Stratified gas–liquid flow is a flow regime typically encountered in multiphase pipelines. The understanding and modeling of this regime is of engineering importance especially for the oil and gas industry. In this work, simulations have been conducted for stratified air–water flow in pipes. We solved the Reynolds-averaged Navier–Stokes (RANS) equations with the volume of fluid (VOF) method. The aim of this work was to evaluate the performance of the k–ω shear stress transport (SST) turbulence model with and without damping of the turbulence at the gas–liquid interface. Simulation results were compared with some of the latest experimental results found in the literature. A comparison between the simulated velocity and kinetic energy profiles and the experimental results obtained with the particle image velocimetry (PIV) technique was conducted. The characteristics of the interfacial waves were also extracted and compared with the experiments. It is shown that a proper damping of the turbulence close to the interface is needed to obtain agreement with the experimental pressure drop and liquid hold-up. In its current form, however, RANS with the k–ω turbulence model is still not able to give an accurate prediction of the velocity profiles and of the interface waves.

Rapid development of smart manufacturing techniques in recent years is influencing production facilities. Factories must both keep up with smart technologies as well as upskill their workforce to remain competitive. One of the recent concerns is how businesses can contribute to environmental sustainability and how to reduce operating costs. In this article authors present a method of measuring gas waste using Industrial Internet of Things (IIoT) sensors and open-source solutions utilised on a brownfield production asset. The article provides a result of an applied research initiative in a live manufacturing facility. The design followed the Reference Architectural Model for Industry 4.0 (RAMI 4.0) model to provide a coherent smart factory system. The presented solution’s goal is to provide factory supervisors with information about gas waste which is generated during the production process. To achieve this an operational technology (OT) network was installed and Key Performance Indicators (KPIs) dashboards were designed. Based on the information provided by the system, the business can be more aware of the production environment and can improve its efficiency.

Digital twin (DT) technology has become a key enabler for prognostics and health management (PHM) in complex industrial systems, yet scaling predictive models for multi-component degradation (MCD) scenarios remains challenging, particularly when transferring insights from predictive models of smaller systems developed with limited data to larger systems. To address this, a physics-informed neural network (PINN) framework that integrates a standardized scaling methodology, enabling scalable DT analytics for MCD prognostics, was developed in this paper. Our approach employs a systematic DevOps workflow that features containerized PINN DT analytics deployed on a Kubernetes cluster for dynamic resource optimization, a real-time DT platform (PTC ThingWorx™), and a custom API for bidirectional data exchange that connects the cluster to the DT platform. A key contribution of this paper is the scalable DT model, which facilitates transfer learning of degradation patterns across heterogeneous hydraulic systems. Three (3) hydraulic system configurations were modeled, analyzing multi-component filter degradation under pump speeds of 700–900 RPM. Trained on limited data from a reference system, the scaled PINN model achieved 88.98% accuracy for initial degradation detection at 900 RPM—outperforming an unscaled baseline of 64.13%—with consistent improvements across various speeds and thresholds. This work advances PHM analytics by reducing costs and development time, providing a scalable framework for cross-system DT deployment.

The rising cost of energy and the urgent need for sustainability have driven industries to adopt smarter solutions for monitoring and optimizing resource consumption. In this study, we present an Industrial Internet of Things (IIoT)-based approach for real-time energy and air consumption monitoring in manufacturing, focusing on a legacy Turret Punch Press (TPP) at Mitsubishi Electric Air Conditioning Systems Europe Ltd. (M-ACE). Due to its age and lack of modern monitoring capabilities, the machine was suspected to be inefficient, requiring a retrofitting strategy for improved transparency and optimization. To address these challenges, a structured IIoT-enabled monitoring system was deployed, integrating KEYENCE MP-F series sensors, an energy monitoring module, and Ethernet communication via Modbus TCP/IP. A comprehensive dashboarding system was developed for real-time visualization and analysis of energy consumption trends, identifying inefficiencies and optimizing machine usage. The data-driven approach revealed significant energy savings of up to 56% and uncovered hidden inefficiencies, including a persistent air leak. By implementing a smart shut-off valve triggered by real-time power consumption data, unnecessary air leakage was eliminated, reducing compressed air waste and overall energy costs. The results demonstrate the effectiveness of IIoT-based retrofitting for industrial energy efficiency, showcasing a scalable framework that can be applied across various machines and production environments. This study highlights the importance of data-driven decision-making in smart manufacturing, contributing to both cost reduction and sustainability goals in industrial settings.

An experimental technique has been developed to measure the flow characteristics of slugs in dense phase pneumatic conveying using pressure measurements. This method is based on the unique characteristics of slug flows in pipes, i.e., an axial pressure fluctuation along the pipeline and a pressure difference in the radial direction at the back of a slug. Standard differential pressure transducers were used in this study and the influence of the finite response time of these transducers was considered. Experiments were conducted over a range of gas-solids flow conditions and experimental data were analyzed to describe the behavior of solids slugs through pipes. The calculated slug velocity and length using axial pressure measurements were confirmed by video recordings, and the synthesis between axial and radial pressure signals showed reasonable agreement in flow pattern analysis. This relatively simple measuring technique has been found effective in detecting solids slugs traveling through horizontal pipes and will distinguish various flow regimes. It provides a useful and easily applied tool for system optimizing and benchmarking in industrial applications.

The stability of flying of a hummingbird-like flapping-wing micro air vehicle (MAV) has been challenging. In this paper, experimental studies are reported on the tail shapes of hummingbird-like flapping-wing MAVs, since tails play an important role in-flight stability. Dynamics parameters of hummingbird tails are firstly studied and evaluated. Then man-made tails inspired by the natural hummingbirds are designed, manufactured and optimized for experimental tests. The results show that lift generated by the tail is independent of a fan angle, whereas the pitch moment is related to the fan angle. Further, the tail can be applied to stabilising hovering twin-wing flapping wing MAVs.