A Comprehensive Study of Modeling Multiphase Flow through Chokes

Choke is an essential device that controls flow rates at either subsurface or surface. Many models and correlations have appeared for handling multiphase flow through surface chokes. However, direct comparison of their relative performances hasn’t been studied before, it is difficult to choose the right model or correlation for rate calculation. This thesis has evaluated and studied several models and correlations to explore their relative merits and ease of use in field settings. Seven different data sets gathered from laboratory and field, involving 1,004 independent data points, constituted the essence of this study. As expected, models anchored in thermodynamic principles outperformed others. The performance of the slippage effect was also studied. Seven slip equations were studied to determine which slip correlation showed best performance. The study found the constants proposed by Simpson et al. used in Grolmes and Leung equation showed best performance for flow through chokes. The study also found the importance of PVT data in flow through choke calculations. Specifically, changes in density and heat capacity with pressure and temperature should be part of any rigorous effort for flow rates computation. The rate-dependent choke discharge coefficient approach, generated from field data, outperforms the fixed discharge coefficient concept of existing models. Based on the results, the Sachdeva and Brill model performed consistently well among those considered models. Two different approaches of modifications were offered: first, replace the specific-heat capacity ratio k by polytropic-gas-expansion coefficient n. With the optimized discharge coefficient, the accuracy was not changed much but this modification was recommended from the theoretical aspect. Second, introduce a slippage factor. However, the results showed that slippage played a minor role in estimating flow rate. This conclusion was also supported by slip velocity and flow pattern calculated. The Sachdeva and Brill model without slippage factor turned out to outperform others. Among the correlations studied, the Fortunati correlation showed best performance but still not as good as all other models. This correlation also distinguishes flow boundary and flow type, which is an issue for the application of Al-Attar and B-K correlation. The Ashford and Pierce correlation, originally developed for subcritical flow, handles choke size up to 20/64 in. Increasing choke size requires adjustment of the choke discharge coefficient, which leads to unreliable solutions. For B-K correlation and Al-Attar critical correlation, they both use empirical coefficients derived from a particular field, which makes the correlation unreliable for other applications. Both correlations show huge errors, and B-K correlation mainly overestimates results

Combining Simulation and Machine Learning for Materials Optimization: Polymer Compatibilization, Disinfection, and Heat Transfer

In the last half-century, considerable advances have been achieved in molecular simulation techniques aiming at offering a comprehensive understanding of the structure-property relationship of soft materials on several time and length scales. So far, however, the optimal design of candidates for the next-generation soft materials is still a challenging task due to the enormous chemical and configurational space. The machine learning (ML) techniques, which are utilized to extract actionable insights from big data generated from simulations, can overcome the bottlenecks in the tasks of soft materials optimization. Hence, this thesis has developed a framework based on the mutual communication between multiscale simulations (atomistic and coarse-grained) and ML toward rational investigations of soft matter. One objective of this thesis is to evaluate the detailed structure-composition-property-performance rela- tionships of soft materials in a forward way. We firstly investigate the compatibilizing performance of block copolymers (i.e., linear and graft) on the interface between two incompatible polymer phases by dissipative- particle-dynamics (DPD) simulations. A phenomenological analytical power-law fit is developed to quantify the variation of compatibilization efficiency of linear block copolymers with the polymer chemistries, the molecular architecture, and the number of copolymer molecules. However, graft copolymers have larger diversities in the space of architectural parameters as compared to linear block copolymers, which limits the traditional empirical fitting process. Accordingly, we feed DPD results to ML models and find that the combination of DPD/ML is able to accurately predict the compatibilization efficiency of graft copolymers at the molecular level. For a given graft copolymer with several descriptors (e.g., molecular architectures and chemistries), its compatibilization efficiency can be well predicted from the trained ML models. Moreover, ML techniques provide a descriptor importance measure for the correlation between descriptors and DPD predictions. We find that as the blend changes from weakly incompatible to strongly incompatible, the number of side chains of graft copolymers gradually dominates their compatibilization efficiency while the side chain length becomes unimportant. This finding can narrow the search space in further simulations and experiments. Furthermore, we attempt to understand the compatibilization mechanism of the linear and graft copolymers by characterizing the beads distributions, the number of unlike contacts between different species, and the molecular conformations. Specifically, the relative shape anisometry of copolymers, defined as the ratio of their gyration tensor elements in directions normal and parallel to the surface, is strongly correlated with their compatibilization efficiency for both linear and graft copolymers. We also evaluate the alcohol-induced changes on coronavirus membranes of different compositions with DPD models, i.e., pure dipalmitoylphosphatidylcholine, dioleoylphosphatidylcholine, and dimyristoylphos- phatidylcholine as well as their binary and ternary mixed membranes. The principal finding of this study is that a maximum ethanol concentration of 32 mol % (55 wt. % ) in alcoholic-based disinfectants is sufficient to decompose any coronavirus model membranes composed of these three lipids. However, given the wide variations in compositions and structures of mixed membranes, identifying their transitions from the intact to the disrupted state is challenging. For example, we find that the transition point cannot be quantitatively predicted based on physical descriptors such as the area per lipid molecule, the membrane thickness, and the orientational order parameter. Additionally, the visual inspection of simulation profiles is cumbersome to characterize the state of these membranes. Developing a simple and robust tool to characterize the stability of membranes against ethanolic disinfectants, can therefore accelerate the optimization process of disinfection investigations. This target is achieved by the developed DPD/deep-neural-network framework in this study, which accesses the integrity of lipid membranes in place of visual inspections. The other objective of this thesis is to design materials with optimal performance on desired properties, com- positions, and structures in the reverse direction, i.e., inverse design (performance-property-composition- structure). We employ a hybrid framework by combining the genetic algorithm and the atomistic molecular dynamics simulation, to design polyethylene-polypropylene copolymers with high thermal conductivity. We find that polyethylene-polypropylene copolymers with various sequences at the same monomer ratio have a broad distribution of thermal conductivities. This indicates that the monomer sequence has a crucial effect on the thermal energy transport of the copolymers. A non-periodic and non-intuitive optimal sequence is indeed identified by this hybrid framework, which gives the highest thermal conductivity compared with both homopolymers and any regular block copolymers, e.g., diblock, triblock, and hexablock. In comparison to bulk density, chain conformations, and vibrational density of states, the monomer sequence has the strongest impact on the efficiency of the thermal energy transport via inter- and intra-molecular interactions. The success of ML, providing property predictions of materials in both large compositional and confor- mational spaces, relies on the availability of training data from simulations. In turn, ML methods allow a robust posteriori data analysis (e.g., descriptor importance measure) for exploring correlations between descriptors and target properties in simulations, which can narrow the search space of descriptors for further investigations. In short, the computational framework of integrating multiscale simulations with ML algorithms has a significant potential for accelerating the design of soft matter. We believe our work provides efficient and practical approaches to develop the advanced hybrid framework for the material optimization

Thermal energy transport across the interface between phase change material n-heneicosane in solid and liquid phases and few-layer graphene

Molecular dynamics simulations have been performed to investigate the mechanism of thermal energy transport at the interface between n-heneicosane in solid and liquid phases and few-layer graphene at different temperatures under two heating modes (in the “heat-matrix” mode, heat is flowing from the heated heneicosane molecules to the cooled ones through the graphene layers and in the “heat-graphene” mode, the energy is flowing from the heated graphene to the cooled heneicosane). The effect of orientation of the perfect crystal structure (heneicosane molecules are positioned perpendicular and parallel to the graphene basal plane) on the interfacial thermal conductance has been examined. It is observed that the interfacial thermal conductance is 2 orders of magnitude higher under the heat-matrix mode than under the heat-graphene mode, for liquid or solid heneicosane and monolayer graphene. With an increase in the number of graphene layers, the interfacial thermal conductance under the heat-matrix mode decreases and reaches a plateau when the number of the graphene layer is more than eight. This is caused by the decreasing contribution of direct heat transfer from the matrix to matrix across the graphene layers via nonbonded intermolecular interactions. The interfacial thermal conductance becomes similar for both heating modes, once the number of graphene layers in the system is over 15. The influence of temperature on the interfacial thermal conductance is found to be insignificant in the range (175–250 K; 350–400 K). Both the phase and structure of heneicosane significantly influence the interfacial conductance. Spectral analysis suggests that graphene vibrational modes of all frequencies contribute to the interfacial heat transfer

Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus

Large Language Models (LLMs) have gained significant popularity for their impressive performance across diverse fields. However, LLMs are prone to hallucinate untruthful or nonsensical outputs that fail to meet user expectations in many real-world applications. Existing works for detecting hallucinations in LLMs either rely on external knowledge for reference retrieval or require sampling multiple responses from the LLM for consistency verification, making these methods costly and inefficient. In this paper, we propose a novel reference-free, uncertainty-based method for detecting hallucinations in LLMs. Our approach imitates human focus in factuality checking from three aspects: 1) focus on the most informative and important keywords in the given text; 2) focus on the unreliable tokens in historical context which may lead to a cascade of hallucinations; and 3) focus on the token properties such as token type and token frequency. Experimental results on relevant datasets demonstrate the effectiveness of our proposed method, which achieves state-of-the-art performance across all the evaluation metrics and eliminates the need for additional information.Comment: Accepted by EMNLP 2023 (main conference

Negative symptom dimensions and social functioning in Chinese patients with schizophrenia

ObjectiveNegative symptoms can seriously affect social functioning in patients with schizophrenia. However, the role of various components of negative symptoms in social functioning remains unclear. This study aimed to explore the associations among three different dimensions of negative symptoms (i.e., communication, emotion, and motivation) and social functioning to identify potential therapeutic targets.MethodsThis cross-sectional study enrolled 202 Chinese participants with schizophrenia. Negative symptoms were evaluated using the Negative Symptom Assessment (NSA). Social functioning was represented by the Personal and Social Performance Scale (PSP) total score and employment status. Correlation analysis was conducted to clarify the relationship between negative symptoms and the PSP total score. Regression analysis was performed to explore the determinants of the PSP total score and employment status, considering negative symptoms and possible confounders, such as demographic features, positive symptoms, cognitive symptoms, depressive symptoms, and extrapyramidal side effects.ResultsThe PSP total score was correlated with all three dimensions of negative symptoms (i.e., emotion, motivation, and communication; rs = –0.509, –0.662, and –0.657, respectively). Motivation, instead of emotion or communication, predicted both low PSP total scores and unemployment.ConclusionSocial functioning in patients with schizophrenia was significantly related to motivation. Further studies should focus on motivation and consider it as a therapeutic target to improve patients’ social functioning

Learning A Foundation Language Model for Geoscience Knowledge Understanding and Utilization

Large language models (LLMs)have achieved great success in general domains of natural language processing. In this paper, we bring LLMs to the realm of geoscience, with the objective of advancing research and applications in this field. To this end, we present the first-ever LLM in geoscience, K2, alongside a suite of resources developed to further promote LLM research within geoscience. For instance, we have curated the first geoscience instruction tuning dataset, GeoSignal, which aims to align LLM responses to geoscience-related user queries. Additionally, we have established the first geoscience benchmark, GeoBenchmark, to evaluate LLMs in the context of geoscience. In this work, we experiment with a complete recipe to adapt a pretrained general-domain LLM to the geoscience domain. Specifically, we further train the LLaMA-7B model on over 1 million pieces of geoscience literature and utilize GeoSignal's supervised data to fine-tune the model. Moreover, we share a protocol that can efficiently gather domain-specific data and construct domain-supervised data, even in situations where manpower is scarce. Experiments conducted on the GeoBenchmark demonstrate the the effectiveness of our approach and datasets

CCN1, a Pro-Inflammatory Factor, Aggravates Psoriasis Skin Lesions by Promoting Keratinocyte Activation

Psoriasis is a common chronic skin disease characterized by epidermal hyperplasia and inflammation. The pathogenesis of psoriasis is multifactorial and is not fully understood. Here we demonstrate that CCN1 (also called Cyr61, which is short for cysteine-rich 61), an extracellular matrix protein that is also considered a pro-inflammatory factor, is highly expressed in the lesional skin of psoriasis patients, as well as in that of imiquimod (IMQ)- and IL-23-treated psoriasis-like mice. Then we show that blocking CCN1 function in vivo attenuates epidermal hyperplasia and inflammation in psoriasis-like mice. Further, in primary cultured normal human keratinocytes and HaCaT (human keratinocyte cell line) cells, CCN1 promotes keratinocyte activation, including the proliferation and expression of immune-related molecules. Finally, we observe that integrin α6β1 is the receptor of CCN1 in keratinocytes, and CCN1 stimulation activates the downstream phosphoinositide-3 kinase/Akt/NF-κB signaling pathway. Taken together, our findings reveal that CCN1 has a critical role in psoriasis pathogenesis. Moreover, as CCN1 is a secreted extracellular matrix (ECM) protein, our study also provides evidence that ECM, which is involved in psoriatic pathogenesis, could be a potent target for psoriasis treatment

Qwen Technical Report

Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, the first installment of our large language model series. Qwen is a comprehensive language model series that encompasses distinct models with varying parameter counts. It includes Qwen, the base pretrained language models, and Qwen-Chat, the chat models finetuned with human alignment techniques. The base language models consistently demonstrate superior performance across a multitude of downstream tasks, and the chat models, particularly those trained using Reinforcement Learning from Human Feedback (RLHF), are highly competitive. The chat models possess advanced tool-use and planning capabilities for creating agent applications, showcasing impressive performance even when compared to bigger models on complex tasks like utilizing a code interpreter. Furthermore, we have developed coding-specialized models, Code-Qwen and Code-Qwen-Chat, as well as mathematics-focused models, Math-Qwen-Chat, which are built upon base language models. These models demonstrate significantly improved performance in comparison with open-source models, and slightly fall behind the proprietary models.Comment: 59 pages, 5 figure