Temperature Distribution
Temperature Distribution - Related Videos
09:48
Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
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Cited by 8 •
2016
We demonstrate use of a fiber optic distributed sensor for mapping the temperature field of mixing air jets. The Rayleigh scattering-based sensor generates thousands of data points along a single fiber to provide exceptional spatial resolution that is unattainable with traditional sensors such as thermocouples.
06:42
Plasmonic Photothermal Cancer Therapy: Nanoparticle-embedded Tumor-tissue-mimicking Phantoms for Visualizing Photothermal Temperature Distribution
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Cited by 1 •
2025
This article presents the protocol for preparing tumor-tissue phantoms that replicate optical properties for plasmonic photothermal therapy. It details phantom preparation, photothermal evaluations, and validation of the developed numerical model based on photothermal temperature measurements for assessing therapeutic parameters, offering an ethical, cost-effective alternative to in vivo studies for preliminary testing.
03:58
Temperature Gradient Assay: A Method to Test Temperature Preference in Drosophila Larvae
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2023
Drosophila fruit flies can distinguish small changes in temperature and therefore select environments with favorable thermal conditions. This video describes a behavioral assay that tests the temperature preference of Drosophila larvae on a linear thermal gradient.
01:25
Drug Distribution: Volume of Distribution
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2023
The volume of distribution refers to the theoretical volume necessary to contain the entire amount of an administered drug at the same concentration observed in the blood plasma. The body's intracellular fluid compartment, which makes up two-thirds of the total body water, is contrasted with the extracellular fluid compartment—comprising plasma and interstitial fluid—that accounts for one-third. The volume of distribution can vary depending on the characteristics of the drug.
03:40
Temperature Dependence - Instructor Prep
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2020
Source: Smaa Koraym at Johns Hopkins University, MD, USA Preparation of 0.5 M Fe(NO3)3Expand Here, we show the preparation of a solution for 10 students working in pairs, with some excess. Please adjust quantities as needed. To set up for this lab experiment, wear the appropriate personal protective equipment, including a lab coat, chemical splash goggles, and gloves Prepare 250 mL of a 0.5 M aqueous solution of iron (III) nitrate. Measure out 50.5 g of iron nitrate...
FAQs
Material properties govern how readily heat moves, while geometry and boundary conditions determine where heat enters, leaves, or remains constrained. Heat sources add energy locally, and conduction, convection, and radiation redistribute it through the system. Changing any of these factors can steepen or flatten thermal gradients, which directly affects predicted performance and thermal design decisions.
Thermal gradients indicate that different regions experience different temperatures, creating conditions that can drive expansion and deformation. In engineering systems, this matters because uneven thermal behavior may distort components, reduce reliability, or contribute to material failure. Mapping the gradient helps engineers locate potentially hazardous hot or highly stressed regions and improve design safety.
Conduction transfers heat through a material, whereas convection and radiation describe other paths by which thermal energy is redistributed. Considering these mechanisms together prevents an analysis from attributing all temperature changes to a single source. The resulting thermal picture is more useful for evaluating engineered systems exposed to multiple heat-transfer conditions.
A practical workflow starts by identifying the heat sources, material properties, geometry, and boundary conditions that govern the system. Engineers then calculate expected temperatures with numerical models or measure them experimentally, including through thermal imaging. Comparing the resulting distribution with performance, deformation, overheating, or failure concerns supports design refinement and engineering decisions.
Numerical models are useful when engineers need to predict how design choices affect temperatures across a system, while thermal imaging provides a way to observe surface temperature patterns. Used together, they can compare predicted and measured behavior, reveal unexpected hot regions, and support evaluation of energy efficiency, reliability, and safety before or during operation.
In engineering, this analysis informs the design and evaluation of heat exchangers, electronic devices, buildings, engines, and manufacturing processes. Each application can present different heat sources, geometries, materials, and boundary conditions. Examining the resulting patterns helps engineers improve energy efficiency while reducing the likelihood of overheating, unwanted deformation, or material failure.