Search results

Aero-engine exhaust plume length can be more than the aircraft length, making it easier to detect and track by infrared seeker. Aim of this study is to analyze the effect of free stream Mach number (M_∞) on length of potential core of plume. Also, change in infrared (IR) signature of plume and aircraft surface with variation in elevation angle (θ) is examined.

Convergent divergent (CD) nozzle is located outside the rear fuselage of the aircraft. A two dimensional axisymmetric computational fluid dynamics (CFD) study was carried out to study effect of M_∞ on potential core. The CFD data with aircraft and plume was then used for IR signature analysis. The sensor position is changed with respect to aircraft from directly bottom towards frontal section of aircraft. The IR signature is studied in mid wave IR (MWIR) and long wave IR (LWIR) band.

The potential plume core length and width increases as M_∞ increases. At higher altitudes, the potential core length increases for a fixed M_∞. The plume emits radiation in the MWIR band, whereas the aerodynamically heated aircraft surface emits IR in the LWIR band. The IR signature in the MWIR band continuously decreases as the sensor position changes from directly bottom towards frontal. In the LWIR band the IR signature initially decreases as the sensor moves from the directly bottom to the frontal, as the sensor begins to see the wing leading edges and nose cone, the IR signature in the LWIR band slightly increases.

The novelty of this study comes from the data reported on the effect of free stream Mach number on the potential plume core and variation of the overall IR signature of aircraft with change in elevation angle from directly below towards frontal section of aircraft.

The purpose of this study is to develop and test a new deep learning model to predict aircraft fuel consumption. For this purpose, real data obtained from different landings and take-offs were used. As a result, a new hybrid convolutional neural network (CNN)-bi-directional long short term memory (BiLSTM) model was developed as intended.

The data used are divided into training and testing according to the k-fold 5 value. In this study, 13 different parameters were used together as input parameters. Fuel consumption was used as the output parameter. Thus, the effect of many input parameters on fuel flow was modeled simultaneously using the deep learning method in this study. In addition, the developed hybrid model was compared with the existing deep learning models long short term memory (LSTM) and BiLSTM.

In this study, when tested with LSTM, one of the existing deep learning models, values of 0.9162, 6.476, and 5.76 were obtained for R², root mean square error (RMSE), and mean absolute percentage error (MAPE), respectively. For the BiLSTM model when tested, values of 0.9471, 5.847 and 4.62 were obtained for R², RMSE and MAPE, respectively. In the proposed hybrid model when tested, values of 0.9743, 2.539 and 1.62 were obtained for R², RMSE and MAPE, respectively. The results obtained according to the LSTM and BiLSTM models are much closer to the actual fuel consumption values. The error of the models used was verified against the actual fuel flow reports, and an average absolute percent error value of less than 2% was obtained.

In this study, a new hybrid CNN-BiLSTM model is proposed. The proposed model is trained and tested with real flight data for fuel consumption estimation. As a result of the test, it is seen that it gives much better results than the LSTM and BiLSTM methods found in the literature. For this reason, it can be used in many different engine types and applications in different fields, especially the turboprop engine used in the study. Because it can be applied to different engines than the engine type used in the study, it can be easily integrated into many simulation models.

This study aims to focus on verifying the possibility of monitoring the condition of a turboprop engine using data recorded by on-board avionics Garmin G1000. This approach has potential benefits for operators without the need to invest in specialised equipment. The main focus was on the inter-turbine temperature (ITT). An unexpected increase in temperature above the usual value may indicate an issue with the engine. The problem lies in the detection of small deviations when the absolute value of the ITT is affected by several external variables.

The ITT is monitored by engine sensors and stored by avionics 1× per second onto an SD card. This process generates large amount of data that needs to be processed. Therefore, an algorithm was created to detect the steady states of the engine parameters. The ITT value also depends on the flight parameters and surrounding environment. As a solution to these effects, the division of data into clusters that represent the usual flight profiles was tested. This ensures a comparison at comparable ambient pressures. The dominant environmental influence then remain at the ambient air temperature (OAT). Three OAT compensation methods were tested in this study. Compensation for the standard atmosphere, compensation for the standard temperature of the given flight level and compensation for the speed of the generator, where the regression analysis proved the dependence between the ambient temperature and the speed of the generator.

The influence of ambient temperature on the corrected ITT values is noticeable. The best method for correcting the OAT appears to be the use of compensation through the revolutions of the compressor turbine NG. The speed of the generator depends on several parameters, and can refine the corrected ITT value. During the long-term follow-up, the ITT differences (delta values) were within the expected range. The tested data did not include the behaviour of the engine with a malfunction or other damage that would clearly verify this approach. Therefore, the engine monitoring will continue.

This study presents a possible approach to turbine engine condition monitoring using limited on board avionic data. These findings can support the development of an engine condition monitoring system with automatic abnormality detection and low operating costs.

This article represent a practical description of problems in monitoring the condition of a turboprop engine in an aircraft with variable flight profiles. The authors are not aware of a similar method that uses monitoring of engine parameters at defined flight levels. Described findings should limit the influence of ambient air pressure on engine parameters.

Throughout an aircraft development process, the conceptual design phase is an extremely important milestone; hence, the quality and success of this step directly affect the overall cost and lead time of the project. Because of this fact, the purpose of this study is to provide outputs and suggestions to the designing engineer regarding the requirements for reducing overall design time as well as costs and creating an ideal design at the early phases of the project by optimizing the aircraft development process.

The system has been prepared parametrically and presents some performance specifications for the aircraft in the early phases of the design, for example, coefficients for lift C_L as well as drag C_D and weight as well as fuel estimations. The software uses a combination of well-known design techniques within just one platform in contrast to many other applications. Because of this feature, it is not needed to use different sub-platforms which would require an appropriate environment and even though would lead to complications with regard to the connectivity. The system also presents relevant information about the aircraft performance like velocity versus load factor (V-n) diagrams, maximum turn rate of climb, turn rate and climb angle graphs in contrast to many other open-source conceptual design platforms.

In this study, authentic General Dynamics F-16 Fighting Falcon and McDonnell Douglas F-15 Eagle data were used as input to the system, and advanced geometric and/or performance graphs were obtained and compared to the literature where a good agreement of the results was observed. These results with regard to the aircraft performance are typically product specific and quite rare in the literature. These data obtained by use of the software during the aircraft design are, thus, of major interest, especially for the design of new aerospace platforms. In this study, all of these graphs (especially the remarkable V-n diagram) are obtained on one platform.

The aircraft conceptual design and analysis system software provides information and suggestions regarding the requirements for reducing the overall design time, reducing the design costs and creating an optimized design at the early phases of a project by optimizing the aircraft development process within just one convenient, that is, user friendly, platform, where it uses a combination of varying methodologies. Besides presenting one interface, which is quite typical for conceptual design tools, it allows applications of methods like vortex lattices and finite differences for obtaining aerodynamic performance parameters.

Reducing aircraft fuel consumption has become a paramount research area, focusing on optimizing operational parameters like speed and altitude during the cruise phase. However, the existing methods for fuel reduction often rely on complex experimental calculations and data extraction from embedded systems, making practical implementation challenging. To address this, this study aims to devise a simple and accessible approach using available information.

In this paper, a novel analytic method to estimate and optimize fuel consumption for aircraft equipped with jet engines is proposed, with a particular emphasis on speed and altitude parameters. The dynamic variations in weight caused by fuel consumption during flight are also accounted for. The derived fuel consumption equation was rigorously validated by applying it to the Boeing 737–700 and comparing the results against the fuel consumption reference tables provided in the Boeing manual. Remarkably, the equation yielded closely aligned outcomes across various altitudes studied. In the second part of this paper, a pioneering approach is introduced by leveraging the particle swarm optimization algorithm (PSO). This novel application of PSO allows us to explore the equation’s potential in finding the optimal altitude and speed for an actual flight from Algiers to Brussels.

The results demonstrate that using the main findings of this study, including the innovative equation and the application of PSO, significantly simplifies and expedites the process of determining the ideal parameters, showcasing the practical applicability of the approach.

The suggested methodology stands out for its simplicity and practicality, particularly when compared to alternative approaches, owing to the ready availability of data for utilization. Nevertheless, its applicability is limited in scenarios where zero wind effects are a prevailing factor.

The research opens up new possibilities for fuel-efficient aviation, with a particular focus on the development of a unique fuel consumption equation and the pioneering use of the PSO algorithm for optimizing flight parameters. This study’s accessible approach can pave the way for more environmentally conscious and economical flight operations.

This research is associated with the real-time parameters of wide- and narrow-body aircraft to recognize the quantitative relationship framework. This paper aims to find the superiority of aircraft design technology which triggers the reduction in specific fuel consumption (SFC) and economic competitiveness.

The real case study is performed with 22 middle-of-the-market (MoM) aircraft. This paper develops a fuel burn mathematical model for mid-size transport aircraft by a multi-linear regression approach. In addition, sensitivity analysis is performed to establish the authentication of the fuel burn model.

The study reveals that the MoM aircraft would be the future aircraft design in terms of better fuel economy and carbon footprint. From the multi-regression analysis, it is observed that the logarithmic regression model is the best fit for estimating the SFC. Moreover, fineness ratio, aspect ratio, gross weight, payload weight fraction, empty weight fraction), fuel weight fraction, payload, wing loading, thrust loading, range, take-off distance, cruise speed and rate of climb are observed as the suitable parameters which provide the best fitness value as 0.9804.

Several existing literature reveals that a few research has been performed on the MoM aircraft with wide-body configuration. Moreover, mathematical modelling on the fuel consumption was insignificantly found. This study examines several parameters which affect the fuel consumption of a wide-body aircraft. A real-case study for design configurations, propulsive systems, performance characteristics and structural integrity parameters of 22 different MoM aircraft are performed. Moreover, multi-regression modelling is developed to establish the relation between SFC and other critical parameters.

Unconventional machining processes, particularly ultrasonic vibration cutting (UVC), can overcome such technical bottlenecks. However, the precise mechanism through which UVC affects the in-service functional performance of advanced aerospace materials remains obscure. This limits their industrial application and requires a deeper understanding.

The surface integrity and in-service functional performance of advanced aerospace materials are important guarantees for safety and stability in the aerospace industry. For advanced aerospace materials, which are difficult-to-machine, conventional machining processes cannot meet the requirements of high in-service functional performance owing to rapid tool wear, low processing efficiency and high cutting forces and temperatures in the cutting area during machining.

To address this literature gap, this study is focused on the quantitative evaluation of the in-service functional performance (fatigue performance, wear resistance and corrosion resistance) of advanced aerospace materials. First, the characteristics and usage background of advanced aerospace materials are elaborated in detail. Second, the improved effect of UVC on in-service functional performance is summarized. We have also explored the unique advantages of UVC during the processing of advanced aerospace materials. Finally, in response to some of the limitations of UVC, future development directions are proposed, including improvements in ultrasound systems, upgrades in ultrasound processing objects and theoretical breakthroughs in in-service functional performance.

This study provides insights into the optimization of machining processes to improve the in-service functional performance of advanced aviation materials, particularly the use of UVC and its unique process advantages.

Designing new aircraft that are state-of-the-art and beyond always requires the development of new technologies. This paper aims to present lessons learned while designing, building and testing new UAVs in the configuration of the flying wing. The UAV contains a number of aerodynamic devices that are not obvious solutions and use the latest manufacturing technology achievements, such as 3D printing.

The design solutions were applied on an airworthy aircraft and checked during test flights. The process was first conducted on the smaller UAV, and based on the test outcomes, improvements were made and then applied on the larger version of the UAV, where they were verified.

A number of practical findings were identified. For example, the use of 3D printing technology for manufacturing integrated pressure ports, investigation of the adverse yaw effect on the flying wing configuration and the effectiveness of winglet rudders in producing yawing moment.

All designed devices were tested in practice on the flying aircraft. It allowed for improved aircraft performance and handling characteristics. Several of the technologies used improved the speed and quality of aerodynamic device design and manufacturing, which also influences the reliability of the aircraft.

The paper presents how 3D printing technology can be utilized for manufacturing of aerodynamic devices. Specially developed techniques for control surface design, which can affect adverse yaw problem and aircraft handling characteristics, were described.

In this study, the performance analysis of the split flow turbofan engine with afterburners has been examined using the parametric cycle analysis method. The purpose of this study is to examine how engine performance is impacted by design parameters and flight ambient values and to develop a software in this context.

Software has been developed using the open-source PYTHON programming language to perform performance analysis. Mach number, compressor/fan pressure ratio, bypass ratio and density were used as parameters. The effects of these variables on engine performance parameters were investigated.

Parametric cycle analysis has been calculated for different flight conditions in the range of 0–2 M and 0–15,000 m altitude for turbofan engines. With this study, basic data were obtained to optimize according to targeted flight conditions.

As a result of the performance analysis, the association between the flight conditions and design parameters of engine were determined. A software has been developed that can be used in the design of supersonic gas turbine engines for fast and easy simulation of the design parameters.

The variables used in the literature have been analyzed, and the results of the studies have been incorporated into the developed software, which can be used in innovative engine design. Software is capable to be developed further with the integration of new algorithms and models.