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Through-the-thickness z-pinning reinforcements to improve energy absorption capabilities of CFRP crash structures: numerical development
Get PDFThis study employs numerical methods to model through-the-thickness reinforcements in CFRP tubular structures under axial impact, investigating the influence of reinforcement configurations on crashworthiness performance. Experimental validation involves testing unpinned tubular structures to establish a baseline model. LS-DYNA finite element models simulate low-velocity axial impacts, incorporating energy-based tiebreak contacts or solid cohesive elements to describe interlaminar bridging. Through-the-thickness are introduced through a homogenous mesh system or locally refined mesh at pin locations. Various reinforced tube designs with different pin diameters and areal densities are examined to identify the optimal pinned design for crashworthiness. The research demonstrates numerically that pinning enhances crashworthiness performances in axial crushing of composite tubes.Applied Composite Material
Multidisciplinary design optimization of the NASA metallic and composite common research model wingbox: addressing static strength, stiffness, aeroelastic, and manufacturing constraints
Get PDFThis study explores the multidisciplinary design optimization (MDO) of the NASA Common Research Model (CRM) wingbox, utilizing both metallic and composite materials while addressing various constraints, including static strength, stiffness, aeroelasticity, and manufacturing considerations. The primary load-bearing wing structure is designed with high structural fidelity, resulting in a higher number of structural elements representing the wingbox model. This increased complexity expands the design space due to a greater number of design variables, thereby enhancing the potential for identifying optimal design alternatives and improving mass estimation accuracy. Finite element analysis (FEA) combined with gradient-based design optimization techniques was employed to assess the mass of the metallic and composite wingbox configurations. The results demonstrate that the incorporation of composite materials into the CRM wingbox design achieves a structural mass reduction of approximately 17.4% compared to the metallic wingbox when flutter constraints are considered and a 23.4% reduction when flutter constraints are excluded. When considering flutter constraints, the composite wingbox exhibits a 5.6% reduction in structural mass and a 5.3% decrease in critical flutter speed. Despite the reduction in flutter speed, the design remains free from flutter instabilities within the operational flight envelope. Flutter analysis, conducted using the p-k method, confirmed that both the optimized metallic and composite wingboxes are free from flutter instabilities, with flutter speeds exceeding the critical threshold of 256 m/s. Additionally, free vibration and aeroelastic stability analyses reveal that the composite wingbox demonstrates higher natural frequencies compared to the metallic version, indicating that composite materials enhance dynamic response and reduce susceptibility to aeroelastic phenomena. Fuel mass was also found to significantly influence both natural frequencies and flutter characteristics, with the presence of fuel leading to a reduction in structural frequencies associated with wing bending.Aerospac
Life cycle prediction: a comparison of methods for a lighting products retailer
Get PDFProduct life cycle (PLC) prediction is one of the most challenging yet critically important aspects of supply chain management. Lost sales and excess inventory costs arise when there is a mismatch between demand and supply, especially at the beginning of a product’s life cycle when a new product is launched. Our proposed framework contributes to the application of decision-support systems in the prediction of PLCs of new products. In this study, we fit piecewise-linear curves, nth order polynomial curves, and Bass diffusion curves for PLC prediction and compare their effectiveness using real data from a retailer specializing in lighting products. We estimate the PLCs of 2 615 lighting products using these models and select the best-fit curve to predict their PLCs. We also develop an algorithm to address challenges posed by imbalanced datasets and apply it in neural networks for predictive modeling to determine a product’s PLC stage, demand class, and stocking decisions. The findings show that fourth-order polynomial curves can accurately predict the PLCs of 63% of the products. Bass diffusion curves emerge as the second-best performer. Our approach can be generalized to other products in other industries, and it can effectively guide end-of-life inventory decisions.IEEE Transactions on Engineering Managemen
Predicting out-terminals for imported containers at seaports using machine learning: Incorporating unstructured data and measuring operational costs due to misclassifications
Get PDFPersistent bottlenecks at container ports have significantly disrupted global supply chains, necessitating more efficient operations at seaports to address yard density and port congestion. An untapped but potentially critical approach to mitigating these challenges is to leverage container characteristics and machine learning to predict the out-terminals of containers upon their discharge from vessels. The predicted results can then guide the development of a more effective container storage strategy. To formulate such a strategy, this research developed a data-enabled methodological framework that integrates four key components: 1) Utilization of structured and unstructured data to enhance prediction accuracy. 2) Practice and knowledge-informed feature engineering to construct relevant features for the machine learning models. 3) Explanatory machine learning based classification models to understand the factors influencing terminal predictions. 4) Model-induced cost analysis to capture the monetary value of the prediction model including assessing the cost implications of misclassifications. An empirical study conducted at a seaport shows that our framework yields cost savings ranging from 14.90% to 30.45% compared to the Business-as-Usual scenario. Incorporating unstructured data as an additional feature in the machine learning models improves prediction performance by up to 6%. Moreover, integrating this framework into the existing operational system poses minimal risk and can be seamlessly executed. Additionally, the proposed methodological framework and its four components has broad applications beyond the shipping industry.This work was partially supported by the UK Engineering and Physical Sciences Research Council (EPSRC) [grant numbers EP/W028492/1 and EP/Y024605/1].Transportation Research Part E: Logistics and Transportation Revie
Multi-fidelity design optimization of installed aero-engines with non-axisymmetric exhausts
Get PDFLarger ultra-high bypass ratio (UHBR) aero-engines introduce an aerodynamic integration challenge. In close-coupled, podded underwing configurations, the aerodynamic interference between the propulsion system and the airframe could penalize the aircraft net vehicle force (NVF) and erode some of the novel cycle benefits and fuel burn reduction. Non-axisymmetric designs of the bypass nozzle can improve the performance of the aircraft by mitigating some of the penalizing effects induced by the integration of the powerplant. However, due to the prohibitive computational cost of the design methods, only lower-fidelity design approaches have been feasible in an industrial time-scale. This work develops a relatively low-cost multi-fidelity design optimization methodology for non-axisymmetric exhausts where the effects of the propulsion system installation are considered. The methodology combines inviscid and viscous aerodynamic data to formulate multi-fidelity surrogate models which drive a genetic algorithm (GA) optimization. The method enabled the incorporation of the viscosity effects in the optimization process at a reasonable computational cost and led to better designs relative to a methodology based only on lower-fidelity data. Overall, the optimization of non-axisymmetric exhausts can benefit the net vehicle force of the complete engine–aircraft system in cruise by up to 0.9% of the engine standard net thrust which can reduce fuel burn by a similar amount. The optimization with multi-fidelity surrogate models reduced the computational time by a factor of four relative to a method based only on viscous aerodynamic data.Rolls Royce and Cranfield UniversityJournal of Engineering for Gas Turbines and Powe
A novel railway maintenance robot for inspection and repair
Get PDFStarr, Andrew - Associate SupervisorRobotics and automation are widely used in various sectors for their economic
benefits, accuracy, and efficiency. However, the railway industry has been slow
to adopt these technologies for track inspection and repair, despite the increasing
demands of asset management. Advances in robotics, computing, sensors, and
Industry 5.0 are driving companies to explore innovative inspection and repair
methods to optimize resource usage. Mobile manipulators, which combine robot
mobility with industrial precision, have the potential to replace humans in risky
and tedious tasks.
In this research, a Robotic Inspection and Repair System (RIRS) has been
developed to establish an improved track inspection method and a robotic repair
technique. With a 0.27% error rate in calculating defect positions and 1mm
precision in actuation, RIRS demonstrates strong feasibility for railway track
maintenance.
Later, an improved inspection method has been proposed fusing 3D
reconstructed model of the target object from the monocular camera with the
large-scale Global Positioning System (GPS) data, medium-scale environment
perception from Light Detection And Ranging (LiDAR) and small-scale Colour
and Depth (RGB-D) model. Texture of surface and less than 5%-dimensional
error of the 3D model compared to the physical model ensures the credibility of
the improved inspection technique which provides more information of the target
object and sets the foundation for a data-rich digital twin in the future.
Finally, the framework of both human-in-the-loop repair task and autonomous
simulated repair task have been proposed. Successful delivery of the correct tool
based on the detected defect upon receiving command from the human operator
and automatic circular trajectory generation demonstrate the prospects of RIRS
for assisting human in track repair tasks.PhD in Manufacturin
Gust rejection in multirotor aircraft using sliding mode control
No full textMultirotor aircraft are being used in many applications because of their mechanical simplicity and high
manoeuvrability. Many potential applications of multirotor aircraft are in gusty and turbulent environ-
ments, it is imperative for them to have a stable hovering as well as offer resistance to transient gusts
and winds. Many research studies have been carried out with regards to control of the multirotor aircraft
but not much work has been undertaken considering the aerodynamic effects impacting the aircraft, in
particular the rotor tilt angle. As the Vertical Take Off and Landing (VTOL) aircrafts are light-weight,
they are prone to gusty wind conditions and desired landing and hovering is a great challenge during
these disturbances.
In this research work, an analysis of the effect of rotor tilt on the stability and gust rejection properties is
performed with a conceptual planar birotor initially and then extended to quadrotors. The gust rejection
properties of multirotor aircraft are also examined using sliding mode control taking rotor tilt angle into
account. Models of the aircraft were developed in MATLAB/Simulink to implement nonlinear dynamic
equations of the multirotor vehicles. A range of rotor tilt angles have been considered and investigated for
gust rejection through extensive simulation studies using Proportional-Integral-Velocity (PIV) control,
Sliding Mode Control (SMC) of altitude, attitude and PIV position control and a complete SMC control
of altitude, attitude and position control. The performance of the three controllers were compared
through numerical simulations. It was found that the complete sliding mode controller is very robust
at rejecting the gust and shows that the rotor tilt angle does not impact on the vehicle stability when
used with SMC, even in the presence of parametric uncertainties and external disturbances. Finally,
suggestions for further work based on this research are presented on further design and development of
sliding mode controllers for di erent multirotor con gurations.PhD in Aerospac
Insight into the stickiness of faecal sludge from dry sanitation technologies: a path toward sustainable and efficient FSM via thermal processes
Get PDFThis study explores the stickiness behaviour of faecal sludge (FS) during thermal drying—an operational challenge that hampers the performance of faecal sludge management (FSM) systems. Samples were collected from ventilated improved pit (VIP) latrines and urine diversion dry toilets (UDDTs) in Durban, South Africa, and analysed using a texture analyser to measure adhesive and cohesive forces over a temperature range of 25–80 °C and moisture contents between 20–90 wt.%. Complementary tests were conducted to assess water activity, drying kinetics, rheological properties, and plastic behaviour. Maximum stickiness occurred in the 50–60 wt.% moisture range. In this region, FS transitioned from a viscoelastic fluid to lumpy and plastic consistency dominated by interstitial moisture, and eventually to a granular solid at the end of the sticky region, as interstitial water was depleted. The sticky phase coincided with the transition from the first to the second falling-rate period of drying, reflecting a shift from surface to internal moisture evaporation. Cohesive forces were consistently greater than adhesive forces and increased modestly at 80 °C. UDDT sludge was slightly stickier than VIP sludge under similar conditions. The results highlight the strong dependence of FS stickiness on moisture content and its binding properties. To address this issue, the study proposes mitigation strategies such as bypassing the sticky range, using bulking agents, or applying pre-treatments to improve drying performance. These findings provide practical guidance for the design and operation of sludge treatment systems and contribute to more sustainable FSM practices.The authors gratefully acknowledge the Bill and Melinda Gates Foundation for their financial support, which made this research possible.Results in Engineerin
Techno-economic and environmental assessment of floating solar power with innovative charging systems for decarbonizing maritime operations in the UK
No full textMaritime transportation contributes around 3 % of global emissions. As global trade and manufacturing expand, the decarbonization of maritime operations becomes an urgent challenge. Ferry ports in the UK face significant barriers to energy transition, including limited grid capacity, lack of charging infrastructure, and constrained land availability. This study proposes the development of a Floating Photovoltaic (FPV) plant on the sea near the port to independently generate renewable electricity for charging electric vessels operating between UK and France. Four scenarios are analyzed, varying in energy generation targets and ground coverage ratios (GCRs). Energy performance is evaluated using the System Advisor Model (SAM), estimating electricity generation and battery energy storage system (BESS) requirements under limited solar irradiance. A comprehensive economic analysis examines capital expenditure (CAPEX), operational expenditure (OPEX), levelized cost of energy (LCOE), revenue, and payback periods. The study also assesses environmental benefits by quantifying CO2 emissions for FPV lifespan and compares them to diesel-based energy. Moreover, charging technologies are reviewed in relation to current technologies, and a logistics plan for integrating FPV systems and electric vessels is proposed. Results demonstrate that the FPV plant can minimize BESS requirements, and reduce payback periods to as little as 3.62 years, facilitating the pathway of ferry ports to achieve net-zero emissions by 2045, with an estimated reduction of 17million tonnes of CO2 annually. This study is among the first to assess the feasibility of using FPV systems to charge electric vessels at a UK marine port, integrating real-world spatial constraints, phased deployment planning, and life-cycle environmental analysis. It also introduces the conceptual integration of floating wireless charging infrastructure, offering a forward-looking approach to maritime electrification.Engineering and Physical Sciences Research Council UK under Grant Agreement No. EP/Y024605/1.Transport Research and Innovation Grants supported by the UK Department for Transport and Connected Places Catapult under Grant Agreement No. TRIG2023-30066Renewable Energ
Towards in-situ failure assessment: deep learning on DIC results for laminated composites
No full textPredicting fracture load in laminated composites with stress raisers is challenging due to complex failure mechanisms such as delamination, fibre breakage, and matrix cracking, which are heavily influenced by fibre orientation, layup sequence, and notch geometry. This study aims to address this by developing a novel deep learning framework that leverages solely experimental strain field data from Digital Image Correlation (DIC) for accurate, in-situ predictions—bypassing the need for finite element simulations or empirical calibrations. Two alternative architectures are explored: a multi-layer perceptron (MLP) that processes numerical values of maximum principal strain from a targeted rectangular region ahead of the notch, enhanced by advanced feature selection (mutual information, Lasso, and SHAP) to focus on critical data points; and a convolutional neural network (CNN) trained on full-field strain images, bolstered by data augmentation to handle variability and prevent overfitting. Validated across 116 quasi-static tests encompassing 31 distinct configurations—including six layups (quasi-isotropic to highly anisotropic) with four off-axis angles for open-hole specimens, and one cross-ply layup with four off-axis and four on-axis notch orientations for U-notched specimens—the MLP and CNN achieve coefficients of determination (R2) of 0.86 and 0.82, respectively. The framework captures a broad spectrum of damage modes and responses, from brittle fibre-dominated fracture to ductile delamination-driven failure, and due to its computational efficiency and reliance only on DIC measurements, the approach enables practical in-situ fracture load estimation.Composites Part B: Engineerin