Hasil untuk "Hydraulic engineering"

Yujun Zhu, Yulan Ning, Zhipeng Yang et al.

This paper proposes, analyzes, and demonstrates an efficient low-rank solver for the stochastic Stokes-Darcy interface model with a random hydraulic conductivity both in the porous media domain and on the interface. We consider three interface conditions with randomness, including the Beavers-Joseph interface condition with the random hydraulic conductivity, on the interface between the free flow and the porous media flow. Our solver employs a novel generalized low-rank approximation of the large-scale stiffness matrices, which can significantly cut down the computational costs and memory requirements associated with matrix inversion without losing accuracy. Therefore, by adopting a suitable data compression ratio, the low-rank solver can maintain a high numerical precision with relatively low computational and space complexities. We also propose a strategy to determine the best choice of data compression ratios. Furthermore, we carry out the error analysis of the generalized low-rank matrix approximation algorithm and the low-rank solver. Finally, numerical experiments are conducted to validate the proposed algorithms and the theoretical conclusions.

Muhammad Tayyab Khan, Zane Yong, Lequn Chen et al.

Engineering drawings are fundamental to manufacturing communication, serving as the primary medium for conveying design intent, tolerances, and production details. However, interpreting complex multi-view drawings with dense annotations remains challenging using manual methods, generic optical character recognition (OCR) systems, or traditional deep learning approaches, due to varied layouts, orientations, and mixed symbolic-textual content. To address these challenges, this paper proposes a three-stage hybrid framework for the automated interpretation of 2D multi-view engineering drawings using modern detection and vision language models (VLMs). In the first stage, YOLOv11-det performs layout segmentation to localize key regions such as views, title blocks, and notes. The second stage uses YOLOv11-obb for orientation-aware, fine-grained detection of annotations, including measures, GD&T symbols, and surface roughness indicators. The third stage employs two Donut-based, OCR-free VLMs for semantic content parsing: the Alphabetical VLM extracts textual and categorical information from title blocks and notes, while the Numerical VLM interprets quantitative data such as measures, GD&T frames, and surface roughness. Two specialized datasets were developed to ensure robustness and generalization: 1,000 drawings for layout detection and 1,406 for annotation-level training. The Alphabetical VLM achieved an overall F1 score of 0.672, while the Numerical VLM reached 0.963, demonstrating strong performance in textual and quantitative interpretation, respectively. The unified JSON output enables seamless integration with CAD and manufacturing databases, providing a scalable solution for intelligent engineering drawing analysis.

Johan Cederbladh, Loek Cleophas, Eduard Kamburjan et al.

With the current trend in Model-Based Systems Engineering towards Digital Engineering and early Validation & Verification, experiments are increasingly used to estimate system parameters and explore design decisions. Managing such experimental configuration metadata and results is of utmost importance in accelerating overall design effort. In particular, we observe it is important to 'intelligent-ly' reuse experiment-related data to save time and effort by not performing potentially superfluous, time-consuming, and resource-intensive experiments. In this work, we present a framework for managing experiments on digital and/or physical assets with a focus on case-based reasoning with domain knowledge to reuse experimental data efficiently by deciding whether an already-performed experiment (or associated answer) can be reused to answer a new (potentially different) question from the engineer/user without having to set up and perform a new experiment. We provide the general architecture for such an experiment manager and validate our approach using an industrial vehicular energy system-design case study.

Everton Cavalcante, Thais Batista, Flavio Oquendo

Modern systems are increasingly connected and more integrated with other existing systems, giving rise to \textit{systems-of-systems} (SoS). An SoS consists of a set of independent, heterogeneous systems that interact to provide new functionalities and accomplish global missions through emergent behavior manifested at runtime. The distinctive characteristics of SoS, when contrasted to traditional systems, pose significant research challenges within Software Engineering. These challenges motivate the need for a paradigm shift and the exploration of novel approaches for designing, developing, deploying, and evolving these systems. The \textit{International Workshop on Software Engineering for Systems-of-Systems} (SESoS) series started in 2013 to fill a gap in scientific forums addressing SoS from the Software Engineering perspective, becoming the first venue for this purpose. This article presents a study aimed at outlining the evolution and future trajectory of Software Engineering for SoS based on the examination of 57 papers spanning the 11 editions of the SESoS workshop (2013-2023). The study combined scoping review and scientometric analysis methods to categorize and analyze the research contributions concerning temporal and geographic distribution, topics of interest, research methodologies employed, application domains, and research impact. Based on such a comprehensive overview, this article discusses current and future directions in Software Engineering for SoS.

Siddhant Dutta, Iago Leal de Freitas, Pedro Maciel Xavier et al.

Federated Learning (FL) is a decentralized machine learning approach that has gained attention for its potential to enable collaborative model training across clients while protecting data privacy, making it an attractive solution for the chemical industry. This work aims to provide the chemical engineering community with an accessible introduction to the discipline. Supported by a hands-on tutorial and a comprehensive collection of examples, it explores the application of FL in tasks such as manufacturing optimization, multimodal data integration, and drug discovery while addressing the unique challenges of protecting proprietary information and managing distributed datasets. The tutorial was built using key frameworks such as $\texttt{Flower}$ and $\texttt{TensorFlow Federated}$ and was designed to provide chemical engineers with the right tools to adopt FL in their specific needs. We compare the performance of FL against centralized learning across three different datasets relevant to chemical engineering applications, demonstrating that FL will often maintain or improve classification performance, particularly for complex and heterogeneous data. We conclude with an outlook on the open challenges in federated learning to be tackled and current approaches designed to remediate and improve this framework.

Peter R. Davids, Sally Priest, Thomas Hartmann

Abstract This paper investigates the role of flood risk experts in supporting homeowners to implement property‐level flood risk adaption (PLFRA). Homeowners can reduce their flood risks by implementing PLFRA. However, oftentimes they need advice on what sort of and how to implement PLFRA. This means that tailored experts advice is necessary to inform homeowners on such measures. But experience shows that mere information is often insufficient to motivate homeowners to realise measures. This contribution explores the reasons for the ineffectiveness of expert advice by investigating how expert advice responds to homeowners' rationalities. Based on a case study from Flanders, Belgium, this paper reveals how the relation between experts and homeowners differs related to different rationalities of homeowners. The paper uses Cultural Theory to discuss strategies on how experts, providing advice on property‐level risk adaption, could move beyond engineering skills by also using risk communication skills in order to involve homeowners in flood risk governance.

Longgang Sun, Yanyan Li, Pengcheng Guo et al.

Precessing vortex rope (PVR) plays a key role in inducing hydraulic resonance in Francis turbines operating at partial load, possibly degrading power plant stability and availability. Air injection into the runner cone is a suitable mitigating alternative; however, the influence mechanism of air injection on PVR remains unclear. The principal objective of this study was to establish response relationships between the characteristic parameters and air injection by the method of computational fluid dynamics (CFD) considering the fluid components of water, water vapour and air. The findings show that cavitation flow can be completely suppressed by slight air injection; however, the helical vortex structures are persisted at 1.0% and 2.0% air volume fractions, and the static pressure recovery is improved together with a slight increase in the hydraulic loss. At 3.0% air volume fraction, the vortex structure completely disappears, leaving an umbrella-shaped structure, with no pressure vibration arising in the turbine. Moreover, the physical mechanism of reducing the pressure amplitudes is clarified. This results clarify the influence mechanism of air injection on PVR, and contribute to steadily extending the flexibility of the operating range of the turbine during the engineering application.

Glauber Rover Cadorin, André Luis Fachini de Souza, Anelise Destefani et al.

Índices de qualidade de água são ferramentas utilizadas para sintetizar, por meio de um único valor, contribuições de variáveis físicas, químicas e biológicas de corpos hídricos. Dentre as várias ferramentas disponíveis para avaliar a qualidade da água, o Índice de Qualidade das Águas (IQA) é um dos instrumentos mais utilizados. Este trabalho tem como objetivo identificar entre os parâmetros constituintes do IQA, variáveis significativas para otimização do monitoramento da qualidade de corpos hídricos, buscando reduções de parâmetros, tempo e custo para realização de análises. Os dados utilizados contemplam campanhas em 19 rios que compõem a Bacia Hidrográfica do Rio Cachoeira na região nordeste de Santa Catarina (Brasil), no período de 2014 a 2021. Ferramentas estatísticas como análise de componentes principais, análise de similaridade e regressão linear múltipla foram utilizadas para análise dos dados experimentais. Os resultados sugerem oxigênio dissolvido, demanda bioquímica de oxigênio, coliformes, fósforo e turbidez como variáveis significativas para a realização dos monitoramentos. Modelos matemáticos gerados apresentaram assertividade média de 81% quando comparados ao IQA. A proposta de redução das variáveis sugeriu viabilidade de aplicação prática, com redução de tempo e custos.

Jingjing Yang, Taihua Wang, Dawen Yang

The Qinghai–Tibet Plateau (QTP) has responded to remarkable climate warming with dramatic permafrost degradation over the past few decades. Previous studies have mostly focused on permafrost responses to rising air temperature, while the effects of accompanying increases in precipitation remain contentious and largely unknown. In this study, a distributed process-based model was applied to quantify the impacts of increased precipitation on permafrost thermal regimes in a warming climate by employing model experiments in the source region of Yellow River (SRYR) on the eastern QTP. The results showed that the active layer thickness (ALT) of permafrost increased by 0.25 m during 2010–2019 compared to 2000 across the SRYR, which was primarily driven by climate warming. In contrast, the increased annual precipitation played a relatively limited role and just slightly mitigated active layer thickening by 0.03 m. Intriguingly, increased precipitation in the cold and warm seasons exerted opposite effects on permafrost across the SRYR. The increased precipitation in the cold season mainly promoted ALT increases, while the increased precipitation in the warm season mitigated ALT increases. In ∼81.0% of the permafrost across the SRYR, the cooling effects of warm season wetting outweighed the warming effects of cold season wetting; while at the transition zone where permafrost was unstable and degrading to seasonally frozen ground, the warming effects of cold season wetting played a relatively larger role which contributed to permafrost degradation. This study explored the physical mechanisms of permafrost thermal responses to climate wetting, thus providing a better understanding of permafrost change in a warmer and wetter climate on the QTP.

David Lloyd Owen

Abstract Smart water enables utilities, regulators, and customers to make more timely and informed decisions about how they use and regard their water resources. It has been developed to assist demand management by influencing customer behavior and reducing network leakage, lowering energy consumption, and avoiding deploying assets that are not actually needed. Smart water has seen an evolution toward monitoring wastewater applications. Challenges include the need for common operating standards and more cohesive national policy frameworks. As a result, smart water adoption occurs on a utility‐by‐utility basis.

Fabio Calefato, Luigi Quaranta, Filippo Lanubile et al.

Background. Due to the widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) for building software applications, companies are struggling to recruit employees with a deep understanding of such technologies. In this scenario, AutoML is soaring as a promising solution to fill the AI/ML skills gap since it promises to automate the building of end-to-end AI/ML pipelines that would normally be engineered by specialized team members. Aims. Despite the growing interest and high expectations, there is a dearth of information about the extent to which AutoML is currently adopted by teams developing AI/ML-enabled systems and how it is perceived by practitioners and researchers. Method. To fill these gaps, in this paper, we present a mixed-method study comprising a benchmark of 12 end-to-end AutoML tools on two SE datasets and a user survey with follow-up interviews to further our understanding of AutoML adoption and perception. Results. We found that AutoML solutions can generate models that outperform those trained and optimized by researchers to perform classification tasks in the SE domain. Also, our findings show that the currently available AutoML solutions do not live up to their names as they do not equally support automation across the stages of the ML development workflow and for all the team members. Conclusions. We derive insights to inform the SE research community on how AutoML can facilitate their activities and tool builders on how to design the next generation of AutoML technologies.

Tao Chen, Miqing Li

Configurable software systems can be tuned for better performance. Leveraging on some Pareto optimizers, recent work has shifted from tuning for a single, time-related performance objective to two intrinsically different objectives that assess distinct performance aspects of the system, each with varying aspirations. Before we design better optimizers, a crucial engineering decision to make therein is how to handle the performance requirements with clear aspirations in the tuning process. For this, the community takes two alternative optimization models: either quantifying and incorporating the aspirations into the search objectives that guide the tuning, or not considering the aspirations during the search but purely using them in the later decision-making process only. However, despite being a crucial decision that determines how an optimizer can be designed and tailored, there is a rather limited understanding of which optimization model should be chosen under what particular circumstance, and why. In this paper, we seek to close this gap. Firstly, we do that through a review of over 426 papers in the literature and 14 real-world requirements datasets. Drawing on these, we then conduct a comprehensive empirical study that covers 15 combinations of the state-of-the-art performance requirement patterns, four types of aspiration space, three Pareto optimizers, and eight real-world systems/environments, leading to 1,296 cases of investigation. We found that (1) the realism of aspirations is the key factor that determines whether they should be used to guide the tuning; (2) the given patterns and the position of the realistic aspirations in the objective landscape are less important for the choice, but they do matter to the extents of improvement; (3) the available tuning budget can also influence the choice for unrealistic aspirations but it is insignificant under realistic ones.

Mark Runciman, Enrico Franco, James Avery et al.

In this article, we investigate the model based position control of soft hydraulic actuators arranged in an antagonistic pair. A dynamical model of the system is constructed by employing the port-Hamiltonian formulation. A control algorithm is designed with an energy shaping approach which accounts for the pressure dynamics of the fluid. A nonlinear observer is included to compensate the effect of unknown external forces. Simulations demonstrate the effectiveness of the proposed approach, and experiments achieve positioning accuracy of 0.043 mm with a standard deviation of 0.033 mm in the presence of constant external forces up to 1 N.

Sean McGuire, Erin Shultz, Bimpe Ayoola et al.

Background: Sustainable software engineering (SSE) means creating software in a way that meets present needs without undermining our collective capacity to meet our future needs. It is typically conceptualized as several intersecting dimensions or ``pillars'' -- environmental, social, economic, technical and individual. However; these pillars are theoretically underdeveloped and require refinement. Objectives: The objective of this paper is to generate a better theory of SSE. Method: First, a scoping review was conducted to understand the state of research on SSE and identify existing models thereof. Next, a meta-synthesis of qualitative research on SSE was conducted to critique and improve the existing models identified. Results: 961 potentially relevant articles were extracted from five article databases. These articles were de-duplicated and then screened independently by two screeners, leaving 243 articles to examine. Of these, 109 were non-empirical, the most common empirical method was systematic review, and no randomized controlled experiments were found. Most papers focus on ecological sustainability (158) and the sustainability of software products (148) rather than processes. A meta-synthesis of 36 qualitative studies produced several key propositions, most notably, that sustainability is stratified (has different meanings at different levels of abstraction) and multisystemic (emerges from interactions among multiple social, technical, and sociotechnical systems). Conclusion: The academic literature on SSE is surprisingly non-empirical. More empirical evaluations of specific sustainability interventions are needed. The sustainability of software development products and processes should be conceptualized as multisystemic and stratified, and assessed accordingly.

P. Jamwal, Anjali Raj, Lakshmi Raveendran et al.

Abstract Constructed wetlands are a nature-based engineering solution enabling polishing of septic tank effluents at low-cost. However to date, the influence of planting on treatment efficiency remains little understood. Here we report a case study evaluating the performance of two near-identical Horizontal Sub-Surface Flow Constructed Wetlands (HSSF-CW) deployed at a school in southern India. The HSSF-CWs were of similar size and construction with the exception that one system was planted (Canna indica) whilst the other was operated without plants. Both systems were operated at similar hydraulic loading rate (HLR) and hydraulic retention time (HRT) of 84 mm day−1 and 3.7 days, respectively to treat the effluent from septic tanks. The systems were monitored fortnightly for one year and the performance kinetics, nutrient and organics removal efficiencies were evaluated. Significant reduction in biochemical oxygen demand (BOD5) and chemical oxygen demand (COD) (p

Fengrui Zhang, A. Jiang, Xiurong Yang

MENG Chunfang, SONG Xiaoyu, TIAN Kening et al.

【Objective】 Surface water in rivers has multiple functions serving direct sectors. The objective of this paper is to analyze the minimum water flow (e-flow) in Xinxiang Section of the Wei River (XSWR) for environmental service. 【Method】 We considered three demands: ecological demand for water, demand for water self-purification, and demand for sediment transport. Ecological water demand was calculated using the Tennant method based on the water flow data measured from 1963 to 1982; self-purification demand was simulated using the one-dimensional water quality model; the demand for sediment transport was calculated as the amount of water required to transport unit sediment in flooding season. Spatiotemporal change in water quality due to pollution was calculated using cluster analysis (CA) and discriminant analysis (DA). The e-flow calculated using the range of variability approach (RVA) was compared with the recommended e-flows, as well as the average annual flow measured from 1963 to 1982. 【Result】 Ecological water demand varied greatly between the seven hydrometric stations along the river. Under standard sewage discharge, the demand for water self-purification in Xiuwu and Hehewei station was 0.74 m3/s and 5.98 m3/s, respectively. The demand for sediment transport at all stations peaked in August, though it varied significantly between the seven hydrometric stations. Surplus-deficit analysis showed the e-flow in Hehewei, Jixian, and Liuzhuang station was markedly short of water. The guarantee rates of 7 hydrometric stations were as follows: Guarantee rates in flooding season were lower than in non-flooding season; Qimen station had the highest guarantee rate and Hehewei station had the least. 【Conclusion】 The e-flow was 5.98 m3/s for Hehewei station, 0.74 to 7.28 m3/s for Xiuwu station, 0.85 to 18.51 m3/s for Jixian station, 1.55 to 65.73 m3/s for Qimen station, 0.47 to 34.61 m3/s for Hehegong station, 0.53 to 36.63 m3/s for Huangtugang station, and 0.17 to 61.54 m3/s for Liuzhuang station.

Dario Tscholl, Stephan-Daniel Gravert, Aurel X. Appius et al.

Rapid and versatile object manipulation in air is an open challenge. An energy-efficient and adaptive soft gripper combined with an agile aerial vehicle could revolutionize aerial robotic manipulation in areas such as warehousing. This paper presents a bio-inspired gripper powered by hydraulically amplified electrostatic actuators mounted to a quadcopter that can interact safely and naturally with its environment. Our gripping concept is motivated by an eagle's foot. Our custom multi-actuator concept is inspired by a scorpion tail design (consisting of a base electrode with pouches stacked adjacently) and spider-inspired joints (classic pouch motors with a flexible hinge layer). A hybrid of these two designs realizes a higher force output under moderate deflections of up to 25° compared to single-hinge concepts. In addition, sandwiching the hinge layer improves the robustness of the gripper. For the first time, we show that soft manipulation in air is possible using electrostatic actuation. This study demonstrates the potential of untethered hydraulically amplified actuators in aerial robotic manipulation. Our proof of concept opens up the use of hydraulic electrostatic actuators in mobile aerial systems.

Zhilu Lai, Wei Liu, Xudong Jian et al.

The order/dimension of models derived on the basis of data is commonly restricted by the number of observations, or in the context of monitored systems, sensing nodes. This is particularly true for structural systems (e.g., civil or mechanical structures), which are typically high-dimensional in nature. In the scope of physics-informed machine learning, this paper proposes a framework -- termed Neural Modal ODEs -- to integrate physics-based modeling with deep learning for modeling the dynamics of monitored and high-dimensional engineered systems. Neural Ordinary Differential Equations -- Neural ODEs are exploited as the deep learning operator. In this initiating exploration, we restrict ourselves to linear or mildly nonlinear systems. We propose an architecture that couples a dynamic version of variational autoencoders with physics-informed Neural ODEs (Pi-Neural ODEs). An encoder, as a part of the autoencoder, learns the abstract mappings from the first few items of observational data to the initial values of the latent variables, which drive the learning of embedded dynamics via physics-informed Neural ODEs, imposing a modal model structure on that latent space. The decoder of the proposed model adopts the eigenmodes derived from an eigen-analysis applied to the linearized portion of a physics-based model: a process implicitly carrying the spatial relationship between degrees-of-freedom (DOFs). The framework is validated on a numerical example, and an experimental dataset of a scaled cable-stayed bridge, where the learned hybrid model is shown to outperform a purely physics-based approach to modeling. We further show the functionality of the proposed scheme within the context of virtual sensing, i.e., the recovery of generalized response quantities in unmeasured DOFs from spatially sparse data.

Jānis Priede

We consider a special type of hydraulic jumps (internal bores) which, in the vertically bounded system of two immiscible fluids with slightly different densities, conserve not only the mass and impulse but also the circulation and energy. This is possible only at specific combinations of the upstream and downstream states. Two such combinations are identified with arbitrary upstream and downstream interface heights. The first has a cross symmetry between the interface height and shear on both sides of the jump. This symmetry, which is due to the invariance of the two-layer shallow-water system with swapping the interface height and shear, ensures the automatic conservation of the impulse and energy as well as the continuity of characteristic velocities across the jump. The speed at which such jumps propagate is uniquely defined by the conservation of the mass and circulation. The other possibility is a marginally stable shear flow which can have fully conservative jumps with discontinuous characteristic velocities. Both types of conservative jumps are shown to represent a long-wave approximation to the so-called solibores which appear as smooth permanent-shape solutions in a weakly non-hydrostatic model. A new analytical solution for solibores is obtained and found to agree very well with the previous DNS results for partial-depth lock release flow. The finding that certain large-amplitude hydraulic jumps can be fully conservative, while most are not such even in the inviscid approximation, points toward the wave dispersion as a primary mechanism behind the lossy nature of internal bores.