Uptimai Uncertainty Quantification builds the complete surrogate mathematical model of the solved problem. Unlike the preliminary analysis, it allows the user to perform the full study of dependencies of output values on uncertain inputs, not only sensitivities to input variables. On the full model, it is possible to prepare the complete set of visualisations of these dependencies and walk in detail through all statistical characteristics of the model. It also allows statistics-based optimization, in which results are again delivered in the form of easily readable graphics, giving the user straightforward hints for increased performance in a range of e.g. operational and environmental conditions, or manufacturing tolerances. The Uncertainty Quantification method will call for outputs corresponding to specific combinations of input parameters, thus, it is intended to work in connection to engineering computational codes etc.
The general appearance of the program window, especially its left section, is described in detail in the Input preparation link. Here the main focus is on the other part which is to a certain point individual for each of the supported methods. The initial of the GUI window when preparing inputs for the Uncertainty Quantification is shown in Figure 1, as the user starts from scratch and needs to Define Input Variables.
In the beginning, only two controls are available for the user:
- # of Monte-Carlo samples : Entry field setting the number of samples for Monte Carlo simulation, size of input distributions. Must be an integer value between 1,000 and 1,000,000.
- Add input variable : Creating a new variable (parameter) of the input domain. Each added variable appears at the bottom of the list of already existing variables.
The Monte Carlo sampling is used for model propagation and visualizations. The default value of 100,000 is based on the best-practice trade-off between the speed of the solver and postprocessor, file sizes, and model precision.
Adding an input variable enhances the input domain space with one dimension. There has to be at least one for the model to be created. Figure 2 describes the situation with multiple input variables already created (see this link for more info about the borehole problem). One of the displayed variables is about to be edited. The input variable can be set using the following controls:
- Variable name : Label of the input parameter, which is being used throughout the whole process up to the postprocessing. The variable name cannot contain empty spaces, these are automatically replaced with underscores.
- Distribution : Selection box where the user sets the shape of the probabilistic function for the input variable. According to the distribution type selected, additional entries with shape parameters appear. A detailed description of featured probability distribution types can be found in the section Input distribution types.
- Confirm : Any changes need to be confirmed with this button to take effect.
- "X" : Each input variable can be deleted when clicking this icon.
- "=" : Allows input variable dragging to change the ordering of inputs in the projects.
- + Advanced Options: Activation Type : Allows change between Active (by default) and Inactive. Active means that the intrinsic uncertainty of the variable will be propagated and Inactive means that only the nominal value will be used (the variable won’t be studied).
Adding one or more input variables activates the Prepare distributions button. This one invokes the preparation of randomly distributed samples according to the settings. In case there are invalid entries in the input variable definition, the user is informed and not allowed to continue to the next step until everything is by the book. Then, the button itself turns into Tweak distribution options, sending the user to this next step. Also, the Tweak Distribution Options item is activated in the fishbone navigation bar on the left.
At this point, the user adjusts the boundaries of the input domain and the so-called nominal sample. Boundaries are recommended to be adjusted especially for distribution shapes where the user defines parameters like mean value and standard deviation. In these cases, the edges of the domain depend on the randomization of samples within the input variable. Thus, modification is usually required to set the exact range for such inputs. For certain types of distribution shapes like uniform or discrete, edges of the domain are exactly given by the distribution shape definition and cannot be changed after.
The nominal point is a sample acting as a baseline for the created surrogate model and analysis. In the model, the results of all data samples are compared with the result value of the nominal sample. This process allows handling the effects of input parameters and their interactions separately as increments to the nominal value. It must be within the range of each input variable and not be equal to its boundaries. Although not strictly necessary, it is recommended to place the nominal sample into the statistical centre of the domain. Then, the process of the surrogate model creation is most efficient and precise. The nominal sample's default position is suggested as the mean of the probability distribution of each input variable. When changing its position, (shown in Figure 3) it is advised to not shift it by more than 10% of the range of each input. As in the case of input variable distribution definition, all changes must be saved using the Confirm button.
For the sampling of variables leading to periodic or symmetrical functions (typically, but not exclusively, angles of any kind), extra caution is required. It is highly recommended not to set their nominal value exactly to the centre of symmetry of the corresponding input distribution! A typical example can be the angular position of a crankshaft, wave phase, etc.
Clicking the Generate data button at the bottom right invokes the saving
.txt files with randomly distributed samples according to the settings and input domain info.
Then, the button itself turns into a View Data Histogram,
sending the user to this next step. Also, the View Data Histogram item is activated
in the fishbone navigation bar on the left. For fundamental changes in any input distribution,
the user can return to the previous step with the Return to Input Variables button.
In this section, all created input variables can be reviewed to check their probability distribution shapes. Users can see the histograms of randomized samples as these are about to be used. It is recommended to provide such a type of check before an actual uncertainty quantification run to prevent solver crashes and eliminate misinterpretation of results.
To the left of the actual histogram plot, there are controls of the figure to be shown. Box labeled Variables contain the list of input parameters available in the domain. Each item can be selected by mouse clicking, showing the corresponding distribution shape. The appearance of the plot can be changed using the settings in the Plot options section:
- Plot title : Displayed above the plot, input variable name by default.
- X label : Label of the X axis, input variable name by default.
- Y label : Label of the Y axis. The default text contains the number of samples used for the histogram and the number of bins these are split in.
- Title size : Size of the title font.
- Label size : Size of the font for both axis labels.
- Show legend : Switch turning on/off the legend of the plot.
- Legend size : Size of the legend font.
- Range : Double-sided slider allowing to show a slice of the input distribution in detail. Dragging one of the slider's points limits the depicted range, one can move with the section along the X-axis by dragging the green bar of the slider (both edge points are highlighted).
- Adjust axes : Toggle if the X-axis range of the plot should be only the range adjusted with the slider above (on) or the full range of the input distribution (off).
- Normalize plot : Turn on/off normalization of the histogram. Y-axis values change accordingly, Y-axis' default label changes from N to Density.
- Log. vertical axis : Turns on/off logarithmic scaling of the Y-axis.
- Bin count : Set the number of bins for the histogram. The recommended value is below 200. Needs to be confirmed with the Apply button.
There are two more buttons under the plot. Return to Distribution Options brings users back to the previous step of Tweak Distribution Options where they can fix boundaries or the position of the nominal sample. The other button will Close Preprocessor since all the input files required are ready for the next step, which is Core Solver Setup.
Additional vertical lines that can be seen in the plot show the boundaries of the input variable distribution (input domain edges) and the position of the nominal sample. Also, clicking into the plot invokes the cross with a label showing the exact value of the selected point in the histogram.