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A new large‐field, high‐sensitivity, single‐mirror coincident schlieren optical instrument has been installed at the Bauhaus‐Universität Weimar for the purpose of indoor air research. Its performance is assessed by the non‐intrusive measurement of the thermal plume of a heated manikin. The schlieren system produces excellent qualitative images of the manikin's thermal plume and also quantitative data, especially schlieren velocimetry of the plume's velocity field that is derived from the digital cross‐correlation analysis of a large time sequence of schlieren images. The quantitative results are compared with thermistor and hot‐wire anemometer data obtained at discrete points in the plume. Good agreement is obtained, once the differences between path‐averaged schlieren data and planar anemometry data are reconciled.
Wind effects can be critical for the design of lifelines such as long-span bridges. The existence of a significant number of aerodynamic force models, used to assess the performance of bridges, poses an important question regarding their comparison and validation. This study utilizes a unified set of metrics for a quantitative comparison of time-histories in bridge aerodynamics with a host of characteristics. Accordingly, nine comparison metrics are included to quantify the discrepancies in local and global signal features such as phase, time-varying frequency and magnitude content, probability density, nonstationarity and nonlinearity. Among these, seven metrics available in the literature are introduced after recasting them for time-histories associated with bridge aerodynamics. Two additional metrics are established to overcome the shortcomings of the existing metrics. The performance of the comparison metrics is first assessed using generic signals with prescribed signal features. Subsequently, the metrics are applied to a practical example from bridge aerodynamics to quantify the discrepancies in the aerodynamic forces and response based on numerical and semi-analytical aerodynamic models. In this context, it is demonstrated how a discussion based on the set of comparison metrics presented here can aid a model evaluation by offering deeper insight. The outcome of the study is intended to provide a framework for quantitative comparison and validation of aerodynamic models based on the underlying physics of fluid-structure interaction. Immediate further applications are expected for the comparison of time-histories that are simulated by data-driven approaches.
Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters.
Due to the importance of identifying crop cultivars, the advancement of accurate assessment of cultivars is considered essential. The existing methods for identifying rice cultivars are mainly time-consuming, costly, and destructive. Therefore, the development of novel methods is highly beneficial. The aim of the present research is to classify common rice cultivars in Iran based on color, morphologic, and texture properties using artificial intelligence (AI) methods. In doing so, digital images of 13 rice cultivars in Iran in three forms of paddy, brown, and white are analyzed through pre-processing and segmentation of using MATLAB. Ninety-two specificities, including 60 color, 14 morphologic, and 18 texture properties, were identified for each rice cultivar. In the next step, the normal distribution of data was evaluated, and the possibility of observing a significant difference between all specificities of cultivars was studied using variance analysis. In addition, the least significant difference (LSD) test was performed to obtain a more accurate comparison between cultivars. To reduce data dimensions and focus on the most effective components, principal component analysis (PCA) was employed. Accordingly, the accuracy of rice cultivar separations was calculated for paddy, brown rice, and white rice using discriminant analysis (DA), which was 89.2%, 87.7%, and 83.1%, respectively. To identify and classify the desired cultivars, a multilayered perceptron neural network was implemented based on the most effective components. The results showed 100% accuracy of the network in identifying and classifying all mentioned rice cultivars. Hence, it is concluded that the integrated method of image processing and pattern recognition methods, such as statistical classification and artificial neural networks, can be used for identifying and classification of rice cultivars.
The effect of urban form on energy consumption has been the subject of various studies around the world. Having examined the effect of buildings on energy consumption, these studies indicate that the physical form of a city has a notable impact on the amount of energy consumed in its spaces. The present study identified the variables that affected energy consumption in residential buildings and analyzed their effects on energy consumption in four neighborhoods in Tehran: Apadana, Bimeh, Ekbatan-phase I, and Ekbatan-phase II. After extracting the variables, their effects are estimated with statistical methods, and the results are compared with the land surface temperature (LST) remote sensing data derived from Landsat 8 satellite images taken in the winter of 2019. The results showed that physical variables, such as the size of buildings, population density, vegetation cover, texture concentration, and surface color, have the greatest impacts on energy usage. For the Apadana neighborhood, the factors with the most potent effect on energy consumption were found to be the size of buildings and the population density. However, for other neighborhoods, in addition to these two factors, a third factor was also recognized to have a significant effect on energy consumption. This third factor for the Bimeh, Ekbatan-I, and Ekbatan-II neighborhoods was the type of buildings, texture concentration, and orientation of buildings, respectively.
Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering. In this work, a novel solubility estimation tool has been proposed for hydrocarbon gases—including methane, ethane, propane, and butane—in aqueous electrolyte solutions based on extreme learning machine (ELM) algorithm. Comparing the ELM outputs with a comprehensive real databank which has 1175 solubility points yielded R-squared values of 0.985 and 0.987 for training and testing phases respectively. Furthermore, the visual comparison of estimated and actual hydrocarbon solubility led to confirm the ability of proposed solubility model. Additionally, sensitivity analysis has been employed on the input variables of model to identify their impacts on hydrocarbon solubility. Such a comprehensive and reliable study can help engineers and scientists to successfully determine the important thermodynamic properties, which are key factors in optimizing and designing different industrial units such as refineries and petrochemical plants.
Carrier-bound titanium dioxide catalysts were used in a photocatalytic ozonation reactor for the degradation of micro-pollutants in real wastewater. A photocatalytic immersion rotary body reactor with a 36-cm disk diameter was used, and was irradiated using UV-A light-emitting diodes. The rotating disks were covered with catalysts based on stainless steel grids coated with titanium dioxide. The dosing of ozone was carried out through the liquid phase via an external enrichment and a supply system transverse to the flow direction. The influence of irradiation power and ozone dose on the degradation rate for photocatalytic ozonation was investigated. In addition, the performance of the individual processes photocatalysis and ozonation were studied. The degradation kinetics of the parent compounds were determined using liquid chromatography tandem mass spectrometry. First-order kinetics were determined for photocatalysis and photocatalytic ozonation. A maximum reaction rate of the reactor was determined, which could be achieved by both photocatalysis and photocatalytic ozonation. At a dosage of 0.4 mg /mg DOC, the maximum reaction rate could be achieved using 75% of the irradiation power used for sole photocatalysis, allowing increases in the energetic efficiency of photocatalytic wastewater treatment processes. The process of photocatalytic ozonation is suitable to remove a wide spectrum of micro-pollutants from wastewater.
A vast number of existing buildings were constructed before the development and enforcement of seismic design codes, which run into the risk of being severely damaged under the action of seismic excitations. This poses not only a threat to the life of people but also affects the socio-economic stability in the affected area. Therefore, it is necessary to assess such buildings’ present vulnerability to make an educated decision regarding risk mitigation by seismic strengthening techniques such as retrofitting. However, it is economically and timely manner not feasible to inspect, repair, and augment every old building on an urban scale. As a result, a reliable rapid screening methods, namely Rapid Visual Screening (RVS), have garnered increasing interest among researchers and decision-makers alike. In this study, the effectiveness of five different Machine Learning (ML) techniques in vulnerability prediction applications have been investigated. The damage data of four different earthquakes from Ecuador, Haiti, Nepal, and South Korea, have been utilized to train and test the developed models. Eight performance modifiers have been implemented as variables with a supervised ML. The investigations on this paper illustrate that the assessed vulnerability classes by ML techniques were very close to the actual damage levels observed in the buildings.
The growing complexity of modern practical problems puts high demand on mathematical modelling. Given that various models can be used for modelling one physical phenomenon, the role of model comparison and model choice is becoming particularly important. Methods for model comparison and model choice typically used in practical applications nowadays are computationbased, and thus time consuming and computationally costly. Therefore, it is necessary to develop other approaches to working abstractly, i.e., without computations, with mathematical models. An abstract description of mathematical models can be achieved by the help of abstract mathematics, implying formalisation of models and relations between them. In this paper, a category theory-based approach to mathematical modelling is proposed. In this way, mathematical models are formalised in the language of categories, relations between the models are formally defined and several practically relevant properties are introduced on the level of categories. Finally, an illustrative example is presented, underlying how the category-theory based approach can be used in practice. Further, all constructions presented in this paper are also discussed from a modelling point of view by making explicit the link to concrete modelling scenarios.
The derivation of nonlocal strong forms for many physical problems remains cumbersome in traditional methods. In this paper, we apply the variational principle/weighted residual method based on nonlocal operator method for the derivation of nonlocal forms for elasticity, thin plate, gradient elasticity, electro-magneto-elasticity and phase-field fracture method. The nonlocal governing equations are expressed as an integral form on support and dual-support. The first example shows that the nonlocal elasticity has the same form as dual-horizon non-ordinary state-based peridynamics. The derivation is simple and general and it can convert efficiently many local physical models into their corresponding nonlocal forms. In addition, a criterion based on the instability of the nonlocal gradient is proposed for the fracture modelling in linear elasticity. Several numerical examples are presented to validate nonlocal elasticity and the nonlocal thin plate.
Personalized ventilation (PV) is a mean of delivering conditioned outdoor air into the breathing zone of the occupants. This study aims to qualitatively investigate the personalized flows using two methods of visualization: (1) schlieren imaging using a large schlieren mirror and (2) thermography using an infrared camera. While the schlieren imaging was used to render the velocity and mass transport of the supplied flow, thermography was implemented to visualize the air temperature distribution induced by the PV. Both studies were conducted using a thermal manikin to simulate an occupant facing a PV outlet. As a reference, the flow supplied by an axial fan and a cased axial fan was visualized with the schlieren system as well and compared to the flow supplied by PV. Schlieren visualization results indicate that the steady, low-turbulence flow supplied by PV was able to penetrate the thermal convective boundary layer encasing the manikin's body, providing clean air for inhalation. Contrarily, the axial fan diffused the supplied air over a large target area with high turbulence intensity; it only disturbed the convective boundary layer rather than destroying it. The cased fan supplied a flow with a reduced target area which allowed supplying more air into the breathing zone compared to the fan. The results of thermography visualization showed that the supplied cool air from PV penetrated the corona-shaped thermal boundary layer. Furthermore, the supplied air cooled the surface temperature of the face, which indicates the large impact of PV on local thermal sensation and comfort.
Electric trains are considered one of the most eco-friendly and safest means of transportation. Catenary poles are used worldwide to support overhead power lines for electric trains. The performance of the catenary poles has an extensive influence on the integrity of the train systems and, consequently, the connected human services. It became a must nowadays to develop SHM systems that provide the instantaneous status of catenary poles in- service, making the decision-making processes to keep or repair the damaged poles more feasible. This study develops a data-driven, model-free approach for status monitoring of cantilever structures, focusing on pre-stressed, spun-cast ultrahigh-strength concrete catenary poles installed along high-speed train tracks. The pro-posed approach evaluates multiple damage features in an unfied damage index, which leads to straightforward interpretation and comparison of the output. Besides, it distinguishes between multiple damage scenarios of the poles, either the ones caused by material degradation of the concrete or by the cracks that can be propagated during the life span of the given structure. Moreover, using a logistic function to classify the integrity of structure avoids the expensive learning step in the existing damage detection approaches, namely, using the modern machine and deep learning methods. The findings of this study look very promising when applied to other types of cantilever structures, such as the poles that support the power transmission lines, antenna masts, chimneys, and wind turbines.
In this article, I show why it is necessary to abolish the use of predictive algorithms in the US criminal justice system at sentencing. After presenting the functioning of these algorithms in their context of emergence, I offer three arguments to demonstrate why their abolition is imperative. First, I show that sentencing based on predictive algorithms induces a process of rewriting the temporality of the judged individual, flattening their life into a present inescapably doomed by its past. Second, I demonstrate that recursive processes, comprising predictive algorithms and the decisions based on their predictions, systematically suppress outliers and progressively transform reality to match predictions. In my third and final argument, I show that decisions made on the basis of predictive algorithms actively perform a biopolitical understanding of justice as management and modulation of risks. In such a framework, justice becomes a means to maintain a perverse social homeostasis that systematically exposes disenfranchised Black and Brown populations to risk.
Scaling of concrete due to salt frost attack is an important durability issue in moderate and cold climates. The actual damage mechanism is still not completely understood. Two recent damage theories—the glue spall theory and the cryogenic suction theory—offer plausible, but conflicting explanations for the salt frost scaling mechanism. The present study deals with the cryogenic suction theory, which assumes that freezing concrete can take up unfrozen brine from a partly frozen deicing solution during salt frost attack. According to the model hypothesis, the resulting saturation of the concrete surface layer intensifies the ice formation in this layer and causes salt frost scaling. In this study an experimental technique was developed that makes it possible to quantify to which extent brine uptake can increase ice formation in hardened cement paste (used as a model material for concrete). The experiments were carried out with low temperature differential scanning calorimetry, where specimens were subjected to freeze–thaw cycles while being in contact with NaCl brine. Results showed that the ice content in the specimens increased with subsequent freeze–thaw cycles due to the brine uptake at temperatures below 0 °C. The ability of the hardened cement paste to bind chlorides from the absorbed brine at the same time affected the freezing/melting behavior of the pore solution and the magnitude of the ice content.
Marine macroalgae such as Ulva intestinalis have promising properties as feedstock for cosmetics and pharmaceuticals. However, since the quantity and quality of naturally grown algae vary widely, their exploitability is reduced – especially for producers in high-priced markets. Moreover, the expansion of marine or shore-based cultivation systems is unlikely in Europe, since promising sites either lie in fishing zones, recreational areas, or natural reserves. The aim was therefore to develop a closed photobioreactor system enabling full control of abiotic environmental parameters and an effective reconditioning of the cultivation medium in order to produce marine macroalgae at sites distant from the shore. To assess the feasibility and functionality of the chosen technological concept, a prototypal plant has been implemented in central Germany – a site distant from the sea. Using a newly developed, submersible LED light source, cultivation experiments with Ulva intestinalis led to growth rates of 7.72 ± 0.04 % day−1 in a cultivation cycle of 28 days. Based on the space demand of the production system, this results in fresh mass productivity of 3.0 kg m−2, respectively, of 1.1 kg m−2 per year. Also considering the ratio of biomass to energy input amounting to 2.76 g kWh−1, significant future improvements of the developed photobioreactor system should include the optimization of growth parameters, and the reduction of the system’s overall energy demand.
Antimicrobial resistance (AMR) is identified by the World Health Organization (WHO) as one of the top ten threats to public health worldwide. In addition to public health, AMR also poses a major threat to food security and economic development. Current sanitation systems contribute to the emergence and spread of AMR and lack effective AMR mitigation measures. This study assesses source separation of blackwater as a mitigation measure against AMR. A source-separation-modified combined sanitation system with separate collection of blackwater and graywater is conceptually described. Measures taken at the source, such as the separate collection and discharge of material flows, were not considered so far on a load balance basis, i.e., they have not yet been evaluated for their effectiveness. The sanitation system described is compared with a combined system and a separate system regarding AMR emissions by means of simulation. AMR is represented in the simulation model by one proxy parameter each for antibiotics (sulfamethoxa-zole), antibiotic-resistant bacteria (extended-spectrum beta-lactamase E. Coli), and antibiotic re-sistance genes (blaTEM). The simulation results suggest that the source-separation-based sanitation system reduces emissions of antibiotic-resistant bacteria and antibiotic resistance genes into the aquatic environment by more than six logarithm steps compared to combined systems. Sulfa-methoxazole emissions can be reduced by 75.5% by keeping blackwater separate from graywater and treating it sufficiently. In summary, sanitation systems incorporating source separation are, to date, among the most effective means of preventing the emission of AMR into the aquatic envi-ronment.
Bolted connections are commonly used in steel construction. The load-bearing behavior of bolt fittings has extensively been studied in various research activities and the bearing capacity of bolted connections can be assessed well by standard regulations for practical applications. With regard to tensile loading, the nut does not have strong influence on resistances, since the failure occurs in the bolts due to higher material strengths of the nuts. In some applications, so-called “blind holes” are used to connect plated components. In a manner of speaking, the nut is replaced by the “outer” plate with a prefabricated hole and thread, in which the bolt can be screwed and tightened. In such connections, the limit load capacity cannot solely be assessed by the bolt resistance, since the threaded hole in the base material has strong influence on the structural behavior. In this context, the available screw-in depth of the blind hole is of fundamental importance. The German National Annex of EN 1993-1-8 provides information on a necessary depth in order to transfer the full tensile capacity of the bolt. However, some connections do not allow to fabricate such depths. In these cases, the capacity of the connection is unclear and not specified. In this paper, first experiments on corresponding connections with different screw-in depths are presented and compared to limit load capacities according to the standard.
Besides their multiple known benefits regarding urban microclimate, living walls can be used as decentralized stand-alone systems to treat greywater locally at the buildings. While this offers numerous environmental advantages, it can have a considerable impact on the hygrothermal performance of the facade as such systems involve bringing large quantities of water onto the facade. As it is difficult to represent complex entities such as plants in the typical simulation tools used for heat and moisture transport, this study suggests a new approach to tackle this challenge by coupling two tools: ENVI-Met and Delphin. ENVI-Met was used to simulate the impact of the plants to determine the local environmental parameters at the living wall. Delphin, on the other hand, was used to conduct the hygrothermal simulations using the local parameters calculated by ENVI-Met. Four wall constructions were investigated in this study: an uninsulated brick wall, a precast concrete plate, a sandy limestone wall, and a double-shell wall. The results showed that the living wall improved the U-value, the exterior surface temperature, and the heat flux through the wall. Moreover, the living wall did not increase the risk of moisture in the wall during winter and eliminated the risk of condensation.
Few studies have investigated how search behavior affects complex writing tasks. We analyze a dataset of 150 long essays whose authors searched the ClueWeb09 corpus for source material, while all querying, clicking, and writing activity was meticulously recorded. We model the effect of search and writing behavior on essay quality using path analysis. Since the boil-down and build-up writing strategies identified in previous research have been found to affect search behavior, we model each writing strategy separately. Our analysis shows that the search process contributes significantly to essay quality through both direct and mediated effects, while the author's writing strategy moderates this relationship. Our models explain 25–35% of the variation in essay quality through rather simple search and writing process characteristics alone, a fact that has implications on how search engines could personalize result pages for writing tasks. Authors' writing strategies and associated searching patterns differ, producing differences in essay quality. In a nutshell: essay quality improves if search and writing strategies harmonize—build-up writers benefit from focused, in-depth querying, while boil-down writers fare better with a broader and shallower querying strategy.
In this paper we present a theoretical background for a coupled analytical–numerical approach to model a crack propagation process in two-dimensional bounded domains. The goal of the coupled analytical–numerical approach is to obtain the correct solution behaviour near the crack tip by help of the analytical solution constructed by using tools of complex function theory and couple it continuously with the finite element solution in the region far from the singularity. In this way, crack propagation could be modelled without using remeshing. Possible directions of crack growth can be calculated through the minimization of the total energy composed of the potential energy and the dissipated energy based on the energy release rate. Within this setting, an analytical solution of a mixed boundary value problem based on complex analysis and conformal mapping techniques is presented in a circular region containing an arbitrary crack path. More precisely, the linear elastic problem is transformed into a Riemann–Hilbert problem in the unit disk for holomorphic functions. Utilising advantages of the analytical solution in the region near the crack tip, the total energy could be evaluated within short computation times for various crack kink angles and lengths leading to a potentially efficient way of computing the minimization procedure. To this end, the paper presents a general strategy of the new coupled approach for crack propagation modelling. Additionally, we also discuss obstacles in the way of practical realisation of this strategy.