Managing complex projects

Complexity is an issue that affects all projects. Project managers know this, but it can be difficult to express the realities they face in a language that others can easily grasp. In this chapter we draw on research that identifies three different kinds of project complexity – structural, socio-political, and emergent, and look at practical response techniques to these. We offer a complexity framework to help managers deal with these challenges. We then show how this can be used both as a problem-solving tool, and also as a method to draw out lessons learned at gate reviews or at the completion of the work

The impact of heat transfer effects on civil aircraft engine transient performance

Pilidis, Pericles - Associate SupervisorDuring gas turbine transient manoeuvre, heat transfer occurs between the fluid and metal. This results in various heat transfer effects, including heat soakage, tip clearance, and change in component performance map. These will cause heat loss and change in component flow characteristic and efficiency, which further affect gas turbine transient performance. In this work, a comprehensive heat transfer model including heat soakage, tip clearance and compressor map modification has been developed. The proposed heat soakage model improves the current state-of-the-art model by establishing a comprehensive thermal network with the consideration of the combustor temperature distribution and cooling technologies including film cooling, internal cooling, and thermal barrier coatings. Additionally, the proposed novel compressor map modification model can derive numerical correlations for compressor maps based on movement of compressor speed line and map scaling, enabling the modification of adiabatic maps to non-adiabatic maps during transient simulations. It improves the current compressor map modification models by ensuring both flexibility and accuracy. The developed heat transfer model has been integrated into Cranfield gas turbine simulation platform Turbomatch, enhancing the realism of transient simulations. The accuracy of the proposed model has been validated against data from public sources, simulation platforms, and experimental results. A sensitivity analysis has also been conducted to assess the impact of various assumptions on heat flow rate estimation. The impact of heat transfer to overall engine’s performance has been demonstrated by simulating transient operation of a turbojet and two turbofan engines to demonstrate the effects of heat transfer on gas turbine transient performance. Comparing with the conventional heat soakage method, the application of the improved models can capture a delay on engine’s response beyond the one simulated by the existing methods. This is a result of considering the combustor temperature distribution and cooling technologies, not included in the conventional heat soakage models. For the impact of heat transfer effect on compressor characteristic and performance, a 4% reduction in compressor surge margin is observed during a hot reslam transient manoeuvre, as a result of movement in compressor speed line due to heat transfer effect.PhD in Aerospac

Integrated cost-efficient itaconic acid production from waste potatoes via a holistic upstream-to-downstream approach

Itaconic acid (IA) holds enormous potential in the polymer industry as a green substitute for several valuable chemicals derived from fossil fuels. However, its full potential is limited by high production costs, resulting from the use of expensive feedstocks and downstream processing approaches. In this context, this study strategizes an inexpensive approach for the efficient production and recovery of IA using damaged/defective waste potatoes, a major food waste across the globe. Initially, the optimized acidic saccharification process (1.5 % HCl, 20 % w/v solid loading) yielded 170 g glucose/kg potato. The fermentation of activated charcoal-treated potato hydrolysate by Aspergillus terreus DSMZ 23081 resulted in 32 g/L IA with a yield and productivity of 0.21 gIA/gGlu (36.2 gIA/kgWPB) and 0.17 g/L/h, respectively. The downstream processing was performed by implementing a salting-out extraction approach. Among various solvent-salt combinations, maximum IA extraction efficiency was observed when 25 % (w/v) Na2SO4 was used as a salting-out agent with sec-butanol as the extractant. Optimizing the physicochemical parameters (pH, solvent volume, shaking time, etc.) led to 92 % recovery from pure IA solution. Finally, when the process was applied to potato-based fermentation broth, 80.5 % IA was recovered with 85 % purity. By integrating valorisation of a low-cost agro-industrial waste with an optimized salting-out extraction strategy that overcomes key limitations of conventional IA purification, the developed bioprocess demonstrates a simple and economically feasible end-to-end strategy for sustainable IA production.Separation and Purification Technolog

Three-dimensional through-flow modelling of axial compressor rotating stall and surge.

The operation of an aero-engine is limited by the occurrence of compressor stall, and compressor performance is sacrificed to maintain a sufficient margin of operation. Compressor stall also plays an important part in the event of a shaft failure, and in determining if this will result in a rotor burst. In Cranfield University a tool to model the whole engine during shaft failure has been developed, but it requires the knowledge of the compressor performance during stall. Low order 1D, 2D or 3D methods to model compressor stall exist in literature, but they are still at a low maturity level and not applicable for commercial use. The only methods available are expensive experimental testing and transient 3D CFD, which has unacceptable computational costs. The objective of this PhD project has therefore been identified in the development of a fast and robust 3D tool to model compressor stall, and in its validation with data from low-speed experimental rigs. The tool created is a three-dimensional through-flow code which uses empirical correlations to model the blade row performance. A novel methodology has been developed to estimate the performance of blade rows in reverse flow, based on previous models of separated blade passages. The Godunov scheme has been chosen to create the 3D, unsteady, cylindrical, compressible, finite volume method Euler solver on which the code is based. Appropriate body forces and boundary conditions have been chosen and implemented. The validation carried out on two low-speed compressors demonstrates the applicability of the proposed formulation, with successful prediction of the performance during reverse flow, rotating stall and surge. The speed, size and structure of the rotating stall cell have been successfully matched to experimental data. The developed tool can reproduce the forward flow, reverse and rotating stall regions of the map in less than 72 hours, at a computational speed unrivalled by modern commercial CFD codes.PhD in Aerospac

Multi-agent deep reinforcement learning-based key generation for graph layer security

All research work was conducted whilst all authors were at Cranfield University.Recently, the emergence of Internet of Things (IoT) devices has posed a challenge for securing information and avoiding attacks. Most of the cryptography solutions are based on physical layer security (PLS), whose idea is to fully exploit the properties of wireless channel state information (CSI) for generating symmetric keys between two communication nodes. However, accurate channel estimation is vulnerable for attackers and relies on powerful signal processing capability, which is not suitable for low-power IoT devices. In this paper, we expect to apply graph layer security (GLS) to exploit the common features of physical dynamics detected by IoT sensors placed in networked systems to generate keys for data encryption and decryption, which we believe is a new frontier to security for both industry and academic research. We propose a distributed key generation algorithm based on multi-agent deep reinforcement learning (MADRL) approach, which enables communication nodes to cooperatively generate symmetric keys based on their locally detected physical dynamics (e.g., water/gas/oil/electrical pressure/flow/voltage) with low computational complexity and without information exchange. In order to demonstrate the feasibility, we conduct and evaluate our key generation algorithm in both a simulated and real water distribution network. The experimental results show that the proposed algorithm has considerable performance in terms of randomness, bit agreement rate (BAR), and so on.This work has been supported by the PETRAS National Centre of Excellence for IoT Systems Cybersecurity, which has been funded by the UK EPSRC under grant number EP/S035362/1.ACM Transactions on Privacy and Securit

Impact of high-aspect-ratio wing aircraft concepts on conventional tricycle landing gear integration

To comply with the Paris Agreement targets set in 2015, significant reductions in aircraft emissions are required. This demands a fundamental shift in aircraft design. Therefore, it is essential to study how future aircraft designs will affect the integration and design of landing systems. This research project examines the landing gear issues that arise from adopting specific future aircraft configurations. The study focuses on two primary configurations: the high-aspect-ratio wing and the ultra-high-aspect-ratio wing, with selected aircraft concepts from Cranfield University as baselines. It investigates the design and integration of conventional landing systems into these new aircraft concepts, highlighting the limitations posed by the modified airframes. The selected concepts include either telescopic or trailing arm arrangements, with attachment points on the wings or fuselage. A methodology for preliminary sizing of landing systems is presented, emphasizing automation and determining key performance indicators to assess the suitability of each solution for different aircraft architectures. The challenges of these novel airframes highlight opportunities to move away from conventional solutions and explore unconventional methods of interfacing between the aircraft and the ground.SAE International Journal of Aerospac

Total pressure distortion reconstruction methods from velocimetry data within an aero-engine intake at crosswind

The integration of Very High Bypass Ratio (VHBR) turbofan engines with short intakes may present challenges due to increased total pressure distortion, particularly under crosswind conditions. Current industrial practices rely on a limited number of intrusive pressure sensors arranged on rakes at the Aerodynamic Interface Plane (AIP), to characterise this total pressure distortion. However, non-intrusive measurement techniques provide a more effective way to capture the complex, unsteady flow fields within the intake, offering higher spatial resolution compared to conventional methods. In this study, velocity data obtained from Stereoscopic Particle Image Velocimetry (S-PIV) during wind tunnel tests of a short intake configuration were employed to reconstruct the instantaneous total pressure fields at the AIP within the intake. Two reconstruction methods were used: Direct Spatial Integration (DSI) of the momentum equation and the Poisson Pressure Equation (PPE). These methods were first applied to numerical data from RANS simulations. The results of the reconstruction of the total pressure field based on the S-PIV data were compared against rake measurements. The methods enabled a more comprehensive assessment of total pressure distortion, offering improvements over conventional sensor-based ap-proaches in identifying and characterising total pressure non-uniformities within an intake.This work was conducted under the NIFTI project which received funding from the Clean Sky 2 Joint Undertaking (JU) under Grant Agreement No 86491116th European Turbomachinery Conference (ETC16

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Leveraging animal feed supply chain capabilities through big data analytics: a qualitative study

Purpose Although big data analytics (BDA) has gained widespread interest in supply chain management (SCM) literature in recent years, our understanding of how it contributes to improved animal feed supply chains (SCs) is still underexplored. This study provides a greater understanding of the role of BDA in improving animal feed SC capabilities. Design/methodology/approach A qualitative approach was used in this study. Data were collected through 32 semistructured interviews from several actors involved in the production and supply of animal feed concentrates. Findings This study provides rich in-description evidence of how BDA enhances performance in the animal feed supply chain through improved logistics capabilities, quality control and information visibility. Our findings also suggest that organizational culture contributes to leveraging BDA capabilities in the feed-processing SCs. Practical implications The research provides an in-depth qualitative investigation of implementing big data in the feed processing SCs. The study provides practical implications for SC managers in the agri-food sector. Originality/value The study contributes to the growing body of knowledge by providing field evidence of the relevance of BDA to animal feed SCs. Moreover, this study adds to the existing literature by providing an understanding of the role of the internal culture of the organization in leveraging BDA capabilities in the SC.International Journal of Quality & Reliability Managemen

Estimation and visualisation of brain functional and effective connectivity

Functional and effective connectivity are two important concepts in the field of neuroscience that describe how different regions of the brain communicate and work together to support various cognitive and behavioural functions. Despite the many advances in functional and effective connectivity research, there are still several important research gaps that need to be addressed. This thesis explores the novel estimation and visualisation of brain functional and effective connectivity using electroencephalography recordings, with a particular focus on its potential impact on the diagnosis and monitoring of neurological disorders. This thesis proposes two novel methods for estimating brain functional connectivity and effective connectivity. The first method, Revised Hilbert-Huang Transformation, outperforms wavelet-based methods in terms of promising features and time-frequency resolution, providing a potential biomarker and diagnostic tool for Alzheimer's disease. The second method, causality detection attention-based convolutional neural networks, effectively estimates effective connectivity networks and identifies disrupted connectivity in Alzheimer’s disease patients. These methods contribute to the growing literature on connectivity estimation and offer valuable insights into the neural mechanisms underlying cognitive processes and neurodegenerative diseases, providing potential diagnostic and monitoring tools for healthcare professionals. This thesis also introduces a novel directed structure learning GNN (DSL-GNN) to leverage several EBC estimations to extract discriminative biomarkers for dementia classification. In studies of Alzheimer's disease, epilepsy, Parkinson's disease, and workload classification, the thesis demonstrates that the proposed brain connectivity methods have better performance compared with traditional methods based on individual channel. It suggests that functional and effective connectivity may track more changes from healthy people to patients to a certain extent, providing the possibility for earlier and more accurate diagnoses. Specifically, the thesis finds that specific regions of the brain can contribute to the diagnosis of epilepsy and dementia disease as well as workload classification based on brain connectivity. By advising the appropriate placement of electroencephalography sensors based on these identified regions, doctors and researchers can more efficiently and accurately diagnose and classify these neurological disorders, reducing the burden on healthcare systems.PhD in Manufacturin