To effectively get the thermal expansion coef- ficient （CTE） of three-dimensional （3D） braided C/C composites and study the variations, a VC＋＋ program with graphical user interfaces was obtained, based on the ya...To effectively get the thermal expansion coef- ficient （CTE） of three-dimensional （3D） braided C/C composites and study the variations, a VC＋＋ program with graphical user interfaces was obtained, based on the yam unit model and numerical analysis. With the limited basic properties of carbon fibers and carbon matrix, CTE of 3D braided C/C composites is obtained at 85 ~C. The deviation between the simulated and exl^erimental axial CTE of 3D braided C/C composites is no more than 11%. The effects of different parameters （including the braiding angle of 3D braided preform, the fiber volume fraction and the porosity of 3D braided C/C composites, and the elastic modulus, Poisson＇s ratio and CTEs of carbon fibers and carbon matrix） were analyzed with the program. The results show that the axial CTE of C/C composites decreases with the increase of the braiding angle, the fiber volume fraction, and the porosity of 3D braided C/C composites. The transverse elastic modulus of carbon fibers has the greatest effect on the axial CTE among the studied mechanical parameters, followed by the elastic modulus and Poisson＇s ratio of carbon matrix.展开更多
Phosphorus (P) risk indices are commonly used in the USA to estimate the field-scale risk of agricultural P runoff. Because the Ohio P Risk Index is increasingly being used to judge farmer performance, it is important...Phosphorus (P) risk indices are commonly used in the USA to estimate the field-scale risk of agricultural P runoff. Because the Ohio P Risk Index is increasingly being used to judge farmer performance, it is important to evaluate weighting/scoring of all P Index parameters to ensure Ohio farmers are credited for practices that reduce P runoff risk and not unduly penalized for things not demonstrably related to runoff risk. A sensitivity analysis provides information as to how sensitive the P Index score is to changes in inputs. The objectives were to determine 1) which inputs are most highly associated with P Index scores and 2) the relative impact of each input variable on resultant P Index scores. The current approach uses simulations across 6134 Ohio point locations and five crop management scenarios (CMSs), representing increasing soil disturbance. The CMSs range from all no-till, which is being promoted in Ohio, rotational tillage, which is a common practice in Ohio to full tillage to represent an extreme practice. Results showed that P Index scores were best explained by soil test P (31.9%) followed by connectivity to water (29.7%), soil erosion (13.4%), fertilizer application amount (11.3%), runoff class (9.5%), fertilizer application method (2.2%), and finally filter strip (2.0%). Ohio P Index simulations across CMSs one through five showed that >40% scored <15 points (low) while <1.5% scored >45 points (very high). Given Ohio water quality problems, the Ohio P Index needs to be stricter. The current approach is useful for Ohio P Index evaluations and revision decisions by spatially illustrating the impact of potential changes regionally and state-wide.展开更多
基金financially supported by the National Natural Science Foundation of China(Nos.50832004 and 50972120)the 111 Project(No.B08040)
文摘To effectively get the thermal expansion coef- ficient （CTE） of three-dimensional （3D） braided C/C composites and study the variations, a VC＋＋ program with graphical user interfaces was obtained, based on the yam unit model and numerical analysis. With the limited basic properties of carbon fibers and carbon matrix, CTE of 3D braided C/C composites is obtained at 85 ~C. The deviation between the simulated and exl^erimental axial CTE of 3D braided C/C composites is no more than 11%. The effects of different parameters （including the braiding angle of 3D braided preform, the fiber volume fraction and the porosity of 3D braided C/C composites, and the elastic modulus, Poisson＇s ratio and CTEs of carbon fibers and carbon matrix） were analyzed with the program. The results show that the axial CTE of C/C composites decreases with the increase of the braiding angle, the fiber volume fraction, and the porosity of 3D braided C/C composites. The transverse elastic modulus of carbon fibers has the greatest effect on the axial CTE among the studied mechanical parameters, followed by the elastic modulus and Poisson＇s ratio of carbon matrix.
文摘Phosphorus (P) risk indices are commonly used in the USA to estimate the field-scale risk of agricultural P runoff. Because the Ohio P Risk Index is increasingly being used to judge farmer performance, it is important to evaluate weighting/scoring of all P Index parameters to ensure Ohio farmers are credited for practices that reduce P runoff risk and not unduly penalized for things not demonstrably related to runoff risk. A sensitivity analysis provides information as to how sensitive the P Index score is to changes in inputs. The objectives were to determine 1) which inputs are most highly associated with P Index scores and 2) the relative impact of each input variable on resultant P Index scores. The current approach uses simulations across 6134 Ohio point locations and five crop management scenarios (CMSs), representing increasing soil disturbance. The CMSs range from all no-till, which is being promoted in Ohio, rotational tillage, which is a common practice in Ohio to full tillage to represent an extreme practice. Results showed that P Index scores were best explained by soil test P (31.9%) followed by connectivity to water (29.7%), soil erosion (13.4%), fertilizer application amount (11.3%), runoff class (9.5%), fertilizer application method (2.2%), and finally filter strip (2.0%). Ohio P Index simulations across CMSs one through five showed that >40% scored <15 points (low) while <1.5% scored >45 points (very high). Given Ohio water quality problems, the Ohio P Index needs to be stricter. The current approach is useful for Ohio P Index evaluations and revision decisions by spatially illustrating the impact of potential changes regionally and state-wide.
