Mechanically Aspirated Solar Radiation Shields: A CFD and Neural Network Design Analysis

2001 ◽  
Author(s):  
B. M. Fichera ◽  
R. L. Mahajan ◽  
T. W. Horst
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Temperature Sensor ◽  
Temperature Measurements ◽  
Analysis And Design ◽  
Computational Fluid Dynamics Analysis ◽  
Radiation Shields ◽  
Input Variables ◽  
The Relationship

Abstract Accurate air temperature measurements made by surface meteorological stations are demanded by climate research programs for various uses. Heating of the temperature sensor due to inadequate coupling with the environment can lead to significant errors. Therefore, accurate in-situ temperature measurements require shielding the sensor from exposure to direct and reflected solar radiation, while also allowing the sensor to be brought into contact with atmospheric air at the ambient temperature. The difficulty in designing a radiation shield for such a temperature sensor lies in satisfying these two conditions simultaneously. In this paper, we perform a computational fluid dynamics analysis of mechanically aspirated radiation shields (MARS) to study the effect of geometry, wind speed, and interplay of multiple heat transfer processes. Finally, an artificial neural network model is developed to learn the relationship between the temperature error and specified input variables. The model is then used to perform a sensitivity analysis and design optimization.

An improvement to the ANN-enhanced flow rating method

2021 ◽  
Author(s):  
Bernhard Schmid
Keyword(s):  
Neural Network ◽  
Electrical Conductivity ◽  
Water Resources ◽  
Flow Rate ◽  
Major Ions ◽  
Rate Of Change ◽  
Data Set ◽  
Input Variables ◽  
Relationship Of ◽  
The Relationship

<p>The work reported here builds upon a previous pilot study by the author on ANN-enhanced flow rating (Schmid, 2020), which explored the use of electrical conductivity (EC) in addition to stage to obtain ‘better’, i.e. more accurate and robust, estimates of streamflow. The inclusion of EC has an advantage, when the relationship of EC versus flow rate is not chemostatic in character. In the majority of cases, EC is, indeed, not chemostatic, but tends to decrease with increasing discharge (so-called dilution behaviour), as reported by e.g. Moatar et al. (2017), Weijs et al. (2013) and Tunqui Neira et al.(2020). This is also in line with this author’s experience.</p><p>The research presented here takes the neural network based approach one major step further and incorporates the temporal rate of change in stage and the direction of change in EC among the input variables (which, thus, comprise stage, EC, change in stage and direction of change in EC). Consequently, there are now 4 input variables in total employed as predictors of flow rate. Information on the temporal changes in both flow rate and EC helps the Artificial Neural Network (ANN) characterize hysteretic behaviour, with EC assuming different values for falling and rising flow rate, respectively, as described, for instance, by Singley et al. (2017).</p><p>The ANN employed is of the Multilayer Perceptron (MLP) type, with stage, EC, change in stage and direction of change in EC of the Mödling data set (Schmid, 2020) as input variables. Summarising the stream characteristics, the Mödling brook can be described as a small Austrian stream with a catchment of fairly mixed composition (forests, agricultural and urbanized areas). The relationship of EC versus flow reflects dilution behaviour. Neural network configuration 4-5-1 (the 4 input variables mentioned above, 5 hidden nodes and discharge as the single output) with learning rate 0.05 and momentum 0.15 was found to perform best, with testing average RMSE (root mean square error) of the scaled output after 100,000 epochs amounting to 0.0138 as compared to 0.0216 for the (best performing) 2-5-1 MLP with stage and EC as inputs only.    </p><p> </p><p>References</p><p>Moatar, F., Abbott, B.W., Minaudo, C., Curie, F. and Pinay, G.: Elemental properties, hydrology, and biology interact to shape concentration-discharge curves for carbon, nutrients, sediment and major ions. Water Resources Res., 53, 1270-1287, 2017.</p><p>Schmid, B.H.: Enhanced flow rating using neural networks with water stage and electrical conductivity as predictors. EGU2020-1804, EGU General Assembly 2020.</p><p>Singley, J.G., Wlostowski, A.N., Bergstrom, A.J., Sokol, E.R., Torrens, C.L., Jaros, C., Wilson, C.,E., Hendrickson, P.J. and Gooseff, M.N.: Characterizing hyporheic exchange processes using high-frequency electrical conductivity-discharge relationships on subhourly to interannual timescales. Water Resources Res. 53, 4124-4141, 2017.</p><p>Tunqui Neira, J.M., Andréassian, V., Tallec, G. and Mouchel, J.-M.: A two-sided affine power scaling relationship to represent the concentration-discharge relationship. Hydrol. Earth Syst. Sci. 24, 1823-1830, 2020.</p><p>Weijs, S.V., Mutzner, R. and Parlange, M.B.: Could electrical conductivity replace water level in rating curves for alpine streams? Water Resources Research 49, 343-351, 2013.</p>

scholarly journals Testing the relationship between the solar radiation dose and surface DMS concentrations using high resolution in situ data

2009 ◽  
Vol 6 (2) ◽  
pp. 3063-3085
Author(s):  
C. J. Miles ◽  
T. G. Bell ◽  
T. M. Lenton
Keyword(s):  
Radiation Dose ◽  
Solar Radiation ◽  
Mixed Layer Depth ◽  
Light Attenuation ◽  
Climate Feedback ◽  
Solar Ultraviolet Radiation ◽  
In Situ Data ◽  
Linear Relationships ◽  
The Relationship

Abstract. We tested the recently proposed, strong positive relationship between dimethylsulphide (DMS) concentrations and the solar radiation dose (SRD) received into the surface ocean. We utilised in situ daily data sampled concurrently with DMS concentrations from the Atlantic Meridional Transect (AMT) programme for the component variables of the SRD; mixed layer depth (MLD), surface insolation (I0) and a light attenuation coefficient (k), to calculate SRDin situ. We find a significant correlation (ρ=0.53) but the slope of the relationship is approximately half that previously proposed. The correlation is improved (ρ=0.76) by replacing the in situ data with an estimated I0 (which assumes a constant 50% removal of the top of atmosphere value; 0.5×TOA), a MLD climatology and a fixed value for k following a previously described methodology. Equally significant, but non-linear relationships are also found between DMS and both in situ MLD (ρ=0.73) and the estimated I0 (ρ=0.76) alone. The DMS data shows an interesting relationship to an approximated UV attenuation depth profile. Using a cloud adjusted, satellite climatology of surface UVA irradiance to calculate a UV radiation dose (UVRD) provides an equivalent correlation (ρ=0.73) to DMS. With this data, MLD appears the dominant control upon DMS concentrations and remains a useful shorthand to prediction without fully resolving the biological processes involved. However, the implied relationship between incident solar/ultraviolet radiation dose and sea surface DMS concentrations (modulated by MLD) is critical for closing a climate feedback loop.

scholarly journals Prediction of penetration rate in drilling operations: a comparative study of three neural network forecast methods

2021 ◽  
Vol 11 (2) ◽  
pp. 805-818
Author(s):  
Ehsan Brenjkar ◽  
Ebrahim Biniaz Delijani ◽  
Kasra Karroubi
Keyword(s):  
Neural Network ◽  
Drilling Process ◽  
Backpropagation Algorithm ◽  
Nsga Ii ◽  
Time Saving ◽  
Selection Step ◽  
Drilling Operations ◽  
Input Variables ◽  
Southwestern Iran ◽  
The Relationship

AbstractOptimizing purposes of the drilling process include reduction in time, saving costs, and increasing efficiency, which requires optimization of controllable variables and variables affecting the drilling process. Drilling optimization is directly related to maximizing the rate of penetration (ROP). However, estimation of ROP is difficult due to the complexity of the relationship between the variables affecting the drilling process. The main goal of this study is to develop three computational intelligence (CI)-based models including multilayer perceptron neural network optimized by backpropagation algorithm (BP-MLPNN), cascade-forward neural network optimized by backpropagation algorithm, and radial basis function neural network optimized by biogeography-based optimization algorithm (BBO-RBFNN) to estimate ROP. Also, in order to broaden the comparisons, some conventional ROP models from the literature were employed. The required data were collected from the well log unit and the final drilling reports of four drilled wells in two different oil fields in southwestern Iran. Firstly, all data were preprocessed to remove outliers; then the overall noises of the data were reduced by implementing Savitzky–Golay smoothing filter. In the next stage, nine input variables were selected during a feature selection step by combining the BP-MLPNN and NSGA-II algorithm. The results of this study showed that developed CI-based models more accurate than conventional ROP models. Also, a survey of statistical indices and graphical error tools proved that BBO-RBFNN model has the highest performance to predict ROP with values of APRE, AAPRE, RMSE and R2 equal to  − 0.603, 5.531, 0.490 and 0.948, respectively.

scholarly journals Neural Network for Estimating Daily Global Solar Radiation Using Temperature, Humidity and Pressure as Unique Climatic Input Variables

2016 ◽  
Vol 07 (03) ◽  
pp. 94-103 ◽  
Author(s):  
Victor Adrian Jimenez ◽  
Amelia Barrionuevo ◽  
Adrian Will ◽  
Sebastián Rodríguez
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
Input Variables

Development of a KOH Defect Etching Furnace with Absolute In-Situ Temperature Measurement Capability

2005 ◽  
Vol 483-485 ◽  
pp. 283-286 ◽  
Author(s):  
Sakwe Aloysius Sakwe ◽  
Z.G. Herro ◽  
Peter J. Wellmann
Keyword(s):  
Calibration Curve ◽  
Temperature Sensor ◽  
Absolute Temperature ◽  
Temperature Measurements ◽  
Research Groups ◽  
Etch Pits ◽  
Measurement Capability ◽  
Etch Pit ◽  
Defect Characterisation

Etching temperature and time are important parameters in the etching of SiC single crystals in molten KOH for defect studies. However, comparison of results of different research groups is difficult because of the way temperature measurements are being carried out. Until now the temperature of the melt has been measured indirectly with a temperature sensor placed outside the melt on the outer walls of the crucible of the etching furnace, resulting in varying etching conditions for varying setup designs. In this paper we developed an etching furnace with the capability of measuring the absolute temperature in-situ directly in the KOH melt. A new thermoelement, resistant to hot molten KOH was developed. Temperature profile measurements of the molten KOH were carried out and a calibration curve of the furnace was obtained. Based on our temperature measurements, we found that etching at 530 °C for 5 minutes was optimal for defect characterisation, both for defect statistics and for distinguishing between the etch pit morphologies. At 550 °C the etch pits become too large, overlap each other and the etching is no longer defect selective.

Experimental Study on Calibration Model Based on Pt100 Temperature Sensor

2013 ◽  
Vol 798-799 ◽  
pp. 402-406
Author(s):  
Peng Fei Li ◽  
Cheng Yv ◽  
Yong Ping Yang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Bp Neural Network ◽  
Temperature Sensor ◽  
Least Square Method ◽  
Least Square ◽  
Calibration Model ◽  
Calibration Experiment ◽  
The Relationship ◽  
Lm Algorithm

In order to improve measuring-temperature accuracy of the PT100 temperature sensor, we conduct multi-point calibration experiment. The BP neural network based on LM algorithm can process experimental data and the least square method can fit out more accurate formula that express the relationship between the temperature and resistance. It is available that this arithmetic that the interrelated experiment demonstrate its accuracy improve precision of the PT100 temperature sensor.This arithmetic can be applied to the calibration test.

scholarly journals Errors of Naturally Ventilated Air Temperature Measurements in a Spatial Observation Network

2008 ◽  
Vol 25 (11) ◽  
pp. 2145-2151 ◽  
Author(s):  
Matthias Mauder ◽  
R. L. Desjardins ◽  
Zhiling Gao ◽  
Ronald van Haarlem
Keyword(s):  
Solar Radiation ◽  
Air Temperature ◽  
Agricultural Land ◽  
Sensible Heat Flux ◽  
Temperature Sensors ◽  
Radiative Heating ◽  
Temperature Measurements ◽  
Observation Network ◽  
Temporal Averaging ◽  
Radiation Shields

Abstract A spatial network of 25 air temperature sensors was deployed over an area of 3.5 km × 3.5 km of agricultural land, aiming to calculate the sensible heat flux by spatial averaging instead of temporal averaging. Since temperature sensors in naturally ventilated solar radiation shields were used for these measurements, a correction for radiative heating had to be applied. In this study, the approach of Anderson and Baumgartner was adapted to the cube-shaped HOBO solar radiation shields. This semiempirical correction depends on the shield’s area normal to the sun in addition to solar radiation and wind speed. The required correction coefficients, which can be universally applied for this type of shield, were obtained through comparison with fan-aspirated temperature measurements at one site. The root-mean-square error of the HOBO temperature measurements was reduced from 0.49° to 0.15°C after applying this radiation correction.

Profile Imaging of Silicon Surface Reconstructions

1985 ◽  
Vol 43 ◽  
pp. 262-263
Author(s):  
O.L. Krivanek ◽  
G.J. Wood
Keyword(s):  
Electron Microscopy ◽  
Surface Structure ◽  
Structure Determination ◽  
Silicon Surface ◽  
Good Resolution ◽  
Surface Reconstructions ◽  
Bulk Structure ◽  
Point To Point ◽  
The Relationship

Electron microscopy at 0.2nm point-to-point resolution, 10-10 torr specimei region vacuum and facilities for in-situ specimen cleaning presents intere; ing possibilities for surface structure determination. Three methods for examining the surfaces are available: reflection (REM), transmission (TEM) and profile imaging. Profile imaging is particularly useful because it giv good resolution perpendicular as well as parallel to the surface, and can therefore be used to determine the relationship between the surface and the bulk structure.

A combined ultrastructural and gene molecular research for the relationship between human uterine cervical carcinoma and human papillomavirus

1990 ◽  
Vol 48 (3) ◽  
pp. 204-205
Author(s):  
Kun Lee ◽  
Jingyi Si ◽  
Ricai Han ◽  
Wei Zhang ◽  
Bingbing Tan ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Human Papillomavirus ◽  
Intraepithelial Neoplasia ◽  
Hpv Infection ◽  
Dot Blot ◽  
Homologous Sequences ◽  
Normal Cervical Epithelium ◽  
The Relationship ◽  
Chronic Cervicitis

There are more supports for the view that human papillomavirus (HPV) infection might be an etiological factor in the development of cervical cancer when the association of persistent condylomata is considered. Biopsies from 318 cases with squamous cell carcinoma of uterine cervix, 48 with cervical and vulvar condylomata, 14 with cervical intraepithelial neoplasia (CIN), 34 with chronic cervicitis and 24 normal cervical epithelium were collected from 5 geographic regions of China with different cervical cancer mortalities. All specimens were prepared for Dot blot, Southern blot and in situ DNA-DNA hybridizations by using HPV-11, 16, 18 DNA labelled with 32P and 3H as probes to detect viral homologous sequences in samples. Among them, 32 cases with cervical cancer, 27 with condyloma and 10 normal cervical epitheliums were randomly chosen for comparative EM observation. The results showed that: 1), 192 out of 318 (60.4%) cases of cervical cancer were positive for HPV-16 DNA probe (Table I)

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