Cranfield University
  • Cranfield, United Kingdom
Recent publications
For complex or large structures, the model updating process can be long and tedious and numerical methods can be computationally expensive. Hence, practitioners and researchers often resort to meta-modelling techniques when large problems are met. Even so, traditional methodologies, such as the Efficient Global Optimisation, can be slow and give sub-optimal results. This work proposes a new methodology for the model updating of numerical systems based on a novel Kriging approach for the scope of damage detection and quantification. The framework proposed is based on a global-local optimisation strategy recently developed by the authors, the refined Efficient Global Optimisation, herein used to tweak finite element models’ parameters to match the modal data extracted from a numerical system by using the residuals of the modified total modal assurance criterion. The main advantage to existing direct optimisation and meta-modelling frameworks is the more efficient use of computational effort for higher dimensional problems, which is verified with the use of a numerical system.
Hydrogen presents an opportunity for Africa to not only decarbonise its own energy use and enable clean energy access for all, but also to export renewable energy. This paper developed a framework for assessing renewable resources for hydrogen production and provides a new critical analysis as to how and what role hydrogen can play in the complex African energy landscape. The regional solar, wind, CSP, and bio hydrogen potential ranges from 366 to 1311 Gt/year, 162 to 1782 Gt/year, 463 to 2738 Gt/year, and 0.03 to 0.06 Gt/year respectively. The water availability and sensitivity results showed that the water shortages in some countries can be abated by importing water from regions with high renewable water resources. A techno-economic comparative analysis indicated that a high voltage direct current (HVDC) system presents the most cost-effective transportation system with overall costs per kg hydrogen of 0.038 $/kg, followed by water pipeline with 0.084 $/kg, seawater desalination 0.1 $/kg, liquified hydrogen tank truck 0.12 $/kg, compressed hydrogen pipeline 0.16 $/kg, liquefied ammonia pipeline 0.38 $/kg, liquefied ammonia tank truck 0.60 $/kg, and compressed hydrogen tank truck with 0.77 $/kg. The results quantified the significance of economies of scale due to cost effectiveness of systems such as compressed hydrogen pipeline and liquefied hydrogen tank truck systems when hydrogen production is scaled up. Decentralization is favorable under some constraints, e.g., compressed hydrogen and liquefied ammonia tank truck systems will be more cost effective below 800 km and 1400 km due to lower investment and operation costs.
Although the promotion of magnetite in anaerobic (AD) has been widely reported, the role of magnetite plays in the AD of waste activated sludge (WAS) and the mechanism of the promotion effect are still needed to be explored. This study investigated the effects of magnetite in AD of WAS with thermal hydrolysis pretreatment (THP) from four aspects including reactor performances, sludge characteristics, microbial community structures and gene abundance. Results showed that magnetite is able to enhance the consumption of soluble protein (SPN) and soluble polysaccharide (SPS) by increasing the activities of α-glucosidase and protease. With the addition of 0.5–5 g/L magnetite, the cumulative CH4 production could be increased by 38.25 %–85.34 %. The sludge electrical conductivity (EC) grew with the increase of magnetite dosage, which were 3.02, 8.65, 13.00, 15.46 and 17.97 μS/cm for G0-G4 respectively. In the presence of magnetite, the abundance of Proteobacteria increased which contained many genera of electroactive bacteria, such as Pseudomonas. By further metagenomics analysis, hydrogenotrophic methanogenesis pathway was shown to be enhanced with the addition of magnetite. The increasing abundance of pilA genes from Clostridium and Pseudomonas in bacteria and Methanospirillum in archaea confirmed that magnetite stimulated DIET in the AD of WAS.
This article introduces a new web-based decision support system created for early-stage feasibility assessments of renewable energy technologies in England, UK. The article includes a review of energy policy and regulation in England and a critical evaluation of literature on similar decision support systems. Overall, it shows a novel solution for a repeatable, scalable digital evidence base for the policy compliant deployment of renewable energy technologies. Data4Sustain is a spatial decision support system developed to quickly identify the feasibility of seven renewable energy technologies across large areas including wind, solar, hydro, shallow and geothermal. A multi-actor approach was used to identify the key factors that influence the technical feasibility of these technologies to generate electricity or heat for local consumption or regional distribution. The research demonstrates opportunities to improve the links between policy and regulation with deployment of renewable energy technologies using novel approaches to digital planning. Deployed, resilient, cost-effective and societally accepted renewable energy generation infrastructure has a role to play in ensuring universal access to affordable, reliableand modern energy supply. This is central to supporting a concerted transition to a low-carbon future in order to address climate change. The selection and siting of renewable energy technology is driven by natural resource availability and physical and regulatory constraints. These factors inform early-stage feasibility of renewables, helping to focus investment of time and money. Understanding their relative importance and identifying the most suitable technologies is a highly complex task due to the disparate and often unconnected sources of data and information needed. Data4Sustain help to overcome these challenges.
Offshore wind infrastructure modifies benthic habitats, affecting ecosystem services. A natural capital approach allows risks to nature-based assets and ecosystem benefits to be assessed. The UK Natural Capital Committee produced guidance for conducting natural capital assessments to aid decision making processes. Development of an asset register and risk register are key components of this methodology. The former provides an inventory of NC stocks, and the latter considers the likelihood of changes and the scale of their impact on delivery of ecosystem services. In this study, suitability of the methodology in a marine environment context was critically evaluated. Natural capital stocks before and after installation of Greater Gabbard offshore wind farm were compared and risks to delivery of ecosystem services were assessed. It was demonstrated that incorporating an assessment of impacts on natural capital assets in planning and management decisions (as an extension to traditional environmental impact assessment approaches) could further facilitate sustainable use of marine ecosystems. For example, by preventing access to bottom-trawl fisheries activities, wind farms may promote recovery and increase value of seabed natural capital assets. By also introducing aquaculture systems loss of food provision (from reduced fishing activity) could be offset whilst allowing benthic natural capital assets to recover. Natural capital assessment is relevant to the marine context. However, application of the Natural Capital Committee’s methodology was constrained by the limited coverage of standard benthic sampling tools. Given the scale of wind energy plans across the marine environment it is recommended that these shortcomings are appropriately addressed.
The impacts of MHD and heat generation/absorption on lid‐driven convective fluid flow occasioned by a lid‐driven square enclosure housing an elliptic cylinder have been investigated numerically. The elliptic cylinder and the horizontal enclosure boundaries were insulated and the left vertical lid‐driven wall was experienced at a fixed hot temperature, and the right wall was exposed to a fixed cold temperature. COMSOL Multiphysics 5.6 software was used to resolve the nondimensional equations governing flow physics. A set of parameters, such as Hartmann number (0 ≤ Ha ≤ 50 $0\le {Ha}\le 50$), Reynolds number (1 0 2 ≤ Re ≤ 1 0 3 $1{0}^{2}\le {Re}\le 1{0}^{3}$), Grashof number (1 0 2 ≤ Gr ≤ 1 0 5 $1{0}^{2}\le {Gr}\le 1{0}^{5}$), heat generation‐absorption parameter (− 3 ≤ J ≤ 3 $-3\le J\le 3$), and elliptical cylinder aspect ratio (AR) (1.0 ≤ AR ≤ 3.0 $1.0\le {AR}\le 3.0$) have been investigated. The current study discovered that for low Reynolds number, the adiabatic cylinder AR of 2.0 provided the optimum heat transfer enhancement for the model investigated, also the impact of cylinder size diminishes beyond Gr = 104. But for high Reynolds (Re = 1000), the size of the cylinder with AR = 3.0 offered the highest heat transfer augmentation. The clockwise flow circulation reduces because of an increase in AR, which hinders the flow circulation. In addition, heat absorption supports heat transfer augmentation while heat generation can suppress heat transfer improvement.
In operational engineering maintenance situations, limitations on time, resource or the information available often inhibit rigorous analysis on complex decision problems. Decision-makers who are compelled to act in such circumstances, may be informed by some level of analysis if available, or else may have to rely on their unsupported judgement. This paper presents three engineering risk decision-making case studies across a 20 year span from the rail, aerospace, and military aviation contexts, highlighting the fallibilities of using unsupported judgements in an unstructured manner. To help situate this type of decision situation, we provide a descriptive model of the decision space which extends an existing description from the discipline of decision analysis. Furthermore, to help make and describe the distinction between unsupported and supported thinking, we provide another descriptive model, this time drawing parallels with the distinction made between Type 1 and Type 2 reasoning. This model is an extension of the default-interventionist model from cognitive psychology. The paper concludes that there is a pressing need to provide some form of support to engineering decision-makers facing operational decisions under severe time pressure. While the ultimate aim must be to improve the quality of decision-making, improved transparency is an important additional benefit. Increased emphasis on decision justification and self-awareness are suggested as potential ways of improving this situation. A further contribution of this paper is to identify and strengthen linkages between safety science and two other relevant disciplines, decision analysis and psychology. Such linkages make it easier to communicate across traditional disciplinary boundaries and may provide opportunities for interdisciplinary learning or suggest future directions for collaborative research.
Intensification of the hydrological cycle resulting from climate change in West Africa poses significant risks for the region’s rapidly urbanising cities, but limited research on flood risk has been undertaken at the urban domain scale. Furthermore, conventional climate models are unable to realistically represent the type of intense storms which dominate the West African monsoon. This paper presents a decision-first framing of climate research in co-production of a climate-hydrology-flooding modelling chain, linking scientists working on state-of-the-art regional climate science with decision-makers involved in city planning for future urban flood management in the city of Ouagadougou, Burkina Faso. The realistic convection-permitting model over Africa (CP4A) is applied at the urban scale for the first time and data suggest significant intensification of high-impact weather events and demonstrate the importance of considering the spatio-temporal scales in CP4A. Hydrological modelling and hydraulic modelling indicate increases in peak flows and flood extents in Ouagadougou in response to climate change which will be further exacerbated by future urbanisation. Advances in decision-makers’ capability for using climate information within Ouagadougou were observed, and key recommendations applicable to other regional urban areas are made. This study provides proof of concept that a decision-first modelling-chain provides a methodology for co-producing climate information that can, to some extent, bridge the usability gap between what scientists think is useful and what decision-makers need.
During the past few years, Tin (Sn)-based perovskites have been extensively investigated in the field of photovoltaics as promising candidates for new generation lead-free perovskite. Tin-based perovskites (ASnI3) present excellent photoelectric properties. However, there still remains a big concern over unsatisfactory stability. In reality, extensive efforts have been committed to improve the stability of perovskite active layer. In this review, a comprehensive understanding on defect formation, oxidation mechanism of Sn²⁺. Then, a detailed discussion on the recent advance the effect of additive engineering for the stability of FASnI3 PSCs, including antioxidants, 2D perovskite materials and functional passive molecular. Lastly, several key scientific issues and future research prospectives are proposed for achieving stable and high-performance Sn-based perovskite photovoltaics.
Wire and arc-based additively manufacturing (WAAM) is a potential metallic additively manufacturing (AM) technologies for producing large-size metallic components. 316L is one of the most common stainless-steel grades used in WAAM. However , most of previous studies normally adopted process parameters for the WAAM process based on recommendations of welding wire manufacturers for traditional welding processes. In this article, we focus on predicting and optimizing process parameters for the WAAM process of 316L stainless steel. The experiment was designed by using Taguchi method and L16 orthogonal array. Three parameters, consisting of voltage (U), welding current (I), and travel speed (v), were considered as the input variables, and the responses are four geometrical characteristics of single weld beads, including width, height, penetration, and dilution of weld beads (WWB, HWB, PWB, and DWB, respectively). The effects of each input variable on the responses were determined through analysis of variance (ANOVA). The optimal process parameters were identified by using GRA (grey-relational analysis) and TOPSIS (techniques for order-preferences by similarity-to-ideal solution) methods. The obtained results show that the travel speed has the most important effect on WWB and HWB, while the voltage has the highest impact on PWB and DWD. Both GRA and TOPSIS methods give the same optimum process parameters, namely U = 22 V, I = 110 A, and v = 0.3 m/min, which are validated by confirmation experiments. The predicted models of WWB, HWB, PWB, and DWB were also demonstrated to be adequate for selecting the process parameters in specific applications.
Drone‐monitoring radars typically integrate many pulses in order to improve signal to noise ratio and enable high detection performance. Over the course of this coherent processing interval (CPI), many components of the drone signature change and the signature's amplitude and Doppler modulations may hinder coherent integration performance, even in the absence of range‐Doppler cell migrations. A statistical characterisation of these fluctuations aides radar designers in selecting optimal CPI lengths. This paper presents a statistical analysis of experimental data of nine flying drones, collected with a frequency modulated continuous wave Ku‐band radar, and examines the statistical features of the amplitude fluctuations of the drone body and blades as well as the signature decorrelation time. The method of moments is used to estimate the probability density function parameters of different drone spectral components with the aim of informing the development of improved theory for predicting drone signatures and ultimately increasing detection performance. Results show that, on average, the Weibull distribution provided the best mean square error fit to the data for most drone spectral components and drone types, with the Rayleigh distribution being the next best match. These results were further corroborated by a study of detection performance for a fluctuating target. Whilst decorrelation times of the various signatures varied significantly, even for the same drone, results show that an approximate inverse relationship between drone spectral component bandwidth and decorrelation time held, with individual spectral lines decorrelating after tens to hundreds of msec.
One of the primary roles of a practitioner in the field of digital forensics (DF) is to conduct the examination of any lawfully seized digital device content and report upon any findings that may support an inquiry being conducted. While there are many intricacies to this task, in some cases, an inquiry will commence with a practitioner carrying out the necessary examination work required to report any findings at a "technical level." Such technical reports are often used for intelligence gathering purposes in an attempt to establish the potential evidential value of a device or data set and are often a precursor to, and catalyst for, further and often more extensive forensic work being commissioned. Therefore, the ability to report at a technical level should be considered a fundamental skill required of all practitioners in this discipline and any attempts to provide guidance and support for conducting this task effectively should be encouraged. This work explores the role of technical reporting, where a series of reporting examples are presented that explore the intricacies involved with conveying digital forensic findings at a technical level. Procedural and linguistic challenges are investigated and evaluated in order to acknowledge the pitfalls that practitioners may encounter and to identify potential technical reporting best practices.
This paper addresses in-schedule dependent task allocation problems for multi-robot systems. One of the main issues with those problems is the inherent NP-hardness of combinatorial optimisation. To handle this issue, this paper develops a decentralised task allocation algorithm by leveraging the submodularity concept and a sampling process of task sets. Our theoretical analysis reveals that the proposed algorithm can provide an approximation guarantee of 1/2 of the optimal solution for the monotone submodular case and 1/4 for the non-monotone submodular case, both with polynomial time complexity. To examine the performance of the proposed algorithm and validate the theoretical analysis, we introduce two task allocation scenarios and perform numerical simulations. The simulation results confirm that the proposed algorithm achieves a solution quality which is comparable to state-of-the-art algorithms in the monotone case and much better quality in the non-monotone case with significantly lower computational complexity.
The aim of this paper is to analyse why and how air traffic conflicts occur as a result of the stochastic behaviour of both the ownship and the intruder and to show how system-level characteristics can be derived from such an analysis. Ensemble dynamics in a given traffic scenario have already been analysed using multi-agent simulations by many; however, such an analysis is hardly ever backed up and interpreted in terms of an analytical study. By making use of directional conflict probability maps, characteristics of integral, system-level quantities can be explained, providing further insight into the relationship of speed distribution parameters and system performance quantities, namely, safety and throughput.
This paper presents a novel holistic modeling approach for investigating and analyzing the relationship of qualitative variables such as training and absenteeism with quantifiable shopfloor key performance indicators such as quality, inventory, and production rate. Soft variables, supervisor support and work environment, and their relationships with the hard variables, facility layout, and production strategies were investigated in this research. It was found in the literature that increasing absenteeism reduces the rate of production and causes a decrease in motivation, while training can increase the level of motivation if effective. A causal loop diagram was developed based on the evidence in the literature, and a system dynamics simulation model was created to depict these relations. It was confirmed that absenteeism affected the cycle time and motivation inversely, but it was not possible to always maintain a desired level of motivation. A discrete event simulation model was also built for the current and the future state maps of the production system. The model used output from the system dynamics model as its input to investigate the effects of the qualitative variables on the production system performance. This paper discusses in detail the stages of building the simulation models and the results recorded.
Acetate is emerging as a promising feedstock for biorefineries as it can serve as an alternate carbon source for microbial cell factories. In this study, we expressed acetyl-CoA synthase in Yarrowia lipolytica PSA02004PP, and the recombinant strain grew on acetate as the sole carbon source and accumulated succinic acid or succinate (SA). Unlike traditional feedstocks, acetate is a toxic substrate for microorganisms; therefore, the recombinant strain was further subjected to adaptive laboratory evolution (ALE) to alleviate toxicity and improve tolerance against acetate. At high acetate concentrations, the adapted strain Y. lipolytica ACS 5.0 grew rapidly and accumulated lipids and SA. Bioreactor cultivation of ACS 5.0 with 22.5 g/L acetate in a batch mode resulted in a maximum cell OD600 of 9.2, with lipid and SA accumulation being 0.84 and 5.1 g/L, respectively. However, its fed-batch cultivation yielded a cell OD600 of 23.5, SA titer of 6.5 g/L and lipid production of 1.5 g/L with an acetate uptake rate of 0.2 g/L.h, about 2.86 times higher than the parent strain. Co-fermentation of acetate and glucose significantly enhanced SA titer and lipid accumulation to 12.2 and 1.8 g/L, respectively, with marginal increment in cell growth (OD600: 26.7). Furthermore, metabolic flux analysis has drawn insights into utilising acetate for the production of metabolites that are downstream to acetyl-CoA. To the best of our knowledge, this is the first report on SA production from acetate by Y. lipolytica and demonstrates a path for direct valorisation of sugar-rich biomass hydrolysates with elevated acetate levels to SA.
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8,762 members
Ian Baillie
  • Food Security and Environmental Health
Anne-Marie Oostveen
  • Department of Aerospace Engineering
Konstantinos Georgarakis
  • School of Aerospace, Transport and Manufacturing
Srinivasan Munisamy
  • School of Aerospace,Transport and Manufacturing (SATM)
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