Department of Power and Propulsion

POWER PLANT SELECTION & OPERATION

TERA (Technoeconomic Environmental Risk Analysis)

Development into asset management Pablo Bellocq - TERA Researcher Pericles Pilidis PhD-MBA - Head Department of Power & Propulsion, School of Engineering, Cranfield University, UK

Department of Power and Propulsion

PROBLEM Environmental concern - Many different technologys available or in development -Complex power and propulsion systems -Operation and maintenance strategies -

SOLUTION ? - TERA (Technoeconomic Environmental Risk Analysis) - Conceived at Cranfield – 1990s - Initially for Aviation: VITAL, NEWAC (EU Projects) - Extended to Marine, Power Generation, Oil and Gas

Department of Power and Propulsion Role of TERA

Objective:

Invest early to reduce commitments and risk during design and operation

Department of Power and Propulsion TERA for Marine Applications AMEPS - EPSRC 3 Universities: Cranfield Manchester Strathclyde

Cranfield role: GT Behaviour TERA

Department of Power and Propulsion TERA - Large Fast Ferry Main Parameters

Combination of Journey, Ship and Powerplant Power demand arising from: S1 Calm Sea S2 Rough Sea S3 Rough Sea & Fouled Hull

Propeller diameter, Dp Pitch diameter ratio, P/Dp

Length at water level, LWL

184.55 m

Expanded area ratio AE/Ao

Maximum beam, B

25.0 m

Propeller rotational speed N

Average design draft, T

6.4 m

Number of blades, Nblade Open water efficiency,

Block coefficient, Cb

0.55

Gas turbine rating, PPT (x2)

25 MW

Midship coefficient, Cm

0.93

Turbine entry temperature, TET

1500

Compressor Pressure Ratio, PRc

17.8

Department of Power and Propulsion Marine TERA - GT Power & Ship Speed v. Time

Engine Power (MW) Ship Speed(knots)

30 Ship Speed S1 Ship Speed S2 Ship Speed S3 Ship Power S1 Ship Power S2 Ship Power S3

27 24 21 18 11

14

17

20

23

Time of Day (hh)

02

05

07

Department of Power and Propulsion Fuel Flow & TET v. Time

Fuel Flow (kg/s)

1.5 1.2

Fuel Flow S1 Fuel Flow S2 Fuel Flow S3 TET S1 TET S2 TET S3

0.9 0.6

1650 1550 1450

0.3 0

1350 11 14 17 20 23 02 05 07 Time of Day (hh)

Turbine Entry Temperature (K)

1750

Department of Power and Propulsion

Output (to meet demand) includes

Used to predict

fuel power output temperatures pressures stress levels mass flow Etc,

emissions costs (complete life cycle) revenues noise life consumption risk - financial risk – technology health evolution

Some examples follow:

Department of Power and Propulsion Aircraft Emissions World NOX Dist Flights London Tokyo

Similar for Noise

Parts Per Trillion By Volume (Pptv)

Department of Power and Propulsion CCGT Technoeconomic Analysis 150,000,000

Case A: Reference Case B: Emission Case C: Fuel Case D: O&M

100,000,000

50,000,000

0 -50,000,000

5

10

15

20

Years

-100,000,000

Cash Flow - Sensitivity

25

30

Department of Power and Propulsion CCGT Technoeconomic Risk Analysis 100

90 80

Cumulative frequency

70

Frequency

% 60 50 40 30

20 10

Cumulative Cash Flow - Risk Analysis

1,326,569

1,060,697

794,825

528,953

263,082

-2,790

-268,662

-534,534

-800,406

-1,066,278

0

Cum Cash in k$

Department of Power and Propulsion Power plant choice for different operations

Standard vs ICR Turbofan: Influence of Aircraft Size on Fuel Weight

(Fig. 6.3 from Whellens, M.W., 2003, “Multidisciplinary Optimisation of Aero-Engines Using Genetic Algorithms and Preliminary Design Tools”, PhD Thesis, School of Engineering, Cranfield University)

Department of Power and Propulsion Optimiser Developments

Plant Design

-

Operations and maintenance

Department of Power and Propulsion

TERA for Asset Management Optimise asset deployment

Example: Take-off and climb of an airliner Manage asset for:

1 - Minimum time 2 - Minimum fuel

ISABE 2009

14

Department of Power and Propulsion

Asset management optimisation: Take-off and climb of an airliner

ALi [m]

ALf [m]

M [--]

R [km]

457 3048 3048 7000

3048 3048 7000 10668

0.38 0.46 0.58 0.80

20.0 10.0 60.0 100.0

Obj. Fuction / Optmizer Time - Optimizer Time - MADS [23] Time - GAs [23] Fuel - Optimizer Fuel - MADS [23] Fuel - GAs [23]

Flight Time [%]

Fuel Burned [%]

-16.2 -16.3 -16.3 3.7 3.1 4.7

50.5 52.6 53.0 -6.7 -6.7 -6.0

Time - Optimizer Fuel - Optimizer

Time - MADS [23] Fuel - MADS [23]

12000 10000

Altitude [m]

Segment No. 1 2 3 4

Baseline Time - GAs [23] Fuel - GAs [23]

8000

6000 4000 2000 0 0

20

40

60

80

100

120

140

160

180

200

Ground Range [km]

ISABE 2009

15

Department of Power and Propulsion

Asset management optimisation: Take-off and climb of an airliner 0.9

0.7

1750

Fuel Burned [kg]

Mach Number [--]

0.8

2000

Baseline Time - Optimizer Fuel - Optimizer

0.6 0.5 0.4

1500

Baseline Time - Optimizer Fuel - Optimizer

1250 1000 750 500 250 0

0.3 1

2

3

1

4

2

1600

Baseline Time - Optimizer Fuel - Optimizer

1500 1400 1300 1200 1100 1000 1

2

Climb Segment

3

4

Change (%) Related to Base Case

Turbine Entry Temperature [K]

1800 1700

3

4

Climb Segment

Climb Segment

694.2

700 600

Time - Optimizer Fuel - Optimizer

500 400 300 200 100 0 -100

55.6

50.5 -16.2 3.7 Flight Time

-6.7 Fuel Burned

-19.3 Oxides of Nitrogen

55.7 -7.0

Carbon Dioxide

-6.9 Water (vapor)

Minimum fuel: early high fuel burn to reduce overall fuel burn

Minimum time: Significant increase in NOx emissions

ISABE 2009

16

Department of Power and Propulsion THE TERA Development into asset management Description (Synergy of representative models) Deployment scenarios Assett management – (procurement – O&M) Sows pros and cons of Asset Mgt decisions Care in : Assett representation (along life cycle) Demand assessment Local conditions Risk

Current work: Integrate diagnostics for optimizing operation and Maintenance strategies

Department of Power and Propulsion

THE TERA (Technoeconomic Environmental Risk Analysis) Development into asset management Thank you for your kind attention Questions?

Pericles Pilidis - Department of Power & Propulsion, School of Engineering, Cranfield University, UK

Department of Power and Propulsion Role of TERA

Objective:

Increase early spend to reduce commitments and risk

Cash

Key Committed (No TERA) Committed (TERA) Spent (No TERA) Spent (TERA)

Time Preliminary Evaluation & Design - Competition

Department of Power and Propulsion Role of TERA Objective: Greatly reduce design space and investment costs Original Design Space

Reduction Via TERA

Final Solution