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Related Concept Videos

Precipitation Processes01:12

Precipitation Processes

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The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
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Precipitation Gravimetry01:03

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Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
In determining nickel by gravimetric analysis, a precipitant of ethanolic dimethylglyoxime is added to a hot nickel salt solution. This is quickly followed by the dropwise addition of dilute ammonia solution until precipitation occurs. A...
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Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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Typical Model Studies01:30

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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data
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Machine-learning informed macro-meteorological models for the near-maritime environment.

Christopher Jellen, Miles Oakley, Charles Nelson

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    New machine learning models improve optical turbulence prediction accuracy in near-maritime environments. These advanced models better track atmospheric turbulence (Cn2) compared to existing methods.

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    Area of Science:

    • Atmospheric Science
    • Optical Engineering
    • Machine Learning

    Background:

    • Macro-meteorological models predict optical turbulence (Cn2) using weather data.
    • Existing models exhibit limitations in accurately forecasting rapid Cn2 fluctuations, especially in near-maritime settings.

    Purpose of the Study:

    • To assess the predictive accuracy of current macro-meteorological models for Cn2 in near-maritime environments.
    • To develop and evaluate novel machine learning-based macro-meteorological models for enhanced Cn2 prediction.

    Main Methods:

    • Collected seven months of Cn2 field data using an 890 m scintillometer link over the Severn River.
    • Augmented Cn2 data with local meteorological measurements for comprehensive atmospheric data.
    • Applied machine learning techniques to train new models using measured Cn2 and environmental parameters.
    • Compared predictions from existing models against newly developed machine learning models.

    Main Results:

    • Existing macro-meteorological models were analyzed for prediction accuracy under various conditions.
    • New machine learning models demonstrated superior Cn2 prediction accuracy in most scenarios.
    • The developed models showed improved ability to track optical turbulence dynamics.

    Conclusions:

    • Machine learning integration significantly enhances macro-meteorological model performance for optical turbulence prediction.
    • The developed models offer a more accurate approach to forecasting Cn2 in challenging near-maritime environments.
    • Further research into model tuning and architecture can potentially yield even greater performance improvements.