## This content will become publicly available on January 11, 2020

# Parametric analysis and optimization of CO _{2} trans-critical cycle for chiller application in a warm climate

## Abstract

This article presents parametric analysis and subsequent optimization of a CO _{2} trans-critical chiller system operating in a warm climate. Additionally, high side pressure and gas cooler face velocities are two controllable parameters investigated. COP of the system is analyzed and optimized using a developed and validated mathematical model. The mean relative error of prediction in COP is found to be within ±10% for physics-based model and within ±1% for Artificial Neural Network (ANN) based model of the experimental findings. The validated mathematical model is utilized to predict optimal high side pressure as well as gas cooler face velocity for the varying ambient and evaporation conditions to achieve best possible COP. A possibility of 5.31% improvement in COP is found based on the optimization of parameters. In conclusion, the proposed methodology is deemed suitable for design and testing of control system for maximization of energy efficiency.

- Authors:

- BITS-Pilani, Rajasthan (India)
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- IITRAM, Ahmedabad, Gujarat (India)

- Publication Date:

- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

- Sponsoring Org.:
- USDOE

- OSTI Identifier:
- 1558562

- Grant/Contract Number:
- AC05-00OR22725

- Resource Type:
- Accepted Manuscript

- Journal Name:
- Applied Thermal Engineering

- Additional Journal Information:
- Journal Volume: 150; Journal Issue: C; Journal ID: ISSN 1359-4311

- Publisher:
- Elsevier

- Country of Publication:
- United States

- Language:
- English

- Subject:
- 42 ENGINEERING; CO2 trans-critical; Chiller; Warm climate; Mathematical modelling; ANN

### Citation Formats

```
Purohit, Nilesh, Sharma, Vishaldeep, Fricke, Brian A., Gupta, Dileep Kumar, and Dasgupta, Mani Sankar. Parametric analysis and optimization of CO2 trans-critical cycle for chiller application in a warm climate. United States: N. p., 2019.
Web. doi:10.1016/j.applthermaleng.2019.01.023.
```

```
Purohit, Nilesh, Sharma, Vishaldeep, Fricke, Brian A., Gupta, Dileep Kumar, & Dasgupta, Mani Sankar. Parametric analysis and optimization of CO2 trans-critical cycle for chiller application in a warm climate. United States. doi:10.1016/j.applthermaleng.2019.01.023.
```

```
Purohit, Nilesh, Sharma, Vishaldeep, Fricke, Brian A., Gupta, Dileep Kumar, and Dasgupta, Mani Sankar. Fri .
"Parametric analysis and optimization of CO2 trans-critical cycle for chiller application in a warm climate". United States. doi:10.1016/j.applthermaleng.2019.01.023.
```

```
@article{osti_1558562,
```

title = {Parametric analysis and optimization of CO2 trans-critical cycle for chiller application in a warm climate},

author = {Purohit, Nilesh and Sharma, Vishaldeep and Fricke, Brian A. and Gupta, Dileep Kumar and Dasgupta, Mani Sankar},

abstractNote = {This article presents parametric analysis and subsequent optimization of a CO2 trans-critical chiller system operating in a warm climate. Additionally, high side pressure and gas cooler face velocities are two controllable parameters investigated. COP of the system is analyzed and optimized using a developed and validated mathematical model. The mean relative error of prediction in COP is found to be within ±10% for physics-based model and within ±1% for Artificial Neural Network (ANN) based model of the experimental findings. The validated mathematical model is utilized to predict optimal high side pressure as well as gas cooler face velocity for the varying ambient and evaporation conditions to achieve best possible COP. A possibility of 5.31% improvement in COP is found based on the optimization of parameters. In conclusion, the proposed methodology is deemed suitable for design and testing of control system for maximization of energy efficiency.},

doi = {10.1016/j.applthermaleng.2019.01.023},

journal = {Applied Thermal Engineering},

number = C,

volume = 150,

place = {United States},

year = {2019},

month = {1}

}