Effects of Fuel Consumption of Commercial Turbofans on Global Warming

Onder Turan and T. Hikmet Karakoc

13.1 Introduction

The main objective of this study is to parametrically investigate the fuel consumption effect of commercial turbofans on global warming. In this regard, of the important parameters, specific fuel consumption of commercial turbofans is taken into consideration. In order to minimize the effect of fuel consumption on global warming, the values of engine design parameters are optimized for maintaining minimum specific fuel consumption (SFC , g/kN s) of high-bypass turbofan engine under different flight conditions and design criteria. The backbones of optimization approach consisted of elitism-based genetic algorithm coupled with real parametric cycle analysis of a turbofan engine. For solving optimization problem a new software program is developed in MATLAB, while objective function is determined for minimizing the specific fuel consumption by considering parameters such as the fan pressure ratio (n), bypass ratio (a), and the fuel heating value [hPR (kJ/kg)]. Accordingly, it may be concluded that the software program developed can successfully solve optimization problems at 1.2 <TC/ < 2, 2 <a< 8, and 23,000 <hPR < 120,000 with aircraft flight Mach number < 0.8. Fuel types used in preliminary engine cycle analysis were JP-4, JP-5, JP-8, and hydrogen in this chapter. As a conclusion, SFC was found to be 5.50, 18.31, and 34.25 g/kN s at cruising condition for hydrogen, kerosene, and ethanol, respectively.

For making aircraft propulsion systems applicable for all types of aircraft, the following development goals were being pursued (Mattingly, 2006):

• higher overall efficiency;

• larger power output engines;

• larger ratios of power output to engine weight, volume, and frontal area;

• greater service life, endurance, and reliability; and

I. Dincer et al. (eds.), Global Warming, Green Energy and Technology,

DOI 10.1007/978-1-4419-1017-2_13, © Springer Science+Business Media, LLC 2010

For supersonic flights, the overall efficiency of turbojet engines was clearly noticeable. However, for high subsonic flight speeds, the velocity of the exhaust gas jet was too high to obtain a best propulsive efficiency. Under these conditions, the bypass engine became a very good choice for improving the propulsive efficiency. Highest possible thermodynamic and propulsive efficiencies of aircraft propulsion systems led to some engine characteristics (Oates, 1997) as very high compressor pressure ratios, turbine inlet temperature, and bypass ratios.

The advantages of the high-bypass ratio turbofan engines can be summarized as follows:

• high overall efficiency, resulting in long flight range;

• lower jet velocity, leading to great noise reduction;

• increase in thrust; and

• low specific fuel consumption, which reduces chemical emissions.

Since the deregulation of the airline market in 1978, the pressure on the engine manufacturers to produce more efficient, low-cost aircraft has increased dramatically (Schipper and Rietveld, 1997). The increased competition forced the airline companies to reduce their commitment as launch customers for new airframe and engines (Nightingale, 2000). In addition, environmental concerns pushed for more stringent legislation on pollutant emissions and noise. The standard regulating NOx emissions of aero-engines was first adopted in 1981 (ICAO, 1999), then was made more stringent in 1993 with a reduction of the permitted levels by 20%. It was followed in 1999 by a further reduction of the standard by about 16% on average for engines to be certified from December 31, 2003.

The financial uncertainties pushed manufacturers to reduce their time to market from 5 years to 39 months for the Trent series (Robins, 1996). In addition Rolls-Royce now plans to reduce its engine development timescale by a further 30% (Anand and Priddin, 2001). The increased competition in conjunction with the environmental concerns changed the market drivers which could be classified as follows (Mari, 2001):

• Life cycle cost: acquisition, fuel burn, maintenance.

• Environmental impact: pollutants emissions, noise.

• Performance: thrust, weight, specific fuel consumption.

Owing to growing up of the intercontinental transportation necessity, it has become inevitable to make more powerful engines. It is estimated that there are currently 16,800 jet airplanes in the world and this figure is expected to grow to 35,300 by 2024. Also, the passenger traffic activities have increased on average 4.8% per year (Boeing, 2007). On the other hand, 2-5% of the world energy consumption belongs to aviation industries (Koroneos et al., 2007). Rolls-Royce predicts continued strong long-term growth in all major segments of the commercial aircraft and jet engine market. Over the next 20 years, the forecasts demand for 132,000 engines, worth $701 billion. Markets within Asia, both short-haul and intercontinental, will drive much of this growth. However, the more mature markets in Europe and North America require over 6000 new airliner deliveries to replace older aircraft in today's fleet. Delivery of these engines also creates an aftermarket opportunity of $550 billion for services in lifetime (Rolls-Royce, 2007).

Over the last 20 years Rolls-Royce's outlook has seen a steady move toward higher thrust engines. Airlines have demanded aircraft with better payload-range performance, more flexibility to takeoff from short runways and improved climb rates. There is no sign of the drive for performance declining. Therefore, Rolls-Royce continues to forecast that the sectors above 200.17 kN (45,000 lb) takeoff thrust will be the largest in terms of value. The forecast for the engine market naturally reflects the size distribution and dynamics of the aircraft market. The market has been segmented into takeoff thrust categories, which can be roughly matched against aircraft classes. For example, below 26.7 kN (6,000 lb) is the domain of smaller business jets, while 26.7-97.86 kN (6, 000-22,000 lb) engines predominantly power business jets and regional jets. The 97.86-200.17 kN (22,000-45,000 lb) category covers the single-aisle market and engines above 200.17 kN (45,000 lb) thrust are for twin-aisle aircraft. While there has been little change in the relationship between takeoff thrust required and the maximum takeoff weight (MTOW) of the aircraft, there have been continued reductions in the MTOW required for a given mission. This is due to more fuel-efficient engines, which require less weight of fuel to be carried and lighter airframe structures. There is also now more focus on 'hot-and-high' engine performance, with airlines wishing to have the flexibility to operate without payload restrictions from regions such as the Middle East, India, and Latin America. Although the largest quantity of engines is for the 97.86-200.17 kN (22,000-45,000 lb) thrust band, the market value is dominated by high thrust engines for long-haul twin-aisle aircraft. This sector has expanded at a rapid rate over the last 15-20 years and is forecast to continue to grow in the coming decades (Rolls-Royce, 2007).

High-bypass turbofan engines can be modeled at various levels of detail, ranging from simple algebraic relations to full three-dimensional (3-D) description of the gas path. Aerothermodynamic models are considered in this study. Some models are massively used by the manufacturers throughout an engine program: for preliminary design and performance prediction, for the synthesis of the control laws, for condition monitoring, as well as for the engine-airframe integration (Borguet et al., 2007).

In this study, we report the development of a modular aircraft high-bypass turbofan engine simulation in the MATLAB (Matrix Laboratory) environment. A new software program was developed for multi-design point optimization of a high-bypass turbofan engine. The newly developed software program's name is TURBOGENf (turbofan genetic fan). It can search optimum thermodynamic points of a high-bypass turbofan engine coupled with elitism-based genetic algorithm method (EBGA) for minimum specific fuel consumption for different fuel usages. The genetic algorithms (GAs), initially developed by Holland (1975), are the most recognized and practiced form of evolutionary algorithms which are stochastic optimization techniques that mimic Darwin's principles of natural selection and survival of the fittest. GAs can be used in the case of discontinuous objective functions, within disjoined and/or non-convex design spaces, and together with discrete, continuous, or integer design variables. With respect to local search methods (e.g., gradient-based) GAs minimize the risk to converge to a local optimum, thanks to the simultaneous processing of the whole candidate solutions.

Moreover, they are particularly suitable for multiobjective optimization problems which are often encountered in real design problems. Because of these advantages, GAs are more and more widely used in various disciplines. However, GAs generally require a large number of iterations and they converge slowly. Optimization using genetic algorithms is thus advantageous when the objective functions evaluation is not too expensive in terms of calculating time (Borguet et al., 2007). Therefore, GAs are efficient when coupled to approximation methods (Pierret, 2005), to parametric reconstructions (Grondin et al., 2005), or to 0-D (zero-dimensional) models.

13.2 High-Bypass Turbofan Engine Modeling

In the following, we focus on a particular type of jet engine: the separated flows and non-afterburning turbofan. With the current level of technology, this one has revealed to be the optimum configuration for high subsonic commercial aircraft (Cumpsty, 2000). A schematic of the engine is sketched in Fig. 13.1.

Engine Station Numbering
Fig. 13.1 General station numbering of a turbofan engine.

The assumptions for the analysis of the turbofan engine cycle with losses are as follows:

• Perfect gas upstream of main burner with constant properties Y, Rc, Cpc.

• Perfect gas downstream of main burner with constant properties Y, Rt, Cpt.

• All components are adiabatic (no turbine cooling).

• The efficiencies of the compressor, fan, and turbine are described through the use of (constant) polytrophic efficiencies ec, ef, and et, respectively.

The steps of cycle analysis can be easily seen in Mattingly (1996). But the most important parameters, specific thrust and specific fuel consumption, are given as follows:

F mn

Table 13.1 Parameters of genetic algorithm and design point of a high-bypass turbofan engine-I.

Flight conditions and design-point parameters of TURBOGENf


T0=220 K

hPR=23,000 kJ/kg

nc =20

T4 (K)=1,500

Cpc kJ/(kg. K)=1.00488

Cp=1.147 kJ/(kg K)

Yc =1.4

Yt =1.33

pt J pt3 =°."

Pm! Pt13 =0.99


e= et =0.89

nb=nm = 0.99

pj P9=0.90

pj P19=0.90

Genetic algorithm parameters of TURBOGENf







13.3 Preliminary Design Curves with TURBOGENf

In the following, TURBOGENf (turbofan genetic fan) software program developed by Turan (2007) is introduced. TURBOGENf is a software program developed in MATLAB programming environment which analyses parametric cycle of a non-afterburning, separate exhaust flow turbofan engine at different design points in SI unit and gets optimum design points at different flight conditions and design criteria via elitism-based genetic algorithm simultaneously. Main purpose of TURBOGENf is minimizing specific fuel consumption of a high-bypass turbofan engine under different design criteria, different fuels and flight conditions. Decision variables of TURBOGENf are fan pressure ratio (nf) and bypass ratio (a). It is possible to see-some 3-D performance curves of an engine in TURBOGENf. TURBOGENf is able to draw 3-D color-scaled counter plot corresponding to specific fuel consumption, specific thrust, propulsive, thermal, and overall efficiency coupled with decision variables such as the fan pressure ratio and the bypass ratio. Table 13.1 consists of design-point parameters of an example turbofan engine. From Figs. 13.2 to Fig. 13.4 3-D color-scaled counter plot coupled with decision variables (nf and a) and specific fuel consumption (SFC) according to Tables 13.1-13.3 for which hPR is 23,000, 43,000, and 120,000 kJ/kg, respectively can be seen. Each mesh plot color in these figures represents value of the specific fuel consumption as a objection function curve.

Table 13.2 Parameters of genetic algorithm and design points of a high-bypass turbofan engine-II._

Flight conditions and design-point parameters of TURBOGENf


T0 =220 K hPR=43,100 kJ/kg


T4 (K)=1,500

C„c kJ/(kg K)=1.00488 CDÍ=1.147 kJ/(kg K)


Y =03

PWPt 3=0.99 pm/p,n=0.99


ef= e, =0.89

Vb= Vm = 0.99 p0/p9=0.90


Genetic algorithm parameters of TURBOGENf


Gn=300 Cr=0.6


1.2 <nf < 2

hasn=0.1 2 <a< 8

has a=0.1

Table 13.3 Parameters of genetic algorithm and design points of a high-bypass turbofan engine-III.

Flight conditions and design-point parameters of TURBOGENf


T0 =220 K hPR=120,00 kJ/kg


T4 (K)=1,500

C„c kJ/(kg K)=1.00488 Cp,=1.147 kJ/(kg K)

Yc =1.4

Y =03

P,4/p, 3=0.99 Pt19 / Pt13 =0.99


e= et =0.89

Vb= Vm = 0.99 p0/p9=0.90


Genetic algorithm parameters of TURBOGENf


Gn=300 Cr=0.6


1.2 <Kf < 2

hasn=0.1 2 < a < 8

has a=0.1

Until now, fossil fuels have contributed to over 80% of energy expenses, and among them, oil played the dominant role. It is expected that its use will not decline until the next two or three decades. The transportation sector, including aviation, an essential part of our modern society, represents the largest part of the petroleum-based fuels consumption. Its importance has continuously grown at a very fast rate over the last century. Future global energy and environmental issues have imposed changes in the operating conditions of jet engines. As in other sectors, research is now oriented on saving energy, in parallel with enhanced protection of our environment (reduction of the emissions of pollutants and green house gases) and fuel reformulation. The detailed modeling of the combustion of jet fuels is a useful tool to solve the problem of combustion control as well as to reduce emissions and fuel consumption.

Table 13.4 Main characteristics of kerosene jet fuel.

Property JP-8


JP-8/Jet A-1

Jet A



Molecular weight



Approximate -formula





atoms in the fuel





H/C ratio -






Boiling range 140-300 °C

Average 204


Average 216



Specific grav- 0.81 ity at 15°C




Av. composition in vol%

Aromatics 20


18(monoaro.) 10-20


Cycloalkenes 20




Paraffin 58


28(n-par.) + 50-65 29(i-par.)

Olefins 2




Source: Dagaut and Cathonette (2006).

Table 13.5 Main characteristics of kerosene jet fuel.

Fuel type

Energy density (M/kg)

Energy per unit volume (MJ/L)

Motivity factor

Specificcarbon emission (kg C/kg fuel)

Liquid hydrogen





Gaseous hydrogen





Fuel oil










Jet fuel

























Bio diesel





Natural gas










Source: Midilli et al. (2005).

Table 13.6 Computer experiment results for different fuel types usage sign of turbofans.

in preliminary de-

Engine number

hpR (kJ/kg)

SFC*[g/(kN s)]










Fig. 13.2 Specific fuel consumption-fan pressure ratio-bypass ratio 3-D curves in TURBOGENf for hpr=23,000 kJ/kg.


Fig. 13.2 Specific fuel consumption-fan pressure ratio-bypass ratio 3-D curves in TURBOGENf for hpr=23,000 kJ/kg.

Pressure Bypass Curve

Fig. 13.3 Specific fuel consumption-fan pressure ratio-bypass ratio 3-D curves in TURBOGENf for hpr=43,000 kJ/kg.

Fig. 13.3 Specific fuel consumption-fan pressure ratio-bypass ratio 3-D curves in TURBOGENf for hpr=43,000 kJ/kg.

Pressure Bypass Curve
Fig. 13.4 Specific fuel consumption-fan pressure ratio-bypass ratio 3-D curves in TURBOGENf for hpr = 120,000 kJ/kg.

Such a modeling represents a real challenge because practical jet fuels are complex mixtures of several hundreds of hydrocarbons including alkenes, cyc-loalkenes, aromatics, and polycyclic compounds. Table 13.4 gives the main characteristics of JP-8 and Jet A-1 reported by several authors compared with the general characteristic of kerosene (Guibet, 1999; Edwards and Maurice, 2001; Violi et al., 2002; Dagaut and Cathonette, 2006). Kerosene usage chain with indications of inputs and outputs including environmental impacts is shown in Fig. 13.5. Main characteristics of kerosene jet fuel can be listed in Table 13.5.

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