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The influence of surface determination in x-ray computed tomography
Get PDFSun, Wenjuan - Industrial SupervisorX-ray computed tomography (XCT) is a non-destructive technique that enables the dimensional inspection of the internal and external features of modern components. All dimensional attributes, including form, size and surface texture, are derived from the surface of the component, therefore, surface determination (SD) is a critical step in X-ray computed tomography (XCT) for dimensional metrology. Threshold-based and gradient-based algorithms are widely used. However, these algorithms are often sensitive to systematic errors and can be largely influenced by the input parameters selected by operators.
Current research gaps include the limited knowledge related to the quantifiable impact of SD algorithms on the traceability of the XCT geometric measurements and to the SD algorithm robustness to threshold setting. It was postulated here that Marker-controlled watershed (MCW), successfully applied in the medical field, can overcome the potential threshold errors encountered with current SD algorithms and provide robust results even in the presence of typical XCT errors.
As a result, this thesis presents the development of a robust SD algorithm for XCT based on the MCW method, which combined weighted gradient voxel refinement and automated marker generation. The validation and comparison of MCW were accomplished by employing calibrated workpiece for experimental XCT scanning, and simulation.
The work has further evaluated the performance of MCW algorithm and two commonly applied SD algorithms, Canny and ‘advanced mode in VGStudio’ (VG) algorithms, under beam hardening and noise effect. SD thresholding parameters were optimised by Taguchi methods when evaluating different types of surface conditions, including smooth and rough surfaces.
Canny presented the worst performance during the measurement of rough surfaces but performed well in smooth surface measurement cases. VG performed worst in bi-directional measurement under the influence of beam hardening but successfully computed the rough surface when beam hardening was corrected. The proposed MCW demonstrated that it is an all-rounded algorithm. Compared to other surface determination algorithms, the MCW algorithm presented consistent results with little influence from the beam hardening, noise and threshold settings. This demonstrates a great potential that the proposed SD algorithm can be fully automated in XCT.PhD in Manufacturin
Biochar amendment and water level optimization enhance nitrogen removal and reduce N2O emissions in vertical flow constructed wetlands via metagenomic analysis
Get PDFTo explore how biochar influences nitrogen cycling in unsaturated, capillary, and saturated zones of partially saturated vertical flow constructed wetlands (VFCWs), three parallel VFCWs were established to examine the effects of biochar's better water holding capacity on nitrogen removal and N2O emissions. Microbial mechanisms involved were studied by conducting ETS activity, metagenomic sequencing and performing high-throughput sequencing of 16S rRNA. Results indicated that the combination of adding 40 % biochar and maintaining water level of 45 cm facilitated TN removal and suppressed N2O emissions, achieving TN removal efficiency of 73.4 % and N2O/removed TN value of 0.3 %. Within the unsaturated zone, the relative abundance of amoA, hao, and nxrB increased by 929 %, 454 %, and 38.3 %, respectively, enhancing nitrification capacity microorganisms carrying these genes and involved in the oxidation of NH4+-N to NO3--N included Nitrosomonas, Methylosarcina, Nitrosospira, and Methylomonas, whose relative abundance increased by 75.2 %. In the capillary zone, the 19.2 % increase in nosZ (involved in the reduction of N2O to N2) transformed it into a potential N2O consumption layer. The functional genera involved in N2O reduction (Ferrovibrio, Thauera, Ramlibacter, and Hyphomicrobium) in the capillary zone increased by 1724 %, 357 %, 707 %, and 78.5 %, respectively, and the ETS activity in the 40W-CW capillary zone was 72.5 % higher than that of QS-CW. Within the saturated zone, the relative abundance of amoA increased by 591 %, hao by 149 %, and nxrB decreased by 20.0 %, potentially facilitating short-cut denitrification.This research is financially supported by the National Natural Science Foundation of China (52260024, 52360024), the Guangxi Key Research and Development Program (Guike AB22080067).Journal of Environmental Managemen
Design of ultra-precision piston/cylinders for directly calibrated hydraulic amplification
Get PDFForce calibrations in the Mega Newton range are not currently providing the accuracy
required by industry. This work addresses a hydraulic force standards, which perform
such calibrations, which work b amplifying the force produced on a small piston/cylinder
assembly (PCA) by connecting it hydraulically to a larger PCA. The force standards are
currently calibrated using a transducer to the more accurate deadweight machines, but not
without a resulting uncertainty of 0.01 % - 0.02 % imparted by the transducer performing
the calibration. Because of this, the potential for evaluating uncertainties within the
machine from first principles and using it as a primary machine, with traceability
maintained to the weights is evaluated. First of all the potential for the resulting reduction
in uncertainties is evaluated analytically, then FEA/CFD work is carried out to produce
analytical formulae which allow for the design of a directly calibrated system. The
analysis is carried out for both the rotational and non-rotational variants of PCA. Finally,
the design of a primary hydraulic standard is carried out using FEA/CFD.PhD in Manufacturin
An interpretable temporal convolutional framework for Granger causality analysis
Get PDFMost existing parametric approaches for detecting linear or nonlinear Granger causality (GC) face challenges in estimating appropriate time delays, a critical factor for accurate GC detection. This issue becomes particularly pronounced in nonlinear complex systems, which are often opaque and consist of numerous components or variables. In this paper, we propose a novel temporal convolutional network (TCN)-based end-to-end GC detection approach called the Interpretable Temporal Convolutional Framework (ITCF). Unlike conventional deep learning models, which act like a “black box” and are difficult to analyse the interactions between variables, the proposed ITCF is able to detect both linear and nonlinear GC and automatically estimate time delay during the multivariant time series prediction. Specifically, GC is obtained by employing the Least Absolute Shrinkage and Selection Operator (Lasso) regression during the prediction of multivariate time series using TCN. Then, time delays can be estimated by interpreting the TCN kernels. We propose a convolutional Hierarchical Group Lasso (cHGL), a hierarchical regularisation approach to effectively utilise temporal information within each TCN channel for enhanced GC detection. Additionally, as far as we are concerned, this paper is the first to integrate the Iterative Soft-Thresholding Algorithm into the backpropagation of TCN to optimise the proposed cHGL, which enabling causal channel selection and inducing sparsity within each TCN channel to remove redundant temporal information, ultimately creating an end-to-end GC detection framework. The testing results of four experiments, involving two simulations and two real data, demonstrate that the proposed ITCF, in comparison with state-of-the-art, offers a more reliable estimation of GC relationships in complex systems featuring intricate dynamics, limited data lengths, or numerous variables.IEEE/CAA Journal of Automatica Sinic
Renewable energy enhanced combined cycle for LH2 carrier ship
Get PDFSampath, Suresh - Associate SupervisorAchieving global climate change mitigation targets requires serious solutions to the
global energy sources decarbonisation. Hydrogen as a clean alternative fuel represents a
successful and viable option to achieve the future zero-carbon target, and it is
considered a leading contender as a fuel for the future economy, and it is anticipated
that there will be a strong demand for vessels capable of transporting liquefied hydrogen
from the production to the consumption sites. However, the seaborn transportation of
hydrogen represents a significant gap in the hydrogen supply chain; this leads to the
following philosophical question: what would the hydrogen carrier ship look like? The
novel contributions of this study are to present a design and evaluation for a liquefied
hydrogen (LH2) tanker fuelled by hydrogen named JAMILA in support of
decarbonisation, storage and transportation of liquefied hydrogen with a total capacity
of ~280,000 mᶟ as a cargo and uses the boil-off gas for propulsion for the loaded leg of
the journey. A hydrogen-fuelled combined-cycle gas turbine was modelled and
examined as a ship prim-mover to achieve the twin objectives of high efficiency and
zero-carbon footprint. Also, the study presents an economic analysis of a liquefied
hydrogen tanker, to determine the viability of using such ships to transport hydrogen in
the future to contribute toward implementing a green hydrogen economy. In terms of
operation, the LH2 tanker was simulated for different journeys under various conditions
to assess the ship's performance in terms of efficiency and environmental aspects.
Moreover, the design was developed by implementing a set of 6 Flettner rotors for the
JAMILA ship to achieve the fuel-saving and emission reduction targets. Established
methods were employed using state-of-the-art design and analysis for determining the
LH2 tank sizing, ship hull design, ship stability, and ship characteristics. Additionally,
the ship propulsion system was designed and evaluated based on the ship resistance
requirements in off-design and degraded performance of the gas-turbine topping cycle.
Moreover, a techno-economic and environmental risk assessment (TERA) method were
developed to evaluate the suggested design in different conditions such as loaded and
unloaded conditions, a normal and 6% degraded engine, and various weather conditions
in different scenarios. The results indicated that the LH2 tanker could carry 20,000
tonnes of liquefied hydrogen in a fully loaded with a displacement tonnage of 232,000
tonnes, and the design has been shown to be stable. The liquid hydrogen boil-off is
supplied to the ship's main engine, thereby saving 29% of the liquefied hydrogen fuel
consumption of the ship in the fully loaded condition. Its propulsion system contains a
combined-cycle gas turbine of approximately 50 MW. The results reveal that the output
power allows ship operation at a great speed even with a degraded engine and adverse
ambient conditions. However, implementing 6 Flettner rotors for the LH2 tanker ship
impacts the performance and leads to environmental benefits. A maximum contribution
power of around 1.8 MW was achieved, saving approximately 3.6% of the combined-
cycle gas turbine total output power (50 MW) and causing a 3.5% reduction in NOx
emissions. Economically, the results indicate that the JAMILA ship's implementation
can cover the ship's capital cost within no more than 2.5 and 6 years in the best and
worst-case maritime shipping prices conditions, respectively. The economic assessment
results indicate a promising perception of the future development of sustainable energy
systems and provide new information that is important for bridging the gap between
research, development, and implementation of a green hydrogen economy that will
contribute to mitigating climate change. Investments in ships such as the vessel
designed and evaluated in this study represent an essential constituent of the
decarbonisation process and could be one of the solutions to achieving almost the zero-
emissions target in the future. This study definitively answers the question regarding the
hydrogen tankers design and assessments.PhD in Aerospac
Study of visualization modalities on industrial robot teleoperation for inspection in a virtual co-existence space
Get PDFIn accordance with institutional ethics protocols and the project proposal submitted regarding participant data retention, the dataset supporting this study’s findings was subject to a 12-month storage limit, which has now expired. Therefore, the data is no longer available.Effective teleoperation visualization is crucial but challenging for tasks like remote inspection. This study proposes a VR-based teleoperation framework featuring a ‘Virtual Co-Existence Space’ and systematically investigates visualization modalities within it. We compared four interfaces (2D camera feed, 3D point cloud, combined 2D3D, and Augmented Virtuality-AV) for controlling an industrial robot. Twenty-four participants performed inspection tasks while performance (time, collisions, accuracy, photos) and cognitive load (NASA-TLX, pupillometry) were measured. Results revealed distinct trade-offs: 3D imposed the highest cognitive load but enabled precise navigation (low collisions). 2D3D offered the lowest load and highest user comfort but slightly reduced distance accuracy. AV suffered significantly higher collision rates and participant feedback usability issues. 2D showed low physiological load but high subjective effort. No significant differences were found for completion time, distance accuracy, or photo quality. In conclusion, no visualization modality proved universally superior within the proposed framework. The optimal choice is balancing task priorities like navigation safety versus user workload. Hybrid 2D3D shows promise for minimizing load, while AV requires substantial usability refinement for safe deployment.This work is supported by the BNBU Research Grant with No. UICR0700120-25 at Beijing Normal-Hong Kong Baptist University, Zhuhai, PR China. This work is also supported by the Centre for Digital Engineering and Manufacturing (CDEM) of Cranfield University in the UK.Virtual World
A 4WD drift assist control framework on general rally track
No full textIn this paper, we present a novel state machine-based 4-Wheel-Drive (4WD) drift assist controller with four drive modes, to allow the driver transition into and out of drifting in corners intuitively. To interpret the driver’s intention to drift, we rely on the driver’s inputs and vehicle motion signals. During drifting, without prior knowledge of path geometry, we introduce a ‘Trajectory Radius Predictor’ to assist the driver stay on the desired course and provide the necessary reference yaw rate for the subsequent torque vectoring controller. Additionally, we restrict the sideslip rate to allow the driver sufficient reaction time to counter steer and follow the desired track when the sideslip changes during drifting. To validate our controller, we consider different driving behaviours from both the CarMaker driver model and a human driver using the driving simulator of Cranfield University. The Pikes Peak rally track in IPG CarMaker is utilised for evaluation, and the results have demonstrated the effectiveness of the proposed framework.The financial and intellectual contributions of Rimac Technology are acknowledged for their role in supporting this research.Vehicle System Dynamic
A systematic review of decision tools for process selection and performance improvement in manufacturing
No full textThe growing complexity of manufacturing processes and the increasing diversity of decision-making tools present challenges in selecting effective approaches for process optimisation. Many existing tools are either too narrowly focused or inconsistently applied across sectors, limiting their broader impact. Additionally, the lack of clear integration strategies often hinders their full implementation in industrial settings. This systematic review examines decision-making tools that enable comparative assessments applied at the unit process level in manufacturing, covering both the selection between competing manufacturing routes and the optimisation of specific processes. A total of 37 journal articles were selected through a structured database search and evaluation process. The review analyses commonly used tools such as Multi-Criteria Decision Analysis (MCDA), Life Cycle Assessment (LCA), and Direct Comparison, highlighting their applications, benefits and limitations. Findings show that MCDA offers robust, multi-dimensional evaluations but is often constrained by complexity and data demands. In contrast, simpler methods like Direct Comparison provide more accessible insights but with a limited scope. Advanced tools such as Deep Learning and Computational Simulations hold promise but face challenges in scaling beyond the process level. Notably, there is limited integration of sustainability metrics within process-level decision-making. To address this, the study proposes a structured framework to guide future research and implementation, focusing on data management, AI integration and tool scalability. The results highlight the need for hybrid approaches that combine different tools to balance trade-offs and support long-term sustainability and operational efficiency in manufacturing systems.The authors would like to acknowledge the UK EPSRC project “Transforming the Foundation Industries Research and Innovation Hub (TranFIRe)” (EP/V054627/1) for the support of this work.The International Journal of Advanced Manufacturing Technolog
Trajectory optimization with sparse gauss-hermite quadrature
Get PDFThis thesis aims to innovate knowledge on the trajectory optimization problem for aerospace
applications by adopting a numerical integration in optimal control designs. Quadrature
point scheme is considered to substitute a derivative of an optimal cost-to-go function and
system dynamics approximated by Gaussian Quadrature rule.
Based on the Differential Dynamic Programming (DDP) algorithm, a sequence of optimal control input is derived by sparsely chosen quadrature points with the Smolyak’s
rule over an exponential weighting function on the Gaussian quadrature, called as Sparse
Gauss-Hermite Quadrature (SGHQ). The sampling points propagated via system dynamics computes the mean and covariance of a probability distribution of the value function
avoiding a numerical differentiation in the DDP. This approach improves an accuracy and
robustness against the numerical calculation in the highly nonlinear environment despite
the less number of the quadrature points compared with the fully composed by Gauss-
Hermite Quadrature. Besides, the number of sampling points can be determined by Smolyak’s rule definitely, while the other sampling point-based approach chooses by heuristic/empirical method such as a trial and error. The proposed method is carried out and
validate via numerical simulation: a fixed-wing aircraft controller and missile guidance.
Considering a stochastic environment and control policy, trajectory optimization problem can be extended to a stochastic trajectory optimization and maximum entropy problem by adding an entropy term in a deterministic trajectory optimization problem: a
Guided Policy Search (GPS). This entropy term in a stochastic problem enables to prevent
a control policy from falling into local minima by maximizing exploration in the given
unknown environment, and improves robustness to respond to a potentially flexible environment. A local policy is updated by DDP framework where the SGHQ-DDP can be
implemented to find a mean and covariance of policy distribution by solving the soft Bellman equation. The numerical simulation shows the feasibility of the SGHQ-GPS method
under an unknown system dynamics under the fitted model.Education Department - Korean GovernmentPhD in Aerospac
Supersonic projectile flow field reconstruction using background oriented schlieren and physics informed convolutional neural networks
Get PDFSession: Supersonics Design and AnalysisThis work explores the use of axisymmetric background-oriented schlieren (BOS) imaging for reconstructing supersonic flow fields over a scaled NATO 5.56 mm M855 projectile at Mach 1.50, 2.00, and 2.50, as well as a 15° cone at Mach 2.50. A method for recovering density fields from BOS displacement maps was implemented, with results compared to a Taylor–Maccoll solution for the cone and a RANS CFD wind tunnel model for the projectile. Density field reconstructions showed errors below 15% overall and under 10% across most of the field, with the largest deviations near shock boundaries and stagnation regions. Additionally, force balance measurements were conducted on the projectile at Mach 2.50, showing an agreement of 1.2% with firing data from the literature and 8% with the RANS model. A custom U-Net was subsequently trained to predict pressure, temperature, and velocity fields from grid-transformed numerical density inputs over the cone, using a physics-exclusive loss function derived from the Euler conservation laws and specified boundary conditions. However, large residuals near the shock and stagnation point due to grid interpolation were found to impede the network’s performance. A purely data-driven model demonstrated good accuracy for pressure and temperature, a moderate performance for radial velocity, and poor accuracy for axial velocity. The model failed to generalize when fed with experimental data, reinforcing the need for strong physical constraints.AIAA Aviation Forum and Ascend 202