高性能氯化物熔盐的结构与热物性分子动力学研究

doi:10.19799/j.cnki.2095-4239.2024.0731

摘要/Abstract

摘要：

近年来可再生能源的大力发展以及火电深度调峰调频的推进对熔盐储热材料工作温度范围和热物性提出了更高的要求，MgCl₂-NaCl-KCl(MgNaK)熔盐是优秀的候选熔盐之一。然而，MgNaK熔盐的热物性数据不完善。本工作基于第一性原理分子动力学模拟(FPMD)得到的能量和原子受力信息，开发了一种能够精确地描述MgNaK熔盐(45.4%-33%-21.6%，摩尔分数)微观粒子间相互作用的机器学习势函数并对其可靠性进行了验证。该机器学习势函数模拟计算的偏径向分布函数(PRDF)和配位数(CN)与FPMD基本重合。本工作从原子和电子的角度对局域结构和热性能随温度的变化进行了详细的研究。Na⁺或K⁺离子的引入破坏了原本紧密连接的MgCl _x 网状结构，从而影响输运性质。采用机器学习势函数模拟计算的密度(ρ)和恒压比热容(C_p )与实验数据高度一致，偏差均小于2%。通过动能理论讨论了MgNaK熔盐导热系数(λ)随温度负线性相关的原因，得到与其他氯化物熔盐类似的结论。最后，基于分子模拟和实验测量，对MgNaK熔盐在整个工作温度范围内的λ和黏度(η)给出了推荐值。

关键词: 高性能氯化物熔盐, 储能技术, 微结构与热物性, 机器学习势函数

Abstract:

The rapid development of renewable energy technologies and improvements in deep load adjustment and frequency regulation in thermal power systems have placed higher demands on the operating temperature range and thermophysical properties of molten salt storage materials. MgCl₂-NaCl-KCl (MgNaK) molten salt stands out among the potential candidates. However, complete thermophysical property data for MgNaK molten salt is lacking. In this study, we developed a machine learning potential function to accurately describe the microscopic particle interactions in MgNaK molten salt (with a composition of 45.4% mol MgCl₂, 33% mol NaCl, and 21.6% mol KCl). This model is based on energy and atomic force information obtained from first-principles molecular dynamics (FPMD) simulations. The reliability of this machine learning potential function was validated by comparing the partial radial distribution function (PRDF) and coordination number (CN) with FPMD results, showing excellent agreement. We explored how the local structure and thermal properties of MgNaK vary with temperature from atomic and electronic perspectives. The introduction of Na $+$ or K $+$ ions disrupts the tightly connected MgCl _x network structure, thereby affecting transportation properties. The density (ρ) and constant-pressure specific heat capacity (C_p ) obtained from machine learning potential function simulations were found to closely match the experimental data, with deviations of less than 2%. Furthermore, we examined the thermal conductivity (λ) of MgNaK molten salt using kinetic theory, we examined a negative linear correlation with temperature, which aligns with observations in other chloride molten salts. Based on molecular simulations and experimental measurements, we provide recommended values for λ and viscosity (η) of MgNaK molten salt across the entire operating temperature range.

Key words: high performance chloride molten salt, energy storage technology, microstructures and thermophysical properties, machine learning potential

中图分类号:

TB 33

于超, 潘格川崎. 高性能氯化物熔盐的结构与热物性分子动力学研究[J]. 储能科学与技术, 2024, 13(12): 4368-4380.

Chao YU, Gechuanqi PAN. Molecular dynamics study on structure and thermal properties of high-performance chloride molten salt[J]. Energy Storage Science and Technology, 2024, 13(12): 4368-4380.

图/表 18

图1

图2

图3

图4

图5

表1

图6

图7

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图9

图10

图11

图12

图13

图14

图15

图16

表2

黏度数据VTF拟合参数"

项目		Vogel-Tamman-Fulcher fit
项目	$η A$ /(cP∙K^-1/2)	A/K	T₀/K
ML	0.794 × 10^-2	1028	363
exp	2.10 × 10^-2	845	363

表2

参考文献 29

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不同配位	T/K	r_max/Å	h_max	r_min/Å	N
Mg-Cl	660	2.389	8.054	3.575	5.230
	700	2.382	8.064	3.570	5.145
	800	2.371	8.041	3.534	4.958
	900	2.363	7.992	3.486	4.805
	1000	2.331	7.908	3.491	4.748
	1100	2.299	7.849	3.433	4.655
	1200	2.348	7.795	3.555	4.618

Mg-Mg	660	3.652	1.671	5.401	3.075
	700	3.652	1.605	5.303	2.895
	800	3.690	1.482	5.368	2.843
	900	3.708	1.389	5.332	2.701
	1000	3.718	1.330	5.258	2.608
	1100	3.665	1.292	5.174	2.500
	1200	3.773	1.263	5.335	2.482

Na-Cl	660	2.787	4.193	4.010	6.400
	700	2.783	4.109	3.997	6.287
	800	2.778	3.906	4.008	6.106
	900	2.764	3.753	4.045	6.000
	1000	2.721	3.621	4.098	6.047
	1100	2.679	3.509	3.998	5.760
	1200	2.736	3.425	4.079	5.573

K-Cl	660	3.186	3.241	4.557	8.413
	700	3.184	3.163	4.568	8.299
	800	3.169	2.988	4.592	8.060
	900	3.159	2.859	4.551	7.657
	1000	3.117	2.743	4.578	7.646
	1100	3.075	2.655	4.525	7.392
	1200	3.129	2.584	4.603	7.066

Cl-Cl	660	3.621	2.548	5.304	12.342
	700	3.626	2.472	5.314	12.219
	800	3.654	2.333	5.327	11.875
	900	3.665	2.233	5.327	11.505
	1000	3.639	2.153	5.353	11.597
	1100	3.613	2.089	5.358	11.588
	1200	3.708	2.036	5.439	11.009

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