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Sustainable fuel aviation infrastructure and transition: hybrid agent-based modeling and system dynamics
No full textThe adoption of sustainable fuels in aviation, such as Sustainable Aviation Fuels (SAF), hydrogen (LH2), and ammonia (NH3), is a significant step toward achieving green and sustainable air transport. However, transitioning the aviation industry to sustainability is a complex process that requires an integrated transformation of both technological and social systems. With multiple potential fuel alternatives, it is important to identify the most viable fuel pathways. This paper presents the concept of a hybrid, Agent-Based Modeling (ABM) and modular System Dynamics (SD) model, developed as a computational framework to analyze the interactions among technology, industry, markets, and society. A hybrid ABM and SD approach is an ideal choice for analyzing the transition of the ATS to sustainable fuels because of the complexity of the problem, which involves various entities with distinct behaviors and decision-making processes. The modular SD model consists of multiple stock and flow modules of passenger demand, aircraft orders, fleet development, fuel demand, sustainable fuel production, and infrastructure, providing a holistic but detailed, granular analysis of interactions and impacts within the system. The ABM approach facilitates the integration of SD modules by incorporating agents such as airlines, fuel suppliers, airports, aircraft manufacturers, and fuel producers, each with their unique behaviors. The proposed model serves as a reference framework for simulation and analysis, supporting future research on aviation sustainability and providing a decision support tool for understanding the long-term feasibility of different sustainable aviation fuels.This work was performed under the Out of Cycle NExt generation highly efficient air transport (ONEheart) project funded by the UK Research and Innovation.10th International Conference on Computer and Communication Engineering (ICCCE
Semantic integration of heterogeneous aircraft though-life documentation
No full textAvdelidis, Nicolas P. - Associate SupervisorThis thesis investigates the challenges of integrating heterogeneous aircraft
maintenance records through the development of a novel ontology and prototype
application, addressing the complexity and heterogeneity of the data sources, formats,
and semantics inherent in aircraft maintenance documentation. This research focuses
on the maintenance records of a Boeing 737-400 available at Cranfield to develop
Aircraft Maintenance Records Ontology (AMRO). Ontology provides a structured
representation of domain knowledge, facilitating improved data consistency by
addressing semantic and syntactic inconsistencies. This study aims to minimise manual
processes in handling maintenance records, thereby enhancing efficiency, accuracy,
and reducing errors related to data inconsistencies.
This study outlines an agile development for ontology-based application (ADOBA)
methodology, integrating ontology and application development to ensure continuous
feedback and improvement. The developed prototype demonstrates practical application
in correcting inconsistencies and enhancing record accuracy using Named Entity
Recognition (NER) for component categorisation with the corrected data. The
performance of the AMRO model and prototype was evaluated using standard metrics,
such as precision, recall, and F-measure, confirming their effectiveness in improving data
consistency and operational efficiency.
The key contributions of this research include a systematic review of aircraft operations
and maintenance knowledge management, highlighting challenges related to data
heterogeneity and integration, introduction of the ADOBA methodology, and
development of AMRO based on real-world maintenance records. This thesis
demonstrates the potential of semantic technologies in improving data consistency and
operational efficiency in aircraft maintenance, setting a foundation for future
advancements and standardisation efforts in the aviation industry.
Future work should focus on aligning the AMRO model with international standards,
expanding its coverage to include predictive maintenance and lifecycle management,
and integrating advanced Natural Language Processing (NLP) models to enhance data
interpretation and automation. This research emphasises the importance of robust
knowledge management systems to ensure compliance with aviation regulations and
improve the overall efficiency of managing aircraft maintenance records.PhD in Transport System
Analytical transonic flow simulation in streamline curvature methods for axial-flow compressor design
No full textAircraft gas-turbine engine component design and analysis is still a pleasant art to be further explored to satisfy the optimum engineering limits. One of the challenges for aero-engines is to provide high efficiencies while being light and compact. As a responding solution for these requisites, modern axial-flow transonic fans and compressors provide high shock-induced single-stage pressure ratios, reducing weight and size. The problem becomes thus to attain this within acceptable isentropic efficiencies and working ranges, at least comparable to those from the subsonic axial-flow compressors.
The constant and crescent demand to obtain more accurate turbomachinery blading performance in the analysis and design process has led the designer to explore different levels of simulation fidelities and optimisation strategies. The extensive use of Computational Fluid Dynamics (CFD) methods in aerodynamics has made the three-dimensional (3-D) Reynolds-averaged Navier-Stokes (RANS) numerical simulations the preferred technique for turbomachinery analysis. Despite their high-order resolution and extensive flow field information that can be collected, it comes at intolerably high computational costs in terms of time and resources, especially if they are used as solvers within an optimisation framework. In contrast, two-dimensional (2-D) through-flow methods such as streamline curvature (SLC), keystone in the turbomachinery design, provide a rapid flow solution whilst offering accurate results.
In the context of transonic axial-flow fans and compressors, previous 2-D SLC tools have failed to replicate the real physics related to compressible flow. More specifically, the prediction of the highly-complex shock-system shape and location for an accurate estimation of shock-associated losses has always been assumed and oversimplified. The situation aggravates, when the assumed overall shock configuration applies only for design point at unstarted operations, requiring of empirical correlations to estimate the shock-loss coefficient for off-design operations. The overall performance prediction of the fan and compressor is thereby highly-dependant on the shock modelling quality.
For this reason, an analytical transonic-flow simulation package was developed and implemented into an existing in-house 2-D SLC compressor performance simulator with the aim of enhancing the aerodynamic prediction for transonic axial-flow fans and compressors. The novel toolkit to handle transonic flow and fully coupled to the 2-D SLC software consists of the following contributions: (1) a 3-D blade-element-layout method; (2) an adaptation of the full radial-equilibrium equation (REE) to handle the effects of 3-D blade shaping; (3) a physics-based shock- structure and loss model for unstarted and started operations that uses an iterative-solution method to locate the choke-induced passage-shock; (4) a choking mass flow redistribution model.
In this way, shock losses were determined throughout the blade span and for various off-design operating conditions, including those at choking, where the mass flow was limited and redistributed spanwise according to the unique incidence. 2-D SLC simulations were conducted for the NASA Rotor 67 and the NASA Advanced Duct Propulsor (ADP) Fan to calibrate and validate the models accordingly against experimental rig-test data and 3-D CFD results. The analytical shock- loss and structure model improved the shock-loss prediction between 40-50% with respect of the state-of-the-art models, and showed satisfactory agreement against measured data within 0.6% at the blade tip and 0.3% at mid-span sections. A parametric study was conducted for the NASA Rotor 67 to demonstrate design trends when varying 2-D and 3-D blade-element parameters utilizing the transonic-flow toolkit in its entirety, evidencing the impact on the shock-loss radial distribution.
A new cutting-edge variable-pitch fan (VPF) was designed using the NASA ADP Fan 2-D SLC model, which was further optimised using an evolutionary genetic algorithm (EGA) to demonstrate the application of the novel transonic-flow package. The single-objective optimisation consisted in the variation of sweep and lean angles with the purpose of studying the pure influence of shock losses on the overall isentropic efficiency. The optimised geometry resulted in a backward-swept and mostly pressure-side leaned VPF that incremented the isentropic efficiency by 0.4% with respect of the baseline configurationPhD in Aerospac
Non-invasive cardiovascular and vital signs monitoring techniques: review, challenges, and perspectives
No full textCardiovascular diseases are the leading cause of global fatalities, necessitating effective diagnostic solutions. Traditional methods, while valuable, often require invasive procedures or require subjects to remain stationary, limiting their real-time monitoring capability in dynamic environments. This article reviews the emerging field of contactless and distraction-free cardiovascular monitoring, which offers distraction-free, flexible, and user-friendly alternatives for enhanced accessibility. We examine various techniques, including radar-based methods, optical measurements, ballistocardiography, contactless electrocardiogram (ECG), and wearable devices, comparing their working principles, advantages, and limitations against traditional diagnosis methods. The novelty of this review lies in its comprehensive evaluation of these methods across eight key dimensions, including application breadth, time efficiency, reliability, distraction-free operation, safety, bandwidth, information value, and working distance. Another new perspective involves how advanced hardware, digital filters, and artificial intelligence (AI)-driven signal processing methods address challenges associated with relatively poor signal quality. Additionally, this article discusses these techniques’ key values on healthcare, challenges, and emerging opportunities.Measuremen
Fault diagnosis in time series data with application to railway assets
No full textEmmanouilidis, Christos - Associate SupervisorThis thesis focuses on diagnosing incipient faults in railway train assets through large data sets. It investigates data mining methods to detect changes in big time series data to enable service by employing three case studies that separately investigate (1) the doors, (2) the engines, and (3) the wheels. Each case study comprises data with different characteristics: the first case study examining engines presents datasets with variable speed and load; the second case study examining door data highlights start-stop characteristics, with discontinuities in the data; and the third case study examining wheel data contains fast transitions from normal to slip behaviours in acceleration and deceleration.
A time series architecture is proposed. It describes key steps for multivariate time series analysis and enables iterative improvement reusing results for the next iteration. A fault diagnosis is made for each case scenario, and procedures for synchronisation and alignment; pre-processing; and methods for feature extraction, classification or clustering are thus presented.
For engine fault diagnosis, the proposed graphical method has the best performance. In the case of the door fault diagnosis, the K-nearest neighbours method has the best performance. In the wheel slip diagnosis case study, the combined wavelet and LSTM methods present the best accuracy. Limitations include data quality issues, key input data uncertainties, and applied classification deficiencies.
The main challenges include big datasets that are desynchronised, wrongly time-indexed, noisy, redundant, and unlabelled; infrequent faults; the lack of monitoring in several subsystems; and incomplete or missing maintenance records.PhD in Manufacturin
Wire + arc additive manufacturing for high-speed flight
Get PDFRodrigues Pardal, Goncalo - Associate SupervisorThe use of Wire + Arc Additive Manufacturing (WAAM) to manufacture high-
speed projectiles, such as missiles, is currently an industry challenge due to the
nature of high-speed flight and the extreme environment that components are
exposed to. Alloys that are suitable for high-speed flight are creep resistant
superalloys, this is due to the aggressive heating environment experienced by
objects in high-speed flight, and the need for performance at extremely high
temperatures. These materials are currently expensive and difficult to
manufacture, which is less than ideal for non-recoverable systems such as
airborne weapons. The development of missile systems requires flight tests to be
affordable and operate in quick succession, to which rapid prototyping offers a
significant advantage. The use of traditional manufacturing methods and supply-
chain for this purpose are logistically challenging and expensive, mainly due to
loss of material though machining. The use of WAAM in a rapid prototyping
capability is the driver for this research. To be able to use the process to
manufacture and prototype components for high-speed applications, would, if
possible, be an excellent solution to reducing the amount of time and money that
it currently costs to flight-test and develop these systems. WAAM could also be
used for final design production.
The effect WAAM route has on the high temperature properties of superalloys is
largely unknown. This research is therefore focused on the development of the
WAAM process, and selection of alloys suitable for high-speed flight and for
WAAM deposition. Four creep-resistant superalloys underwent deposition using
a plasma WAAM process and the resulting material was characterised to
understand how WAAM affects high temperature performance. The research also
investigates post-deposition heat-treatment of these alloys and develops
parameters for inter-pass machine hammer peening to improve material
performance.
The findings from this project increases the understanding between the WAAM
process and superalloy strengthening mechanisms and develops a method to
increase the performance of additive manufactured material. The mostii
appropriate alloys for both WAAM and the high-speed flight application were
ranked and down selected based on their anticipated performance and
weldability. The selected alloys then underwent extensive testing from room
temperature to 1000 °C, to understand the performance of WAAM built structures
at high temperature. The microstructure is examined throughout and found key
differences between solid-solution strengthened and age hardened alloys which
effects performance. Finally, in-process machine hammer peening was
investigated for age hardened Rene 41 and found to greatly increase the
performance to match that of the wrought material.Defence Science and Technology Laboratory (DSTL)PhD in Manufacturin
Introduction of social benefits to the tera – gas turbines and pipelines
Get PDFIgie, Uyioghosa - Associate SupervisorThe aim of this work is the techno-economic and environmental risk analysis
(TERA) of the Trans-Saharan Gas Pipeline on gas turbine compressor stations.
A pipeline project encompasses many aspects, viz., choice of compressor station
location, power of each compressor station, compressor station availability,
pipeline sizing, and it includes socioeconomic impacts. Therefore, this research
considered the impacts of engine availability, compressor station location, and
socioeconomic impact in the TERA for pipelines while optimising for the lowest
lifecycle cost.
The pipeline and gas compressor modules were evaluated considering
segmented pipe length, elevation, and station location ambient temperature
variation at varying flow conditions. The design and off-design points
performance of the selected gas turbine models were simulated using
Turbomatch to obtain essential performance data required for the techno socio-
economic analysis. The unit availability was evaluated based on a developed
local maintenance schedule and failure rate retrieved from literature studies. The
analysis considered the social impacts and benefits of compressor station
locations. A scenario-based techno socio-economic analysis was performed to
show the sensitivities of the compressor station and pipeline systems to social
and technical aspects of the project in terms of social benefits and availabilities.
The economic model was developed based on social benefit algorithms and the
variation in compressor station location ambient temperatures at varying flow
conditions.
Results show compressor station system availabilities of 0.2542, 0.4657, and
0.9926 with corresponding lifecycle costs of 23.05 billion, and
$24.11 billion assuming a 15% discount rate for scenarios 1, 2, and 3,
respectively. An increase in availability leads to a corresponding increase in the
lifecycle cost estimate. The employment and road benefit ratios would increase
by a factor of 10 and reciprocal of new locations. This ratio is for every 10 km
decrease in the distance at each station location. Results show the significance
of the modelling and optimisation approaches utilised in this research for
compressor station locations optimisation of the integrated pipeline TERA. This
will guide decision-makers on the ultimate selection of engine configurations that
will give the optimum lifecycle cost and socio-economic benefits at the optimised
station locations.Petroleum Technology Development Fund (PTDF)PhD in Aerospac
Framework for anomaly detection of flight-crew deviation from standard operating procedures: a data analytics approach
Get PDFJennions, Ian K. - Associate SupervisorDeviations from Standard Operating Procedure form a significant part of aviation incidents today
involving loss of lives and other related costs. Previous work tailored towards detecting
procedure deviations in flight operations have primarily been rule-based. The current method
being used by airlines to detect operational, component fault and crew action anomalies within
flight data is a rule-based Exceedance Detection technique which is only able to flag up known
flight abnormalities. Lately, Anomaly Detection methods have been introduced to find, not just
known, but unknown anomalies that deviate from the expected normal flight profile. There is a
need to explore flight data using anomaly detection methods to detect subtle underlying
misunderstandings of the flight crew in relation to deviations from laid down procedures which
do not lead to incidents, under most conditions, or are hard to detect by the state-of-the-art
method. However, these detection methods are limited in the type of anomalies they can find
when implemented individually on heterogeneous flight dataset thereby missing critical
anomalous flight incidents. In this work, Flight Data Recorder data of a fleet from a United
Kingdom airline and a structurally similar publicly available dataset from the National Aeronautics
& Space Administration are used. This study proposes a framework integrating an Ensemble
anomaly detection technique (combining individual anomaly detection techniques into a single
method) and a Case Based Reasoning system. The findings reveal that combining existing
anomaly detection methods into an Ensemble can detect a wider variety of anomalies that were
not flagged by individual methods. Also, the proposed reasoning design aims to filter for
procedure deviations from the pool of anomalous incidents detected by the Ensemble. Detecting
these procedure deviations is not just aimed at complementing crew training, improving
procedures, and understanding automation design to put in place mitigation strategies but also
to aid accident investigations by informing of accident flights with procedure deviations that may
have been contributing factors.The Petroleum Technology Development Fund, NigeriaPhD in Transport System
A global synthesis of genotypic variation in crop greenhouse gas emissions under variable nitrogen fertilisation
No full textTargeted crop selection offers a promising potential pathway to reduce greenhouse gas (GHG) emissions from global croplands. Yet, the influence of crop genotypes on GHG emissions remains poorly studied, limiting our ability to understand its global potential. To address this challenge, we conducted a global synthesis of GHG and crop yield data from 42 field experiments across 180 genotypes of major cereal (predominantly rice) and oilseed crops (soybeans and canola) and nitrogen (N) fertilisation rates (40kg ha-1 to 390kg ha-1) (n =390). To test the influence of genotype, we removed measurements from genotypes with fewer than three independent replicates (n = 97) and apply linear mixed-effects models to control for study and latitude effects. Across a range of environmental and experimental conditions, we analysed the influence of N application rate on crop nitrous oxide (N2O) and methane (CH4) emissions, alongside yield. We found significant differences in N2O-N cumulative fluxes between crop types and mean annual precipitation ranges. When expressed per unit of crop yield, N2O-N and CH4-C cumulative fluxes revealed a significant difference between N application rate groups (a = < 50, b = 50-100, c = 100-150, d = 150-200, e = 200-250, f = 250-300, g = > 300), with a positive yield response to N fertilisation. While yield-scaled N2O-N cumulative fluxes declined with N application rate, yield-scaled CH4-C cumulative fluxes increased; however, all CH4 measurements were derived from rice systems. Regression relationships between cumulative N2O, CH4, crop yield and N application rate were consistent with previous global syntheses, showing that N2O and CH4 emissions increased exponentially with N application, while crop yield exhibited a quadratic response. Our results indicate that N application rate was the primary driver of N2O emissions and crop yield, while genotypic differences significantly influenced CH4 emissions. These findings underscore the importance of integrating genotype selection with nitrogen management to improve GHG mitigation while optimising crop productivity.This work was funded by the Cranfield Industrial Partnership PhD Programme and Premium Crops awarded to NG.Frontiers in Agronom
Investigating the role of grain boundary hydrogen in dual atmosphere effects for solid oxide cells interconnect applications
No full textFerritic stainless steel (FSS) is widely used as an interconnect material in solid oxide cells (SOCs). However, these interconnects degrade faster under simultaneous exposure to oxidizing and reducing atmospheres, a phenomenon known as the dual atmosphere effect. This study used SUS430 to investigate the mechanisms behind this effect. Oxidation behavior was compared for single air atmosphere, and dual atmosphere at 750 °C after 50, 100, and 200 h. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) showed significant hydrogen enrichment at grain boundaries after 200 h in the dual atmosphere exposure as compared to the single atmosphere. To explore hydrogen’s role, first-principles calculations were performed evaluating its adsorption energy on the (110) Fe-Cr crystal plane and its impact on Cr diffusion. The results revealed that hydrogen’s presence raises the energy barrier for Cr diffusion and alters its pathway. This suggests that hydrogen enrichment at grain boundaries is a major factor in the dual atmosphere effect, as it hinders Cr diffusion, contributing to accelerated degradation of interconnect materials.This research was supported by the Centre for Energy Engineering at Cranfield University (UK).Corrosion Scienc