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Neural-Network-Based State Estimation for Nonlinear Stochastic Systems Under Token Bucket Communication Protocol

2026-03-10T00:00:00Z

Title: Neural-Network-Based State Estimation for Nonlinear Stochastic Systems Under Token Bucket Communication Protocol Authors: Wang, D; Wang, Z; Wen, C Abstract: This article is concerned with the recursive neural network (NN)-based state estimation problem for a class of stochastic discrete time-varying systems subjected to both unknown nonlinear dynamics and the token bucket communication protocol. The token bucket protocol is utilized to determine whether the sensor signal is granted access to the network at each transmission instant, wherein the transmission may fail due to an insufficient number of tokens in the bucket. The objective of the addressed problem is to design a recursive NN-based state estimator such that, under the influence of the unknown nonlinear dynamics and the token bucket communication protocol, certain upper bounds of both the state estimation error covariance and the NN-weight (NNW) error covariance are guaranteed, while the explicit expressions of the NN-based estimator gain and the NN tuning parameters are derived. By employing two sets of matrix difference equations, two upper bounds for the state estimation error covariance and the NNW error covariance are established, and these upper bounds are subsequently minimized by parameterizing the NN-based estimator gain in terms of the solutions to the matrix difference equations. Finally, an illustrative example is provided to demonstrate the feasibility and effectiveness of the proposed estimation approach.

A Simple Phase Synchronization Algorithm for Aeroacoustics Studies of Multi Propeller system

2026-06-07T00:00:00Z

Title: A Simple Phase Synchronization Algorithm for Aeroacoustics Studies of Multi Propeller system Authors: Bhardwaj, M; Chong, TP; Chaitanya, P; Joseph, P Abstract: Phase synchronization between propellers is a critical factor influencing aerodynamic interactions and their noise generation in electric vertical take-off and landing (eVTOL) vehicles and unmanned aerial systems (UAS). While several studies have demonstrated the benefits of phase control of propellers for noise reduction purposes, practical methods for accurately identifying phase differences in experimental environments are complicated and remain limited. This paper introduces a phase detection and filtering algorithm utilizing Gaussian weighting technique to extract data corresponding to the required phase differences between two propellers. It also assists in decomposing the noise into different angular positions. The algorithm compares the timing signals from two tachometers and separates the target data according to the defined phase difference between the propellers. If both the flow and noise fields are measured simultaneously, both data can then be decomposed based on the defined phase difference and the angular position of the propellers. Validation results confirm the reliability of this method in separating the datasets into defined phase difference with good accuracy, facilitating the systematic analysis of tonal noise behavior in synchronized propeller configurations. Description: Data availability: Data will be made available on request.

Enhancing out-of-plane seismic resistance of masonry walls with fiber-reinforced overlays: tests, modeling, and fragility analysis

2026-01-13T00:00:00Z

Title: Enhancing out-of-plane seismic resistance of masonry walls with fiber-reinforced overlays: tests, modeling, and fragility analysis Authors: Wen, T-H; Yuen, TYP; Zhou, X Abstract: This study investigates the out-of-plane seismic performance and fragility of masonry walls retrofitted with basalt fiber grid (BFG), carbon fiber–reinforced polymer (CFRP), glass fiber–reinforced polymer (GFRP), and ultra-high-performance concrete (UHPC). Large-scale masonry wall specimens were tested under lateral loading to examine strength, deformation capacity, and failure mechanisms. This study focuses on the pure out-of-plane response of masonry walls, without considering in-plane forces or deformations. All retrofitted walls significantly outperformed the unreinforced masonry (URM) wall, which failed at 11.34 kN. Peak strengths reached 40.79 kN (GFRP), 30.92 kN (UHPC), 24.88 kN (BFG), and 21.39 kN (CFRP), with varying ductility and crack patterns. Discrete finite element models, calibrated against experimental results, were employed to perform incremental dynamic analysis and derive seismic fragility curves. Results show that retrofitting markedly reduced failure probabilities, raising median spectral acceleration thresholds by factors of 3–7 compared with URM. Among the methods, GFRP achieved the most favorable fragility performance, exhibiting both high capacity and low drift exceedance. These findings demonstrate that retrofit material choice and configuration are critical for improving seismic resilience and reducing collapse risk in masonry structures.

Development of social sustainability assessment methods for solar thermal energy systems applied to industrial processes

2026-01-01T00:00:00Z

Title: Development of social sustainability assessment methods for solar thermal energy systems applied to industrial processes Authors: Zafar, Imaad Abstract: This thesis assessed the social sustainability of a newly developed Solar Thermal Energy (STE) system using the following methods. First, a Social Life Cycle Assessment (S-LCA) was conducted using Social Hotspot Database (SHDB) inventory data to assess social risks on employees involved in producing the system’s three components: Fresnel solar collectors (SunDial), the Phase Change Material (PCM) storage tank, and the Control Unit. Next, surveys involving 56 employees engaged in the technology’s Product Development phase were conducted to assess social impact in the following impact categories: Training Satisfaction (TS), Professional Development (PD), and Working Intensity (WI). Industrial and social acceptance of the technology was then assessed through surveys with 318 industries and 279 members of the public, capturing perceptions of the system’s adoption potential, greenhouse gas (GHG) reduction, and economic savings. Finally, a regression model was conducted to predict future trends in social impact and industrial acceptance over a 10-year timeframe, providing insights into long-term technological and financial improvements. Results of the S-LCA revealed substantial Health & Safety (H&S) risks for employees involved in the technology’s production, particularly in aluminium manufacturing of the PCM storage tank due to non-compliance with regional H&S policies. In the Product Development phase, positive impact was observed in PD and WI, whereas a negative impact score of -0.5 in TS revealed training provision gaps. Regression analysis identified strong correlations between social impact and influencing investments in Human Resource Management (HRM) including provision of training; PT (ρ = 0.54), employee engagement in R&D; EE, (ρ = 0.48), provision of professional development opportunities; PPD (ρ = 0.80), and task allocation; AT (ρ = 0.63), all statistically significant (p < 0.05). Next, surveys gauging the acceptance of worldwide industries showed strong results for the STE system’s technical compatibility (82%), costs (82%), and impact on standard compliance (87%), with highest scores reported by the Aerospace (92%), Metallic (89%), and Automotive (86%) industries. Acceptance was particularly strong among large companies (84%) and medium-sized companies (87%), whereas lower rates were observed for small (45%) and micro (37%) enterprises, largely due to the upfront costs of STE systems, which posed a greater financial burden for firms of this scale. Moreover, surveys involving the general public showed strong agreement with STE’s environmental benefits (86%) and willingness to consume products manufactured using STE’s clean energy goods (79%). Results of the future predictions showed that social impact on employees improved over the decade, as indicated by the probability of observing TS = 5 increased from 0.10 to 0.30 and TS = 4 from 0.35 to 0.60 due to annual investment in EE. Similarly, the probability of observing PD = 5 increased from 0.20 to 0.50 and WI = 4 from 0.25 to 0.50 from investment in PPD and AT, respectively. Industrial acceptance also marginally improved over the decade; most notably, AC = 5 rose from 0.44 to 0.52, and AC = 4 increased from 0.41 to 0.46. A large improvement was found amongst small and micro-sized companies as the probability of observing a high score rose 0.45 to 0.67 for small companies and 0.37 to 0.62 for micro-sized companies. The findings provide nuanced technological and monetary improvement measures to enhance the long-term sustainability and industrial relevance of newly developed STE systems. The findings of this thesis demonstrate the strong influence of targeted investments in workforce development, technological improvements, and financial support mechanisms on enhancing both social impact and industrial acceptance of STE systems over the decade. These thesis insights on influencing factors provide valuable implications for workforce managers, industrial stakeholders, and policymakers by offering practical guidance on forecasting, prioritising, and strategically allocating resources to maximise stakeholder satisfaction and support industrial adoption of current and future emerging STE deployments. Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London