# Reliability-based Design Optimization

Reliability-based Design Optimization (RBDO) is an optimization method that can be used to provide optimum designs in the presence of uncertainty.

A two-phase approach is implemented in OptiStruct to solve the RBDO problems efficiently, while preserving accurate reliability assessment of the final design. The design variables, constraints, and objective are tested for reliability based on user-defined reliability requirements.

Three types of variables, including Deterministic design variables, Random design variables (also known as random control factors) and Random parameters (also known as noise factors) can be selected in a RBDO problem. Design Constraints and Objectives can be specified as Deterministic (mean) or Percentile values.

## Implementation

The original problem of reliability-based design optimization (RBDO) is mathematically a nested two-level structure that is computationally time consuming for real engineering problems.

To overcome the computational difficulties, many formulations have been proposed in the
literature. These include SORA (sequential optimization and reliability assessment)
that decouples the nested problems. SLA (single loop approach) further improves
efficiency in that reliability analysis becomes an integrated part of the
optimization problem. However, even SLA method can become computationally high
expensive for real engineering problems involving many reliability constraints. An
enhanced version of SLA has now been implemented where the first phase is based on
approximation at nominal design point. After convergence of first iterative phase is
reached the process transitions to a second phase where approximation of reliability
constraints is carried out at their respective Minimum Performance Target Point
(MPTP). The first phase of the proposed method is as efficient as deterministic
optimization. The accuracy of reliability assessment is ensured in the second phase.
Thus, the RBDO problems can be solved more efficiently while preserving accurate
reliability assessment of the final design. Examples show that the proposed
two-phase approach consumes much less finite element analyses while achieving equal
solution quality. ^{1}

### Variables

The following design variables and parameters can be used to define the structural design space in OptiStruct:

Random design variables are defined via the `RAND` continuation lines on the
DESVAR Bulk Data Entry. Various random distribution types can
be selected and their parameters are defined accordingly. In an RBDO process, during
reliability and/or robustness analysis, the design should satisfy optimality based
on the specified distributions. Additionally, loading can be selected as a design
variable using the DVPREL1 and DVPREL2 Bulk
Data Entries.

The definition of Random parameters is similar to that of Random Design Variables, using
`RANP` definition. However, the important difference is that,
while the mean values of random variables are changed to improve the design, the
mean values of random parameters remain constant. For example, typically sheet metal
thickness can be a random variable, due to fabrication variance, while the Young's
modulus of a material would typically be a random parameter, if its variance is
accounted for.

### Objective

The following design objective types are available in OptiStruct:

`PROB`argument can be used to define the required parameters.

Where, $f\left(x\right)$ is the objective function, and $$r$$ is the probability level (for example, 95%). The right and left percentile values are available. MINP minimizes the right percentile value and MAXP maximizes the left percentile value.

Where, $f\left(x\right)$ is the objective function.

### Constraints

The following design constraint types are available in OptiStruct:

The probability of one constraint satisfying its bounds should not be less than the
predefined reliability value. The reliability value is defined via the
`PROB` field on the DCONSTR Bulk Data
Entry.

Where, $c\left(x\right)$ is the constraint value, `UB` is
the upper bound of the constraint, `LB` is the lower bound of the
constraint, and $$r$$ is the probability level (for example, 95%).

For the $P\left(c\left(x\right)\le UB\right)\ge r$ constraint, the right percentile value of $LB$ is forced to be less than or equal to the upper
bound `UB`. For the $P\left(c\left(x\right)\ge LB\right)\ge r$ constraint, the left percentile value of $c\left(x\right)$ is forced to be greater than or equal to the lower
bound `LB`.

Where, $c\left(x\right)$ is the constraint value, `UB` is
the upper bound of the constraint, and `LB` is the lower bound of
the constraint.

### Example

Problem with mean of random variables as design variables (Yi et al. 2008).

Where, $j=1,2$.

- ${G}_{1}\left(X\right)={X}_{1}^{2}{X}_{2}/20-1$
- ${G}_{2}\left(X\right)={\left({X}_{1}+{X}_{2}-5\right)}^{2}/30+{\left({X}_{1}-{X}_{2}-12\right)}^{2}/120-1$
- ${G}_{3}\left(X\right)=80/\left({X}_{1}^{2}+8{X}_{2}+5\right)-1$

The results of this example are summarized in Table 1. For the two-phase approach, in the first phase one function evaluation at each iteration can generate all the needed response values (performance functions are all evaluated at the same point). So the total number of function evaluations is much less compared to evaluations during second phase. The values in the bottom row of each column, such as 7.268 (3.609, 3.659), are the final objective function and optimal design.

It is clear that the two-phase approach implemented in OptiStruct is much more efficient than SAP. In this example, the distribution types of the random variables have very little influence on the computational efficiency of the two-phase approach while SAP performs quite differently for the different distribution types. The two-phase approach consumes the same computational effort for all the five distribution types except Gumbel, in which number of function evaluations is slightly less than others. For Gumbel distribution, SAP consumes more than 1.6 times of the computational effort of that with Normal distribution.

Distribution Type | SAP (Yi et al. 2008) | Two-Phase Approach | |
---|---|---|---|

Normal | Number of function evaluations | 54 | Phase 1: 7 Phase 2: 15 Total: 22 |

Objective (Variables) | 7.268 (3.609,3.659) | 7.268 (3.609,3.659) | |

Lognormal | Number of function evaluations | 54 | Phase 1: 7 Phase 2: 15 Total: 22 |

Objective (Variables) | 7.055 (3.556, 3.499) | 7.114 (3.571,3.544) | |

Weibull | Number of function evaluations | 42 | Phase 1: 7 Phase 2: 15 Total: 22 |

Objective (Variables) | 7.513 (3.668,3.845) | 7.549 (3.682,3.866) | |

Gumbel | Number of function evaluations | 90 | Phase 1: 7 Phase 2: 12 Total: 19 |

Objective (Variables) | 6.836 (3.491,3.345) | 6.817 (3.497,3.320) | |

Uniform | Number of function evaluations | 66 | Phase 1: 7 Phase 2: 15 Total: 22 |

Objective (Variables) | 6.869 (3.521,3.348) | 7.106 (3.597,3.509) |

The OptiStruct RBDO (the two-phase approach) offers an efficient tool to consider uncertainty involved in design. For most problems this approach yields reasonable solution accuracy. It should be noted that the two-phase approach is derived from the SLA methods and, therefore, inherits the potential shortcomings in terms of solution accuracy of SLA. Insufficient solution accuracy is observed in the study of the above example. Accurate reliability analysis should be carried out if accurate satisfaction of reliability requirements is critical. Also note that for the OptiStruct RBDO approach, reliability analysis is performed only for retained constraints for which sensitivity is available. You can adjust the screening criteria using the DSCREEN Bulk Data Entry, if required.

^{1}Zhou, M & Luo, Z (2017). A Two-Phase Approach based on Sequential Approximation for Reliability-based Design Optimization.