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Entrapped air pockets can cause failure in water distribution systems if air valves have not been appropriately designed for expelling air during filling manoeuvres performed by water utilities. One-dimensional mathematical models recently developed for studying this phenomenon do not consider the effect of blocking columns inside water pipelines. This research presents the development of a mathematical model for analysing the filling process in a pipeline with an undulating profile with various air valves, including blocking columns during starting-up water installations. The results show how different air pocket pressure peaks can be produced over transient events, which need to be analysed to ensure a successful procedure that guarantees pipeline safety during the pressure surge occurrence. In this study, an experimental set-up is analysed to observe the behaviour of two blocking columns during filling by comparing the air pocket pressure pulses.

Air pockets can become trapped at high points in pipelines with irregular profiles, particularly during service interruptions. The resulting issues, primarily caused by peak pressures generated during pipeline filling, are a well-documented topic in the literature. However, it is surprising that this subject has not received comprehensive attention. Using a model developed by the authors, this paper identifies the key parameters that define the phenomenon, presenting equations in a dimensionless format. The main advantage of this study lies in the ability to easily compute pressure surges without the need to solve a complex system of differential and algebraic equations. Numerous cases of filling operations were analysed to obtain dimensionless charts that can be used by water utilities to compute pressure surges during filling operations. Additionally, it provides charts that facilitate the rapid and reasonably accurate estimation of peak pressures. Depending on their transient characteristics, pressure peaks are either slow or fast, with separate charts provided for each type. A practical application involving a water pipeline with an irregular profile demonstrates the model’s effectiveness, showing strong agreement between calculated and chart-predicted (proposed methodology) values. This research provides water utilities with the ability to select the appropriate pipe’s resistance class required for water distribution systems by calculating the pressure peak value that may occur during filling procedures.

Recent droughts have severely threatened water security in many regions worldwide. Reservoirs, designed to combat droughts and secure water supply partially, are reported failing to fill up to the total capacity due to severe droughts. How bad is climate affecting reservoir filling up on a global scale? This issue has not been studied. We present a big picture of reservoirs in crisis using satellite altimetry. Thanks to the unique characteristics of CryoSat‐2, 525 reservoirs worldwide were investigated during 2010–2022. Results show that most reservoirs (93%) are found not fully filled up at least once. About 21% of reservoirs, which are mainly located in the Southern Hemisphere, show a significant decline in water levels. Moreover, about 20% of reservoirs with larger level fluctuations (>3 m) are located in less developed economies, indicating informed operation rules are needed. Further analyses indicate reservoirs are largely affected by extreme climate events, such as ENSO. Plain Language Summary A reservoir is an artificial lake where water is collected and stored for various purposes, such as flood control, irrigation, hydropower generation, industrial use, etc. In a changing climate, drought events can cause a decline in the natural flow of streams and rivers to the reservoirs. Consequently, many of the functions provided by the reservoir might be halted if the drought continues, just like the cases of Lake Powell and Lake Mead. The past decade saw several record‐breaking global annual temperatures. How have global reservoirs been affected in terms of the filling up? Leveraging more than a decade of CryoSat‐2 altimetry observations, we provided a global picture of this issue. We found that 93% of studied reservoirs have not been fully filled up at least once during 2010–2022. Our analyses revealed that droughts are the most probable culprits. About 86% of the 398 reservoirs with accessible SPEI data exhibited significant susceptibility to drought, while 43% of the 525 reservoirs demonstrated notable sensitivity to ENSO events. These findings have important implications for future reservoir operations to cope with more intensive drought events. It also means the benefits and costs of both existing and planned reservoirs need to be re‐assessed to take adaptation strategies. Key Points Water levels of reservoirs in the southern hemisphere show a declining trend About 93% of the 525 studied reservoirs have not been fully filled up at least once in the past 12 years Less developed economies need to develop informed reservoir operation rules to cope with climate change

A crucial, both from theoretical and practical points of view, problem in preference modelling is the number of questions to ask from the decision maker. We focus on incomplete pairwise comparison matrices based on graphs whose average degree is approximately 3 (or a bit more), i.e., each item is compared to three others in average. In the range of matrix sizes we considered, n=5,6,7,8,9,10, this requires from 1.4n to 1.8n edges, resulting in completion ratios between 33% (n=10) and 80% (n=5). We analyze several types of union of two spanning trees (three of them building on additional ordinal information on the ranking), 2-edge-connected random graphs and 3-(quasi-)regular graphs with minimal diameter (the length of the maximal shortest path between any two vertices). The weight vectors are calculated from the natural extensions, to the incomplete case, of the two most popular weighting methods, the eigenvector method and the logarithmic least squares. These weight vectors are compared to the ones calculated from the complete matrix, and their distances (Euclidean, Chebyshev and Manhattan), rank correlations (Kendall and Spearman) and similarity (Garuti, cosine and dice indices) are computed in order to have cardinal, ordinal and proximity views during the comparisons. Surprisingly enough, only the union of two star graphs centered at the best and the second best items perform well among the graphs using additional ordinal information on the ranking. The union of two edge-disjoint spanning trees is almost always the best among the analyzed graphs.

Quantum computing devices are believed to be powerful in solving hard computational tasks, in particular, combinatorial optimization problems. In the present work, we consider a particular type of the minimum bin packing problem, which can be used for solving the problem of filling spent nuclear fuel in deep-repository canisters that is relevant for atomic energy industry. We first redefine the aforementioned problem it in terms of quadratic unconstrained binary optimization. Such a representation is natively compatible with existing quantum annealing devices as well as quantum-inspired algorithms. We then present the results of the numerical comparison of quantum and quantum-inspired methods. Results of our study indicate on the possibility to solve industry-relevant problems of atomic energy industry using quantum and quantum-inspired optimization.

In this paper, multi-dimensional global optimization problems are considered, where the objective function is supposed to be Lipschitz continuous, multiextremal, and without a known analytic expression. Two different approximations of Peano-Hilbert curve applied to reduce the problem to a univariate one satisfying the Hölder condition are discussed. The first of them, piecewise-linear approximation, is broadly used in global optimization and not only whereas the second one, non-univalent approximation, is less known. Multi-dimensional geometric algorithms employing these Peano curve approximations are introduced and their convergence conditions are established. Numerical experiments executed on 800 randomly generated test functions taken from the literature show a promising performance of algorithms employing Peano curve approximations w.r.t. their direct competitors.

In this paper, the problem of approximating and visualizing the solution set of systems of nonlinear inequalities is considered. It is supposed that left-hand parts of the inequalities can be multiextremal and non-differentiable. Thus, traditional local methods using gradients cannot be applied in these circumstances. Problems of this kind arise in many scientific applications, in particular, in finding working spaces of robots where it is necessary to determine not one but all the solutions of the system of nonlinear inequalities. Global optimization algorithms can be taken as an inspiration for developing methods for solving this problem. In this article, two new methods using two different approximations of Peano–Hilbert space-filling curves actively used in global optimization are proposed. Convergence conditions of the new methods are established. Numerical experiments executed on problems regarding finding the working spaces of several robots show a promising performance of the new algorithms.

The operating limits of oscillating heat pipes (OHP) are crucial for the optimal design of cooling systems. In particular, the dryout limit is a key factor in optimizing the functionality of an OHP. As shown in previous studies, experimental approaches to determine the dryout limit lead to contradictory results. This work proposes a compact theory to predict a dryout threshold that unifies the experimental and analytical data. The theory is based on the influence of vapor quality on the flow pattern. When the vapor quality exceeds a certain limit (x = 0.006), the flow pattern changes from slug flow to annular flow and the heat transfer decreases abruptly. The results indicate a uniform threshold value, which has been validated experimentally and by the literature. With that approach, it becomes possible to design an OHP with an optimized filling ratio and, hence, substantially improve its cooling abilities.

The inherent variability and unpredictability of renewable energy output pose significant challenges to power grid stability. Pumped Storage Power Stations (PSPS) play a pivotal role in mitigating these challenges, enhancing the grid’s reliability and operational efficiency. This study proposes an advanced linear analytical method based on Mixed-Integer Linear Programming (MILP) to optimize the short-term scheduling of PSPSs. The goal is to simultaneously maximize the reduction in equivalent load fluctuations and improve power generation benefits. The model linearizes the objective functions, constraints, and decision variables, applying MILP to efficiently derive optimal dispatch solutions. Using the Heimifeng (HMF) PSPS in Hunan Province as a case study, data from four representative daily load scenarios in 2023 are employed to optimize both power output and pumping processes. The results highlight the following nonlinear, competitive relationship between load fluctuation improvements and power generation benefits: as power benefits increase the rate of improvement in load fluctuations tends to decrease. The optimal solutions demonstrate significant outcomes, with improvements exceeding 11.5% in equivalent load fluctuations across all scenarios and daily power benefits surpassing$41,100, reaching a peak of $ 55,700. This study introduces a robust linear analytical framework capable of simultaneously enhancing power benefits and stabilizing load fluctuations, thereby offering valuable technical support for decision-makers.

This study presents an innovative energy management framework for multi‐microgrids, integrating the burgeoning domain of cryptocurrency mining. Cryptocurrencies, a novel fusion of encryption technology and financial currency, are witnessing exponential global growth. This expansion correlates with a surge in the prevalence of mining activities, amplifying electricity consumption and necessitating accelerated advancements in urban transmission and distribution infrastructures, coupled with increased financial investments. Despite cryptocurrencies' growth, comprehensive research to capitalize on their potential is scarce. This article introduces an operation cost model for miners in the proposed dual‐stage framework. The first stage is dedicated to day‐ahead scheduling, focusing on peak shaving and valley filling in the electricity demand curve, while concurrently optimizing operational costs. The second stage, updating each 5 min, minimizes imbalances in response to uncertain network conditions. A pivotal feature of this framework is the allocation of revenues generated from mining operations towards enhancing renewable energy resources. Empirical simulations underscore the framework's efficacy, evidenced by a substantial peak shaving of 482.833 kW and valley filling of 4084.42 kW. Furthermore, this approach effectively maintains operational costs within a feasible spectrum. Notably, the demand curve's peak‐to‐valley distance extends to 4 MW, with the revenue from mining activities alone sufficient to offset operational expenditures. This study introduces an energy management framework integrating cryptocurrency mining into multi‐microgrids to address the rising electricity consumption associated with mining activities. The framework employs a dual‐stage approach, focusing on day‐ahead scheduling for optimizing operational costs and real‐time adjustments to minimize imbalances in response to uncertain network conditions.