 Assessing Errors in Analysis Models
This article discusses errors in analysis and methods to reduce or
quantify them. The approach described in the SAFESA series of
documents, published by NAFEMS (Ref. R0039
, R0040
, R0041
) attempts to formalise the measurement and treatment of error in
analysis. This article gives an interpretation of some of the ideas
behind this. While quantifying error is a laudable concept, it may
not always be viable. But a consideration (if not a quantification)
of errors should be a minimum requirement of any analysis quality
system. Errors are inevitable in numerical analysis and experienced
analysts have a ‘feel’ for the accuracy of a particular
result.
Many analysts might argue that producing a result that is
meaningful and sufficiently accurate does not require a formal
quantification or error, just the supervision of an analyst with
suitable experience. Attempting to quantify all errors is akin to
quantifying professional experience, and can be difficult.
Nonetheless, even an informal consideration of the different types
of errors that arise in numerical analysis can be a useful way of
building confidence in results.
Uncertainty and Error
Many of the differences in the actual behaviour of a structure and
that predicted by analysis are due to uncertainty in the physical
description of the structure - due to natural variability in the
loading, in environmental conditions, methods of manufacture or the
operating regime. Uncertainties are often dealt with by specifying
conservative loadings or material properties in codes of practice
or standards. The analyst in these cases does not have to make
further allowance for these uncertainties in the model.
A good example of uncertainty is wind loading on a building or
structure. Codes of practice address the uncertainty in wind
loading by prescribing a design wind speed based on conservative
probabilistic considerations, so that the loads will account, with
a high degree of confidence, for all wind conditions likely to be
encountered by the structure during its life.
In this context uncertainties can be distinguished from modelling
errors. In simple terms, this is a distinction between variability
in the definition of the problem (uncertainty) and variability in
the definition of the model created to represent it (error). (The
analyst normally has full responsibility for the latter and perhaps
some of the former.)
Modelling Error
Different categories of modelling approximation are listed below.
These are grouped according to the SAFESA documents; but are not
definitive. Some examples of sources of error are given for each
category.
Mathematical Model
Domain
Boundary Conditions
Applying point loads or bolt loads
Primitives (Elements)
Elements have limitations on the behaviour that they can represent.
This may not just be limited to the accuracy of the approximation
of displacement or stress (for example) across an element but can
also include an inability to represent some types of behaviour
entirely. Examples in structural analysis include shear
representation in certain types of shell elements and more
obviously, beam elements not representing local stress
concentrations for example, where a bracket might be attached or
two beams are connected together.
The aim of the SAFESA approach is to put a figure on the maximum
possible analysis error (i.e. bound the error). This can be
achieved by first bounding individual errors such as those
identified above, and then combining them to give a total maximum
error value. It is usual in any analysis project to carry out
several runs of improving accuracy and provide validating
calculations. The method of quantifying individual errors in the
SAFESA approach is achieved by extending and refining this process.
Analyses typically solve for a maximum value of a result such as
stress, deflection or energy absorption, and compare the maximum
value with an ‘allowable’ value of the same parameter.
Safety factors on the input load or allowable value can be
specified which account for the uncertainties in some or all of the
data. Strictly speaking however, the purpose of safety factors
should not be to allow for errors in the analysis process. Instead,
a total maximum error value obtained using the SAFESA or a similar
approach, can be used to factor the results before comparing them
with the allowable values.
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