Hasil untuk "Hydraulic engineering"

M. Deshpande, Vijay R. Thombare, S. Talegaonkar

Alexander Korn, Lea Zaruchas, Chetan Arora et al.

Large Language Models, particularly decoder-only generative models such as GPT, are increasingly used to automate Software Engineering tasks. These models are primarily guided through natural language prompts, making prompt engineering a critical factor in system performance and behavior. Despite their growing role in SE research, prompt-related decisions are rarely documented in a systematic or transparent manner, hindering reproducibility and comparability across studies. To address this gap, we conducted a two-phase empirical study. First, we analyzed nearly 300 papers published at the top-3 SE conferences since 2022 to assess how prompt design, testing, and optimization are currently reported. Second, we surveyed 105 program committee members from these conferences to capture their expectations for prompt reporting in LLM-driven research. Based on the findings, we derived a structured guideline that distinguishes essential, desirable, and exceptional reporting elements. Our results reveal significant misalignment between current practices and reviewer expectations, particularly regarding version disclosure, prompt justification, and threats to validity. We present our guideline as a step toward improving transparency, reproducibility, and methodological rigor in LLM-based SE research.

Alexandra González, Xavier Franch, Silverio Martínez-Fernández

Integrating Artificial Intelligence into Software Engineering (SE) requires having a curated collection of models suited to SE tasks. With millions of models hosted on Hugging Face (HF) and new ones continuously being created, it is infeasible to identify SE models without a dedicated catalogue. To address this gap, we present SEMODS: an SE-focused dataset of 3,427 models extracted from HF, combining automated collection with rigorous validation through manual annotation and large language model assistance. Our dataset links models to SE tasks and activities from the software development lifecycle, offering a standardized representation of their evaluation results, and supporting multiple applications such as data analysis, model discovery, benchmarking, and model adaptation.

Mairieli Wessel, Daniel Feitosa, Sangeeth Kochanthara

Rising publication pressure and the routine use of generative AI tools are reshaping how software engineering research is produced, assessed, and taught. While these developments promise efficiency, they also raise concerns about skill degradation, responsibility, and trust in scholarly outputs. This vision paper employs Design Fiction as a methodological lens to examine how such concerns might materialise if current practices persist. Drawing on themes reported in a recent community survey, we construct a speculative artifact situated in a near future research setting. The fiction is used as an analytical device rather than a forecast, enabling reflection on how automated assistance might impede domain knowledge competence, verification, and mentoring practices. By presenting an intentionally unsettling scenario, the paper invites discussion on how the software engineering research community in the future will define proficiency, allocate responsibility, and support learning.

Golnoush Abaei, Mojtaba Shahin, Maria Spichkova

The inclusion of internship courses in Software Engineering (SE) programs is essential for closing knowledge gaps and improving graduates' readiness for the software industry. Our study focuses on year-long internships at RMIT University (Melbourne, Australia), which offers in-depth industry engagement. We analysed how the course evolved over the last 10 years to incorporate students' needs and summarised the lessons learned that can be helpful for other educators supporting internship courses. Our qualitative analysis of internship data based on 91 reports during 2023-2024 identified three challenge themes the students faced, and which courses were found by students to be particularly beneficial during their internships. On this basis, we proposed recommendations for educators and companies to help interns overcome challenges and maximise their learning experience.

Mehrbod Zarifi, Mohamad Amin Jamshidi, Zolfa Anvari et al.

Hydraulic systems have been one of the most used technologies in many industries due to their reliance on incompressible fluids that facilitate energy and power transfer. Within such systems, hydraulic cylinders are prime devices that convert hydraulic energy into mechanical energy. Some of the genuine and very common problems related to hydraulic cylinders are leakages. Leakage in hydraulic systems can cause a drop in pressure, general inefficiency, and even complete failure of such systems. The various ways leakage can occur define the major categorization of leakage: internal and external leakage. External leakage is easily noticeable, while internal leakage, which involves fluid movement between pressure chambers, can be harder to detect and may gradually impact system performance without obvious signs. When leakage surpasses acceptable limits, it is classified as a fault or failure. In such cases, leakage is divided into three categories: no leakage, low leakage, and high leakage. It suggests a fault detection algorithm with the basic responsibility of detecting minimum leakage within the Hydraulic system, and minimizing detection time is the core idea of this paper. In order to fully develop this idea, experimental data collection of Hydraulic systems is required. The collected data uses pressure sensors and other signals that are single-related. Due to the utilization of Long Short-Term Memory (LSTM) recurrent neural networks, more complex data analysis was enabled, which the LSTM-based leakage detection algorithm successfully achieved, providing almost 96% accuracy in classifying leakage types. Results demonstrate that the proposed method can perform real-time and online fault diagnosis for each cycle, reducing maintenance costs and prolonging the hydraulic system's lifespan.

Max Ofsa, Taylan G. Topcu

Systems engineering (SE) is evolving with the availability of generative artificial intelligence (AI) and the demand for a systems-of-systems perspective, formalized under the purview of mission engineering (ME) in the US Department of Defense. Formulating ME problems is challenging because they are open-ended exercises that involve translation of ill-defined problems into well-defined ones that are amenable for engineering development. It remains to be seen to which extent AI could assist problem formulation objectives. To that end, this paper explores the quality and consistency of multi-purpose Large Language Models (LLM) in supporting ME problem formulation tasks, specifically focusing on stakeholder identification. We identify a relevant reference problem, a NASA space mission design challenge, and document ChatGPT-3.5's ability to perform stakeholder identification tasks. We execute multiple parallel attempts and qualitatively evaluate LLM outputs, focusing on both their quality and variability. Our findings portray a nuanced picture. We find that the LLM performs well in identifying human-focused stakeholders but poorly in recognizing external systems and environmental factors, despite explicit efforts to account for these. Additionally, LLMs struggle with preserving the desired level of abstraction and exhibit a tendency to produce solution specific outputs that are inappropriate for problem formulation. More importantly, we document great variability among parallel threads, highlighting that LLM outputs should be used with caution, ideally by adopting a stochastic view of their abilities. Overall, our findings suggest that, while ChatGPT could reduce some expert workload, its lack of consistency and domain understanding may limit its reliability for problem formulation tasks.

Zirui Li, Stephan Husung, Haoze Wang

Cross-organizational collaboration in Model-Based Systems Engineering (MBSE) faces many challenges in achieving semantic alignment across independently developed system models. SysML v2 introduces enhanced structural modularity and formal semantics, offering a stronger foundation for interoperable modeling. Meanwhile, GPT-based Large Language Models (LLMs) provide new capabilities for assisting model understanding and integration. This paper proposes a structured, prompt-driven approach for LLM-assisted semantic alignment of SysML v2 models. The core contribution lies in the iterative development of an alignment approach and interaction prompts, incorporating model extraction, semantic matching, and verification. The approach leverages SysML v2 constructs such as alias, import, and metadata extensions to support traceable, soft alignment integration. It is demonstrated with a GPT-based LLM through an example of a measurement system. Benefits and limitations are discussed.

Jake Zappin, Trevor Stalnaker, Oscar Chaparro et al.

This position paper examines the substantial divide between academia and industry within quantum software engineering. For example, while academic research related to debugging and testing predominantly focuses on a limited subset of primarily quantum-specific issues, industry practitioners face a broader range of practical concerns, including software integration, compatibility, and real-world implementation hurdles. This disconnect mainly arises due to academia's limited access to industry practices and the often confidential, competitive nature of quantum development in commercial settings. As a result, academic advancements often fail to translate into actionable tools and methodologies that meet industry needs. By analyzing discussions within quantum developer forums, we identify key gaps in focus and resource availability that hinder progress on both sides. We propose collaborative efforts aimed at developing practical tools, methodologies, and best practices to bridge this divide, enabling academia to address the application-driven needs of industry and fostering a more aligned, sustainable ecosystem for quantum software development.

Qingfeng Miao, Xiaoyu Liu, Haibin Shi et al.

Examining lake-area evolution and influencing factors is essential for understanding global environmental and societal changes and supporting ecological sustainability. Inner Mongolia, China, given its unique geographical and climatic conditions, serves as a natural laboratory for investigating the complex coupling mechanisms of “climate–hydrology–humanities.” Accordingly, we analyzed data regarding annual area changes in 655 lakes across five basins obtained from Landsat, Sentinel-2, and pushbroom multispectral scanner (1987–2023), combined with meteorological, hydrological, and human factors affecting lake-area changes. Results indicated that lake areas varied from 4059.36 to 6489.46 km2 in 1987–2023, exhibiting an overall decline of 38.06 km2/a (R2 = 0.39, p < 0.001). This trend was nonlinear, exhibiting area expansion (1987–1998), rapid shrinkage (1998–2010), and stabilization after a slight rebound (2010–2023). Natural factors dominated lake-area dynamics in the Songhua and Northwest River Basins, while human activities, particularly agriculture, were key drivers in the Liaohe, Haihe, and Yellow River Basins. These findings provide critical insights into the drivers of lake-area changes and establish a scientific basis for developing effective water-resource management and ecological protection strategies.

Marco Autili, Martina De Sanctis, Paola Inverardi et al.

As testified by new regulations like the European AI Act, worries about the human and societal impact of (autonomous) software technologies are becoming of public concern. Human, societal, and environmental values, alongside traditional software quality, are increasingly recognized as essential for sustainability and long-term well-being. Traditionally, systems are engineered taking into account business goals and technology drivers. Considering the growing awareness in the community, in this paper, we argue that engineering of systems should also consider human, societal, and environmental drivers. Then, we identify the macro and technological challenges by focusing on humans and their role while co-existing with digital systems. The first challenge considers humans in a proactive role when interacting with digital systems, i.e., taking initiative in making things happen instead of reacting to events. The second concerns humans having a reactive role in interacting with digital systems, i.e., humans interacting with digital systems as a reaction to events. The third challenge focuses on humans with a passive role, i.e., they experience, enjoy or even suffer the decisions and/or actions of digital systems. The fourth challenge concerns the duality of trust and trustworthiness, with humans playing any role. Building on the new human, societal, and environmental drivers and the macro and technological challenges, we identify a research roadmap of digital systems for humanity. The research roadmap is concretized in a number of research directions organized into four groups: development process, requirements engineering, software architecture and design, and verification and validation.

Oluwatosin Ogundare, Srinath Madasu, Nathanial Wiggins

Large Language Models (LLMs) have shown great potential in solving complex problems in various fields, including oil and gas engineering and other industrial engineering disciplines like factory automation, PLC programming etc. However, automatic identification of strong and weak solutions to fundamental physics equations governing several industrial processes remain a challenging task. This paper identifies the limitation of current LLM approaches, particularly ChatGPT in selected practical problems native to oil and gas engineering but not exclusively. The performance of ChatGPT in solving complex problems in oil and gas engineering is discussed and the areas where LLMs are most effective are presented.

Oliver Karras, Felix Wernlein, Jil Klünder et al.

[Background.] Empirical research in requirements engineering (RE) is a constantly evolving topic, with a growing number of publications. Several papers address this topic using literature reviews to provide a snapshot of its "current" state and evolution. However, these papers have never built on or updated earlier ones, resulting in overlap and redundancy. The underlying problem is the unavailability of data from earlier works. Researchers need technical infrastructures to conduct sustainable literature reviews. [Aims.] We examine the use of the Open Research Knowledge Graph (ORKG) as such an infrastructure to build and publish an initial Knowledge Graph of Empirical research in RE (KG-EmpiRE) whose data is openly available. Our long-term goal is to continuously maintain KG-EmpiRE with the research community to synthesize a comprehensive, up-to-date, and long-term available overview of the state and evolution of empirical research in RE. [Method.] We conduct a literature review using the ORKG to build and publish KG-EmpiRE which we evaluate against competency questions derived from a published vision of empirical research in software (requirements) engineering for 2020 - 2025. [Results.] From 570 papers of the IEEE International Requirements Engineering Conference (2000 - 2022), we extract and analyze data on the reported empirical research and answer 16 out of 77 competency questions. These answers show a positive development towards the vision, but also the need for future improvements. [Conclusions.] The ORKG is a ready-to-use and advanced infrastructure to organize data from literature reviews as knowledge graphs. The resulting knowledge graphs make the data openly available and maintainable by research communities, enabling sustainable literature reviews.

QIU Dapeng 1, 2, CHEN Jianyun 3, WANG Wenming 1, 2, CAO Xiangyu 4

The increase dynamic analysis (IDA) curves of seismic responses of the underground large-scale frame structure (ULSFS) are investigated during the single horizontal earthquakes and horizontal-vertical earthquakes, respectively. The influence mechanism of vertical earthquakes on the seismic responses of different vulnerable positions is revealed. Aiming at the interlayer drift deformation and flexural deformation in the ULSFS, the interlayer drift ratio (IDR) and interlayer rotation angle (IRA) are employed as the seismic performance evaluation indexes. Therefore, the influence mechanism of vertical earthquakes on structural seismic performance is further revealed. The seismic fragility curves of the ULSFS are achieved during horizontal earthquakes and horizontal-vertical earthquakes, respectively. The results show that the vertical earthquakes have small seismic influences on the seismic fragility of the ULSFS based on the IDR. However, the vertical earthquakes enlarge the local flexural deformation of the ULSFS and decrease the seismic performance of the ULSFS based on the IRA. The seismic fragility considerably increases after considering the vertical seismic effects. The IDR aiming at the horizontal drift deformation and the IRA aiming at the interlayer flexural deformation are advised to be employed to assess the seismic fragility of large underground structures during both horizontal and vertical earthquakes comprehensively.

ZHANG Tonggang, HU Xinglu, LUO Min et al.

【Objective】 Saline water has been used as a supplementary irrigation resource in most countries to sustain agricultural production. The purpose of this paper is to study the influence of ionic composition in saline water on soil properties and growth of corn under mulched drip irrigation. 【Method】 The field experiment was conducted from April to September 2021 in the Hetao Irrigation District (HID) in Inner Mongolia. The saline water was created by adding different chloride salts: NaCl (T1), KCl (T2), CaCl2 (T3) and MgCl2 (T4) to fresh water. Irrigation with fresh underground water served as the control (CK). During each treatment, change in soil structure, transport of water and salt in soil, and crop growth were measured. 【Result】 ① Compared to CK, T1 significantly increased the number of small pores in the top 0~20 cm soil layer. This had a detrimental impact on soil structure but increased its water storage, particularly during the jointing stage. Compared to CK, T3 and T4 reduced the small porosity by 147.73% and 132.01%, respectively, but increased permeability of the soil. ② Compared to CK, all treatments increased the concentrations of Na+, K+, Ca2+ and Mg2+ in the 0~60 cm soil layer. Except T1, other treatments increased the concentrations of K+, Ca2+, and Mg2+, compared to CK. T1 resulted in salt accumulation in the soil surface, while other treatments did not show noticeable impact on ion composition. It was found that T3 and T4 moved the salt out the zone controlled by the mulch in the lateral direction, particularly T4. The electrical conductivity (EC) of the saturated extract from the root zone soil was influenced by the EC of irrigation water and varied in the range of 3~4 dS/m. ③ Na+ and K+ contents were the lowest and highest in the aboveground part, respectively. Na+, K+, and Cl- contents were higher in leaves than in stems, while Ca2+ and Mg2+ contents were higher in stem than in leaves. Increasing irrigation amount enhanced crop yield only in some treatments. Among all treatments, T4 gave the highest corn seed yield, 17.33% more than CK. 【Conclusion】 Irrigation with saline water containing high Na+ increased the small porosity of the topsoil, affecting soil water infiltration. In contrast, increasing K+, Ca2+, and Mg2+ contents reduced small porosity, promoting salt leaching due to the increased soil permeability. Keeping concentration of K+, Ca2+ and Mg2+ at appropriate levels under saline water irrigation is beneficial to increasing dry matter accumulation and the final yield, as it offsets the negative effects of excessive Na+. These findings provide guidance for safe utilization of saline water for irrigation in HID.

Nikolas Martelaro, Carol J. Smith, Tamara Zilovic

Embedding artificial intelligence into systems introduces significant challenges to modern engineering practices. Hazard analysis tools and processes have not yet been adequately adapted to the new paradigm. This paper describes initial research and findings regarding current practices in AI-related hazard analysis and on the tools used to conduct this work. Our goal with this initial research is to better understand the needs of practitioners and the emerging challenges of considering hazards and risks for AI-enabled products and services. Our primary research question is: Can we develop new structured thinking methods and systems engineering tools to support effective and engaging ways for preemptively considering failure modes in AI systems? The preliminary findings from our review of the literature and interviews with practitioners highlight various challenges around integrating hazard analysis into modern AI development processes and suggest opportunities for exploration of usable, human-centered hazard analysis tools.

Sebastián Pizard, Diego Vallespir, Barbara Kitchenham

Context: Evidence-based software engineering (EBSE) can be an effective resource to bridge the gap between academia and industry by balancing research of practical relevance and academic rigor. To achieve this, it seems necessary to investigate EBSE training and its benefits for the practice. Objective: We sought both to develop an EBSE training course for university students and to investigate what effects it has on the attitudes and behaviors of the trainees. Method: We conducted a longitudinal case study to study our EBSE course and its effects. For this, we collect data at the end of each EBSE course (2017, 2018, and 2019), and in two follow-up surveys (one after 7 months of finishing the last course, and a second after 21 months). Results: Our EBSE courses seem to have taught students adequately and consistently. Half of the respondents to the surveys report making use of the new skills from the course. The most-reported effects in both surveys indicated that EBSE concepts increase awareness of the value of research and evidence and EBSE methods improve information gathering skills. Conclusions: As suggested by research in other areas, training appears to play a key role in the adoption of evidence-based practice. Our results indicate that our training method provides an introduction to EBSE suitable for undergraduates. However, we believe it is necessary to continue investigating EBSE training and its impact on software engineering practice.

Yanpin Li, Lihong Zhang, Jinbao Chen et al.

To study the influence of rotational speed on the performance of hydraulic turbine in turbine mode (T-type turbine), the performance of turbine at different rotational speed was predicted through theoretical analysis, based on the Navier-Stokes equation and standard k-ε turbulence model, numerical simulation was used to study the performance of turbine at different rotational speed. The head loss, pressure distribution, turbulence kinetic energy, and unsteady pressure pulsation were also clarified. The results shows that with the increase of rotational speed, the high efficiency area of turbine gradually becomes wider, and there is little difference of the maximum efficiency at different rotational speeds. Under the optimal working condition, the pressure difference between inlet and outlet increase gradually, and turbulent kinetic energy of runner also increase. The inlet circulation increases with the increase of rotational speed, the outlet circulation increases with the increase of speed under small flow conditions, and decreases with the increase of rotational speed under large flow conditions. The study of pressure pulsation shows that the increase of rotating speed can effectively reduce the pressure pulsation in the runner. When the rotating speed is 2100 r/min, the amplitude of pressure pulsation at P7 is 10.39% lower than that at 900 r/min. Considering the hydraulic characteristics and the pulsation characteristics, it is recommended that the hydraulic turbine operates at or above the rated speed as far as possible.

TANG Xiaolin, ZHOU Yifan, ZHU Zhaoyin et al.

The rain-sewage diversion project in the dense housing area of urban villages involves a large amount of trench excavation,which leads to prominent safety problems in construction.The safety monitoring and analysis of trench excavation are characterized by high requirements,complex data association,and poor data conditions,and thus we propose a theoretical method of organic integration of land subsidence mechanisms and data analysis process.The intelligent analysis method for land subsidence in trench excavation is based on digital twin technology,which integrates data and knowledge to realize visualized real-time monitoring,prediction,and control of land subsidence in urban villages.The method extracts and expresses the scenes,processes,objects,problems,and knowledge of the whole life cycle of land subsidence in survey and design,trial excavation,and formal excavation construction,and a big data analysis model of land subsidence based on data-knowledge fusion is developed.The research results can provide a scientific basis for the safety decision-making of excavation construction in the dense housing area of urban villages.

Maycon Breno Macena da Silva, Igor Antônio de Paiva Brandão, Márcia Maria Rios Ribeiro

ABSTRACT Urban areas in semi-arid regions are under chronic water stress. In this scenario, expanding water supply with decentralized sources that collaborate with Water-Sensitive Urban Design (WSUD) may be relevant, such as rainwater harvesting (RWH) systems. In this respect, this study aimed to analyze the potential for the use of rainwater in public buildings in the Brazilian semi-arid region, integrating three aspects: environmental and economic feasibility, seasonality, and reliability. The results provide substantial evidence on the benefits of using rainwater, both from an environmental and an economic point of view. This use can significantly reduce the annual consumption of water from the public supply, which would reduce the demand from water bodies. It has also been found that there is considerable variation in the potable water savings potential throughout the year; the systems, however, still provide reliability.