techshanghai2016 - model based development of mechatronic systems

MODEL BASED DEVELOPMENT OF

MECHATRONIC SYSTEMS

Challenges and opportunities

Dr. Yuan Liu

© 2016 Altair Engineering, Inc. Proprietary and Confidential. All rights reserved.

The Old Development Approach

BUILD IT

PAINT IT

ADD CONTROLS

Design errors

Requirement misses

Longer development times

Higher development costs


Today’s Products are Complex


Today’s Products are Complex

Clever use of sensors and microprocessors

can significantly reduce energy consumption


Model Based Development

Extended V-model according to Eigner et al.


Challenges

• INTEGRATING VARIOUS USER COMMUNITIES• Math, Signal-, Physical based Modeling

• ENABLING COLLABORATION• Across Departments

• OEM – Suppliers

• 1D vs. 3D Models

• Flexibility and Affordability


Vision

DRIVING INNOVATION THROUGH SIMULATION

From Concept Design to HIL, SIL Validation

Combining Math, Signal-Based, Physical and 3D Modeling


HVAC EXAMPLE

© 2016 Altair Engineering, Inc. Proprietary and Confidential. All rights reserved.Copyright © 2014 Altair Engineering, Inc. Proprietary and Confidential. All rights reserved.

Abstract: model

We have created, using the 1D modeling approach, an easy-to-use/physics-based

model in VisSim™ for managing heating, ventilating , air conditioning (HVAC) and

controls in vehicle cabins to be used as a tool to:

1. Improve the passenger comfort

2. Monitoring the electric consumption due to the A/C usage

3. Improve the concept design of HVAC Systems


Abstract: objective

The work is about the analysis of the influence of some input variables on the

cabin temperature trend during the cooling down phase. The analysis is carried out

by using HyperStudy®.

Tem

pera

ture

time

AVERAGE AIR TEMPERATURE IN

CABIN ALONG THE SIMULATION

DESIGN VARIABLES

RESPONSES

- System parameters

- Modeling parameters


Abstract: benefits of the study

• Better understanding of the system

• Identify the most influence parameters on the

studied responses

• If, as design variables, we consider some modeling

parameters, the knowledge of their influence on the

responses is useful during the validation phase

[°C]

• In a second phase, the analysis could be useful to

choose the most important parameters to be used

for optimizing a specific phenomenon of interest


HVAC System Model

HVAC CABIN


• Fast exploration of the design space:

o Runs are very fast

o No need of modifying 3D CAD or CFD

grids for different configurations

• Valuable insight of the overall system performance

• Easy integration in the simulation environment of the

control algorithms

• Possibility to monitor the humidity variation inside the

cabin

• Easy managing of input parameters

• Useful in the early stages of the product development as

a design tool

Vehicle HVAC: 1D approach strength

WATER VAPOR LOADS


Vehicle HVAC: 1D approach weakness

• Hard to analyze the flow and temperature

distribution inside the cabin taking into account the

cabin geometry

• Hard to monitor the temperature distribution along

the human body surface

• Not usually used as a verification tool where

experimental tests or CFD analysis are preferred.


Top Level Simulation Interface


Analysis Results: Final temperature Tf

1. k_hA: The higher is the evaporator cooling capacity (k_hA) the lower is the cabin

regime temperature since temperature of the inlet air in cabin is lower.

2. Xr_envi: If the inlet air humidity to evaporator is high, a big amount of the

evaporator capacity is involved in the water vapor condensation process rather

than in the air cooling so that the temperature of the inlet flow to the cabin and

the final temperature in cabin are higher.

Δ𝑇𝑓Δ𝑇𝑓

For every simulation, all the

other factors are taken at the

middle value.


Best of the Two Worlds

1D World 3D World

Concept studies

Control design

System performance optimization

Controller implementation and testing

Generation of

parameterized 1D-models

Subsystem optimization

Co-simulation with1D


solidThinking

COMPOSE – ACTIVATE – EMBED

Altair‘s Solution for



solidThinking Compose


solidThinking Compose – Data Fitting


solidThinking Compose – Algorithm Development


solidThinking Compose – Plotting


solidThinking Activate


solidThinking Activate – Rapid Model Building


solidThinking Activate – Example: Oxygen Mask


solidThinking Activate – Co-simulation with Multibody Dynamics


solidThinking Activate – Example: Optimal Gear Ratios


solidThinking Activate – Modelica Example: Heating Control


Implementation


Let’s Get Real

IMPLEMEN-

TATION

PLANT

MODELING

CONTROL

DESIGN

&

SIMULATION

CONCEPT REALITY


Let’s Get Real

• Real-time a must

• Memory restrictions

• Word size restrictions

• Debugging information not easily accessible

• Need to configure A/D, D/A, CAN, PWM, I2C, ….


Implementation Concerns

EFFICIENCY OF AUTOMATIC CODE GENERATORS

Will it fit into memory?

Will it be fast enough?

Can I read it?

SUPPORT OF ON-CHIP PERIPHERALS

PWM

Quadrature encoder

Event capture

CAN, I2C, serial, SPI, ADC, GPIO, …

ISN’T IT BETTER TO HAND-CODE?


Code Efficiency

Autogenerated code as efficient as hand-coded

Fixed Point operations are more efficient

4.16 number: 1011.100011011001

vs


Why Fixed Point?


Fixed Point: Overflow Detection and AutoScaling


Debugging in the Real World

Not Needed

• Microcontroller Debugger

• Embed Communication Interface• Plots

• Logging

• Inputs/Disturbances


Addressing Implementation Concerns

EFFICIENCY OF AUTOMATIC CODE GENERATORS

Will it be fast enough?

Will it fit into memory?

Can I read it?

SUPPORT OF ON-CHIP PERIPHERALS

PWM

Quadrature encoder

Event capture

CAN, I2C, serial, SPI, ADC, GPIO, …

ISN’T IT BETTER TO HAND-CODE? NO


solidThinking Embed – Code Generation


ROBOTICS DEVELOPMENT

Case Study


Robotics Development


Robotics Development - Inverse Kinematics


Robotics Development - Structural Optimization


Robotics Development - Control Design


Robotics Development – Detailed Performance Evaluation


Best of the Two Worlds

1D World 3D World

Concept studies

Control design

System performance optimization

Controller implementation and testing

Generation of

parameterized 1D-models

Subsystem optimization

Co-simulation with 1D


Addressing the Challenges

• INTEGRATING VARIOUS USER COMMUNITIES• Math, Signal-, Physical based Modeling

• ENABLING COLLABORATION• Across Departments

• OEM – Suppliers

• 1D vs. 3D Models

• Flexibility and Affordability


Opportunities for Model Based Development

DRIVING INNOVATION THROUGH SIMULATION

From Concept Design to HIL, SIL Validation

Combining Math, Signal-Based, Physical and 3D Modeling


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