In the present work,paraffin phase change material is used as quenchant for the heat treatment of 42CrMo4 alloy and compared with water,air,and CuO doped paraffin.The samples were prepared based on ASTM E 8M-98 standa...In the present work,paraffin phase change material is used as quenchant for the heat treatment of 42CrMo4 alloy and compared with water,air,and CuO doped paraffin.The samples were prepared based on ASTM E 8M-98 standard for tensile test and then heated up to 830°C,kept for 4 h in an electric resistance furnace and then quenched in the mentioned media.Elastic modulus,yield strength,ultimate tensile strength,elongation,and modulus of toughness were determined according to the obtained stress?strain curves.Moreover,the hardness and microstructural evolution were investigated after the heat treatment at different media.The samples quenched in paraffin and CuO-doped paraffin are higher in ultimate tensile strength(1439 and 1306 MPa,respectively)than those quenched in water(1190 MPa)and air(1010 MPa).The highest hardness,with a value of HV 552,belonged to the sample quenched in CuO-doped paraffin.The microstructural studies revealed that the non-tempered steel had a ferrite/pearlite microstructure,while by quenching in water,paraffin and CuO-doped paraffin,ferrite/martensite microstructures were achieved.It is also observed that using the air as quenchant resulted in a three-phase bainite/martensite/ferrite microstructure.展开更多
A Trombe wall-heating system is used to absorb solar energy to heat buildings.Different parameters affect the system performance for optimal heating.This study evaluated the performance of four machine learning algori...A Trombe wall-heating system is used to absorb solar energy to heat buildings.Different parameters affect the system performance for optimal heating.This study evaluated the performance of four machine learning algorithms—linear regression,k-nearest neighbors,random forest,and decision tree—for predicting the room temperature in a Trombe wall system.The accuracy of the algorithms was assessed using R^(2)and root mean squared error(RMSE)values.The results demonstrated that the k-nearest neighbors and random forest algorithms exhibited superior performance,with R^(2)and RMSE values of 1 and 0.In contrast,linear regression and decision tree showed weaker performance.These findings highlight the potential of advanced machine learning algorithms for accurate room temperature prediction in Trombe wall systems,enabling informed design decisions to enhance energy efficiency.展开更多
文摘In the present work,paraffin phase change material is used as quenchant for the heat treatment of 42CrMo4 alloy and compared with water,air,and CuO doped paraffin.The samples were prepared based on ASTM E 8M-98 standard for tensile test and then heated up to 830°C,kept for 4 h in an electric resistance furnace and then quenched in the mentioned media.Elastic modulus,yield strength,ultimate tensile strength,elongation,and modulus of toughness were determined according to the obtained stress?strain curves.Moreover,the hardness and microstructural evolution were investigated after the heat treatment at different media.The samples quenched in paraffin and CuO-doped paraffin are higher in ultimate tensile strength(1439 and 1306 MPa,respectively)than those quenched in water(1190 MPa)and air(1010 MPa).The highest hardness,with a value of HV 552,belonged to the sample quenched in CuO-doped paraffin.The microstructural studies revealed that the non-tempered steel had a ferrite/pearlite microstructure,while by quenching in water,paraffin and CuO-doped paraffin,ferrite/martensite microstructures were achieved.It is also observed that using the air as quenchant resulted in a three-phase bainite/martensite/ferrite microstructure.
文摘A Trombe wall-heating system is used to absorb solar energy to heat buildings.Different parameters affect the system performance for optimal heating.This study evaluated the performance of four machine learning algorithms—linear regression,k-nearest neighbors,random forest,and decision tree—for predicting the room temperature in a Trombe wall system.The accuracy of the algorithms was assessed using R^(2)and root mean squared error(RMSE)values.The results demonstrated that the k-nearest neighbors and random forest algorithms exhibited superior performance,with R^(2)and RMSE values of 1 and 0.In contrast,linear regression and decision tree showed weaker performance.These findings highlight the potential of advanced machine learning algorithms for accurate room temperature prediction in Trombe wall systems,enabling informed design decisions to enhance energy efficiency.
