Derivation of a scattering model for rarefied gas-solid surface by an unsupervised machine learning approach

Benchmarked by MD results, the performance of the GM-driven scattering model is evaluated and compared with the CLL scattering model in a combined Fourier-Couette flow problem. For the Ar-Au system, the correlation plots based on both scattering kernels are in good agreement with MD results. However, looking at the predicted PDFs of the outgoing velocity components, mismatches between the CLL and MD results at the peak of the tangential velocity components are observed. For the H2-Ni system, it is shown that the results from the GM model are also in good agreement with the MD results. However, except for the rotational energy mode, the CLL model does not show a good performance in predicting the other energy modes, nor in predicting the translational velocity components accurately.Using the predicted velocity components by the GM and CLL scattering kernels, the corresponding ACs are computed and compared with the original MD results. For the different partial velocity ACs ( ) for the Ar-Au system. it was observed that the results obtained by both scattering kernels are in good agreement with the MD simulation results. However, it is found that obtained by the CLL model is slightly higher that the MD results, whereas the results obtained by the GM model are always in perfect agreement with the MD ones. For the H2-Ni system, it is shown that both scattering kernels have an acceptable accuracy in predicting , as well as . However, while the GM model results for and are just slightly higher than the MD results, there is a significant discrepancy between the results obtained from the CLL model and MD simulations.The observed high precision of the GM predictions indicates that it can be considered a promising candidate to compute important discontinuity phenomena such as temperature jumps and velocity slips in rarefied gas flow systems. In addition, the accuracy of the GM model results indicates the high potential of this approach to construct a generalized scattering kernel for rarefied gas-solid surface interactions.In rarefied gas flows, the non-continuum effects, such as velocity slip and temperature jump commonly appear in the gas layer adjacent to a solid boundary. Due to the physical complexity of the interactions at the gas-solid interface, particularly in the case of systems with local nonequilibrium, scattering models with a limited number of parameters cannot completely capture the reflection of gas molecules at the solid boundary. In this work, the Gaussian Mixture (GM) approach, an unsupervised machine learning technique, is employed to construct a statistical gas-surface scattering model. The main input required to train the GM model are the MD collisional data. In this paper we consider two cases: a monoatomic (Ar-Au) and a diatomic (H2-Ni) gas-wall interaction

A hybrid gaussian mixture-DSMC approach applied to the Fourier thermal problem

In rarefied gas dynamics, gas-surface scattering kernels play an important role in evaluating the gas flow field properties since they include crucial information about the effects of the physical and chemical properties of the gas-surface interface on the gas scattering process. In our previous work, an unsupervised machine learning approach, the Gaussian Mixture (GM) approach, is employed to establish a statistical gas-surface scattering model. In this paper, the GM- scattering kernel is coupled to a Direct Simulation Monte Carlo (DSMC) solver as a boundary model to study the Fourier thermal problem. Two gas-solid pairs are considered here: Ar-Au and H2-Ni.To fully examine the coupling mechanism between the GM scattering kernel and the DSMC approach, a one-to-one correspondence between MD and DSMC particles is considered here. Benchmarked by MD results, the performance of the Gaussian Mixture model coupled to DSMC (GM-DSMC) is assessed against the Cercignani-Lampis-Lord (CLL) kernel incorporated into DSMC simulation (CLL-DSMC). To assess the performance of the applied scattering kernels, the number density and temperature profiles between two walls are utilized. The accuracy of the simulation results is determined by measuring the deviations of the DSMC results from full MD simulation results.For the Ar-Au case, the predicted results by DSMC incorporating both scattering models are consistent with the MD data. Here, the average deviations of the predicted density results are 0.6% for both GM-DSMC and CLL-DSMC approaches. In the case of the temperature profile, the trend predicted by the GM-DSMC in the bulk of the domain matches well the pure MD results. Here, the deviations are 0.2%. On the other hand, in most parts of the simulation domain, the results predicted by the CLL-DSMC method deviate from the MD results, observing the highest deviation close to the top wall, which is 4%.For the H2-Ni system,it is observed that within the bulk of the simulation domain, the GM-DSMC approach the results for the number density match well the MD data. Here, the deviations of the GM-DSMC results are around 0.6% on average. However, a notable discrepancy between the density profiles obtained from the MD and CLL-DSMC is observed: the highest deviation is measured near the top wall, which is 8%. The predicted temperature profiles based on both GM-DSMC and CLL-DSMC approaches deviate from the reference MD results. Nevertheless, the GM-DSMC approach still outperforms the CLL-DSMC approach. Herein, the highest deviations based on the GM-DSMC approach are measured at the first and last bins, which are 2% and 1%, respectively. On the other hand, using the CLL-DSMC approach, the deviations on the same bins are 9% and 10%, respectively.These results confirm the better performance of the GM-DSMC approach compared to the CLL-DSMC approach, especially for the diatomic systems

Emoto - visualising the online response to London 2012.

In recent years we have moved from data scarcity to data abundance. As a response, a variety of methods have been adopted in art, design, business, science and government to understand and communicate meaning in data through visual form. emoto (emoto2012.org) is one such project, it visualised the online audience response to a major global event, the London 2012 Olympic and Paralympic Games. emoto set out to both give expression to and augment online social phenomena, that are emergent and only recently made possible by access to huge real-time data streams. This report charts the development and release of the project, and positions it in relation to current debates on data and visualisation, for example, around the bias and accessibility of the data, and how knowledge practices are changing in an era of so-called 'big data.

How Do Different Forms of Vascular Brain Injury Relate to Cognition in a Memory Clinic Population: The TRACE-VCI Study

Background: Memory clinic patients frequently present with different forms of vascular brain injury due to different etiologies, often co-occurring with Alzheimer’s disease (AD) pathology. / Objective: We studied how cognition was affected by different forms of vascular brain injury, possibly in interplay with AD pathology. / Methods: We included 860 memory clinic patients with vascular brain injury on magnetic resonance imaging (MRI), receiving a standardized evaluation including cerebrospinal fluid (CSF) biomarker analyses (n = 541). The cognitive profile of patients with different forms of vascular brain injury on MRI (moderate/severe white matter hyperintensities (WMH) (n = 398), microbleeds (n = 368), lacunar (n = 188) and non-lacunar (n = 96) infarct(s), macrobleeds (n = 16)) was assessed by: 1) comparison of all these different forms of vascular brain injury with a reference group (patients with only mild WMH (n = 205) without other forms of vascular brain injury), using linear regression analyses also stratified for CSF biomarker AD profile and 2) multivariate linear regression analysis. / Results: The cognitive profile was remarkably similar across groups. Compared to the reference group effect sizes on all domains were <0.2 with narrow 95% confidence intervals, except for non-lacunar infarcts on information processing speed (age, sex, and education adjusted mean difference from reference group (β: – 0.26, p = 0.05). Results were similar in the presence (n = 300) or absence (n = 241) of biomarker co-occurring AD pathology. In multivariate linear regression analysis, higher WMH burden was related to a slightly worse performance on attention and executive functioning (β: – 0.08, p = 0.02) and working memory (β: – 0.08, p = 0.04). / Conclusion: Although different forms of vascular brain injury have different etiologies and different patterns of cerebral damage, they show a largely similar cognitive profile in memory clinic patients regardless of co-occurring AD pathology

Assessing the Placement of a Cochlear Electrode Array by Multidimensional Scaling

Disorders of the head and nec

Editorial for the special issue on non-equilibrium gas flows

International audienc

Local clothing thermal properties of typical office ensembles under realistic static and dynamic conditions

An accurate local thermal sensation model is indispensable for the effective development of personalized conditioning systems in office environments. The output of such a model relies on the accurate prediction of local skin temperatures, which in turn depend on reliable input data of the local clothing thermal resistance and clothing area factor. However, for typical office clothing ensembles, only few local datasets are available in the literature. In this study, the dry thermal resistance was measured for 23 typical office clothing ensembles according to EN-ISO 15831 on a sweating agile manikin. For 6 ensembles, the effects of different air speeds and body movement were also included. Local clothing area factors were estimated based on 3D scans. Local differences can be found between the measured local insulation values and local area factors of this study and the data of other studies. These differences are likely due to the garment fit on the manikin and reveal the necessity of reporting clothing fit parameters (e.g., ease allowance) in the publications. The increased air speed and added body movement mostly decreased the local clothing insulation. However, the reduction is different for all body parts, and therefore cannot be generalized. This study also provides a correlation between the local clothing insulation and the ease allowance for body parts covered with a single layer of clothing. In conclusion, the need for well-documented measurements is emphasized to get reproducible results and to choose accurate clothing parameters for thermo-physiological and thermal sensation modeling

Validation of human thermo-physiology models for personalized predictions of thermal responses

Current thermal comfort models used in practice do not consider physiological and psychological differences of people. With this respect, human thermo-physiology models (HTPM) are useful tools to assess human thermal comfort at local and overall levels of the human body. All published HTPM models claim a high level of agreement with experimental data; however, in most cases, the comparison was made with data from third-party experimentation. This paper presents an independent detailed evaluation of two sophisticated human thermo-physiology models, JOS3 and ThermoSEM, considering individual parameters (sex, height, weight, etc.) as input. Both models are similar in heat transfer calculation but differ in thermoregulation control. For validation, the experimental study was conducted in a climatic chamber with male and female subjects sitting relaxed and standing sorting under different environmental conditions (22-28°C). The measured core temperature and the skin temperature at 14 locations were used to evaluate the predicted values. The paper highlights the capabilities and limitations of HTPMs and, furthermore, discusses the application of HTPMs in the field of individualized thermal comfort. The results show that HTPMs are valuable tools for predicting individual local thermal response but in order to reach a better accuracy models need more refinement on assumptions of local thermal characteristics.Current thermal comfort models used in practice do not consider physiological and psychological differences of people. With this respect, human thermo-physiology models (HTPM) are useful tools to assess human thermal comfort at local and overall levels of the human body. All published HTPM models claim a high level of agreement with experimental data; however, in most cases, the comparison was made with data from third-party experimentation. This paper presents an independent detailed evaluation of two sophisticated human thermo-physiology models, JOS3 and ThermoSEM, considering individual parameters (sex, height, weight, etc.) as input. Both models are similar in heat transfer calculation but differ in thermoregulation control. For validation, the experimental study was conducted in a climatic chamber with male and female subjects sitting relaxed and standing sorting under different environmental conditions (22-28°C). The measured core temperature and the skin temperature at 14 locations were used to evaluate the predicted values. The paper highlights the capabilities and limitations of HTPMs and, furthermore, discusses the application of HTPMs in the field of individualized thermal comfort. The results show that HTPMs are valuable tools for predicting individual local thermal response but in order to reach a better accuracy models need more refinement on assumptions of local thermal characteristics