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Why ESL?

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Engineering design concerns the process of creating and optimizing solutions in the form of technical systems for problems within the design space spanned by needs, requirements, and constraints that are set by material, technological, economic, legal, environmental and human-related considerations. 1

The needs, requirements, and constraints are usually specified in text documents written in natural language, and are often referred to as system specifications2. Design engineers have to interpret these system specifications and convert them into technical designs.

An important step in the conversion of needs, requirements and constraints into technical solutions is the design of the system's architecture, for multiple reasons3. First of all, the system's architecture has a significant impact on the system's performance, e.g., reliability, availability, maintainability, and cost of the system45. Secondly, insight into the system's architecture is required for effective change management6. That is, requirements often change during the design process, which may cause re-design of one or more components. Such design changes may propagate through the system yielding re-design of other components7. A model of the system architecture can be used to evaluate the impact of design changes8.

It is common practice to relate needs, requirements, and constraints to technical aspects of the system12. Therefore, engineers need methods to structure, visualize, and analyze the relations between system architecture, needs, requirements, and constraints.

Ulrich9 defines system architecture as the mapping of a system's functions to the physical components within the system, and to the dependencies between those components. The work of Wilschut et al.10 shows that the intended system architecture can be derived from structured function specifications. That is, using a fixed grammar to describe component functions, we can automatically derive dependencies between components, between functions, between variables, and combinations thereof, and visualize those dependencies. This presents an effective method to generate a dependency structure matrix (DSM) model of the system architecture.

This method10, however, is limited to a single level description of the system. The functions are specified at a single granularity level. Design processes are usually hierarchical in nature11. Different parts of a system are usually described at different levels of granularity in system specifications12. As the design process progresses, the level of granularity of system specifications may change. That is, as more details of the system become apparent, the grain size at which a system is described becomes smaller13.

Furthermore, in the piece by Wilschut et al.10 functions of the system at hand are described and therefore only intended functional dependencies can be derived. In engineering design, however, the design, behavior and physics of systems is specified and analyzed as well. It is essential to have insight in the network of design dependencies, e.g. a spatial dependency between two components that have to fit in a predefined space, logical dependencies, e.g. (software) dependencies between sensors and actuators, and physical dependencies, e.g., heat exchange due to dissipation of energy.

Enter ESL

Therefore, we took the concepts of the earlier piece10 as a basis and developed the Elephant Specification Language (ESL). ESL is a computer readable language for writing multiple level system specifications that describe the function, behavior, design and physics of a systems and its subcomponents at various levels of granularity.

An ESL-compiler has been developed which checks the consistency of ESL specifications and derives dependencies between components, variables, needs, goal-specifications, transformation-specifications, behavior-specifications, design-specifications, relations, and combinations thereof across all decomposition levels of the specification, following a predefined set of mathematical rules.

Tip

For the most up-to-date reference of ESL we invite you to take a look at the Reference pages for the syntax and semantics as well as dependency derivation rules. For the evolution of the language and the latest additions, you can check the Governance pages.

Where to from here?

  1. G. Pahl and W. Beitz. Engineering design: a systematic approach. Springer Science and Business Media, London, England, 2007. ↩↩

  2. E. Hull, K. Jackson, and J. Dick. Requirements engineering. Springer, London, England, 2nd edition, 2017. ↩↩

  3. R. Eggert. Engineering design. Pearson/Prentice Hall, Upper Saddle River, New Jersey, USA, 2005. ↩

  4. J. Jiao, T. W. Simpson, and Z. Siddique. Product family design and platform-based product development: a state-of-the-art review. Journal of Intelligent Manufacturing, 18(1):5–29, 2007. ↩

  5. K. G. Lough, R. Stone, and I. Y. Tumer. The risk in early design method. Journal of Engineering Design, 20(2):155–173, 2009. ↩

  6. M. Giffin, O. de Weck, G. Bounova, R. Keller, C. Eckert, and P. J. Clarkson. Change propagation analysis in complex technical systems. Journal of Mechanical Design, 131(8):1–14, 2009. ↩

  7. T. A. W. Jarratt, C. M. Eckert, N. H. M. Caldwell, and P. J. Clarkson. Engineering change: an overview and perspective on the literature. Research in Engineering Design, 22(2):103–124, 2011. ↩

  8. P. J. Clarkson, C. Simons, and C. Eckert. Predicting Change Propagation in Complex Design. Journal of Mechanical Design, 126(5):788–797, 2004. ↩

  9. K. Ulrich. The role of product architecture in the manufacturing firm. Research Policy, 24(3):419–440, 1995. ↩

  10. T. Wilschut, L. F. P. Etman, J. E. Rooda, and J. A. Vogel. Automated generation of a function-component-parameter multi-domain matrix from textual function specifications. Research in Engineering Design, 29(4):531––546, 2018. ↩↩↩↩

  11. J. A Estefan. Survey of model-based systems engineering (MBSE) methodologies. Incose MBSE Focus Group, 2007. ↩

  12. J. F. Maier, C. M. Eckert, and J. P. Clarkson. Model granularity in engineering design – concepts and framework. Design Science, 3(1):1–29, 2017. ↩

  13. S. D. Eppinger, N. R. Joglekar, A. Olechowski, and T. Teo. Improving the systems engineering process with multilevel analysis of interactions. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 28(04):323–337, nov 2014. ↩