Towards the development of A Technoeconomic, environmental risk analysis for an aircraft operator

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1 ISABE Towards the development of A Technoeconomic, environmental risk analysis for an aircraft operator Alex Nind, Dr V. Sethi, Dr S. Sampath Cranfield University, College Road, Cranfield, Bedford, MK43 0AL Abstract TERA (Techno-economic and Environmental Risk Analysis) is a multi-disciplinary framework that has been developed at Cranfield University over the last twenty years. It is essentially a collection of models that interact with each other and is useful for assessing promising design choices at preliminary stages for further study. It is used primarily as a way to assess the viability of current and future gas turbine engines at a system level, but can also be used to assess other technologies and also for asset management. This paper describes the creation of a TERA framework that is to be used to assess current and future gas turbine engines from an aircraft operator's perspective. At the heart of the TERA framework is TURBOMATCH, Cranfield University's in house gas turbine performance code, used to 'build' and assess the performance of current and potential aircraft engines. The outputs from this code feed into the other models within the TERA framework. HERMES is the University's aircraft mission performance code. It uses thrust and SFC data from TURBOMATCH, combined with user defined aircraft geometric and weight details, and mission profile data to evaluate mission time and fuel burn. ATLAS is a simple model used to predict the weight of potential future engine designs, by using engine component weight correlations and sizing. This is then fed into the aircraft performance model. HEPHAESTUS is used to predict engine emissions, in particular NOx emissions. It can be configured to use the P3T3 method to more accurately predict current certified engines, or use a stirred reactor model to assess potential engine designs. It uses outputs from TURBOMATCH, atmospheric data and combustor sizing as inputs. HESTIA is an engine life, economics and risk code. It uses creep and fatigue analysis of life limited parts to determine the maintenance period for the engine. This is then combined with economic details such as staff salaries, maintenance costs and fuel costs to determine aircraft mission operating costs and through life costs. It can also be used to assess different potential future emission taxation and policy scenarios. The models are brought together within isight, a commercial optimization code, which allows for both interaction between the models and specific objectives to be optimized. The purpose of the framework will then be to assess whether, from the point of view of an aircraft operator, there are any emergent engine technologies which are more attractive than others, for a variety of different potential future scenarios. Nomenclature ACARE BPR CO 2 DOC DREAM EI EU ICRTF ISA Advisory Council for Aeronautical Research in Europe ByPass Ratio Carbon Dioxide Direct Operating Costs validation of Radical Engine Architecture systems Emissions Index (g/kg fuel) European Union InterCooled-Recuperated TurboFan International Standard Atmosphere 1

2 LEMCOTEC Low EMissions Coreengines TEChnology LTO Landing and Take-Off cycle M Mach number MTOW Maximum Take-Off Weight (kg) N Engine Rotational Speed (rpm) NEWAC NEW Aero engine Core concepts NO Nitric Oxide N 2O Nitrous Oxide NOx Nitrogen Oxides NPC Net Present Cost OEM Original Equipment Manufacturer PSR Perfectly Stirred Reactor PaSR Partially Stirred Reactor PSRS series of Perfectly Stirred ReactorS SFC Specific Fuel Consumption (mg/ns) SLS Sea Level Static T Total Temperature (K) TERA Techno-economic and Environmental Risk Analysis TET Turbine Entry Temperature (K) TOC Top Of Climb VITAL environmentally friendly aero engine VTOL Vertical Take-Off and Landing into account all of the constraints, and reduce overall design costs accordingly. This is where a Technoeconomic, Environmental Risk Analysis (TERA) framework comes in. What is TERA? TERA is a tool that can be used to assess mainly gas turbine engines (although there is no reason why this couldn't be expanded to other areas) with "minimum [environmental] impact and lowest cost of ownership in a variety of emission legislation scenarios, emissions taxation policies, fiscal and air traffic management environments" [3]. TERA uses a modular, multi-disciplinary (in that several different disciplines are used such as gas turbine performance, noise modelling, emission modelling, aircraft performance e.t.c.) approach to optimize a particular 'goal' function, such as fuel burn, noise or global warming potential (ibid. p. 4). Figure 1 shows the typical TERA philosophy that was used in the European Union's (EU) environmentally friendly aero-engine (VITAL) project. The framework is explained in detail in Ogaji et al [3] and is summarized below. Introduction The focus of jet engine design has evolved over the years as our understanding of the science and technology has improved. The traditional approach to jet engine design in the 1960's was to focus on specific thrust and specific fuel consumption (SFC) for an uninstalled engine [1,2]. This has moved on to now consider a whole raft of issues including direct operating costs (DOC - fuel costs, purchase costs, maintenance costs), environmental considerations (including CO 2 and NOx emissions) as well as the performance of the engine [1,2]. In order to design future generations of engines, a more 'front-loaded' approach is required at a preliminary stage to design the engines effectively, which takes Figure 1: PHILOSPHY OF TERA [3] The TERA framework consists of several modules which are integrated together with an optimizer. The optimizer used for VITAL was a commercial optimizer called isight, although other user made geneticalgorithm optimizers have also been used, such as in Whellens et al [4]. The different modules are individual 2

3 models that perform a specific function, such as an engine performance model or aircraft performance model. These are brought together within the optimizer, which can then give a set of optimum results for a number of different objectives. History of TERA A brief history of TERA is given in Kyprianidis et al [5], which is expanded upon here. TERA models have been developed at Cranfield University since the mid 1990's. One of the first to use this kind of approach was Vincente [6], who studied the "effect of bypass ratio on long range subsonic engines". Further work on TERA continued with Whellens et al [4], who combined an engine performance model, engine weight model, aircraft mission model with a geneticalgorithm based optimizer, to study the benefits of an intercooled recuperated turbofan (ICRTF) over existing turbofan engines for a long-distance passenger aircraft. The objective of the optimization was to reduce mission fuel burn by optimizing various cycle parameters of the engine (such as bypass ratio, fan pressure ratio, overall pressure ratio and turbine entry temperature). The formalization and coining of 'TERA' occurred during the EU's VITAL project. This project ran between 2005 and 2007 [7], and sought to investigate new direct drive, geared and contra-rotating turbofans to see if they could achieve the goals set out in the European Aeronautics: A Vision for Europe 2020 [8] document, now administered by the Advisory Council for Aeronautical Research in Europe (ACARE). The EU s vision of TERA was to identify the most promising designs at a preliminary stage of the design process for OEM s to investigate further using higher fidelity tools and proprietary data. The project was carried out by a group of different organizations across Europe and it fell to Cranfield University to develop the 'TERA 2020' architecture for this project [3]. This was developed so that these new technologies could be assessed quickly to see if they meet the ACARE goals and then used to help steer the design of the engines and speed up the design process. Further EU funded projects where Cranfield University and TERA were utilized are 'Clean Sky' [9], 'DREAM' (validation of Radical Engine Architecture systems) [10], 'NEWAC' (NEW Aero-engine Core concepts) [11] and LEMCOTEC (Low EMissions Core engines TEChnologies [12]. TERA for Novel Powerplant Selection for an Aircraft Operator This paper describes the conception and development of a TERA framework for an aircraft operator, which would then be used to assess different powerplants suitable for a short to medium range passenger aircraft. The aircraft chosen as the baseline is the Airbus A320 with CFM56-5B/4 engines, which currently form a portion of easyjet's fleet [13]. The idea is to use TERA to assess novel powerplant technologies against different hypothetical future taxation, policy and fuel price scenarios to give an idea of what a promising future powerplant for an operator might be. Figure 2: TERA FRAMEWORK Figure 2 shows the TERA framework that will be described in the rest of the paper, showing the different modules and how they link together. 3

4 Engine Module TURBOMATCH The engine module can be thought of as being the 'heart' of TERA. The engine model was created using TURBOMATCH, Cranfield University's in-house gas turbine performance code. TURBOMATCH was created at Cranfield University and is a modular, '0-D' code where 'bricks' are used to simulate individual components within a gas turbine. Good descriptions about the functionality and use of TURBOMATCH can be found in [14,15]. The outputs from the engine module are used in most of the other modules. The aircraft performance module uses thrust and SFC as its main inputs from the engine module, the emissions module uses the combustor inlet total pressures and temperatures along with TET, while the economics and lifing module uses TET and cooling flow information. Parameter CFM56-5B/4 Data CUEJ56 Delta At 35,000ft, M=0.8, ISA (DP) TOC Thrust (kn) % TOC SFC (mg/ns) Unknown TOC TET (K) Unknown 1,489 - Cruise Thrust (kn) % Cruise SFC (mg/ns) % Cruise TET (K) Unknown 1,430 - At 0ft, M=0, ISA, SLS T-O Thrust (kn) % T-O SFC (mg/ns) Unknown T-O TET (K) Unknown 1,634 - T-O BPR % T-O Mass Flow Rate % (kg/s) Table 1: Comparison of CUEJ56 with CFM56-5B/4 Data from Janes [15 p ] As the table shows, the model CUEJ56 gives good correlation with public data for the CFM56-5B/4, with negligible differences between known parameters. 25 SFC as a Function of Altitude and Mach No. (Constant TET) Figure 3: SCHEMATIC OF CUEJ56 The baseline engine used as a demonstration is based on the CFM56-5B/4 engine currently powering the Airbus A320. The model created in TURBOMATCH, called the 'CUEJ56' (Cranfield University EasyJet 56), is a two-spool turbofan. A schematic of CUEJ56 is shown in Figure 3. The design point of the engine was selected as top of climb and was verified using public domain data from Janes Aero Engines [16]. Table 1 compares the engine model with the real engine parameters. SFC (mg/ns) Mach No. Figure 4: VARIATION OF SFC WITH ALTITUDE AND MACH N O FOR CUEJ56 ENGINE MODEL 0m 1,000m 2,000m 3,000m 4,000m 5,000m 6,000m 7,000m 8,000m 9,000m 10,000m 11,000m 12,000m A secondary verification was also carried out. This involves running the model across a broad spectrum of operating points and determining whether the model behaves as a real gas turbine would. This was carried 4

5 out for the CUEJ56 model and some of the results are described here. Figure 4 shows how the SFC of the CUEJ56 engine model varies with altitude and Mach number while keeping the TET constant. The trends are as would be expected, with SFC increasing as the flight Mach number increases. Three factors influence the performance here: momentum drag, ram compression and ram temperature rise. The figure shows that the engine model behaves as would be expected, thereby increasing the confidence of the model. Aircraft Performance Model HERMES The Aircraft Performance Module, called HERMES is Cranfield University's aircraft performance code. It calculates lift and drag coefficients, take-off distance and other aircraft performance characteristics using geometric and weight data inputted by the user. The user specifies a mission profile, including a main and diversion mission, and combined with output data from TURBOMATCH, HERMES calculates the mission time and either fuel burn or mission range, depending on which one was chosen as the variable in the code. HERMES calculates the lift and drag polars for each stage of the mission, converting this into a thrust requirement for the engines. TURBOMATCH then provides the thrust data for each operating condition, along with the fuel flow for each point. This way the mission fuel burn can be calculated. HERMES and TURBOMATCH therefore work in conjunction, with TURBOMATCH providing the engine performance data needed by HERMES, with HERMES providing the operating condition and thrust requirement data to TURBOMATCH. A detailed description of HERMES is given in [17]. The baseline aircraft being modelled is based on the Airbus A with a MTOW of 73,500kg. The aircraft geometric details were entered into the code using scale drawings from the Airbus website. The aircraft weights were all obtained from Airbus [18] for the particular variant of the aircraft. The baseline aircraft mission profile used is the standard approach given in [19]. To verify the model, a payloadrange diagram was prepared using HERMES and compared with published Airbus data, using the same conditions [18]. The model was run to find the maximum range at the three main points which make up the payloadrange diagram: max payload, max fuel and ferry range. The comparison between the Cranbus EJ320 model and Airbus A320 is shown in Figure 5. The diagram shows a fairly good correlation with the Airbus data at all points, with the maximum error being less than 2%. Payload (t) Airbus A320 Cranbus EJ320 (With CUEJ56 Engines) Payload Range Diagram Comparison Airbus A320 (MTOW 73.5t) and Cranbus EJ Range (km) Figure 5: PAYLOAD RANGE DIAGRAM COMPARING CRANBUS EJ320 AND AIRBUS A320 [18] Emissions Prediction Module - HEPHAESTUS HEPHAESTUS is Cranfield University's emission prediction module and is used primarily for NOx prediction. These predictions are important as NOx emissions are regulated and NOX emissions within the landing and take-off (LTO) cycle are a certification requirement for aero-engines. There has also been some debate over the years for potential future aero-engine NOx taxation, which is able to be assessed within a TERA framework. A summary of emissions prediction modelling by HEPHAESTUS is given below, while a detailed description is given in [20]. 5

6 HEPHAESTUS is able to model NOx emissions using two methods: an empirical based P3-T3 method and a physics based stirred reactor model. CFD is also used for emissions prediction, however it is too computationally expensive to be included within the TERA framework. Work currently being undertaken at Cranfield University using largeeddy simulation (LES) for emissions prediction is being used to inform and improve HEPHAESTUS [22]. The stirred-reactor method will be used for the TERA framework as the P3-T3 method is only suitable for modelling emissions of existing engines where some emissions data is already known. The stirred reactor model will be used as it is able to predict NOx emissions for concept combustors and engines, perfect for assessing the viability of potential future engines on an aircraft. HEPHAESTUS utilizes three types of stirred reactors to determine emissions: a perfectly-stirred reactor (PSR), a series of perfectly-stirred reactors (PSRS) and a partially-stirred reactor (PaSR) explained in [20]. HEPHAESTUS calculates NOx emissions based on three different mechanisms: Thermal NO, nitrous oxide (N 2O) mechanism and prompt NO. A fourth NOx formation mechanism, fuel NO, is not used in HEPHAESTUS as kerosene and other aviation fuels do not contain significant levels of nitrogen. HEPHAESTUS uses as its inputs user entered details of combustor sizing, including the inlet and outlet areas and lengths of the four main zones - flame front, primary, secondary and dilution zones along with the fraction of incoming air that is introduced within each of these zones. The remaining inputs come from either HERMES or TURBOMATCH. HERMES supplies the operating condition data of altitude and ambient temperature, while TURBOMATCH supplies the engine data of fuel and air mass flow rates, and combustor inlet total pressures and temperatures. In order to validate the model, the baseline engine model CUEJ56 was run across a range of power settings from idle to full thrust at sea level and the emissions index (EI) of NOx (grams NOx produced per kilogram of fuel burned) obtained from HEPHAESTUS. The comparison of the results from HEPHAESTUS and the NOx emissions for the CFM56-5B/4 from the ICAO emissions exhaust databank [23] is shown in Figure 6. Figure 6: NOx COMPARISON OF CUEJ56 IN HEPHAESTUS AND CFM56-5B/4 ICAO DATA Figure 6 shows a reasonable correlation between the model and real engine data, particularly at the higher power settings. The data from HEPHEASTUS clearly shows an asymptotic trend, with a smooth curve obtained. The model data does not show the 'spike' at 35% thrust setting that comes from the certification results. The accuracy of the model is considered suitable for the purposes of the TERA, as it is the trends, rather than absolute values, that will be assessed. Weight Estimation Module ATLAS ATLAS is the name of Cranfield University's gas turbine weight estimation model. Weight estimation is an important part of assessing the viability of a future powerplant as an uninstalled engine may give good SFC, but may give poor overall fuel burn performance if it is too heavy compared to a lighter engine with a worse SFC. The version of ATLAS used for this TERA framework has been chosen for its ease of use and speed of calculation rather than outright accuracy, which in any event is very difficult to achieve at an early design stage [24]. The method chosen is based on the work by Sagerser et al [25] and is a 6

7 correlation based method, which provides engine weight and dimension details using basic thermodynamic and geometrical parameters. This method was originally designed for use with vertical take-off and landing (VTOL) engines, but is considered by Jackson to based on good physical principles and suitable for use with modern cruise engines, albeit in a modified form [24]. The method works by calculating the weight of individual components such as the fan, combustor and 'accessories' along with groups of compressor and turbine stages. The model uses inputs from TURBOMATCH including component inlet and outlet total pressures and temperatures, mass flows and stage loadings. This is then combined with user component details such as hub-tip ratios, mean combustor diameter and fan aspect ratio, which can all be determined by a first principle turbo-machinery design method if the engine is a concept. To test the model, the dimensions for the input were scaled from the CFM56-5B schematic in Janes Aero Engines [16] and then combined with thermodynamic data from TURBOMATCH. The model gave a dry weight for the engine as 1775 kg, which doesn t compare favorably with the published dry weight of the engine of 2,381 kg [16]. Jackson [24] also found that the model underestimated the weight quite significantly and put this down to the correlations being based on VTOL engines, which are generally lighter than conventional engines. Accordingly, he applied a correlation factor to take account of this discrepancy. Based on the correlation factor he used for the IAE V2500 engine (which is used as alternative propulsion on the Airbus A320 family of aircraft) of 1.316, the corrected dry weight of the CUEJ56 engine is 2,335 kg, a difference of less than 2% to the published CFM56-5B weight. Economic, Lifing and Risk Module - HESTIA HESTIA, the mythical Greek goddess of the 'home and hearth' is the name given to Cranfield University's economics model. The model is made up of three separate parts: an economic module, a risk module and a lifing module and was created by Pascovici [26,27]. The lifing module uses creep and low cycle fatigue analysis of the high pressure turbine blades and disk to determine the time between overhaul of the engine. The module uses the assumption that these are the primary life limited parts of the engine, thus determine the time between service of the whole engine. The lifing model was Created at Cranfield University by Vigna Suria and a detailed description can be found at [28]. The user specifies the material of the turbine blades and disk from an easily updatable database. The module performs both creep and fatigue analyses and uses the lesser of the two as the time between overhaul and takes into account n appropriate safety factor that can be specified by the user is also included, as well as the effect of thermal barrier coating and the effect of the cooling flow. The user specifies the dimensions of the turbine blade and disc (which is split into hoops), which can be obtained from schematics for existing engines, and estimated through analysis for a concept engine. Mission data from HERMES, including mission segments (cruise, take-off, descent e.t.c.), ambient temperatures, and LTO cycles per day is used as inputs to the module, along with gas temperatures, rotational speeds, and cooling flows from TURBOMATCH. The risk module utilizes a Monte Carlo simulation technique to assess the effect that uncertain factors such as fuel price, inflation, interest rates, aircraft downtime, maintenance labor costs and emission taxes have on the net present cost (NPC) of the engine and aircraft over its lifetime. The module analyses these factor's influence over 10,000 scenarios, and fits it to a Gaussian distribution to assess the most likely scenarios. The economics module combines the work of Roskam [29] and Jenkinson et al [19]. The module estimates the direct operating costs (DOC) of the 7

8 aircraft and mission according to the parameters shown in figure 7. that provides the wrappe' for the different modules. isight allows for the different modules to interact by allowing the results from one model to be specified as the inputs of another. This allows for many loops of TERA to be run very quickly in order optimize for a number of different objectives, such as lowest mission fuel burn, lowest NOx emissions or lowest ownerships costs under a wide range of different scenarios, such as changing fuel prices, emissions taxation and changing labor rates. Conclusions Figure 7: Components of Direct Operating Costs [19] The figure can be interpreted as having three main parts: standing charges, flight costs and maintenance costs. Standing charges are the costs incurred by an airline during their business of operating aircraft, but which are not directly linked to a mission. These overheads include interest charges on loans, cost of capital, aircraft depreciation or leasing costs and insurance costs. Flight costs consist of mission incurred costs and include crew wages, fuel costs and airport fees, and potentially CO 2 and NOx taxation. Maintenance costs include labor costs for maintaining the aircraft and engines as well as the cost of spare parts. The economics module uses the equations given by Roskam [29], with updated factors to account for the trends of costs for modern engines. The user specifies various parameters such as crew and maintenance labor rates, interest rates, fuel costs and potential emission taxation levels. Inputs from other modules include maximum take-off weight, block fuel burn and range from HERMES; Take-off thrust from TURBOMATCH and; engine emissions from HEPHAESTUS. Optimizer - isight isight is a commercial optimizer tool from Dassault Systemes [30] In this work, a Techno-economic and environmental Risk Analysis framework has been described. TERA can be used in a wide variety of ways to aid the early design of gas turbines, and potentially other technologies, to find an optimum potential solution to a wide range of different potential scenarios. The TERA framework described in this paper is intended to be used to assess potential future aircraft powerplants under a different range of legislation, taxation and fuel price scenarios to find which of those engines might offer the lowest overall cost of running and ownership for an aircraft operator. The TERA framework described consists of five different modules - an engine performance module (TURBOMATCH), an aircraft performance module (HERMES), an emissions prediction module (HEPHAESTUS), a weight estimation module (ATLAS), and an economics, risk and lifing module (HESTIA). These modules are brought together in a commercial optimizer, isight, which can be used to optimize for specific goals and scenarios. Acknowledgments This study has been carried out with financial help by a DTA award from easyjet Airline Company Limited and the Engine and Physical Sciences Research Council (ESPRC), for which the author is eminently grateful. 8

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