| ID | Competence Statement |
| OPTpr4 | Familiarity with at least two of the traditional problem definition methods such as Simplex methods, Linear Programming, Geometric Programming, Quadratic Programming |
| OPTpr5 | Familiarity with gradient search methods such as steepest descent |
| OPTpr6 | Understanding of unconstrained and constrained strategies |
| OPTpr7 | Understanding of at least one of the -modern-methods of search strategy such as Neural Networks, Genetic Algorithms, etc. |
| OPTpr8 | Ability to carry out Linear Static Analysis or similar level of analyses in other core disciplines and produce validated results |
| OPTpr9 | Thorough awareness of effects of bad modelling practice and need for adequate checking |
| OPTpr10 | Awareness of difference between global and local minima |
| OPTpr11 | Awareness of parametric controls such as CAD geometry dimensions. |
| OPTkn1 | List the various steps in a general optimisation study. |
| OPTkn2 | List the various types of optimisation search algorithms available in the system(s) you use. |
| OPTkn3 | State whether the optimisation system(s) you use are controlling CAD geometry or finite element parameters (or both). |
| OPTkn4 | State the maximum problem size recommended for your optimization tool in terms of design variables and constraints |
| OPTkn5 | Define the convergence criteria used in your optimization tool for establishing an optimum |
| OPTkn6 | List some direct and indirect methods used for the optimum solution of a constrained nonlinear programming problem. |
| OPTkn7 | State whether your system can handle multiobjective functions |
| OPTkn8 | Outline via a sketch a typical 2 variable optimization problem using variables as x and y axes and show objective function and constraints on the sketch. |
| OPTkn9 | State if linearization of local design space can be used during an optimization with your system |
| OPTkn10 | List which Artificial Intelligence based approaches that your system uses |
| OPTkn11 | State whether your system can deal with discrete variables as well as continuous variables |
| OPTkn12 | State whether your system can define objective functions of more than one term, such as weight AND cost |
| OPTkn13 | List the various methods of establishing feasible search directions |
| OPTkn14 | List methods which transform constrained problems to unconstrained problems |
| OPTkn15 | Define a discrete design variable |
| OPTco1 | Explain the terms goal (objective function), variable and constraint. |
| OPTco2 | Explain why an optimum solution is not always a robust solution. |
| OPTco3 | Describe the basic methodology used to achieve shape modification in any system(s) you use. |
| OPTco4 | Describe the basic methodology used to create structural holes in any system(s) you use. |
| OPTco5 | Explain the concept and usage of a Pareto Set. |
| OPTco6 | Explain the concept of Objective Space and Design Space. |
| OPTco7 | Explain the terms local minima, global minima and saddle point. |
| OPTco8 | Describe the advantages and disadvantages of the search algorithms available in the software tools you use. |
| OPTco9 | Describe Basis Vector methods to reduce the number of design variables |
| OPTco10 | Describe Design Variable Linking |
| OPTco11 | Describe the Kuhn Tucker conditions |
| OPTco12 | Explain how you would investigate the design evaluation trends shown by your software using GUI based graphs, tables tec. |
| OPTco13 | Describe how you would confirm that the optima found is not a local minima |
| OPTco14 | Explain the importance of the definition of the applied loading case set to be used in the optimization |
| OPTco15 | Describe the process to take the optimum solution found and map it into a practical CAD design |
| OPTco16 | Describe how you would review the final design to understand what the main driving parameters are |
| OPTco17 | Describe what steps you may take to understand why an optimization problem will not converge to a solution and how to improve the strategy |
| OPTco18 | Discuss how important it is to find the absolute minima relative to practical limits on design, manufacturing etc. |
| OPTco19 | Define the difference between sizing, shape and topology optimization |
| OPTco20 | Describe how linearization of design space is used, with pros and cons |
| OPTco21 | Explain the difference between parameter based and non-parameter based optimization and where each is most effective |
| OPTco22 | Discuss why mutation in a gene pool is important in a Genetic Algorithm |
| OPTco23 | Describe the difference in approach to an objective function between topology optimization and sizing optimization |
| OPTco24 | Describe what is meant by a stochastic approach to optimization |
| OPTco25 | Describe what is meant by an optimality criterion based method and give an example |
| OPTco26 | Describe typical ways of dealing with discrete variables and their pros and cons |
| OPTco27 | Describe a typical multi-term objective function and mention any drawbacks with this approach |
| OPTco27b | Discuss the importance of an accurate baseline FE Analysis with validated results as the starting point for optimization |
| OPTco28 | Describe parameter linking in a design variable with pros and cons |
| OPTco29 | Describe synthetic type constraints created from multiple responses with pros and cons |
| OPTco30 | Explain why an optimum solution may actually violate one or more constraints |
| OPTco31 | Discuss and sketch what is implied by a "best infeasible" solution |
| OPTco32 | Describe the terms mean and standard deviation |
| OPTco33 | Describe the Normal Probability distribution |
| OPTco34 | Describe the method of Genetic Algorithms |
| OPTco35 | Explain the process of Neural Network based optimization |
| OPTco36 | Describe the training phase of a Neural Network |
| OPTco37 | Describe the Quasi-Newton root finding method |
| OPTco38 | Describe the Secant root finding method |
| OPTco38b | Describe Convex and Non-Convex sets |
| OPTco39 | Explain the difference between a gradient based and non-gradient based search method |
| OPTco40 | Describe how various optimization strategies such as Shape, Sizing, Topology may be combined in a single project |
| OPTco41 | Describe DOE usage in optimization and how the resulting surface model may be used |
| OPTco42 | Describe the various DOE search strategies |
| OPTco43 | Describe Topometry optimization and its relationship to shape optimization |
| OPTco44 | Describe Topography optimization and how it is used in the overall optimization process |
| OPTco45 | Describe the implications of non-linear Optimization |
| OPTco46 | Explain the design variable and Objective function options available to Composite Structural Analysis as opposed to Isotropic Structural Analysis |
| OPTco47 | What special care is needed when carrying out optimization of Composite Structures |
| OPTap1 | Employ available software tools to carry out parameter, shape and topology optimisation studies. |
| OPTap2 | Use appropriate software tools to carry out multidisciplinary optimisation studies, if relevant. |
| OPTap3 | Conduct sensitivity studies to inform optimisation studies. |
| OPTap4 | Utilise appropriate and efficient optimisation algorithms, where a choice is given. |
| OPTap5 | Demonstrate the definition and execution of an optimization task, starting with a baseline FE Analysis |
| OPTap6 | Conduct an optimization analysis of a composite based structure |
| OPTan1 | Analyse the results from sensitivity studies and draw conclusions from trends. |
| OPTan2 | Determine whether the results from an optimisation study represent a robust solution. |
| OPTan3 | Determine whether an optimization study should use discrete variables and the practical benefits gained from this approach |
| OPTan4 | Determine the best design variables and optimization technique to use for composite structures |
| OPTsy1 | Plan effective analysis strategies for optimisation studies. |
| OPTsy2 | Formulate a series of simple benchmarks in support of a complex optimisation study. |
| OPTsy3 | Plan an evaluation study for a new optimization tool to brought into your operation |
| OPTsy4 | Create a process to take an FE based optimum design and evolve into a practical CAD design |
| OPTsy5 | Formulate a check list of do's and dont's for setting up a realistic optimization problem, include practical, logistic and FE solver and optimizer specific issues |
| OPTsy6 | Prepare an overview of your complete optimization process from concept to product |
| OPTsy7 | Describe how your company uses optimization and recommend areas for improvement |
| OPTsy8 | Describe a range of ideas for objective functions, other than weight minimization, with practical examples |
| OPTsy9 | Describe the technical and resource management issues associated with Multidisciplinary Optimization |
| OPTsy10 | Create a presentation to give at Management Level to justify a major purchase and implementation of Optimization in the design and manufacturing process |
| OPTev1 | Justify the appropriateness of goals, constraints and variables used in an optimisation study. |
| OPTev2 | Select suitable idealisations for optimisation studies. |
| OPTev3 | Provide effective specialist advice on optimisation to colleagues. |
| OPTev4 | Assess appropriate hardware and software solutions to meet the needs of planned optimisation studies. |
| OPTev5 | Justify an optimum design configuration by comparing with initial solution and simple variations or information from the optimization tool. |
| OPTev6 | Justify an optimum design based on its applicability to manufacture and assembly |
| OPTev7 | Assess the application and effectiveness of using EXCEL Solver, MATLAB, open source or programmatic in-house solutions to an optimization problem as an alternative to COTS |
