State Model Research Articles

Characteristic Curves of Polar Fluids: (II) The Two-Center Lennard–Jones Plus Quadrupole Fluid

A systematic study of Brown’s characteristic curves of the two center Lennard–Jones plus point quadrupole (2CLJQ) fluid was carried out using molecular simulation and molecular-based equation of state (EOS) modeling. The model parameters (elongation and quadrupole moment) were varied systematically covering the range relevant for real fluid models. In total, 36 model fluids were studied. The independent predictions from the EOS and the computer experiments are found to be in very good agreement. Based on these results, the influence of the quadrupole moment on the fluid behavior at extreme conditions is elucidated. The quadrupole interactions are found to have a surprisingly minor influence on the extreme state fluid behavior. In particular, for the Amagat curve, the quadrupole moment is found to have an almost negligible influence in a wide temperature range. The results also provide new insights into the applicability of the corresponding states principle, which is compared to other molecular property features. Interestingly, for a wide range of quadrupole moments, the fluid behavior at extreme conditions is conform with the corresponding states principle—opposite to the influence of other molecular features. This is attributed to the symmetry of the quadrupole interactions. Moreover, an empirical correlation for the characteristic curves was developed as a global function of the model parameters and tested on real substance models. Additionally, the applicability of Batschinski’s linearity law for the Zeno curve was assessed using the results for the 2CLJQ fluid.

Quantifying Unbiased Conformational Ensembles from Biased Simulations Using ShapeGMM.

Quantifying the conformational ensembles of biomolecules is fundamental to describing mechanisms of processes such as protein folding, interconversion between folded states, ligand binding, and allosteric regulation. Accurate quantification of these ensembles remains a challenge for conventional molecular simulations of all but the simplest molecules due to insufficient sampling. Enhanced sampling approaches, such as metadynamics, were designed to overcome this challenge; however, the nonuniform frame weights that result from many of these approaches present an additional challenge to ensemble quantification techniques such as Markov State Modeling or structural clustering. Here, we present rigorous inclusion of nonuniform frame weights into a structural clustering method entitled shapeGMM. The result of frame-weighted shapeGMM is a high dimensional probability density and generative model for the unbiased system from which we can compute important thermodynamic properties such as relative free energies and configurational entropy. The accuracy of this approach is demonstrated by the quantitative agreement between GMMs computed by Hamiltonian reweighting and direct simulation of a coarse-grained helix model system. Furthermore, the relative free energy computed from a shapeGMM probability density of alanine dipeptide reweighted from a metadynamics simulation quantitatively reproduces the underlying free energy in the basins. Finally, the method identifies hidden structures along the actin globular to filamentous-like structural transition from a metadynamics simulation on a linear discriminant analysis coordinate trained on GMM states, illustrating how structural clustering of biased data can lead to biophysical insight. Combined, these results demonstrate that frame-weighted shapeGMM is a powerful approach to quantifying biomolecular ensembles from biased simulations.

A flexible and deep peak shaving scheme for combined heat and power plant under full operating conditions

Improving the flexible and deep peak shaving capacity of combined heat and power (CHP) plant under full operating conditions to facilitate renewable energy consumption is the main choice of novel power system. Accordingly, a dry/wet state automatic conversion control scheme fuses the precise mechanism structure, reinforcement learning and multi-objective model predictive control (MPC) algorithms is designed to promote the deep peak shaving ability of CHP plant in this paper. Firstly, the detailed mechanism models of once-through boiler at dry and wet state are presented by analyzing the dynamic characteristics of steam-water flow. Secondly, the large-scale unknown parameters in mechanism models are identified via the Takagi-Sugeno fuzzy modeling, reinforcement learning algorithms and actual dry/wet state conversion data. Thanks to the proposed hybrid modeling strategy, the high-precision boiler-steam unit models at dry and wet state are rapid obtained. Then, to meet different peak shaving demands, the multi-objective MPC algorithm is respectively constructed under dry and wet state with the comprehensive consideration of operational constraints, load tracking error, algorithm stability, power generation cost, CO2 and NOx emission costs. Aiming at maximizing the peak shaving efficiency and flexible operation ability of plant, a dry/wet automatic control scheme combined the designed multi-objective MPC algorithms and identified dry/wet state models is proposed. Finally, the load variation rate of 5 % and 2 % RCM/min is respectively achieved in the dry/wet state conversion tests on a 350 MW CHP plant based on the proposed control scheme. Thus, the rapid and thorough dry/wet state conversion performance of once-through boiler has successfully improved the operational flexibility of CHP plant under full operating conditions.

Analysis and experimental verification of the polarization characteristics of cube-corner reflectors

The polarization effect of cube-corner reflectors (CCRs), which influences the performance of optical systems, requires comprehensive analysis. This study developed a model for the polarization state of uncoated solid and hollow CCRs using the Jones matrix derivation and Zemax software simulations. The accuracies of theoretical analyses and simulations were verified using an experimental setup. Theoretical analysis, simulation, and experimental results revealed that hollow CCRs are insensitive to the polarization state of the incident light, exhibiting average variations of 0.8° and 0.7° in the polarization direction and ellipticity, respectively. Contrastingly, the high sensitivity of solid CCRs to the polarization state of the incident light varied across different incident regions. The propagation paths 2–1–3 and 3–1–2 with minor polarization effects involved light that entered from one side of the CCR, traversed the bottom, and emitted from the other side. In these regions, the average variations in the polarization direction and ellipticity were 10.7° and 6.6°, respectively, whereas more affected regions exhibited corresponding values of 44.8° and 20.0°. These findings guide the enhancement and optimization of the performance of optical systems using CCRs.

Photochemical Transformation of Monochloramine Induced by Triplet State Dissolved Organic Matter

The photoexcited dissolved organic matter (DOM) could produce reactive intermediates, affecting chemical oxidant transformation in UV based advanced oxidation processes (AOPs). This study confirmed the critical role of triplet state DOM (3DOM*), generated from DOM photoexcitation, in the transformation of monochloramine (NH2Cl), a commonly used chemical oxidant and disinfectant in water treatment. NH2Cl (42.25 μM, as Cl2) was decayed by 17.4–73.4 % within 60 min, primarily due to 3DOM* , in DOM (2–30 mgC L−1) solutions irradiated by 365 nm, where NH2Cl has no absorption. The second-order quenching rate constants of triplet state model photosensitizers by NH2Cl were determined to be 0.95(± 0.04)–4.49(± 0.04)× 108 M−1 s−1 by using laser flash photolysis. As a reductant, 3DOM* reacted with NH2Cl through one-transfer mechanism, leading to amino radical (NH2•) generation, which then transferred to ammonia (NH4+, pKa 9.25) through H-abstraction by the phenolic moieties in DOM. Additionally, the intermediate product of 3DOM* oxidized by NH2Cl or those triplet state quinones can hydrolyze to form phenolic moieties, elevating NH4+ yield to higher than 99% upon 365 nm irradiation. These findings suggest that the widespread DOM can be applied to convert NH2Cl via 3DOM* with minimal toxic risks.

Advanced computational approaches to understand protein aggregation.

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

Early Aggregation Mechanism of SOD128-38 Based on Force Field Parameter of 5-Cyano-Tryptophan.

The aggregation of superoxide dismutase 1 (SOD1) results in amyloid deposition and is involved in familial amyotrophic lateral sclerosis, a fatal motor neuron disease. There have been extensive studies of its aggregation mechanism. Noncanonical amino acid 5-cyano-tryptophan (5-CN-Trp), which has been incorporated into the amyloid segments of SOD1 as infrared probes to increase the structural sensitivity of IR spectroscopy, is found to accelerate the overall aggregation rate and potentially modulate the aggregation process. Despite these observations, the underlying mechanism remains elusive. Here, we optimized the force field parameters of 5-CN-Trp and then used molecular dynamics simulation along with the Markov state model on the SOD128-38 dimer to explore the kinetics of key intermediates in the presence and absence of 5-CN-Trp. Our findings indicate a significantly increased probability of protein aggregate formation in 5CN-Trp-modified ensembles compared to wildtype. Dimeric β-sheets of different natures were observed exclusively in the 5CN-Trp-modified peptides, contrasting with wildtype simulations. Free-energy calculations and detailed analyses of the dimer structure revealed augmented interstrand interactions attributed to 5-CN-Trp, which contributed more to peptide affinity than any other residues. These results explored the key events critical for the early nucleation of amyloid-prone proteins and also shed light on the practice of using noncanonical derivatives to study the aggregation mechanism.

Entity Footprinting: Modeling Contextual User States via Digital Activity Monitoring

Our digital life consists of activities that are organized around tasks and exhibit different user states in the digital contexts around these activities. Previous works have shown that digital activity monitoring can be used to predict entities that users will need to perform digital tasks. There have been methods developed to automatically detect the tasks of a user. However, these studies typically support only specific applications and tasks, and relatively little research has been conducted on real-life digital activities. This article introduces user state modeling and prediction with contextual information captured as entities, recorded from real-world digital user behavior, called entity footprinting —a system that records users’ digital activities on their screens and proactively provides useful entities across application boundaries without requiring explicit query formulation. Our methodology is to detect contextual user states using latent representations of entities occurring in digital activities. Using topic models and recurrent neural networks, the model learns the latent representation of concurrent entities and their sequential relationships. We report a field study in which the digital activities of 13 people were recorded continuously for 14 days. The model learned from this data is used to (1) predict contextual user states and (2) predict relevant entities for the detected states. The results show improved user state detection accuracy and entity prediction performance compared to static, heuristic, and basic topic models. Our findings have implications for the design of proactive recommendation systems that can implicitly infer users’ contextual state by monitoring users’ digital activities and proactively recommending the right information at the right time.

A study on modeling of boiling heat transfer in core debris bed of SFR

In case of a hypothetical severe accident in a Sodium-cooled Fast Reactor (SFR), coolability of the debris bed in the post-accident phase plays a vital role in mitigating the accident and ensuring the structural integrity of the reactor vessel. Few numerical studies are reported in literature, in which the boiling heat transfer in debris bed is expressed as equivalent heat conduction using similarity law between heat conduction and two-phase heat transfer. However, these studies assumed steady state mass conservation for the boiling zone and neglected the gravity force. Hence, a detailed study has been carried out for various particle sizes and porosities of SFR debris to investigate the influence of above considerations. The effect of gravity on debris bed coolability is studied using steady state model of Lipinski, which showed that gravity has a non-negligible effect, for particle size of 0.3 mm and porosity of 0.5. However, the gravitation force was found to have a negligible effect in dryout heat flux estimation for the bottom cooled configuration. A transient numerical model is developed for simulating the boiling phenomena in debris beds and validated with the published experimental results. The assumption of steady state mass conservation is verified by carrying out transient analysis, which indicated early prediction of the dryout inception. For time dependent heat generation case, the unsteady mass conservation predicted higher DHF compared to constant heat generation.

Reconnaissance of Allostery via the Restoration of Native p53 DNA-Binding Domain Dynamics in Y220C Mutant p53 Tumor Suppressor Protein.

Allosteric regulation of protein dynamics infers a long-range deliberate propagation of information via micro- and macroscale interactions. The Y220C structural mutant is one of the most frequent cancerous p53 mutants. The mutation is distally located from the DNA-binding site of the p53 DNA-binding domain yet causes changes in DNA recognition. This system presents a unique opportunity to examine the allosteric control of mutated proteins under a drug design paradigm. We focus on the key case study of p53 Y220C mutation restoration by a series of new compounds suggested to have Y220C reactivation properties in comparison to our previous findings on the restorative potential of PK11000, a compound studied extensively for reactivation in vitro and in vivo. Previously, we implemented all-atom molecular dynamics (MD) simulations and our lab's techniques of MD-Sectors and MD-Markov state models on the wild type, the Y220C mutant, and Y220C with PK11000 to characterize the effector's restorative properties in terms of conformational dynamics and hydrogen bonding. In this study, we turn to probing the effects made by docking the battery of a new but less well-tested set of aminobenzothiazole derivative compounds reported by Baud et al., which show promise of Y220C rescue. We find that while complete and precise reconstitution of p53 WT molecular dynamics may not be observed as was the case with PK11000, dispersed local reconstitution of loop dynamics provides evidence of rescuing effects by aminobenzothiazole derivative N,2-dihydroxy-3,5-diiodo-4-(1H-pyrrol-1-yl)benzamide, Effector 22, like what we observed for PK11000. Generalizable insights into the mutation and allosteric reactivation of p53 by various effectors by reconstitution of WT dynamics observed in statistical conformational ensemble analysis and network inference are discussed, considering the development of allosteric drug design rooted in first principles.

Reference site selection based on state-and-transition models for soil health gap evaluation within cropland reference ecological units

Comparative soil health studies are critical in soil conservation and gauging the success of different management practices in soil health improvement. A primary challenge in these studies is the selection of a consistent natural reference site. Current choices vary widely, from minimally disturbed areas to pristine prairies. This methodological paper underscores the need for deliberate and thoughtful reference site selection for benchmark soil properties. Utilizing the State &Transition models, the study introduces a framework for this selection, drawing upon the ecological site (ES) and reference plant communities detailed in an ecological site description (ESD) within a respective Major Land Resource Area (MLRA). This study advocates for a localized classification within the framework of the Cropland Reference Ecological Unit (CREU), emphasizing the significance of local precipitation and soil data to ensure unbiased comparisons. Soil samples from eastern (MLRA 106) and western (MLRA 67A) Nebraska were collected, representing distinct pedogenetic and climatic differences. Analysis of soil organic matter between MLRAs displayed substantial variations, suggesting potential biases and complexities in soil health gap calculations when using reference sites and croplands not in the same MLRA, soil types (texture class), or precipitation zones. However, refining the comparisons by delineating the MLRA, soil, and precipitation zones within the framework of CREU yielded more consistent and realistic comparative data. Integrating the MLRA and ES, complemented by granular soil and precipitation data, provides a robust method for establishing soil health benchmark data.

Online two-dimensional filter for anti-interference aging features extraction to accurately predict the battery health

Reliable and precise extraction of features plays a critical role in predicting the State of Health (SOH) of lithium batteries. Nevertheless, noise-tainted measurement signal can cause the distortion of differential curves, consequently compromising the accuracy of feature extraction. This paper develops an online two-dimensional filtering framework by fusing the Adaptive Taylor Kalman filter (ATKF) and the Improved Gaussian filter (IGF), applying it to both incremental capacity analysis and differential thermal voltammetry methods. Firstly, the Taylor series is harnessed to construct a state model for the noise-affected measurement signal. Then, an adaptive algorithm is introduced to diminish the dependence on human judgment when configuring variances. Finally, the IGF filters the curve from the two-dimensions of time and period. Experimental results demonstrate that the two-dimensional filtering method exhibits robust noise mitigation capabilities, enhancing the stability of feature extraction while maintaining system causality. Furthermore, this method has strong robustness to battery inconsistency and temperature uncertainty. When applying the proposed filter to extract aging features from battery data provided by CALCE and Oxford datasets, the correlation coefficient between the extracted aging features and SOH increases by 34.75% and 10.71%, respectively. Additionally, the RMSE of the SOH prediction results decreases by 60.57% and 59.46%, respectively.

Design of AR system tracking registration method using dynamic target light-field

In the process of tracking registration for an augmented reality (AR) system, it's essential first to obtain the system's initial state, as its accuracy significantly influences the precision of subsequent three-dimensional tracking registration. At this point, minor movements of the target can directly lead to calibration errors. Current methods fail to address the challenge of capturing the initial state of dynamic deformation in optically transparent AR systems effectively. To tackle this issue, the concept of a static light-field is expanded to a four-dimensional dynamic light-field, and a tracking registration method for an optical see-through AR system based on the four-dimensional dynamic light-field is introduced. This method begins by analyzing the relationship between the components of the optical see-through AR system and studying the impact of a dynamic target on the initial state model. Leveraging the fundamental principle of light-field correlation, the theory and model for four-dimensional dynamic light-field tracking registration are developed. A lot of experiments have confirmed the algorithm's accuracy, enhanced its stability, and demonstrated the superior performance of the three-dimensional tracking registration algorithm.

Machine-learning-based plasma metabolomic profiles for predicting long-term complications of cirrhosis.

The complications of liver cirrhosis occur after long asymptomatic stages of progressive fibrosis and are generally diagnosed late. We aimed to develop a plasma metabolomic-based score tool to predict these events. We enrolled 64,005 UK biobank participants with metabolomic profiles. Participants were randomly divided into the training (n=43,734) and validation cohorts (n=20,271). Liver cirrhosis complications were defined as hospitalization for liver cirrhosis or presentation with HCC. An interpretable machine-learning framework was applied to learn the metabolomic states extracted from 168 circulating metabolites in the training cohort. An integrated nomogram was developed and compared to conventional and genetic risk scores. We created 3 groups: low-risk, middle-risk, and high-risk through selected cutoffs of the nomogram. The predictive performance was validated through the area under a time-dependent receiver operating characteristic curve (time-dependent AUC), calibration curves, and decision curve analysis. The metabolomic state model could accurately predict the 10-year risk of liver cirrhosis complications in the training cohort (time-dependent AUC: 0.84 [95% CI: 0.82-0.86]), and outperform the fibrosis-4 index (time-dependent AUC difference: 0.06 [0.03-0.10]) and polygenic risk score (0.25 [0.21-0.29]). The nomogram, integrating metabolomic state, aspartate aminotransferase, platelet count, waist/hip ratio, and smoking status showed a time-dependent AUC of 0.930 at 3 years, 0.889 at 5 years, and 0.861 at 10 years in the validation cohort, respectively. The HR in the high-risk group was 43.58 (95% CI: 27.08-70.12) compared with the low-risk group. We developed a metabolomic state-integrated nomogram, which enables risk stratification and personalized administration of liver-related events.

Arginine Kinase Activates Arginine for Phosphorylation by Pyramidalization and Polarization.

Arginine phosphorylation plays numerous roles throughout biology. Arginine kinase (AK) catalyzes the delivery of an anionic phosphoryl group (PO3-) from ATP to a planar, trigonal nitrogen in a guanidinium cation. Density functional theory (DFT) calculations have yielded a model of the transition state (TS) for the AK-catalyzed reaction. They reveal a network of over 50 hydrogen bonds that delivers unprecedented pyramidalization and out-of-plane polarization of the arginine guanidinium nitrogen (Nη2) and aligns the electron density on Nη2 with the scissile P-O bond, leading to in-line phosphoryl transfer via an associative mechanism. In the reverse reaction, the hydrogen-bonding network enforces the conformational distortion of a bound phosphoarginine substrate to increase the basicity of Nη2. This enables Nη2 protonation, which triggers PO3- migration to generate ATP. This polarization-pyramidalization of nitrogen in the arginine side chain is likely a general phenomenon that is exploited by many classes of enzymes mediating the post-translational modification of arginine.

Cognitive state detection with eye tracking in the field: an experience sampling study and its lessons learned

Abstract In the future, cognitive activity will be tracked in the same way how physical activity is tracked today. Eye-tracking technology is a promising off-body technology that provides access to relevant data for cognitive activity tracking. For building cognitive state models, continuous and longitudinal collection of eye-tracking and self-reported cognitive state label data is critical. In a field study with 11 students, we use experience sampling and our data collection system esmLoop to collect both cognitive state labels and eye-tracking data. We report descriptive results of the field study and develop supervised machine learning models for the detection of two eye-based cognitive states: cognitive load and flow. In addition, we articulate the lessons learned encountered during data collection and cognitive state model development to address the challenges of building generalizable and robust user models in the future. With this study, we contribute knowledge to bring eye-based cognitive state detection closer to real-world applications.

Multi-Objective Optimization of Cell Voltage Based on a Comprehensive Index Evaluation Model in the Aluminum Electrolysis Process

In the abnormal situation of an aluminum electrolysis cell, the setting of cell voltage is mainly based on manual experience. To obtain a smaller cell voltage and optimize the operating parameters, a multi-objective optimization method for cell voltage based on a comprehensive index evaluation model is proposed. Firstly, a comprehensive judgment model of the cell state based on the energy balance, material balance, and stability of the aluminum electrolysis process is established. Secondly, a fuzzy neural network (FNN) based on the autoregressive moving average (ARMA) model is designed to establish the cell-state prediction model in order to finish the real-time monitoring of the process. Thirdly, the optimization goal of the process is summarized as having been met when the difference between the average cell voltage and the target value reaches the minimum, and the condition of the cell is excellent. And then, the optimization setting model of cell voltage is established under the constraints of the production and operation requirements. Finally, a multi-objective antlion optimization algorithm (MOALO) is used to solve the above model and find a group of optimized values of the electrolysis cell, which is used to realize the optimization control of the cell state. By using actual production data, the above method is validated to be effective. Moreover, optimized operating parameters are used to verify the prediction model of cell voltage, and the cell state is just excellent. The method is also applied to realize the optimization control of the process. It is of guiding significance for stabilizing the electrolytic aluminum production and achieving energy saving and consumption reduction.

Rites for/of Power: Research on the Harmony of Three Teachings in Vietnam during the Former LL (LL SB) Dynasty (1428—1527)

This article studies the harmony of Three Teachings in Dai Viet during the period of the Former Lê dynasty (Lê Sơ period) (1428–1527). Most of the previous studies have suggested that Lê Sơ was the period advocating for the monopoly of Confucianism and rejecting Buddhism and Taoism. However, this article will prove that the Lê Sơ dynasty was thoroughly using Confucian classics under the Song dynasty, applying the model of Confucian state of the Great Ming dynasty, while still developing management policies on Taoism and Buddhism. The “The Convergence of Three Teachings” was a dynastic policy, in which Confucianism played a major role in ideological, political and cultural activities, while Buddhism and Taoism played a supporting role in ritual and religious activities of the dynasty and folklore. The results show that the “The Convergence of Three Teachings” is a constant of Vietnamese history from the Lý – Trần dynasties onward. The Lê Sơ royal court both developed Confucian cultural institutions and managed Buddhist and Taoist monasteries, temples and rituals. This can also be considered the foundation for the Three Teachings to come together in the later stages.

CIBSE TM54 energy projections I: A case study using quasi-steady state modelling

CIBSE TM54 was recently revised and covers best practice methods to evaluate the operational energy use of buildings. TM54 is a guidance document that can be used for performance evaluation at every stage of the design and construction process, and during the occupied stage, to ensure that long-term operational performance aligns with the design intent. The main performance evaluation principles in TM54 are a step-by-step modelling approach and scenario testing, to improve the robustness of the design proposal calculations. The latest edition of this technical memorandum brings an updated perspective to the modelling approaches, including detailed Heating, Ventilation and Air Conditioning (HVAC) modelling and simulation. It also incorporates more detailed guidance around risks, target setting, scenario testing and sensitivity analysis. A case study approach is used to explore and demonstrate some important aspects described in TM54. TM54 recommends three modelling approaches (aka implementation routes) that a project can follow depending on its scale and complexity: using quasi-steady state tools; dynamic simulation with a template HVAC system; and dynamic simulation with detailed HVAC system modelling. As part of a series of three, this case study provides an application of the first implementation route: modelling using quasi-steady state tools. Practical Application This case study provides detailed guidance on undertaking CIBSE TM54 modelling and projecting design stage building performance. The study covers the interpretation and clarifications of how TM54 can be applied, through the quasi-steady-state modelling tools.

Effect of elastic deformation on the heat transfer characteristic of refrigerator gasket based on static analysis of structural assemblies

ABSTRACT Soft and hard contact deformation is most common issue of the refrigerator gasket and it has almost been ignored in previous studies. In this paper, the novelty is to propose a thermal-solid coupling simulation to study the effect of assembly states on the heat transfer of gaskets, improving the accuracy of heat transfer simulation analysis. The numerical results indicate that the calculated temperature based on physical model of assembly state is closer to measured temperature. The assembly distance affects the assembly state of the gasket by changing the contact positions and lengths of contact interfaces. As a result, the heat transfer path and condition at the gasket region also changes. As the deformation increases form −1.0 mm to 5.0 mm, the heat leakage load of gasket rises firstly and then falls, in the range of 1.61 ~ 3.33 W•m−1. The heat leakage at the transfer paths of internal cold air, outer shell and cold bridge accounts for over 20%. Thus, it is recommended to reduce the contact interface length of internal cold air, increase the thermal resistance of auxiliary air-bag, reduce the thermal conductivity and thickness of magnetic strip and increase the contact thermal resistance of cold bridge.