What are the key concepts covered in Quantitative Methods in Systems Engineering?

The key concepts covered in the course "Quantitative Methods in Systems Engineering" include:

1. 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.

2. Tradespace Exploration:

   • 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.

3. 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.

4. 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.

5. Systems Architecture:

   • 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.

6. 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.

7. 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.