Constraints on Ωm\Omega_\mathrm{m} and σ8\sigma_8 from the potential-based cluster temperature function

The abundance of galaxy clusters is in principle a powerful tool to constrain cosmological parameters, especially $\Omega_\mathrm{m}$ and $\sigma_8$, due to the exponential dependence in the high-mass regime. While the best observables are the X-ray temperature and luminosity, the abundance of galaxy clusters, however, is conventionally predicted as a function of mass. Hence, the intrinsic scatter and the uncertainties in the scaling relations between mass and either temperature or luminosity lower the reliability of galaxy clusters to constrain cosmological parameters. In this article, we further refine the X-ray temperature function for galaxy clusters by Angrick et al., which is based on the statistics of perturbations in the cosmic gravitational potential and proposed to replace the classical mass-based temperature function, by including a refined analytic merger model and compare the theoretical prediction to results from a cosmological hydrodynamical simulation. Although we find already a good agreement if we compare with a cluster temperature function based on the mass-weighted temperature, including a redshift-dependent scaling between mass-based and spectroscopic temperature yields even better agreement between theoretical model and numerical results. As a proof of concept, incorporating this additional scaling in our model, we constrain the cosmological parameters $\Omega_\mathrm{m}$ and $\sigma_8$ from an X-ray sample of galaxy clusters and tentatively find agreement with the recent cosmic microwave background based results from the Planck mission at 1$\sigma$-level.Comment: 10 pages, 5 figures, 2 tables; accepted by MNRAS; some typos correcte

Method for rating power cables buried in surface troughs

An alternative method is detailed by which the ambient temperature parameter as applied to the calculation of ratings of cables buried in surface trough installations can be determined. Improvement in the accuracy of cable rating calculations will allow greater utilisation of the cable asset and assist for example in the planning of system outages for maintenance work. The proposed model calculates the temperature at the cable burial depth based on measurements of solar radiation, windspeed and air temperature. The model is based on physical laws rather than empirical approaches that have been shown to be generally conservative in application. Results based on weather data monitored over a two-year period show that the ambient temperature of the soil at cable depth can be accurately determined and the model provides a significant improvement on existing methods

Prediction of Thermal Behavior of Pervious Concrete Pavements in Winter

Because application of pervious concrete pavement (PCPs) has extended to cold-climate regions of the United States, the safety and mobility of PCP installations during the winter season need to be maintained. Timely application of salt, anti-icing, and deicing agents for ice/snow control is most effective in providing sufficient surface friction when done at a suitable pavement surface temperature. The aim of this project was to determine the thermal properties of PCP during the winter season, and to develop a theoretical model to predict PCP surface temperature. The project included a laboratory and a field component. In the laboratory, thermal conductivity of pervious concrete was determined. A linear relationship was established between thermal conductivity and porosity for pervious concrete specimens. In the field, the pavement temperature in a PCP sidewalk installation at Washington State University was monitored via in-pavement instrumentation. Based on the field data, the Enhanced Integrated Climatic Model (EICM) was developed and validated for the site, using PCP thermal properties and local climatic data. The EICM-predicted PCP surface temperature during the winter season agreed well with the field temperature. Overall, the predicted number of days that the pavement surface fell below 32°F agreed well with the number based on field data for 85% of the days. Therefore, the developed model is useful in identifying those days to apply deicer agents. Finally, a regression model using climatic indices was developed for PCP surface temperature prediction in the absence of a more advanced temperature model

ANN for Predicting Temperature and Humidity in the Surrounding Environment

Abstract: In this research, an Artificial Neural Network (ANN) model was developed and tested to predict temperature in the surrounding environment. A number of factors were identified that may affect temperature or humidity. Factors such as the nature of the surrounding place, proximity or distance from water surfaces, the influence of vegetation, and the level of rise or fall below sea level, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using data from several regions in the surrounding environment. The evaluation of testing the dataset shows that the ANN model is capable of correctly predicting the temperature with 100% accuracy