Florian Brokhausen’s research while affiliated with Technische Universität Berlin and other places

Due to the heterogeneous nature of wastewater, the operation of wastewater pumps exhibits challenges beyond the optimization of efficiency and hydraulic performance. Instead, operationality is a paramount feature. The main cause for malfunctions in wastewater pumps is the phenomenon of clogging of the pump impeller to a state of total blockage. There exist automatic procedures to resolve this type of clogging, most prevailing the so-called cleaning sequence. However, the current implementation in practice exhibits the downside of under-determined triggering solutions. This work presents a step towards the identification of the clogging state of a pump and the subsequent leverage as a trigger for an automated cleaning sequence. By analyzing the discharge pressure of an exemplary wastewater pump in the frequency domain, intricate characteristics in salient frequencies are demonstrated to be an indicative proxy for the clogging state of the pump impeller. In detail, the pump under observation showed a highly increased amplitude at the blade passing frequency as well as its second harmonic. Compared to clear water operation, the magnitude of these pressure pulsations showed an average increase of over 600% due to clogging of the impeller.

Auf dem Prüfstand am Fachgebiet Fluidsystemdynamik der TU Berlin wurden bislang über 60 Pumpen im langjährigen Betrieb einem standardisierten Testverfahren unterzogen. Daraus gewonnene Daten und Erkenntnisse sind für einen effizienten Pumpenbetrieb wichtig.

The effects of clogging in pumps are manifold and change over time. This is demonstrated with time-resolved observations of the operating data on a test rig at the chair of fluid system dynamics of the Technische Universität Berlin. On the test rig, the head, flow, power consumption and efficiency of different pumps operating with artificial wastewater are recorded. In the data, different clogging behavior reflect in the operating parameters. There are three major clogging-induced characteristics to be differentiated in the time-resolved measurements. For some aggregates there is a rapid drop in the hydraulic parameters right from the start due to initial clogging, with following stagnating performance. This is showcased on a semi-open two-channel impeller, where efficiency drops by 40 % at the very beginning of the measurement and subsequently stagnates. In other cases, like demonstrated here on a closed two-channel impeller, the operating parameters decline continuously over the entire measurement period due to increasing clogging. In the showcased example, the efficiency drops by over 40% in the first six minutes of the measurement due to initial clogging and keeps declining till the end for another 6%. Lastly, there are cases where self-cleansing mechanisms are identified. It is further shown that this phenomenon also highly varies in its timing and hydraulic effects. Finally, it is concluded that all operating parameters must be monitored to generate sufficient knowledge about the clogging behavior of a given aggregate. Additionally, longer observation times are necessary to sufficiently identify effects like the self-cleansing of an impeller.

The continuous testing of small changes to systems has proven to be useful and is widely adopted in the development of software systems. For this, software is tested in environments that are as close as possible to the production environments. When testing IoT systems, this approach is met with unique challenges that stem from the typically large scale of the deployments, heterogeneity of nodes, challenging network characteristics, and tight integration with the environment among others. IoT test environments present a possible solution to these challenges by emulating the nodes, networks, and possibly domain environments in which IoT applications can be executed. This paper gives an overview of the state of the art in IoT testing. We derive desirable characteristics of IoT test environments, compare 18 tools that can be used in this respect, and give a research outlook of future trends in this area.KeywordsInternet of ThingsCyber-physical systemsFog computingEdge computingTestingIterative software development

The continuous testing of small changes to systems has proven to be useful and is widely adopted in the development of software systems. For this, software is tested in environments that are as close as possible to the production environments. When testing IoT systems, this approach is met with unique challenges that stem from the typically large scale of the deployments, heterogeneity of nodes, challenging network characteristics, and tight integration with the environment among others. IoT test environments present a possible solution to these challenges by emulating the nodes, networks, and possibly domain environments in which IoT applications can be executed. This paper gives an overview of the state of the art in IoT testing. We derive desirable characteristics of IoT test environments, compare 18 tools that can be used in this respect, and give a research outlook of future trends in this area.

Simulink is an example of a successful application of the paradigm of model-based development into industrial practice. Numerous companies create and maintain Simulink projects for modeling software-intensive embedded systems, aiming at early validation and automated code generation. However, Simulink projects are not as easily available as code-based ones, which profit from large publicly accessible open-source repositories, thus curbing empirical research. In this paper, we investigate a set of 1734 freely available Simulink models from 194 projects and analyze their suitability for empirical research. We analyze the projects considering (1) their development context, (2) their complexity in terms of size and organization within projects, and (3) their evolution over time. Our results show that there are both limitations and potentials for empirical research. On the one hand, some application domains dominate the development context, and there is a large number of models that can be considered toy examples of limited practical relevance. These often stem from an academic context, consist of only a few Simulink blocks, and are no longer (or have never been) under active development or maintenance. On the other hand, we found that a subset of the analyzed models is of considerable size and complexity. There are models comprising several thousands of blocks, some of them highly modularized by hierarchically organized Simulink subsystems. Likewise, some of the models expose an active maintenance span of several years, which indicates that they are used as primary development artifacts throughout a project’s lifecycle. According to a discussion of our results with a domain expert, many models can be considered mature enough for quality analysis purposes, and they expose characteristics that can be considered representative for industry-scale models. Thus, we are confident that a subset of the models is suitable for empirical research. More generally, using a publicly available model corpus or a dedicated subset enables researchers to replicate findings, publish subsequent studies, and use them for validation purposes. We publish our dataset for the sake of replicating our results and fostering future empirical research.

Leakages and other infrastructure failures represent major challenges confronting the reliability, efficiency, and resilience of water distribution networks (WDNs). Timely identification and accurate localization of these anomalies is paramount to mitigate water and revenue losses, and avoid unwanted cascading effects. Previous resilience studies have demonstrated that the topology of WDNs affects their resilience, for instance to pipe failures. Moreover, several methodologies have been proposed in the last decades to identify optimal sensor locations in a WDN and monitor physical variables (e.g., pressure, flow, concentration of contaminants) in relation to specific objectives, including water contamination monitoring and leakage detection. Yet, most of the studies on optimal sensor placement in WDNs assume constant data logging frequency or number of available sensors, and do not comparatively analyze the influence of different sensor network architectures in relation to different WDN topologies. Here, we develop a simulation-based approach for WDN resilience assessment to quantify the influence and trade-offs of different sensor network architectures, data sampling frequencies, and WDN topologies on automatic leakage identification and localization capabilities.