20505 research outputs found
Sort by
Predicting penetration performance in current and novel sustainable tissue simulants using quasi‐static compression data
No full textHistorically, ballistic gelatine has been used as the industry standard when conducting experiments associated with survivability, ammunition lethality, and wound ballistics. This biopolymer is used to replicate human tissue and examine permanent and temporary cavitation. However, previous works have highlighted a promise in using foodstuffs gelatine to provide economic benefit to research programmes and reduce the environmental impact caused by a single source supplier. This work compares alternative foodstuffs’ porcine gelatine to vegan alternatives using quasi‐static compression testing to investigate differences in performance, repeatability, manufacturing considerations, and a comparison on transparency when compared to 10% ballistic gelatine. Vegan alternatives were identified as suitable candidates to reduce the ethical and environmental issues associated with traditional gelatine manufacture. It was found that both porcine and vegan alternatives can provide increased performance, transparency, and repeatability at a lower strain rate when using 10% ballistic gelatine as the control. The work reported within this study highlights that at a lower strain rate, researchers can use an alternative material to produce the same results as 10% ballistic gelatine; however, dynamic testing revealed no correlation between datasets, generated as part quasi‐static and dynamic research programmes, can be made, with significant differences in global and local response to impact.This research was supported by Cranfield Forensics Institute and Defence Equipment and Support.Nano Selec
General fault factor method for health monitoring of a liquid rocket engine
No full textThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. RS-2022-00164702).Journal of Spacecraft and Rocket
An exploration of new methods to assess energy availability in the English oak (Quercus robur)
No full textExisting methods of measuring the capability of foods to drive ecosystem processes
are reviewed and found to be inadequate; these included bomb calorimetry and plant
quality assays. This thesis explores two alternative methods, one in vivo and one in
vitro, based on the rate of supply of energy and compares these to plant quality
measurements and calorific content. A pilot study was undertaken using cow parsley
(Anthriscus sylvestris) to determine sampling regime and establish laboratory
techniques for fibre content measurement. The energy availability of different parts of
the English oak (Quercus robur) are compared by measuring their decomposition
rates in oak woodland soil. In this form the soil is acting as an in vivo microbial
digester and the rates at which weight loss occurs in the samples is converted to an
energy release rate dimensioned in kW.kg-¹. This unit mass power measurement is
compared to heat of combustion data derived from bomb calorimetry which is
dimensioned in joules.kg-¹. Variation in unit mass heat of combustion between plant
parts is small at 10% compared to the 400% (at 18 days) and 720% (at 115 days)
variation in unit mass power output between plant parts as measured in the tests
undertaken. Unit mass power output correlated with conventional plant quality
assays of the oak. A third set of experiments has begun the development of the
laboratory based measurement of energy availability under standard conditions. Each
plant material was macerated in a roller mill for thirty minutes (after hammer milling
where required) and subjected to a cellulose digest. A biological oxygen demand
measurement was made before and after the cellulose digest. These measurements of
metabolisable energy were converted to an energy supply rate by establishing a
theoretical gut retention time based on the time a standard amount of cellulose takes
to digest the cellulose in a unit mass of sample. The results of the in vivo and in vitro
measurements were qualitatively similar but with the in vitro test energy was
accessed more quickly and therefore had a higher power output rate. It is suggested
that ecosystem or community energetics are dependent on energy availability rather
than total energy content and that the importance of this distinction will vary between
biomes.International Ecotechnology Research CentrePhD in International Ecotechnology Research Centr
Comparative analysis of sum-of-squares optimization and Neural Network Lyapunov Functions for region of attraction estimation
No full textRegion of Attraction (ROA) estimation with accuracy is crucial for effective control design in nonlinear dynamical systems. Sum-of-squares (SOS) optimization refines Lyapunov function representations and expands ROA estimation for polynomial dynamical systems. However, traditional SOS methods tend to be overly conservative. In contrast, deep learning has emerged as a powerful tool in robotics, enabling data-driven ROA estimation. While deep learning offers strong empirical performance, its lack of stability guarantees remains challenging in safety-critical applications. This paper compares two ROA estimation methods: Sum-of-squares (SOS) optimization and Neural Network Lyapunov Functions (NLFs). We examine their effectiveness in approximating Lyapunov functions for stability assessment, highlighting their strengths, limitations, and practical relevance. Using the Van der Pol oscillator as a benchmark, we conduct detailed simulations to evaluate each method's performance. We provide a comprehensive comparison, considering computational efficiency, accuracy, and scalability, offering insights into their applicability in real-world aerospace systems.Ministry of Higher Education, Science, Research and Innovation, Thailand2025 33rd Mediterranean Conference on Control and Automation (MED
Technology acceptance, enabling adoption in the public sector - a practitioner’s perspective
Get PDFYates, Nicky - Associate SupervisorWithin the UK Public Sector, the railway is a publicly owned asset whose operating of trains is contracted through to different organisations. Network Rail features as the
infrastructure owner and charges out access to the train operators. In doing so it overseas the timetable, infrastructure assets and the majority of technology introduction due to the investment in renewals and enhancement programmes. Widely publicised in 2021, the need for industry reform to better support passengers and freight was articulated in the paper presented by Shapps and Williams (2021) including the need to improve the acceptance of technology. Responding to this need, the aims of the research are as follows;
To offer credible and usable information and guidance which can be applied to policy and practice that will enable Network Rail to be successful in technology acceptance. Influencing company policy and real-world practices relating to technology acceptance will increase the chance of successful technology acceptance meaning improved efficiency as well as further cost avoidance. Making sure that changes in the way the business works effect both its leadership and those who inform positive outcomes at a project level will enable the business to redefine how it engages with the technology acceptance process to maximise efficiency.
An aim of the research is to also look at the practical value of the available frameworks and models which are available and how they can be applied in practice for both technology acceptance and success. When considering the model and frameworks, some may not be applicable within the railway environment. This could be due to numerous reasons such as they were developed specifically for mining which is not an activity or environment undertaken by the railway, however determinants within the model or framework may still be applicable. The availability of technology could be a determinant related to mining which also relates to the railway even if the overarching framework does not offer synergies. This is why both the determinants, and the model or framework are important when considering the application. Therefore, recommendations of the research will consider the suitability of the models identified. The research objectives which follow inspire the research questions which guide the whole thesis and study.Network RailDoctor of Business Administratio
Data "OFS 2025 Manuscript Data - FSI Humidity Sensors"
No full textTwo .csv files comprising the total data set. One contains two columns of data for an independent RH sensor, with headers of "Time_Sensor (s)" and "RH(%)". The second .csv file contains five columns of data for the multiplexed interferometers, with headers of "Time (s)", "Int1 (rad)", "Int2 (rad)", "Int3(rad)", "Int4 (rad)".This research demonstrates a novel approach to distributed humidity sensing using polyimide-coated optical fibres and fibre segment interferometry (FSI). FSI interrogates the cumulative strain along fibre segments formed between pairs of FBG reflectors, significantly enhancing sensitivity by scaling with segment length. Polyimide, a hygroscopic coating material, induces strain on the optical fibre in response to humidity changes, creating measurable phase shifts. Experiments were conducted using an FSI array comprising three 10 mm fibre segments, with polyimide coatings on two segments and the third uncoated for control. This work highlights the advantages of FSI over direct FBG interrogation, including increased sensitivity, scalability, and reduced complexity of interrogation hardware.Engineering and Physical Sciences Research Council (EPSRC
Machine learning (ml) approaches to model interdependencies between dynamic loads and crack propagation
Get PDFStarr, Andrew - Associate SupervisorThe application of machine learning in structural health and crack prediction is of
paramount importance, as it offers the potential to enhance the accuracy,
efficiency, and reliability of detecting and predicting damage in various materials
and structures. This research presents an in-depth exploration of machine
learning (ML) applications in the field of Structural Health Monitoring (SHM)
across various materials, including composites, metals, and polymers. The study
identifies the current challenges in implementing ML in SHM, such as data
sparsity, interpretability of ML models, overfitting, and the absence of general
guidelines for ML model selection.
The research analyses the dynamic response data of different materials and
establishes significant crack depth predictors for materials such as aluminum,
concrete, and 3D-printed Acrylonitrile Butadiene Styrene (ABS). It further
investigates and validates selected ML models to predict crack depth in different
materials. The models' performance is evaluated using Mean Squared Error
(MSE) on both training and test sets, demonstrating their ability to capture
meaningful patterns within the data and make reasonably accurate predictions.
A significant contribution of this study is the proposal of an automated model
utilizing the H2O library for crack propagation prediction in ABS materials. This
model demonstrates the potential of automation in SHM, offering substantial
benefits for structural integrity assessment, maintenance strategies, and
materials design in various industries. This research concludes with
recommendations for future research, including the exploration of advanced ML
algorithms, investigation of additional predictive features, and evaluation of the
models in different real-world scenarios.PhD in Manufacturin
Energy scavenging piezoelectric powered led system for use in tracer ammunition
No full textAlmond, Heather - Associate SupervisorFuel-oxidizer tracer ammunition is the standard technology used to produce bright light
for projectile observation. However, a modern electronic tracer system has the
potential to eliminate the safety risks associated with combustible materials and open
flame systems by replacing them with a safer, integrated energy harvester-powered
electronic light-emitting system. The goal of this research is to investigate the
technologies necessary to convert kinetic energy from the bullet propulsion into
electrical power and to assess whether an integrated energy harvesting system,
coupled with electrical storage and an LED with the accompanying circuitry, could
feasibly replace the current technology in the future.
The study focuses on analysing existing mechanical-to-electrical transduction
technologies, understanding their design and use limitations, and evaluating their
suitability for implementation with small arms munitions that undergo high linear and
rotational acceleration. Additionally, this research examines the complexity of
manufacturing, construction, and adaptability of these technologies to smaller and
larger of munitions. After reviewing and filtering previous system designs and
technology prototypes, piezoelectric energy harvesting technology was selected due
to its energy density, material and structural compatibility for withstanding large forces
and lower mechanical system complexity for further development.
A prototype piezoelectric system was designed and simulated using commercial
software to model both structural and electrical behaviour. Experimental validation
tests were conducted with high compressive loads and rotational forces experienced
in real-world conditions. The research developed three novel spring structures that
significantly increase the power density of linear and rotational piezoelectric energy
harvesters. These spring structures feature enhanced shearing capabilities with disc
spring optimisation allowing 39% energy harvesting improvement and a prototype
system tuned for the 7.62 mm tracer outputting 3 V. and can be manufactured with
relatively low complexity compared to other energy harvesting technologies.
With the novel energy harvesting system in place, additional modelling was conducted
to design the accompanying LED circuit and capacitive energy storage, thereby
completing the development of the Electronic Tracer system.Engineering and Physical Sciences Research Council (EPSRC)PhD in Energy and Powe
Prolonged heat stress in Brassica napus during flowering negatively impacts yield and alters glucosinolate and sugars metabolism
Get PDFOilseed rape (Brassica napus), one of the most important sources of vegetable oil worldwide, is adversely impacted by heatwave-induced temperature stress especially during its yield-determining reproductive stages. However, the underlying molecular and biochemical mechanisms are still poorly understood. In this study, we investigated the transcriptomic and metabolomic responses to heat stress in B. napus plants exposed to a gradual increase in temperature reaching 30°C in the day and 24°C at night for a period of 6 days. High-performance liquid chromatography (HPLC) and liquid chromatography–mass spectrometry (LC-MS) was used to quantify the content of carbohydrates and glucosinolates, respectively. Results showed that heat stress reduced yield and altered oil composition. Heat stress also increased the content of carbohydrate (glucose, fructose, and sucrose) and aliphatic glucosinolates (gluconapin and progoitrin) in the leaves but decreased the content of the indolic glucosinolate (glucobrassicin). RNA-Seq analysis of flower buds showed a total of 1,892, 3,253, and 4,553 differentially expressed genes at 0, 1, and 2 days after treatment (DAT) and 4,165 and 1,713 at 1 and 7 days of recovery (DOR), respectively. Heat treatment resulted in downregulation of genes involved in respiratory metabolism, namely, glycolysis, pentose phosphate pathway, citrate cycle, and oxidative phosphorylation especially after 48 h of heat stress. Other downregulated genes mapped to sugar transporters, nitrogen transport and storage, cell wall modification, and methylation. In contrast, upregulated genes mapped to small heat shock proteins (sHSP20) and other heat shock factors that play important roles in thermotolerance. Furthermore, two genes were chosen from the pathways involved in the heat stress response to further examine their expression using real-time RT-qPCR. The global transcriptome profiling, integrated with the metabolic analysis in the study, shed the light on key genes and metabolic pathways impacted and responded to abiotic stresses exhibited as a result of exposure to heat waves during flowering. DEGs and metabolites identified through this study could serve as important biomarkers for breeding programs to select cultivars with stronger resistance to heat. In particular, these biomarkers can form targets for various crop breeding and improvement techniques such as marker-assisted selection.Biotechnology and Biological Sciences Research CouncilFrontiers in Plant Scienc
Decentralized mission planning for multiple unmanned aerial vehicles
Get PDFTsourdos, Antonios - Associate SupervisorThe focus of this thesis is the mission planning challenge for multiple unmanned
aerial vehicles (UAVs), with a particular emphasis on their stable operations in
a stochastic and dynamic environment. Mission planning is a crucial module in
automated multi-UAV systems, allowing for efficient resource allocation, conflict
resolution, and reliable operation. However, the distributed nature of the system
and physical and environmental constraints make it challenging to develop effective
mission planning algorithms.
The thesis begins with a review of the taxonomy, frameworks, and techniques in
multiple-UAV mission planning. Following this, it identifies four critical research
challenges, encompassing scalability, efficiency, adaptability and robustness, and energy management and renewable strategies. In response to these challenges, four
objectives have been defined with the overall aim of developing a generic decentralized mission planning paradigm for multi-UAV systems. The thesis subsequently
concentrates on accomplishing these objectives, with notable contributions in the
development of 1) a decentralized task coordination algorithm, 2) an efficient route
planner in consideration of recharging, and 3) an energy-aware planning framework.
This research first proposes a decentralized auction-based coordination strategy for
task-constrained multi-agent stochastic planning problems. Through casting the
problem as task-constrained Markov decision processes (MDPs), the task dependency due to an exclusive constraint is despatched from Multi-agent Markov decision
processes (M-MDPs) and then resolved by adopting an auction-based coordination
method. For multi-agent stochastic planning problems, the suggested technique
resolves the trade-off concern between computational tractability and solution quality. The proposed method ensures convergence, achieves at least 50% optimality
under the assumption of a submodular reward function, and greatly reduces the
computational complexity compared to multi-agent MDPs. Deep Auction is then
proposed as an approximate modification of the suggested auction-based coordination method, where two neural network approximators are introduced to facilitate
scaled-up implementations. By theoretical analysis, these two proposed algorithms
achieve better robustness and feature less computing complexity compared to the
state-of-the-art. Finally, a case study of drone delivery with time windows is implemented for validation. Simulation results demonstrate the theoretical benefits of
the recommended methodologies.
Then, an efficient route planner for individual UAVs accounting for recharging services is proposed. Despite extensive research in decision-making algorithms, existing
models have limitations in accurately representing real-world scenarios in terms of
UAV’s physical restrictions and stochastic operating environments. To address this,
a drone delivery problem with recharging (DDP-R) is proposed. The problem is characterized by directional edges and stochastic edge costs affected by winds. To solve
DDP-Rs, a novel edge-enhanced attention model (AM-E) is proposed and trained
via the REINFOCE algorithm to map the optimal policy. AM-E consists of a series
of edge-enhanced dot-product attention layers that capture the heterogeneous relationships between nodes in DDP-Rs by incorporating adjacent edge information.
Simulation results show that the edge enhancement achieves better results with a
simpler architecture and fewer trainable parameters, compared to other deep learning models. Extensive simulations demonstrate that the proposed DRL method
outperforms state-of-the-art heuristics in solving the DDP-R problem, especially at
large sizes, for both non-wind and windy scenarios.
Finally, we integrate the above route planning algorithm into an online energy inference framework, namely, the Energy-aware Planning Framework (EaPF), with
the aim of optimizing solution quality in consideration of possible time-window
violation and battery depletion. The framework comprises a statistical energy predictive model, a risk assessment module, and a route optimizer, with functions of
modelling energy costs, estimating risks, and optimizing the risk-sensitive objective. Concretely, a Mixture Density Network (MDN) is established for predicting
the distribution of future energy consumption taking account of wind conditions.
The MDN is trained by historical data and continuously updated as new data is
collected. Then, a risk-sensitive criterion is formed based on the MDN model of
energy consumption to assess the risk of task lateness and battery depletion. To
minimize the risk-sensitive objective, the EaPF incorporates the proposed AM-E
planner using a Model-based Multi-Sampling (MBMS) route construction strategy,
to further improve solution quality and planning robustness. In the context of drone
deliveries, simulations validate the effectiveness of the MDN energy model and the
EaPF. Results show that the integration of EaPF achieves an average cost reduction
of 25%, which implies a lower energy cost, a higher task accomplishment rate, and
a smaller battery depletion risk compared to the stand-alone DRL planner.PhD in Aerospac