Simulation and Analysis of Urban Production–Living–Ecological Space Evolution Based on a Macro–Micro Joint Decision Model

The precise simulation of urban space evolution and grasping of the leading factors are the most important basis for urban space planning. However, the simulation ability of current models is lacking when it comes to complicated/unpredictable urban space changes, resulting in flawed government decision-making and wasting of urban resources. In this study, a macro–micro joint decision model was proposed to improve the ability of urban space evolution simulation. The simulation objects were unified into production, living and ecological space to realize “multiple planning in one”. For validation of the proposed model and method, remote sensing images, geographic information and socio-economic data of Xuzhou, China from 2000 to 2020 were collected and tested. The results showed that the simulation precision of the cellular automata (CA) model was about 87% (Kappa coefficient), which improved to 89% if using a CA and multi-agent system (MAS) joint model. The simulation precision could be better than 92% using the prosed model. The result of factor weight determination indicated that the micro factors affected the evolution of production and living space more than the macro factors, while the macro factors had more influence on the evolution of ecological space than the micro factors. Therefore, active policies should be formulated to strengthen the ideological guidance towards micro individuals (e.g., a resident, farmer, or entrepreneur), and avoid disordered development of living and production space. In addition, ecological space planning should closely link with the local environment and natural conditions, to improve urban ecological carrying capacity and realize urban sustainable development.

Development of a Phase Field Tool Coupling With Thermodynamic Data for Microstructure Evolution Simulation of Alloys in Nuclear Reactors

We have developed a new phase field tool PHAFIS to automatically incorporate the thermodynamic data for both of WBM and KKS phase field simulations, which are widely used in the simulation of microstructure evolution of nuclear materials. Based on the generic C/C++ programming language, PHAFIS is capable of automatically parsing the standard TDB files, extracting the free energy and diffusion potential varying with the composition in an analytical way. Based on the two diffrerent TDB files of Fe-Cr binary system and the interpolated data, the phase morphologies during spinodal decomposition at 700 K and liquid-solid transition at high temperatures above 1800 K are reproduced and compared with each other by WBM and KKS model, respectively. Specifically, both of interface-controlled and diffusion-controlled phase transition mechanisms are successfully revealed for solidification through our KKS simulation, consistent with classic phase transition theories. It can be concluded that even slight differences in thermodynamic data will cause significant changes in the microstructure evolution. The integrity of our software tool will facilitate the coupling of phase field methods with thermodynamic data for other materials, paving a fundamental step for coupling more factors required in microstructure simulation.

The impact of (n,γ) reaction rate uncertainties of unstable isotopes on the i-process nucleosynthesis of the elements from Ba to W

ABSTRACT The abundances of neutron (n)-capture elements in the carbon-enhanced metal-poor (CEMP)-r/s stars agree with predictions of intermediate n-density nucleosynthesis, at Nn ∼ 1013–1015 cm−3, in rapidly accreting white dwarfs (RAWDs). We have performed Monte Carlo simulations of this intermediate-process (i-process) nucleosynthesis to determine the impact of (n,γ) reaction rate uncertainties of 164 unstable isotopes, from 131I to 189Hf, on the predicted abundances of 18 elements from Ba to W. The impact study is based on two representative one-zone models with constant values of Nn = 3.16 × 1014 and 3.16 × 1013 cm−3 and on a multizone model based on a realistic stellar evolution simulation of He-shell convection entraining H in a RAWD model with [Fe/H] = −2.6. For each of the selected elements, we have identified up to two (n,γ) reactions having the strongest correlations between their rate variations constrained by Hauser–Feshbach computations and the predicted abundances, with the Pearson product–moment correlation coefficients |rP| > 0.15. We find that the discrepancies between the predicted and observed abundances of Ba and Pr in the CEMP-i star CS 31062−050 are significantly diminished if the rate of 137Cs(n,γ)138Cs is reduced and the rates of 141Ba(n,γ)142Ba or 141La(n,γ)142La increased. The uncertainties of temperature-dependent β-decay rates of the same unstable isotopes have a negligible effect on the predicted abundances. One-zone Monte Carlo simulations can be used instead of computationally time-consuming multizone Monte Carlo simulations in reaction rate uncertainty studies if they use comparable values of Nn. We discuss the key challenges that RAWD simulations of i process for CEMP-i stars meet by contrasting them with recently published low-Z asymptotic giant branch (AGB) i process.

How does DEM error impact the optimal grid resolution of soil evolution simulation?

<p>The soil thickness is the key controlling factor of local hydrology and geomorphologic characteristics. The accuracy, reliability and coverage of soil thickness map are required for reliable application. Though with quite distinct structures, models for simulating soil thickness take modern topographic data (normally digital elevation model, DEM) as one of the most important inputs. Understanding the effect of grid resolution on soil thickness prediction and selecting an appropriate resolution is crucial for the macro-scale modeling. In this study, we further explored the relationship between topographic resolution and simulation accuracy of soil thickness, and propose a new method to determine the optimal simulation resolution. A series of abstract hillslopes with different terrain noise and terrain complexity were construct and different resolutions of DEM were generated. We used a simple geomorphic based model to calculate topographic index (slope, aspect and curvature) and soil thickness. The results show that the truncation error and noise of DEM will propagate during the simulation process. Furtherly, the correlation curve between DEM resolution and the simulation error of soil thickness is a hook curve. The shape of the curve is mainly controlled by two factors, terrain noise and terrain complexity. By fitting the correlation curve of all hillslopes, the curve can be predicted by them, and the resolution corresponding to the error minimum be found out, which can be called the optimal simulation resolution of the soil thickness prediction model.</p>