Vertical take-off and landing (VTOL) vehicles are gaining traction in both the delivery drone market and passenger transportation, driving the development of urban air mobility (UAM) systems. UAM seeks to alleviate road congestion in dense urban areas by leveraging urban airspace. To handle UAM traffic, vertiport terminals (vertiminals) play a critical role in supporting VTOL vehicle operations such as take-offs, landings, taxiing, passenger boarding, refuelling or charging and maintenance. Efficient scheduling algorithms are essential to manage these operations and optimise vertiminal throughput while ensuring safety protocols. Unlike fixed-wing aircraft, which rely on runways for take-off and climbing in fixed directions, VTOL vehicles can utilise multiple surface directions for climbing and approach. This flexibility necessitates specialised scheduling methods. We propose a mixed integer linear programme (MILP) formulation to holistically optimise vertiminal operations, including taxiing, climbing (or approach) using multiple directions and turnaround at gates. The proposed MILP reduces delays by up to 50%. Additionally, we derive equations to compute upper bounds of the throughput capacity of vertiminals, considering its core elements: the touchdown and lift-off (TLOF) pad system, taxiway system and gate system. Our results demonstrate that the MILP achieves throughput levels consistent with the theoretical maximum derived from these equations. As a case study, we applied our throughput analysis on a vertiminal topology in the literature and used our MILP to find the optimal configuration. This dual approach, MILP and throughput analysis, allows for comprehensive capacity analysis without requiring simulations while enabling efficient scheduling through the MILP formulation.

In this study, a hybrid propulsion-powered small fixed-wing unmanned aerial vehicle (UAV) was designed to enhance endurance using solar energy. The UAV, a solar-powered vertical take-off and landing (VTOL) with a 1.8 m wingspan and a take-off mass of 3.3 kg, was equipped with a propulsion system comprising solar cells, a battery, a supercapacitor and a DC/DC converter, which was modelled in MATLAB/Simulink to evaluate energy management strategies. To optimise energy utilisation, fuzzy logic (FL), equivalent consumption minimisation strategy (ECMS) and quantum particle swarm optimisation (QPSO) algorithms were implemented. Notably, the QPSO algorithm was integrated into the solar energy management system for the first time. Optimisation results indicate that the QPSO algorithm harnesses solar energy more rapidly and efficiently than other strategies, significantly improving the UAV’s endurance. The time required for the QPSO algorithm to reach maximum power is 1.7948 s and 1.5028 s, shorter than that of the FL and ECMS algorithms, respectively. This result demonstrates that the QPSO algorithm exhibits a fast dynamic response and adapts more efficiently to sudden power demands. Furthermore, considering the time required to reach the maximum power output of 77 W from the solar cell, the corresponding contributions to endurance are calculated as 1.22 h for QPSO, 0.51 h for FL and 0.06 h for ECMS.

This study presents a surrogate-model-assisted Quasi-Newton optimisation framework for simultaneously improving the aerodynamic performance and radar stealth characteristics of an unmanned aerial vehicle (UAV). High-fidelity computational fluid dynamics (CFD) and computational electromagnetics (CEM) simulations are integrated through surrogate models generated via a face-centred central composite design within a design of experiments framework. Quadratic polynomial response surface equations are constructed for key aerodynamic and radar cross-section (RCS) metrics, enabling analytical gradient evaluation. A gradient-based quasi-Newton method with Broyden–Fletcher–Goldfarb–Shanno Hessian updates is employed to minimise a scalarised objective function combining normalised maximum lift coefficient, overall RCS and frontal RCS. Constraints are imposed on the lift-to-drag ratio ($L/D \geq 10$) and static longitudinal stability (${C_{m0}} \geq 0$). Analytical derivatives from the response surface equations (RSEs) eliminate the need for direct numerical differentiation of CFD/CEM outputs, reducing computational cost and eliminating simulation noise. An interior-point sequential quadratic programming strategy is used to ensure satisfaction of nonlinear constraints during the optimisation process. The optimised UAV design demonstrates a $12{\rm{\% }}$ increase in maximum lift coefficient and a $30{\rm{\% }}$ reduction in both overall and frontal RCS compared to the baseline configuration. The results are confirmed through high-fidelity CFD and RCS simulations and are further validated experimentally in an anechoic chamber, with close agreement across all measured frequencies. The proposed methodology provides an efficient and experimentally verified approach for integrated aerodynamic and stealth optimisation in UAV design.

There is great potential for engineering design approaches in medicine to personalize treatments according to unique patient physiology and needs. However, it is challenging to optimize solutions such as medical implants given the complex biomechanical interactions between the body and implant. Here, we review personalization for clinical needs, biomechanical modelling, and computational design for interbody spinal cage implants. By reviewing relevant literature, research suggests specific clinical needs are addressable by redesigning cages with multi-objective optimization or artificial intelligence methods integrated with finite element modelling of the spine. Such an approach is generalizable to further biomechanical design cases, where personalized design provides promise to deliver higher quality solutions for the clinic.

Managing high-variant product portfolios effectively is a crucial competitive advantage in offering mass customized products on saturated markets. Association Rule Mining (ARM) is a field of data mining determining frequent itemsets from historic transactions and deriving patterns of conclusion. This paper introduces a new approach to transfer ARM to feature-based configuration e.g. in the German automotive industry. Combined, existing apriori product knowledge is used in constraints to effectively lowering runtime by reducing the number of candidate-sets through introduction of a Boolean satisfiability check. For an efficient implementation, three different Apriori algorithms are tested and benchmarked on a generic dataset for different parameters. Results show a significant improvement in using SAT-based pre-screening while efficiency of the implementation depends on the given example.

Substantial engineering efforts are dedicated to reducing injury risks in crash scenarios during the development of new vehicles. This is achieved by performing crash simulations to optimize the nonlinear behavior of systems. However, the complexity makes their behavior difficult and time-consuming for engineers to understand. To reduce the analysis time, this study introduces a modular framework combining Explainable Artificial Intelligence and Large Language Models (LLM). Shapley Additive Explanation values allow for simulation-wise feature importance attribution and generate a data-driven understanding. An LLM assists by making result data interactively accessible and supports technical report generation. Validated through a real-world vehicle side crash optimization use case, the framework demonstrates enhanced and accessible insights into system behavior within virtual engineering.

Light weight design Plans am cranial role in enhancing efficiency and sustainability. The strategic use of advanced materials, such as fiber-reinforced plastics, can help achieving lightweight designs. However, the anisotropic material properties of composite materials also lead to new challenges in the design and manufacturing process. Additionally, due to the layered structure of composite parts, the number of design points is increased drastically. Moreover, the complex manufacturing process, including curing, makes composite parts prone to variations. Therefore, this research paper presents an innovative lightweight design approach that aims to overcome the described difficulties by linking the individual simulation steps, providing a continuous simulation strategy and taking variations into account. Finally, the presented simulation strategy is applied to an electrified cross skate.

Topology optimization combined with additive manufacturing enables the creation of complex, high-performance products. However, industrial applications often involve numerous and complex requirements, making it challenging to align the design and manufacturing process to meet all demands. A particular challenge is to determine which requirements should be included in the optimization problem statement. This paper presents a procedure model to integrate requirements and feasibility constraints into the design and manufacturing process. It includes two major steps: organizing requirements and constraints in the process and identifying the problem statement. The procedure is applied to the requirements of an engine bracket of AUDI AG, demonstrating its ability to handle numerous requirements and to specify the problem statement.

The application of Generative Artificial Intelligence (AI) in early-stage design processes has emerged as a promising method for generating innovative solution concepts. However, AI-driven concepts may introduce secondary problems when implemented practically. This study proposes a systematic framework integrating Generative AI (GPT-4o), patent analysis using Retrieval-Augmented Generation (RAG), and Failure Mode and Effects Analysis (FMEA) to predict, evaluate, and mitigate potential risks. Applied to a case study on nickel recovery through froth flotation, the framework significantly enhanced the feasibility, usefulness, and sustainability of solution concepts. The research highlights the scientific contribution and practical benefits of combining Generative AI with structured risk-analysis methods for sustainable innovation.

Need analysis is essential for organisations to design efficient knowledge management (KM) practices, especially in contexts where knowledge is a critical asset and evolving fast. The research explores the application of large language model (LLM)-based agents in automating need analysis for KM practices. A two-layered model using Retrieval-Augmented Generation (RAG) architecture was developed and tested on datasets, including interviews with managers and consultants. The system automates NLP analysis, identifies stakeholder needs, and generates insights comparable to manual methods. Results demonstrate high efficiency and accuracy, with the model aligning with expert conclusions and offering actionable recommendations. This study highlights the potential of LLM-based systems to enhance KM processes, addressing challenges faced by non-technical professionals and optimising workflows.

Product development is a dynamic, multidisciplinary field shaped by evolving customer demands and the need for individualized products, increasing product variety. Key factors include economic performance, customer satisfaction, and sustainability. Lightweight design drives innovation by enhancing weight-specific performance, optimizing resources, and reducing CO2 emissions, especially in transportation. However, conflicts arise as lightweight design focuses on individual variants, neglecting broader product family implications, while Design for Variety strategies often exclude lightweight design. This study examines the interplay between product variety and lightweight design, proposing a measurement framework to support the development of variant products and their components within product families in the context of lightweight design.

Accurate estimation of finger joint stiffness is important in assessing the hand condition of stroke patients and developing effective rehabilitation plans. Recent technological advances have enabled the efficient performance of hand therapy and assessment by estimating joint stiffness using soft actuators. While joint modular soft actuators have enabled cost-effective and personalized stiffness estimation, existing approaches face limitations. A corrective approach based on an analytical model suffers from actuator–finger and inter-actuator interactions, particularly in multi-joint systems. In contrast, a data-driven approach struggles with generalization due to limited availability of labeled data. In this study, we proposed a method for energy conservation-based online tuning of the analytical model using an artificial neural network (ANN) to address these challenges. By analyzing each term in the analytical model, we identified causes of estimation error and introduced correction parameters that satisfy energy balance within the actuator–finger complex. The ANN enhances the analytical model’s adaptability to measurement data, thereby improving estimation accuracy. The results show that our method outperforms the conventional corrective approach and exhibits better generalization potential than the purely data-driven approach. In addition, the method also proved effective in estimating stiffness in human subjects, where errors tend to be larger than in prototype experiments. This study is an essential step toward the realization of personalized rehabilitation.

Yield is impacted by the environmental conditions that plants are exposed to. Controlled environmental agriculture provides growers with an opportunity to fine-tune environmental conditions for optimising yield and crop quality. However, space and time constraints will limit the number of experimental conditions that can be tested, which will, in turn, limit the resolution to which environmental conditions can be optimised. Here we present an innovative experimental approach that utilises the existing heterogeneity in light quantity and quality across a vertical farm to evaluate hundreds of environmental conditions concurrently. Using an observational study design, we identify features in light quality that are most predictive of biomass in different kinds of microgreens (kale, radish and sunflower) that may inform future iterations of lighting technology development for vertical farms.

Designing optimal assistive wearable devices is a complex task, often addressed using human-in-the-loop optimization and biomechanical modeling approaches. However, as the number of design parameters increases, the growing complexity and dimensionality of the design space make identifying optimal solutions more challenging. Predictive simulation, which models movement without relying on experimental data, provides a powerful tool for anticipating the effects of assistive devices on the human body and guiding the design process. This study aims to introduce a design optimization platform that leverages predictive simulation of movement to identify the optimal parameters for assistive wearable devices. The proposed approach is specifically capable of dealing with the challenges posed by high-dimensional design spaces. The proposed framework employs a two-layered optimization approach, with the inner loop solving the predictive simulation of movement and the outer loop identifying the optimal design parameters of the device. It is utilized for designing a knee exoskeleton with a damper to assist level-ground and downhill gait, achieving a significant reduction in normalized knee load peak value by $ 37\% $ for level-ground and by $ 53\% $ for downhill walking, along with a decrease in the cost of transport. The results indicate that the optimal device applies damping torques to the knee joint during the Stance phase of both movement scenarios, with different optimal damping coefficients. The optimization framework also demonstrates its capability to reliably and efficiently identify the optimal solution. It offers valuable insight for the initial design of assistive wearable devices and supports designers in efficiently determining the optimal parameter set.

This paper describes a reverse engineering methodology so as to accomplish an aero-propulsive modelling (APM) through implementing a drag polar estimation for a case study jet aircraft in case of the absence of the thrust data of the aircraft’s engine. Since the available thrust force can be replaced by the required thrust force for the sustained turn, this approach allows the elimination for the need of the thrust parameter in deriving an aero-propulsive model utilising equations of motion. Two different modelling approaches have been adopted: (i) implementing the 6-DOF model data for sustained turn and climb flight to achieve induced drag model; and then incorporating the glide data to obtain the total drag polar model; (ii) using the 6-DOF model data together with introducing the effect of CL-α dependency. The error assessments showed that the derived CSA models were able to predict the drag polar values accurately, providing linear correlation coefficient (R) values equal to 0.9982 and 0.9998 for the small α assumption and CL-α dependency, respectively. A direct comparison between the trimmed CD values of 6-DOF model and the values predicted by the CSA model was accomplished, which yielded highly satisfactory results within high subsonic and transonic CL values.

This work shows that direct combustion of cotton gin waste (CGW) at cotton gins can profitably generate electricity. Many bioenergy processing centres emphasise very large-scale operations, which require a large and stable bio-stock supply that is not always available. Similarly, a small biorefinery processing gin trash at a cotton gin must wrestle with the high volatility of cotton yields and price variation in cotton and electricity. Fortunately, the smaller scale allows these risks to be somewhat countervailing. Low cotton yields allow the limited gin trash available to be applied to the highest peak electricity prices in winter. Similarly, high yields with low cotton prices generate revenue from power generation throughout high winter electric prices.

To assess the profitability of an onsite power plant requires high-resolution data. We utilise hourly electricity price data from 2010 to 2021 in West Texas and obtain a small data array of 15 years of gin trash at a medium-sized gin. Prior analyses have had neither. We leverage limited CGW data to better leverage generous electricity price data by generating a Bayesian distribution for CGW. We simulate 10,000 annual CGW outcomes and electricity prices. Using engineering parameters for combustion efficiency, we show the expected internal rates of return of 19–22% for a 1 MWe and a 2 MWe plant at a small gin. Simulations then compare economic returns to the variance of those returns, which allows the analyst to present to investors a frontier of stochastic dominant return outcomes (risk-returns trade-off) for plants of different sizes at different sized gins.

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.