20505 research outputs found
Sort by
Aircraft design system requirements analysis with Bayesian Networks
No full textThis paper introduces readers with the application of Bayesian Networks in conceptual design tasks. These models can represent in a graph the cause-and-effect logic of requirements and enable the analysis of uncertainty in decision making. A syntax for converting a requirement into a Bayesian network query is also presented. These concepts are applied on a wing design example to demonstrate the flexibility of a Bayesian Network in answering design-related questions. In particular, a feasible design space is identified through inference of the requirements onto the input parameters. This is useful to narrow down configurations for further detailed analysis with an optimization algorithm. The model is capable of estimating the quantities of interest and the effect of the requirement uncertainty on the system variables. Through proper data collection and integration with digital engineering methods, a system-wide Bayesian network would enable reasoning between systems and components, accelerating the industrial development of complex products.This project has received funding from Innovate UK under Grant Agreement No 10003388.AIAA SCITECH 2025 Foru
An agent-based model of farmer decision making: application to shared water resources in Arid and semi-arid regions
Get PDFThe study presents an agent-based modelling framework that integrates behavioural and biophysical models to investigate shared irrigation water management in an arid region. The behavioural model simulates farmers' decisions about their water irrigation sources (dam or groundwater) and whether to continue cultivating in the face of drought. This model was parameterised using survey data. The biophysical model component quantifies the impact of water availability and irrigation sources on soil salinity accumulation and its effects on crop productivity. Applied to the Al Haouz Basin, in Morocco, the integrated model reveals several key findings: (1) Increased groundwater access through water abstraction authorization can initially boost productivity but leads to widespread salinisation and farm abandonment, particularly under climate change scenarios. (2) Scenarios with reduced dam water availability demonstrate that mixed irrigation strategies mitigate short-term productivity losses but fail to prevent long-term soil salinity issues. (3) Land abandonment is significantly influenced by the level of water abstraction authorizations, with higher abstraction leading to more severe environmental degradation and social impacts. (4) Policy scenarios reveal that there is a theoretical optimal level of groundwater abstraction that maximises productivity while minimising land abandonment and salinity build-up. These results highlight the complex trade-offs between short-term gains and long-term sustainability, emphasising the need for holistic water governance policies that balance individual and collective interests.Rothamsted ResearchThis project is funded by Office Chérifien des Phosphate (OCP group) through the collaboration between Cranfield University (CU), Rothamsted Research (RRes) and Mohammed VI Polytechnic University (UM6P).Agricultural Water Managemen
Data "WAAM of a complex superalloy component"
No full textTime-lapse videoWire Arc Additive Manufacturing complex Inconel 718 superalloy component. Research explored the feasibility of additively manufacturing superalloy components for high-speed flight and methods to enhance the performance of Wire Arc AM superalloy components.DST
How an institutional setting shape and limit the mitigation of accidents in complex work settings
Get PDFIntroduction:
Research suggests that accidents due to failed coordination arising from the disruption of everyday activity can be mitigated by empowered employees through sensemaking activities: observing or recognizing cues, voicing concern, and considering alternative perspectives. Unfortunately, the literature also observes limits to such activities due to the influence from technology, power, and language. However, there is negligible understanding of the mutual influence of these phenomena on (the failure of) sensemaking to prevent escalation.
Method:
Using an institutional and sociomaterial approach to sensemaking, we integrate the influence of technology, power, and language to investigate accident commission data (e.g., talk between different actors and interviews), from a railway accident in Sweden in 1987, showing how a minor disruption in everyday work escalated into a situation that exceeded the limits for effective sensemaking.
Results:
Technology, power, and language in institutional settings, expressed through actors’ habitual repertoire, influence sensemaking and its outcomes. The findings indicate that actors’ habits encourage the continuation of immanent sensemaking and that it takes strong, specific, cues to shift to deliberative sensemaking. Moreover, also deliberative sensemaking is influenced by actor’s habitual repertoire, limiting its quality.
Conclusions:
The efforts to mitigate the escalation to tragedy in this case failed because of the mutual influences of technology, power and language operating within an institutionalized and heavily regulated work environment. This resulted in fragmented or minimal sensemaking that, in hindsight, did not match the complexity in the accident and the response that would have been required.
Practical Applications:
To enable sufficient articulation of concerns and collaborative problem-solving in complex safety–critical systems, there is a need to break with hierarchical relations, to create a shared language, and employees should be made aware of the potential misleading signals from technologies designed to ensure safety.Journal of Safety Researc
Dataset DrivAer hp-F: Wake Total Pressure Measurements in Yaw Conditions
No full textDataset for the wake total pressure measurements conducted on the 35% scale DrivAer hp-F model at various yaw angles in the 8x6 Wind Tunnel at Cranfield University. The measurements are performed on the DrivAer hp-F rear wing configuration with an angle of attack of 15°. The dataset includes the total pressure coefficient results from measurements on the P1, P2, and P3 wake planes, which are located 400 mm, 700 mm, and 1000 mm downstream of the vehicle model respectively. Additionally, the horizontal and vertical measurements positions (in mm) are provided for each wake plane. A horizontal sweep on the P3 wake plane has been conducted three times for repeatability.
In reference to the publication: Steven Rijns, Tom-Robin Teschner, Kim Blackburn, Anderson Ramos Proenca, James Brighton; Experimental and numerical investigation of the aerodynamic characteristics of high-performance vehicle configurations under yaw conditions. Physics of Fluids 1 April 2024; 36 (4): 045112. https://doi.org/10.1063/5.0196979
CAD files for the DrivAer hp-F rear wing configuration are available at: Rijns, Steven; Teschner, Tom-Robin; Blackburn, Kim; Ramos Proenca, Anderson; Brighton, James (2024). DrivAer hp-F: Spoiler & Rear Wing Configurations Geometry Pack. Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.25715202
Note: The updated dataset retains all original data while adding calibrated data to provide (new) users with an additional reference option
Past, present, and future of battlefield forensics - Presentation
Get PDFThe full report on the conference, is available at: https://ccdcoe.org/library/publications/report-on-the-conference-on-the-law-applicable-to-the-use-of-biometrics-by-armed-forces-tallinn-7th-8th-of-may-2024/Conference in the Law Applicable to the use of Biometrics by Armed Forces 202
Ontology-driven knowledge graphs for personnel management within the UK Ministry of Defence: a conceptual overview
Get PDFOntology-driven knowledge graphs visualise complex relationships between entities such as people and concepts. This conceptual paper explores the potential for using ontology-driven knowledge graphs to enhance personnel management within the Ministry of Defence (MOD). It reviews existing literature on ontologies, structured frameworks to store domain knowledge based on relationships between data, and knowledge graphs and outlines the concept of an ontology-driven knowledge graph for a skill management system. The paper argues that this approach can provide a unified, standardised method for managing personnel skills, improving decision-making, and enhancing operational efficiency. A further benefit identified is the potential for the system to be expanded to exchange information with other systems, such as the NATO Defence Planning Process (NDPP), allowing external data to improve the quality of inferences made by the system.DSTLDefence and Security Doctoral Symposia 2024 (DSDS24
Identifying enablers for a circular healthcare supply chain: an integrated fuzzy DEMATEL-MMDE approach with hesitant information
Get PDFOver the past decade, the healthcare industries have been facing the issue of resource scarcity due to socio-political wars, the COVID-19 pandemic, the luxury lifestyles of people, and the prevalence of single-use medical devices. The circular economy (CE) can be vital in accommodating the evolving patient and provider demands. The CE defies the conventional take-make-dispose process and advocates for optimized resource use throughout its entire lifecycle. Finding the critical enabler for a circular supply chain in the Indian healthcare sector is urgently needed because the CE is still nascent in developing nations like India. This paper identifies the enablers of a circular healthcare supply chain (CHSC) through a comprehensive analysis of cause and effect, interrelationships, and priorities using integrated alpha-level sets based on the fuzzy DEMATEL-MMDE approach with hesitant information. Experts' opinions were gathered using a triangular fuzzy linguistic scale, incorporating hesitant information to minimize data vagueness. The results of this study offer valuable insights into various enablers, emphasizing their criticality and interdependence, thereby benefiting healthcare organizations aiming to implement circular supply chains. Additionally, the findings will assist policymakers in creating policies to accelerate the adoption of CE practices in the healthcare industry.Cleaner Engineering and Technolog
Source detection and tracking for underwater distributed acoustic sensing
Get PDFDistributed Optical Fiber Sensing (DOFS) transforms conventional fiber optic cables into an extensive network of continuous sensors. It achieves this by exploiting the spectral, polarization and/or phase sensitivity of the propagating light to measurands of temperature, strain, pressure, vibrations etc. To harness the novel capabilities of optical fibers to remotely capture, process and coherently analyze ambient vibration (e.g., acoustic) fields, it is crucial to address the challenges of the diversity of noise introduced in DOFS measurements, in particular, within the under-explored submarine environment. This research introduces a
comprehensive workflow for the detection of active (uncontrolled) acoustic sources, comprised of successive denoising steps that deal with the distinctive properties of such environments. Leveraging the spatio-temporal density of DOFS measurements, we develop a method based on data covariances for the automatic extraction of features in an unsupervised manner, together with additional features introduced to distinguish active source signals from noise. Consequently, this work takes the denoising of underwater DOFS data one step further through the application of a tracking algorithm on real, novel submarine DOFS data, laying the foundation for broader applications of DOFS data analysis in marine environmental sensing
and monitoring.Engineering and Physical Sciences Research Council (EPSRC)This work was supported by UK Research and Innovation Centre for Doctoral Training in Machine Intelligence for Nano-electronic Devices and Systems [EP/S024298/1] and the Defence Science and Technology Laboratory.Defence and Security Doctoral Symposia 2024 (DSDS24
Modeling of overloads in cyclic loading
No full textWhile researchers have tried to predict mechanical response in cyclic loading, the mod-
elling of overloads (monotonic loads among cycles) is still missing in the literature. This
work presents a new crystal plasticity approach to predict the overload response.
Even though crystal plasticity models have been available for decades, quantification
of material parameters is still a matter of debate. Polycrystalline experimental results can
normally be reproduced by multiple sets of parameters, raising concerns about the best
parameterization to predict the grain-level response. Crystal plasticity parameters has
been optimised by not only fitting experimental stress-strain curves but also independent
prediction of mesoscale structures in this work. We employ a unique set of parameters
with limited uncertainty to reproduce the mechanical response of FCC single- and poly-
crystals in monotonic loading. We demonstrate that mesoscale parameters are material-
invariant and can be used to model FCC metals with similar dislocation substructures such
as for Cu, Ni and Al. Furthermore, the model is validated by comparing to experimental
single- and poly-crystalline stress-strain curves and mesoscale dislocation substructure
images.
This work also innovates with a new optimization method to calibrate the proposed
crystal plasticity model parameters. We estimated model parameters for Cu, Ni, and Al
and showed that these parameters are applicable to predict stainless steel response without
single crystal data.
Finally, we used the model with optimised parameters to predict the overload response
for Cu and NiCr alloy. The agreement with experiments for not only macroscopic re-
sponse, but also mesoscale structures attributes shows robust prediction power of this
work.PhD in Manufacturin