• Energy Research
• Embargo close
• US close
• PL close
• Energy Research close

The lifetime and reliability of power transformers are primarily dependent on the hot-spot temperature in the windings, as temperature is the most important factor determining the insulation degradation rate. Key to removing the heat from the transformer is the radiator which must be carefully designed to keep the temperatures within limits under all operating conditions whilst minimizing the transformer size, weight and cost. This paper compares the analytical method used to predict the radiator performance with computational fluid dynamics (CFD) models in terms of heat dissipation. It is found that the analytical method and CFD models give similar results in the air natural (AN) cooling modes, whereas the analytical method overestimates the heat dissipation in the air forced (AF) cooling modes. Moreover, the thermal conduction effect in the radiator wall is investigated under different operating conditions and for different radiator sizes using the CFD models. The simulation results indicate that the radiator wall contributes to 6%-10% of the total heat dissipation under some circumstances and therefore should not be simply ignored in radiator models.

The lifetime and reliability of power transformers are primarily dependent on the hot-spot temperature in the windings, as temperature is the most important factor determining the insulation degradation rate. Key to removing the heat from the transformer is the radiator which must be carefully designed to keep the temperatures within limits under all operating conditions whilst minimizing the transformer size, weight and cost. This paper compares the analytical method used to predict the radiator performance with computational fluid dynamics (CFD) models in terms of heat dissipation. It is found that the analytical method and CFD models give similar results in the air natural (AN) cooling modes, whereas the analytical method overestimates the heat dissipation in the air forced (AF) cooling modes. Moreover, the thermal conduction effect in the radiator wall is investigated under different operating conditions and for different radiator sizes using the CFD models. The simulation results indicate that the radiator wall contributes to 6%-10% of the total heat dissipation under some circumstances and therefore should not be simply ignored in radiator models.

Modelling the economics of climate change is daunting. Many existing methodologies from social and physical sciences need to be deployed, and new modelling techniques and ideas still need to be developed. Existing bread-and-butter micro- and macroeconomic tools, such as the expected utility framework, market equilibrium concepts and representative agent assumptions, are far from adequate. Four key issues—along with several others—remain inadequately addressed by economic models of climate change, namely: (1) uncertainty, (2) aggregation, heterogeneity and distributional implications (3) technological change, and most of all, (4) realistic damage functions for the economic impact of the physical consequences of climate change. This paper assesses the main shortcomings of two generations of climate-energy-economic models and proposes that a new wave of models need to be developed to tackle these four challenges. This paper then examines two potential candidate approaches—dynamic stochastic general equilibrium (DSGE) models and agent-based models (ABM). The successful use of agent-based models in other areas, such as in modelling the financial system, housing markets and technological progress suggests its potential applicability to better modelling the economics of climate change.

Modelling the economics of climate change is daunting. Many existing methodologies from social and physical sciences need to be deployed, and new modelling techniques and ideas still need to be developed. Existing bread-and-butter micro- and macroeconomic tools, such as the expected utility framework, market equilibrium concepts and representative agent assumptions, are far from adequate. Four key issues—along with several others—remain inadequately addressed by economic models of climate change, namely: (1) uncertainty, (2) aggregation, heterogeneity and distributional implications (3) technological change, and most of all, (4) realistic damage functions for the economic impact of the physical consequences of climate change. This paper assesses the main shortcomings of two generations of climate-energy-economic models and proposes that a new wave of models need to be developed to tackle these four challenges. This paper then examines two potential candidate approaches—dynamic stochastic general equilibrium (DSGE) models and agent-based models (ABM). The successful use of agent-based models in other areas, such as in modelling the financial system, housing markets and technological progress suggests its potential applicability to better modelling the economics of climate change.

In industrialized countries, large amounts of mineral wastes are produced. They are re-used in various ways, particularly in road and earth constructions, substituting primary resources such as gravel. However, they may also contain pollutants, such as heavy metals, which may be leached to the groundwater. The toxic impacts of these emissions are so far often neglected within Life Cycle Assessments (LCA) of products or waste treatment services and thus, potentially large environmental impacts are currently missed. This study aims at closing this gap by assessing the ecotoxic impacts of heavy metal leaching from industrial mineral wastes in road and earth constructions. The flows of metals such as Sb, As, Pb, Cd, Cr, Cu, Mo, Ni, V and Zn originating from three typical constructions to the environment are quantified, their fate in the environment is assessed and potential ecotoxic effects evaluated. For our reference country, Germany, the industrial wastes that are applied as Granular Secondary Construction Material (GSCM) carry more than 45,000 t of diverse heavy metals per year. Depending on the material quality and construction type applied, up to 150 t of heavy metals may leach to the environment within the first 100 years after construction. Heavy metal retardation in subsoil can potentially reduce the fate to groundwater by up to 100%. One major challenge of integrating leaching from constructions into macro-scale LCA frameworks is the high variability in micro-scale technical and geographical factors, such as material qualities, construction types and soil types. In our work, we consider a broad range of parameter values in the modeling of leaching and fate. This allows distinguishing between the impacts of various road constructions, as well as sites with different soil properties. The findings of this study promote the quantitative consideration of environmental impacts of long-term leaching in Life Cycle Assessment, complementing site-specific risk assessment, for the design of waste management strategies, particularly in the construction sector.

In industrialized countries, large amounts of mineral wastes are produced. They are re-used in various ways, particularly in road and earth constructions, substituting primary resources such as gravel. However, they may also contain pollutants, such as heavy metals, which may be leached to the groundwater. The toxic impacts of these emissions are so far often neglected within Life Cycle Assessments (LCA) of products or waste treatment services and thus, potentially large environmental impacts are currently missed. This study aims at closing this gap by assessing the ecotoxic impacts of heavy metal leaching from industrial mineral wastes in road and earth constructions. The flows of metals such as Sb, As, Pb, Cd, Cr, Cu, Mo, Ni, V and Zn originating from three typical constructions to the environment are quantified, their fate in the environment is assessed and potential ecotoxic effects evaluated. For our reference country, Germany, the industrial wastes that are applied as Granular Secondary Construction Material (GSCM) carry more than 45,000 t of diverse heavy metals per year. Depending on the material quality and construction type applied, up to 150 t of heavy metals may leach to the environment within the first 100 years after construction. Heavy metal retardation in subsoil can potentially reduce the fate to groundwater by up to 100%. One major challenge of integrating leaching from constructions into macro-scale LCA frameworks is the high variability in micro-scale technical and geographical factors, such as material qualities, construction types and soil types. In our work, we consider a broad range of parameter values in the modeling of leaching and fate. This allows distinguishing between the impacts of various road constructions, as well as sites with different soil properties. The findings of this study promote the quantitative consideration of environmental impacts of long-term leaching in Life Cycle Assessment, complementing site-specific risk assessment, for the design of waste management strategies, particularly in the construction sector.

The stability of winter wheat-flowering-date is crucial for ensuring consistent and robust crop performance across diverse climatic conditions. However, the impact of climate change on wheat-flowering-dates remains uncertain. This study aims to elucidate the influence of climate change on wheat-flowering-dates, predict how projected future climate conditions will affect flowering date stability, and identify the most stable wheat genotypes in the study region. We applied a multi-locus genotype-based (MLG-based) model for simulating wheat-flowering-dates, which we calibrated and evaluated using observed data from the Northern China winter wheat region (NCWWR). This MLG-based model was employed to project flowering dates under different climate scenarios. The simulated flowering dates were then used to assess the stability of flowering dates under varying allelic combinations in projected climatic conditions. Our MLG-based model effectively simulated flowering dates, with a root mean square error (RMSE) of 2.3 days, explaining approximately 88.5 % of the genotypic variation in flowering dates among 100 wheat genotypes. We found that, in comparison to the baseline climate, wheat-flowering-dates are expected to shift earlier within the target sowing window by approximately 11 and 14 days by 2050 under the Representative Concentration Pathways 4.5 (RCP4.5) and RCP8.5 climate scenarios, respectively. Furthermore, our analysis revealed that wheat-flowering-date stability is likely to be further strengthened under projected climate scenarios due to early flowering trends. Ultimately, we demonstrate that the combination of Vrn and Ppd genes, rather than individual Vrn or Ppd genes, plays a critical role in wheat-flowering-date stability. Our results suggest that the combination of Ppd-D1a with winter genotypes carrying the vrn-D1 allele significantly contributes to flowering date stability under current and projected climate scenarios. These findings provide valuable insights for wheat breeders and producers under future climatic conditions.