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This work responds to isolating urban places, and contributes new ways for thinking about placemaking. Progressing through autoethnography and prototyping, we critique design proposals with Lefebvre’s theory of utopia. There inhabitants can enjoy and shape their place together without risking depletion of their abilities and motivations to do so. The critique produces political sensibilities that help us make sense of common tensions among inhabitants, landowners, and visitors, and generate possible responses. The critique process itself illustrates how designing through critique with theory can help us think in new ways. This paper contributes a display of how design with critical theory can happen, ultimately to support our abilities and motivations to envision and make places of social flourishing that can respond to our socio-environmental crises., QC 20230804

Improving the frictional response of a functional surface interface has been a significant research concern. During the last couple of decades, lubricant oils have been enriched with several additives to obtain formulations that can meet the requirements of different lubricating regimes from boundary to full-film hydrodynamic lubrication. The possibility to improve the tribological performance of lubricating oils using various types of nanoparticles has been investigated. In this study, we proposed a data-driven approach that utilizes machine learning (ML) techniques to optimize the composition of a hybrid oil by adding ceramic and carbon-based nanoparticles in varying concentrations to the base oil. Supervised-learning-based regression methods including support vector machines, random forest trees, and artificial neural network (ANN) models are developed to capture the inherent non-linear behavior of the nano lubricants. The ANN hyperparameters were fine-tuned with Bayesian optimization. The regression performance is evaluated with multiple assessment metrics such as the root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R2). The ANN showed the best prediction performance among all ML models, with 2.22 × 10−3 RMSE, 4.92 × 10−6 MSE, 2.1 × 10−3 MAE, and 0.99 R2. The computational models’ performance curves for the different nanoparticles and how the composition affects the interface were investigated. The results show that the composition of the optimized hybrid oil was highly dependent on the lubrication regime and that the coefficient of friction was significantly reduced when optimal concentrations of ceramic and carbon-based nanoparticles are added to the base oil. The proposed research work has potential applications in designing hybrid nano lubricants to achieve optimized tribological performance in changing lubrication regimes., Validerad;2023;Nivå 2;2023-06-12 (sofila)

This paper constitutes the third part of a series of works on lower-dimensional models in lubrication. In Part A, it was shown that implicit constitutive theory must be used in the modelling of incompressible fluids with pressure-dependent viscosity and that it is not possible to obtain a lower-dimensional model for the pressure just by letting the film thickness go to zero, as in the proof of the classical Reynolds equation. In Part B, a new method for deriving lower-dimensional models of thin-film flow of fluids with pressure-dependent viscosity was presented. Here, in Part C, we also incorporate the energy equation so as to include fluids with both temperature and pressure dependent viscosity. By asymptotic analysis of this system, as the film thickness goes to zero, we derive a simplified model of the flow. We also carry out an asymptotic analysis of the boundary condition, in the case where the normal stress is specified on one part of the boundary and the velocity on the remaining part., Validerad;2023;Nivå 2;2023-04-18 (joosat);Licens fulltext: CC BY License

Bearings are the significant components among the rolling machine elements subjected to high wear and tear. The timely detection of faults in such components rotating at higher frequencies can save substantial maintenance costs and production setbacks. Physical examination and fault detection by human experts is always challenging at runtime. Predictive maintenance and real-time condition monitoring are gaining higher utility with the advent of suitable instrumentation and machine learning classifiers. A convolutional neural network (CNN) based bearing fault detection scheme is developed in this research work. The acquired sensory data of vibration signals are converted into the frequency domain and then fed to the classifier for spectral feature extraction and fault classification. The CNN architecture is trained and tested using a bearing dataset available online. The model is further tested and validated with the data acquired from an indigenously designed bearing test rig. The proposed scheme has successfully detected inner and outer race faults and no fault or normal state. This multiclass fault classification has shown promising results with 97.68% accuracy, 96.9% precision, 99.14% sensitivity, 98.01% F1-score, and 93.65% specificity. The achieved results validate the utility of the proposed detection system. Hence the presented scheme has deployment potential for real-time condition monitoring and predictive maintenance applications.