https://bura.brunel.ac.uk/handle/2438/8624 2026-10-16T06:27:13Z https://bura.brunel.ac.uk/handle/2438/34002 Title: Large language models in the patent lifecycle: A systematic review Authors: Jiang, Pingfei; Arena, Andrea Pietro; Sorce, Salvatore; Atherton, Mark Abstract: Patents constitute the world's largest repository of codified technical knowledge, yet their length, legal terminology, and linguistic ambiguity have limited the effectiveness of traditional text mining and keyword-based retrieval. Large Language Models (LLMs) offer new capabilities for processing this complexity, but the resulting literature is fragmented across disciplines, with no comprehensive synthesis of how these tools are being applied across the patent lifecycle. This study systematically reviews the literature on LLMs and Generative AI in patent analytics, following the PRISMA 2020 protocol, to map current applications, characterise dominant architectural trends, and identify priority research gaps. Using Scopus and Web of Science, we identified and screened a corpus of 129 peer-reviewed studies published from 2018 to early 2026, organised according to three patent lifecycle stages: creation and pre-filing (N = 36), examination and prosecution (N = 34), and post-grant management (N = 60). The review shows that research activity has grown sharply since 2024, accompanied by a shift from exploratory, single-task studies toward applied, multi-component systems. Across this literature, Retrieval-Augmented Generation (RAG) has become the dominant architectural response to hallucination risk, grounding generated content in verified external sources. The review further identifies persistent gaps in domain-specific evaluation metrics, multimodal reasoning, and governance frameworks for AI-generated patent content. For patent professionals and institutions, the findings indicate that LLMs are best positioned as assistive tools requiring human oversight rather than autonomous replacements, and that the central challenge going forward is not whether but how the patent system governs this transition. Description: Data availability: No data was used for the research described in the article. 2026-09-26T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33984 Title: Numerical optimisation of ply orientation for aeroelastic tailoring Authors: Canpolat, Hakan Berk Abstract: This research explores the aeroelastic performance of a composite cantilever wing. The aim is clear: design a structure that resists flutter while staying light. We connect aerodynamic analysis with structural dynamics to reach this goal. First, we run CFD simulations to measure lift and drag coefficients at different flow speeds. We then convert these coefficients into forces. Next, we apply these forces as uniform pressure loads on the wing surfaces in ANSYS Workbench. This step directly links fluid flow results to structural response. We model the structure in both shell and solid forms. Each model captures stiffness and mass differently. The modal analysis reveals the natural frequencies and mode shapes. We track changes in torsional and bending modes as the ply count increases. More plies make the structure stiffer, but the gain slows after a certain thickness. The 8-ply configuration offers the best balance between stiffness, weight, and mode separation. Harmonic response simulations test the wing under realistic oscillating loads. We focus on vibration amplitudes near resonance. We compare multiple stacking sequences, especially for the 12-ply case. Some layups reduce torsion–bending coupling and shift resonance away from critical flight frequencies. One configuration shows the lowest displacement peaks at both 60 m/s and 80 m/s, making it the top candidate. These results give engineers a direct method to link aerodynamic loads with structural vibration data. The workflow is simple, fast, and repeatable. Future work will extend the method to high-speed compressible flows and validate results with wind tunnel experiments. Description: This thesis was submitted for the award of Master of Philosophy and was awarded by Brunel University London 2026-01-01T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33963 Title: Flow boiling of HFE-7100 in a single microchannel Authors: Widgington, Joseph Jake Abstract: The global drive towards electrification and carbon reduction has created an urgent requirement for compact, high-performance thermal management systems capable of dissipating high heat fluxes while maintaining component reliability. Flow boiling in microchannels has emerged as a promising solution due to its high heat transfer capability arising from latent heat effects and large surface area to volume ratios. However, industrial implementation remains limited by incomplete understanding of microscale flow behaviour, heat transfer, pressure drop, and critical heat flux (CHF). Existing predictive methods are often empirical, fluid-specific, and lack general applicability. This thesis addresses these limitations through an experimental investigation of flow boiling of HFE-7100 in a single rectangular microchannel. An experimental facility was designed and constructed to obtain accurate measurements of wall temperature, pressure drop, and high-speed flow visualisation. Two oxygen-free copper test sections with rectangular channels and hydraulic diameters of 0.75 mm were fabricated, with heated lengths of 30 mm and 75 mm to investigate length effects. Experiments were conducted over mass fluxes from 100–1000 kg/m²s, wall heat fluxes up to CHF, and inlet pressures of 1–2 bar. HFE-7100 was selected due to its dielectric properties and lack of prior single-channel flow boiling data. Single-phase validation and uncertainty analysis confirmed the reliability of the methodology. Flow visualisation identified bubbly, slug, churn, and annular flow regimes, with transitions strongly dependent on mass flux, heat flux, inlet pressure, and heated length. Stratified flow was not observed, confirming the confined nature of microscale boiling. Nucleation consistently initiated at channel corners due to geometric confinement. Increasing mass flux and pressure altered bubble coalescence and stability, shifting transition boundaries accordingly. Shorter heated lengths promoted earlier slug formation due to accelerated bubble growth at higher local heat fluxes. Transition maps demonstrated that existing predictive models only partially capture the combined effects of confinement, evaporation momentum, interfacial shear, and geometry. Heat transfer measurements showed that boiling incipience occurred at lower wall superheats with increasing inlet pressure. Heat flux significantly enhanced heat transfer coefficients through increased nucleation activity, while mass flux primarily extended the subcooled boiling region. Higher inlet pressures further improved heat transfer by stabilising nucleation. The 30 mm channel produced higher average heat transfer coefficients than the 75 mm channel at equivalent outlet vapour qualities due to intensified nucleation from concentrated heat input. Nucleate boiling persisted throughout the vapour quality range, limiting the applicability of convective boiling models. Evaluation of twelve existing correlations showed that the best-performing models were those capable of accounting for nucleate boiling dominance. Two-phase pressure drop increased with heat flux due to increased vapour generation and interfacial shear. Increasing pressure generally reduced pressure drop by delaying flow regime transitions through changes in fluid properties. Shorter heated lengths produced steeper pressure gradients due to intensified bubble activity and frictional losses. Existing pressure drop correlations showed considerable discrepancies, although the homogeneous flow model provided reasonable agreement. CHF investigations in the 30 mm channel showed that dryout occurred primarily through liquid film depletion in annular flow. Higher mass flux and pressure increased CHF, with mass flux having the strongest influence due to improved liquid replenishment and bubble removal. Existing CHF correlations performed reasonably well overall but did not accurately capture the weak pressure dependence observed experimentally. Finally, a novel neural network framework for predicting microscale flow pattern transitions was developed. A multi-stage feed-forward neural network architecture was trained using a large experimental database with robust preprocessing, cross-validation, and hyperparameter optimisation. The model achieved an overall classification accuracy of 95.5%, outperforming conventional empirical approaches and demonstrating the potential of data-driven methods for predicting complex microscale two-phase flow behaviour. Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London 2026-01-01T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33920 Title: Smart Bioinks for 4D Bioprinting: Requirements, Design, and Applications Authors: Chen, Shangsi; Lai, Jiahui; Zeng, Qiongjiao; Zhou, Liangbin; Yang, Boguang; Zhang, Bin; Wang, Min; Zhou, Jiajing; Lau, Kieran; Lim, Khoon S; Li, Zhong Alan; Tuan, Rocky S Abstract: 3D bioprinting is known for its high precision and reproducibility in fabricating complex and customized biomedical constructs. However, its applications are limited by their static nature; i.e., unlike native tissues, they cannot change shape or functionality over time. To overcome this, 4D bioprinting has emerged as a groundbreaking strategy by incorporating time as the fourth dimension, enabling dynamic structures that adapt in response to stimuli, thereby more accurately replicating living tissues. The success of 4D bioprinting hinges on the development of advanced smart bioinks, as their physicochemical properties uniquely dictate the shape-morphing behavior, functionality, and performance of bioprinted constructs. These bioinks must be precisely engineered to respond to specific stimuli. This review first introduces 4D bioprinting technologies for tissue engineering scaffolds. We then outline essential requirements for smart bioinks and highlight how AI, particularly machine learning, is revolutionizing their design. Additionally, we examine widely used biomaterials for 4D bioprinting and discuss promising candidates for 4D printing. We also present cutting-edge bioink applications in tissue engineering, drug screening, and disease modeling, showcasing their potential in regenerative medicine and personalized therapeutics. Finally, we discuss current challenges and future perspectives, underscoring the transformative impact of smart bioinks and 4D bioprinting on biomedical innovation. Description: Data Availability Statement: Data will be available upon reasonable request.; Supporting Information is avaoilable online at: https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.77791#support-information-section . 2026-09-27T00:00:00Z