Hasil untuk "Hydraulic engineering"

Anatoly A. Krasnovsky

Chaos engineering reveals resilience risks but is expensive and operationally risky to run broadly and often. Model-based analyses can estimate dependability, yet in practice they are tricky to build and keep current because models are typically handcrafted. We claim that a simple connectivity-only topological model - just the service-dependency graph plus replica counts - can provide fast, low-risk availability estimates under fail-stop faults. To make this claim practical without hand-built models, we introduce model discovery: an automated step that can run in CI/CD or as an observability-platform capability, synthesizing an explicit, analyzable model from artifacts teams already have (e.g., distributed traces, service-mesh telemetry, configs/manifests) - providing an accessible gateway for teams to begin resilience testing. As a proof by instance on the DeathStarBench Social Network, we extract the dependency graph from Jaeger and estimate availability across two deployment modes and five failure rates. The discovered model closely tracks live fault-injection results; with replication, median error at mid-range failure rates is near zero, while no-replication shows signed biases consistent with excluded mechanisms. These results create two opportunities: first, to triage and reduce the scope of expensive chaos experiments in advance, and second, to generate real-time signals on the system's resilience posture as its topology evolves, preserving live validation for the most critical or ambiguous scenarios.

Georgi Markov, Jon G. Hall, Lucia Rapanotti

Many organisational problems are addressed through systemic change and re-engineering of existing Information Systems rather than radical new design. In the face of widespread IT project failure, devising effective ways to tackle this type of change remains an open challenge. This work discusses the motivation, theoretical foundation, characteristics and evaluation of a novel framework - referred to as POE-$Δ$, which is rooted in design and engineering and is aimed at providing systematic support for representing, structuring and exploring change problems of a socio-technical nature, including implementing their solutions when they exist. We generalise an existing framework of greenfield design as problem solving for application to change problems. From a theoretical perspective,POE-$Δ$ is a strict extension to its parent framework, allowing the seamless integration of greenfield and brownfield design to tackle change problems. A Design Science Research methodology was applied over a decade to define and evaluate POE-$Δ$, with significant case study research conducted to evaluate the framework in its application to real-world change problems of varying criticality and complexity. The results show that POE-$Δ$ exhibits desirable characteristics of a design approach to organisational change and can bring tangible benefits when applied in practice as a holistic and systematic approach to change in socio-technical contexts.

Sebastian Baltes, Florian Angermeir, Chetan Arora et al.

Large Language Models (LLMs) are now ubiquitous in software engineering (SE) research and practice, yet their non-determinism, opaque training data, and rapidly evolving models threaten the reproducibility and replicability of empirical studies. We address this challenge through a collaborative effort of 22 researchers, presenting a taxonomy of seven study types that organizes the landscape of LLM involvement in SE research, together with eight guidelines for designing and reporting such studies. Each guideline distinguishes requirements (must) from recommended practices (should) and is contextualized by the study types it applies to. Our guidelines recommend that researchers: (1) declare LLM usage and role; (2) report model versions, configurations, and customizations; (3) document the tool architecture beyond the model; (4) disclose prompts, their development, and interaction logs; (5) validate LLM outputs with humans; (6) include an open LLM as a baseline; (7) use suitable baselines, benchmarks, and metrics; and (8) articulate limitations and mitigations. We complement the guidelines with an applicability matrix mapping guidelines to study types and a reporting checklist for authors and reviewers. We maintain the study types and guidelines online as a living resource for the community to use and shape (llm-guidelines$.$org).

CHEN Quanfu, LI Xungui

In recent years, significant changes in hydrological and engineering conditions have been observed in the Yujiang River Basin, and climate anomalies have increased flood control pressures. The distribution of rainstorms and floods within the basin varies significantly, which makes it insufficient to describe flood risk solely from the perspective of flood peak or flood volume, and further study is necessitated. This paper takes the runoff data (1947—2015) from the Guigang Station as the research object and extracts flood sequences through the annual maximum (AM) method and the peaks over threshold (POT) method. Three common Archimedean Copula functions are employed to establish the multivariate joint distribution of flood variables, and their joint return periods and co-occurrence return periods are compared and analyzed. Results show that the optimal marginal distributions respectively obtained through the AM method and the POT method differ, with Clayton Copula being the optimal fitting function for the AM method and Frank Copula for the POT method. While the joint and co-occurrence return periods calculated through both methods show little difference, under the same return period, the POT method yields more reliable results for flood peak and flood volume. The findings can provide a valuable reference for decision-making in aspects of flood mitigation, engineering construction, and management in the Yujiang River Basin.

Lin Cheng, Yongkang Sun, Zhaohan Wang et al.

Water pipelines in water diversion projects can leak, leading to soil deformation and ground subsidence, necessitating research into soil deformation monitoring technology. This study conducted model tests to monitor soil deformation around leaking buried water pipelines using distributed fiber optic strain sensing (DFOSS) technology based on optical frequency domain reflectometry (OFDR). By arranging strain measurement fibers in a pipe–soil model, we investigated how leak location, leak size, pipe burial depth, and water flow velocity affect soil strain field monitoring results. The results showed that pipeline leakage creates a “saddle-shaped” spatial distribution of soil strain above the pipeline, effectively indicating ground subsidence locations. When only one survey line is arranged, it is preferable to place the optical fiber directly above the pipeline. Surface monitoring fibers primarily detected tensile strain, with more pronounced peak values observed under conditions of larger leak size, higher flow velocity, shallow burial depth, and top-pipe leakage location. Monitoring fibers below the pipeline showed mainly unimodal distribution, with peak strain coinciding with the leak location. The sequential timing of strain changes at different fiber positions enabled the determination of soil seepage direction. This study demonstrates that DFOSS technology can provide important support for the early warning of such geological disasters.

Zheng Wei, Wei Cui, Ri-cheng Miao

The viscosity of cement-bentonite (CB) slurry has an important impact on its construction quality and engineering utility. This study focuses on the mechanism of how Na-bentonite (Na-B) influences the viscosity of CB slurry. Four types of Na-B were selected to prepare bentonite slurry and CB slurry, followed by the design of tests to analyze the viscosity differences. Based on these results, microscopic tests, including X-ray diffraction (XRD), nuclear magnetic resonance (NMR), and field emission scanning electron microscopy (FESEM), were conducted to explain the reasons for the different properties of different bentonite slurries and their CB slurries. The analysis demonstrates that the variation in viscosity of bentonite slurry can be elucidated by the principles of viscosity recovery, low shear viscosity failure, and differences in bentonite composition. Meanwhile, the viscosity of CB slurry is influenced by charge adsorption, hydration products, and the interaction between bentonite and cement.

Oliver Karras

In the last two decades, several researchers provided snapshots of the "current" state and evolution of empirical research in requirements engineering (RE) through literature reviews. However, these literature reviews were not sustainable, as none built on or updated previous works due to the unavailability of the extracted and analyzed data. KG-EmpiRE is a Knowledge Graph (KG) of empirical research in RE based on scientific data extracted from currently 680 papers published in the IEEE International Requirements Engineering Conference (1994-2022). KG-EmpiRE is maintained in the Open Research Knowledge Graph (ORKG), making all data openly and long-term available according to the FAIR data principles. Our long-term goal is to constantly 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. Besides KG-EmpiRE, we provide its analysis with all supplementary materials in a repository. This repository contains all files with instructions for replicating and (re-)using the analysis locally or via executable environments and for repeating the research approach. Since its first release based on 199 papers (2014-2022), KG-EmpiRE and its analysis have been updated twice, currently covering over 650 papers. KG-EmpiRE and its analysis demonstrate how innovative infrastructures, such as the ORKG, can be leveraged to make data from literature reviews FAIR, openly available, and maintainable for the research community in the long term. In this way, we can enable replicable, (re-)usable, and thus sustainable literature reviews to ensure the quality, reliability, and timeliness of their research results.

Mehrdad Sabetzadeh, Chetan Arora

Natural Language Processing (NLP) is now a cornerstone of requirements automation. One compelling factor behind the growing adoption of NLP in Requirements Engineering (RE) is the prevalent use of natural language (NL) for specifying requirements in industry. NLP techniques are commonly used for automatically classifying requirements, extracting important information, e.g., domain models and glossary terms, and performing quality assurance tasks, such as ambiguity handling and completeness checking. With so many different NLP solution strategies available and the possibility of applying machine learning alongside, it can be challenging to choose the right strategy for a specific RE task and to evaluate the resulting solution in an empirically rigorous manner. In this chapter, we present guidelines for the selection of NLP techniques as well as for their evaluation in the context of RE. In particular, we discuss how to choose among different strategies such as traditional NLP, feature-based machine learning, and language-model-based methods. Our ultimate hope for this chapter is to serve as a stepping stone, assisting newcomers to NLP4RE in quickly initiating themselves into the NLP technologies most pertinent to the RE field.

Yu Xu, You-peng Xu, Qiang Wang et al.

Suzhou City, located in the Yangtze River Delta in China, is prone to flooding due to a complex combination of natural factors, including its monsoon climate, low elevation, and tidally influenced position, as well as intensive human activities. The Large Encirclement Flood Control Project (LEFCP) was launched to cope with serious floods in the urban area. This project changed the spatiotemporal pattern of flood processes and caused spatial diversion of floods from the urban area to the outskirts of the city. Therefore, this study developed a distributed flood simulation model in order to understand this transition of flood processes. The results revealed that the LEFCP effectively protected the urban areas from floods, but the present scheduling schemes resulted in the spatial diversion of floods to the outskirts of the city. With rainstorm frequencies of 10.0% to 0.5%, the water level differences between two representative water level stations (Miduqiao (MDQ) and Fengqiao (FQ)) located inside and outside the LEFCP area, ranged from 0.75 m to 0.24 m and from 1.80 m to 1.58 m, respectively. In addition, the flood safety margin at MDQ and the duration with the water level exceeding the warning water level at FQ ranged from 0.95 m to 0.43 m and from 4 h to 22 h, respectively. Rational scheduling schemes for the hydraulic facilities of the LEFCP in extreme precipitation cases were developed according to flood simulations under seven scheduling scenarios. This helps to regulate the spatial flood diversion caused by the LEFCP during extreme precipitation.

Caihong Tang, Yiling Leng, Pengyu Wang et al.

Hydropower plants with a small installed capacity, which are widely distributed in mountainous areas with abundant rainfall and steep rivers, play an important role in resolving energy problems in remote rural areas. These plants are a crucial source of clean electricity generated from water power. Harnessing local water resources not only helps alleviate energy shortages, but also reduces reliance on fossil fuels, contributing significantly to China’s national goals of achieving peak carbon emissions and carbon neutrality. This study investigates the carbon footprint of the Huangshadong Reservoir Project in Chongqing, China. The entire life cycle of the hydropower plant is assessed, including the preparation, construction, operation and maintenance, and demolition phases. The uncertainty was evaluated using the error propagation method. Following analysis, suggestions for carbon footprint reduction measures were proposed. Results showed that the total carbon footprint and the carbon intensity of the Huangshadong Reservoir Project over its entire life cycle are 33,148.29 t CO2e and 417.75 g CO2e/kWh, respectively. Of the total carbon footprint, the preparation phase, construction phase, operation and maintenance phase, and demolition phase account for 0.04%, 67.06%, 26.2%, and 6.7%, respectively. It means that the requirement for cement during the construction phase represents an important contribution to the entire life cycle carbon footprint of a small hydropower plant. As an integrated water conservancy project, the carbon intensity of the Huangshadong Reservoir Project is higher than that of medium-sized and large hydropower plants. However, its carbon intensity is lower than the emission factor of fossil power plants. The research results provide reference for both planning and construction of small hydropower plants and low-carbon development of rural hydraulic engineering.

TAN Junli, WANG Xi’na, MA Xiaofu et al.

【Objective】 Brackish water has been used as a supplementary irrigation resource in some arid and semi-arid regions in northwestern China, but the impact of its long-term application on soil environment and crop growth and quality is not well understood. The objective of this paper is to fill this knowledge gap using watermelon as an example. 【Method】 The experiment was conducted in fields with sand mulch, where brackish water has been used as a supplementary irrigation resource for varying periods ranging from 2 to 14 years. During the experiment, we measured the distribution of soil water and salt in the 0~60 cm soil layer, soil bulk density, as well as the yield and quality of watermelon. An uncultivated piece of land nearby was used as the control. 【Result】 Soil salt content increased with the increase in duration of brackish water irrigation in both tendril elongation and harvest stage. The highest soil salt content was in the 20~40 cm soil layer for the control, and in the 40~60 cm soil layer for the sand-mulched field. The supplementary brackish water irrigation increased the soil moisture content in flowering and fruit-setting stages, but the increase varied in that with the increase in the duration of the brackish water irrigation, the soil water content increased first and then declined. The highest soil moisture content in the sand-mulched field was in the surface layer (0~10 cm), while for the control it was in the 10~20 cm soil layer. Sand mulch increased soil bulk density in the 0~40 cm soil layer and soil compaction in the 0~8 cm soil layer. It was also found that with the increase in length of brackish water irrigation, the yield and soluble solids content in the watermelon decreased, while the fruit quality improved first followed by a decline. 【Conclusion】 Long-term use of brackish water for supplementary irrigation in the sand-mulched fields increased soil bulk density and compaction in the upper soil layers, as well as soil moisture and salt content. As the years of brackish water irrigation increased, the yield of watermelon decreased while the quality of its fruits showed an initial improvement before deteriorating.

Ping Xie, Weijun He, Min An et al.

Industrial Park is a part of urban development and plays an indispensable role in promoting regional economic development. However, the high consumption of resources and the concentrated emission of pollution pose a great threat to the ecological balance and human health. Therefore, the future of industrial park needs to be balanced with economic growth while realizing resource conservation and environmental improvement as much as possible. However, there is still a research gap in analyzing the relationship between ecological environment quality (EEQ) and economic development (ED) at the industrial park scale. This paper first constructs the new remote sensing ecological index (RSEInew) model to measure the EEQ of Suzhou Industrial Park (SIP) from 2000 to 2020. Then this study constructs an ED index system to evaluate its economic situation. Finally, this paper utilizes the Environmental Kuznets Curve (EKC) model and the coupled coordination degree (CCD) model to deeply analyze the relationship between EEQ and ED. The results show that: (1) The average annual growth rate of EEQ in SIP is −1.50 %, with a general downward trend. Over the past 20 years, the ecological environment of SIP has not deteriorated extensively. (2) The ED of SIP is steady and rising, with an average annual growth rate of 10.17 %. (3) From 2000 to 2020, the EKC between EEQ and the ED of the SIP takes on an inverted “N” shape, with its CCD rising from barely coordinated to highly coordinated. This study can provide a reference for studying the quality development of areas at the industrial park level.

Chi-Min Chiu, Laurence Zsu-Hsin Chuang, Wei-Liang Chuang et al.

This study aims to establish a comprehensive workflow for developing emergency response plans for both actual and scenario oil spill incidents in the Taiwan waters while addressing the resource allocation for oil spill containment as well. This workflow comprises two vital components. The first component involves the integration of numerical tools and observational data, which includes the incorporation of wind data from sources such as the National Centers for Environmental Prediction (NCEP) or meteorological stations. Additionally, it incorporates ocean current data simulated by the semi-implicit cross-scale hydroscience integrated system model (SCHISM) into the general NOAA operational modeling environment (GNOME) model, which is a new approach for this purpose. In order to assess the efficacy of this component, two distinct case studies were conducted. The first case study focused on an incident in a northern coastal area of Taiwan under open sea conditions, whereas the second case study examined an incident within a major commercial harbor in central Taiwan. The second component of this workflow involves creating oil risk maps by integrating the results from the first component with specific geographical factors into Google Earth. These oil risk maps serve multiple purposes. They offer real-time information to emergency response commanders regarding oil spill hazard prediction, and they also enable the effective development of emergency response strategies and disposal plans for potential oil spill incidents. This is achieved by generating risk maps for various scenarios using the approach outlined in the first component. Additionally, these maps assist in the assessment and planning of resource allocation for oil containment.

R. Chapuis

Armin Norouzi, Saeid Shahpouri, David Gordon et al.

Machine learning (ML) and a nonlinear model predictive controller (NMPC) are used in this paper to minimize the emissions and fuel consumption of a compression ignition engine. In this work machine learning is applied in two methods. In the first application, ML is used to identify a model for implementation in model predictive control optimization problems. In the second application, ML is used as a replacement of the NMPC where the ML controller learns the optimal control action by imitating or mimicking the behavior of the model predictive controller. In this study, a deep recurrent neural network including long-short term memory (LSTM) layers are used to model the emissions and performance of an industrial 4.5 liter 4-cylinder Cummins diesel engine. This model is then used for model predictive controller implementation. Then, a deep learning scheme is deployed to clone the behavior of the developed controller. In the LSTM integration, a novel scheme is used by augmenting hidden and cell states of the network in an NMPC optimization problem. The developed LSTM-NMPC and the imitative NMPC are compared with the Cummins calibrated Engine Control Unit (ECU) model in an experimentally validated engine simulation platform. Results show a significant reduction in Nitrogen Oxides (\nox) emissions and a slight decrease in the injected fuel quantity while maintaining the same load. In addition, the imitative NMPC has a similar performance as the NMPC but with a two orders of magnitude reduction of the computation time.

Klaus Schmid, Sascha El-Sharkawy, Christian Kröher

Experimentation with software prototypes plays a fundamental role in software engineering research. In contrast to many other scientific disciplines, however, explicit support for this key activity in software engineering is relatively small. While some approaches to improve this situation have been proposed by the software engineering community, experiments are still very difficult and sometimes impossible to replicate. In this paper, we propose the concept of an experimentation workbench as a means of explicit support for experimentation in software engineering research. In particular, we discuss core requirements that an experimentation workbench should satisfy in order to qualify as such and to offer a real benefit for researchers. Beyond their core benefits for experimentation, we stipulate that experimentation workbenches will also have benefits in regard to reproducibility and repeatability of software engineering research. Further, we illustrate this concept with a scenario and a case study, and describe relevant challenges as well as our experience with experimentation workbenches.

Janusan Baskararajah, Lei Zhang, Andriy Miranskyy

The Software Engineering (SE) community is prolific, making it challenging for experts to keep up with the flood of new papers and for neophytes to enter the field. Therefore, we posit that the community may benefit from a tool extracting terms and their interrelations from the SE community's text corpus and showing terms' trends. In this paper, we build a prototyping tool using the word embedding technique. We train the embeddings on the SE Body of Knowledge handbook and 15,233 research papers' titles and abstracts. We also create test cases necessary for validation of the training of the embeddings. We provide representative examples showing that the embeddings may aid in summarizing terms and uncovering trends in the knowledge base.

Rafael de Mello, Matheus Coelho

Context: Empirical studies in software engineering are typically centered on human subjects, ranging from novice to experienced developers. The experience of these individuals is a key context factor that should be properly characterized for supporting the design of empirical studies and interpreting their results. However, the criteria adopted for characterizing the experience of subjects do not follow a standard and are frequently limited. Goal: Our research aims at establishing an optimized and comprehensive scheme to characterize the subjects' experience for studies in software engineering. Method: Based on previous work, we defined the first version of this scheme, composed of three experience attributes, including time, number of projects, and self-perception. In the last years, we applied the characterization scheme over four empirical studies, reaching the characterization of 79 subjects in three different skills. Results: We found that the attributes from our scheme are positively but moderately correlated. This finding suggests these attributes play a complementary role in characterizing the subjects' experience. Besides, we found that study subjects tend to enumerate the technical diversity of their background when summarizing their professional experience. Conclusion: The scheme proposed represents a feasible alternative for characterizing subjects of empirical studies in the field. However, we intend to conduct additional investigations with developers to evolve it.

Silverio Martínez-Fernández, Justus Bogner, Xavier Franch et al.

AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image- and speech-recognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems. To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state of the art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula.

Egor Dontsov

The purpose of this study is to investigate morphology of simultaneously propagating hydraulic fractures. Simultaneous propagation of hydraulic fractures occurs during stimulation of horizontal wells, and, in particular, several initiation points or perforation intervals along the well are often used to promote the growth of multiple hydraulic fractures at the same time. Numerical simulations demonstrate that there are situations, in which stress interaction between the fractures is minimal and this results in a very similar geometry of each fracture. At the same time, for some other parameters, the stress interaction is very strong, so that the fractures interact with each other and develop complex shapes. By focusing on the constant height hydraulic fractures, it is shown that there is a dimensionless parameter that controls such a behavior. In particular, fractures propagating in the toughness dominated regime lead to complex shapes, while when fluid viscosity dominates the response, then the fractures are more regular and uniform. A series of numerical examples is presented to illustrate the findings.