Aircraft play a major role in meeting the fast and efficient transportation needs of modern society, thanks to their advanced features. However, gas turbine engines used in aircraft have many negative effects on human health. One of the negative effects is the exhaust gases released by these engines to nature. In this study, it is discussed to present alternative models based on heuristic methods to reduce the emission values of the synthetic fuel mixture used in the combustion chamber of gas turbine engines. For this purpose, a model based on artificial neural networks (ANN) based on the back-tracking search optimisation (BSO) algorithm is proposed by using experimentally obtained emission values found in the literature. In the proposed model, the parameters of the optimum ANN structure are first determined by the BSO algorithm. Then, by using the optimum ANN structure, the most appropriate input values were found with the BSO algorithm, and the emission values were reduced. The simulation results have shown that the proposed method will be a fast and safe alternative method for reducing emission values.

Congested airports benefit from parallel-point merge systems (P-PMSs) for efficient arrival route control. However, the decline in air traffic due to COVID-19 has curtailed its optimal utilisation, especially with the reduced need for long sequencing legs. As air traffic is poised to rebound, the evident volatility seen during and post COVID-19, as well as the daily fluctuations between peak and off-peak hours, underscore the importance of the dynamic utilisation of sequencing legs in P-PMSs. EUROCONTROL proposes various leg configurations to manage fluctuating traffics, ensuring both efficiency and safety. First, we proposed two additional leg configurations for the Istanbul Airport, offering continuous descent with the engines operating at idle thrust during leg flights; partially overlapped and fully dissociated. While they offer an alternative for controllers during low to medium traffic scenarios, current long and fully overlapped parallel legs may be used in high traffic due to the volatility of traffic density throughout a day. Therefore, we suggest an approach that provides dynamic utilisation of these configurations. We first modeled and analysed the configurations for various traffic numbers and scenarios. Then, we introduced a new stochastic matheuristic model that considers the configuration changes throughout the day and provides feasible and robust sequences applicable to all configurations by combining the benefits of mathematical models with the adaptability and speed of heuristic methods. Several test problems were evaluated using the terminal manoeuvering area structure of Istanbul Airport as a case study. The results indicate that by changing configurations, an average of 35 kg in fuel savings per aircraft can be achieved. The results also show that the proposed approach outperforms traditional stochastic mathematical models and the first-come first-serve (FCFS) strategy, ensuring efficient air traffic management in terms of fuel and delay with robust sequencing by eliminating the need for re-sequencing during configuration changes.

This paper discusses approaches for tradespace analysis, exploration, and visualization to address multi-objective decision-making. Next, computational tools for early-stage tradespace analysis to enhance programmatic decision-making are introduced via a vehicle design example to demonstrate the effectiveness and capability of the method. Using a smaller sample of technologies in this problem a synthetic tradespace spans the space of potential and available solutions and provides an opportunity for design engineers to develop an insight into possible technologies and solutions within the tradespace.

The usage areas of rotary or fixed wing unmanned aerial vehicles (UAV) have become very widespread with technological developments. For this reason, UAV designs differ in terms of aerodynamic design, flight performance and endurance depending on the intended use. In this study, maximising of the autonomous flight performance of the unmanned helicopter produced at Erciyes University using an optimisation algorithm is discussed. For this purpose, the input parameters of the dynamic model are chosen as blade length, blade mass density, blade chord width and blade twist angle of the unmanned helicopter and the proportional, integral, derivative gain coefficients of the lateral axis of the autopilot. The output parameters of the dynamic model are selected as settling time, rise time and maximum overshoot, which are autonomous performance parameters. The dynamic model consisting of helicopter and autopilot parameters is integrated into the back-tracking search optimisation (BSO) algorithm as an objective function. In the optimization process, where mean squared error (MSE) is used as the performance criterion, optimum input and output values were determined. Thus, helicopter and autopilot parameters, which are among the factors affecting autonomous performance, are taken into account with equal importance and simultaneously. Simulations show that the obtained values are satisfactory. With this approach based on the optimisation method, complex and time-consuming dynamic model calculations are reduced, time and cost are saved, and practicality is achieved in applications. Therefore, this approach can be an innovative and alternative method to improve UAV designs and increase flight performance compared to classical methods.

The idea that plants would be efficient, frugal or optimised echoes the recurrent semantics of ‘blueprint’ and ‘program’ in molecular genetics. However, when analysing plants with quantitative approaches and systems thinking, we instead find that plants are the results of stochastic processes with many inefficiencies, incoherence or delays fuelling their robustness. If one had to highlight the main value of quantitative biology, this could be it: plants are robust systems because they are not efficient. Such systemic insights extend to the way we conduct plant research and opens plant science publication to a much broader framework.

Unmanned aerial vehicles (UAVs), which are available in our lives in many areas today, bring with them new expectations and needs along with developing technology. In order to meet these expectations and needs, main subjects such as reducing energy consumption, increasing thrust and endurance, must be taken into account in UAV designs. In this study, Backtracking search optimisation (BSO) algorithm-based adaptive neuro-fuzzy inference system (ANFIS) model is proposed for the first time to improve UAV thrust. For this purpose, first, different batteries and propellers were tested on the thrust measuring device and a data set was obtained. Propeller diameter and pitch, current, voltage and the electronic speed controller (ESC) signal were selected as input, and UAV thrust was selected as output. ANFIS was used to relate input and output parameters that do not have a direct relationship between them. In order to determine the ANFIS parameters at the optimum value, ANFIS was trained with the obtained data set by using BSO algorithm. Then, the objective function based on the optimum ANFIS structure was integrated into BSO algorithm, and the input values that gave the optimum thrust were calculated using BSO algorithm. Simulation results, in which parameters such as engine, battery and propeller affecting the thrust are taken into account equally, emphasise that the proposed method can be used effectively in improving the UAV thrust. This hybrid method, consisting of ANFIS and BSO algorithm, can reduce the cost and time loss in UAV designs and allows many possibilities to be tested.

AR/VR applications are a valuable tool in product design and lifecycle. But the integration of AR/VR is not seamless, as CAD models need to be prepared for the AR/VR applications. One necessary data transformation is the tessellation of the analytically described geometry. To ensure the usability, visual quality and evaluability of the AR/VR application, time consuming optimisation is needed depending on the product complexity and the performance of the target device.

Widespread approaches to this problem are based on iterative mesh decimation. This approach ignores the varying importance of geometries and the required visual quality in engineering applications. Our predictive approach is an alternative that enables optimisation without iterative process steps on the tessellated geometry.

The contribution presents an approach that uses surface-based prediction and enables predictions of the perceived visual quality of the geometries. This contains the investigation of different geometric complexity metrics gathered from literature as basis for prediction models. The approach is implemented in a geometry preparation tool and the results are compared with other approaches.

Digital design tools and technologies offer new opportunities for designers to generate a diverse range of design solutions. Previous research have discussed the multifaceted use of such technologies for 1) rapid visualisations, 2) generating design options, and 3) predicting design solutions. However, such research have focused more on simplifying design for fabrication and less on the integration of individual needs in design processes. This research adopts a human-centric design approach to merge user-to-design and design-to-fabrication processes. Through a scoping review on homelessness, design, and fabrication, we contribute a user-design-fabrication framework devised for the specific and dynamic needs of homeless individuals living in Melbourne, Australia. Our findings suggests that to optimise digital design processes for individuals with specific and dynamic needs, designers need to understand, translate, and embed the social, design, and fabrication complexities of a design problem. Future research should therefore test the real-world application of our user-design-fabrication framework and evaluate the impact of such digital design processes, for the provision of more individualised homeless housing design solutions.

This paper explores the suitability of Artificial Neural Networks (ANNs) as an enabler of Design Automation in the turbomachinery industry. Specifically, the paper provides 1) a preliminary estimation of the effectiveness of ANNs to define values for design variables of reciprocating compressors (RC) and 2) a comparison of ANNs performance with traditional and more computationally demanding methods like CFD. A tailored ANN trained on a dataset composed by 350+ Baker Hughes’ RC automatically assigns values to 8 geometrical variables belonging to multiple parts of the RC in order to satisfy two target conditions linked to their thermodynamic performance. The results highlight that the ANN-assigned parameters return an optimal solution for RC also when the target values do not belong to the training dataset. Their predictive capacity for RC thermodynamic performance, with respect to CFD, are comparable (i.e. less than 2% in terms of calculated absorbed power) and the approach enables a significant gain in terms of computational time (i.e. 2 minutes vs 10 hours). Future perspectives of this work may involve the integration of this tool in an advanced DA method to lead Design Engineers (DEs) during the whole design process.

In the development and production of new products, interdepartmental knowledge transfer is essential. Successful knowledge transfer faces several challenges, such as a lack of willingness to transfer knowledge or an inappropriate selection of tools. These can lead to the reduction of efficiency and effectiveness of knowledge transfers. Therefore, the InKTI – Interdepartmental Knowledge Transfer Improvement Method is developed to support the improvement (in terms of speed and quality) of knowledge transfers, particularly in product and production engineering.

This paper presents the first validation of the InKTI Method through a field study at the company Protektorwerk Florenz Maisch GmbH & Co. KG, which is a leading European company in the construction industry, to support the successful knowledge transfer into practice. Therefore, the research need is pointed out, and a concept for validation is developed and implemented. Afterward, the InKTI Method is evaluated based on its success, support as well as applicability.

Over the last 20 years, finite element analysis (FEA) has become a standard analysis tool for metal joining processes. When FEA tools are combined with design of experiments (DOE) methodologies, academic research has shown the potential for virtual DOE to allow for the rapid analysis of manufacturing parameters and their influence on final formed products. However, within the domain of bulk-metal joining, FEA tools are rarely used in industrial applications and limit DOE trails to physical testing which are therefore constrained by financial costs and time.

This research explores the suitability of an FEA-based DOE to predict the complex behaviour during bulk-metal joining processes through a case study on the staking of spherical bearings. For the two DOE outputs of pushout strength and post-stake torque, the FEA-based DOE error did not exceed ±1.2% and ± 1.5 Nm respectively which far surpasses what was previously capable from analytically derived closed-form solutions. The outcomes of this case study demonstration the potential for FEA-based DOE to provide an inexpensive, methodical, and scalable solution for modelling bulk-metal joining process

The tolerancing of products for manufacturing is usually performed at the end of the design process and the responsibility of the designer. Although components are commonly tolerated to ensure functionality, time-based influences, like wear, that occur during operation, are often neglected. This could result in small amounts of scrap after production, but high quantities of failure during operation. To overcome this issue, this paper presents an approach to perform a multi-objective optimization considering tolerances based on a wear simulation. Thereby, mean shifts serve as optimization variables, while the aim of the optimization is to generate an optimal ratio of scrap to failure. In addition, the optimization results are interpreted and further options for the designer are presented. Moreover, the approach is exemplary applied to a use case.

The product engineering process as part of the product life cycle includes product and production system development as well as production. In integrated product and production engineering (PPE), knowledge transfer is an important success factor. Optimizing the efficiency and effectiveness of knowledge transfers can, for example, support the avoidance of costly, production-related changes to the product design. The current state of research describes different models of knowledge transfer as well as factors that influence it. Some results show how the speed and quality of knowledge transfer can be improved by implementing so-called interventions. However, those models either represent abstract contexts of knowledge transfer or focus only on product engineering. Therefore, a literature analysis is conducted to identify the system of objectives for a method, that supports the improvement of knowledge transfer in PPE. Subsequently, the system of objectives is operationalized to provide the basis for the InKTI – Interdepartmental Knowledge Transfer Improvement Method, which is applicable, supports the user in improving knowledge transfers in PPE, and aims to increase the quality and speed of knowledge transfers.

The contradictions of TRIZ are now widespread and recognized as an effective inventive design tool. They make it possible to find solution concepts to problems that cannot be solved by optimization approaches. However, many contradictions could be formulated and it could be difficult to choose the priority one. The authors propose here two methods to formulate the contradictions and identify the priority contradiction: an experimental approach on the one hand, and a multiphysics approach on the other hand. This analysis, illustrated through an example of 3D printing of parts, shows that these two approaches are similar in terms of result, and indeed make it possible to formulate contradictions taking into account all the complexity of a system.

The use of sensor networks (SNs) on the surface or the inside of large-scale components allows the continuous acquisition of data on the applied loads and their structural integrity. A lot of publications on SN's system reliability deal with this topic from a hardware- or a data- and energy-oriented viewpoint. To give an overview on the state of the art in the field of reliability-oriented concept-optimization of SNs, a Systematic Literature Review is conducted. The found literature is used to investigate how different models combine the different viewpoints to analyse the system reliability. By analysing the results regarding the used reliability indicators and methods to assess the system reliability from the different viewpoints, it can be observed that most publications deal with the accuracy, loss and delay of data as well as the energy consumption in SNs. Few publications use common modelling methods like reliability block diagrams or Markov chains with a focus on the hardware reliability. Furthermore, none of the found publications combines the data, hardware and energy perspective and uses them to optimize a SN regarding its reliability from all three viewpoints.

Tuned mass dampers may be used to improve vibrational behavior of structures. However, they require space to move. This paper presents an approach to incorporate tuned mass dampers into a lightweight-optimized structure without extra space requirement. It is based on (1) topology optimization (TopOpt) with unit cells and (2) vibration reduction with multiple tuned mass dampers (m-TMD) within the unit cells. The topology optimization is performed with a physics-informed penalty factor, unique to the chosen unit cell. Subsequently, the weight optimal density distribution is realized by populating the design domain with unit cells of ten different densities. To reduce the induced vibration, m-TMDs are placed inside the cavities of the unit cells in the grey scale regions. The effectiveness of the approach is demonstrated for the design of a 2-segment robot arm. The resulting unit cell robotic arm (UC-Arm) is 3.6% lighter than the reference model, maintains the same static performance, and shows a 60% smaller dynamic displacement in the observed frequency range. No extra space is required for the motion of the m-TMD.

Due to the continuous progress in information technology, complex problems of machine elements can be investigated using numerical methods. The focus of these investigations and optimizations often aims to reduce the stresses that occur or to increase the forces and torques that can be transmitted. Interference fit connections are an essential machine element for drive technology applications and are characterized by their economical fabrication. The transmission of external loads over a large contact surface between the shaft and hub makes it less vulnerable to impact loads. These advantages contrast with disadvantages such as the limited transmittable power, the risk of friction fatigue, and stress peaks at the hub edges, which can lead to undesirable and sudden failure, especially in the case of brittle hub materials. Analytical approaches already exist for optimizing these connections, which are expensive, time-consuming, and complex, so a high degree of expert knowledge is required to apply these methods in practice successfully. This paper presents a novel method using the example of optimizing the pressure distribution in the interface of a shrink-fit connection.