Hasil untuk "Hydraulic engineering"

J. Davidovits

Mingjun Wang, Yingjie Wang, W. Tian et al.

Abstract CFD method has the potential to simulate the detailed three-dimensional flow and heat transfer features in nuclear reactors, and is promising to play a more important role in the future reactor design and thermal hydraulics analysis. In recent years, series of research achievements in nuclear engineering based on the CFD method, including the single-phase application, two-phase model development and multi-scale and multi-physics coupling, are accomplished around the world. XJTU-NuTheL has also been committed to the development and application of high fidelity thermal–hydraulic models using CFD method. The three-dimensional CFD models of key equipments in nuclear power plants, such as RPV, SG, valves, T-junctions and passive residual heat exchanger, are developed. The mathematical models of complicate two-phase boiling phenomena and thermal hydraulic features under the motion conditions are established. In addition, with the high fidelity simulation requirement of the whole reactor system, the nuclear reactor multi-scale and multi-physics coupling platforms are developed on the basis of CFD codes. In this paper, the latest progress of nuclear reactor thermal–hydraulic research using CFD method is outlined, especially at XJTU-NuTheL. The major challenges and promising directions of CFD method in the nuclear reactor engineering are proposed, which would be beneficial for the promotion of its further applications.

H. Motz, J. Geiseler

M. Awais, N. Ullah, J. Ahmad et al.

Abstract Nanofluid is considered to be a new generation heat transfer medium which has attracted significant attention from the research community for the past two decades due to its reported high effectiveness in various heat transfer applications. Dispersed solid nanoparticles with enhanced thermal conductivity in base fluid possess substantial competence in augmenting thermal performance at the expense of moderate pumping power. This inclusive study elaborates the impact of nanofluids on thermo-hydraulic performance of thermal devices in order to ensure the appropriate selection and implementation of nanofluids in various engineering thermal devices. Inclusion of nanofluid as heat transfer fluid in various systems requiring high heat transfer rate e.g. solar thermal conversion systems, HVAC systems, electronic equipment, heat exchangers, nuclear reactors have imparted greater role in reducing negative impacts of climate change. The influence of size, concentration, type and shape of nanoparticles, working temperature, compound passive techniques and magnetic field effect on heat transfer and pressure drop performance of nanofluids are extensively discussed along with the drawbacks of nanofluids such as formation of fouling on heat transfer surfaces. This comprehensive review will be beneficial for engineers, researchers, and academics to commend the significance of various nanofluids and comprehend their remarkable impact on various heat transfer applications.

Junwei Yu, Mufeng Yang, Yepeng Ding et al.

The proliferation of AI-powered search engines has shifted information discovery from traditional link-based retrieval to direct answer generation with selective source citation, creating new challenges for content visibility. While existing Generative Engine Optimization (GEO) approaches focus primarily on semantic content modification, the role of structural features in influencing citation behavior remains underexplored. In this paper, we propose GEO-SFE, a systematic framework for structural feature engineering in generative engine optimization. Our approach decomposes content structure into three hierarchical levels: macro-structure (document architecture), meso-structure (information chunking), and micro-structure (visual emphasis), and models their impact on citation probability across different generative engine architectures. We develop architecture-aware optimization strategies and predictive models that preserve semantic integrity while improving structural effectiveness. Experimental evaluation across six mainstream generative engines demonstrates consistent improvements in citation rate (17.3 percent) and subjective quality (18.5 percent), validating the effectiveness and generalizability of the proposed framework. This work establishes structural optimization as a foundational component of GEO, providing a data-driven methodology for enhancing content visibility in LLM-powered information ecosystems.

Minho Lee, Hyeonseok Kim, Jin Tak Kim et al.

The simulation-to-reality (sim-to-real) transfer of large-scale hydraulic robots presents a significant challenge in robotics because of the inherent slow control response and complex fluid dynamics. The complex dynamics result from the multiple interconnected cylinder structure and the difference in fluid rates of the cylinders. These characteristics complicate detailed simulation for all joints, making it unsuitable for reinforcement learning (RL) applications. In this work, we propose an analytical actuator model driven by hydraulic dynamics to represent the complicated actuators. The model predicts joint torques for all 12 actuators in under 1 microsecond, allowing rapid processing in RL environments. We compare our model with neural network-based actuator models and demonstrate the advantages of our model in data-limited scenarios. The locomotion policy trained in RL with our model is deployed on a hydraulic quadruped robot, which is over 300 kg. This work is the first demonstration of a successful transfer of stable and robust command-tracking locomotion with RL on a heavy hydraulic quadruped robot, demonstrating advanced sim-to-real transferability.

J. Donnelly, S. Abolfathi, J. Pearson et al.

The computational limitations of complex numerical models have led to adoption of statistical emulators across a variety of problems in science and engineering disciplines to circumvent the high computational costs associated with numerical simulations. In flood modelling, many hydraulic and hydrodynamic numerical models, especially when operating at high spatiotemporal resolutions, have prohibitively high computational costs for tasks requiring the instantaneous generation of very large numbers of simulation results. This study examines the appropriateness and robustness of Gaussian Process (GP) models to emulate the results from a hydraulic inundation model. The developed GPs produce real-time predictions based on the simulation output from LISFLOOD-FP numerical model. An efficient dimensionality reduction scheme is developed to tackle the high dimensionality of the output space and is combined with the GPs to investigate the predictive performance of the proposed emulator for estimation of the inundation depth. The developed GP-based framework is capable of robust and straightforward quantification of the uncertainty associated with the predictions, without requiring additional model evaluations and simulations. Further, this study explores the computational advantages of using a GP-based emulator over alternative methodologies such as neural networks, by undertaking a comparative analysis. For the case study data presented in this paper, the GP model was found to accurately reproduce water depths and inundation extent by classification and produce computational speedups of approximately 10,000 times compared with the original simulator, and 80 times for a neural network-based emulator.

Hashini Gunatilake, John Grundy, Rashina Hoda et al.

Empathy plays a crucial role in software engineering (SE), influencing collaboration, communication, and decision-making. While prior research has highlighted the importance of empathy in SE, there is limited understanding of how empathy manifests in SE practice, what motivates SE practitioners to demonstrate empathy, and the factors that influence empathy in SE work. Our study explores these aspects through 22 interviews and a large scale survey with 116 software practitioners. Our findings provide insights into the expression of empathy in SE, the drivers behind empathetic practices, SE activities where empathy is perceived as useful or not, and the other factors that influence empathy. In addition, we offer practical implications for SE practitioners and researchers, offering a deeper understanding of how to effectively integrate empathy into SE processes.

Justus Bogner, Roberto Verdecchia

From its early foundations in the 1970s, empirical software engineering (ESE) has evolved into a mature research discipline that embraces a plethora of different topics, methodologies, and industrial practices. Despite its remarkable progress, the ESE research field still needs to keep evolving, as new impediments, shortcoming, and technologies emerge. Research reproducibility, limited external validity, subjectivity of reviews, and porting research results to industrial practices are just some examples of the drivers for improvements to ESE research. Additionally, several facets of ESE research are not documented very explicitly, which makes it difficult for newcomers to pick them up. With this new regular ACM SIGSOFT SEN column (SEN-ESE), we introduce a venue for discussing meta-aspects of ESE research, ranging from general topics such as the nature and best practices for replication packages, to more nuanced themes such as statistical methods, interview transcription tools, and publishing interdisciplinary research. Our aim for the column is to be a place where we can regularly spark conversations on ESE topics that might not often be touched upon or are left implicit. Contributions to this column will be grounded in expert interviews, focus groups, surveys, and position pieces, with the goal of encouraging reflection and improvement in how we conduct, communicate, teach, and ultimately improve ESE research. Finally, we invite feedback from the ESE community on challenging, controversial, or underexplored topics, as well as suggestions for voices you would like to hear from. While we cannot promise to act on every idea, we aim to shape this column around the community interests and are grateful for all contributions.

Rajesh Kumar Bhagat

Hydraulic jumps in thin films are traditionally explained through gravity-driven shallow-water theory, with surface tension assumed to play only a secondary role via Laplace pressure. Recent experiments, however, suggest that surface tension can be the primary mechanism. In this work we develop a theoretical framework for surface tension driven hydraulic jumps in planar thin-film flows. Starting from the full interfacial stress conditions, we show that the deviatoric component of the normal stress enters at leading order and fundamentally alters the balance. A dominant-balance analysis in the zero-gravity limit yields parameter-free governing equations, which admit a similarity solution for the velocity profile. Depth-averaged momentum conservation then reveals a singularity at unit Weber number, interpreted as the criterion for hydraulic control. This singularity is regularised by a non-trivial pressure gradient at the jump. This work establishes the theoretical basis for surface-tension-driven hydraulic jumps, providing analytical predictions for the jump location and structure.

Qiulin Li, Jinchao He, Dewei Mu et al.

Dissolved oxygen (DO) is a vital water quality index influencing biological processes in aquatic environments. Accurate modeling of DO levels is crucial for maintaining ecosystem health and managing freshwater resources. To this end, the present study contributes a Bayesian-optimized explainable machine learning (ML) model to reveal DO dynamics and predict DO concentrations. Three ML models, support vector regression (SVR), regression tree (RT), and boosting ensemble, coupled with Bayesian optimization (BO), are employed to estimate DO levels in the Mississippi River. It is concluded that the BO-SVR model outperforms others, achieving a coefficient of determination (CD) of 0.97 and minimal error metrics (root mean square error = 0.395 mg/L, mean absolute error = 0.303 mg/L). Shapley Additive Explanation (SHAP) analysis identifies temperature, discharge, and gage height as the most dominant factors affecting DO levels. Sensitivity analysis confirms the robustness of the models under varying input conditions. With perturbations from 5% to 30%, the temperature sensitivity ranges from 1.0% to 6.1%, discharge from 0.9% to 5.2%, and gage height from 0.8% to 5.0%. Although the models experience reduced accuracy with extended prediction horizons, they still achieve satisfactory results (CD > 0.75) for forecasting periods of up to 30 days. The established models also exhibit higher accuracy than many prior approaches. This study highlights the potential of BO-optimized explainable ML models for reliable DO forecasting, offering valuable insights for water resource management.

SHI Rongqing, HUANG Lingmei, LI Jia et al.

To identify flood-prone areas in the Daqing River Basin and classify flood risk levels, the Spearman's rank correlation coefficient and the random forest method were employed to analyze the correlation and importance between flood loss rates and influencing factors. Based on the most significant indicator, a flood loss rate function was constructed, incorporating four key factors: precipitation, flood control and disaster mitigation capacity, socio-economic development, and natural resources. By using this function, flood loss under five shared socioeconomic pathways (SSPs) from 2030 to 2050 was estimated. The results indicate that the flood loss rate across provinces and cities is generally positively correlated with precipitation factors but negatively correlated with flood control and disaster mitigation capacity, socio-economic development, and natural resources. The multiple regression function effectively captures the variation pattern of flood loss rates, with a coefficient of determination (<italic>R</italic>²) ≥ 0.89 and a root mean square error (RMSE) ≤ 0.047 3. Under the rainfall scenarios of 1996 and 2012, future economic flood losses are projected to increase significantly by 392%–452% at maximum, while the number of affected people is expected to decrease, with a minimum reduction of 22%–35%.

V.N. Kitaev, R.L. Afanasyev, M.V. Petrov

Background. The inertia switches are traditionally used in mobile vehicles for commutating the electric circuits of the engineering systems. Triggering of such devices takes place mainly when having taken integral along linear acceleration while the vehicle’s space motion. To integrate the linear acceleration, both magnetic induction and hydraulic dampers are customary used. In a number of cases the hydraulic dampers, simplifying design of inertia switchers, are preferred. Given work presents the results of development of the inertia switch design, its mathematical model: differential equations, describing motion of mobile design elements, and the initial motion conditions, as well. For differential equating as follows assumptions are taken: the liquid is incompressible; no account to inertia switch components variation in dimension due to environmental variation in temperature; no account to dumping liquid viscosity variation due to environmental temperature variation. Materials and methods. The major feature of inertiaswitch designed to distinguish it from similar inertia devices is its feasible actuation at acceleration along either of the two axial directions. The contact system switches from the initial state at releasing and following turning of the jumper strap. Design of the inertia switch enables reliable retention of the initial state of a contact system at any operation conditions of the mobile vehicles as well as fail-safe switching while vehicle motion during specified period of time (speedup, braking) with acceleration of no less than certain (specified) value. Presented results demonstrate possibility of development of the reliable and technologically effective inertia switch, designed for engineering systems of the independent mobile vehicles.

Bohui Zhang, Valentina Anita Carriero, Katrin Schreiberhuber et al.

Ontology engineering (OE) in large projects poses a number of challenges arising from the heterogeneous backgrounds of the various stakeholders, domain experts, and their complex interactions with ontology designers. This multi-party interaction often creates systematic ambiguities and biases from the elicitation of ontology requirements, which directly affect the design, evaluation and may jeopardise the target reuse. Meanwhile, current OE methodologies strongly rely on manual activities (e.g., interviews, discussion pages). After collecting evidence on the most crucial OE activities, we introduce \textbf{OntoChat}, a framework for conversational ontology engineering that supports requirement elicitation, analysis, and testing. By interacting with a conversational agent, users can steer the creation of user stories and the extraction of competency questions, while receiving computational support to analyse the overall requirements and test early versions of the resulting ontologies. We evaluate OntoChat by replicating the engineering of the Music Meta Ontology, and collecting preliminary metrics on the effectiveness of each component from users. We release all code at https://github.com/King-s-Knowledge-Graph-Lab/OntoChat.

Isaiah Lahr, Saghir Alfasly, Peyman Nejat et al.

Searching for similar images in archives of histology and histopathology images is a crucial task that may aid in patient matching for various purposes, ranging from triaging and diagnosis to prognosis and prediction. Whole slide images (WSIs) are highly detailed digital representations of tissue specimens mounted on glass slides. Matching WSI to WSI can serve as the critical method for patient matching. In this paper, we report extensive analysis and validation of four search methods bag of visual words (BoVW), Yottixel, SISH, RetCCL, and some of their potential variants. We analyze their algorithms and structures and assess their performance. For this evaluation, we utilized four internal datasets ($1269$ patients) and three public datasets ($1207$ patients), totaling more than $200,000$ patches from $38$ different classes/subtypes across five primary sites. Certain search engines, for example, BoVW, exhibit notable efficiency and speed but suffer from low accuracy. Conversely, search engines like Yottixel demonstrate efficiency and speed, providing moderately accurate results. Recent proposals, including SISH, display inefficiency and yield inconsistent outcomes, while alternatives like RetCCL prove inadequate in both accuracy and efficiency. Further research is imperative to address the dual aspects of accuracy and minimal storage requirements in histopathological image search.

Jianfeng Liu, Wang Xi, Weigang Lu

Influenced by the clearance flow between stator and rotor, the operational performance and hydraulic performance of full cross-flow pump units are often worse than that of semi-cross-flow pumps. In order to explore the influence mechanism of clearance structural parameters on clearance flow and provide a reliable scientific support for the improvement of both external and internal characteristics of full cross-flow pump units, firstly, the optimization of the stator–rotor clearance structure was studied as research entry point and the radial inlet and outlet clearance width were taken to set up design variables. Secondly, to establish a comprehensive optimization objective function considering both the operational performance and the hydraulic performance of the pump, the information weight method was adopted by weighting four evaluation indexes, namely, head coefficient, efficiency coefficient, vortex average radial deflection coefficient and axial velocity uniformity coefficient, which were calculated by numerical simulation. Finally, the relevant optimization design analysis was carried out by establishing the response surface model, with the optimal objective value obtained by conducting the steepest-descent method. The results show that the response of the radial inlet and outlet clearance width coefficient between stator and rotor to the comprehensive objective function is not directly coupled and the influence of the radial inlet clearance width coefficient on the objective function is higher than that of the radial outlet clearance width coefficient. The parameter optimization outcomes are as follows: the width coefficient of radial inlet clearance between stator and rotor is 2.2 and that of radial outlet clearance is 3.6, in which case the disturbance effect of clearance flow on the mainstream flow pattern in the pump can be significantly reduced, with the export cyclic quantity of the guide vane obviously decreased and the outlet flow pattern of the pump unit greatly improved. Verified by the model test, the average lift of the pump unit was increased by about 7.6% and the maximum promotion of the unit efficiency reached 5.2%.

Seyed Mostafa Emadi Baladehi

Introduction Landfill leachate, a liquid resulting from waste decomposition, contains nutrients like ammoniacal-N, Na, K, and organic matter. Biological treatments effectively remove degradable organics from young landfill leachate, but aged leachate with recalcitrant organics requires combined physical-chemical and biological methods or advanced technologies, leading to higher treatment costs. Even after treatment, leachate may not meet environmental standards for release. In arid and semi-arid regions with water scarcity and low soil organic matter, leachate application to soil presents a potential solution. Soil&rsquo;s properties enable it to retain and degrade pollutants while utilizing leachate&rsquo;s nutrients to enhance fertility and crop growth. However, leachate composition and application rates are critical factors due to potential negative impacts from total nitrogen, salinity, and heavy metals. Alkaline pH in aged leachate reduces heavy metal contamination risk. Detailed leachate characterization before soil application is crucial to prevent environmental and functional problems. This review examines existing research on leachate irrigation&rsquo;s effects on soil properties and plant nutrition, contributing to sustainable leachate management and agricultural practices in water-limited regions. Additionally, the review explores potential risks associated with leachate irrigation, including soil salinization, heavy metal accumulation, and groundwater contamination. By understanding both the benefits and drawbacks, informed decisions can be made regarding the suitability and implementation of leachate irrigation in specific contexts. &nbsp; Materials and Methods To carry out this study, keywords such as "Landfill leachate", "Composition of landfill leachate" and "Landfill leachate irrigation" were searched in the Web of Science, Google Scholar, ScienceDirect, and SID databases. For these keywords, 205 articles were found from 1989 to 2023. After the screening, quality review, and removal of repetitive and unrelated articles, 110 relevant articles were used. The main criterion for selecting articles was the effects of landfill leachate irrigation on the various properties of soil, and the nutrition of different plant species. The quality of the articles was evaluated through the Scimago Journal Rank (SJR) index, the citation, the Impact Factor, and the source normalized impact per paper (SNIP) index. &nbsp; Results and Discussion Landfill leachate presents a complex environmental challenge due to its potential for both soil contamination and enrichment. Leachate's xenobiotic and heavy metal components can induce soil contamination, altering the natural environment. Studies have documented reduced hydraulic conductivity, increased gas production, and altered microbial communities, ultimately impacting soil productivity.&nbsp; Leachate percolation can also modify physicochemical characteristics, including reduced microbial biomass, phosphorus-fixing capacity, and pH shifts, depending on waste composition. Conversely, research highlights the potential benefits of leachate application in arid and semi-arid regions facing water scarcity and low soil organic matter. Leachate can contribute to the increased organic content, improved soil structure, and regulated pH, enhancing soil fertility and crop productivity.&nbsp; The presence of macro and micro-nutrients such as Fe, Mn, N, P, and Zn further supports leachate's potential as a fertilizer. However, concerns remain regarding inhibitory chemicals in leachate and their potential detrimental effects on plant growth and yield. Studies report instances of leaf injury, reduced yield, and poor survival rates in certain plant species.&nbsp; In contrast, research demonstrates the positive effects of diluted or low-strength leachate application, stimulating plant growth and enhancing yield, particularly for Brassica species and tree species like Acacia confusa, Leucaena leptocephali, and Eucalyptus tortellini. These contradictory findings underscore the intricate interplay of factors influencing leachate irrigation outcomes. Soil characteristics, plant species, leachate source and composition, application methods, and their interactions all play significant roles in determining the success or failure of leachate irrigation. Conclusion Landfill leachate, characterized by its elevated nitrogen and nutrient levels, presents a potential alternative water and fertilizer source for agricultural practices, particularly in arid and semi-arid regions facing water scarcity. However, responsible leachate utilization necessitates a comprehensive approach that balances maximizing benefits with minimizing environmental risks. Prior to agricultural application, detailed leachate characterization is crucial to determine its precise composition and suitability for irrigation. This includes quantifying heavy metal concentrations, salinity levels, and the presence of potentially toxic organic compounds.&nbsp; Concurrent plant selection is equally important, prioritizing species with demonstrated tolerance to leachate constituents. Given the potential for salinity and heavy metal accumulation, continuous application of raw leachate, especially for sensitive crops, should be avoided. Implementing alternating irrigation regimes with conventional water sources can mitigate these risks while providing essential nutrients for plant growth.&nbsp; Monitoring soil health indicators, including pH, organic matter content, and microbial activity, is vital to assess long-term impacts and implement necessary soil amendments. Determining optimal leachate application rates requires a multifaceted approach that considers plant-specific nitrogen requirements, leachate toxicity levels, and soil infiltration capacity.&nbsp; This ensures adequate nutrient supply without exceeding the assimilative capacity of plants and soil, preventing environmental contamination. Further research is needed to investigate the long-term impacts of leachate irrigation on soil health, crop quality, and potential groundwater contamination. Developing standardized guidelines for leachate treatment and application, tailored to specific regional contexts and crop types, is crucial for promoting sustainable and responsible leachate utilization in agriculture.

Zixuan Liu, Yao Gao, Tingyu Li et al.

The paper addresses the overlooked interaction between power-to-gas (P2G) devices and carbon capture and storage (CCS) equipment, along with the stepwise carbon trading mechanism in the context of current multi-park integrated energy microgrids (IEMGs). Additionally, it covers the economic and coordinated low-carbon operation issues in multi-park IEMGs under the carbon trading system. It proposes a multi-park IEMG low-carbon operation strategy based on the synchronous Alternating Direction Method of Multipliers (ADMM) algorithm. The algorithm first enables the distribution of cost relationships among multi-park IEMGs. Then, using a method that combines a CCS device with a P2G unit in line with the tiered carbon trading scheme, it expands on the model of single IEMGs managing thermal, electrical, and refrigeration energy. Finally, the comparison of simulation cases proves that the proposed strategy significantly reduces the external energy dependence while keeping the total cost of the users unchanged, and the cost of interaction with the external grid is reduced by 56.64%, the gas cost is reduced by 27.78%, and the carbon emission cost is reduced by 29.54% by joining the stepped carbon trading mechanism.

Magnus V. Paludan, Benjamin Dollet, Philippe Marmottant et al.

Soft intertwined channel systems are frequently found in fluid flow networks in nature. The passage geometry of these systems can deform due to fluid flow, which can cause the relationship between flow rate and pressure drop to deviate from Hagen-Poiseuille's linear law. Although fluid-structure interactions in single deformable channels have been extensively studied, such as in Starling's resistor and its variations, the flow transport capacity of an intertwined channel with multiple self-intersections (a "hydraulic knot"), is still an open question. We present experiments and theory on soft hydraulic knots formed by interlinked microfluidic devices comprising two intersecting channels separated by a thin elastomeric membrane. Our experiments show flow-pressure relationships similar to flow limitation, where the limiting flow rate depends on the knot configuration. To explain our observations, we develop a mathematical model based on lubrication theory coupled with tension-dominated membrane deflections that compares favorably to our experimental data. Finally, we present two potential hydraulic knot applications for microfluidic flow rectification and attenuation.

Rudrajit Choudhuri, Dylan Liu, Igor Steinmacher et al.

Conversational Generative AI (convo-genAI) is revolutionizing Software Engineering (SE) as engineers and academics embrace this technology in their work. However, there is a gap in understanding the current potential and pitfalls of this technology, specifically in supporting students in SE tasks. In this work, we evaluate through a between-subjects study (N=22) the effectiveness of ChatGPT, a convo-genAI platform, in assisting students in SE tasks. Our study did not find statistical differences in participants' productivity or self-efficacy when using ChatGPT as compared to traditional resources, but we found significantly increased frustration levels. Our study also revealed 5 distinct faults arising from violations of Human-AI interaction guidelines, which led to 7 different (negative) consequences on participants.