• Energy Research
• 2025-2025close
• other engineering and technologies close
• PK close
• KR close

This study explores the relationship between grid reliability and energy efficiency in China’s energy landscape. Findings highlight the influence of energy efficiency in the past while revealing random effects of smart grid penetration. Demand-side management and renewable energy integration significantly increase energy efficiency. Grid reliability and technology investment enhance efficiency, although consumption pattern changes show a limited impact. Implications for policy and energy strategies are discussed.

This study explores the relationship between grid reliability and energy efficiency in China’s energy landscape. Findings highlight the influence of energy efficiency in the past while revealing random effects of smart grid penetration. Demand-side management and renewable energy integration significantly increase energy efficiency. Grid reliability and technology investment enhance efficiency, although consumption pattern changes show a limited impact. Implications for policy and energy strategies are discussed.

A fin is an extended surface used to enhance the surface area of a heat transfer surface between a hot source and the outside environment. To maximise the rate of heat transportation, the exterior surface of heated equipment is equipped with fins of various geometries. Heat is exchanged using fins in radiators, refrigeration systems, superheaters, combustion engines, electrical equipment, electric transformers, space vehicles, and aircraft engines. Reflecting these applications, we analyse the effect of a magnetic field on the thermal properties of radiating porous rectangular fins. The proposed model is numerically analysed using the shooting method under the influence of radiation and convection, then compared with the DTM solution and both the solution found closer to each other. The effect of various dimensionless parameters on temperature transmission in magnetized rectangular-shaped porous fins is revealed using numerical results. It is revealed that, when the Raleigh, Hartmann, Peclet numbers, convective and radiative parameters increase, the fin?s thermal profile decreases, whereas the thermal profile increases with an increase in surface temperature, porosity, and ambient temperature. It is observed that the magnetic effect increases the heat transfer rate from porous rectangular fin surfaces. Accordingly, efficiency increases as Hartmann number, Raleigh number, and radiative parameter rise. Increasing Peclet number, surface temperature, and ambient temperature leads to a reduction in efficiency.

A fin is an extended surface used to enhance the surface area of a heat transfer surface between a hot source and the outside environment. To maximise the rate of heat transportation, the exterior surface of heated equipment is equipped with fins of various geometries. Heat is exchanged using fins in radiators, refrigeration systems, superheaters, combustion engines, electrical equipment, electric transformers, space vehicles, and aircraft engines. Reflecting these applications, we analyse the effect of a magnetic field on the thermal properties of radiating porous rectangular fins. The proposed model is numerically analysed using the shooting method under the influence of radiation and convection, then compared with the DTM solution and both the solution found closer to each other. The effect of various dimensionless parameters on temperature transmission in magnetized rectangular-shaped porous fins is revealed using numerical results. It is revealed that, when the Raleigh, Hartmann, Peclet numbers, convective and radiative parameters increase, the fin?s thermal profile decreases, whereas the thermal profile increases with an increase in surface temperature, porosity, and ambient temperature. It is observed that the magnetic effect increases the heat transfer rate from porous rectangular fin surfaces. Accordingly, efficiency increases as Hartmann number, Raleigh number, and radiative parameter rise. Increasing Peclet number, surface temperature, and ambient temperature leads to a reduction in efficiency.

The recently completed Horizon-2020 project “Management and Uncertainties of Severe Accidents (MUSA)” has reviewed uncertainty sources and Uncertainty Quantification methodology for assessing Severe Accidents (SA), and has made a substantial effort at stimulating uncertainty applications in predicting the radiological Source Term of reactor and Spent Fuel Pool accident scenarios. The key motivation of the project has been to bring the advantages of the Best Estimate Plus Uncertainty approach to the field of Severe Accident modelling. With respect to deterministic analyses, expected gains are avoiding adopting conservative assumptions, identifying uncertainty bands of estimates, and gaining insights into dominating uncertain parameters. Also, the benefits for understanding and improving Accident Management were to be explored. The reactor applications brought together a large group of participants that set out to apply uncertainty analysis (UA) within their field of SA modelling expertise – in particular reactor types, but also SA code used (ASTEC, MELCOR, MAAP, RELAP/SCDAPSIM), uncertainty quantification tools used (DAKOTA, SUSA, URANIE, self-developed tools based on Python code), detailed accident scenarios, and in some cases SAM actions. The setting up of the analyses, challenges faced during that phase, and solutions explored, are described in Brumm et al. ANE 191 (2023). This paper synthesizes the reactor-application work at the end of the project. Analyses of 23 partners are presented in different categories, depending on whether their main goal is/are (i) uncertainty bands of simulation results; (ii) the understanding of dominating uncertainties in specific sub-models of the SA code; (iii) improving the understanding of specific accident scenarios, with or without the application of SAM actions; or, (iv) a demonstration of the tools used and developed, and of the capability to carry out an uncertainty analysis in the presence of the challenges faced. A cross-section of the partners’ results is presented and briefly discussed, to provide an overview of the work done, and to encourage accessing and studying the project deliverables that are open to the public. Furthermore, the partners’ experiences made during the project have been evaluated and are presented as good practice recommendations. The paper ends with conclusions on the level of readiness of UA in SA modelling, on the determination of governing uncertainties, and on the analysis of SAM actions.

The recently completed Horizon-2020 project “Management and Uncertainties of Severe Accidents (MUSA)” has reviewed uncertainty sources and Uncertainty Quantification methodology for assessing Severe Accidents (SA), and has made a substantial effort at stimulating uncertainty applications in predicting the radiological Source Term of reactor and Spent Fuel Pool accident scenarios. The key motivation of the project has been to bring the advantages of the Best Estimate Plus Uncertainty approach to the field of Severe Accident modelling. With respect to deterministic analyses, expected gains are avoiding adopting conservative assumptions, identifying uncertainty bands of estimates, and gaining insights into dominating uncertain parameters. Also, the benefits for understanding and improving Accident Management were to be explored. The reactor applications brought together a large group of participants that set out to apply uncertainty analysis (UA) within their field of SA modelling expertise – in particular reactor types, but also SA code used (ASTEC, MELCOR, MAAP, RELAP/SCDAPSIM), uncertainty quantification tools used (DAKOTA, SUSA, URANIE, self-developed tools based on Python code), detailed accident scenarios, and in some cases SAM actions. The setting up of the analyses, challenges faced during that phase, and solutions explored, are described in Brumm et al. ANE 191 (2023). This paper synthesizes the reactor-application work at the end of the project. Analyses of 23 partners are presented in different categories, depending on whether their main goal is/are (i) uncertainty bands of simulation results; (ii) the understanding of dominating uncertainties in specific sub-models of the SA code; (iii) improving the understanding of specific accident scenarios, with or without the application of SAM actions; or, (iv) a demonstration of the tools used and developed, and of the capability to carry out an uncertainty analysis in the presence of the challenges faced. A cross-section of the partners’ results is presented and briefly discussed, to provide an overview of the work done, and to encourage accessing and studying the project deliverables that are open to the public. Furthermore, the partners’ experiences made during the project have been evaluated and are presented as good practice recommendations. The paper ends with conclusions on the level of readiness of UA in SA modelling, on the determination of governing uncertainties, and on the analysis of SAM actions.

Data deficiency is often a problem in regression analysis. The problem, for example, may be due to non-availability of data on some variable, missing observations, lack of information due to multicollinearity and measurement errors, etc. Various approaches have been suggested to deal with the problem depending on its precise nature. One such problem we want to focus our attention on is the lack of time disaggregated data in time-series regression analysis. In particular, observations on some variables over a shorter time interval like a quarter may be limited in number while the corresponding observations over a longer time interval like a year are available for a long period of time The number of quarterly observations may not be sufficient to estimate the desired relationship with acceptable degrees of freedom. On the other hand, estimation with yearly data may require the use of a long time series going way back into the past. The estimates thus obtained may not capture the relationship prevailing at present or in the recent past and, therefore, mislead the researcher. In addition, the use of yearly data may also result in lack of degrees of freedom.

Data deficiency is often a problem in regression analysis. The problem, for example, may be due to non-availability of data on some variable, missing observations, lack of information due to multicollinearity and measurement errors, etc. Various approaches have been suggested to deal with the problem depending on its precise nature. One such problem we want to focus our attention on is the lack of time disaggregated data in time-series regression analysis. In particular, observations on some variables over a shorter time interval like a quarter may be limited in number while the corresponding observations over a longer time interval like a year are available for a long period of time The number of quarterly observations may not be sufficient to estimate the desired relationship with acceptable degrees of freedom. On the other hand, estimation with yearly data may require the use of a long time series going way back into the past. The estimates thus obtained may not capture the relationship prevailing at present or in the recent past and, therefore, mislead the researcher. In addition, the use of yearly data may also result in lack of degrees of freedom.