Abstract
One of the most challenging next-generation issues is examining a novel and sustainable aircraft to reduce emissions and fuel burn. IATA addressed these issues in collaboration with the EU, GARDEN, and Clean Sky programs to tackle the challenges using retrofit design and upgraded systems. This study aims to create a sustainable aircraft design to solve the fuel burn issue by minimizing the maximum take-off weight (MTOW). An optimized wide-body aircraft is established by selecting twelve aerodynamic design variables and fourteen flight mission constraints (retrofit, operational performance, and stability). To develop a sustainable wide body aircraft structure, this study proposes a hybrid design optimization algorithm combined with the multicriteria decision-making (MCDM) technique. The CRITIC-WASPAS technique is used to obtain the baseline aircraft, and the optimal design of the baseline aircraft is achieved by solving the proposed nonlinear constrained optimization model. The selected baseline aircraft was compared with the optimized result to validate and determine the robustness of the objective function. The findings reflect that the formulation has improved the MTOW, empty weight fraction, fuel weight, and performance by 3.75%, 28.38%, 2.06%, and 8.63%, respectively.
1. Introduction
The International Civil Aviation Organization (ICAO) postulated strict sustainability targets for the new transport aircraft, significant reduction in emissions and enhancement in fuel economy. Recently, NASA scientists and AIAA researchers developed a concept on the total technology readiness level (TTRL) framework to boost productivity with less risk and more readiness through innovations. But the pandemic indeed influenced the global gross domestic product (GDP) and posed a threat to the airline’s revenue. The latest study says the global aircraft manufacturing market (GAMM) will be worth USD 626.25 billion by 2030. The report forecasts for future market growth demand are reviving by increasing production to meet resurgent demand for wide-body jets [1]. In the year 2015, Boeing identified a market niche for a new airplane configuration called middle-of-the-market (MoM) aircraft. The concept of MoM was gleaned from an advanced twin-aisle medium range (ATMR) from 5,000 km to 7,500 km and a seating capacity of 180 to 280. Johnsson [2] reported that executing a wide-body configuration can produce 30% more revenue and 40% lower transit expenses than narrow bodies. Likewise, other researchers undergoing various TRL and have proven to improve aerodynamic performance by integrating advanced technologies. Aside from the generic concern of designing an aircraft incorporating new technologies and combinations into fuselage design is a challenge all on its own. The study reveals that aircraft fuselage produces 40% to 50% of the overall drag. A conceptual fuselage design and optimization, including a comprehensive fuselage layout, are more complicated than a detailed main wing design or wing and empennage integration. The additional inescapable challenge is to optimize the seating capacity and meet the airworthy performance. To improve aerodynamic efficiency, several N + 3 aircraft designs such as blended wing body (BWB) and twin-fuselage (TF) aircraft, and other novel aircraft motives to improve aerodynamic efficiency through innovations [3]. This study motivates to improve performance by reducing the structural weight of an aircraft. The selection of aircraft was based on the future market segment. According to Boeing’s statements suggest that wide-body aircraft configuration is the future to generate market competitiveness for the airlines. There are IATA, EU, Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA), Continuous Lower Energy, Emissions and Noise (CLEEN) and Clean Sky programs that took the initiative to reduce the structural weight and better retrofit design to improve fuel economy and emission [3, 4].
2. Literature Review
This work aims to develop a novel and efficient fuselage design by implementing a design optimization approach to minimize the MTOW. Considerable research studies shared that the parameters drastically improve aircraft performance. Dhara et al. [5] reported the four “common-to-all-types” categories as design parameters, propulsive systems, structural integrations, and operational performances, as shown in Figure 1. The design process of a novel MoM transport aircraft has been carried out based on the study the prospects of integrating with advanced airframe technologies to minimize MTOW. Mathematically, MTOW () is a summation of passenger, crew, and cargo weight (), empty weight (), and fuel weight () as expressed in equation (1). Further simplification of the above equation (1), yields
Based on the seating capacity, the number of crew members and cargo capacity are predefined as per FAR. The succeeding parameter, is called a fuel weight fraction, which depends on the flight mission and 6% of fuel reservation as per FAR policy, as indicated in equation (3). The values of the fuel weight fraction of each mission starting from the engine warm-up () to the landing phase () are estimated from historical data and are clearly illustrated in Figure 2where,
The fraction values for stages 5 and 6 represent the cruise and loiter as per the mission objective, and the loitering endurance (E) of flight can fly with a loaded of fuel for 45 mins need to be adopted as per FAR standards and expanded in equations (4a) and (4b). These equations represent the operational factors that depend on the fuel weight fraction of a flightwhere, R represents the range of an aircraft travelled in km, Cj denotes the specific fuel consumption in hr−1, and defines the cruise velocity in km/hr, E refers to the endurance of flight in hr, and L/D indicates the lift-to-drag ratio of an aircraft.
The endmost term is symbolizing the empty weight of an aircraft, which depends on the aircraft’s structural and system weight [6]. However, numerically, is a summation of the aircraft component’s weight (includes wing , fuselage , horizontal tail , vertical tail , and landing gear ), engine weight (), and systems weight (), marked as equation (5).
Many researchers have used several methodologies such as Raymer’s formula [6], Torenbeek’s formula [7], Roskam’s formula [8], and the Nicolai and Carichner formula [9] to find the component weight. The interaction of aerodynamic features with structural interaction and the choice of material used in aircraft play a vital role in weight reduction and the performance of the flight. However, the aerodynamic-structural installation factor () and the density of aircraft material () were not considered in the abovementioned literature. Therefore, this study presented Sadrey’s [10] formula for the component weight equation expressed in the following equations (6a)–(6e):
The contribution of aircraft components’ weight indulges the aerodynamic-structural installation, and the material used is indicated using the component density factor () and tabulated in Table 1.
The second factor of empty weight is engine weights () obtained from the jet-engine manufacturer and scaling factors represented in equation (6f). These weights imply exhaust, cooling, turbo-supercharger, and lubrication systems
As per the thumb rule, empty weight lies between 40% and 60% of MTOW, whereas, avionics systems weight lies between 1.5% and 2% of empty weight. The avionics group includes flight controls, hydraulics, pneumatics, electrical, instrumentation, and other avionics equipment. The design thumb rule says, . The equation (6g) refers to
Besides the design parameters, many researchers approached designing an efficient aircraft using design optimization techniques [1], especially for industrial design. Design optimization techniques are an approachable method that has been drastically applied to transport aircraft and unmanned aerial vehicles (UAV) in the last 6 years. Thus, a detailed study of design optimization methods for transport aircraft is carried out by different researchers under different methodologies, as tabulated in Table 2. Galloway [21] has designed an aircraft fuselage with payload constraints included. Singh et al., [12] discussed ways to reduce MTOW by utilizing genetic algorithms optimization techniques. Furthermore, obtaining the objective using the same design constraint can be extended through other optimization techniques. Chai et al. [14] described how to enhance fuel efficiency using multipoint over single-point optimization techniques. Li et al. [1] applied the fidelity optimization technique to improve wide-body aircraft’s competitiveness. Ma and Elham [22] designed a novel TF configuration using a surrogated model and compared it with the conventional configuration yields to improve 20% fuel economy and 7% MTOW. Liu and Jiang [23] represent the design optimization of tail design to minimize the MTOW by 3.28% and provid insight into further reduction that can be achieved using boundary layer ingestion (BLI) and other active flow control (AFC) systems. The aviation industry has made remarkable achievements in boosting the economy of today’s aircraft, particularly in lowering the fuel consumption of aircraft engines. NASA’s Glenn Research Centre is researching propulsion technologies to improve the fuel economy of future aeroplanes, lowering total consumer costs and the environmental impact [24]. Budziszewski and Friedrichs [24] suggested the concept of BLI be introduced, not only to reduce drag but to improve power utility to the maximum. Moreover, the application of innovative analyses to examine the economic growth of these technologies as well as life cycle analysis to assess the ecological footprint across all phases of the aircraft life cycle is critical for future research projects. The examination of different aircraft types and mission ranges will also provide insights into how the advantages of this technology are influenced by aircraft size and how system sensitivities change [25].
The MCDM method helps to characterize the sorts of decision-making challenges that come throughout the various design phase, technical, financial, and safety strategies for solving issues incorporated in commercial or defence [5, 11, 26]. Some literature is also worked on MCDM techniques used on airlines’ quality services and passenger comforts [3, 15, 27]. Evaluating technical parameters and conflicting aspects among the alternatives can be solved using a suitable MCDM approach [13]. A benefit of the MCDM approach aims to predict both measurable and subjective factors using single and hybrid techniques.
According to the abovementioned author’s contribution, the key findings and future insights are to be extended in this research work. This research postulated an answer to the following questions:(a)What are the advanced technologies incorporated in a novel design?(b)What are the critical parameters required to calculate the MTOW of an aircraft?(c)To validate the novel design, which wide-body aircraft will be chosen as the baseline model?(d)Which approach is to be used to validate the optimized design result?
The answers and solutions to the following questions are provided in the upcoming sections.
3. Materials and Methods
According to Boeing’s statement, wide-body aircraft configuration is the future to generate market competitiveness for airlines. This section aims to appropriate techniques to minimize the MTOW of an aircraft by applying novel design technologies in the fuselage and comparing the result with the existing aircraft. Data were collected based on 22 real-time wide-body transport aircraft, which includes a detailed study of “common-to-all types” such as retrofit configuration, propulsive systems, structural parameters, and operational performances. Further investigation of the design parameters was carried out from previous literature, as mentioned in Table 2. In this section, the authors contributed a proposed methodology to fulfill the objective goal, which is illustrated in Figure 3. At this point, a novel conceptual design is needed to develop the flight mission and evaluate the result. In consequence, the proposed method is segmented into four steps:
First, choose the baseline aircraft based on fuel economy. The role of decision-making strategy plays a vital role in choosing the right aircraft as the baseline aircraft. A literature review of several MCDM approaches in the aviation sector was conducted. The CRITIC-WASPAS MCDM approach was used in this study to select the optimum baseline aircraft of choice. Second, once the selection of baseline aircraft is accepted, according to the mission profile of the flight, it will be considered a base flight mission. The baseline aircraft will be used to validate the result with a novel conceptual one in terms of retrofit design structural contributions or operational performances. Third, indulging in parametric changes and incorporating advanced technology to find the MTOW of the novel aircraft. The current manuscript focuses on MCDM techniques integrated with the interior point design optimization approach. In the proposed optimization technique, interior point methods have been adopted. Its solution can be either a global optimum or no feasible solution will exist. Finally, the results found in an optimized solution are reported and compared with the baseline discussed in this study. The robustness of the novel design was validated by comparing the operational performance of the baseline aircraft.
3.1. Multicriteria Decision Making (MCDM) Approach
This subsection employing the critical parameters ought to find the baseline aircraft using the multicriteria decision-making (MCDM) approach. Dhara et al. [5] provide insight into how the design parameters affect performance in terms of fuel economy or weight reduction. Table 2 clearly states that an implementation of technical aspects and variant subparameters is considered based on the objective finding. It shows a wide opportunity and challenge in the execution of MCDM to select or evaluate the appropriate aircraft based on the objective function. Based on the literature survey, “criteria importance through intercriteria correlation” (CRITIC) approaches are not yet used in the aerospace sector to obtain the objective weights of considered criteria. “The weighted aggregates sum product assessment” (WASPAS) Method proved to have a highly significant rate of accuracy [28]. This study proposes a hybrid MCDM called the CRITIC-WASPAS approach applied to find the best and worst midsize aircraft, as shown in Figure 4.
3.1.1. CRITIC Approach
The main motive of the CRITIC approach is to find out the object weights of the criteria considered. The fundamental principle of this method is used to measure conflicts through statistical data [29]. This approach provides a weight priority based on the measures of conflicts through the standard deviation for each criterion. A formulation of objective weights begins with four simple steps. First, starts with creating a decision matrix, framing normalized matrix based on beneficial and nonbeneficial criteria, developing a correlation matrix and applying an objective index to obtain weights of criteria. Step 1: create a decision matrix. The construction of a decision criteria matrix for wide-body transport aircraft is a combination of alternatives and criteria used in aircraft design. There are 22 different alternatives based on 13 different criteria. The generalized criteria-decision matrix “D” expressed in equation (7) is as follows: where, “m” is the number of alternatives and “n”is the number of criteria. Applying equation (7), Table 3 represents the criteria decision matrix D for a midsize aircraft. Next, calculate the linear normalized decision matrix by screening the benefit as the maximum value and the nonbenefit as the minimum value from the decision matrix Dij using equations (8a) and (8b) expressed as follows: For benefit criteria For nonbenefit criteria, where i = 1, 2, …, m and j = 1, 2, …, n. Setting the Best and worst parameters, Table 4 represents the normalized decision matrix Nij and the standard deviation of each jth criterion from C1 to C13. Mathematically, the standard deviation (σ) can be derived through MS Excel STDEVPA of each criterion. Followed by evaluating the correlation coefficient relation matrix rij of each criteria applying MS Excel operator CORREL. In order to predict the conflicting index and weights of each decision criteria, a sum of all coefficient relation matrix rij are tabulated in Table 4. Step 2: measures of conflict index (Ij) and weights (). This step is to find out the value of conflict index Ij and objective weights to obtain the weightage of each criterion using equations (9) and (10) as represented in Table 4. It has been noticed that aerodynamic considerations cause a 17.7 percent contribution, while structural consideration attributes 26.10 percent, Performance impacts about 53.5 percent, and economic reliability provides about 9.70 percent
3.1.2. WASPAS Method
The implication of the WASPAS method was developed by Zavadskas et al. [28], motto to obtain higher rank accuracy. This approach is a combination of two basic techniques called WSM and WPM. These approach portraits a benefit in rank accuracy, rationality, and practicality preceded with basic four steps are as follows: Step 1: obtain the linear normalized decision matrix. The first step of the WASPAS method is to generate a linear normalized matrix based on benefit and nonbenefit criteria on the jth column as shown in equations (11a) and (11b), respectively. For beneficial criteria For nonbeneficial criteria where is the normalized value of Step 2: computing the normalized weighted matrix. In this step, need to evaluate weighted sum and product matrix implementing WSM and WPM. This value can be obtained from the mathematical expression in equations (12a) and 12b), respectively where, refers to the objective weights obtained from the CRITIC method for each criterion. The weightage of C1 to C12 criteria are considered from Table 4. Implementation of the equations (12a) and (12b), is computed and tabulated in Table 5. According to Zavadskas et al. [28], introduced generalized equation expressed as equation (13). This equation (13) is the combination of WSM and WPM used in equations (12a) and (12b), with a WASPAS coefficient of λ. The value of λ ranges from 0 to 1 (0, 0.1, 0.2, …, 1). In Table 5, illustrates by compiling the equation (13) and varying the value of λ from 0 to 1 at a unit step of 0.1
This WASPAS technique is a unique combination of the WSM and WPM methods. If the value of λ is 0, then becomes . Similarly, if the value of λ is 1, then becomes i.e., equivalent to WSM.
3.1.3. Evaluate Rank
The values obtained for different values of λ from 0 to 1. The value of λ closest to 1 refers to the highest rank among the alternatives. This phenomenon yields different rank for different values of λ. Considering the average value of all values will determine the best and worst selection of midsize aircraft. By employing the WASPAS MCDM technique, the rank of midsize aircraft is selected as follows:
λ = 0.5: A2 > A9 > A1 > A16 > A8 > A5 > A6 > A4 > A13 > A21 > A20 > A15 > A18 > A19 > A22 > A17 > A14 > A3 > A12 > A7 > A11 > A10, i.e., Airbus A310-200 > … > Boeing B747-100.
3.2. Flight Mission Problem Statement
Based on the MCDM approach, an appropriate selection of baseline aircraft, the A310-200 was obtained and collected the flight mission profile [30]. A baseline aircraft with a seating capacity for 275 passengers to travel a distance of 6500 km, similar to the A310-200 flight mission, is discussed. An aircraft has a cruise speed of Macr = 0.79 at a cruise altitude of 12.2 km and a service ceiling of 43,100 feet. As per airworthiness standards, a necessity-reserved range of 370 km, along with a 6% reserved fuel, and a 45-minute hold at 1,500 feet are adopted. A summary of the flight mission considered from engine warm-up to the landing phase is shown in Figure 2. The flight mission is confined to the top-level design requirement into three categories represented in Table 6.
3.3. Design Optimization
A novel MoM transport aircraft with seats for 275 people combined with sophisticated technology to reduce MTOW. The present work is concerned with the design parameter selection and optimizing of a transport aircraft to minimize the MTOW using a design optimization approach. Usually, the optimization design problem employs a set of design variables that are required to minimize (or maximize) the objective function. First, twelve design variables are used as input data to minimize the objective function MTOW by the regulated constraints. The Figure 5 depicts the determined input parameter, and constraints are incorporated to obtain the objective goal of improving the structural integrity. This study aims to utilize multidomain analysis to fulfill the design objective. These three elements—design goals, constraints, and design variables—are described in the following paragraphs:
3.3.1. Design Goal
As per the flight mission, the design sizing of a novel design is suggested to follow airworthiness standards in FAR part 25. A novel MoM transport aircraft with a seating capacity of 275 passengers can travel a distance of 6,500 km and is integrated with advanced technologies to minimize MTOW. However, the predictions on the impact of modern technology on this aircraft are provided in Table 7. The study aims to design an efficient transport aircraft with three prime emerging technologies such as the implementation of lightweight structures and materials, the boundary layer ingestion concept, and an upgraded engine. Based on the literature survey, emerging technologies are incorporated to improve the performance of the aircraft at certain operational missions by improving aerodynamic efficiency as well as fuel economy. The currently operated and efficient engine GEnx-1B54/P2 is considered for this design aircraft with a Twin Annular Premixing Swirler (TAPS) and bypass ratio (BPR) of 9.39. As per the track measured, the specific fuel consumption caused by this engine is pretty low at 0.37 hr−1. Nonetheless, a synergistic interaction with the structure is essential to meet aviation’s environmental policy through the boundary layer ingestion (BLI) concept [31]. Additionally, the constant parameters obtained from the baseline aircraft are represented in Table 8, and the objective model is developed in equation (14).
Thus, to minimize the objective function was stated as follows:
3.3.2. Design Variables
As far as the conceptual design is concerned, the design variables (X) are obtained accordingly. There are twelve design considered variables such as wing area (), wing span (), wing taper ratio (), fuselage length (Lf), fuselage diameter (Df), horizontal tail surface area (SHT), horizontal tail span length (bHT), horizontal tail taper ratio (λHT), vertical tail surface area (SVT), vertical tail span length (bVT), vertical tail taper ratio (λVT), and aerodynamic efficiency (L/D) expressed in equation (14). The design geometry of other parameters has been assumed to be similar to the wing-tail configuration of the Airbus A310-200, and the engines have a wing-mounted arrangement [30]. This study presumes the use of advanced lightweight materials in the airframes. Hence, the mathematical function of MTOW is as follows:
In addition to it, the sizing of the design parameter should be pursued based on the objective function, the “dedicated-to-a-type” design illustrated in Table 5. The upper and lower bounds of design variables were determined and chosen based on the statistical dataset.
3.3.3. Constraints
The most crucial part is to formulate an acceptable set of constraints for a certain design process. In this research, 14 constrained are considered to satisfy the aircraft’s performance as per airworthiness standards [32] and marked in Table 8.
3.3.4. Tool Analysis
To enhance the aircraft design, a set of design variables with a limited constraint helps examine the value of MTOW. Such a framework can create an improved design to fulfil the objective using a design optimization approach. Several researchers used different design optimization approaches to solve effective design models. Many authors followed the genetic algorithm (GA) to optimize the required objective. Merely, GA can obtain global solutions and results to converge local optimum value. Thus, the current study prefers the interior point optimization technique over other techniques due to its solver algorithms and its ability to obtain a globally optimized solution. The robustness of this approach solves all kinds of problems such as linear and nonlinear. The optimization has been carried out on the GNA University workstation with a 2.4 GHz CPU and 16 GB of RAM, utilizing MATLAB version R2014a to estimate the objective function. Based on the considered input values, the objective MTOW value was obtained and tabulated in Table 9. In order to validate the result, the outcomes are compared with the parameters of the Airbus A310-200.
4. Results and Discussion
This section shows the obtained design solution for the novel aircraft components. This section shows a stepwise design solution for the novel aircraft. Initially, the WASPAS technique was implemented to identify the baseline aircraft, which was the Airbus A310-200. Further, a detailed study of this model aircraft was performed. The flight mission analysis and detailed multidomain analyses were done to improve the objective model. In addition to it, advanced technology has been assumed to be incorporated into the baseline model, as noted in Table 7. Based on the domain considered, this study focuses on implementing interior point optimization to minimize the MTOW. The optimized results converged after 231 iterations, as shown in Figure 6. After the convergence was made, the optimized values were obtained in the comment section. The optimized parameters provide the layout configuration of our processed model as indicated in Figure 7.
The overview of the proposed model is indicated with the red colour zone, whereas the baseline model is highlighted using the green colour. From the perspective of a side view of the aircraft, that clearly shows the difference in the streamlined body postulated as per optimized value. It is noticed that the rear portion of the fuselage has a highly cambered and a slight increase in fuselage length. The parametric design has been created using solid works design software through the loft, and spline command tools. The indications of the design parameters are discussed with the following consequences:(a)The values of wing area and wing span have been reduced to 200 m2 and 42.87 m, respectively. As a result, an increase in the aspect ratio (AR) from 8.8 to 9.2 has been noticed. Due to its impact, there is a possible way to improve aircraft performance. Further, an increase in the taper ratio from 0.26 to 0.32 has been adopted. Aerodynamically, this value will help reduce the induced drag. Hence, a drastic change in the taper ratio has been indicated.(b)The values of fuselage length and diameter have both increased to 47.2 m and 5.9 m, respectively. As a result, a slight decrease in the fineness ratio (FR) from 8.3 to 8.0 has been noticed. The reduction in fineness ratio up to the optimized value will favour to cut-down the parasite drag.(c)The value of horizontal tail (HT) wing area and wing span have been reduced to 69.1 m2 and 16 m, respectively. As a result, an increase in the HT aspect ratio (AR) from 3.4 to 3.7 has been identified. Similarly, the taper ratio leads to an increase in the value from 0.34 to 0.385.(d)The value of vertical tail (VT) wing area and wing span have increased to 52.2 m2 and reduced to 7.97 m, respectively. As a result, a reduction in the VT aspect ratio (AR) from 1.64 to 1.22 has been predicted. Further, the taper ratio leads to an increase in the value from 0.3 to 0.31, which has been obtained.(e)A drastic improvement in aerodynamic efficiency, as called the lift-to-drag ratio, has been noticed, and the value has increased from 16.3 to 16.7. As a result, the parasite drag coefficient has decreased from 0.0207 to 0.0197. The reduction of the drag coefficient has been caused by the parametric design change and the incorporation of advanced technologies. Hence, it is proven that this optimization result shows a reduction in the MTOW of a novel aircraft as shown in Figure 8.
4.1. Comparative Studies
To understand the robustness of the design, it splits into two cases: with and without the advanced technologies mentioned in Table 7. The first case is to investigate a novel aircraft design by applying assumed technologies in the solver, and the second case is just the baseline aircraft model, the Airbus A310-200. In the first case, a 4.42% increase in aspect ratio and a 3.3% decrease in fineness ratio, a 7.7% increase in the aspect ratio of the horizontal tail by reducing the span area over the span, a 25.7% increase in the aspect ratio of the vertical tail, and a 2.5% improvement in aerodynamic efficiency. The outcome of the optimization shows aerodynamic efficiency has high fidelity due to its design threshold values for aspect ratio, taper ratio, and other considered parameters. Aerodynamically, the increase in aspect ratio and taper ratio helps to decrease induced drag, and as a result, improves the fuel consumption of an aircraft. The fineness of the fuselage ranges from 8 to 12 at a certain Reynolds number, leading to improved parasite drag in an aircraft. The aerodynamic efficiency of the optimized value seems to have greater value as compared with baseline aircraft. Additionally, advanced structural material and an upgraded engine initiate the reduction of the structural weight of the aircraft, thereby, creating a low drag profile, fuel economy, and emissions as well. The concept of fuselage shape is considered boundary layer ingestion (BLI) technology. Therefore, the flow over a fuselage body is optimized to be streamlined to reduce drag. Additionally, the parasite drag coefficient (CD0) is also evaluated to predict the improvement in the novel aircraft, which shows a 4.67% reduction of drag as compared with the A310-200 model. Figure 9(a) illustrates the overall improvement of aircraft design parameters and results to minimize the MTOW. The use of the optimization approach resulted in an overall improvement in the MTOW value of around 3.75%.
(a)
(b)
As previously mentioned, equation (2) shows that factors affect the MTOW of an aircraft. Furthermore, the analysis of this model has been performed. The outcome of these optimization results shows an improved value of the empty weight, fuel weight, and performance factors that have been noticed in Figure 9(b). The first factor is the empty weight, which significantly depends on the design parameters of the aircraft’s components and is marked in equations (6a)–(6e). The obtained model yields an empty weight fraction of 0.42 by implementing advanced materials into the airframe and component installation factors. Therefore, a drastic decrease in empty weight fraction was around 25.59% as compared to the baseline aircraft, the Airbus A310-200. A significant change in empty weight has a substantial impact on design parameters. and mathematical relations are expressed in equations (6a)–(6f). A notable improvement in and is due to the application of lightweight structures, the materials used in the structure, and changes in design parameters as per the design objective. Looking into the fuel weight factor, which depends on the mission profile (i.e., range and flight velocity) along with the engine configuration and aerodynamic efficiency, is expressed in equation (3). As per the considered optimization model, 2.06% and 6.04% have been increased in fuel weight and fuel weight fraction compared to the baseline, respectively. There is a possibility for an extra capacity of fuel to endure better range compared to baseline aircraft. Hence, improved aerodynamic efficiency and specific fuel consumption for the required mission could explain a slight increase in fuel weight and fraction. Thirdly, to study the performance factor of the aircraft through take-off field distance (TLO). Theoretically, TLO is the sum of ground distance (), rotational distance (Sr), and transition distance (Str) by keeping the constant value of take-off velocity(), rotation time (rt), stall velocity (), load factor during take-off, and radius of turn (R), respectively. Hence, the optimized TLO value turns out to be around 1461 km and improved by 8.63% compared to the baseline. Likewise, numerous parametric analyses can be performed to find the robustness of this optimized result.
5. Conclusion
This study presents a novel conceptual design for MoM transport aircraft. Nowadays, research studies report that the MoM aircraft leads a streamlined progression in transportation and tourism. The motivation of this study is to minimize the gross aircraft weight (MTOW) using an optimization approach. The main feature of novel transport aircraft is a low-wing configuration with advanced lightweight materials, an efficient engine, and an improved retrofit design. The results show that implementing advanced technology and design parameters triggered a decrease in the MTOW. Additionally, implementing efficient twin GEnX engines boosts performance, improves fuel economy, and reduces emissions. The influence of aerodynamic parameters on the wing, fuselage, and empennage has affected the MTOW value. As a result, the aerodynamic characteristics of wing planform geometry, fuselage geometry, and empennage planform geometry are vital for aircraft design. The correct change of these geometrical characteristics, concerning their impacts on the aircraft’s aerodynamic parameters, can enhance the performance. Notably, the aspect ratio increased in the wing, and the empennage section has been noticed; hence, drag decreased. A rise of 4.42%, 12.1%, and 18.3% in wing aspect ratio, horizontal tail aspect ratio, and vertical tail aspect ratio, respectively, were observed compared to the baseline aircraft. Simultaneously, the results showed an improvement of 23.9%, 13.1%, and 3.33% in the taper ratio of the wing, horizontal tail, and vertical tail, respectively, which indicates better aircraft performance. Numerous engineers aim to optimize the fuselage fineness ratio to decrease fuselage drag. This optimization approach obtained a fineness ratio of 8.12 and was comparatively reduced by 1.81%. As the design variables directly connected with the lift-to-drag ratio (L/D) have been estimated, the result is improved by 1.8%, thus achieving better aerodynamic efficiency compared to the baseline aircraft.
On the other hand, a preliminary performance study of a novel aircraft has been performed. This study targeted finding the impact of empty weight, fuel weight, and performance parameters which affect the design. However, the parametric studies reported that there is a significant potential to improve the performance in terms of structural weight, fuel weight, and take-off distance, which were reduced by 28.38%, increased by 2.83%, and reduced by 8.63%, respectively. Implementation of novel transport aircraft will potentially lower drag, especially after embedding the concept of BLI. Therefore, the conclusion shows that the novel design with the assumed technologies triggered the environmental and economic pillars of sustainability. The proposed aircraft design obtained from the optimization modelling, improved the MTOW compared to the baseline aircraft. Moreover, the empty weight, the fuel weight, and the take-off distance were improved, which reveals better structural integrity, fuel consumption, and aircraft performance.
The article states the prospects and extension of the work in two ways. First, the robustness of the optimization model can be compared using parametric analysis. In continuation to this, comparison can be shown among the combinations of technologies with one another or by adding upgraded retrofit designs. Second, both computational and experimental work can be carried out to characterize the performance of the flight. After successful flight testing, the certification of novel aircraft can proceed for better airline profitability and fuel economy. Third, as indicated in the research study, different multidisciplinary design optimization models can be performed by using other parameters. Besides the difficulties of acquiring data, certain parameters are evaluated, and the interactive parametric study is not taken into account [12, 33]. As a result, more analysis of the interaction between the parameters depending on specific application situations can be recommended for further study. In future research, the proposed model can be merged with favourable hypotheses to increase performance efficiency and result accuracy.
Data Availability
The data used to support the findings of this study are available from the corresponding author upon request.
Conflicts of Interest
The authors declasre that they have no conflicts of interest.
Acknowledgments
The authors would like to acknowledge that this work was funded by the GNA University under the department of Research and Development (GU/RESEARCH/2022-23/028).