The key concepts covered in the course "Quantitative Methods in Systems Engineering" include:
Quantitative Analysis in Systems Engineering:
- Defining criteria and decisions for quantitative analysis.
- Evaluating concept alternatives and recommending preferred alternatives.
- Structuring trade studies.
- Identifying key cost and benefit criteria for system decision-making.
- Constructing value hierarchies to inform system design decisions.
- Articulating the core concepts of value-based thinking.
- Choosing relevant axes and representations for tradespaces.
- Interpreting tradespace results.
- Identifying the fuzzy Pareto front in a tradespace.
- Performing sensitivity analysis.
- Critiquing decision analysis models and identifying sources of uncertainty.
Model-Based Systems Engineering (MBSE):
- Understanding the core tenets of MBSE and its differences from traditional systems engineering.
- Scoping and defining the purpose and approach for an MBSE project.
- Describing the intent and basic structure of SysML (Systems Modeling Language).
- Critiquing MBSE implementations and building a model management plan.
- Referencing industry examples of MBSE and communicating potential approaches.
Model Development and Use:
- Enumerating the purposes of models in engineering and evaluating their success.
- Describing the model development process and increasing fidelity.
- Demonstrating how models are used for decision-making and optimization.
- Evaluating the credibility and fidelity of existing models.
- Exploring single models versus ensembles of models and resolving conflicts.
- Combining subsystem models into a system model.
- Verifying and validating models.
- Examining tradeoffs between physical and virtual prototypes for system testing.
- Contrasting definitions of system architecture and constructing personal definitions.
- Applying systems thinking and defining systems.
- Describing system boundaries and identifying system interfaces.
- Understanding architectural elements and their role in system documentation.
- Critiquing system architecture representations for completeness and consistency.
Design Structure Matrix (DSM) and Change Propagation:
- Constructing design and process DSMs.
- Applying sequencing algorithms to sort components or tasks.
- Analyzing change propagation from a database of changes.
Role of the Architect:
- Defining the role of the architect and collaborating with stakeholders.
- Identifying deliverables of the architect and referencing architectural frameworks.
By studying these key concepts, learners will gain a solid understanding of quantitative methods in systems engineering, model-based systems engineering, tradespace exploration, systems architecture, and design structure matrix analysis. They will develop skills in quantitative analysis, decision-making, model development and usage, and critical evaluation of systems and architectures.