Engineering SMART MODEL BASED SYSTEMS ENGINEERING Author: M.E. - - PowerPoint PPT Presentation

Systems Engineering

8/14/2018

SMART MODEL BASED SYSTEMS ENGINEERING

Author: M.E. Alejandro Ayala, University of Detroit Mercy Coauthor: Ph.D. Jonathan Weaver, University of Detroit Mercy Coauthor: M.E. Ruben Ochoa, Hochschule Esslingen Coauthor: B.S.E. Jenifer Fuentes, Unitec University

Systems Engineering

8/14/2018

Content

• Background
• Model Based Systems Engineering (MBSE)
• Smart Model Based Systems Engineering (SMBSE)
• Reusability
• Automation
• Dynamic Simulation
• Optimization
• Further Development
• Conclusions
• Q&A

“Do complex systems meet stakeholder needs and deliver value? Do they integrate easily, evolve flexibly, and operate simply and reliable? Well architected systems do!” Bruce Cameron MIT

2

Systems Engineering

8/14/2018 3

Background Increasing Complexity

The increasing complexity of disruptive technologies and resulting ultra large1 and complex systems engineering, requires the development of fully integrated modeling and iterative analysis platforms.

1 Sillitto, Hillary. Design Principles for Ultra Large Scale Systems, 2010

Disruptive Technologies

Industry 4.0 Quantum Computing Genome Editing City Brain

Systems Engineering

8/14/2018

• MBSE is the formalized application of systems engineering

modeling to manage requirements traceability, identify constraints and interfaces, define verification and validation activities, and predict emergence results.

• A wide range of current Systems Engineering models were

analyzed to identify opportunities for adding functional capabilities to current Model Based Systems Engineering methodologies.

2 Cameron, Bruce “Systems Architecture”, MIT 2

Model Based Systems Engineering

4

Systems Engineering

8/14/2018 5

• MBSE has been a dominant methodology for defining and

modeling complex systems; however, it has not yet been paired with cutting-edge digital engineering transformation.

Model Based Systems Engineering

Complexity Time

MBSE

Systems Engineering Complexity Growing gap between complex systems engineering and MBSE functionality

Systems Engineering

8/14/2018 6

• MBSE is constrained to represent a whole system, but

lacks other capabilities, such as dynamic simulation and

• ptimization, as well as integration of the model with

software and hardware.

Model Based Systems Engineering

Systems Engineering

8/14/2018 7

Smart MBSE

• This research provides the key elements for developing a

Smart MBSE (SMBSE) modeling approach that integrates Systems Engineering (SE) with the full suite of other development tools utilized to create today’s complex products and services.

Systems Engineering

8/14/2018

Smart MBSE

• SMBSE will provide the required functionality in accordance

with cyber-physical systems engineering requirements in terms of speed, robustness and anticipated results.

Source: https://www3.nd.edu/~dwang5/courses/spring17/

8

Systems Engineering

8/14/2018

Smart MBSE

• This research was oriented towards exploring the
• pportunity to develop a SMBSE methodology by

integrating reusability, automation, simulation and

• ptimization to the conventional MBSE.
• SMBSE is a compelling requirement for the increasing

systems engineering complexity, while taking full advantage

• f cyber tools.

9

Systems Engineering

8/14/2018

Smart MBSE

• The main benefit of SMBSE is to close the gap between

the growing business needs of complex systems engineering and conventional MBSE.

Complexity Time

MBSE

Systems Engineering Complexity Opportunity to close the gap through SMBSE methodology

10

Systems Engineering

8/14/2018

Smart MBSE

• An additional benefit is the design integration between

software and hardware components with multiple features, often with conflicting targets.

SMBSE functionalities to validate hardware, software and targets before prototype build

11

Systems Engineering

8/14/2018

Smart MBSE

• The above sequence is the recommended path for

developing SMBSE methodology, leveraging the reusability of proven prior models in order to focus on the unique elements of the new product or services.

12

Systems Engineering

8/14/2018

Reusability

• The models developed using a SMBSE approach are able

to reuse the requirements, signals and interfaces from the carryover subsystems, and apply them to the new systems.

13

Systems Engineering

8/14/2018

Reusability

• Reusability of prior proven models provides opportunity

to reduce cost and timing for developing new complex models.

• Quality history, quality tools and lessons learned

translated into design requirements and design rules are critical elements for preventing systemic issues on the new models.

Time

Time

Cost

Reusability

Quality

14

“The cheapest defect to fix is the one you prevented”*

*Lenny Delligati, “SysML Distilled”

Systems Engineering

8/14/2018

Automation

• In most of ground vehicle applications, about 40% of the

components are carryover from the prior product generation, even for products with revolutionary innovation.

https://www.bmwblog.com/2014/05/06/pondering-bmws-cfrp-past-present- future/http://www.electricmotorengineering.com/italian-automotive-components-industry/ https://www.continental-automotive.com/en-gl/Passenger-Cars/Chassis-Safety/Software-Functions/Passive- Safety/Pedestrian-Protection

40% of carryover subsystems parts

Electric Car Gasoline Car

15

Pedestrian Protection Regulation compliance is extended through the products variants based on the same architecture

Systems Engineering

8/14/2018

Automation

• The development of new products based on a common

architecture, provides the opportunity to automate the systems engineering model creation, from common parameters for new product variants.

• SMBSE automation generates models from parametric

modeling algorithms that replicate existing models for new products with carryover architectures or subsystems.

http://www10.mcadcafe.com/nbc/articles/1/965427/Concepts-NREC-Advancing-Clean-Efficient-Turbine-Technology-ASME-

16

3D Parametric Automated Models

Systems Engineering

8/14/2018

Dynamic Simulation

• Current common practice is to conduct the modeling,

simulation and analysis on separate software platforms with minimum integration between these three central elements of systems engineering.

Current MBSE practice

17

Systems Engineering

8/14/2018

Dynamic Simulation

• A common constraint in the conventional MBSE is the

limited simulation functionality with linear models which are not representative of real complex systems.

• An effective design of cyber physical systems requires the

proper modeling representation, but fully integrated with simulation and analysis functionalities.

SMBSE integrated elements

18

Systems Engineering

8/14/2018

Optimization

• The central benefit of SMBSE is to develop the simplest

solution for complex and large system engineering, as well as to facilitate the decision-making process on the early architecture definition.

• Once the system solution is developed the optimization

stage is activated by the application of smart algorithms to maximize performance and minimize costs.

Elegant Solution = Sufficient + Simple

19

Systems Engineering

8/14/2018

Optimization

• The SMBSE optimization functionality is oriented to

identify different systems architecture options before proceeding to develop a new subsystem. The wider the scope of architecture optimization, the simpler the solution at different levels of decomposition.

*Gwangki, M., Eun Suk, S., & Katja, H. O. (2015 de Dec de 21). System Architecture, Level of Decomposition, and Structural Complexity: Analysis and Observations.** Cfatmaneshnik & Ryan,2015

A) Integral Architecture * B) Linear Modular Architecture C) Bus-modular Architecture

A B C

Elegance metric** Ei = Cp / Csi Where: Ei= elegance of the i sufficient solution Cp = complexity of the problem Csi = complexity of the I complexity solution

20

Systems Engineering

8/14/2018

Optimization

3 Salado, Alejandro, “Painting Systems: From Art to Systems Architecting” 2016, *Gwangki, M., Eun Suk, S., & Katja, H. O 4 Hillary Sillito .

*

• Complexity elasticity metric is a recommended option to

quantify the solution elegance 3 before and after an

• ptimization process is applied.

21

Don’t optimize parts at the expense of the whole 4

Systems Engineering

8/14/2018

Further Development

• A growing need to lower costs and shorten the product

development cycle has forced high-tech industries to virtual simulate engineering systems and gradually replace the full physical prototyping testing validation and verification.

Physical prototypes units to be gradually replaced by virtual verification units

22

Systems Engineering

8/14/2018

Further Development

• Following this trend, the virtual product development

(VPD) systems are leading the modeling simulation and visualization tools integration.

• MBSE software tools are limited with static variables,

making a challenging integration with VPD systems.

https://hvm.catapult.org.uk/impact/case-studies/mtc-inspires-siemens-digital-factory-to-use-virtual-reality-for-product- and-factory-design/

23

Systems Engineering

8/14/2018

Further Development

• The solution is SMBSE by adding the missing dynamic

simulation and visualization functionalities and integrating mathematical and CAE models in the systems engineering modeling tools.

https://www.gov.uk/government/news/british-army-set-to-redefine-warfare-with-joint-autonomous-warrior

Autonomous Vehicle Dynamic Signals

Absorption (1/m) Wavelength

24

Opportunity to incorporate Artificial Intelligence algorithms

Systems Engineering

8/14/2018

Conclusions

• Current MBSE functionalities are behind the industry´s

digital transformation challenges.

• Smart MBSE methodology integrates reusability,

automation, simulation and optimization capabilities to conventional MBSE.

• SMBSE will provide the required functionality in

accordance with cyber-physical systems, in terms of speed, reliability and anticipated results.

25

Systems Engineering

8/14/2018

Conclusions

• The technologies required to develop the SMBSE

methodology are available, but a significant effort is needed to develop and integrate simulation and

• ptimization functionalities without extensive and

expensive adaptation.

• An efficient Systems Engineering model solution is simple

and sufficient.

• An efficient Systems Architecture is able to increase

elements at a lower rate than the problem complexity is increased.

26

Systems Engineering

8/14/2018 27

• The further progress on modeling the system, the higher

necessity to work fully integrated with validation and verification functionalities and work teams.

• Available digital systems technology provides an
• pportunity to develop SMBSE with an integrated

framework from MIL, SIL, HIL, CAD, CAE, CAM, AI, Quality and Optimization tools in order to evolve to SMBSE as a fundamental step towards a disruptive evolution in Product Development methodologies.

Conclusions

Systems Engineering

8/14/2018

Questions & Answers

“A designer knows he has achieved perfection not when there is nothing left to add, but when there is nothing left to take away.” Antoine de Saint- Exupery

Smart MBSE

28