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BlockSim and RENO appear to have overlapping capabilities. What
are the core differences between BlockSim and RENO?
BlockSim uses
logic diagrams in the form of Reliability Block Diagrams (RBDs)
or fault trees. These logic diagrams describe the ways a system
can fail or succeed. In BlockSim, blocks represent
components, subsystems, assemblies, events, etc. and have a
number of reliability and maintainability properties which
determine the outcome of a simulation. How the blocks are joined
illustrates the reliability relationship (e.g. series and
parallel (redundant) configurations).
More complex constructs are also available in order to create
more realistic models (e.g. load sharing containers or
standby containers).
RENO employs a
visual and intuitive flowcharting approach to allow you to build
models to analyze complex probabilistic or deterministic
scenarios. With
RENO, you can create
scenarios graphically (through a flowchart type of interface), using a set of predefined constructs and function
variables. Once the model has been constructed, simulation
(through sequential execution of the flowchart) can be utilized.
RENO is unique in the fact that
it gives you the flexibility of a computer language, but instead
of writing computer code, you use the familiar flowcharting
concept to build your analysis (to write your "program"). This
flexibility does require that the user is adept at building
simulation scenarios from the ground up.
Which software tool is best for
my application?
BlockSim provides a sophisticated discrete event
simulation engine for reliability, maintainability,
availability, throughput, life cycle cost summaries and related
analyses. Additionally, an extensive array of RBD configurations
and fault tree analysis gates and events, designed specifically
for these types of analyses, are supported. Included are
advanced capabilities to model complex configurations, load
sharing, standby redundancy, phases, duty cycles, etc.
On the other hand, if you are
trying to build a generic probabilistic simulation for
applications including probabilistic risk analysis, event
modeling, reliability analysis, financial analysis, forecasting,
maintenance planning, optimization or operational research
problems, then RENO provides that flexibility.
Where can I find more
information about the software? Who should I contact if I have
more questions about the differences and uses of BlockSim and
RENO?
Our Web site has more detailed information about each of these
software packages. Use the links below to find out more, or
contact us
directly.
http://BlockSim.ReliaSoft.com
http://RENO.ReliaSoft.com
Does DOE++ offer variability analysis?
For two level full factorial, two
level fractional factorial and Plackett-Burman designs with more
than one replicate, DOE++
gives you the ability to determine the variability of the
response(s) across runs and to analyze that standard deviation
information. This offers valuable insight into the sources of
variation within the experimental data, helping you to identify
the treatment (or combination of factor settings) that results
in the least amount of variation in the response(s) being
studied.
To select the response(s) that you
want to see variability analysis for, choose Data >
Variability Analysis or click the Variability Analysis
icon on the Main page of the Standard Folio Design tab Control
Panel.
In the Variability Analysis window
that appears, select the check box beside each response that you
want to see variability analysis for. In the Factors to Include
area, select the check box beside each factor that should be
considered in the variability analysis. You can also select to
consider blocks in the variability analysis.
For each selected response, a
standard deviation column will be inserted beside it in the
Standard Folio Design tab Data Sheet. This column displays the standard
deviation of the observations taken for each factor setting
combination across replicates. Such columns can be selected to
be used as responses in the analysis.
For example, consider a two level
full factorial design with three factors, A, B and C, and two
responses. The following selections in the Variability Analysis
window:
will yield a Standard Folio Design
tab like the one shown next.
You can see that the standard
deviation column that has been inserted for Response 1 includes
a value for the standard deviation at the following settings:
|
Factor A |
Factor B |
| -1 |
-1 |
| 1 |
-1 |
| -1 |
1 |
| 1 |
1 |
Factor C
settings are ignored, as this factor was not selected for inclusion
in the variability analysis.
Selecting the standard
deviation column for Response 1 to be included in the
analysis allows you to see how each factor setting
combination affects the variability in Response 1, as shown
next.
In the Regression Information
table, AB is shown in red, indicating that the interaction
of factors A and B is a significant source of variability in
Response 1.
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