Manufacturing and Production Engineering

Agriculture / Analysis of Mechancial Design / ANSYS / Modelling / Simulations

Production engineering is a combination of manufacturing technology, engineering sciences with management science. A production engineer typically has a wide knowledge of engineering practices and is aware of the management challenges related to production. The goal is to accomplish the production process in the smoothest, most-judicious and most-economic way.

Production engineering encompasses the application of castings, machining processing, joining processes, metal cutting & tool design, metrology, machine tools, machining systems, automation, jigs and fixtures, die and mould design, material science, design of automobile parts, and machine designing and manufacturing. Production engineering also overlaps substantially with manufacturing engineering and industrial engineering. The names are often interchangeable.

In industry, once the design is realized, production engineering concepts regarding work-study, ergonomics, operation research, manufacturing management, materials management, production planning, etc., play important roles in efficient production processes. These deal with integrated design and efficient planning of the entire manufacturing system, which is becoming increasingly complex with the emergence of sophisticated production methods and control systems.

The production engineer possesses a wide set of skills, competences and attitudes based on market and scientific knowledge. These abilities are fundamental for the performance of coordinating and integrating professionals of multidisciplinary teams.^[1] The production engineer should be able to:

• Scale and integrate resources. Usually required to consider physical, human and financial resources at high efficiency and low cost, yet considering the possibility of continuous further improvement;
• Make proper use of math and statistics to model production systems during decision making process;
• Design, implement and refine products, services, processes and systems taking in consideration that constraints and particularities of the related communities;
• Predict and analyze the demand. Select among scientific and technological appropriate knowledge in order to design, redesign or improve product/service functionality;
• Incorporate concepts and quality techniques along all the productive system. Deploy organizational standards for control proceedings and auditing;
• Stay up-to-date with technological developments, enabling them to enterprises and society;
• Understand the relation between production systems and the environment. This relates to the use of scarce resources, production rejects and sustainability;
• Manage and optimize flow (information and production flow).
• 
  

Classification of the Manufacturing Process:

The manufacturing process used in engineering industries basically perform one or more of the following functions:

Change the physical properties of the work material

Change the shape and size of the work piece.

Produce desired dimensional accuracy and surface finish.

Based on the nature of work involved these processes may be divided into following seven categories:

Processes for changing physical properties of the materials – Hardening, Tempering, Annealing, Surface Hardening.

Casting Processes – Sand Casting, Permanent mold casting, die casting, Centrifugal casting

Primary metal working processes – Rolling, forging, extrusion, wire drawing

Shearing and Forming processes – Punching, blanking, drawing, bending, forming

Joining processes – Welding, brazing, soldering, joining

Machining Processes – Turning, drilling, milling, grinding

Surface finishing processes – Lapping, honing, superfinishing

Manufacturing or Production Engineering

Manufacturing or Production Engineering is the subset / specialization of a Mechanical Engineering. Mechanical Engineering with the focus only on Machine Tools, Materials Science, Tribology, and Quality Control is known as Manufacturing Engineering. Professional manufacturing engineers are responsible for all aspect of the design, development, implementation, operation and management of manufacturing system. Manufacturing is the most important element in any engineering process & Manufacturing Engineers are key personnel in many organization. The manufactured products range from aero planes, turbines, engines and pumps - to integrated circuits and robotic equipment.

 

What does a Manufacturing / Production Engineer do?

Production Engineers work towards Choosing machinery and equipments for the particular manufacturing process

Production Engineers will be planning & scheduling the production in any manufacturing industry. [e.g. Automobile Manufacturing industry].

Production Engineers will be programming the CNC machines to produce engineering components such as gears, screws, bolts, etc

They are responsible for quality control, distribution and inventory control.

What is the future for a Manufacturing / Production Engineering graduate?

The job of manufacturing/production Engineer involves the use of machine tools, materials and human resources in the most effective way to produce any goods. They can find opportunities in any of the following category.....

Top Sectors for Production Engineers to work

1.     Research Labs

2.     Manufacturing sector

3.     Communication sector

4.     Transportation

5.     Banking

6.     Pharmaceuticals

7.     Finance

8.     Travel

9.     Semiconductor

10.   e-business

11.   Sports

12.   Health

13.   Information Technology

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Design and modification of invelox wind power generation system through cfd analysis

Agriculture / Analysis of Mechancial Design / ANSYS / Modelling / Simulations

INVELOX is a wind delivery system suitable for wind power harnessing. One of its innovative features is its capability of incorporating multiple wind turbine generator system in the venturi section. Its first innovative feature is the elimination of tower mounted turbine. Secondly, INVELOX captures wind flow through an omnidirectional intake or multi-unidirectional intakes and thereby there is no need for a passive and active yaw control to orient the wind turbine. Third, it accelerates the flow within a shrouded venturi section which is subsequently expanded and release into the ambient environment through a diffuser. When two or three turbines are in the venturi section, the wind power harnessed by second and third turbine is lesser than the first turbine power conservation.

It is a new concept of wind harnessing which overcomes every drawback of traditional turbine and provide power output with reduced cost. Its peculiarity is that it eliminates the tower loading turbines. Today standardization of wind energy generation is hoisting the turbines with massive blades top on the tower. But as economic perspective they are not affordable also unsuitable causing harm to people and wildlife. Considering INVELOX on the other hand captures the wind Omni-directionally and funnel it to the ground level where impact of the high velocity wind on the turbine causes energy generation. And thereby INVELOX has proven to be a solution of all the problems arises due to the traditional windmills like low turbine reliability, downtime issues adverse environmental impact. the overall objective of this work is to model and to understand actual fluid flow inside the INVELOX where the wind turbines are situated. Various computations are carried out to check the relation between wind direction and inside geometry of system and now the study shows it is possible to capture, accelerate and concentrate to obtain higher power output. And hence INVELOX is better way of harnessing wind energy any time any were.

II.LITERATURE REVIEW

Utilizing the wind energy for the wind energy for various application is an ancient concept. Initially wind energy was used for sailing boats, circulating the outside air in the houses for cooling purpose, agricultural purposes for cleaning harvested grains.

 

Innovation has helped in building massive devices for harnessing this wind energy, and better example of it is INVELOX. Till now we all are familiar of traditional wind turbines but we are unaware of its drawbacks. But INVELOX technology had succeeded in overcoming all the drawbacks of this traditional wind mills and is promising a better energy generation.

·       This do not require site selection unlike in case of traditional wind. This INVELOX system can be retrofitted to residential buildings and factories.

·       INVELOX [9] can be installed in the sites which are not suitable for traditional wind mills.

·       It can be very helpful in militaries, since there are now sources of energy.

·       With proper planning this technology can also serve the purpose of ventilation in the places where there is scarcity of ventilation.

·       Irrespective of velocity of wind this technology can be installed in places with low velocity (2 m/s) to extremely high velocity like in typhoon conditions too.

·       We are living in the era where meeting the energy need is the most crucial factor to be considered, but along with this we should also be concern of the maintaining the eco-friendly relations with the environment.

·       Unlike traditional wind energy generation INVELOX does not cause any harm to the birds, biodiversity, wildlife.

·       As compared to traditional wind mills the INVELOX [10] produce 5-6 times of energy acquiring the same amount of area.

·       Traditional wind turbines have tendency to produce electromagnetic radiations which causes harm to the electronic devices. Gadgets like cellphones, televisions, etc. cannot be used in this areas.

III.CFD (COMPUTATIONAL FLUID DYNAMICS) ANALYSIS

From literature review it is concluded that CFD test conducted on INVELOX system using ANSYS. The speed ratio is important design factor to be considered at the time of designing. From this result [6] it is concluded that the speed ratio is almost twice, speed ratio is the ratio of the velocity of wind at venturi section to the free stream velocity at inlet. The dimensions considered during the CFD analysis in ANSYS was 1.8 m diameter of the venturi section, height of 18m which is considered from the center of the inlet to the ground level. The result obtained showed that the inlet free stream velocity of the wind was 6 m/s and the velocity at the venturi was 12m/s. All this analysis shown in the figure (1.a) given below.

IV.CONCLUSION

Form this literature study it is concluded that wind is captured omni directionally and then funneled to get high power output [8]. It is also known that this technology can replace traditional wind mill in upcoming era. After going through all research paper it is known that power output is increased by increasing the mass flow rate or total energy drop across the turbine.

From the study of the research papers it is concluded that power obtained through Invelox systems is 5-6 times more than that power obtained by traditional wind mills with respect to size. Also there are no adverse impact on the environment. Hence there is no harm to the locality. There is no sound pollution caused due to Invelox unlike traditional wind mills.

Besides of having all the advantages it has one drawback, i.e. its cost increases with respect to its capacity. After studying this we came up with an idea of making further inventions in this system.

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Modeling and Simulating Mechanical Systems on a Transforming Dicycle

Agriculture / Analysis of Mechancial Design / ANSYS / Modelling / Simulations

Modeling and Simulating Mechanical Systems on a Transforming Dicycle

By Danaan Metge, BPG Motors

The Uno III is unlike any other vehicle in the world. Originally prototyped as a self-balancing, electric dicycle, the Uno III can transform into a motorcycle by deploying a third wheel on the fly (

Figure 1).

Figure 1. The Uno III dicycle in Uno mode (left) and in motorcycle mode (right). During the transformation to motorcycle mode, the third wheel, stored in Uno mode, is moved forward while the rear traction wheels shift backwards, providing greater stability at high speeds.

Figure 1. The Uno III dicycle in Uno mode (left) and in motorcycle mode (right). During the transformation to motorcycle mode, the third wheel, stored in Uno mode, is moved forward while the rear traction wheels shift backwards, providing greater stability at high speeds.

Much like aircraft control systems, the Uno’s controllers must manage roll, pitch, and yaw, as well as forward motion and the transformation from dicycle to motorcycle. They must also handle throttle and steering inputs from the rider, as well as side-to-side and front-to-rear shifts in the rider’s position. With five independent motors, six gyroscopes and accelerometers, and four potentiometers onboard, testing and tuning the Uno’s control systems is a complex task, made even more challenging by the need to ensure rider safety at all times.

When we began the Uno III redesign, it was clear that manually tuning controllers and testing on the actual vehicle would be inefficient and risky. Instead, we used Simulink®, Simscape™, and SimMechanics™ to model and simulate the Uno’s mechanical systems.

During real-world tests, things move so fast it is impossible to understand everything that is happening. In simulations, however, we can use Simulink to freeze time and inspect every aspect of the model to get a clear picture of how the mechanics are behaving. We can then see exactly how to improve the control system.

A Brief History of the Uno

Company founder Ben Gulak conceived the idea for the Uno while still a teenager. On a trip to China in 2006, struck by the pollution caused by heavy city traffic, he decided to create an eco-friendly commuter vehicle capable of being driven and stored in congested areas.

Gulak built the original Uno on an angle-iron frame from wheelchair motors, batteries, and gyroscopes. The design won a Grand Award at the Intel International Science and Engineering Fair, and appeared on the cover of Popular Mechanics magazine.

After securing startup funding, Gulak and a team of engineers designed the Uno II, which enabled the relatively low-speed dicycle to convert into a motorcycle for higher-speed operation.

To increase the stability and safety of the Uno, the company embarked on a complete redesign for the Uno III. This redesign included improvements to the transforming technology and the gyroscopic tilt system.

Modeling the Tilt System

Like a motorcycle, the Uno tilts when turning. The control systems must maintain proper balance throughout each turn. We initially tried to develop and test the balance and tilt control loops independently, but early hardware tests showed us that they were coupled. To better understand these systems and their interdependencies, we ran simulations in Simulink. We could then examine moments of inertia, velocity, control signals, and other system characteristics that would be difficult or impossible to measure in real-world tests.

We built the basic framework by importing the mechanical design from SolidWorks® 3D CAD software into SimMechanics (Figure 2). To this framework we added the electric motor, a potentiometer, updated ball joints to link the push rods, and the mass of the rider. Via simulation, we applied motor torque and measured the tilt of the system for a range of motor angles.

After postprocessing the simulation results in MATLAB®, we developed a transfer function between motor rotation and bike tilt. This analysis enabled us to better understand how the Uno would respond to changes in tilt motor torque (Figure 3). We used the same approach to understand the relationship between differential torque applied to the wheels and the radius of the resulting turn.

The complete tilt model (Figure 4) enabled us to experiment with different sensors. We supplemented the Inertial Measurement Unit with a potentiometer in the tilt system. Based on simulation results acquired from the Simulink model, we later moved to a higher-resolution analog-to-digital converter, which we ultimately used in the final design.

Figure 4. Simulink model of the Uno tilt system.

Tuning Controller Gains

BPG software engineers developed the proportional integral derivative (PID) controllers for the Uno III in C, intending to use data gathered during hardware tests to tune the controller gains. In practice, this proved to be impractical using a traditional Ziegler-Nichols method because when we increased the proportional gain, the system’s output never stabilized as we expected.

To resolve this problem, we built a simple PID controller in Simulink and ran simulations with the plant model of the tilt system. We placed scopes throughout the model to collect data, which we postprocessed in MATLAB. This analysis enabled us to first better understand the tilt system and then tune the controller gains until the system worked in simulation. We then adjusted the controller gains in our C code to match the ratio that we had verified in Simulink. The adjustment led to a breakthrough—we were able to actually ride the Uno III for the first time.

Modifying the Hardware and Controller Software

In addition to helping us tune controller gains, Simulink simulations also provided design insights that led to control algorithm changes and hardware modifications. For example, we ran multiple simulations in Simulink to see if gain scheduling could improve the tilt system. We found that a stepwise loop that used one set of gains for tilt angles between -3 and +3 degrees and different gain values for progressively larger tilt angle ranges produced better overall performance than a linear PID loop.

Later, we used Simulink and SimMechanics to explore mechanical changes to the system. In one instance, we ran simulations to ensure that the tilt motor had enough torque to move the 350-pound Uno and its rider from side to side. After conducting these simulations, we weren’t convinced that the motor at its current size would be capable of moving the Uno quickly enough. While the results were not definitive, we decided to err on the side of caution and use a larger motor.

Simulink simulations also helped us identify a deficiency with our analog-to-digital converter (ADC). Using some basic ADC blocks in Simulink, we built and simulated a simple model that helped us identify dead spots in our control algorithm that were affecting performance. To address the problem, we replaced the hardware ADC on the Uno with one that had a resolution four times higher.

Simulating the Transition and Power Systems

Because the Uno’s transformation from dicycle to motorcycle occurs while the vehicle is moving, ensuring the safety of this transition is a top priority. To simulate the transition with Simulink and SimMechanics, we combined a simple model of the balance control system with an inverted pendulum model. We then estimated the position of the Uno’s center of mass relative to the traction wheels at several states in the transition process, and used Simulink to verify that the mechanical model was controllable for each state.

As the Uno moves from prototype to preproduction, we are expanding our use of Simulink to model and simulate aspects of the Uno that would be too costly, dangerous, or time-consuming to experiment with on the actual hardware. We recently used SimPowerSystems™ to model the Uno's 48V power system, including the batteries, switches, and motors, to capture inductance and capacitance effects. Instead of hooking up probes and meters to the actual power system, we simulated it to track down the source of the spikes that we had identified during testing.

Going forward, we plan to reuse the model for additional reliability checks and to estimate battery life for various drive cycles and conditions.

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Agriculture / Analysis of Mechancial Design / ANSYS / Modelling / Simulations

Wind Turbine Design

Primary objective in wind turbine design is to maximize the aerodynamic efficiency, or power extracted from the wind. But this objective should be met by well satisfying mechanical strength criteria and economical aspects. In this video we will see impact of number of blades, blade shape, blade length and tower height on wind turbine design.

Effect of Number of Blades 

Fig.1 Efficiency gain as number of blades in wind turbine is increased

As the number of blades in the wind turbine increases aerodynamic efficiency increases, but in a diminishing manner. When we move from 2 blades to 3 blades design efficiency gain is about 3%. But as we move from blades to 4 blades dsign, efficiency gain is marginal.

As we increase number of blades, cost of the system increases drastically. Along with that mechanical design of blades also becomes a difficult affair. With more number of blades, blades should be thinner to be aerodynamically efficient. But blades with thinner portion at the root may not withstand bending stress induced due to axial wind load. So generally wind turbines with 3 blades which can accommodate a thicker root cross-section are used.

Fig.2 Wind turbine blades have got thicker root to withstanad huge bending moment induced

Wind Turbine Blade Design

The next big factor which is affecting performance of wind turbine is shape and orientation of blade cross section. A moving machine experiences fluid flow at a different velocity than the actual velocity. It is called as relative or apparent velocity. Apparent velocity of flow is difference between actual flow and blade velocity. Absolute velocity of the flow is shown in first figure, while apparent velocity in the second figure. It is clear that apparent velocity of flow is vectorial difference between actual and blade velocity. The vector difference is shown in the first figure at a particular cross section. A rotating blade will experience an apparent velocity of flow.

Fig.3 Absolute & apparent velocity of wind

A close look at wind turbine blade will reveal that, it is having airfoil cross sections from root to tip. The driving force of wind turbine is, lift force generated, when wind flows over such airfoils. Lift force will be perpendicular to apparent velocity. Generally lift force increases with angle of attack. Along with that undesirable drag force also increases. While tangential component of lift force supports blade rotation, drag force opposes it. So a wind turbine can give maximum performance, when lift to drag ratio is maximum. This is called as, optimum angle of attack. Airfoil cross sections are aligned in a way to operate at this optimum angle of attack.

Fig.4 Lift and drag force induced over a wind turbine blade

Even though flow velocity is uniform along the length of the blade, blade velocity increases linearly as we move to the tip. So angle and magnitude of relative velocity (apparent velocity) of wind varies along the length of the blade. Apparent velocity becomes more aligned to chord direction as we move to the tip.

Fig.5 Change in apparent velocity along length of the blade

So there should be a continuous twist in the blade, so that at every airfoil cross section angle of attack is optimum.

Pitching of Blades

Wind condition can change at any time. So it is also possible to rotate wind turbine blades in its own axis, in order to achieve optimum angle of attack with varying wind condition. This is known as pitching of blades. A clever algorithm which uses wind condition and characteristics of wind turbine as input, governs the pitch angle for the maximum power production.

Fig.6 Schematic of algorithm which governs blade pitching

Blade Length

Next big factor affecting performance of wind turbine is length of the blade. As we discussed in first video lecture, power extracted by the wind turbine varies according to this equation. So it is clear that, a longer blade will favor the power extraction. But, with increase in blade length, deflection of blade tip due to axial wind force also increases. So blind increase in length of the blade may lead to dangerous situation of collision of blade and tower.

Fig.7 Blade bending due to wind load acting on it

With increase in blade length tip velocity increases. Noise produced by the turbine is a strong function of tip velocity. So, it is not permissible to increase blade length after a limit. Other factor which goes against long blades is requirement of huge mechanical structures which leads to heavy investment.

Determination of Tower Height

Most critical factor of wind turbine design is determination of proper tower height. Power input available for wind turbine varies as cube of wind speed. So a small change in wind speed will have huge effect on power production. A typical wind speed increase from ground level is shown in figure. So from power extraction point of view, it is desired to have tower height as high as possible. But difficulty in road transportation and structural design problems put a limit on maximum tower height possible.

Fig.8 Wind velocity increases with altitude resulting in more power extraction

There are many forms of renewable energy . Most of these renewable energies depend in one way or another on sunlight. Wind and hydroelectric power are the direct result of differential heating of the Earth's surface which leads to air moving about (wind) and precipitation forming as the air is lifted. Solar energy is the direct conversion of sunlight using panels or collectors. Biomass energy is stored sunlight contained in plants. Other renewable energies that do not depend on sunlight are geothermal energy, which is a result of radioactive decay in the crust combined with the original heat of accreting the Earth, and tidal energy, which is a conversion of gravitational energy.

Solar. This form of energy relies on the nuclear fusion power from the core of the Sun. This energy can be collected and converted in a few different ways. The range is from solar water heating with solar collectors or attic cooling with solar attic fans for domestic use to the complex technologies of direct conversion of sunlight to electrical energy using mirrors and boilers or photovoltaic cells. Unfortunately these are currently insufficient to fully power our modern society.

Wind Power. The movement of the atmosphere is driven by differences of temperature at the Earth's surface due to varying temperatures of the Earth's surface when lit by sunlight. Wind energy can be used to pump water or generate electricity, but requires extensive areal coverage to produce significant amounts of energy.

Hydroelectric energy. This form uses the gravitational potential of elevated water that was lifted from the oceans by sunlight. It is not strictly speaking renewable since all reservoirs eventually fill up and require very expensive excavation to become useful again. At this time, most of the available locations for hydroelectric dams are already used in the developed world.

Biomass is the term for energy from plants. Energy in this form is very commonly used throughout the world. Unfortunately the most popular is the burning of trees for cooking and warmth. This process releases copious amounts of carbon dioxide gases into the atmosphere and is a major contributor to unhealthy air in many areas. Some of the more modern forms of biomass energy are methane generation and production of alcohol for automobile fuel and fueling electric power plants.

Hydrogen and fuel cells. These are also not strictly renewable energy resources but are very abundant in availability and are very low in pollution when utilized. Hydrogen can be burned as a fuel, typically in a vehicle, with only water as the combustion product. This clean burning fuel can mean a significant reduction of pollution in cities. Or the hydrogen can be used in fuel cells, which are similar to batteries, to power an electric motor. In either case significant production of hydrogen requires abundant power. Due to the need for energy to produce the initial hydrogen gas, the result is the relocation of pollution from the cities to the power plants. There are several promising methods to produce hydrogen, such as solar power, that may alter this picture drastically.

Geothermal power. Energy left over from the original accretion of the planet and augmented by heat from radioactive decay seeps out slowly everywhere, everyday. In certain areas the geothermal gradient (increase in temperature with depth) is high enough to exploit to generate electricity. This possibility is limited to a few locations on Earth and many technical problems exist that limit its utility. Another form of geothermal energy is Earth energy, a result of the heat storage in the Earth's surface. Soil everywhere tends to stay at a relatively constant temperature, the yearly average, and can be used with heat pumps to heat a building in winter and cool a building in summer. This form of energy can lessen the need for other power to maintain comfortable temperatures in buildings, but cannot be used to produce electricity.

Other forms of energy. Energy from tides, the oceans and hot hydrogen fusion are other forms that can be used to generate electricity. Each of these is discussed in some detail with the final result being that each suffers from one or another significant drawback and cannot be relied upon at this time to solve the upcoming energy crunch.

Can A Country Achieve 100% Renewable Energy?

If you think 100% renewable energy will never happen, think again. Several countries have adopted ambitious plan to obtain their power from renewable energy. These countries are not only accelerating RE installations but are also integrating RE into their existing infrastructure to reach a 100% RE mix. Read our article..

What are renewable energy sources? Solar power can be used directly for heating and producing electricity or indirectly via biomass, wind, ocean thermal, and hydroelectric power. Energy from the gravititational field can be harnessed by tidal power; and the internal heat of the Earth can be tapped geothermally.

These tools and more can help make the transition from non-renewable to renewable and environmentally friendly energy. However, none of these is sufficiently developed or abundant enough to substitute for fossil fuels use. Every one of these power sources (with the exception of hydroelectric) has low environmental costs, and combined have the potential to be important in avoiding a monumental crisis when the fossil fuel crunch hits. These energy sources are often non-centralized, leading to greater consumer control and involvement.

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Optimizing Performance and Fuel Economy of a Dual-Clutch Transmission Powertrain with Model-Based Design

Agriculture / Analysis of Mechancial Design / ANSYS / Modelling / Simulations

Tight vehicle emission regulations and high fuel prices have intensified the demand for fuel-efficient cars. At the same time, customers continue to expect the same vehicle performance that they had when fuel prices were lower. These conflicting demands present an optimization challenge for automotive manufacturers: how to minimize fuel consumption and CO2emissions without sacrificing performance?

In the distant past, automobile manufacturers tackled the problem by optimizing the power efficiency of each powertrain component separately. During the 1970s fuel crisis, large automakers began developing in-house computer simulation models to achieve optimal system-level performance. Despite this move toward system-level optimization, it is still common in some emerging markets to optimize individual components. This piecemeal approach misses a large opportunity to reduce vehicle-level fuel consumption by coordinating the operating points of the components.

Model-Based Design with MATLAB® and Simulink® enables all automakers and suppliers to achieve optimization results once reserved for a few large automakers with the resources to develop large internal simulation models and optimization programs. By using a system model that incorporates the engine, transmission, axle ratio, driver, and vehicle, engineers can precisely match powertrain components and optimize hardware variables, such as axle ratios, and calibration parameters, such as shift schedules, simultaneously. Instead of rough estimates of fuel economy impact derived from expensive technology alternatives, they then have hard metrics upon which to base crucial hardware-selection decisions.

For example, suppose we want to optimize the powertrain for an economy car with a five-speed, dual-clutch transmission (DCT) and a turbo-charged, 2-liter, 4-cylinder engine (Table 1). The goal is to use as little fuel as possible over a Federal Test Procedure (FTP75) drive cycle while maintaining a minimum performance threshold of 10 seconds for the 0–100 kph acceleration time (the time it takes to reach 100 kph from a standing start).

Engine	Turbocharged 2.0L 4-cylinder I4 SI engine with dual-VVT
Vehicle class (mass)	Small to midsize (1600kg)
Transmission	5-speed DCT
Drag coefficient Cd	0.4
Drive cycles	FTP75 (fuel economy) 0–100 kph acceleration (performance)

Table 1. Vehicle characteristics.

To find the combination of gear shift schedule calibrations and axle ratio that meets these requirements, we test a range of axle ratios. For each ratio, we use numerical optimization to find the most fuel-efficient shift schedule calibration for the FTP75 cycle, as well as a separate shift schedule calibration that minimizes 0–100 kph acceleration time. In keeping with current practice, the production powertrain controller chooses which of the two optimal shift schedules to use depending upon the magnitude of the torque demand sent by the driver through the accelerator pedal. Fuel cost and 0–100 kph acceleration time corresponding to each axle ratio are then plotted together to form a tradeoff graph of fuel economy and performance vs. axle ratio (Figure 1).

Figure 1. Simulation results showing optimal FTP75 fuel consumption and optimal 0–100 kph time for 7 axle ratio values. The blue line plots fuel cost as a function of axle ratio. The green line plots acceleration performance measured as 0–100 kph time against axle ratio.

Instead of using expensive and time-consuming in-vehicle testing with axle hardware changes and shift-schedule recalibration, which can produce inconclusive results due to measurement noise, we now have a definitive result: Given a 10-second 0–100 kph design constraint, an axle ratio of 3.0 in combination with an optimal shift schedule calibration provides the best fuel efficiency.

Why DCT?

A DCT combines the convenience of an automatic transmission with the fuel efficiency of a manual transmission. It houses two separate clutches, one for odd and one for even gear sets (Figure 2), eliminating the need for a torque converter. To ensure smooth shifting and optimal efficiency, DCTs need sophisticated controllers capable of preselecting the next gear and engaging the appropriate clutch precisely when required.

Figure 2. A dual-clutch transmission.

DCTs are 3–5% more fuel-efficient than manual transmissions, which in turn are 5–10% more efficient than automatic transmissions. This efficiency gain has contributed to the recent growth of the DCT market, particularly in Europe and China.

Developing the System-Level Model

Working in Simulink, we build a system-level model that includes an accurate engine model derived from engine mapping data, the DCT (including its controller), the vehicle, and an autodriver component to drive the simulation through a specific drive cycle (Figure 3).

Figure 3. The Simulink system-level model.

Engine Modeling and Calibration

Since the engine is the heart of a conventional powertrain and directly consumes fuel, an accurate model of engine fuel consumption and torque production is crucial. To get the most accurate results, we must create this model from measured data. Using Model-Based Calibration Toolbox™ we fit statistical models to engine test data from an engine dynamometer and export those models automatically into Simulink (Figure 4).

Figure 4. A Simulink engine model block, created from measured data.

Using the Calibration Generation (CAGE) tool in Model-Based Calibration Toolbox, we generate engine calibration tables for use in the engine controller model of the overall simulation. These tables capture optimal spark advance, air-fuel ratio, intake cam phasing, and exhaust phasing as a function of engine speed and load (Figure 5).

Figure 5. An engine calibration block, created using Model-Based Calibration Toolbox.

Transmission and Vehicle Modeling

We model the DCT using actuator, dog clutch, gear, and shaft blocks from SimDriveline™ (as shown in the Simulink system model in Figure 3). The transmission controller, which includes the fuel economy and performance shift schedules, is modeled using Simulink and Stateflow®.

We also use SimDriveline to model a vehicle dynamics subsystem that incorporates the vehicle's mass and road-load characteristics.

Drive Cycle and Autodriver Implementation

To complete the system-level model, we add the FTP75 drive cycle and the autodriver subsystems. The FTP75 block incorporates the vehicle speed trace representing the standard Federal Test Procedure 75 drive cycle, which includes a mix of low- and mid-speed driving and is typically used for fuel economy and emission certification testing. The autodriver takes as input the vehicle speed command from the FTP75 block and the actual vehicle speed. It uses a proportional-integral (PI) controller to produce a torque demand signal that commands engine torque so that the actual vehicle speed matches the commanded vehicle speed from the FTP75 drive cycle.

Simulation and Optimization

Once we have a complete system-level model, we can run a simulation for any shift schedule and axle ratio that we want to test. The total fuel consumed and the 0–100 kph time can be calculated by the simulation.

We will test seven axle ratios, ranging from 1.75 to 4.25, and determine the optimal gear shift schedule for each. A search that simulates every possible shift schedule is not feasible because the shift schedule comprises 32 different parameters (Figure 6). Assuming that each shift schedule calibration value can be varied over a range of +/-10 mph at an engineering resolution of 1 mph (not accounting for constraints), an exhaustive search would require the investigation of 2.05 x 1042 (2132) possible shift schedule simulations.

Figure 6. A gear shift schedule, showing throttle position and vehicle speed for each shift.

Instead of this brute force approach, we will use the pattern search optimization algorithm in Global Optimization Toolbox, reducing the number of simulations required—in this example, to 15,400.

An individual simulation of the FTP75 cycle takes only 250 seconds—about 5 times faster than real time. However, an optimization requiring 15,400 simulation runs on a single processor would take more than 44 days to get a result. This computationally intensive problem is a good candidate for parallel computing because the simulations can be run independently on separate processors.

We set up a computing cluster with 16 quad-core PCs, for a total of 64 workers. To further accelerate the search, we build a standalone executable target using Rapid Accelerator mode in Simulink to maintain complete simulation model parameter independence between runs.

We begin the optimization process (Figure 7) by setting up the optimization parameters. Next, the pattern search algorithm in Global Optimization Toolbox identifies the parameter variations to be simulated using the 2N optimization method of pattern search. In this example there are two shift schedules, each with 16 variable points (N=32). This means that pattern search will run 64 (2N) simulations at a time, perfectly matching the number of workers available. Each variation is simulated for the current axle ratio on a different processor in the cluster. If the search space resolution, or mesh size, does not fall below a predetermined threshold, then a new set of parameter variations is generated and the process repeats. When the search space mesh size falls below the threshold, the algorithm has located a global minimum in fuel consumption of 0–100 kph time, and the results are reported.

Distributing this process on a 64-worker cluster reduces the total computation time from more than 44 days to about 26 hours.

Figure 7. The optimization process.

After running a complete axle sweep and finding the optimal shift schedule for each axle ratio, we generate the performance and fuel economy tradeoff plot shown in Figure 1. The results show that the lowest fuel consumption is found at an axle ratio of about 2.6. This ratio, however, results in a 0–100 kph time of more than 10 seconds—above our 10-second performance threshold for the target market of the vehicle we are designing. The optimal axle ratio that falls below our desired performance threshold is 3.0.

Figure 8. Simulation results showing the optimal axle ratio for a 0–100 kph time of less than 10 seconds.

Before running the optimization, we estimated an optimal shift schedule and axle ratio. This baseline configuration, with an axle ratio of 3.8, resulted in a vehicle fuel efficiency of 31.85 mpg and a 0–100 kph time of 8.03 seconds (Table 2). After running the optimization to find the most fuel-efficient shift schedule for this axle ratio, we increased fuel efficiency by 5.8%. By reducing the axle ratio to 3.0, we can trade off performance (0–100 kph time increases from 8.03 to 9.54) to achieve a fuel efficiency increase of 12.5% over the baseline.

If we had changed the axle ratio to 3.0 in a manual process without also reoptimizing the shift schedule, we would not have achieved the maximum possible fuel consumption reduction because it would have been difficult or impossible to adjust all 32 shift points manually to achieve the same result.

Shift Schedule	Base	Opt 3.8	Opt 3.0
Axle Ratio	3.8	3.8	3.0
MPG	31.85	5.8%↑	12.5%↑
Performance Time 0–100 kph (s)	8.03	8.03	9.54

Conclusions

Accurate vehicle simulation models enable engineers to quantitatively determine the optimal tradeoff between the conflicting demands of vehicle performance and fuel economy given a set of available hardware choices.

By using the approach described in this article, engineers can continue to respond to evolving market demands, changing the drivetrain axle ratio to select a tradeoff that matches customer expectations for a given class of vehicle.

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