Reliability and reliability-based sensitivity analysis of shell and tube heat exchangers using Monte Carlo Simulation

Azarkish, Hassan; Rashki, Mohsen

doi:10.1016/j.applthermaleng.2019.113842

Cited by 23 publications

(7 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Where, is denoted as diameter, is indicated as Reynolds number, and is represented as Prandtl number. The friction factor is attained utilizing the correlation of Konakov that is mathematically denoted in equation (2). Table 2 represents the heat transfer coefficient and Reynolds's number of STHEs.…”

Section: Tube Side Heat Transfer Coefficientmentioning

confidence: 99%

“…In recent times, world's electricity generation mainlybased onconventional fossil and nuclear energy sources. Majority of the electricity supply is generated from fossil fuels based thermal power plants using coal, oil and natural gas [1][2]. Presently, these energy sources are facing numerous issues like increasing prices, over dependence on fewer countries thosehave fossil fuel supplies, and concerns over change in climatic conditions [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimum Design of Shell and Tube Heat Exchangers using Modified Kinetic Gas Molecule Optimizer for the use of Low Temperature in Organic Rankine Cycles

Reddy,

Reddy

2020

IJEAT

View full text Add to dashboard Cite

Shell and Tube Heat Exchangers (STHEs) plays a crucial role in an effective design of Organic Rankine Cycle (ORC) power plants.The main aim of this research work is to design a cost-effective ORC in order to exploit low to medium temperature geothermal fluid or low grade industrial waste heat. In this research work, modified Kinetic Gas Molecule Optimization (KGMO) algorithm was developed forfinding the optimized parameter settings of the power plant. In modified KGMO algorithm, feedback learning stage was included for improving the fitness of individual worst particles. In addition, the proposed optimization algorithm was tested on two dissimilar fluids such asR245fa and R134a in order to show the effectiveness of proposed scheme. The experimental investigation showed that the proposed scheme effectively improved the heat exchanger performance as related to the existing schemes.The enhancement factor of proposed scheme was 2.8063 for R245fa fluid and 1.9346 for R134a fluid, which was better compared to the existing schemes; KGMO and Bell-Delaware method.

show abstract

Section: Tube Side Heat Transfer Coefficientmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimum Design of Shell and Tube Heat Exchangers using Modified Kinetic Gas Molecule Optimizer for the use of Low Temperature in Organic Rankine Cycles

Reddy,

Reddy

2020

IJEAT

View full text Add to dashboard Cite

show abstract

“…He et al (2018) combined BP neural network with the Monte Carlo method to evaluate the reliability of the gas storage unit. Azarkish and Rashki (2019) conducted a reliability-based sensitivity analysis of a shell and tube heat exchanger using Monte Carlo simulation. Chakraborty et al (2020) proposed a Monte-Carlo Markov Chain simulation approach for evaluating coverage-area reliability of mobile wireless sensor networks with multistate nodes.…”

Section: Introductionmentioning

confidence: 99%

Reliability assessment of ultra-deep oil and gas wellbore casing using data statistics and numerical simulations

Yang

Zhang

Wang

et al. 2021

Journal of Loss Prevention in the Process Industries

View full text Add to dashboard Cite

Ultra-deep oil and gas wells have become a new development trend in onshore oil and gas exploitation. However, Ultra-deep oil and gas wellbore casing is with high failure risk due to the harsh environment. It is essential to evaluate the reliability of wellbore casing. This paper assesses the operational reliability of wellbore casing using data statistics and numerical simulation. Firstly, the theoretical model for reliability analysis of wellbore casing is established, and the variables in the model are determined, including rock mechanics, cement ring, and casing string strength factors. Subsequently, considering the random distribution of model variables, many statistics and analyses are performed to determine the distribution parameters of the model variables. Eventually, Monte Carlo based numerical simulations are carried out to obtain the residual strength distribution and the reliability of wellbore casing. The production casing in the ultra-deep well with a depth of 6.5 km in China as an industrial case is used to illustrate the present study. It is observed that this study can be useful to guide a more accurate assessment of the reliability of ultra-deep wellbore casing.

show abstract

“…These approaches include support vector regression [18] and the use of artificial neural networks and have been adopted in the optimization of the thermal design of spacecraft. However, these methodologies require a detailed thermal mathematical model (DTMM) [19]- [21] to guarantee adequate accuracy of the metamodel and are time consuming to employ [22], [23]. Stout and Thumnnissen [24] proposed a Bayesian-based thermal modeling approach to optimize the thermal design of spacecraft, but the computational efficiency of their approach struggles to satisfy engineering requirements.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Optimization Strategy Based on Statistical Machine Learning for Spacecraft Thermal Design

et al. 2020

View full text Add to dashboard Cite

The thermal design of spacecraft becomes increasingly complicated as various advanced technologies are continuously introduced to the spacecraft. Determining and optimizing the uncertainties of a spacecraft thermal control system through global sensitivity analysis has long been an essential task for thermal engineers. It is a difficult task that relies heavily on engineering experience and is a timeintensive, trial-and-error endeavor that may not even lead to global optimization. Hence, an intelligent optimization strategy based on statistical machine learning for spacecraft thermal design, called IOSML, is proposed. An intelligent batch processing system (IBPS) based on MATLAB, Python, and NX/TMG realtime data interaction is designed. The IBPS uses a surrogate model to reduce the computational cost of global sensitivity analysis while using a detailed thermal mathematical model to maintain accuracy. We combine a Bayesian inference framework with a neural network surrogate spacecraft-thermophysical model that is 100× faster than numerical solvers. This paper first reports on a density-based global sensitivity analysis that evaluates the effect of design parameters on the temperature difference between the complementary metal-oxide-semiconductor and cold screen of the Lehman Alpha Solar Space Telescope detector. From 42 design parameters, the most sensitive four are selected for optimization, and the temperature difference and the boundary temperature are used as the objective function. Adopting IOSML, under no supervision, four design parameters are optimized through the IBPS, and the effectiveness of the algorithm is verified by comparison with traditional methods. Additionally, IOSML is versatile and can be used in various complex engineering applications to provide guidance for the better selection of appropriate parameters and optimization.

show abstract

Reliability and reliability-based sensitivity analysis of shell and tube heat exchangers using Monte Carlo Simulation

Cited by 23 publications

References 26 publications

Optimum Design of Shell and Tube Heat Exchangers using Modified Kinetic Gas Molecule Optimizer for the use of Low Temperature in Organic Rankine Cycles

Optimum Design of Shell and Tube Heat Exchangers using Modified Kinetic Gas Molecule Optimizer for the use of Low Temperature in Organic Rankine Cycles

Reliability assessment of ultra-deep oil and gas wellbore casing using data statistics and numerical simulations

Intelligent Optimization Strategy Based on Statistical Machine Learning for Spacecraft Thermal Design

Contact Info

Product

Resources

About