \(U = k\,u\) with coverage factor \(k\) (often 2 for ~95%) Reporting in publications; comparison to simulation confidence intervals
Sensitivity coefficient Partial derivative \(\partial f / \partial x_i\) showing how output \(f\) responds to input \(x_i\) Essential when outputs are nonlinear functions of calibrated readings

Propagation methods

When a measured quantity \(y\) is a function of several inputs \(x_1,\ldots,x_n\), choose a propagation route consistent with linearisation error and reporting needs.

Method When it applies Limitation
First-order Taylor (GUM) Smooth functions; uncertainties small relative to curvature Breaks down for strongly nonlinear maps unless inputs redefined
Root-sum-square of components Independent bias and precision budgets on a single output Cross-correlations between inputs must be included when present
Monte Carlo Nonlinear calibration surfaces; correlated inputs; asymmetric distributions Requires defensible input PDFs; heavier to document

For a scalar output \(a\) with bias \(B_a\) and precision \(S_a\) treated independently and both stated at about 95% coverage (if both are standard uncertainties, their root-sum-square is the combined standard uncertainty, and \(U_a\) is \(k\) times it), a common reporting form is:

\[ U_a = \sqrt{B_a^2 + S_a^2} \]

The probe note applies this structure to pressure coefficients and derived flow angles, with explicit partial derivatives at the operating point.

Practical workflow

  1. Define the measurand: the quantity reported (e.g. yaw angle, effectiveness, metal temperature)
  2. Write the model: algebraic map from raw readings to the measurand, including calibration polynomials
  3. List uncertainty sources: instrument, installation, facility, data-reduction choices
  4. Estimate component uncertainties: Type A repeats and Type B bounds
  5. Propagate: Taylor or Monte Carlo; document assumptions
  6. State results: value ± expanded uncertainty; archive enough detail for reproduction

This sequence is the backbone of the instrumentation & measurement theme and the Oxford probe publication track.

About this work

Lucas Rey, aerothermal systems engineer and academic tutor.

  • University of Oxford: DPhil Researcher, Thermofluids Institute.
  • University of Cambridge: Alumnus.
  • Rolls-Royce: Sponsored researcher (High-pressure turbine programme).

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