One method of nuclear energy development involves using helium. Its properties make using extended surfaces obligatory. However, currently nuclear technology does not typically use finned tubes. This study explores ways of enhancing heat transfer efficiency in a high-temperature gas-cooled reactor system by using novel fin designs in the heat exchanger for residual heat removal. Four different types of fins were studied: annular, serrated, square, and helical. The effect of fin height, thickness, and number was evaluated. Serrated and helical fins demonstrated superior performance compared to conventional annular fin designs, which was expressed in enhanced efficiency. The thickness of fins was found to have the strongest influence on the efficiency, while the height and number of fins per meter had weaker effects. In addition, the study emphasized the significance of considering complex effects when optimizing fin design, like the effect of fin geometry on the velocity of helium. The findings highlight the potential of creative fin designs to greatly enhance the efficiency and dependability of gas-cooled reactor systems, opening up possibilities for advancements in nuclear power plant technology.
{"title":"Effect of Fin Type and Geometry on Thermal and Hydraulic Performance in Conditions of Combined-Cycle Nuclear Power Plant with High-Temperature Gas-Cooled Reactors","authors":"K.A.A. Ramadan, K.V. Slyusarskiy","doi":"10.3390/thermo4030020","DOIUrl":"https://doi.org/10.3390/thermo4030020","url":null,"abstract":"One method of nuclear energy development involves using helium. Its properties make using extended surfaces obligatory. However, currently nuclear technology does not typically use finned tubes. This study explores ways of enhancing heat transfer efficiency in a high-temperature gas-cooled reactor system by using novel fin designs in the heat exchanger for residual heat removal. Four different types of fins were studied: annular, serrated, square, and helical. The effect of fin height, thickness, and number was evaluated. Serrated and helical fins demonstrated superior performance compared to conventional annular fin designs, which was expressed in enhanced efficiency. The thickness of fins was found to have the strongest influence on the efficiency, while the height and number of fins per meter had weaker effects. In addition, the study emphasized the significance of considering complex effects when optimizing fin design, like the effect of fin geometry on the velocity of helium. The findings highlight the potential of creative fin designs to greatly enhance the efficiency and dependability of gas-cooled reactor systems, opening up possibilities for advancements in nuclear power plant technology.","PeriodicalId":516035,"journal":{"name":"Thermo","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141921690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In thermodynamic theory, free energy (i.e., available energy) is the concept facilitating the combined applications of the theory’s two fundamental laws, the first and the second laws of thermodynamics. The critical step was taken by Kelvin, then by Helmholtz and Gibbs—that in natural processes, free energy dissipates spontaneously. With the formulation of the second law of entropy growth, this may be referred to as the dissymmetry proposition manifested in the spontaneous increase of system/environment entropy towards equilibrium. Because of Kelvin’s pre-entropy law formulation of free energy, our concept of free energy is still defined, within a framework on the premise of primacy of energy, as “body’s internal energy or enthalpy, subtracted by energy that is not available.” This primacy of energy is called into question because the driving force to cause a system’s change is the purview of the second law. This paper makes a case for an engineering thermodynamics framework, instead, to be based on the premise of the primacy of dissymmetry over free energy. With Gibbsian thermodynamics undergirded with dissymmetry proposition and engineering thermodynamics with a dissymmetry premise, the two branches of thermodynamics are unified to become classical thermodynamics.
{"title":"Unified Classical Thermodynamics: Primacy of Dissymmetry over Free Energy","authors":"Lin-Shu Wang","doi":"10.3390/thermo4030017","DOIUrl":"https://doi.org/10.3390/thermo4030017","url":null,"abstract":"In thermodynamic theory, free energy (i.e., available energy) is the concept facilitating the combined applications of the theory’s two fundamental laws, the first and the second laws of thermodynamics. The critical step was taken by Kelvin, then by Helmholtz and Gibbs—that in natural processes, free energy dissipates spontaneously. With the formulation of the second law of entropy growth, this may be referred to as the dissymmetry proposition manifested in the spontaneous increase of system/environment entropy towards equilibrium. Because of Kelvin’s pre-entropy law formulation of free energy, our concept of free energy is still defined, within a framework on the premise of primacy of energy, as “body’s internal energy or enthalpy, subtracted by energy that is not available.” This primacy of energy is called into question because the driving force to cause a system’s change is the purview of the second law. This paper makes a case for an engineering thermodynamics framework, instead, to be based on the premise of the primacy of dissymmetry over free energy. With Gibbsian thermodynamics undergirded with dissymmetry proposition and engineering thermodynamics with a dissymmetry premise, the two branches of thermodynamics are unified to become classical thermodynamics.","PeriodicalId":516035,"journal":{"name":"Thermo","volume":"119 47","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141822092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The paper revisits the formulation of the second law in continuum physics and investigates new methods of exploitation. Both the entropy flux and the entropy production are taken to be expressed by constitutive equations. In three-dimensional settings, vectors and tensors are in order and they occur through inner products in the inequality representing the second law; a representation formula, which is quite uncommon in the literature, produces the general solution whenever the sought equations are considered in rate-type forms. Next, the occurrence of the entropy production as a constitutive function is shown to produce a wider set of physically admissible models. Furthermore the constitutive property of the entropy production results in an additional, essential term in the evolution equation of rate-type materials, as is the case for Duhem-like hysteretic models. This feature of thermodynamically consistent hysteretic materials is exemplified for elastic–plastic materials. The representation formula is shown to allow more general non-local properties while the constitutive entropy production proves essential for the modeling of hysteresis.
{"title":"On the Second Law of Thermodynamics in Continuum Physics","authors":"C. Giorgi, A. Morro","doi":"10.3390/thermo4020015","DOIUrl":"https://doi.org/10.3390/thermo4020015","url":null,"abstract":"The paper revisits the formulation of the second law in continuum physics and investigates new methods of exploitation. Both the entropy flux and the entropy production are taken to be expressed by constitutive equations. In three-dimensional settings, vectors and tensors are in order and they occur through inner products in the inequality representing the second law; a representation formula, which is quite uncommon in the literature, produces the general solution whenever the sought equations are considered in rate-type forms. Next, the occurrence of the entropy production as a constitutive function is shown to produce a wider set of physically admissible models. Furthermore the constitutive property of the entropy production results in an additional, essential term in the evolution equation of rate-type materials, as is the case for Duhem-like hysteretic models. This feature of thermodynamically consistent hysteretic materials is exemplified for elastic–plastic materials. The representation formula is shown to allow more general non-local properties while the constitutive entropy production proves essential for the modeling of hysteresis.","PeriodicalId":516035,"journal":{"name":"Thermo","volume":"15 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141356682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Md Shazzad Hossain, Ibrahim A. Sultan, Truong H. Phung, Apurv Kumar
In this work, an artificial neural network (ANN)-based model is proposed to describe the input–output relationships in a Limaçon-To-Circular (L2C) gas expander with an inlet valve. The L2C gas expander is a type of energy converter that has great potential to be used in organic Rankine cycle (ORC)-based small-scale power plants. The proposed model predicts the different performance indices of a limaçon gas expander for different input pressures, rotor velocities, and valve cutoff angles. A network model is constructed and optimized for different model parameters to achieve the best prediction performance compared to the classic mathematical model of the system. An overall normalized mean square error of 0.0014, coefficient of determination (R2) of 0.98, and mean average error of 0.0114 are reported. This implies that the surrogate model can effectively mimic the actual model with high precision. The model performance is also compared to a linear interpolation (LI) method. It is found that the proposed ANN model predictions are about 96.53% accurate for a given error threshold, compared to about 91.46% accuracy of the LI method. Thus the proposed model can effectively predict different output parameters of a limaçon gas expander such as energy, filling factor, isentropic efficiency, and mass flow for different operating conditions. Of note, the model is only trained by a set of input and target values; thus, the performance of the model is not affected by the internal complex mathematical models of the overall valved-expander system. This neural network-based approach is highly suitable for optimization, as the alternative iterative analysis of the complex analytical model is time-consuming and requires higher computational resources. A similar modeling approach with some modifications could also be utilized to design controllers for these types of systems that are difficult to model mathematically.
{"title":"An Optimized Artificial Neural Network Model of a Limaçon-to-Circular Gas Expander with an Inlet Valve","authors":"Md Shazzad Hossain, Ibrahim A. Sultan, Truong H. Phung, Apurv Kumar","doi":"10.3390/thermo4020014","DOIUrl":"https://doi.org/10.3390/thermo4020014","url":null,"abstract":"In this work, an artificial neural network (ANN)-based model is proposed to describe the input–output relationships in a Limaçon-To-Circular (L2C) gas expander with an inlet valve. The L2C gas expander is a type of energy converter that has great potential to be used in organic Rankine cycle (ORC)-based small-scale power plants. The proposed model predicts the different performance indices of a limaçon gas expander for different input pressures, rotor velocities, and valve cutoff angles. A network model is constructed and optimized for different model parameters to achieve the best prediction performance compared to the classic mathematical model of the system. An overall normalized mean square error of 0.0014, coefficient of determination (R2) of 0.98, and mean average error of 0.0114 are reported. This implies that the surrogate model can effectively mimic the actual model with high precision. The model performance is also compared to a linear interpolation (LI) method. It is found that the proposed ANN model predictions are about 96.53% accurate for a given error threshold, compared to about 91.46% accuracy of the LI method. Thus the proposed model can effectively predict different output parameters of a limaçon gas expander such as energy, filling factor, isentropic efficiency, and mass flow for different operating conditions. Of note, the model is only trained by a set of input and target values; thus, the performance of the model is not affected by the internal complex mathematical models of the overall valved-expander system. This neural network-based approach is highly suitable for optimization, as the alternative iterative analysis of the complex analytical model is time-consuming and requires higher computational resources. A similar modeling approach with some modifications could also be utilized to design controllers for these types of systems that are difficult to model mathematically.","PeriodicalId":516035,"journal":{"name":"Thermo","volume":"44 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141358860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Keyhole mode laser welding is a valuable technique for welding thick materials in industrial applications. However, its susceptibility to fluctuations and instabilities poses challenges, leading to defects that compromise weld quality. Observing the keyhole during laser welding is challenging due to bright process radiation, and existing observation methods are complex and expensive. This paper alternatively presents a novel numerical modeling approach for laser spot welding of aluminum through a modified mixture theory, a modified level-set (LS) method, and a thermal enthalpy porosity technique. The effects of laser parameters on keyhole penetration depth are investigated, with a focus on laser power, spot radius, frequency, and pulse wave modulation in pulsed wave (PW) versus continuous wave (CW) laser welding. PW laser welding involves the careful modulation of power waves, specifically adjusting the pulse width, pulse number, and pulse shapes. Results indicate a greater than 80 percent increase in the keyhole penetration depth with higher laser power, pulse width, and pulse number, as well as decreased spot radius. Keyhole instabilities are also more pronounced with higher pulse width/numbers and frequencies. Notably, the rectangular pulse shape demonstrates substantially deeper penetration compared to CW welding and other pulse shapes. This study enhances understanding of weld pool dynamics and provides insights into optimizing laser welding parameters to mitigate defects and improve weld quality.
{"title":"Comparative Numerical Analysis of Keyhole Shape and Penetration Depth in Laser Spot Welding of Aluminum with Power Wave Modulation","authors":"Saeid SaediArdahaei, Xuan-Tan Pham","doi":"10.3390/thermo4020013","DOIUrl":"https://doi.org/10.3390/thermo4020013","url":null,"abstract":"Keyhole mode laser welding is a valuable technique for welding thick materials in industrial applications. However, its susceptibility to fluctuations and instabilities poses challenges, leading to defects that compromise weld quality. Observing the keyhole during laser welding is challenging due to bright process radiation, and existing observation methods are complex and expensive. This paper alternatively presents a novel numerical modeling approach for laser spot welding of aluminum through a modified mixture theory, a modified level-set (LS) method, and a thermal enthalpy porosity technique. The effects of laser parameters on keyhole penetration depth are investigated, with a focus on laser power, spot radius, frequency, and pulse wave modulation in pulsed wave (PW) versus continuous wave (CW) laser welding. PW laser welding involves the careful modulation of power waves, specifically adjusting the pulse width, pulse number, and pulse shapes. Results indicate a greater than 80 percent increase in the keyhole penetration depth with higher laser power, pulse width, and pulse number, as well as decreased spot radius. Keyhole instabilities are also more pronounced with higher pulse width/numbers and frequencies. Notably, the rectangular pulse shape demonstrates substantially deeper penetration compared to CW welding and other pulse shapes. This study enhances understanding of weld pool dynamics and provides insights into optimizing laser welding parameters to mitigate defects and improve weld quality.","PeriodicalId":516035,"journal":{"name":"Thermo","volume":"31 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141104222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Inverse gas chromatography at infinite dilution was used to determine the surface thermodynamic properties of silica particles and PMMA adsorbed on silica, and more particularly, to quantify the London dispersive energy γsd, the Lewis acid γs+, and base γs− polar surface energies of PMMA/silica composites as a function of the temperature and the recovery fraction θ of PMMA. The polar acid-base surface energy γsAB and the total surface energy of the different composites were then deduced as a function of the temperature. In this paper, the Hamieh thermal model was used to quantify the surface thermodynamic energy of polymethyl methacrylate (PMMA) adsorbed on silica particles at different recovery fractions. A comparison of the new results was carried out with those obtained by applying other molecular models of the surface areas of organic molecules adsorbed on the different solid substrates. An important deviation of these molecular models from the thermal model was proved. The determination of γsd, γs+, γs−, and γsAB of PMMA in both the bulk and adsorbed phases showed an important non-linearity variation of these surface parameters as a function of the temperature. The presence of maxima in the curves of γsd(T) highlighted the second-order transition temperatures in PMMA showing beta-relaxation, glass transition, and liquid–liquid temperatures. These three transition temperatures depended on the adsorption rate of PMMA on silica. The proposed method gave a new relation between the recovery fraction of PMMA and its London dispersive energy, showing an important effect of the temperature on the surface energy parameters of the adsorption of PMMA on silica. A universal equation relating γsd(T,θ) of the systems PMMA/silica to the recovery fraction and the temperature was proposed.
{"title":"The Effect of Temperature on the London Dispersive and Lewis Acid-Base Surface Energies of Polymethyl Methacrylate Adsorbed on Silica by Inverse Gas Chromatography","authors":"T. Hamieh","doi":"10.3390/thermo4020012","DOIUrl":"https://doi.org/10.3390/thermo4020012","url":null,"abstract":"Inverse gas chromatography at infinite dilution was used to determine the surface thermodynamic properties of silica particles and PMMA adsorbed on silica, and more particularly, to quantify the London dispersive energy γsd, the Lewis acid γs+, and base γs− polar surface energies of PMMA/silica composites as a function of the temperature and the recovery fraction θ of PMMA. The polar acid-base surface energy γsAB and the total surface energy of the different composites were then deduced as a function of the temperature. In this paper, the Hamieh thermal model was used to quantify the surface thermodynamic energy of polymethyl methacrylate (PMMA) adsorbed on silica particles at different recovery fractions. A comparison of the new results was carried out with those obtained by applying other molecular models of the surface areas of organic molecules adsorbed on the different solid substrates. An important deviation of these molecular models from the thermal model was proved. The determination of γsd, γs+, γs−, and γsAB of PMMA in both the bulk and adsorbed phases showed an important non-linearity variation of these surface parameters as a function of the temperature. The presence of maxima in the curves of γsd(T) highlighted the second-order transition temperatures in PMMA showing beta-relaxation, glass transition, and liquid–liquid temperatures. These three transition temperatures depended on the adsorption rate of PMMA on silica. The proposed method gave a new relation between the recovery fraction of PMMA and its London dispersive energy, showing an important effect of the temperature on the surface energy parameters of the adsorption of PMMA on silica. A universal equation relating γsd(T,θ) of the systems PMMA/silica to the recovery fraction and the temperature was proposed.","PeriodicalId":516035,"journal":{"name":"Thermo","volume":"104 50","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141126282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shaveshwar Deonarine, Navindra Soodoo, L. Bouzidi, R. Emery, Sanela Martic, Suresh S. Narine
The phase behavior of lipids extracted from Astrocaryum vulgare (AV) and Astrocaryum aculeatum (AA) pulp and kernels and their microstructural, thermal and flow properties were studied. The lipid profiles, crystal structures, microstructures, thermal stabilities and flow behaviors of these lipids provided important structure–function information that are useful to assess potential applications in the food, cosmetic and pharmaceutical industries. AV and AA fruits were sourced from the lowlands and rainforests, respectively, of Guyana. AV and AA pulp oils (AVP and AAP) were distinguished from each other in composition and unsaturation, with AVP oils being predominated by a di-unsaturated TAG (2-(palmitoyloxy)propane-1,3-diyl dioleate (POO)) and AAP oils predominated by propane-1,2,3-triyl trioleate (OOO); there were unsaturation levels of 65% and 80%, respectively. The main fatty acids in AVP oils were oleic, palmitic and stearic; for AAP, these were oleic, linoleic, palmitic and stearic. The kernel fats of AV and AA were similar in composition and had saturation levels of 80%, being mainly comprised of tri-saturated TAGs propane-1,2,3-triyl tridodecanoate (LLL) and 3-(tetradecanoyloxy)propane-1,2-diyl didodecanoate (LML). The onset of mass loss (T5%on) of AV and AA pulp oils were similar at 328 ± 6 °C, which were 31 °C ± 9 higher compared to that of the kernel fats, which demonstrated similar T5%on = 293 ± 7 °C. AA and AV pulp oils were liquid at room temperature, with melting points of −5 ± 1 °C and 3 ± 1 °C, respectively; both kernel fats were solid at room temperature, packing in β′ (90% of crystals) and β (10% of crystals) polymorphic forms and melting almost identically at 30 ± 1 °C. Pulp oils demonstrated sporadic nucleation at the onset of crystallization with slow growth into rod-shaped crystallites, leading to an approximately 50% degree of crystallization at undercooling of approximately 40K. Nucleation for kernel fats was instantaneous at undercooling of approximately 23K, demonstrating a spherulitic growth pattern incorporating crystalline lamella and a 90% degree of crystallization. Kernel fats and pulp oils demonstrated Newtonian flow behavior and similar dynamic viscosity in the melt, approximately 28.5 mPa·s at 40 °C. The lipid profiles of AVP and AAP oils were dominated by unsaturated TAGs, suggesting potential nutrition and health benefits, particularly compared to other tropical oils with higher saturation levels, such as palm oil. AAP oil in particular is as unsaturated as olive oil, contains high levels of beta carotene and provides a unique flavor profile. The AAK and AVK lipid profiles and phase transformation indicate potential for applications where a high solid fat content and medium-chain fatty acids are required. Their high lauric and myristic acid content makes them similar to industrially important tropical oils (coconut and palm kernel), suggesting their use in similar formulations. The melting point and plasticity o
{"title":"Molecular, Crystalline, and Microstructures of Lipids from Astrocaryum Species in Guyana and Their Thermal and Flow Behavior","authors":"Shaveshwar Deonarine, Navindra Soodoo, L. Bouzidi, R. Emery, Sanela Martic, Suresh S. Narine","doi":"10.3390/thermo4010009","DOIUrl":"https://doi.org/10.3390/thermo4010009","url":null,"abstract":"The phase behavior of lipids extracted from Astrocaryum vulgare (AV) and Astrocaryum aculeatum (AA) pulp and kernels and their microstructural, thermal and flow properties were studied. The lipid profiles, crystal structures, microstructures, thermal stabilities and flow behaviors of these lipids provided important structure–function information that are useful to assess potential applications in the food, cosmetic and pharmaceutical industries. AV and AA fruits were sourced from the lowlands and rainforests, respectively, of Guyana. AV and AA pulp oils (AVP and AAP) were distinguished from each other in composition and unsaturation, with AVP oils being predominated by a di-unsaturated TAG (2-(palmitoyloxy)propane-1,3-diyl dioleate (POO)) and AAP oils predominated by propane-1,2,3-triyl trioleate (OOO); there were unsaturation levels of 65% and 80%, respectively. The main fatty acids in AVP oils were oleic, palmitic and stearic; for AAP, these were oleic, linoleic, palmitic and stearic. The kernel fats of AV and AA were similar in composition and had saturation levels of 80%, being mainly comprised of tri-saturated TAGs propane-1,2,3-triyl tridodecanoate (LLL) and 3-(tetradecanoyloxy)propane-1,2-diyl didodecanoate (LML). The onset of mass loss (T5%on) of AV and AA pulp oils were similar at 328 ± 6 °C, which were 31 °C ± 9 higher compared to that of the kernel fats, which demonstrated similar T5%on = 293 ± 7 °C. AA and AV pulp oils were liquid at room temperature, with melting points of −5 ± 1 °C and 3 ± 1 °C, respectively; both kernel fats were solid at room temperature, packing in β′ (90% of crystals) and β (10% of crystals) polymorphic forms and melting almost identically at 30 ± 1 °C. Pulp oils demonstrated sporadic nucleation at the onset of crystallization with slow growth into rod-shaped crystallites, leading to an approximately 50% degree of crystallization at undercooling of approximately 40K. Nucleation for kernel fats was instantaneous at undercooling of approximately 23K, demonstrating a spherulitic growth pattern incorporating crystalline lamella and a 90% degree of crystallization. Kernel fats and pulp oils demonstrated Newtonian flow behavior and similar dynamic viscosity in the melt, approximately 28.5 mPa·s at 40 °C. The lipid profiles of AVP and AAP oils were dominated by unsaturated TAGs, suggesting potential nutrition and health benefits, particularly compared to other tropical oils with higher saturation levels, such as palm oil. AAP oil in particular is as unsaturated as olive oil, contains high levels of beta carotene and provides a unique flavor profile. The AAK and AVK lipid profiles and phase transformation indicate potential for applications where a high solid fat content and medium-chain fatty acids are required. Their high lauric and myristic acid content makes them similar to industrially important tropical oils (coconut and palm kernel), suggesting their use in similar formulations. The melting point and plasticity o","PeriodicalId":516035,"journal":{"name":"Thermo","volume":"39 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140250262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Francesca Villano, Gerardo Maria Mauro, Alessia Pedace
Given the climate change in recent decades and the ever-increasing energy consumption in the building sector, research is widely focused on the green revolution and ecological transition of buildings. In this regard, artificial intelligence can be a precious tool to simulate and optimize building energy performance, as shown by a plethora of recent studies. Accordingly, this paper provides a review of more than 70 articles from recent years, i.e., mostly from 2018 to 2023, about the applications of machine/deep learning (ML/DL) in forecasting the energy performance of buildings and their simulation/control/optimization. This review was conducted using the SCOPUS database with the keywords “buildings”, “energy”, “machine learning” and “deep learning” and by selecting recent papers addressing the following applications: energy design/retrofit optimization, prediction, control/management of heating/cooling systems and of renewable source systems, and/or fault detection. Notably, this paper discusses the main differences between ML and DL techniques, showing examples of their use in building energy simulation/control/optimization. The main aim is to group the most frequent ML/DL techniques used in the field of building energy performance, highlighting the potentiality and limitations of each one, both fundamental aspects for future studies. The ML approaches considered are decision trees/random forest, naive Bayes, support vector machines, the Kriging method and artificial neural networks. The DL techniques investigated are convolutional and recursive neural networks, long short-term memory and gated recurrent units. Firstly, various ML/DL techniques are explained and divided based on their methodology. Secondly, grouping by the aforementioned applications occurs. It emerges that ML is mostly used in energy efficiency issues while DL in the management of renewable source systems.
{"title":"A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management","authors":"Francesca Villano, Gerardo Maria Mauro, Alessia Pedace","doi":"10.3390/thermo4010008","DOIUrl":"https://doi.org/10.3390/thermo4010008","url":null,"abstract":"Given the climate change in recent decades and the ever-increasing energy consumption in the building sector, research is widely focused on the green revolution and ecological transition of buildings. In this regard, artificial intelligence can be a precious tool to simulate and optimize building energy performance, as shown by a plethora of recent studies. Accordingly, this paper provides a review of more than 70 articles from recent years, i.e., mostly from 2018 to 2023, about the applications of machine/deep learning (ML/DL) in forecasting the energy performance of buildings and their simulation/control/optimization. This review was conducted using the SCOPUS database with the keywords “buildings”, “energy”, “machine learning” and “deep learning” and by selecting recent papers addressing the following applications: energy design/retrofit optimization, prediction, control/management of heating/cooling systems and of renewable source systems, and/or fault detection. Notably, this paper discusses the main differences between ML and DL techniques, showing examples of their use in building energy simulation/control/optimization. The main aim is to group the most frequent ML/DL techniques used in the field of building energy performance, highlighting the potentiality and limitations of each one, both fundamental aspects for future studies. The ML approaches considered are decision trees/random forest, naive Bayes, support vector machines, the Kriging method and artificial neural networks. The DL techniques investigated are convolutional and recursive neural networks, long short-term memory and gated recurrent units. Firstly, various ML/DL techniques are explained and divided based on their methodology. Secondly, grouping by the aforementioned applications occurs. It emerges that ML is mostly used in energy efficiency issues while DL in the management of renewable source systems.","PeriodicalId":516035,"journal":{"name":"Thermo","volume":"31 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140262656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：