2.2.1 Design assumptions

In this chapter, the assumptions for the input data are described in more detail and are categorised as general, operation and maintenance assumptions. If not mentioned otherwise, the inputs are identical for both the baseline and HLFC aircraft configurations.

General

The input data in the general category are:

• • The entry into service date is 01.01.2030 and both aircraft are operated for 20 years.

• • Both aircraft are sold at the end of their operational life for a residual value of 10%.

• • Both aircraft are bought, i.e. not leased, by the operator. The financing structure is based on Clark [Reference Clark21].

• • The base year for the assessment is 2023, i.e. all monetary values are expressed in USD for the year 2023.

• • The HLFC system is activated during cruise only.

• • The HLFC system requires annual maintenance but does not need to be replaced during the 20 years of operational life.

Operation

The operational schedule is shown in Fig. 3. This route network is based on an analysis of Lufthansa’s A330-343 fleet as of the year 2020. As the network shows, the aircraft are based in Frankfurt (FRA), Germany and fly to destinations in the USA, Africa, the Middle East and a few in West Asia. This schedule is flown randomly while respecting the relative frequencies of each origin and destination pair, which leads to a realistic distribution of flown missions throughout the lifecycle. The average distance and average flight time are 3,410 NM and 7:45hrs, respectively. This results in about 657 flight cycles per year, which is relatively close to the actual operation of most of Lufthansa’s A330-343. Note that the payload (incl. passengers) is treated as an uncertainty and hence described later (see Chapter 2.2.2). Furthermore, it is assumed that the aircraft only carry passengers, i.e. the airline’s operational revenue stream comes only from sold fares as well as ancillary revenues.

As for the HLFC aircraft, it is assumed that the airline carries an extra amount of contingency fuel for cases of unforeseen losses of laminarity. The amount of carried contingency fuel is treated as an uncertainty and is also described later. Note that the additionally carried mass affects the fuel performance of the HLFC aircraft negatively, but allows for a more realistic assessment.

Figure 3.

Route network, based on Lufthansa’s A330-343 fleet as of 2020, depicted as great circles.

Table 1.

Used maintenance schedule, based on data from Aircraft Commerce papers [23, 24] for the Airbus A330.

^*L: line maintenance, B: base maintenance, H: heavy component maintenance.

Maintenance

For the maintenance schedule, it is assumed that the operator uses the maintenance plan of the Airbus A330, which consists of line maintenance (transit-checks, daily-checks, and weekly-checks), base maintenance (A-checks and C-checks with a repeating pattern of 8 years), and heavy component maintenance (e.g. for tires, landing gear, thrust-reversers, as well as engine shop visits). This maintenance schedule is summarised in Table 1 alongside the cost and assumed downtime. These maintenance events are automatically triggered by the simulation and contribute to a more realistic assessment – despite being identical for both aircraft designs and ultimately cancelling each other out when calculating the ${{\Delta }}$ values. Unscheduled maintenance is calculated as a time-dependent function where the ratio of unscheduled maintenance cost to scheduled maintenance cost varies from 1.0 (in the first operational year) to 1.75 (in the last operational year), according to Suwondo [Reference Suwondo22].

As for the HLFC maintenance, only an estimation of the expected cost can be given at this point due to the lack of operational data (e.g. frequency of clogged surfaces or average/standard deviation of compressor failures). To account for the uncertainty in the estimation of the HLFC maintenance, experts from the advisory board have been interviewed. More information follows in the next chapter.

2.2.2 Uncertainties

As is common in prospective assessments of aircraft and technologies at this stage of the development, uncertainties are inevitable and should be considered and quantified where possible. When data for uncertainty quantification (UQ) are available, probabilistic techniques such as probability density function (PDF) fitting and subsequent quasi-random sampling are employed. If data are scarce or entirely unavailable, subject matter experts (SMEs) have been interviewed in order to utilise their knowledge. Both approaches are described below.

TSFC penalty during HLFC operation

To operate, the HLFC system requires electrical energy, which is drawn from the engine generators during cruise. This introduces a thrust specific fuel consumption (TSFC) penalty, which has to be outweighed by the gains in drag reduction for a successful HLFC aircraft. The quantification of said TSFC penalty depends on a variety of factors, out of which the electrical efficiencies of the components play a major role. The basis for this quantification is the suction power ${{\text{P}}_{{\text{suc}}}}$ , for which an aerodynamic analysis was conducted. In this analysis, the flight level (FL), cruise Mach number (Ma) and lift coefficient ${{\text{C}}_{\text{L}}}$ have been varied and the suction power has been computed for each combination applying the suction chamber design programme (SCDP) by Schrauf [Reference Schrauf25]. Herein, the suction power is defined as the power necessary to move the air through both, the micro-perforated suction surface and the throttle holes as well as providing a realistic compressor outflow velocity. Table 2 shows an excerpt for Ma = 0.83, out of which an average suction power of 48 kW was obtainedFootnote ¹ . The same approach was taken for the other two cruise Mach numbers 0.81 and 0.83, for which the average suction powers are 56 kW and 49 kW, respectively. This averaging was needed due to the way the engine performance decks are created, out of which the fuel performance is calculated during the mission simulation in AMC.

Table 2.

Excerpt of the aerodynamic analysis for the estimation of the suction power ${{\text{P}}_{{\text{suc}}}}$ per half wing for Ma = 0.83.

^* Service ceiling of FL430 was extrapolated using linear regression due to missing data.

To incorporate uncertainty, the expected electrical efficiencies of the compressors, inverters, generators and power lines are used. Based on a compressor feasibility study, which places its efficiencyFootnote ² at ${\eta _{{\text{compr}}}} \approx 0.55$ , along with publicly available data for the remaining components [Reference Pohya, Wicke and Kilian26], the overall suction system efficiency uncertainty is:

(1) \begin{align}\eta_{\text{suc}} = \prod \eta_i = \underbrace{(0.55)}_{\text{compressor}} \cdot \underbrace{(0.81 \ldots 0.87)}_{\text{generator}} \cdot \underbrace{(0.95)}_{\text{power lines}} \sim \mathcal{U}(0.424, 0.455).\end{align}

Due to the lack of more precise information regarding the generator’s efficiency, a uniform distribution is assumed, which, in turn, leads to a uniform distribution for ${\eta _{{\text{suc}}}}$ . With this, the shaft power offtakes (i.e. those drawn from the generator) can be calculated using

(2) \begin{align}{P_{{\text{HLFC}}}}\!\left( {Ma} \right) = \frac{{{P_{{\text{suc}}}}\!\left( {Ma} \right)}}{{{\eta _{{\text{suc}}}}}}.\end{align}

Table 3 shows the ranges of shaft-power offtakes considering the uncertainties in the electrical efficiencies. These have then been translated to TSFC penalties using literature values for an engine suitable for the Airbus A330, with the range from 0.5 to 0.7 % per 100 kW [Reference Young27]. These represent the final ranges that are implemented in the lifecycle-based performance assessment. More specifically, the Min. column of the TSFC penalty is used to interpolate from the so-called “advantageous” scenario. In the same manner, the Mean and Max. columns are used for the intermediate and disadvantageous scenarios, respectively.

Table 3.

Cruise Mach specific shaft-power offtakes.

Mach number during cruise and its effect on the laminar efficacy

The cruise Mach number affects the laminar area of the HLFC aircraft and consequently its fuel efficiency. To integrate this into the assessment, two steps needed to be performed. The first step was to determine the variations in real cruising speed. Secondly, the effect of this variation on the laminarity had to be quantified. For the first step, representative flight trajectories of an Airbus A330-300 aircraft from FlightRadar24 [28] were analysed. As these only provide the current time, altitude, latitude and longitude (and hence its ground speed) of an aircraft, the cruise Mach number is not readily accessible. Therefore, these trajectories were coupled with the weather database from the ECMWF [Reference Hersbach, Bell, Berrisford, Biavati, Horányi and Muñoz Sabater29] to calculate the present wind speed, temperature, pressure and speed of sound that the aircraft experiences. The resulting Mach number histogram is depicted in grey in Fig. 4 (top left). From this distribution, it can be observed that the A330 flies with an average speed close to its design cruise Mach number of 0.82. Since the XRF1 as well as the HLFC aircraft have a design Mach number of 0.83, this distribution has been shifted by +0.01 (see blue histogram).

Figure 4.

Histogram of calculated cruise Mach number of an Airbus A330-343 (top left), high-fidelity CFD results with laminar efficacy values (compared to itself when turbulent), colour coded by the skin friction coefficient (right), and the regression model obtained from it (bottom left) [Reference Pohya, Wicke and Kilian26].

For the second step, i.e. the effect of different cruise speeds on laminarity, the results of a high-fidelity computational fluid dynamics (CFD) simulation of the configuration were used. Here, cruise Mach number have been varied and the impact on the transition and drag reduction potential was calculated. The right plot in Fig. 4 illustrates the spanwise transition, whereas the bottom-left plot depicts the results of the Mach number variation on the laminar efficacy ${\eta _{{\text{L}},{\text{mach}}}} = f\!\left( {Ma} \right)$ . Here, the drag reduction at Ma = 0.83 was set to the reference point and hence normalised to 100 %. As the data shows, lower Mach numbers result in a significant loss of laminar efficacy, whereas higher speeds seem to be favourable. For more information on this parameter, consider Pohya [Reference Pohya, Wicke and Kilian26].

In the lifecycle simulation, one cruise Mach number is sampled according to the aforementioned distribution before each flight. Both aircraft fly to their destination with this cruise speed. To quantify the impact of the laminar efficacy on the fuel performance, the degradation due to cloud encounter (described next) is needed.

Time in cloud

The degradation of laminarity due to cloud encounter at high altitudes has been proven in various flight tests. For this analysis, the statistics of the relative time in cloud ${t_c}$ obtained from flight test data were used. Before each simulated flight, one value for ${t_c}$ is sampled according to the distribution shown in the top of Fig. 5 and then translated using the linear regression model ${\eta _{{\text{L}},{\text{cloud}}}} = f\!\left( {{t_c}} \right)$ depicted at the bottom. This results in an overall laminar efficacy of

(3) \begin{align}{\eta _L} = {\eta _{L,{\text{mach}}}} \cdot {\eta _{,{\text{cloud}}}}.\end{align}

Figure 5.

Histogram of average time in cloud en route (top) and its impact on the laminar efficacy (bottom) [Reference Pohya, Wicke and Kilian26].

The fuel performance of the HLFC aircraft is then calculated using an interpolation between the laminar and non-laminar aerodynamic performance map, where ${\eta _L}$ is used as the interpolation coefficient.

Passenger load factor

The number of passengers carried varies with the airline, route, season and other factors. For this project, published data from the US Department of Transportation (DoT) [30] has been used, which averages to about 80 %. Before each flight, a passenger load factor is sampled according to the distribution shown in Fig. 6. Both aircraft then fly their route with the same passenger load factor.

Figure 6.

Histogram of average load factors based on data between 2002 and 2019 from the US DoT [30].

Note that the passenger load factor is applied equally to both the reference and HLFC-equipped aircraft to ensure a fair comparison. While medium- to long-term changes in passenger demand may affect overall operations, they are assumed to influence both aircraft similarly and are therefore treated consistently in this assessment.

Carried contingency fuel for the HLFC system

Operators of HLFC aircraft may wish to include some additional contingency fuel ${m_{{\text{fuel}},{\text{cont}}}}$ to account for unforeseeable losses of laminarity, e.g. due to an unexpectedly high time in cloud or failures of components. Depending on the operators’ preferences, this contingency can range from zero (risk-tolerant) to a maximum (risk-averse). The latter is likely to be equal to the amount of fuel that the HLFC system would save on the given route. To model this uncertainty, an interpolant ${c_{{\text{int}}}}$ between these two extreme values is introduced and varied uniformly from 0 to 1, i.e.:

(4) \begin{align}{m_{{\text{fuel}},{\text{cont}}}} = {c_{{\text{int}}}} \cdot \left( {{m_{{\text{fuel}},{\text{turb}}}} - {m_{{\text{fuel}},{\text{lami}}}}} \right)\;\;\;\;{\text{with}}\;\;\;\;{c_{{\text{int}}}} \in \left[ {0,1} \right]\end{align}

Here, ${m_{{\text{fuel}},{\text{turb}}}}$ refers to the planned block fuel of the HLFC aircraft if the HLFC system is turned on but does not work, e.g. because it is fully contaminated. ${m_{{\text{fuel}},{\text{lami}}}}$ refers to the block fuel of the HLFC aircraft if there is no loss of laminarity. Because this uncertainty is more of an airline specific value, rather than something that is decided anew day to day, ${c_{{\text{int}}}}$ is not sampled before each flight like the previous uncertain parameters. Instead the aforementioned scenarios are used, where a value is fixed for each lifecycle simulation:

1. 1. Disadvantageous: ${c_{{\text{int}}}} = 1.0 \Rightarrow {m_{{\text{fuel}},{\text{cont}}}} = {m_{{\text{fuel}},{\text{turb}}}} - {m_{{\text{fuel}},{\text{lami}}}}$ . This means that the operator always carries an amount of contingency fuel that is equal to the maximum savings of the HLFC system. This in turn reduces the overall fuel efficiency due to the additional mass.

2. 2. Neutral: ${c_{{\text{int}}}} = 0.5$ . Here, the operator carries half of the maximum savings.

3. 3. Advantageous: ${c_{{\text{int}}}} = 0.0 \Rightarrow {m_{{\text{fuel}},{\text{cont}}}} = 0$ . Here, no additional contingency fuel is carried. Thus, the operator assumes that the generally carried contingency fuels are sufficient for any expected losses.

Note that ${m_{{\text{fuel}},{\text{cont}}}}$ is treated as a generic added mass in the simulation. There is no advanced en-route fuel planning algorithm implemented.

Future fuel price

The price per unit of fuel is known to vary greatly and predictions of the future fuel price development are naturally uncertain. The US Department of Transportation therefore uses multiple scenarios in their annual energy outlook (AEO) [31] considering various market assumptions. Figure 7 shows the so-called reference case as well as two boundary case scenarios named “low oil price” and “high oil price” provided by the AEO alongside the historic development. These fuel price progressions are therefore used for the lifecycle simulation:

1. 1. Disadvantageous: Ranging from 0.52 USD/kg (1.60 USD/gal) in 2030 to 0.58 USD/kg (1.77 USD/gal) in 2050.

2. 2. Neutral: Ranging from 0.90 USD/kg (2.77 USD/gal) in 2030 to 1.01 USD/kg (3.10 USD/gal) in 2050.

3. 3. Advantageous: Ranging from 1.57 USD/kg (4.82USD/gal) in 2030 to 1.79 USD/kg (5.49 USD/gal) in 2050.

Figure 7.

Historic and projected kerosene price with low, reference, and high oil price scenarios (taken from the US Energy Information Administration [31]).

Uncertainties without plentiful data

As opposed to the previously described uncertain parameters, there is little to no reliable data for estimating the increase in maintenance cost and aircraft price. These are therefore modelled with a new approach that is based on evidence theory. For this approach, SMEs have been interviewed and asked to provide so-called belief values for intervals in which the value of interest may lie in. In other words, the experts’ belief in the parameter is elicited and used to create a distribution of potential values. More information on this approach can be found in Pohya [Reference Pohya, Wicke and Wende32].

The two uncertainties treated with evidence theory are the increase in maintenance cost and the aircraft price due to the introduction of the HLFC system. The maintenance cost increase has two components: (a) the scheduled portion, expressed in USD per flight hour (USD/FH), and (b) the ratio of unscheduled to scheduled HLFC maintenance cost, expressed as a percentage. The aircraft price increase is defined in USD/kg. For each element, three SMEs provided intervals and their associated beliefs, which are visualised in the top part of the figure.

In the top plots in Fig. 8, the horizontal axis represents the range of possible values for each parameter: the scheduled HLFC maintenance cost, the ratio of unscheduled to scheduled maintenance cost and the aircraft price increase. The vertical axis shows the belief assigned to each interval by the SMEs. Each shaded box corresponds to an interval provided by one SME, where the width of the box indicates the range of possible values the SME considered plausible, and the height reflects the degree of belief that the true value lies within this interval. A higher box signifies a stronger belief, meaning more samples are drawn from that interval during the uncertainty modelling.

Figure 8.

Increase in maintenance cost and aircraft price, treated with evidence theory.

The lower plots in Fig. 8 show histograms of the sampled values resulting from the belief-based allocation. Here, the horizontal axis shows the possible values that were sampled based on the experts’ inputs, while the vertical axis represents the relative frequency of these sampled values. This part of the figure illustrates how the distribution of samples reflects the SMEs’ beliefs and the uncertainty intervals they provided.

All three of these elements are assumed to be fixed for one lifecycle simulation, i.e. they are not assumed to vary from year to year within a lifecycle. Therefore, we follow the scenario approach outlined before, defining these three cases:

1. 1. Advantageous: The scheduled maintenance cost is assumed to be 5 USD/FH. The unscheduled maintenance cost is assumed to be 100% of the maintenance cost, adding another 5 USD/FH, which results in total HLFC maintenance cost of 10 USD/FH. The cost of HLFC is assumed to be 1,410 USD/kg, whereby the kg value represents the mass difference between the HLFC aircraft and the baseline. As will be discussed later, this mass difference computes to 1,330 kg, resulting in an aircraft price increase of 1.9 million USD.

2. 2. Neutral: The scheduled maintenance cost is assumed to be 8 USD/FH. The unscheduled maintenance cost ratio is 140%, adding 11 USD/FH, which results in total maintenance cost of 19 USD/FH. The HLFC cost is assumed to be 1,750 USD/kg, resulting in an aircraft price increase of 2.3 million USD.

3. 3. Disadvantageous: Here, the scheduled maintenance cost is 10 USD/FH and the unscheduled ratio is 180%, adding 18.5 USD/FH, resulting in total HLFC maintenance cost of 29 USD/FH. The HLFC cost is 2053 USD/kg, resulting in an aircraft price of 2.7 million USD.

These values have been defined using the lower third, centre and upper third of the intervals of Fig. 8 using the ${0^{{\text{th}}}}$ , ${33^{{\text{rd}}}}$ , ${66^{{\text{th}}}}$ , and ${100^{{\text{th}}}}$ percentile of the sample space, resulting in a weighted and more representative split. Within these thirds, the average value was used for the scenario values.

It should be noted that even with the estimation approach presented, determining the increase in aircraft price remains challenging due to the confidential and undisclosed nature of negotiations between manufacturers and airlines. This underscores the inherent difficulty of accurately estimating aircraft prices, despite efforts to use consistent and transparent assumptions.

Table 4.

Top-level aircraft requirements of the reference aircraft [Reference Fröhler, Häßy and Abu-Zurayk13].

3.3.1 Geometry

The design of the HLFC wing was based on the specifications of the AIRBUS XRF1 aircraft, as detailed in Chapter 3.1. Figure 10 shows a perspective view of the HLFC aircraft, the basis for CFD calculations and far-field drag analysis. For the shape design, a cruise flight condition with ${{\text{M}}_\infty }$ = 0.83, ${{\text{C}}_{\text{L}}}$ = 0.5 and a flight altitude of 36,000 ft was considered. Off-design scenarios were also investigated, including a variation of ${{\text{M}}_\infty }$ (0.83 $ \pm $ 0.02), ${{\text{C}}_{\text{L}}}$ (0.5 $ \pm $ 0.05) and flight altitude (36,000 ft $ \pm $ 3000 ft), resulting in a total of nine different flow conditions. In terms of wing shape modifications, the changes were limited to the outer wing, outboard of the engine position (see Fig. 10, shaded area in red). The wing thickness remained unchanged in order to maintain the necessary fuel volume and avoid detrimental effects on the wing’s structural integrity. The leading edge incorporated four HLFC suction panels, each with an individual length of 5 m (see Fig. 10, shaded area in green). The segment length was limited due to maintenance and handling considerations. The wing geometry for the current study was derived from this shape design optimisation, as described by Streit et al. [Reference Streit, Kruse, Kilian and Petropoulos35]. In particular, modifications were made to the aerofoils to optimise laminar flow conditions over the wing surface.

Figure 10.

Depiction of the HLFC outer wing (red) and leading-edge suction panels (green) [Reference Streit, Kruse, Kilian and Petropoulos35].

3.3.2 Aerodynamics

Subsequently, the ADE is calibrated to match the aerodynamic performance provided by the findings from Streit et al. [Reference Streit, Kruse, Kilian and Petropoulos35]. Table 6 lists the aerodynamic performance without the horizontal tail plane (HTP) at cruise flight condition of the baseline and the HLFC aircraft. It shows an L/D ratio of 20.5 for the baseline and is compared to the other configurations. When changing from the baseline to the HLFC aircraft, two things can be observed. First, the HLFC aircraft with a fully turbulent outer flow (e.g. the HLFC system not operating) reduces the aerodynamic performance by 1.1 %, mainly due to the increase in wave drag, inherent to HLFC aerofoils at high Mach numbers. Once the HLFC system is turned on, the wing friction drag is reduced together with the viscous pressure drag due to a downstream shift of transition. The transition location as well as the aerodynamic drag is computed using the RANS solver Tau [Reference Rossow, Kroll and Schwamborn37] coupled with the linear stability method LILO [Reference Schrauf38]. The aerofoil shape of the baseline wing has been tailored to meet the requirement of maximising the laminar benefits of HLFC. Particular attention was paid to the pressure gradient across the wing box downstream of the suction section to maintain laminar flow while limiting the increase in wave drag, especially at higher Mach number flight conditions. More details on the design philosophy can be found in Streit et al. [Reference Streit, Kruse, Kilian and Petropoulos35]. In order to have a more realistic model of the aerodynamics, the details of the suction segments were considered. The suction area was adjusted to the actual extent of the suction surface, which results in turbulent wedges originating from the spanwise interface between suction panels (see Fig. 11). This results in an L/D ratio of 21.2 for the HLFC aircraft with wedges and thus, an increase in aerodynamic performance by 3.5 %. Note, that the detrimental effect of turbulent wedges at suction segment boundaries is a merely integrational aspect and depends in magnitude on design specifics, such as chordwise laminar extent, number of suction segments and segment interface layout.

Table 6.

Aerodynamic performance comparison between the baseline and HLFC aircraft configuration (without HTP) at cruise flight condition ( ${{\text{M}}_\infty }$ = 0.83, ${{\text{C}}_{\text{L}}}$ $ \approx $ 0.5, flight altitude of 36,000 ft).

Figure 11.

Skin friction coefficient for the HLFC wing’s upper side. Laminar areas coloured blue [Reference Kilian and Schrauf39].

3.3.3. Mass

To fully assess the performance of the HLFC technology, it is necessary to integrate the HLFC system into a more realistic aircraft model that takes into account all relevant factors. This requires a holistic approach that considers the impact of the HLFC system on the entire aircraft. To account for the changes in the wing assembly resulting from the integration of the HLFC system, an upscaling strategy is used, as introduced by van de Kamp et al. [Reference van de Kamp, Kleineberg and Schröder12]. This upscaling strategy was necessary because only a detailed mass breakdown of the outer wing with the HLFC system integrated was available. The strategy involves a linear extrapolation of the geometry and laminate thickness of the outer-wing. Assuming that the changes introduced by the HLFC system are relatively small, this approach provides a rough estimate of the outer wing mass distribution. The outer wing is simply tapered and a homogeneous laminate thickness distribution is assumed, with no areas of massive local load introduction such as landing gear or pylon attachments. The process starts with the full wing of the baseline aircraft, which is scaled down to the half wing and then to the outer wing by linear extrapolation of the geometry. At this stage, the available data for the outer wing, incorporating structural changes from the HLFC system, is used and can be exchanged between the baseline and HLFC aircraft. Finally, the outer wing of the HLFC aircraft, with integrated structural modifications, is upscaled back to the half wing and full wing to provide aircraft level mass information. Overall, the integration of the HLFC system into a realistic aircraft model using an upscaling strategy is crucial to assess the effectiveness of the technology at a holistic aircraft level. However, a more detailed wing design would be required to reduce the uncertainties.

For the estimation of the complete HLFC wing, some additional assumptions had to be made compared to the upscaling strategy by van de Kamp et al. [Reference van de Kamp, Kleineberg and Schröder12] in order to arrive at the total wing mass. Table 7 provides an overview of the total wing mass breakdown and the change from the baseline to the HLFC aircraft. Since the design and mass estimation of the ribs is highly complex and depends not only on the wing loading but also on the landing gear or pylon attachments, the rib mass is assumed to be equal to the baseline mass and is therefore not changed. For the other components of the wing box, the upscaling strategy is used. In addition, the fixed leading-edge component is split into the leading edge structure and the suction glove, which represents the microperforated titanium skin. The leading-edge structure is scaled according to the upscaling strategy, while the suction glove is added to the HLFC aircraft model. The movable leading edge was changed from a slat system to the Kruger flap high-lift system. The evaluation of the Kruger flap system was critical to the assessment as it is a new system introduced to the HLFC aircraft. In this work a Torenbeek method [Reference Torenbeek40] is applied to estimate the mass of the Kruger flap system. The Kruger flap mass is estimated by

(5) \begin{align}{m_{Kruger}} = \frac{{220}}{g} \cdot {S_{Kruger}} \cdot {f_{Calibration}},\end{align}

with a constant specific weight divided by the gravity $g$ , as well as the planform area of the Kruger flap ${S_{Kruger}}$ in the retracted position and a calibration factor ${f_{Calibration}}$ . The process of obtaining the calibration factor started with the reference aircraft. The known wing mass of the reference aircraft was used to calibrate the method for calculating the total wing mass. Next, a technology factor was applied to the calibration factor to reflect the projected technology level of the baseline aircraft, assuming an EIS in 2035 (see Chapter 3.2). This updated calibration factor was then used to estimate the mass of the Kruger flap. The results show that the mass of the total movable leading edge is very similar to the baseline. As the other components remain unchanged with respect to the HLFC system, a total wing mass increase of 941 kg is estimated.

Table 7.

Wing mass breakdown estimation from the Baseline to HLFC Aircraft in kg.

3.3.4 Systems

The next step is to install all the systems associated with the introduction of the HLFC system, as described in detail by Wahlich et al. [Reference Wahlich, Bismark, Radestock, Menchel, Milot and Cruz41]. To maintain laminarity on the upper surface of the outer wing, the compressors are used to partially suck in the airflow, which reduces the friction drag on the wing. The compressors ensure a negative pressure gradient between the ambient air on the suction surface and the sealed chambers inside the leading edge. An external company, Safran Cabin, with experience in the design and certification of compressors used in civil aviation, performed a preliminary design of a suitable compressor model according to the specifications. In general, the suction area spans 20 m of the outer wing, which is divided into four equally spaced segments, as shown in Fig. 12. The mass flow of each compressor has been estimated from the aerodynamic requirements and is described in more detail in Streit et al. [Reference Streit, Kruse, Kilian and Petropoulos35]. According to the compressor designed by Safran Cabin, the expected mass is up to 11.4 kg per unit (compressor and attached inverter). However, additional mounting elements have to be taken into account and an additional margin of 10 % is assumed, resulting in a total of 13 kg per unit. Therefore, the sum of the seven compressors per half span results in an additional mass of 176 kg.

Figure 12.

Rib-integrated compressors to provide suction for the HLFC system [Reference van de Kamp, Kleineberg and Schröder12].

In order to accommodate the HLFC system in the narrow leading edge, the concept of a suction rib was developed (see Fig. 13). In addition to supporting the compressor and sealing the suction system, it can also fulfil the structural requirements of a classic rib. The compressor itself is accessible through a maintenance hatch in the suction rib after the Kruger panel has been deployed, making maintenance straightforward. During operation, the suction system has to achieve a certain pressure in the chamber under the titanium skin. Air is drawn in from the compressor inlet, forcing it to flow through the micro-perforated titanium skin of variable porosity and ventilation holes in the CFRP structure. These flow resistances in turn create a defined pressure drop, the characteristics of which are known from tests and analyses. In this way, the suction distribution along the skin can be adjusted as required by simply controlling the mass flow of the compressor. Compression ratio and mass flow can thus be derived for several operating points, allowing the suction power to be estimated.

Figure 13.

Schematic diagram of the HLFC suction system.

In addition to the development of the HLFC system, another system component has been replaced. The wing ice protection system (WIPS), as the name suggests, is used to protect the wing from ice build-up, which can lead to a degradation of aerodynamic lift and a disturbance of laminar flow. The most common conditions that require the use of WIPS include flying through clouds with temperatures at or below freezing, flying in visible moisture such as rain or snow, or flying in high humidity areas where supercooled water droplets may be present. In order to allow for a proper system design, the certification regulations clearly define the icing conditions that need to be covered. WIPS functionality is conventionally provided by the bleed air system, but an inductive WIPS (iWIPS) has been introduced due to the limited space available in the leading edge. Furthermore, the use of a non-contact induction-based heating system allows for a merely undisturbed HLFC suction surface and provides an efficient maintenance approach by being implemented on independent, individually replaceable modules. From van de Kamp et al. [Reference van de Kamp, Kleineberg and Schröder12], the mass of the iWIPS is estimated by the coil structure shown in Fig. 14. The iWIPS works on the principle that an alternating current in the coils creates a magnetic field, which induces eddy currents in the electrically conductive titanium skin. This causes the skin to heat up as a result of the Joule effect. The alternating current is provided by frequency converters. The coil layout was optimised using numerical simulations and tests to match the local heating power requirements in the titanium skin along the chord while ensuring the best efficiency. In the end, there are three coils with different inductance values, each consisting of two layers of Litz-wires. The coils are held in place by 3D-printed support elements attached to the CFRP structure. A coil contains 60 wires with a cross-section of 4  ${{\text{mm}}^2}$ and a material density of 8.69 mg/ ${{\text{mm}}^2}$ for copper. With an additional margin of 20 % and the inverter, a mass of 50 kg is estimated for a 5 m segment with three coils. For the complete wing, two segments are heated on each side, giving a total system mass of 200 kg.

Figure 14.

Integration of inductive wing ice protection system (iWIPS) into the outer wing leading edge.

3.3.5 Engine

The HLFC system and WIPS require electrical power to operate. HLFC systems also add mass to the overall configuration leading to changes in thrust requirements. The resulting demand must be provided by the engine and has implications for its design and fuel consumption. Consideration of the changes in engine requirements is critical in assessing overall system performance. The HLFC system not only introduces a mass penalty due to the change in wing assembly and additional system components such as the rib-integrated compressors, but also increases the power requirement of the engines to drive the compressors as mentioned in Chapter 2.2.2. In addition, the change from a bleed air operated WIPS to an electric iWIPS, which requires shaft power take-off instead of bleed air, changes the engine power requirement.

The power requirements of the iWIPS depend on the weather conditions or the operating condition of the aircraft. Typically, a WIPS is active at lower altitudes up to approximately 25,000 ft. Above this altitude, the air is generally too dry for ice to form on the wings, and the use of the WIPS is not necessary. The power requirement of the iWIPS at aircraft level is estimated to be 100 kW to 120 kW depending on the atmospheric conditions. For the purpose of this study and comparability between the baseline and the HLFC aircraft, the design case with a shaft power take-off of 120 kW is used. For simplicity, it is assumed that iWIPS is active during the take-off, climb, descent and approach and landing segments and is only deactivated during the cruise segment.

Therefore, the engine design requirements for HLFC aircraft have changed from bleed air to more shaft power take-off, but the thrust requirements have also changed due to the change in aircraft mass, while the thrust-to-weight ratio remains constant. Table 8 provides an overview of the required engine off-takes for the baseline configuration and different operating conditions. An engine design is derived for the HLFC configuration and performance maps for all relevant off-take scenarios are provided.

Table 8.

Engine offtakes overview for different operating conditions and engine settings.