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Vertical green system for gray water treatment: Analysis of the VertiKKA-module in a field test
(2022)
This work presents a modular Vertical Green System (VGS) for gray water treatment, developed at the Bauhaus-Universität Weimar. The concept was transformed into a field study with four modules built and tested with synthetic gray water. Each module set contains a small and larger module with the same treatment substrate and was fed hourly. A combination of lightweight structural material and biochar of agricultural residues and wood chips was used as the treatment substrate. In this article, we present the first 18 weeks of operation. Regarding the treatment efficiency, the parameters chemical oxygen demand (COD), total phosphorous (TP), ortho-phosphate (ortho-P), total bound nitrogen (TNb), ammonium nitrogen (NH4-N), and nitrate nitrogen (NO3-N) were analyzed and are presented in this work. The results of the modules with agricultural residues are promising. Up to 92% COD reduction is stated in the data. The phosphate and nitrogen fractions are reduced significantly in these modules. By contrast, the modules with wood chips reduce only 67% of the incoming COD and respectively less regarding phosphates and the nitrogen fraction.
For the safe and efficient operation of dams, frequent monitoring and maintenance are required. These are usually expensive, time consuming, and cumbersome. To alleviate these issues, we propose applying a wave-based scheme for the location and quantification of damages in dams.
To obtain high-resolution “interpretable” images of the damaged regions, we drew inspiration from non-linear full-multigrid methods for inverse problems and applied a new cyclic multi-stage full-waveform inversion (FWI) scheme. Our approach is less susceptible to the stability issues faced by the standard FWI scheme when dealing with ill-posed problems. In this paper, we first selected an optimal acquisition setup and then applied synthetic data to demonstrate the capability of our approach in identifying a series of anomalies in dams by a mixture of reflection and transmission tomography. The results had sufficient robustness, showing the prospects of application in the field of non-destructive testing of dams.
Der vorliegende Beitrag ist in zwei thematische Teilebereiche gegliedert. Der erste Teil beschäftigt sich mit der Analyse von Graphen, insbesondere von Graphen, die Straßennetzwerke repräsentieren. Hierzu werden Methoden aus der Graphentheorie angewendet und Kenngrößen aus der Space Syntax Methode ausgewertet. Ein Framework, welches basierend auf der Graphentheorie in Architektur und Stadtplanung Einzug gehalten hat, ist die Space Syntax Methode. Sie umfasst die Ableitung unterschiedlicher Kenngrößen eines Graphen bzw. Netzwerkes, wodurch eine Analyse für architektonische und stadtplanerische Zwecke ermöglicht wird.
Der zweite Teil dieses Berichts beschäftigt sich mit der Generierung von Graphen, insbe-sondere der von Straßennetzwerkgraphen. Die generativen Methoden basieren zum Teil auf den gewonnenen Erkenntnissen der Analyse von Straßennetzwerken. Es werden unterschiedliche Ansätze untersucht, um verschiedene Parameterwerte zur Generierung von Straßengraphen festzulegen. Als Ergebnis der Arbeiten ist ein Softwaretool entstanden, welches es erlaubt, auf Grundlage einer Voronoi-Tesselierung realistische Straßennetzwerkgraphen zu erzeugen.
A safe and economic structural design based on the semi-probabilistic concept requires statistically representative safety elements, such as characteristic values, design values, and partial safety factors. Regarding climate loads, the safety levels of current design codes strongly reflect experiences based on former measurements and investigations assuming stationary conditions, i.e. involving constant frequencies and intensities. However, due to climate change, occurrence of corresponding extreme weather events is expected to alter in the future influencing the reliability and safety of structures and their components. Based on established approaches, a systematically refined data-driven methodology for the determination of design parameters considering nonstationarity as well as standardized targets of structural reliability or safety, respectively, is therefore proposed. The presented procedure picks up fundamentals of European standardization and extends them with respect to nonstationarity by applying a shifting time window method. Taking projected snow loads into account, the application of the method is exemplarily demonstrated and various influencing parameters are discussed.
Urban planners are often challenged with the task of developing design solutions which must meet multiple, and often contradictory, criteria. In this paper, we investigated the trade-offs between social, psychological, and energy potential of the fundamental elements of urban form: the street network and the building massing. Since formal mehods to evaluate urban form from the psychological and social point of view are not readily available, we developed a methodological framework to quantify these criteria as the first contribution in this paper. To evaluate the psychological potential, we conducted a three-tiered empirical study starting from real world environments and then abstracting them to virtual environments. In each context, the implicit (physiological) response and explicit (subjective) response of pedestrians were measured. To quantify the social potential, we developed a street network centrality-based measure of social accessibility.
For the energy potential, we created an energy model to analyze the impact of pure geometric form on the energy demand of the building stock. The second contribution of this work is a method to identify distinct clusters of urban form and, for each, explore the trade-offs between the select design criteria. We applied this method to two case studies identifying nine types of urban form and their respective potential trade-offs, which are directly applicable for the assessment of strategic decisions regarding urban form during the early planning stages.
In machine learning, if the training data is independently and identically distributed as the test data then a trained model can make an accurate predictions for new samples of data. Conventional machine learning has a strong dependence on massive amounts of training data which are domain specific to understand their latent patterns. In contrast, Domain adaptation and Transfer learning methods are sub-fields within machine learning that are concerned with solving the inescapable problem of insufficient training data by relaxing the domain dependence hypothesis. In this contribution, this issue has been addressed and by making a novel combination of both the methods we develop a computationally efficient and practical algorithm to solve boundary value problems based on nonlinear partial differential equations. We adopt a meshfree analysis framework to integrate the prevailing geometric modelling techniques based on NURBS and present an enhanced deep collocation approach that also plays an important role in the accuracy of solutions. We start with a brief introduction on how these methods expand upon this framework. We observe an excellent agreement between these methods and have shown that how fine-tuning a pre-trained network to a specialized domain may lead to an outstanding performance compare to the existing ones. As proof of concept, we illustrate the performance of our proposed model on several benchmark problems.
Design-related reassessment of structures integrating Bayesian updating of model safety factors
(2022)
In the semi-probabilistic approach of structural design, the partial safety factors are defined by considering some degree of uncertainties to actions and resistance, associated with the parameters’ stochastic nature. However, uncertainties for individual structures can be better examined by incorporating measurement data provided by sensors from an installed health monitoring scheme. In this context, the current study proposes an approach to revise the partial safety factor for existing structures on the action side, γE by integrating Bayesian model updating. A simple numerical example of a beam-like structure with artificially generated measurement data is used such that the influence of different sensor setups and data uncertainties on revising the safety factors can be investigated. It is revealed that the health monitoring system can reassess the current capacity reserve of the structure by updating the design safety factors, resulting in a better life cycle assessment of structures. The outcome is furthermore verified by analysing a real life small railway steel bridge ensuring the applicability of the proposed method to practical applications.
Bolted connections are widely employed in structures like transmission poles, wind turbines, and television (TV) towers. The behaviour of bolted connections is often complex and plays a significant role in the overall dynamic characteristics of the structure. The goal of this work is to conduct a fatigue lifecycle assessment of such a bolted connection block of a 193 m tall TV tower, for which 205 days of real measurement data have been obtained from the installed monitoring devices. Based on the recorded data, the best-fit stochastic wind distribution for 50 years, the decisive wind action, and the locations to carry out the fatigue analysis have been decided. A 3D beam model of the entire tower is developed to extract the nodal forces corresponding to the connection block location under various mean wind speeds, which is later coupled with a detailed complex finite element model of the connection block, with over three million degrees of freedom, for acquiring stress histories on some pre-selected bolts. The random stress histories are analysed using the rainflow counting algorithm (RCA) and the damage is estimated using Palmgren-Miner's damage accumulation law. A modification is proposed to integrate the loading sequence effect into the RCA, which otherwise is ignored, and the differences between the two RCAs are investigated in terms of the accumulated damage.
The production of a desired product needs an effective use of the experimental model. The present study proposes an extreme learning machine (ELM) and a support vector machine (SVM) integrated with the response surface methodology (RSM) to solve the complexity in optimization and prediction of the ethyl ester and methyl ester production process. The novel hybrid models of ELM-RSM and ELM-SVM are further used as a case study to estimate the yield of methyl and ethyl esters through a trans-esterification process from waste cooking oil (WCO) based on American Society for Testing and Materials (ASTM) standards. The results of the prediction phase were also compared with artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS), which were recently developed by the second author of this study. Based on the results, an ELM with a correlation coefficient of 0.9815 and 0.9863 for methyl and ethyl esters, respectively, had a high estimation capability compared with that for SVM, ANNs, and ANFIS. Accordingly, the maximum production yield was obtained in the case of using ELM-RSM of 96.86% for ethyl ester at a temperature of 68.48 °C, a catalyst value of 1.15 wt. %, mixing intensity of 650.07 rpm, and an alcohol to oil molar ratio (A/O) of 5.77; for methyl ester, the production yield was 98.46% at a temperature of 67.62 °C, a catalyst value of 1.1 wt. %, mixing intensity of 709.42 rpm, and an A/O of 6.09. Therefore, ELM-RSM increased the production yield by 3.6% for ethyl ester and 3.1% for methyl ester, compared with those for the experimental data.
Care of ageing adults has become a dominant field of application for assistive robot technologies, promising support for ageing adults residing in care homes and staff, in dealing with practical routine tasks and providing social and emotional relieve. A time consuming and human intensive necessity is the maintenance of high hygiene quality in care homes. Robotic vacuum cleaners have been proven effective for doing the job elsewhere, but—in the context of care homes—are counterproductive for residents’ well-being and do not get accepted. This is because people with dementia manifest their agency in more implicit and emotional ways, while making sense of the world around them. Starting from these premises, we explored how a zoomorphic designed vacuum cleaner could better accommodate the sensemaking of people with dementia. Our design reconceptualises robotic vacuum cleaners as a cat-like robot, referring to a playful behaviour and appearance to communicate a non-threatening and familiar role model. Data from an observational study shows that residents responded positively to our prototype, as most of them engaged playfully with it as if it was a pet or a cat-like toy, for example luring it with gestures. Some residents simply ignored the robot, indicating that it was not perceived as frightening or annoying. The level of activity influenced reactions; residents ignored our prototype if busy with other occupations, which proves that it did not cause significant disturbance. We further report results from focus group sessions with formal and informal caregivers who discussed a video prototype of our robot. Caregivers encouraged us to enhance the animal like characteristics (in behaviour and materiality) even further to result in richer interactions and provoke haptic pleasure but also pointed out that residents should not mistake the robot for a real cat.