OptiStruct is a proven, modern structural solver with comprehensive, accurate and scalable solutions for linear and nonlinear
analyses across statics and dynamics, vibrations, acoustics, fatigue, heat transfer, and multiphysics disciplines.
Elements are a fundamental part of any finite element analysis, since they completely represent (to an acceptable
approximation), the geometry and variation in displacement based on the deformation of the structure.
The different material types provided by OptiStruct are: isotropic, orthotropic, and anisotropic materials. The material property definition cards are used to
define the properties for each of the materials used in a structural model.
High Performance Computing leverages computing power, in standalone or cluster form, with highly efficient software,
message passing interfaces, memory handling capabilities to allow solutions to improve scalability and minimize run
times.
Contact is an integral aspect of the analysis and optimization techniques that is utilized to understand, model, predict,
and optimize the behavior of physical structures and processes.
OptiStruct and AcuSolve are fully-integrated to perform a Direct Coupled Fluid-Structure Interaction (DC-FSI) Analysis based on a
partitioned staggered approach.
Uniaxial Fatigue Analysis, using S-N (stress-life) and E-N (strain-life) approaches for predicting the life (number
of loading cycles) of a structure under cyclical loading may be performed by using OptiStruct.
Multiaxial Fatigue Analysis, using S-N (stress-life), E-N (strain-life), and Dang Van Criterion (Factor
of Safety) approaches for predicting the life (number of loading cycles) of a structure under cyclical
loading may be performed by using OptiStruct.
Seam Weld Fatigue analysis is available to facilitate Fatigue analysis for seam welded structures. It allows you to
simulate the Fatigue failure at the seam weld joints to assess the corresponding fatigue failure characteristics like
Damage and Life.
When there is no underlying random vibration but there are a sufficient number of simultaneously occurring sine tones,
it can be considered random vibration.
Sine-sweep on random vibration is a superposition of swept sinusoidal vibration on random vibration. It is considered
as a series of single sine tones on top of random vibration.
Aeroelastic analysis is the study of the deflection of flexible aircraft structures under aerodynamic loads, wherein
the deformation of aircraft structures in turn affect the airflow.
OptiStruct provides industry-leading capabilities and solutions for Powertrain applications. This section aims to highlight OptiStruct features for various applications in the Powertrain industry. Each section consists of a short introduction, followed
by the typical Objectives in the field for the corresponding analysis type.
This section provides an overview of the capabilities of OptiStruct for the electronics industry. Example problems pertaining to the electronics industry are covered and common solution
sequences (analysis techniques) are demonstrated.
OptiStruct generates output depending on various default settings and options. Additionally,
the output variables are available in a variety of output
formats, ranging from ASCII (for example, PCH) to binary files (for example,
H3D).
A semi-automated design interpretation software, facilitating the recovery of a modified geometry resulting from a
structural optimization, for further use in the design process and FEA reanalysis.
The OptiStruct Example Guide is a collection of solved examples for various solution sequences and optimization types and provides
you with examples of the real-world applications and capabilities of OptiStruct.
Surface condition is an extremely important factor influencing fatigue strength, as
fatigue failures nucleate at the surface. Surface finish and treatment factors are
considered to correct the fatigue analysis results.
Surface finish correction factor is used to characterize the roughness of the
surface. It is presented on diagrams that categorize finish by means of qualitative
terms such as polished, machined or forged. 1
Surface treatment can improve the fatigue strength of components. NITRIDED,
SHOT-PEENED, and COLD-ROLLED are considered for surface treatment correction. It is
also possible to input a value to specify the surface treatment factor .
In general cases, the total correction factor is
If treatment type is NITRIDED, then the total correction is .
If treatment type is SHOT-PEENED or COLD-ROLLED, then the total correction is = 1.0. It means you will ignore the effect of
surface finish.
The fatigue endurance limit FL will be modified by as: . For two segment S-N curve, the stress at the
transition point is also modified by multiplying by .
Surface conditions may be defined on a PFAT Bulk
Data Entry. Surface conditions are then associated with sections of the model
through the FATDEF Bulk Data Entry.
Fatigue Strength Reduction Factor
In addition to the factors mentioned above, there are various other factors that could affect the
fatigue strength of a structure, that is, notch effect, size effect, loading type.
Fatigue strength reduction factor is introduced to account for the combined effect of
all such corrections. The fatigue endurance limit FL will be modified by as:
The fatigue strength reduction factor may be defined on a
PFAT Bulk Data Entry. It may then be associated with
sections of the model through the FATDEF Bulk Data
Entry.
If both and are specified, the fatigue endurance limit FL will
be modified as:
and have similar influences on the E-N formula through
its elastic part as on the S-N formula. In the elastic part of the E-N formula, a
nominal fatigue endurance limit FL is calculated internally from the reversal limit
of endurance Nc. FL will be corrected if and are presented. The elastic part will be modified as
well with the updated nominal fatigue limit.
Scatter in Fatigue Material Data
The S-N and E-N curves (and other fatigue properties) of a material is obtained from
experiment; through fully reversed rotating bending tests. Due to the large amount
of scatter that usually accompanies test results, statistical characterization of
the data should also be provided (certainty of survival is used to estimate the
worst mean log(N) according to the standard error of the curve and a higher reliability level
requires a larger certainty of survival).
To understand these parameters, let us consider the S-N curve as an example. When S-N
testing data is presented in a log-log plot of alternating nominal stress amplitude
Sa or range SR versus cycles to failure N, the relationship between S and N can be
described by straight line segments. Normally, a one or two segment idealization is
used.
Consider the situation where S-N scatter leads to variations in the possible S-N
curves for the same material and same sample specimen. Due to natural variations,
the results for full reversed rotating bending tests typically lead to variations in
data points for both Stress Range (S) and Life (N). Looking at the Log scale, there
will be variations in Log(S) and Log(N). Specifically, looking at the variation in
life for the same Stress Range applied, you may see a set of data points which look
like this.
S
2000.0
2000.0
2000.0
2000.0
2000.0
2000.0
Log (S)
3.3
3.3
3.3
3.3
3.3
3.3
Log (N)
3.9
3.7
3.75
3.79
3.87
3.9
As with many processes, the distribution of Log(N) is assumed to be a Normal
Distribution. There is a full population of possible values of log(N) for a
particular value of log(S). The mean of this full population set is the true
population mean and is unknown. Therefore, statistically estimate the worst true
population mean of log(N) based on the user input sample mean (SN curve on
MATFAT) and Standard Error (SE
on MATFAT) of their sample. The SN material data input on the
MATFAT entry is based on the mean of the normal
distribution of the scatter in the particular user sample used to generate the
data.
The experimental scatter exists in both Stress Range and Life data. On the
MATFAT entry, the Standard Error of the scatter of log(N) is
required as input (SE field for S-N curve). The sample mean is
provided by the S-N curve as , whereas, the standard error is input via the
SE field of the
MATFAT entry.
If the specified S-N curve is directly utilized, without any perturbation, the sample
mean is directly used, leading to a certainty of survival of 50%. This implies that
OptiStruct does not perturb the sample mean provided
by the user on the MATFAT entry. Since a value of 50%
survival certainty may not be sufficient for all applications, OptiStruct can internally perturb the S-N material data to the required
certainty of survival defined by you. To accomplish this, the following data is
required.
Standard Error of log(N) normal distribution (SEon
MATFAT).
Certainty of Survival required for this analysis (SURVCERT on FATPARM).
A normal distribution or gaussian distribution is a probability density function
which implies that the total area under the curve is always equal to 1.0.
The user-defined SN curve data is assumed as a normal distribution, which is
typically characterized by the following Probability Density
Function:(1)
Where,
The data value () in the user sample.
The sample mean .
The standard deviation of the sample (which is unknown, as the user
inputs only Standard Error (SE) on
MATFAT).
The above distribution is the distribution of the user-defined sample, and not the
full population space. Since the true population mean is unknown, the estimated
range of the true population mean from the sample mean and the sample SE and
subsequently use the Certainty of Survival defined by the user (SURVCERT) to perturb the sample mean.
Standard Error is the standard deviation of the normal distribution created by all
the sample means of samples drawn from the full population. From a single sample
distribution data, the Standard Error is typically estimated as , where is the standard deviation of the sample, and is the number of data values in the sample. The mean
of this distribution of all the sample means is actually the same as the true
population mean. The certainty of survival provided by the user is applied on this
distribution of all the sample means.
The general practice is to convert a normal distribution function into a standard
normal distribution curve (which is a normal distribution with mean=0.0 and standard
error=1.0). This allows us to directly use the certainty of survival values via
Z-tables.
Note: The certainty of survival is equal to the area of the curve under
a probability density function between the required sample points of interest.
It is possible to calculate the area of the normal distribution curve directly
(without transformation to standard normal curve), however, this is
computationally intensive compared to a standard lookup Z-table. Therefore, the
generally utilized procedure is to first convert the current normal distribution
to a standard normal distribution and then use Z-tables to parameterize the
input survival certainty.
For the normal distribution of all the sample means, the mean of this distribution is
the same as the true population mean
, the range of which is what you want to estimate.
Statistically, you can estimate the range of true population mean as:(2)
That is, (3)
Since the value on the left hand side is more conservative, use the following
equation to perturb the SN curve:(4)
Where,
Perturbed value
User-defined sample mean (SN curve on MATFAT)
Standard error (SE on MATFAT)
The value of is procured from the standard normal distribution
Z-tables based on the input value of the certainty of survival. Some typical values
of Z for the corresponding certainty of survival values are:
Z-Values (Calculated)
Certainty of Survival (Input)
0.0
50.0
0.5
69.0
1.0
84.0
1.5
93.0
2.0
97.7
3.0
99.9
Based on the above example (S-N), you can see how the S-N curve is modified to the
required certainty of survival and standard error input. This technique allows you
to handle Fatigue material data scatter using statistical methods and predict data
for the required survival probability values.
References
1 Yung-Li Lee, Jwo. Pan, Richard B. Hathaway and Mark E.
Barekey. Fatigue testing and analysis: Theory and practice, Elsevier,
2005