Data-Driven Radial Compressor Design Space Mapping

Abstract Estimates of turbomachinery performance trends inform system-level compromises during preliminary design. Existing empirical correlations for efficiency use limited experimental data, while analytical loss models require calibration to yield predictive results. From a set of 3708 radial compressor computations, this paper maps efficiency as a function of mean-line aerodynamics, and determines the governing loss mechanisms. An open-source turbomachinery design code creates annulus and blade geometry, then runs a Reynolds-averaged Navier–Stokes simulation for compressors sampled from the mean-line design space. Polynomial surface fits yield a continuous eight-dimensional representation of the design space for analysis, predicting efficiency with a root-mean-square error of 1.2% points. The results show a balance between surface dissipation in boundary layers and mixing loss due to casing separations sets optimum values for inlet Mach number, hub-to-tip ratio, de Haller number, and backsweep angle. Surface dissipation drives the effect of flow coefficient, with high surface areas at low values, and high velocities at high values. Compact compressor designs are achieved by increasing inlet Mach number, reducing hub-to-tip ratio, and minimizing the radial loading coefficient—all of which reduce efficiency approaching design space boundaries. An interactive web-based tool makes the results available to practising engineers, demonstrating large ensembles of automated designs and simulations as a higher-fidelity replacement for legacy empirical correlations in preliminary design.</jats:p

Digital twins for urban underground space

Digital twins (DTs) offer promising benefits to address several inherent problems in underground construction, yet confusion surrounds the concept due to its context-specific nature which hinders more widespread adoption. This paper seeks to clarify DT-related terminologies from a built environment perspective and define the features and maturity levels of DTs for underground spaces, considering their unique challenges. A layered architecture for constructing DTs is proposed, offering various options based on functionality, technological advancement, and expected value. Additionally, a comprehensive literature review of technologies enabling the development of digital twins for underground spaces is presented, including data-driven ground modelling, site investigation and design process integration with BIM, computational BIM, and advanced sensing and instrumentation. The paper identifies synergies between DTs and the observational method in geotechnical engineering, highlights research gaps, and proposes a transition to a prescriptive, knowledge-based DT. Furthermore, exemplar use cases of underground DTs throughout their lifecycle are explored, demonstrating their potential value

Predictive alarm models for improving radio access network robustness

With the widespread expansion of telecommunication networks, the increase in the number and complexity of base stations has led to an exponential growth in the volume of alarms. Traditional alarm prediction based on expert experience or rules has posed significant challenges due to the demand for engineers' expertise and workload. It has become imperative to enhance efficiency by employing data-driven approaches for network alarm prognosis. In this paper, a data-driven alarm prediction model is proposed to support the alarm prognosis in base stations. To improve model performance, the proposed approach utilises ensemble deep learning methods to address the heterogeneity and highly imbalanced alarm dataset. The model is trained and validated using a dataset provided by British Telecom (BT) group. The validation results demonstrate that the proposed method achieves a top-5 accuracy of up to 90\% in predicting alarms across 170 categories on the validation set

Knowledge-aware audio-grounded generative slot filling for limited annotated data

Manually annotating fine-grained slot-value labels for task-oriented dialogue (ToD) systems is an expensive and time-consuming endeavour. This motivates research into slot-filling methods that operate with limited amounts of labelled data. Moreover, the majority of current work on ToD is based solely on text as the input modality, neglecting the additional challenges of imperfect automatic speech recognition (ASR) when working with spoken language. In this work, we propose a Knowledge-Aware Audio-Grounded generative slot filling framework, termed KA2G, that focuses on few-shot and zero-shot slot filling for ToD with speech input. KA2G achieves robust and data-efficient slot filling for speech-based ToD by 1) framing it as a text generation task, 2) grounding text generation additionally in the audio modality, and 3) conditioning on available external knowledge (e.g. a predefined list of possible slot values). We show that combining both modalities within the KA2G framework improves the robustness against ASR errors. Further, the knowledge-aware slot-value generator in KA2G, implemented via a pointer generator mechanism, particularly benefits few-shot and zero-shot learning. Experiments, conducted on the standard speech-based single-turn SLURP dataset and a multi-turn dataset extracted from a commercial ToD system, display strong and consistent gains over prior work, especially in few-shot and zero-shot setups

Flexural behaviour of functionally graded reinforced concrete slabs with vertical or horizontal layers

Concrete floor and ground slabs in building frames represent a significant embodied CO2 footprint. One promising way to reduce the environmental impact is to tailor the concrete properties within a slab to meet spatially varied performance requirements such that concrete with a higher cement content is used only where necessary. An experimental programme to investigate simply-supported functionally layered slabs with either horizontal or vertical layers was undertaken to ascertain whether a desired serviceability performance can be achieved in tandem with a relatively lower embodied CO2. The test matrix compared three single mix slabs, two horizontally layered slabs with different layer thicknesses and two vertically layered slabs with different zonations. The slabs were 1200 mm long by 500 mm wide and either 120 or 160 mm thick. Conventional concrete materials consisting of a high strength (target compressive strength of 60 MPa) and a low strength (target compressive strength of 20 MPa) concrete mix were layered. The influence of the concrete mix properties, the tensile horizontal high strength layer thickness (20 mm or 56 mm) and the location of the high strength concrete (in the interior 233 mm width or exterior 133 mm wide regions) in a slab with vertical interfaces were probed. The layers were cast in the fresh-state with less than a 30 min pour delay to promote hydration across the interfaces. The one-way spanning slabs were tested in four-point bending where the distance between supports was 1000 mm and the shear span was 250 mm. The horizontally layered slab with a 56 mm high strength concrete layer had a higher elastic stiffness and provided an uplift in first cracking moment of around 60% relative to an analogous single mix low strength concrete slab. Beneficial restraint between weaker and stronger mixes across vertical interfaces led to an approximately 30% increase in cracking load relative to the single mix lower strength slab. It was observed that the placement of the stronger mix in the exterior regions bounding the weaker mix was the preferable vertical layout in terms of reaching a greater peak load at failure. Functionally layered concrete can help to rationalise the overall embodied CO2 of reinforced concrete slabs while concurrently satisfying desired serviceability and ultimate limit state requirements

Hyperbolic Site Percolation

Percolation was introduced in 1957 by Broadbent and Hammersley \cite{BH57} as a model for the spread of fluid through a random medium. Percolation provides a natural mathematical setting for such topics as the study of disordered materials, magnetization, and the spread of disease. See \cite{HDC18,GP,LP} for recent accounts of the theory. We consider here site percolation on a graph $G=(V,E)$, assumed to be infinite, locally finite, connected, and planar. The current work has two linked objectives

Impact of stacking faults on the luminescence of a zincblende InGaN/GaN single quantum well

Abstract In this paper, we investigate the optical properties of a zincblende InGaN single quantum well (SQW) structure containing stacking faults (SFs). Cathodoluminescence studies revealed the presence of sharp emission features adjacent to SFs, identified as quantum wires (Qwire) via their spatial anisotropy. Scanning transmission electron microscopy provided evidence of indium rich regions adjacent to SFs which intersect the QW along the [110] and [1–10] directions, whilst atom probe tomography revealed that the indium rich regions have an elongated structure, creating a Qwire. This work sheds light on the intricate relationship between SFs and Qwires in zincblende InGaN SQW structures, offering insights into the underlying mechanisms governing their optical behavior.</jats:p

Topology Bench: systematic graph-based benchmarking for core optical networks

Topology Bench is a comprehensive topology dataset designed to accelerate benchmarking studies in optical networks. The dataset, focusing on core optical networks, comprises publicly accessible and ready-to-use topologies, including (a) 105 georeferenced real-world optical networks and (b) 270,900 validated synthetic topologies. Prior research on real-world core optical networks has been characterized by fragmented open data sources and disparate individual studies. Moreover, previous efforts have notably failed to provide synthetic data at a scale comparable to our present study. Topology Bench addresses this limitation, offering a unified resource, and represents a 61.5% increase in spatially referenced real-world optical networks. To benchmark and identify the fundamental nature of optical network topologies through the lens of graph-theoretical analysis, we analyze both real and synthetic networks using structural, spatial, and spectral metrics. Our comparative analysis identifies constraints in real optical network diversity and illustrates how synthetic networks can complement and expand the range of topologies available for use. Currently, topologies are selected based on subjective criteria, such as preference, data availability, or perceived suitability, leading to potential biases and limited representativeness. Our framework enhances the generalizability of optical network research by providing a more objective and systematic approach to topology selection. A statistical and correlation analysis reveals the quantitative range of all of these graph metrics and the relationships between them. Finally, we apply unsupervised machine learning to cluster real-world topologies into distinctive groups based on nine optimal graph metrics using K-means. It employs a two-step optimization process: optimal features are selected by maximizing feature uniqueness through principal component analysis, and the optimal number of clusters is determined by maximizing decision boundary distances via support vector machines. We conclude the analysis by providing guidance on how to use such clusters to select a diverse set of topologies for future studies. Topology Bench, openly available via Dataset 1 (https://zenodo.org/records/13921775) and Code 1 (https://github.com/TopologyBench), promotes accessibility, consistency, and reproducibility.</jats:p