First and Second-Law Efficiency Analysis and ANN Prediction of a Diesel Cycle with Internal Irreversibility, Variable Specific Heats, Heat Loss, and Friction Considerations

Rashidi, Majid; Hajipour, A.; Mousapour, A.; Ali, Mohamed; Xie, Gongnan; Freidoonimehr, Navid

doi:10.1155/2014/359872

Cited by 9 publications

(6 citation statements)

References 23 publications

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“…where Ra is the gas constant of the air, which is 0.287 kJ/kgK and Rrgthe gas constant of the residual gas, which is 0.293 kJ/kgK. The compression ratio, r, of the engine is [26][27][28][29]:…”

Section: Theoretical Modelmentioning

confidence: 99%

The effects of different engine material properties on the performance of a diesel engine at maximum combustion temperatures

Palacı

Gonca

2020

THERM SCI

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In this study, the influences of various engine materials such as palladium, titanium, thorium, zirconium, vanadium, alumina, aluminum bronze, copper, iron (gray cast), manganese, nickel, cobalt, and carbon steel on the effective efficiency and effective power with respect to the variation of equivalence ratio at the maximum combustion temperatures. In-cylinder gas temperatures have been determined with respect to the melting temperatures and the performance values have been calculated with respect to the variation of the gas temperatures. The results indicated that alumina provides the maximum performance values as aluminum bronze gives the minimum performance values due to the combustion temperatures. Furthermore, the equivalence ratios which give the maximum performance characteristics have been parametrically described. The obtained results can be assessed by engine designers and manufacturers to choose suitable engine material.

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Section: Theoretical Modelmentioning

confidence: 99%

The effects of different engine material properties on the performance of a diesel engine at maximum combustion temperatures

Palacı

Gonca

2020

THERM SCI

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“…In the simulation, the performance parameters at the standard condition are determined as in Table . The EFP ( P ef ) and EFPD ( P d ) are obtained as below:

P_{ef} = {\dot{Q}}_{in} - {\dot{Q}}_{out} - P_{fr}, P_{d} = \frac{P_{italicef}}{V_{T}},

where

{\dot{Q}}_{in}

stands for the total heat input (processes 2‐3, 3‐4, and 4‐5) and

{\dot{Q}}_{out}

stands for the heat output (processes 6‐7 and 7‐1).

\begin{matrix} lefttrue \\ {\overset{Q}{true}}_{italicin} = {\overset{Q}{true}}_{f, c} - {\overset{Q}{true}}_{italicht} = {\overset{m}{true}}_{T} [\int_{T_{2}}^{T_{3}} C_{V} dT + \int_{T_{3}}^{T_{4}} C_{P} dT + R_{g} T_{5} ln (r_{T})] = \\ {\overset{m}{true}}_{T} (\begin{matrix} left \\ ([],, (\begin{matrix} left \\ 2.506 \cdot 10^{- 11} \frac{T^{3}}{3} + 1.454 \cdot 10^{- 7} \frac{T^{2.5}}{2.5} - 4.246 \cdot 10^{- 7} \frac{T^{2}}{2} + 3.162 \cdot 10^{- 5} \frac{T^{1.5}}{1.5} + \\ 1.0433 T - 1.512 \cdot 10^{4} (- \frac{T^{- 0.5}}{0.5}) + 3.063 \cdot 10^{5} (- T^{- 1}) \end{matrix})) \end{matrix}) \end{matrix}

…”

Section: Theoretical Modelmentioning

confidence: 99%

“… β is named as pressure ratio. T 2 and T 6 are derived as follows:

T_{2} = \frac{T_{2S} + T_{1} ((), η_{C} - 1)}{η_{normalC}},

T_{6} = T_{5} + η_{E} ((), T_{6S} - T_{5}),

where η C and η E are the isentropic efficiencies of compression process and expansion process, respectively. Other cycle design parameters are defined as follows: cycle pressure ratio ( λ ), cycle temperature ratio ( α ), Takemura cycle ratio ( r T ), exhaust temperature ratio ( ζ ), and cutoff ratio ( ρ ), which are ordered as follows:

λ = P_{\max} / P_{\min} = P_{3} / P_{1},

α = \frac{T_{\max}}{T_{\min}} = \frac{T_{4}}{T_{1}} = \frac{λρ}{r} = 1 + \frac{r^{k - 1} - 1}{η {}_{C}},

r_{T} = v_{5} / v_{4},

ζ = \frac{T_{6}}{T_{7}},

ρ = v_{4} / v_{3} = T_{4} / T_{3}

…”

Section: Theoretical Modelmentioning

confidence: 99%

See 1 more Smart Citation

Performance analysis of a novel eco‐friendly internal combustion engine cycle

Gonca

Şahi̇n

2019

Int J Energy Res

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Summary The Miller cycle applications have been performed to diminish NOx released from internal combustion engines (ICEs), in recent years. The Miller cycle provides decreased compression ratio and enhanced expansion ratio; hereby, maximum in‐cylinder combustion temperatures diminish, and NOx formations slow down remarkably. Another less‐known method is Takemura cycle application, which provides heat addition into engine cylinder at constant combustion temperatures. In this study, a novel cycle including the Miller cycle and the Takemura cycle has been developed by using novel numerical models and computing methods with seven processes and a novel way to decrease NOx emissions at higher levels compared with the single applications of known cycles. A comprehensive performance examination of the proposed cycle engine in terms of performance characteristics such as effective power (EFP), effective power density (EFPD), exergy destruction (X), exergy efficiency (ε), and ecological coefficient of performance (ECOP) has been conducted. The impacts of engine operating and design parameters on the performance characteristics have been computationally examined. Furthermore, irreversibilities depending on incomplete combustion loss (INCL), exhaust output loss (EXOL), heat transfer loss (HTRL), and friction loss (FRL) have been considered in the performance simulations. The minimum exergy destruction and maximum performance specifications have been observed with 30 of the compression ratio. Maximum effective power values have been obtained at range between 1 and 1.2 of equivalence ratio. The optimum range for exergy efficiency is between 0.8 and 1 of equivalence ratio. Increasing engine speed has provided enhancing effective power. However, an optimum range has been found for the exergy efficiency that is interval of 3000 to 4000 rpm. The results obtained can be assessed by researchers studying on modeling of the engine systems and designs.

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“…Interested readers can refer to Wang et al 20 They used genetic algorithm (GA) to search the optimal parameter combination of support vector regression (SVR) 21 and the results showed that their GA-SVR method was better than the mathematical regression model and artificial neural network (ANN). [22][23][24]…”

Section: Svddmentioning

confidence: 99%

An effective multivariate control chart for detecting small mean shifts using support vector data description

Xia

Zheng

Liu

et al. 2018

Advances in Mechanical Engineering

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Conventional multivariate cumulative sum control charts are more sensitive to small shifts than [Formula: see text] control charts, but they cannot get the knowledge of manufacturing process through the learning of in-control data due to the characteristics of their own structures. To address this issue, a modified multivariate cumulative sum control chart based on support vector data description for multivariate statistical process control is proposed in this article, which is named [Formula: see text] control chart. The proposed control chart will have both advantages of the multivariate cumulative sum control charts and the support vector data description algorithm, namely, high sensitivities to small shifts and learning abilities. The recommended values of some key parameters are also given for a better application. Based on these, a bivariate simulation experiment is conducted to evaluate the performance of the [Formula: see text] control chart. A real industrial case illustrates the application of the proposed control chart. The results also show that the [Formula: see text] control chart is more sensitive to small shifts than other traditional control charts (e.g. [Formula: see text] and multivariate cumulative sum) and a D control chart based on support vector data description.

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First and Second-Law Efficiency Analysis and ANN Prediction of a Diesel Cycle with Internal Irreversibility, Variable Specific Heats, Heat Loss, and Friction Considerations

Cited by 9 publications

References 23 publications

The effects of different engine material properties on the performance of a diesel engine at maximum combustion temperatures

The effects of different engine material properties on the performance of a diesel engine at maximum combustion temperatures

Performance analysis of a novel eco‐friendly internal combustion engine cycle

An effective multivariate control chart for detecting small mean shifts using support vector data description

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