Similarity-Based Framework for Unsupervised Domain Adaptation: Peer Reviewing Policy for Pseudo-Labeling

The inherent dependency of deep learning models on labeled data is a well-known problem and one of the barriers that slows down the integration of such methods into different fields of applied sciences and engineering, in which experimental and numerical methods can easily generate a colossal amount of unlabeled data. This paper proposes an unsupervised domain adaptation methodology that mimics the peer review process to label new observations in a different domain from the training set. The approach evaluates the validity of a hypothesis using domain knowledge acquired from the training set through a similarity analysis, exploring the projected feature space to examine the class centroid shifts. The methodology is tested on a binary classification problem, where synthetic images of cubes and cylinders in different orientations are generated. The methodology improves the accuracy of the object classifier from 60% to around 90% in the case of a domain shift in physical feature space without human labeling

Conditional Generative Adversarial Networks for modelling fuel sprays

In this study, the probabilistic, data driven nature of the generative adversarial neural networks (GANs) was utilized to conduct virtual spray simulations for conditions relevant to aero engine combustors. The model consists of two sub-modules: (i) an autoencoder converting the variable length droplet trajectories into fixed length, lower dimensional representations and (ii) a Wasserstein GAN that learns to mimic the latent representations of the evaporating droplets along their lifetime. The GAN module was also conditioned with the injection location and the diameters of the droplets to increase the generalizability of the whole framework. The training data was provided from highly resolved 3D, transient Eulerian–Lagrangian, large eddy simulations conducted with OpenFOAM. Neural network models were created and trained within the open source machine learning framework of PyTorch. Predictive capabilities of the proposed method was discussed with respect to spray statistics and evaporation dynamics. Results show that conditioned GAN models offer a great potential as low order model approximations with high computational efficiency. Nonetheless, the capabilities of the autoencoder module to preserve local dependencies should be improved to realize this potential. For the current case study, the custom model architecture was capable of conducting the simulation in the order of seconds after a day of training, which had taken one week on HPC with the conventional CFD approach for the same number of droplets (200,000 trajectories)

Model Predictive Evolutionary Temperature Control via Neural-Network-Based Digital Twins

In this study, we propose a population-based, data-driven intelligent controller that leverages neural-network-based digital twins for hypothesis testing. Initially, a diverse set of control laws is generated using genetic programming with the digital twin of the system, facilitating a robust response to unknown disturbances. During inference, the trained digital twin is utilized to virtually test alternative control actions for a multi-objective optimization task associated with each control action. Subsequently, the best policy is applied to the system. To evaluate the proposed model predictive control pipeline, experiments are conducted on a multi-mode heat transfer test rig. The objective is to achieve homogeneous cooling over the surface, minimizing the occurrence of hot spots and energy consumption. The measured variable vector comprises high dimensional infrared camera measurements arranged as a sequence (655,360 inputs), while the control variable includes power settings for fans responsible for convective cooling (3 outputs). Disturbances are induced by randomly altering the local heat loads. The findings reveal that by utilizing an evolutionary algorithm on measured data, a population of control laws can be effectively learned in the virtual space. This empowers the system to deliver robust performance. Significantly, the digital twin-assisted, population-based model predictive control (MPC) pipeline emerges as a superior approach compared to individual control models, especially when facing sudden and random changes in local heat loads. Leveraging the digital twin to virtually test alternative control policies leads to substantial improvements in the controller’s performance, even with limited training data

MDCT Findings of Denim-Sandblasting-Induced Silicosis: a cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Denim sandblasting is as a novel cause of silicosis in Turkey, with reports of a recent increase in cases and fatal outcomes. We aimed to describe the radiological features of patients exposed to silica during denim sandblasting and define factors related to the development of silicosis.</p> <p>Methods</p> <p>Sixty consecutive men with a history of exposure to silica during denim sandblasting were recruited. All CT examinations were performed using a 64-row multi-detector CT (MDCT). The nodules were qualitatively and semi-quantitatively analyzed by grading nodular profusion (NP) on CT images.</p> <p>Results</p> <p>Silicosis was diagnosed radiologically in 73.3% of patients (44 of 60). The latency period (the time between initial exposure and radiological imaging) and duration of silica exposure was longer in patients diagnosed with silicosis than in those without silicosis (p < 0.05). Nodules were present in all cases with centrilobular type as the commonest (63.6%). All cases of silicosis were clinically classified as accelerated and 11.4% had progressive massive fibrosis (PMF). Mild NP lesions were the most prevalent in all six zones of the lung. The NP score was significantly correlated with the duration of silica exposure, the latency period, presence of PMF, and pleural thickening. Enlarged lymphadenopathy was present in 45.5% of patients.</p> <p>Conclusions</p> <p>The duration of exposure and the latency period are important for development of silicosis in denim sandblasters. MDCT is a useful tool in detecting findings of silicosis in workers who has silica exposure.</p

Analyzing the Interaction of Vortex and Gas–Liquid Interface Dynamics in Fuel Spray Nozzles by Means of Lagrangian-Coherent Structures (2D)

Predictions of the primary breakup of fuel in realistic fuel spray nozzles for aero-engine combustors by means of the SPH method are presented. Based on simulations in 2D, novel insights into the fundamental effects of primary breakup are established by analyzing the dynamics of Lagrangian-coherent structures (LCSs). An in-house visualization and data exploration platform is used in order to retrieve fields of the finite-time Lyapunov exponent (FTLE) derived from the SPH predictions aiming at the identification of time resolved LCSs. The main focus of this paper is demonstrating the suitability of FTLE fields to capture and visualize the interaction between the gas and the fuel flow leading to liquid disintegration. Aiming for a convenient illustration at a high spatial resolution, the analysis is presented based on 2D datasets. However, the method and the conclusions can analoguosly be transferred to 3D. The FTLE fields of modified nozzle geometries are compared in order to highlight the influence of the nozzle geometry on primary breakup, which is a novel and unique approach for this industrial application. Modifications of the geometry are proposed which are capable of suppressing the formation of certain LCSs, leading to less fluctuation of the fuel flow emerging from the spray nozzle

Significance of particle concentration distribution on radiative heat transfer in circulating fluidized bed combustors

In this study, effect of particle concentration distribution on radiative heat transfer in circulating fluidized bed combustors (CFBCs) is investigated. The aim is to identify how important it is to include axial and radial variations of particle concentration along the splash and dilute zones in radiative heat transfer calculations and to determine the predictive accuracy of simple OD and 1D approximations for particle concentration distribution in the riser by benchmarking their predictions against a semi-empiric 2D axisymmetric model developed for a wide range of operating conditions and systems. Input data required for the radiation model are provided from measurements carried out in a 150 kWt cylindrical Circulating Fluidized Bed Combustor (CFBC) test rig burning low calorific value Turkish lignite with high volatile matter/fixed carbon (VM/FC) ratio in its own ash. Radiative transfer equation (RTE) is solved for 2-D axisymmetric cylindrical enclosure which contains gray, absorbing, emitting gas mixture with gray, absorbing, emitting, anisotropically scattering particles bounded by diffuse, gray/black walls. Incident heat fluxes and source terms along the riser are predicted by the Method of Lines (MOL) solution of Discrete Ordinates Method (DOM) with Leckner's correlations for combustion gases, geometric optics approximation for particles and normalized Henyey-Greenstein for the phase function. Comparisons reveal that OD and 1D representations of particle concentration distribution lead to overprediction of incident heat fluxes, in both splash and dilute zones, where discrepancy of OD model is larger. Similarly, errors in source term predictions introduced by simplifying the parade concentration distribution via deploying OD and 1D models are found to be significantly large. These findings indicate that rigorous evaluation of particle concentration distribution is essential for accurate prediction of radiative heat transfer in CFBCs despite its high CPU requirements. (C) 2017 Published by Elsevier Ltd

Influence of gray particle assumption on the predictive accuracy of gas property approximations

In this study, influence of gray particle assumption on the predictive accuracy of gas property models is investigated for conditions typically encountered in industrial coal-fired furnaces. The aim is (i) to identify how the share of gas radiation is influenced by the presence of particles and particle properties and (ii) to determine the effect of gray particle assumption on the predictive accuracy of gas property approximations. For that purpose, predictive accuracy of a simple gas property model is benchmarked against that of Spectral Line-Based Weighted Sum of Grey Gases Model (SLW) in the presence of gray/non-gray particles with different ash compositions, particle loads and boundary conditions. Input data required for the radiation code and its validation are provided from two combustion tests previously carried out in a 300 kWt Atmospheric Bubbling Fluidized Bed Combustor (ABFBC) test rig burning low calorific value Turkish lignite with high volatile matter/fixed carbon (VM/FC) ratio in its own ash. Comparisons reveal that gray particle assumption leads to over-estimation of particle radiation, which leads to under-estimation of gas radiation share in total radiative heat exchange. This under-estimation is found to be reflected on the predictive accuracy of gas property models, that is, a simple gas property model can be found to be "accurate" if particles are assumed to be gray although that is not the case. Furthermore, share of particle radiation in total radiative heat exchange is demonstrated to be strongly dependent on the spectral nature of particle properties. The results show that accurate gas property models such as SLW are needed to represent the spectral behavior of combustion gases even at high particle loads