Refrigerant falling film evaporation review: Description, fluid dynamics and heat transfer

On thermoeconomic analysis of a single-effect mechanical vapor compression desalination system

A low-temperature multi-effect desalination system powered by the cooling water of a diesel engine

The paper introduces the artificial intelligence (AI) approach as a general method for the design and optimization study of heat exchangers. Genetic Algorithms (GA) and Artificial Neural Networks (ANN) are applied in the paper. An AGENN model, combining Genetic Algorithms with Artificial Neural Networks, was developed and validated against the desired data on a large falling film evaporator. A broad range of operating conditions and geometric configurations are considered in the study. Four kinds of tubes are deliberated, including plain and enhanced tubes. Different tube pass arrangements, i.e., top-to-bottom, bottom-to-top, and side-by-side, are discussed. Finally, the effects of liquid refrigerant mass flow rate, as well as the number of flooded tubes on the performance of the evaporator, are analyzed. The total heat transfer rate of the evaporator, predicted by the model, is in good agreement with the desired data; the maximum error is lower than ±3%. The highest heat transfer rate of the evaporator is 1140.01 kW and corresponds to Turbo EHP tubes, and bottom-to-top tubes pass arrangements, which guarantee the best thermal energy conversion. The presented approach can be referred to as a complementary technique in heat exchanger design procedures, besides the common rating and sizing tasks. It is an effective and alternative method for the existing approaches, considering the complexity of analytical and numerical techniques as well as the high costs of experiments.

https://www.mdpi.com/1996-1073/12/23/4441/pdf

A General Approach in Optimization of Heat Exchangers by Bio-Inspired Artificial Intelligence Methods

A comparison of flow characteristics of refrigerants flowing through adiabatic straight and helical capillary tubes

The performance of the horizontal heat transfer tube bundles in falling film evaporators in large compression refrigeration systems was investigated with numerical simulation in this paper. Four types of tubes, including plain tubes, and enhanced surface tubes of Turbo-B, Turbo-BII and Turbo-EHP, were employed in the simulation. Some factors, such as tube kind, tube pass arrangement, dry patch area on tube surface, liquid refrigerant flow rate, and number of flooded tubes, were analyzed based on simulated results. In the study, the maldistribution of liquid refrigerant flow caused by the distributor apparatus was discussed, which severely affects the performance of falling film evaporators according to the simulation. Some calculated results were verified by the experiment. These discussions and results can be used to guide the design of falling film evaporators under realistic flow conditions.

The heat transfer performance of horizontal tube bundles in large falling film evaporators

The capillary tube is often served as an expansion device in small refrigeration and air-conditioning systems. In this paper, a generalized correlation for predicting the refrigerant mass flow rate through the adiabatic capillary tube is developed with approximate analytic solutions based on the extensive data for R12, R22, R134a, R290, R600a, R410A, R407C, and R404A, in which a homogeneous equilibrium model for two-phase flow is employed, and there is a subcooled liquid or saturated two-phase mixture at the inlet of the capillary tubes. The collected database about capillary tubes covers the inner diameter from 0.5 mm to 2 mm, the tube length from 0.5 m to 5 m, the condensing temperature from 20 °C to 60 °C, the subcooling from 0 °C to 20 °C, and the quality from 0 to 0.3 at the inlet. Assessments for the correlation are made with some experimental data for R12, R22, R134a, R290, R407C, R410A, and R404A obtained from the open literature and some existing correlations based on the experimental database also. The present correlation yields an average deviation of −0.83% and a standard deviation of 9.02% from the database.

A generalized correlation for the characteristics of adiabatic capillary tubes

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Li Yang

Author Tools

Chat about Author

Papers

The heat transfer performance of horizontal tube bundles in large falling film evaporators

A generalized correlation for the characteristics of adiabatic capillary tubes