Enhanced Molecular Dynamics Approach for Thermal Transport Phenomena in Complex Systems
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Bibliographic record
Abstract
This thesis introduces an enhanced Molecular Dynamics (MD) approach, blended with fine-tuned Force Field (FF) models to reflect more realistic experimental conditions and achieve a precise representation of the atomic interactions in complex systems. Firstly, an enhanced MD algorithm consisting of an upgraded non-equilibrium integration scheme, namely eHEX, coupled with an augmented TraPPE-UA force field, was generated and put to use to predict Soret effect in a binary mixture: n-pentane/n-decane. The results were compared to other MD approaches and validated with respect to benchmarked experimental data. The suggested method showed a closer agreement with experimental data than the previous MD findings. The reinforced potential field (TraPPE-UA) was capable of reflecting the real molecular interactions between the hydrocarbons and reproduce the liquid mixture properties at different conditions. Moreover, the extended HEX method succeeded in conserving the system’s overall energy with minor fluctuations and attaining a stationary state, ensuring the precision of the integration scheme and the satisfaction of local equilibrium. Secondly, the performance of the previously proposed approach was further studied to test its performance on a ternary mixture of methane/n-butane/n-dodecane at five different compositions. Thermodiffusion separation ratio of each component was assessed at 333.15 K and 35 MPa, and compared to the experimental data as well as 3 other MD models from the literature. A good qualitative agreement between the experimental data and the MD model observed in this work was observed, displaying the least deviation when compared to the other MD approaches. The method was capable of adequately representing the physics behind the thermodiffusive separation and deepening the microscopic understanding of the segregation process in a ternary mixture undergoing large thermal gradients. Put differently, the approach elucidates the relative contribution of the cross-interactions found between the unlike species in the mixture and their corresponding composition. Next, an enhanced MD approach was also presented to predict the dynamics and thermophysical properties of suspended γ-alumina nanoparticles (NPs) in acidic aqueous solutions. The previous MD work have unveiled numerous impediments in terms of reproducing the thermal transport phenomena in nanofluids. A hybrid potential field, comprised of refined orce field models (ClayFF and SPC/E), was implemented to allow a precise integration of the nanoscale phenomena into the dynamics and structure of charged alumina NPs, thereby bridging the challenging gap between the solid-liquid interfacial chemistry and the overall thermodynamic properties. The original CLAYFF was augmented to properly account for the energy and momentum transfer between the water molecules and the positively charged NPs, while keeping the number of parameters small enough to allow modeling of a relatively large nanofluidic system.The results were in good agreement with the experimental data. An increase of the NPs volumetric concentration (φ) lead to the enhancement of thermal conductivity along with an increase of viscosity. The results demonstrate the crucial role played by the repulsive electrostatic forces yielding well-dispersed NP suspensions, specially at low φ. The post analysis of Mouromtseff number demonstrated that at lower φ, the system show a higher propensity for stability and enhancement for φ less than 2%, specially at high temperatures. On the contrary, for volumetric concentrations higher than 2%, the system thermal performance deteriorates which is expected due to the fact that the system exhibit a critical condition of aggregation and clogging. With all of the above findings in mind, the MD framework presented in this thesis represents an improved step towards a precise and computationally balanced MD modelling that bridges the relation between molecular signatures and macroscopic features, capable of overcoming the shortcomings present in mainly two emerging thermal applications: 1) Soret effect in hydrocarbon mixture and 2) thermal transport of alumina-water nanofluids.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it