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A sustainability-based framework for predicting the remaining useful life of a complex engineering asset
Get PDFAs climate change became recognised as a major global challenge, the ability to define and account for the environmental performance of an asset became an important attribute aiding the sustainable development strategies towards net-zero. Remaining Useful Life (RUL) indicator allows for optimised maintenance scheduling and the life extension of an asset. However, the existing RUL prediction methods do not fully consider the environmental performance (EP) of an asset. This paper aims to develop a sustainability-based framework for complex engineering assets’ RUL prediction based on a systematic review of key literature. The proposed framework introduces a new concept, so-called ‘sustainable-RUL’ (SRUL), which refers to the estimated remaining lifetime that an item is able to function reliably and be environmentally sustainable. The Scopus database is used to develop the PRISMA framework. Finally, a generic S-RUL framework is introduced which incorporates the environmental sustainability aspect into the RUL prediction. Hence, the decision-maker is provided with a single predictive indicator, that accounts for the asset reliability and EP at the same level of granularity, thus facilitating the selection of maintenance policies that establishes a condition for ecological and economic stability.This research was supported by the Centre of Digital Engineering and Manufacturing (CDEM) at Cranfield University. The authors acknowledge Rolls-Royce Plc for supporting this work.12th International Conference on Through-life Engineering Services – TESConf202
Advancing fruit preservation: ecofriendly treatments for controlling fruit softening
Get PDFTextural softening is a major factor that limits the storage potential of fruit. Fresh produce markets incur severe financial losses due to excessive fruit softening. The application of preservation strategies aimed at mitigating fruit softening is crucial for optimising the marketability of fruit. Proposed preservation strategies include ecofriendly treatments, namely, hexanal, edible coatings, heat treatments, ozone and UV-C irradiation. These treatments optimise firmness retention by targeting the factors that affect fruit softening, such as ethylene, respiration rates, enzymes and pathogens. This review discusses the mechanisms by which ecofriendly treatments inhibit fruit softening, providing insights into their effect on ethylene biosynthesis, cell wall metabolism and disease resistance. Although ecofriendly treatments offer a promising and sustainable approach for delaying fruit softening, the optimisation of treatment application protocols is needed to improve their efficacy in retaining fruit firmness. Studies reporting on the molecular mechanisms by which ecofriendly treatments inhibit fruit softening are limited. Future studies should prioritise proteomic and transcriptome analyses to advance our understanding of the underlying molecular mechanisms by which ecofriendly treatments delay the fruit-softening process.This research was funded by the National Research Foundation of South Africa (Grant numbers: SFH220123657394 and CSRP2205046155).Horticultura
Predicting emergent animal biodiversity patterns across multiple scales
Get PDFRestoring biodiversity-based resilience and ecosystem multi-functionality needs to be informed by more accurate predictions of animal biodiversity responses to environmental change. Ecological models make a substantial contribution to this understanding, especially when they encode the biological mechanisms and processes that give rise to emergent patterns (population, community, ecosystem properties and dynamics). Here, a distinction between ‘mechanistic’ and ‘process-based’ ecological models is established to review existing approaches. Mechanistic and process-based ecological models have made key advances to understanding the structure, function and dynamics of animal biodiversity, but are typically designed to account for specific levels of biological organisation and spatiotemporal scales. Cross-scale ecological models, which predict emergent co-occurring biodiversity patterns at interacting scales of space, time and biological organisation, is a critical next step in predictive ecology. A way forward is to first capitalise on existing models to systematically evaluate the ability of scale-explicit mechanisms and processes to predict emergent patterns at alternative scales. Such model intercomparisons will reveal mechanism to process transitions across fine to broad scales, overcome approach-specific barriers to model realism or tractability and identify gaps which necessitate the development of new fundamental principles. Key challenges surrounding model complexity and uncertainty would need to be addressed, and while opportunities from big data can streamline the integration of multiple scale-explicit biodiversity patterns, ambitious cross-scale field studies are also needed. Crucially, overcoming cross-scale ecological modelling challenges would unite disparate fields of ecology with the common goal of improving the evidence-base to safeguard biodiversity and ecosystems under novel environmental change.Natural Environment Research Council. Grant Number: NE/W003031/1Global Change Biolog
Data relating to "High resolution (cm scale) elevation data of Cockermouth Town, UK"
No full textThe project is focused on 'Harnessing long-term gridded rainfall data and microtopographic insights to characterise risk from surface water flooding'. The data provides the microtopography information of Cockermouth Town in England and the property resilience and resistance information. Three data sets are provided; 1.Elevation model at 25 cm resolution generated from lidar point clouds captured from aircraft; 2. Elevation model at 10 cm resolution generated from stereo photos captured by photographic cameras mounted on UAV; 3. shapefile having attributes related to flood resilience and resistance information for the residential buildings of Cockermouth Town.EP/N010329/
Constructive deviance in the defence context
Get PDFDenyer, David - Associate Supervisor“Do not conform to the pattern of this world, but be transformed
by the renewing of your mind”. (Romans 12:2)
This study addresses the question: what motivates and influences constructively
deviant behaviour in the UK Defence context? It answers this question by asking
interviewees, in semi-structured interviews, to reflect on two occasions when they
had the opportunity to constructively deviate. By comparing the enablers and
barriers from an episode in which they destructively conformed with those from
one in which they constructively deviated, the research focuses on the
organisational and contextual factors that affect people’s decisions to
constructively deviate or destructively conform. The research suggests that
individuals in the Defence context are motivated to constructively deviate to
improve their immediate environment or increase operational effectiveness or
efficiency. It finds that there are factors in the Defence context which influence
constructive deviance that are common to those in less normative environments
such as supportive leadership, felt empowerment and a sense of responsibility.
It also finds that there are Defence-specific factors that influence constructive
deviance such as the relative importance of leaders compared to peers, leader
rhetoric, the performance appraisal system and Defence bureaucracy. The
research adds to the body of knowledge through its exploration of enablers and
barriers to constructive deviance and a deeper understanding of the UK Defence
context.Doctor of Business Administratio
The effect of hydrogen fuel on the performance and emissions of 3 kWe natural gas fuelled microturbine
Get PDFHydrogen is an alternative fuel to power microturbines. In this work, the application of H2 in a 3 kWe microturbine combustor is investigated. First, the combustor is tested with different molar concentrations of hydrogen in methane fuel (XH2= 5%, 10% and 20%). Afterward, the operation of the microturbine is verified using thermodynamic analysis of the microturbine cycle. The combustion of the fuels is investigated using CFD analysis. The level of gaseous emissions including (CO2, CO, and NOX) and the microturbine overall operability in terms of turbomachine mechanical and thermal efficiencies are compared in each case to find out the influence of hydrogen addition on the natural gas combustion in the microturbine (MT). Findings show that the application of hydrogen in the MT combustor decreases the level of CO2 and CO emissions while increasing NOX emissions. Despite the improvement in combustion, hydrogen could deteriorate the MT effectiveness and overall efficiency. The findings demonstrate that if the hydrogen mole percent in the fuel rises from 0 to 10, the cycle efficiency decreases from 4.73% to 4.7% and if it increases to 20 percent, the efficiency of the cycle increases from 4.7% to 4.92%.Fue
A study into the correlation between single array-hull configurations and wave spectrum for floating solar photovoltaic systems
Get PDFFloating photovoltaic (FPV) systems offer a viable renewable energy solution due to easy installation and cost-effectiveness compared to other renewable energy generation methods. On the other hand, land-based solar photovoltaics face challenges such as space scarcity and environmental impacts. Shifting to nearshore locations unlocks vast ocean space potential, though waves expose significant challenges to FPV systems. Several novel FPV system designs are proposed, inspired by high-speed vessel multihulls, including catamaran, trimaran, quadrimaran, and pentamaran configurations, as floating supports for solar panels. Simulations were conducted to determine Response Amplitude Operators (RAOs) under various irregular wave spectrum conditions in a free-floating initial state. The FPV motion problem was solved using linear potential-flow theory with the Boundary Element Method (BEM) with Green-Function approach. Superposition of wave spectral energy and motion RAOs was used to obtain spectral structural responses. Motion in heave, roll, and pitch modes was evaluated across wave spectrum types. Results show that adding hulls reduces the significant amplitude response in all motion modes. In summary, valuable insights into floater designs and the hydrodynamic evaluation of FPV systems are presented.The research scheme is financed by the Higher-Education Endowment Fund (DAPT) through the Enhancing Quality Education for International University Recognition (EQUITY) initiated by the Lembaga Pengelola Dana Pendidikan (LPDP), Ministry of Finance, the Republic of Indonesia.Ocean Engineerin
Infrared thermography- a study into its current capabilities and future prospects
Get PDFThe fourth industrial revolution has brought forward a paradigm shift in the analyses and interpretation of data. Emerging technologies such as artificial intelligence, Internet of Things (IoT), and cyber physical systems have accelerated the process of concepts such as decentralised decision making, automation, and digitalisation. In the context of Non-destructive Evaluation (NDE), adopting these technologies has substantially improved the efficiency of existing techniques. Currently, Ultrasound Testing (UT) has been dubbed as the “gold standard” for Non-destructive Testing (NDT). However, a major drawback to this technique is its contact-based inspection. Infrared Thermography (IRT) on the other hand offers a non-contact non-intrusive inspection and is a growing area of interest to researchers. This paper explores the impact of infrared thermography within the maintenance industry. Firstly, the current state-of-the-art in IRT is presented followed by the limitations of the technique, the current research and knowledge gap that exists in thermographic testing. Potential solutions that can overcome the limitations are proposed. These cover specific aspects of the technique such as the working principle, mathematical modelling, data
interpretation and processing, automation, and digitalisation. Finally, future prospects of the technique are briefly presented.12th International Conference on Through-life Engineering Services – TESConf202
Effect of nozzle shape on fluid flow and heat transfer characteristics of an impinging jet system – a numerical study
Get PDFImpinging jet is one of the most efficient techniques to achieve a high heat transfer coefficient and is used in many engineering applications. The present study focuses on the effect of nozzle shape on fluid behavior and heat transfer characteristics. For the current investigation, circular, square, rectangular, and elliptical nozzles with identical hydraulic diameters are used with Reynolds number Re ranging from 15,000–35,000. The circular nozzle results are validated with the published numerical and experimental data. In the current study, it is found that as the Reynolds number increases, the value of the averaged Nusselt number increases in all circumstances. When examining the different nozzle shapes, the value of the averaged Nusselt number is higher when an elliptical nozzle is used. The contours of the surface Nusselt number and velocity streamlines are also presented. The contour shows that the heat flux is highest in the stagnation zone and gradually decreases to the sides because they are outside the impingent coverage. Moreover, the area between the jets has a low heat flux. The heat transfer in the impinging zone is initially raised as the jet-induced crossflow increases and achieves a peak value, and then reduced stream-wise because of the crossflow effect
Explaining data-driven control in autonomous systems: a reinforcement learning case study
Get PDFExplaining what does a data-driven control algorithm learns play a crucial role for safety critical control of autonomous platforms in transportation. This is more acute in reinforcement learning control algorithms, where the learned control policy depends on various factors that are hidden within the data. Explainable artificial intelligence methods have been used to explain the outcomes of machine learning methods by analysing input-output relations. However, data-driven control does not pose a simple input-output mapping and hence, the resulting explanations lack depth. To deal with this issue, this paper proposes a explainable data-driven control method that allows to understand what the data-driven method is learning from the data. The model is composed by a Q-learning algorithm enhanced by a dynamic mode decomposition with control (DMDc) algorithm for state-transition function estimation. Both the Q-learning and DMDc provides the elements that are learned from the data and allow the construction of counterfactual explanations. The proposed approach is robust and does not require hyperparameter tuning. Simulation experiments are conducted to observe the benefits and challenges of the method.Engineering and Physical Sciences Research Council (EPSRC)Engineering and Physical Sciences Research Council; EP/V026763/1, EP/X040518/1 and EP/Y037421/110th International Conference on Control, Decision and Information Technologie