Rüchardt’s experiment treated by Fourier transform

Caccamo, Maria Teresa; Castorina, Giuseppe; Catalano, F; Magazù, Salvatore

doi:10.1088/1361-6404/aaf66c

Cited by 7 publications

(7 citation statements)

References 34 publications

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“…Another analysis method that would naturally extract the frequency component would be Fourier transforms (FT) [10,17] and their time-dependent counterpart of wavelet analysis [18,19]. However, care would have to be taken with the interpretation of any FT since the presence of the magnet-coil system means the signal is not just dSHM.…”

Section: Resultsmentioning

confidence: 99%

“…From its initial reporting in 1929 [1] there has been a periodic updating of both the experiments [7][8][9][10][11] and its theoretical basis [12]. There has been a propensity to update the analysis and data-capture as more sophisticated data-capture and computer techniques become commonplace in undergraduate teaching laboratories, for example, with the advent of digital computing in teaching laboratories [13].…”

Section: Introduction To the Rüchardt Experimentsmentioning

confidence: 99%

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The Rüchardt experiment revisited: using simple theory, accurate measurement and python based data analysis

Shearwood

Sloan

2023

Eur. J. Phys.

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This project uses the Rüchardt experiment to determine the ratio of specific heats and hence the number of degrees of freedom f of different gases by measuring the frequency of damped simple harmonic motion where the gas provides the Hooke's law like spring of a cylinder-piston system. This project links mechanics, electromagnetism, thermodynamics, statistical mechanics and quantum mechanics making it an excellent synoptic experiment for a year 2 undergraduate student and we detail our implementation of this team-based learning project. We present, for the Rüchardt experiment, simple derivations of the main relationships that govern the experiment, a detailed data analysis of the physics of the apparatus and of the experimental data. We find f(He) = 3.48 ± 0.14, f(N2) = 4.92 ± 0.24$, f(Air) = 4.96 ± 0.25$, f(CO2) = 6.46 ± 0.39 at room temperature and atmospheric pressure. The results for CO2 requires a statistical analysis of its vibrational modes. These results show that the expected results can be measured using fairly simple apparatus, coupled with careful analysis of large data sets.

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Section: Resultsmentioning

confidence: 99%

Section: Introduction To the Rüchardt Experimentsmentioning

confidence: 99%

The Rüchardt experiment revisited: using simple theory, accurate measurement and python based data analysis

Shearwood

Sloan

2023

Eur. J. Phys.

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“…This is know as Foehn effect. The adiabatic expansions and compressions are well known examples of thermodynamic processes and have been recently investigated also by means of the Rüchardt's experiment [12] and the frequency analysis procedure [13]. The Stau and Foehn effects can be thermodynamically described by making use of the first thermodynamic law, which can be written as:…”

Section: Weather Conditions and Complex Orography: The Cases Of The S...mentioning

confidence: 99%

A didactic approach to the Machine Learning application to weather forecast

Raffaele,

Caccamo,

Castorina

et al. 2020

Preprint

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We propose a didactic approach to use the Machine Learning protocol in order to perform weather forecast. This study is motivated by the possibility to apply this method to predict weather conditions in proximity of the Etna and Stromboli volcanic areas, located in Sicily (south Italy). Here the complex orography may significantly influence the weather conditions due to Stau and Foehn effects, with possible impact on the air traffic of the nearby Catania and Reggio Calabria airports. We first introduce a simple thermodynamic approach, suited to provide information on temperature and pressure when the Stau and Foehn effect takes place. In order to gain information to the rainfall accumulation, the Machine Learning approach is presented: according to this protocol, the model is able to "learn" from a set of input data which are the meteorological conditions (in our case dry, light rain, moderate rain and heavy rain) associated to the rainfall, measured in mm. We observe that, since in the input dataset provided by the Salina weather station the dry condition was the most common, the algorithm is very accurate in predicting it. Further improvements can be obtained by increasing the number of considered weather stations and time interval.

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“…The upward movement of the air on the windward side of a mountain range (known as the Stau effect) facilitates the formation of clouds and rain. Adiabatic expansions and compressions are well-known examples of thermodynamic processes and have recently been investigated also through the Rüchardt experiment [2] and the frequency analysis procedure [3].…”

Section: Introductionmentioning

confidence: 99%

The Role of Physical Parameterizations on the Numerical Weather Prediction: Impact of Different Cumulus Schemes on Weather Forecasting on Complex Orographic Areas

Castorina

Caccamo

Colombo³

et al. 2021

Atmosphere

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Numerical weather predictions (NWP) play a fundamental role in air quality management. The transport and deposition of all the pollutants (natural and/or anthropogenic) present in the atmosphere are strongly influenced by meteorological conditions such as, for example, precipitation and winds. Furthermore, the presence of particulate matter in the atmosphere favors the physical processes of nucleation of the hydrometeors, thus increasing the risk of even extreme weather events. In this framework of reference, the present work aimed to improve the quality of weather forecasts related to extreme events through the optimization of the weather research and forecasting (WRF) model. For this purpose, the simulation results obtained using the WRF model, where physical parametrizations of the cumulus scheme can be optimized, are reported. As a case study, we considered the extreme meteorological event recorded on 25 November 2016, which affected the whole territory of Sicily and, in particular, the area of Sciacca (Agrigento). In order, to evaluate the performance of the proposed approach, we compared the WRF model outputs with data obtained by a network of radar and weather stations. The comparison was performed through statistical methods on the basis of a “contingency table”, which allowed for ascertaining the best suited physical parametrizations able to reproduce this event.

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Rüchardt’s experiment treated by Fourier transform

Cited by 7 publications

References 34 publications

The Rüchardt experiment revisited: using simple theory, accurate measurement and python based data analysis

The Rüchardt experiment revisited: using simple theory, accurate measurement and python based data analysis

A didactic approach to the Machine Learning application to weather forecast

The Role of Physical Parameterizations on the Numerical Weather Prediction: Impact of Different Cumulus Schemes on Weather Forecasting on Complex Orographic Areas

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