Multi layer perceptron model in pattern recognition of surface parameters during pre-monsoon thunderstorm

The concept of Multi Layer Perceptron and Fuzzy logic is introduced in this paper to recognize the pattern of surface parameters pertaining to forecast the occurrence of pre-monsoon thunderstorms over Kolkata (22 ° 32¢ , 88 ° 20¢ ). The results reveal that surface temperature fluctuates significantly from Fuzzy Multi Layer Perceptron (FMLP) model values on thunderstorm days whereas on non-thunderstorm days FMLP model fits well with the surface temperature. The results further indicate that no definite pattern could be made available with surface dew point temperature and surface pressure that can help in forecasting the occurrence of these storms.

Case Study of Condensate Dropout Effect in Unconventional Gas/Condensate Reservoirs with Hydraulically Fractured Wells

Condensate banking is a major issue in the production operations of gas condensate reservoirs. Increase in liquid saturation in the near-wellbore zone due to pressure decline below dew point, decreases well deliverability and the produced condensate-gas ratio (CGR). This paper investigates the effects of condensate banking on the deliverability of hydraulically fractured wells producing from ultralow permeability (0.001 to 0.1 mD) gas condensate reservoirs. Cases where condensate dropout occurs over a large volume of the reservoir, not only near the fracture face, were examined by a detailed numerical reservoir simulation. A commercial compositional simulator with local grid refinement (LGR) around the fracture was used to quantify condensate dropout as a result of reservoir pressure decline and its impact on well productivity index (PI). The effects of gas production rate and reservoir permeability were investigated. Numerical simulation results showed a significant change in fluid compositions and relative permeability to gas over a large reservoir volume due to pressure decline during reservoir depletion. Results further illustrated the complications in understanding the PI evolution of hydraulically fractured wells in "unconventional" gas condensate reservoirs and illustrate how to correctly evaluate fracture performance in such a situation. The findings of our study and novel approach help to more accurately predict post-fracture performance. They provide a better understanding of the hydrocarbon phase change not only near the wellbore and fracture, but also deep in the reservoir, which is critical in unconventional gas condensate reservoirs. The optimization of both fracture spacing in horizontal wells and well spacing for vertical well developments can be achieved by improving the ability of production engineers to generate more realistic predictions of gas and condensate production over time.

The Acoustic Nature of “Storm Nose”, “Supercell” Vortices and Tornadoes

The process of cumulonimbus cloud Cb calvus formation in the middle latitudes of real atmosphere is analyzed in this work. Its transformation from initial lifecycle stage to “maturity” undergoes due to the formation of the waveguide called “aerial acoustic channel” in the troposphere near the level of temperature minimum that is close to 2 km altitude. This “aerial acoustic channel” can be considered as analog of “deep sound channel” that corresponds to the minimal sound speed level. Tropospheric “channel” related to the thermal inversion zone is almost unlimited horizontally. Synchronous generation of two compression waves (ascending one above Cb and descending one inside Cb) is caused by Cb calvus dome ascension. The first one can provoke the aerodynamic draft previously unexplained. The second one results in the growth of its “storm nose” and in the axial and peripheral descending mechanisms in Cb. The penetration of Cb into stratosphere results in the destruction of dynamic balance around Cb top and hence in its unloading in the descending decompression wave. Here the air cools down to the “dew point” in the place of conjugation with parental cloud – due to Snellius law it results in the formation of aerosol “vortex” as condensation front; this “vortex” has calculated value of its generatrix against vertical. Due to D. Snow’s criterion, this vortex forms either “supercell” vortex or tornado vortex.

Observed and Projected Scaling of Daily Extreme Precipitation with Dew Point Temperature at Annual and Seasonal Scales across the Northeast United Sates

This study investigates how extreme precipitation scales with dew point temperature across the Northeast U.S., both in the observational record (1948-2020) and in a set of downscaled climate projections in the state of Massachusetts (2006-2099). Spatiotemporal relationships between dew point temperature and extreme precipitation are assessed, and extreme precipitation – temperature scaling rates are evaluated on annual and seasonal scales using non-stationary extreme value analysis for annual maxima and partial duration series, respectively. A hierarchical Bayesian model is then developed to partially pool data across sites and estimate regional scaling rates, with uncertainty. Based on the observations, the estimated annual scaling rate is 5.5% per °C, but this varies by season, with most non-zero scaling rates in summer and fall and the largest rates (∼7.3% per °C) in the summer. Dew point temperatures and extreme precipitation also exhibit the most consistent regional relationships in the summer and fall. Downscaled climate projections exhibited different scaling rates compared to the observations, ranging between -2.5 and 6.2% per °C at an annual scale. These scaling rates are related to the consistency between trends in projected precipitation and dew point temperature over the 21st century. At the seasonal scale, climate models project larger scaling rates for the winter compared to the observations (1.6% per °C). Overall, the observations suggest that extreme daily precipitation in the Northeast U.S. only thermodynamic scales with dew point temperature in the warm season, but climate projections indicate some degree of scaling is possible in the cold season under warming.

Heat Flux and Thermal Characteristics of Electrically Heated Windows: A Case Study

Energy loss through windows can be high relatively compared to other opaque surfaces because insulation performance of fenestration parts is lower in the building envelope. Electrically heated window systems are used to improve the indoor environment, prevent condensation, and increase building energy efficiency. The purpose of this study is to analyze the thermal behaviors of a heated window under a field experiment condition. Experiments were conducted during the winter season (i.e., January and February) with the energy-efficient house that residents occupy. To collect measured data from the experimental house, temperature and heat flux meter sensors were used for the analysis of heat flow patterns. Such measured data were used to calculate heat gain ratios and compare temperature and dew point distribution profiles of heated windows with input power values under the changed condition in the operating temperature of the heated glazing. Results from this study indicated that the input average heat gain ratio was analyzed to be 75.2% in the south-facing and 83.8% in the north-facing at nighttime. Additionally, compared to January, reducing the operating temperature of the heated glazing by 3 °C decreased the input energy in February by 44% and 41% for the south-facing and north-facing windows, respectively. Through such field measurement study, various interesting results that could not be found in controlled laboratory chamber conditions were captured, indicating that the necessity of establishing various control strategies should be considered for the development and commercialization of heated windows.

Measurement of indoor environmental parameters and analysis of the condensation phenomenon in urban utility tunnels

Condensation is a major issue in the safe operation of utility tunnels. To address the condensation problem, the indoor air temperature, relative humidity (RH) and surface temperature in an urban utility tunnel in Jining were continuously measured, and the condensation conditions were surveyed and analysed. The results indicated that under natural ventilation conditions, the air temperature in the comprehensive cabin varied from 23.4°C to 24.5°C, the RH fluctuated between 86.4% and 95.3%, and the corresponding air dew point temperature (DPT) remained in the range of 22.2°C–22.9°C. The surface temperature of the water supply pipeline ranged from 17.8°C to 18.5°C, which was far lower than the DPT in the tunnel, resulting in serious condensation. A water supply pipeline with an anti-condensation design was developed based on environmental test data. A 25-mm-thick rubber plastic sponge insulation layer was used to thermally insulate the water supply pipeline, preventing further dew condensation. Furthermore, mechanical ventilation had little effect on reducing the RH in the tunnel and may actually cause dew condensation; therefore, a ventilation control mode was proposed in this study. These results are expected to provide basic data for further research and reference for the safe management of utility tunnels.

Establishing Coupled Models for Estimating Daily Dew Point Temperature Using Nature-Inspired Optimization Algorithms

Potential of a classic adaptive neuro-fuzzy inference system (ANFIS) was evaluated in the current study for estimating the daily dew point temperature (Tdew). The study area consists of two stations located in Iran, namely the Rasht and Urmia. The daily Tdew time series of the studied stations were modeled through the other effective variables comprising minimum air temperature (Tmin), extraterrestrial radiation (Ra), vapor pressure deficit (VPD), sunshine duration (n), and relative humidity (RH). The correlation coefficients between the input and output parameters were utilized to determine the most effective inputs. Furthermore, novel hybrid models were proposed in this study in order to increase the estimation accuracy of Tdew. For this purpose, two optimization algorithms named bee colony optimization (BCO) and dragonfly algorithm (DFA) were coupled on the classic ANFIS. It was concluded that the hybrid models (i.e., ANFIS-BCO and ANFIS-DFA) demonstrated better performances compared to the classic ANFIS. The full-input pattern of the coupled models, specifically the ANFIS-DFA, was found to present the most accurate results for both the selected stations. Therefore, the developed hybrid models can be proposed as alternatives to the classic ANFIS to accurately estimate the daily Tdew.

Forecasting of minimum temperature over Gangtok

A study has been conducted to assess day-to-day changes, departure and persistence of minimum temperature and the frequency of cold wave and severe cold wave over Gangtok for five winter months i.e., November-March for the years 1969 to 1992. Regression models have also been formulated to forecast minimum temperature with the knowledge of dew point, cloud amount, maximum temperature and minimum temperature recorded on previous day. In case of changes, ‘little change’ and ‘no change’ constitute about four-fifth of total changes. The cases of nearly normal were found maximum when departure of minimum temperature from normal was considered. Frequency of cold wave and severe cold wave has been recorded more in January and February respectively. It has been observed that there is a gradual fall in the percentage frequency with the increase in the magnitude of variation. Regression model gives good results from November to February.