Hasil untuk "Hydraulic engineering"

Ian A. Cosden, Elizabeth Holtz, Joel U. Bretheim

Research Software Engineers (RSEs) have become indispensable to computational research and scholarship. The fast rise of RSEs in higher education and the trend of universities to be slow creating or adopting models for new technology roles means a lack of structured career pathways that recognize technical mastery, scholarly impact, and leadership growth. In response to an immense demand for RSEs at Princeton University, and dedicated funding to grow the RSE group at least two-fold, Princeton was forced to strategize how to cohesively define job descriptions to match the rapid hiring of RSE positions but with enough flexibility to recognize the unique nature of each individual position. This case study describes our design and implementation of a comprehensive RSE career ladder spanning Associate through Principal levels, with parallel team-lead and managerial tracks. We outline the guiding principles, competency framework, Human Resources (HR) alignment, and implementation process, including engagement with external consultants and mapping to a standard job leveling framework utilizing market benchmarks. We share early lessons learned and outcomes including improved hiring efficiency, clearer promotion pathways, and positive reception among staff.

Lucca Maitan, Lucas Toschi, Cícero Zanette et al.

Force control in hydraulic actuators is notoriously difficult due to strong nonlinearities, uncertainties, and the high risks associated with unsafe exploration during learning. This paper investigates safe reinforcement learning (RL) for hy draulic force control with real-world training using contraction metric certificates. A data-driven model of a hydraulic actuator, identified from experimental data, is employed for simulation based pretraining of a Soft Actor-Critic (SAC) policy that adapts the PI gains of a feedback-linearization (FL) controller. To reduce instability during online training, we propose a quadratic-programming (QP) contraction filter that leverages a learned contraction metric to enforce approximate exponential convergence of trajectories, applying minimal corrections to the policy output. The approach is validated on a hydraulic test bench, where the RL controller is trained directly on hardware and benchmarked against a simulation-trained agent and a fixed-gain baseline. Experimental results show that real-hardware training improves force-tracking performance compared to both alternatives, while the contraction filter mitigates chattering and instabilities. These findings suggest that contraction-based certificates can enable safe RL in high force hydraulic systems, though robustness at extreme operating conditions remains a challenge.

Xiaofeng Liu, Yong G. Lai

Traditional 2D hydraulic models face significant computational challenges that limit their applications that are time-sensitive or require many model evaluations. This study presents a physics-informed Deep Operator Network (DeepONet) framework for computational hydraulics modeling that learns the solution operator of the 2D shallow water equations (SWEs) to create fast surrogate models. The framework can operate in two modes: a purely data-driven SWE-DeepONet that learns from numerical solver such as SRH-2D, and a physics-informed PI-SWE-DeepONet that additionally incorporates the continuous SWEs as constraints during training. Based on a real-world case, steady flows in a reach of the Sacramento River in California, it is demonstrated that PI-SWE-DeepONet possesses much enhanced prediction capability than SWE-DeepONet when applied to out-of-distribution scenarios. The physics-informed model is shown to exhibit slower error growth and larger breakdown distances in comparison with SWE-DeepONet. The gain of the physics-informed training, however, comes with costs, chief among which are the simulated results have slightly higher errors for in-distribution cases. It reflects the existence of a tension between the two competing training objectives: fitting the results from the traditional hydraulic model and satisfying the continuous governing equations. In this study, guidelines are developed for selecting the appropriate approach based on a real-world case: PI-SWE-DeepONet is preferred for out-of-distribution predictions, uncertain training data, or when physical consistency is a priority, while SWE-DeepONet is recommended if the modeling objective is to replicate faithfully the traditional hydraulic model results within the training distribution. Other challenges are also discussed, such as the loss weighting approach.

Lusha Jiang, Hui Wang, Yu Miao et al.

Polymer-modified bentonite (PMB) is much more effective at containing chemically aggressive liquids than conventional bentonite. The PMB manufacturing process typically utilizes natural, high-quality sodium bentonite (NaB) owing to its excellent hydrophilicity and swelling capacity. However, calcium bentonite (CaB), which is much more abundant worldwide, is rarely used for containment applications owing to its poor hydrophilicity. This study proposed a polymerization method that transforms sodium-activated calcium bentonite (NCB) into PMB to achieve low hydraulic conductivity (k) to aggressive liquids. The mechanism for its low k was revealed through characterization techniques and analyses (e.g. X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and Brunauer-Emmett-Teller (BET)). The results showed that the PMB had a small amount of polymer elution (indicating better interface stability) and thus exhibited excellent barrier properties under chemically aggressive conditions, with the k of <10−11 m/s for 0.6 mol/L NaCl solution, which is four orders of magnitude lower than that of the NCB (k = 3 × 10−7 m/s). Various microscopic analyses indicated that the selected monomers were successfully polymerized, and intercalated into and grafted onto the montmorillonite layers of bentonite. The formed polymer network increased the swelling capability of PMB granules, decreased the pore size, and created narrow and tortuous flow pathways leading to a very low k to aggressive liquids.

Sharon Guardado, Risha Parveen, Zheying Zhang et al.

The integration of Large Language Models (LLMs) in Requirements Engineering (RE) education is reshaping pedagogical approaches, seeking to enhance student engagement and motivation while providing practical tools to support their professional future. This study empirically evaluates the impact of integrating LLMs in RE coursework. We examined how the guided use of LLMs influenced students' learning experiences, and what benefits and challenges they perceived in using LLMs in RE practices. The study collected survey data from 179 students across two RE courses in two universities. LLMs were integrated into coursework through different instructional formats, i.e., individual assignments versus a team-based Agile project. Our findings indicate that LLMs improved students' comprehension of RE concepts, particularly in tasks like requirements elicitation and documentation. However, students raised concerns about LLMs in education, including academic integrity, overreliance on AI, and challenges in integrating AI-generated content into assignments. Students who worked on individual assignments perceived that they benefited more than those who worked on team-based assignments, highlighting the importance of contextual AI integration. This study offers recommendations for the effective integration of LLMs in RE education. It proposes future research directions for balancing AI-assisted learning with critical thinking and collaborative practices in RE courses.

Liyuan Chen, Shuoling Liu, Jiangpeng Yan et al.

The advent of foundation models (FMs), large-scale pre-trained models with strong generalization capabilities, has opened new frontiers for financial engineering. While general-purpose FMs such as GPT-4 and Gemini have demonstrated promising performance in tasks ranging from financial report summarization to sentiment-aware forecasting, many financial applications remain constrained by unique domain requirements such as multimodal reasoning, regulatory compliance, and data privacy. These challenges have spurred the emergence of financial foundation models (FFMs): a new class of models explicitly designed for finance. This survey presents a comprehensive overview of FFMs, with a taxonomy spanning three key modalities: financial language foundation models (FinLFMs), financial time-series foundation models (FinTSFMs), and financial visual-language foundation models (FinVLFMs). We review their architectures, training methodologies, datasets, and real-world applications. Furthermore, we identify critical challenges in data availability, algorithmic scalability, and infrastructure constraints and offer insights into future research opportunities. We hope this survey can serve as both a comprehensive reference for understanding FFMs and a practical roadmap for future innovation.

Davide Venturelli, Erik Gustafson, Doga Kurkcuoglu et al.

We review the prospects to build quantum processors based on superconducting transmons and radiofrequency cavities for testing applications in the NISQ era. We identify engineering opportunities and challenges for implementation of algorithms in simulation, combinatorial optimization, and quantum machine learning in qudit-based quantum computers.

Nazanin Ahmadi, Qianying Cao, Jay D. Humphrey et al.

Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws with data-driven methods. Here, we review three main classes of PIML frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and neural operators (NOs), highlighting their growing role in biomedical science and engineering. We begin with PINNs, which embed governing equations into deep learning models and have been successfully applied to biosolid and biofluid mechanics, mechanobiology, and medical imaging among other areas. We then review NODEs, which offer continuous-time modeling, especially suited to dynamic physiological systems, pharmacokinetics, and cell signaling. Finally, we discuss deep NOs as powerful tools for learning mappings between function spaces, enabling efficient simulations across multiscale and spatially heterogeneous biological domains. Throughout, we emphasize applications where physical interpretability, data scarcity, or system complexity make conventional black-box learning insufficient. We conclude by identifying open challenges and future directions for advancing PIML in biomedical science and engineering, including issues of uncertainty quantification, generalization, and integration of PIML and large language models.

Fabian C. Peña

Large Language Models (LLMs) are revolutionizing software engineering (SE), with special emphasis on code generation and analysis. However, their applications to broader SE practices including conceptualization, design, and other non-code tasks, remain partially underexplored. This research aims to augment the generality and performance of LLMs for SE by (1) advancing the understanding of how LLMs with different characteristics perform on various non-code tasks, (2) evaluating them as sources of foundational knowledge in SE, and (3) effectively detecting hallucinations on SE statements. The expected contributions include a variety of LLMs trained and evaluated on domain-specific datasets, new benchmarks on foundational knowledge in SE, and methods for detecting hallucinations. Initial results in terms of performance improvements on various non-code tasks are promising.

LIU Juncai, DONG You

ObjectiveTransmission tower-line structures are representative spatial distribution systems. They typically span hundreds or even thousands of kilometers and inevitably traverse regions with high seismic intensities. Characterized by high flexibility, long spans, and strong nonlinearity, the transmission tower-line system is prone to damage or even collapse ender earthquakes, which can result in regional power outages and substantial economic losses. A substantial amount of historical seismic data has indicated that ground motion generally occurs in the form of sequences, with randomness and multi-dimensional characteristics. Employing only single mainshocks with different input directions to excite the transmission tower-line systems fails to precisely capture the impact of the input directionality of sequential earthquakes on their dynamic responses. Therefore, it is essential to explore the seismic performance and failure risk level of the transmission tower-line system under the excitation of sequential earthquakes from various input directions.MethodsFirstly, taking into account the intricate tower-line coupling effect, a refined finite element model of the transmission tower-line system is developed using ABAQUS software. A nonlinear hysteretic model is employed to mimic the complex mechanical behavior of steel members, encompassing buckling and post-buckling phenomena, as well as the degradation of strength and stiffness. Subsequently, a series of actual mainshocks and their corresponding aftershocks are chosen from the Pacific ground motion database to formulate real mainshock-aftershock sequences. In accordance with the earthquake damage degree and nonlinear pushover analysis of the transmission tower, the multi-objective seismic performance states are defined, and the corresponding state limits are quantified. Owing to the symmetrical nature of the two main axes of the transmission tower-line system, this paper opts to analyze the dynamic response of the transmission tower-line system under the excitation of the mainshock-aftershock sequences from five input directions. Based on the hierarchical approach of the probabilistic demand model, fragility probability, and seismic risk level, the influence pattern of sequential seismic directivity on the seismic performance of the transmission tower-line system is uncovered.Results and Discussions　When compared to the inter-segment drift ratios (<italic>I</italic>_SDR) under single mainshocks, the <italic>I</italic>_SDR of the transmission tower-line system under sequential earthquakes are larger. This phenomenon indicates that aftershocks cause additional damage to the transmission tower-line system. When the peak ground acceleration (<italic>P</italic>_GA) increases to 0.6<italic>g</italic>, the <italic>I</italic>_SDR changes of the transmission tower-line system under single mainshocks and sequential earthquakes are basically consistent. The maximum <italic>I</italic>_SDR occurs between the second and third segments, suggesting that these two segments are the weak regions of the transmission tower. Regarding the different ground motion input directions considered, 16%, 50%, and 84% of the quintile incremental dynamic analysis (IDA) curves of the transmission tower-line system are all below the IDA curves for single mainshocks. This indicates that sequential earthquakes with smaller <italic>P</italic>_GA can lead to larger <italic>ISDR</italic>s, further highlighting the weakening effect of subsequent aftershocks on the seismic performance of the transmission tower-line system. For the IDA curve with a 50% quintile value, the <italic>I</italic>_SDR under a <italic>P</italic>_GA of 0.6<italic>g</italic> ranges from 1.3% to 1.9% under different input directions, suggesting that the seismic directionality has certain impacts on the seismic performance of the transmission tower-line system. When single mainshocks with a <italic>P</italic>_GA of 0.8<italic>g</italic> are input along the five studied directions, the probabilities of the transmission tower-line system reaching or exceeding the collapse state (<italic>D</italic>_S4) are 0.60, 0.54, 0.59, 0.53, and 0.55 respectively. For the sequential earthquakes, the corresponding fragility probabilities are 0.74, 0.73, 0.75, 0.75, and 0.77 respectively. The fragility probability increases by 41.5%, which implies that subsequent aftershocks exacerbate the damage state of the transmission tower-line system and increase its risk of collapse. Compared to the results under single mainshocks, the annual risk probability curves of the <italic>ISDR</italic> of the transmission tower-line system under different input directions are all higher. The differences between the two annual risk probability curves become more significant as the <italic>I</italic>_SDR increases. Taking <italic>I</italic>_SDR=4.0% as an example, the annual risk probability of the transmission tower-line system increases by 0.91%, 3.89%, 1.83%, 3.20%, and 3.70% respectively under sequential earthquakes with input directions of 0, 22.5°, 45.0°, 67.5°, and 90.0°. This further emphasizes that subsequent aftershocks increase the earthquake damage probability of the transmission tower-line system. Compared to single mainshocks, when the input direction is 67.5° and the design reference period is increased from 1 year to 100 years, the risk probability of the transmission tower-line system reaching different performance states increases by 1.28%, 2.52%, 3.13%, 3.62%, and 4.05% respectively. Moreover, for all performance states, the risk probability curve of earthquake damage of the transmission tower-line system under different input directions shows an arc-shaped pattern, indicating that the input direction has relatively little influence.ConclusionsIn the case where the transmission tower-line system is experiencing slight damage (<italic>D</italic>_S1), the variation in the fragility curves across different input directions of ground motions is relatively small. As the performance state shifts from <italic>D</italic>_S1 to <italic>D</italic>_S4, the discrepancy between the vulnerability curves corresponding to different input directions becomes increasingly pronounced. For all performance states, an input direction of 90.0° proves to be the least favorable input direction. As the earthquake intensity rises, the difference in the risk level of the transmission tower-line system between the mainshock-aftershock sequences and single mainshocks first increases and then decreases. This highlights the necessity of taking subsequent aftershocks into account. It is recommended that the impacts of sequential aftershocks be considered when the transmission tower is in a moderate or severe damage state.

Hehua Zhu, Jinxiu Yan, Wenhao Liang

Social development has led to the placement of high standards on ultra-long and ultra-deep mountain tunnels. Disasters may be encountered during the construction and maintenance of such mountain tunnels due to high geostress, high geotemperature, high hydraulic pressure, and special adverse strata, in addition to various other problems caused by engineering activities. To deal with uncertain geological conditions during mountain tunnel construction, comprehensive geological prediction, refined monitoring, and dynamic design and construction methods based on information technology should be adopted. For the operation and maintenance of ultra-long tunnels, the concepts of dynamic evacuation rescue, active protection, energy conservation, and environmental protection should be fully embodied in order to address significant problems related to ventilation, rescue situations, and energy consumption. Moreover, integrated construction and maintenance should be carried out to achieve digital sensing and intelligent maintenance. New ideas and technologies should be adopted to improve the quality and efficiency of the whole process of construction and operation, and to enable the construction of environmentally friendly tunnels, thus achieving the ultimate goals of safety, efficiency, greenness, and intelligence for ultra-long and ultra-deep rock tunnels. With the development of society, the economy, and transportation networks, the construction of ultra-long and ultra-deep tunnels through mountains has become increasingly inevitable. Ultra-long and ultra-deep tunnels are generally defined as tunnels that have a length exceeding 10 km and a depth exceeding 500 m [1]. Mountain tunnels mainly consist of road tunnels, railway tunnels, and hydraulic tunnels. Although an ultra-long and ultra-deep tunnel potentially features the advantages of safety, environmental friendliness, and speed, the cost and difficulty of project establishment, construction, and operation are considerable. Table 1 lists the ultra-long and ultra-deep mountain tunnels that have already been built or are under construction around the world. According to an incomplete survey, there are 56 ultra-long and ultra-deep mountain tunnels in China and 21 abroad. Among these tunnels, the 57.1 km Gotthard Basis Tunnel is the longest and deepest in the world, the 32.7 km New Guanjiao Tunnel is the world’s longest tunnel above 3000 m, the 18 km Highway Tunnel of Qinling Zhongnan Mountain is the world’s longest double-line highway tunnel, and the 16 km Qinling Tianhuashan Tunnel is Asia’s longest single-hole two-lane high-speed rail tunnel. With the increasing demand for and ongoing progress in tunnel construction technologies, the construction of ultra-long and ultra-deep mountain tunnels will usher in new development opportunities. Due to the high geostress, high geotemperature, and ultra-long construction and operation, these complex tunnel projects must handle unprecedented challenges in terms of design, construction, operation, and maintenance, which demand new ideas and engineering measures.

Daniel R. Clarkson, Lawrence A. Bull, Chandula T. Wickramarachchi et al.

Regression is a fundamental prediction task common in data-centric engineering applications that involves learning mappings between continuous variables. In many engineering applications (e.g.\ structural health monitoring), feature-label pairs used to learn such mappings are of limited availability which hinders the effectiveness of traditional supervised machine learning approaches. The current paper proposes a methodology for overcoming the issue of data scarcity by combining active learning with hierarchical Bayesian modelling. Active learning is an approach for preferentially acquiring feature-label pairs in a resource-efficient manner. In particular, the current work adopts a risk-informed approach that leverages contextual information associated with regression-based engineering decision-making tasks (e.g.\ inspection and maintenance). Hierarchical Bayesian modelling allow multiple related regression tasks to be learned over a population, capturing local and global effects. The information sharing facilitated by this modelling approach means that information acquired for one engineering system can improve predictive performance across the population. The proposed methodology is demonstrated using an experimental case study. Specifically, multiple regressions are performed over a population of machining tools, where the quantity of interest is the surface roughness of the workpieces. An inspection and maintenance decision process is defined using these regression tasks which is in turn used to construct the active-learning algorithm. The novel methodology proposed is benchmarked against an uninformed approach to label acquisition and independent modelling of the regression tasks. It is shown that the proposed approach has superior performance in terms of expected cost -- maintaining predictive performance while reducing the number of inspections required.

Yanpeng Cai, Yueying Yang, Qian Tan et al.

Abstract In this research, a modeling approach of rainfall generator coupled with high resolution rainfall products were proposed to generate designed rainfall events under multiple spatial and temporal distributions, which was then employed to analyze the impacts of spatial and temporal rainfall heterogeneities on peak runoff for watersheds. Three scenarios were developed under multiple degrees of impermeable underlying surface areas within an urban watershed in south China. Detailed runoff processes were analyzed through the adoption of a distributed hydrological model (GSSHA). A covariance analysis method combined with rainfall spatio‐temporal heterogeneity characteristic were used to quantify heterogeneity effects on peak runoff. Results indicated that coupling short period (2008–2016) remotely rainfall data and RainyDay results could successfully reproduce designed rainfall events, spatio‐temporal heterogeneity of rainfall contributed significantly to the peak runoff, which was greater than those by rainfall duration and capacity, and the increase in impermeable underlying surface enhanced the complexities of the effects. Over each rainfall duration with increasing rainfall return period, the indicator of rainfall peak coefficient (RWD) would decrease and then increase. Regarding the total rainfall center (tg), 25 mm/h threshold rainfall spatial coverage (A25) decreased with increasing imperviousness, 1‐h maximum rainfall (Rmax) surged with increasing imperviousness at rainfall duration of 2 and 24 h. Innovations of this research lied in: combination of a rainfall generator model based on a stochastic storm transposition technique and remote‐sensing rainfall data to generate designed rainfall events, a rainfall spatial and temporal heterogeneities index system was developed to reveal how the changing characteristics of rainfall distribution and the impacts on peak runoff, and in‐depth analysis of the impacts on runoff peak under multiple urban development scenarios for increasing capability in flood control/prevention.

Yuhao Zhang, Ru Guo, Kaiming Peng et al.

In the context of global efforts to address climate change, research into regional carbon neutrality strategies has become especially critical. For developing countries and regions, it is essential to scientifically and rationally assessing the paths for small-scale regional transformations under carbon neutrality imperatives to effectively implement low-carbon transition measures. This study utilizes Chongming District in Shanghai of China as a case to establish a framework for forecasting carbon emission and sink from a multi-dimensional natural-social perspective. This facilitates the simulation and optimization of pathways for carbon neutrality transformation. The results indicate: (1) From 2000 to 2020, the total regional carbon emission exhibited a rising trend, while the total carbon sink initially declined then increased, indicating potential enhancement zone with significant potential and space for carbon neutrality development. (2) Enhanced management of ecological spaces and land use planning result in notable increases in carbon sink. Strategic measures such as emission and consumption reductions, alongside energy transitions, effectively controlled carbon emission growth and facilitated comprehensive decarbonization. (3) By combining ecological priority with enhanced control and balanced development with enhanced control, the region can achieve carbon neutrality. This showcases the effective role of policy regulation in facilitating high-quality carbon–neutral transformations. (4) Effective ecosystem management along with robust reduction and transition strategies enable county-level carbon–neutral transformations, offering a model and methodological support for other developing regions facing the twin challenges of economic growth and environmental sustainability.

Henry Bory Prevez, Angel Orlando Castellano Sánchez

Una central maremotérmica es un sistema capaz de aprovechar los gradientes térmicos oceánicos para producir electricidad. Se trata de una máquina térmica en la que el agua superficial actúa como fuente de calor, mientras que el agua extraída de las profundidades actúa como refrigerante. El objetivo es modelar matemáticamente las centrales maremotérmicas de ciclo abierto o de Anderson emplazadas en la costa y conectadas al sistema eléctrico. Para esto se revisó la bibliografía relacionada con los componentes de esta central, se modeló matemáticamente cada componente, a partir del diagrama funcional propuesto por los autores de este trabajo, se obtuvo el modelo de dichas centrales en MatLab/SimulinkÒ, lo que permitió determinar para un escenario dado la potencia producida por el generador y la entrega de energía eléctrica al sistema eléctrico.

حلیمه پیری, اسماعیل میر

در این پژوهش اثر بیوچار بر کارایی مصرف آب آبیاری و کارایی مصرف نیتروژن در سطوح مختلف آبی و کود نیتروژن برای گیاه کارلا در شهرستان زاهدان موردبررسی قرار گرفت. آزمایش در شرایط گلخانه به­صورت فاکتوریل و در قالب طرح کاملاًکاملاًکاملاً تصادفی با سه تکرار (کاشت بهمن‌ماه 1398 و برداشت فروردین‌ماه 1399) انجام شد. تیمارها شامل سه تیمار آب آبیاری ((I1)50، (I2)75 و 100(I3) درصد مقدار آب آبیاری، چهار تیمار بیوچار (صفر (B1)، 25/1 (B2)، 5/2 (B3) و 5 (B4) درصد وزنی خاک گلدان) و سه تیمار کود نیتروژن (50 (N1)، 75 (N2) و 100 (N3) درصد نیاز کودی گیاه) بود. سطوح تنش آبی در طول فصل رشد ﺑﺎ ﺗﻮزﯾﻦ روزاﻧﻪ ﮔﻠﺪانﻫﺎ اﻋﻤﺎل ﺷﺪ. برداشت هر هفته یک­بار انجام شد. در مجموع پنج بار برداشت انجام شد. عملکرد و کارایی مصرف آب آبیاری و کارایی مصرف نیتروژن و شوری خاک در پایان فصل کشت در هر تیمار محاسبه شد. هم‌چنین مقدار نیتروژن خاک و قند میوه نیز در هر برداشت اندازه­گیری شد. نتایج نشان داد اثرات سطوح آب آبیاری و بیوچار در سطح احتمال یک و پنج درصد بر پارامترهای اندازه­گیری‌شده معنی­دار بود. بیش‌ترین مقدار عملکرد (5/15 تن در هکتار) از تیمار 100 درصد مقدار آب آبیاری حاصل شد که از این نظر با تیمار 75 درصد آب آبیاری معنی­دار نبود. استفاده از بیوچار تا سطح 5/2 درصد وزنی خاک باعث افزایش عملکرد شد. استفاده بیش‌تر بیوچار (5 درصد وزنی خاک) باعث کاهش عملکرد گیاه شد. بیش‌ترین کارایی مصرف آب (14/3 کیلوگرم بر مترمکعب) و کارایی مصرف نیتروژن (55/94 کیلوگرم بر کیلوگرم) با مصرف 75 درصد کود نیتروژن (150 کیلوگرم در هکتار) و 5/2 درصد وزنی بیوچار به­دست آمد. استفاده از مقدار مناسب بیوچار ﺳﺒﺐ ﮐﺎﻫﺶ اﺛﺮات ﻣﻨﻔﯽ ﺗﻨﺶ رﻃﻮﺑﺘﯽ در ﻣﻘﺎﯾﺴﻪ ﺑﺎ ﺷﺎﻫﺪ ﺷﺪ. ﺑﻨﺎﺑﺮاﯾﻦ ﮐﺎرﺑﺮد آن ﺑﺮای ﮔﯿﺎه و ﺑﻪوﯾﮋه در ﺷﺮاﯾﻄﯽ ﮐﻪ ﮔﯿﺎه ﺗﺤﺖ ﺗﻨﺶ ﺧﺸﮑﯽ اﺳﺖ و ﯾﺎ در ﮔﻠﺨﺎﻧﻪﻫﺎ و ﺧﺰاﻧﻪﻫﺎ ﺑﻪ­ﻣﻨﻈﻮر ﮐﺎﻫﺶ ﻣﯿﺰان آب ﻣﺼﺮﻓﯽ و ﺑﻬﺒﻮد ﻋﻤﻠﮑﺮد ﮔﯿﺎه ﻗﺎﺑﻞ‌ﺗﻮﺻﯿﻪ ﻣﯽﺑﺎﺷﺪ، ﻫﺮﭼﻨﺪ ﭘﯿﺸﻨﻬﺎد ﻣﯽﺷﻮد آزﻣﺎﯾﺶ در ﺷﺮاﯾﻂ ﻣﺰرﻋﻪ ﻧﯿﺰ اﻧﺠﺎم ﺷﻮد.

S. Mitoulis, S. Argyroudis, M. Loli et al.

Abstract Bridges are the most vulnerable assets of our transport networks. They are disproportionately exposed to and hit by multiple natural hazards, with flooding being the leading cause of bridge failures in the world. Their performance is constantly challenged by the combined effects of natural hazard stressors, e.g. flash floods, exacerbated by climate change, ageing, increasing traffic volumes and loads. Bridges are vulnerable to scour and other flood-related impacts, such as hydraulic forces and debris accumulation. In order to assess and quantify the resilience of flood-critical bridges and subsequently deploy bridge resilience models aiming at building resilience into transport networks, it is essential to use reliable fragility, capacity restoration and traffic reinstatement metrics and models. It is surprising that, despite the importance of bridges and their high vulnerability to hydraulic actions, there are no available recovery models. The latter can help quantify the pace of post-flood capacity and functionality gain for facilitating well-informed decision making for reliable prioritisation and efficient allocation of resources in transport networks. The main barrier is the nature and complexity of recovery actions, which encompass engineering, operational, owner resources and organisational challenges, among others. This paper, for the first time in the international literature, aims at filling this gap by generating a set of reliable recovery models that include both bridge reinstatement (traffic capacity) and restoration (structural capacity) models based on a detailed questionnaire that elicits knowledge from experts. Recovery models are then presented and validated for spread and deep foundations for a typical reinforced concrete bridge, including restoration task prioritisation and scheduling, inter-task dependencies, idle times, durations and cost ratios for different damage levels, as well as the evolution of traffic capacity after floods.

Tao You, Keita Yoshioka

Though a number of formulations have been proposed for phase--field models for hydraulic fracture, the definition of the degraded poroelastic strain energy varies from one model to another. This study explores previously proposed forms of the poroelastic strain energy with diffused fracture and assesses their ability to recover the explicit fracture opening aperture. We then propose a new form of degraded poroelastic strain energy derived from micromechanical analyses. Unlike the previously proposed models, our poroelastic strain energy degradation depends not only on the phase--field variable (damage) but also on the type of strain energy decomposition. Comparisons against closed form solutions suggest that our proposed model can recover crack opening displacement more accurately irrespective of Biot's coefficient or the pore--pressure distribution. We then verify our model against the plane strain hydraulic fracture propagation, known as the KGD fracture, in the toughness dominated regime. Finally, we demonstrate the model's ability to handle complex hydraulic fracture interactions with a pre--existing natural fracture.

Mateusz Hämmerling, Natalia Walczak, Tomasz Kałuża

The operation of water structures causes various problems. They are related, for example, to the material carried by the water, hydrological conditions, range of operation of hydroelectric turbines, or water elevations at the lower position of the hydroelectric power plant. Among the various operational problems, this article focuses mainly on the impact of the backwater of Gwda river on the water level elevations at the lower station of the Stary Młyn hydropower plant in Dobrzyca. The power plant is located on Głomia river. The analysis was carried out for different flow variants in both the Gwda and Głomia rivers. The effect of characteristic flows on the water surface level at the lower station of the hydropower plant was examined. It was found that the water surface level at the lower station of the hydropower plant is strongly influenced by flows higher than the average high flow on Gwda river. Due to the extent of the backwater in current operating conditions, the hydroelectric power plant is shut down from flows on Gwda river of 30–28 m³/s (flows that are not much higher than the multi-year average SSQ). The modeling results were confirmed by an analysis of power plant shutdowns of normal operation especially in wet years, when the plant did not operate for almost half of the year (188 days), with losses of 203 MWh. It was also shown that even a small additional damming of water, e.g., of the order of 0.2 m, can extend the operating time of a power plant up to 249 days even under unfavorable hydrological conditions. Factors related to climate change are beginning to play an increasingly important role in the current operating conditions of small lowland hydroelectric power plants. They can contribute to a reduction in electricity production. The proposed solution related to the possibility of greater water retention on dammed-up water barrages allows one to partially offset these problems as well.

Nanée Chahinian, Matias Alcoba, Ndji dit Jacques Dembélé et al.

Abstract Devastating floods have plagued many West African cities in the past decades. In an attempt to reduce flood damage in Bamako (Mali), an early warning system (EWS) demonstrator (Raincell App) was developed for flash floods. On 16 May 2019, while the demonstrator was partially operational, an intense rainfall event led to devastating floods. We carried out an experience feedback on this flood event by comparing EWS simulations to the results of a field survey. Given the synoptic situation and the rapid development pattern of the storm, none of the global forecasting systems were able to foresee its occurrence and magnitude. The hydrological model developed as part of the demonstrator correctly identified most of the locations where overbank flow occurred. In the absence of data, the predicted discharge and volume values could not be validated. However, they are realistic based on the water levels reported in the Post‐Disaster Needs Assessment report. It would be advisable to couple it to a two‐dimensional hydraulic model and add discharge and water level monitoring to the already existing rainfall surveillance scheme to further improve the system's performance. Increasing the local population's awareness of the dangers of clogged waterways is also mandatory.