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Round-robin study for ice adhesion tests
Get PDFIce adhesion tests are widely used to assess the performance of potential icephobic surfaces and coatings. A great variety of test designs have been developed and used over the past decades due to the lack of formal standards for these types of tests. In many cases, the aim of the research was not only to determine ice adhesion values, but also to understand the key surface properties correlated to low ice adhesion surfaces. Data from different measurement techniques had low correspondence between the results: Values varied by orders of magnitude and showed different relative relationships to one another. This study sought to provide a broad comparison of ice adhesion testing approaches by conducting different ice adhesion tests with identical test surfaces. A total of 15 test facilities participated in this round-robin study, and the results of 13 partners are summarized in this paper. For the test series, ice types (impact and static) as well as test parameters were harmonized to minimize the deviations between the test setups. Our findings are presented in this paper, and the ice- and test-specific results are discussed. This study can improve our understanding of test results and support the standardization process for ice adhesion strength measurements.Aerospac
Coupling green hydrogen production to community benefits: a pathway to social acceptance?
Get PDFHydrogen energy technologies are forecasted to play a critical supporting role in global decarbonisation efforts, as reflected by the growth of national hydrogen energy strategies in recent years. Notably, the UK government published its Hydrogen Strategy in August 2021 to support decarbonisation targets and energy security ambitions. While establishing techno-economic feasibility for hydrogen energy systems is a prerequisite of the prospective transition, social acceptability is also needed to support visions for the ‘hydrogen economy’. However, to date, societal factors are yet to be embedded into policy prescriptions. Securing social acceptance is especially critical in the context of ‘hydrogen homes’, which entails replacing natural gas boilers and hobs with low-carbon hydrogen appliances. Reflecting the nascency of hydrogen heating and cooking technologies, the dynamics of social acceptance are yet to be explored in a comprehensive way. Similarly, public perceptions of the hydrogen economy and emerging national strategies remain poorly understood. Given the paucity of conceptual and empirical insights, this study develops an integrated acceptance framework and tests its predictive power using partial least squares structural equation modelling. Results highlight the importance of risk perceptions, trust dynamics, and emotions in shaping consumer perceptions. Foremost, prospects for deploying hydrogen homes at scale may rest with coupling renewable-based hydrogen production to local environmental and socio-economic benefits. Policy prescriptions should embed societal factors into the technological pursuit of large-scale, sustainable energy solutions to support socially acceptable transition pathways.Energy Research & Social Scienc
Non-linear control of a quadrotor with actuator delay
Get PDFDuring the last decade, Unmanned Aerial Vehicles (UAVs) gained significant interest for use in various application domains such as monitoring/surveillance, freight/cargo shipping, and agriculture spraying. Developing such vehicle platforms requires the utilization of robust control approaches to maintain stable and appropriate maneuvering capabilities, as well as to address system uncertainty such as payload variation or dynamic variations (e.g., spraying drones are affected by such uncertainty). Moreover, due to the rotor-based structure, rotorcraft UAVs are quite vulnerable to UAV systems control input signal delay. In terms of maintaining a robust approach for a rotorcraft UAV, it is essential to provide stability against UAV systems control input signal delay. This paper makes an analysis throughout to improve control efficiency against UAV systems control input signal delay. Through investigation and improved fault rejection, three controlling algorithms were designed and applied. The analysis focused on three different scenarios. Insights are discussed within the remit of command tracking performance with UAV systems control input signal delay.AIAA SCITECH 2024 Foru
High-throughput screening of sulfur-resistant catalysts for steam methane reforming using machine learning and microkinetic modeling
Get PDFThe catalytic activity of bimetallic catalysts for the steam methane reforming (SMR) reaction was extensively studied previously. However, the performance of these materials in the presence of sulfur-containing species is yet to be investigated. In this study, we propose a novel process aided by machine learning (ML) and microkinetic modeling for the rapid screening of sulfur-resistant bimetallic catalysts. First, various ML models were developed to predict atomic adsorption energies (C, H, O, and S) on bimetallic surfaces. Easily accessible physical and chemical properties of the metals and adsorbates were used as input features. The Ensemble learning, artificial neural network, and support vector regression models achieved the best performance with R2 values of 0.74, 0.71, and 0.70, respectively. A microkinetic model was then built based on the elementary steps of the SMR reaction. Finally, the microkinetic model, together with the atomic adsorption energies predicted by the Ensemble model, were used to screen over 500 bimetallic materials. Four Ge-based alloys (Ge3Cu1, Ge3Ni1, Ge3Co1, and Ge3Fe1) and the Ni3Cu1 alloy were identified as promising and cost-effective sulfur-resistant catalysts.ACS Omeg
Emerging resilience metrics in an intensely managed ecological system
Get PDFThere is growing interest in understanding resilience of ecosystems because of the potential of abrupt and possibly irreversible shifts between alternative ecosystem states. Tipping points are observed in systems with strong positive feedback, providing early warning signals of potential instability. These points can be detected through metrics like critical slowing down (CSD), such as increased recovery time, variance, and autocorrelation. These indicators have been tested in laboratory experiments and field settings, ignoring trait changes. Here we present a long-term temporal analysis of several large, intensely monitored constructed wetlands, the Everglades Stormwater Treatment Areas (STAs), in which sudden changes in plant community composition have been observed. Using wavelet analysis, significant increases and decreases of variance properties (long-term flow data, water quality and nutrient TP loads) across these systems can indicate when and which STAs are less resilient to perturbations. In this study, continuous wavelet transform (CWT) was used to determine the periodicity of any cyclical activity in the data and to determine changes in autocorrelation and variance as measures of CSD. The change detection methods were used to find significant changes in variations and correlations across the time series. By employing these techniques, we were able to spot substantial shifts in model-observed wavelet correlation and model residual wavelet variance and thereby identify where these systems exhibit CSD. Although our analysis is limited to historical data, the proposed approach has practical value in that it identifies STAs that may be vulnerable to perturbation. The study also presents one of the few studies in which CSD is observed in practice rather than modelled in theory.National Environment Research Council (Restoring Resilient Ecosystems) NE/V006444/1Ecological Engineerin
AFJPDA: a multiclass multi-object tracking with appearance feature-aided joint probabilistic data association
Get PDFThis study addresses a multiclass multi-object tracking problem in consideration of clutters in the environment. To alleviate issues with clutters, we propose the appearance feature-aided joint probabilistic data association filter. We also implemented simple adaptive gating logic for the computational efficiency and track maintenance logic, which can save the lost track for re-association after occlusion or missed detection. The performance of the proposed algorithm was evaluated against a state-of-the-art multi-object tracking algorithm using both multiclass multi-object simulation and real-world aerial images. The evaluation results indicate significant performance improvement of the proposed method against the benchmark state-of-the-art algorithm, especially in terms of reduction in identity switches and fragmentation.This research was supported by the UK Research and Innovation-funded project HADO: project number 10024815Journal of Aerospace Information System
Application of central-weighted essentially non-oscillatory finite-volume interface-capturing schemes for modeling cavitation induced by an underwater explosion
Get PDFCavitation resulting from underwater explosions in compressible multiphase or multicomponent flows presents significant challenges due to the dynamic nature of shock–cavitation–structure interactions, as well as the complex and discontinuous nature of the involved interfaces. Achieving accurate resolution of interfaces between different phases or components, in the presence of shocks, cavitating regions, and structural interactions, is crucial for modeling such problems. Furthermore, pressure convergence in simulations involving shock–cavitation–structure interactions requires accurate algorithms. In this research paper, we employ the diffuse interface method, also known as the interface-capturing scheme, to investigate cavitation in various underwater explosion test cases near different surfaces: a free surface and a rigid surface. The simulations are conducted using the unstructured compressible Navier–Stokes (UCNS3D) finite-volume framework employing central-weighted essentially non-oscillatory (CWENO) reconstruction schemes, utilizing the five-equation diffuse interface family of methods. Quantitative comparisons are made between the performance of both models. Additionally, we examine the effects of cavitation as a secondary loading source on structures, and evaluate the ability of the CWENO schemes to accurately capture and resolve material interfaces between fluids with minimal numerical dissipation or smearing. The results are compared with existing high-order methods and experimental data, where possible, to demonstrate the robustness of the CWENO schemes in simulating cavitation bubble dynamics, as well as their limitations within the current implementation of interface capturing.The authors acknowledge the computing time at Cranfield University Delta2 HPC facility.Fluid
Exploring the dynamic nexus between cross-border dollar claims and global economic growth
Get PDFThis paper addresses the role of the U.S. dollar in fostering global economic growth during the post-war period. The existing literature lacks a comprehensive understanding of the true implications of the U.S. dollar’s status as a reserve currency and a dearth of studies examining its impact. In this study, we explore the dynamic long-run and short-run relationships between cross-border U.S. dollar claims, global GDP, and global trade while gauging the impact of the Global Financial Crisis (GFC) and the COVID-19 pandemic. In doing so, we use ARDL methodology for a data set that spans the period of 1980 to 2022. The estimation results reveal a robust long-run relationship between U.S. dollar claims, global GDP and global trade and no clear evidence of asymmetric effects. Our findings are of great significance for monetary authorities, emphasising the need for a nuanced understanding of the implications of the U.S. dollar’s conducive role in shaping global economic dynamics and fostering growth
DSDCLNet: dual-stream encoder and dual-level contrastive learning network for supervised multivariate time series classification
Get PDFIn recent years, deep learning approaches have shown remarkable advancements in multivariate time series classification (MTSC) tasks. However, the existing approaches primarily focus on capturing the long-term correlations of time series or identifying local key sequence fragments, inevitably neglecting the synergistic properties between global and local components. Additionally, most representation learning methods for MTSC rely on self-supervised learning, which limits their ability to fully exploit label information. Hence, this paper proposes a novel approach termed Dual-Stream Encoder and Dual-Level Contrastive Learning Network (DSDCLNet), which integrates a dual-stream encoder (DSE) and dual-level contrastive learning (DCL). First, to extract multiscale local-global features from multivariate time series data, we employ a DSE architecture comprising an attention-gated recurrent unit (AGRU) and a dual-layer multiscale convolutional neural network (DMSCNN). Specifically, DMSCNN consists of a series of multi-scale convolutional layers and a max pooling layer. Second, to maximize the utilization of label information, a new loss function is designed, which combines classification loss, instance-level contrastive loss, and temporal-level contrastive loss. Finally, experiments are conducted on the UEA datasets and the results demonstrate that DSDCLNet achieves the highest average accuracy of 0.77, outperforming traditional approaches, deep learning approaches, and self-supervised approaches on 30, 23, and 27 datasets, respectively.Knowledge-Based System
Unsteady multiphase simulation of oleo-pneumatic shock absorber flow
Get PDFThe internal flow in oleo-pneumatic shock absorbers is a complex multiphysics problem combining the interaction between highly unsteady turbulent flow and multiphase mixing, among other effects. The aim is to present a validated simulation methodology that facilitates shock absorber performance prediction by capturing the dominant internal flow physics. This is achieved by simulating a drop test of approximately 1 tonne with an initial contact vertical speed of 2.7 m/s, corresponding to a light jet. The flow field solver is ANSYS Fluent, using an unsteady two-dimensional axisymmetric multiphase setup with a time-varying inlet velocity boundary condition corresponding to the stroke rate of the shock absorber piston. The stroke rate is calculated using a two-equation dynamic system model of the shock absorber under the applied loading. The simulation is validated against experimental measurements of the total force on the shock absorber during the stroke, in addition to standard physical checks. The flow field analysis focuses on multiphase mixing and its influence on the turbulent free shear layer and recirculating flow. A mixing index approach is suggested to facilitate systematically quantifying the mixing process and identifying the distinct stages of the interaction. It is found that gas–oil interaction has a significant impact on the flow development in the shock absorber’s upper chamber, where strong mixing leads to a periodic stream of small gas bubbles being fed into the jet’s shear layer from larger bubbles in recirculation zones, most notably in the corner between the orifice plate and outer shock absorber wall.This research was funded by Innovate UK grant number 10002411, under the ATI/IUK Project: LANDOne, with Airbus UK as Industrial Lead.Fluid