Jove
Visualize
Contact Us

Related Concept Videos

Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

152
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
152
Transformers01:26

Transformers

1.1K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.1K
Three-Winding Transformers01:19

Three-Winding Transformers

226
Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
226
Types Of Transformers01:16

Types Of Transformers

976
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
976
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

422
The practical equivalent circuits of single-phase two-winding transformers exhibit significant deviations from their idealized versions due to the inherent properties of winding resistance and finite core permeability. These properties result in real and reactive power losses, affecting the transformer's performance. Understanding these deviations is crucial for designing more efficient transformers.
In a practical transformer, each winding exhibits resistance and leakage reactance. The...
422
Reducing Line Loss01:18

Reducing Line Loss

152
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
152

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Nanostructured Insulated Electrodes: Fabrication and Characterization.

Langmuir : the ACS journal of surfaces and colloids·2026
Same author

Associations Between Microaggressions, Discrimination, and Brain Health Among Black Women Living with HIV.

Journal of racial and ethnic health disparities·2026
Same author

Mapping the mammalian dark metabolome by <i>in vivo</i> isotope tracing.

bioRxiv : the preprint server for biology·2026
Same author

Language model-guided anticipation and discovery of mammalian metabolites.

Nature·2026
Same author

Deployment of Solid-Supported Natural Deep Eutectic Solvents via Unmanned Aerial Vehicles to Preconcentrate Contaminants from Environmental Water Samples.

ACS omega·2025
Same author

Menthol-thymol NADES as a fungicidal and chemosensitizing agent against multidrug-resistant <i>Candida albicans</i>: ROS induction, efflux pump inhibition, and synergy with fluconazole.

Frontiers in pharmacology·2025
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jul 2, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.8K

Predicting the formation of NADES using a transformer-based model.

Lucas B Ayres1, Federico J V Gomez2, Maria Fernanda Silva2

  • 1Department of Chemistry, Clemson University, 211 S. Palmetto Blvd, Clemson, SC, 29634, USA.

Scientific Reports
|February 22, 2024
PubMed
Summary

Machine learning predicts new natural deep eutectic solvents (NADES) for greener chemistry. This approach accelerates the discovery of novel, stable NADES mixtures, advancing pharmaceutical and agricultural applications.

More Related Videos

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

861
Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training
06:20

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training

Published on: December 6, 2024

2.8K

Related Experiment Videos

Last Updated: Jul 2, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.8K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

861
Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training
06:20

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training

Published on: December 6, 2024

2.8K

Area of Science:

  • Green Chemistry
  • Computational Chemistry
  • Materials Science

Background:

  • Natural deep eutectic solvents (NADES) offer sustainable alternatives to traditional organic solvents in various industries.
  • Current NADES development relies heavily on empirical methods, limiting the discovery of novel mixtures.
  • A data-driven approach is needed to overcome the limitations of traditional NADES discovery.

Purpose of the Study:

  • To develop a machine learning model for predicting the formation of stable NADES.
  • To accelerate the discovery and design of new NADES with desired properties.
  • To reduce the reliance on empirical methods in NADES development.

Main Methods:

  • A transformer-based neural network was employed, leveraging natural language processing techniques.
  • The model was pre-trained on general chemical data (SMILES) and fine-tuned for binary classification of stable NADES formation.
  • The approach adapted language learning strategies for efficient pattern recognition with limited data and computational resources.

Main Results:

  • The algorithm successfully predicted 337 new stable eutectic mixtures from a natural compound database.
  • The model demonstrated the ability to predict components and molar ratios for NADES incorporating novel molecules.
  • Validation included previously reported NADES and the development of new ibuprofen-containing solvents.

Conclusions:

  • The proposed machine learning strategy significantly enhances the prediction and discovery of NADES.
  • This approach has the potential to revolutionize NADES screening and streamline the use of bioactive compounds in liquid formulations.
  • The method offers a powerful tool for advancing green chemistry and the pharmaceutical industry.