Investigations about the role of nuclei and nucleation for the inception and formation of cavitation have been part of cavitation research since Harvey et al. (J. Cell. Physiol., vol. 24 (1), 1944, pp. 1–22) postulated the existence of gas filled crevices on surfaces and particles in liquids. In a supersaturated liquid, surface nuclei produce small gas bubbles due to mass transfer of gas or themselves work as weak spots in the liquid that are necessary for a phase change under technically relevant static pressures. Although various theories and models about nuclei and nucleation have found their way into standard literature, there is a lack of experimentally validated theories that describe the process of diffusion-driven nucleation in hydrodynamic cavitation. In order to close this gap we give new theoretical insights into the physics of this nucleation mechanism at technically relevant low supersaturations validated with extensive experimental results. The nucleation rate, the number of produced bubbles per second, is proportional to the supersaturation of the liquid and shows a nonlinear dependence on the shear rate at the surface nucleus. A model for the Strouhal number as dimensionless nucleation rate is derived allowing the estimation of nucleation rates from surface nuclei in hydrodynamic cavitation. The model provides three asymptotes, being a function of Péclet number, Weber number, the supersaturation of the liquid $\unicode[STIX]{x1D701}$ and gas solubility $\unicode[STIX]{x1D6EC}$ for three different detachment mechanisms, $Sr\propto \unicode[STIX]{x1D701}\unicode[STIX]{x1D6EC}We^{n}Pe^{1/3}$ with $n=1/3,3/4,1$ . The theoretical findings are in good agreement with experimental results, leading to a new assessment of the role of diffusion in cavitating flows.

Approximately 1 billion slum dwellers worldwide are exposed to increased health risks due to their spatial environment. Recent studies have therefore called for the spatial environment to be introduced as a separate dimension in medical studies. Hence, this study investigates how and on which spatial scale relationships between the settlement morphology and the health status of the inhabitants can be identified. To this end, we summarize the current literature on the identification of slums from a geographical perspective and review the current literature on slums and health of the last five years (376 studies) focusing on the considered scales in the studies. We show that the majority of medical studies are restricted to certain geographical regions. It is desirable that the number of studies be adapted to the number of the respective population. On the basis of these studies, we develop a framework to investigate the relationship between space and health. Finally, we apply our methodology to investigate the relationship between the prevalence of slums and different health metrics using data of the global burden of diseases for different prefectures in Brazil on a subnational level.

The application of mathematical optimization methods for water supply system design and operation provides the capacity to increase the energy efficiency and to lower the investment costs considerably. We present a system approach for the optimal design and operation of pumping systems in real-world high-rise buildings that is based on the usage of mixed-integer nonlinear and mixed-integer linear modeling approaches. In addition, we consider different booster station topologies, i.e. parallel and series-parallel central booster stations as well as decentral booster stations. To confirm the validity of the underlying optimization models with real-world system behavior, we additionally present validation results based on experiments conducted on a modularly constructed pumping test rig. Within the models we consider layout and control decisions for different load scenarios, leading to a Deterministic Equivalent of a two-stage stochastic optimization program. We use a piecewise linearization as well as a piecewise relaxation of the pumps’ characteristics to derive mixed-integer linear models. Besides the solution with off-the-shelf solvers, we present a problem specific exact solving algorithm to improve the computation time. Focusing on the efficient exploration of the solution space, we divide the problem into smaller subproblems, which partly can be cut off in the solution process. Furthermore, we discuss the performance and applicability of the solution approaches for real buildings and analyze the technical aspects of the solutions from an engineer’s point of view, keeping in mind the economically important trade-off between investment and operation costs.

According to the United Nations, about 1 billion persons live in so-called slums. Numerous studies have shown that this population is particularly vulnerable to infectious diseases. The current COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, emphatically underlines this problem. The often high-density living quarters coupled with a large number of persons per dwelling and the lack of adequate sanitation are reasons why measures to contain the pandemic only work to a limited extent in slums. Furthermore, assignment to risk groups for severe courses of COVID-19 caused by noncommunicable diseases (eg, cardiovascular diseases) is not possible due to inadequate data availability. Information on people living in slums and their health status is either unavailable or only exists for specific regions (eg, Nairobi). We argue that one of the greatest problems with regard to the COVID-19 pandemic in the context of slums in the Global South is the lack of data on the number of people, their living conditions, and their health status.

More than 900 million people worldwide live in slums. These slums mainly can be found in cities of the global south and are characterized by poor living conditions and usually insufficient access to basic infrastructure such as water or energy. In order to improve the living conditions of slum inhabitants, information about the number, location and size of the slums is required to plan supply infrastructure. We therefore identify morphological slums in eight different cities in Africa, South America and Asia, using remote sensing data and analyse their size distributions. We show that 84.6% of all observed morphological slums have a size between 0.001 and 0.1 km². These results rely on a consistent approach using a clear ontology and conceptual frame for classification. However, classification methods for these underserved areas differ. We show slum classifications based on different methods reveal a strong dependency between the particular method and the resulting size distribution. The study shows the relevance of remote sensing for the investigation of slums and the results can be used for infrastructure planning, as infrastructure improvement projects are often limited to the large known slums. Whereas, the large number of small slums distributed across the city is often neglected.