A Holographic Prediction of the Deconfinement Temperature

We argue that deconfinement in AdS/QCD models occurs via a first order Hawking-Page type phase transition between a low temperature thermal AdS space and a high temperature black hole. Such a result is consistent with the expected temperature independence, to leading order in 1/N_c, of the meson spectrum and spatial Wilson loops below the deconfinement temperature. As a byproduct, we obtain model dependent deconfinement temperatures T_c in the hard and soft wall models of AdS/QCD. Our result for T_c in the soft wall model is close to a recent lattice prediction.Comment: 4 pages, 1 figure; v2 ref added, minor changes; v3 refs added, discussion modified, to appear in PR

A literature review on fatigue and creep interaction

Life-time prediction methods, which are based on a number of empirical and phenomenological relationships, are presented. Three aspects are reviewed: effects of testing parameters on high temperature fatigue, life-time prediction, and high temperature fatigue crack growth

Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single and binary component solids

A combination of systematic density functional theory (DFT) calculations and machine learning techniques has a wide range of potential applications. This study presents an application of the combination of systematic DFT calculations and regression techniques to the prediction of the melting temperature for single and binary compounds. Here we adopt the ordinary least-squares regression (OLSR), partial least-squares regression (PLSR), support vector regression (SVR) and Gaussian process regression (GPR). Among the four kinds of regression techniques, the SVR provides the best prediction. In addition, the inclusion of physical properties computed by the DFT calculation to a set of predictor variables makes the prediction better. Finally, a simulation to find the highest melting temperature toward the efficient materials design using kriging is demonstrated. The kriging design finds the compound with the highest melting temperature much faster than random designs. This result may stimulate the application of kriging to efficient materials design for a broad range of applications

On skillful decadal predictions of the subpolar North Atlantic

The North Atlantic is a crucial region for the prediction of weather and climate of North America and Europe and is the focus of this analysis. A skillful decadal prediction of the surface temperature was shown for several Earth system models, with the North Atlantic standing out as one region with higher predictive skill. This skill assessment concentrates on the rapid increase of the annual mean sea surface temperature of the North Atlantic subpolar gyre by about 1 K in the mid‑1990s and the adjacent years. This event-oriented analysis adds creditability to the decadal predictions and reveals the potential for improvements. The ability to simulate the observed sea surface temperature in the North Atlantic is quantified by using four versions of decadal predictions, which differ in model resolution, initialization technique, and the reanalysis data used in the assimilation run. While all four versions can reproduce the mid-1990s warming of the subpolar North Atlantic, the characteristics differ with lead time and version. The higher vertical resolution in the atmosphere and the higher horizontal resolution in the ocean improve the decadal prediction for longer lead times, and the anomaly initialization outperforms the full-field initialization for short lead times. The effect from the two different ocean reanalysis products on the predictive skill is strongest in the first two prediction years; a substantial cooling instead of the warming in the central North Atlantic reduces the skill score for the North Atlantic sea surface temperature in one version, whereas a too large interannual variability, compared with observations, lowers the skill score in the other version. The cooling patches are critical since the resulting gradients in sea surface temperature and their effect on atmospheric dynamics deviate from observations, and, moreover, hinder the skillful prediction of atmospheric variables

Enhanced Thermoelectric Power in Graphene: Violation of the Mott Relation By Inelastic Scattering

We report the enhancement of the thermoelectric power (TEP) in graphene with extremely low disorder. At high temperature we observe that the TEP is substantially larger than the prediction of the Mott relation, approaching to the hydrodynamic limit due to strong inelastic scattering among the charge carriers. However, closer to room temperature the inelastic carrier-optical-phonon scattering becomes more significant and limits the TEP below the hydrodynamic prediction. We support our observation by employing a Boltzmann theory incorporating disorder, electron interactions, and optical phonons.Comment: 5 pages, 4 figure

Connecting dispersion models and wall temperature prediction for laminar and turbulent flows in channels

In a former paper, Drouin et al. (2010) proposed a model for dispersion phenomena in heated channels that works for both laminar and turbulent regimes. This model, derived according to the double averaging procedure, leads to satisfactory predictions of mean temperature. In order to derive dispersion coefficients, the so called ‘‘closure problem’’ was solved, which gave us access to the temperature deviation at sub filter scale. We now propose to capitalize on this useful information in order to connect dispersion modeling to wall temperature prediction. As a first step, we use the temperature deviation modeling in order to connect wall to mean temperatures within the asymptotic limit of well established pipe flows. Since temperature in wall vicinity is mostly controlled by boundary conditions, it might evolve according to different time and length scales than averaged temperature. Hence, this asymptotic limit provides poor prediction of wall temperature when flow conditions encounter fast transients and stiff heat flux gradients. To overcome this limitation we derive a transport equation for temperature deviation. The resulting two-temperature model is then compared with fine scale simulations used as reference results. Wall temperature predictions are found to be in good agreement for various Prandtl and Reynolds numbers, from laminar to fully turbulent regimes and improvement with respect to classical models is noticeable

Evaluation of the heat transfer module (FAHT) of Failure Analysis Nonlinear Thermal And Structural Integrated Code (FANTASTIC)

The heat transfer module of FANTASTIC Code (FAHT) is studied and evaluated to the extend possible during the ten weeks duration of this project. A brief background of the previous studies is given and the governing equations as modeled in FAHT are discussed. FAHT's capabilities and limitations based on these equations and its coding methodology are explained in detail. It is established that with improper choice of element size and time step FAHT's temperature field prediction at some nodes will be below the initial condition. The source of this unrealistic temperature prediction is identified and a procedure is proposed for avoiding this phenomenon. It is further shown that the proposed procedure will converge to an accurate prediction upon mesh refinement. Unfortunately due to lack of time FAHT's ability to accurately account for pyrolysis and surface ablation has not been verified. Therefore, at the present time it can be stated with confidence that FAHT can accurately predict the temperature field for a transient multi-dimensional, orthotropic material with directional dependence, variable property, with nonlinear boundary condition. Such a prediction will provide an upper limit for the temperature field in an ablating decomposing nozzle liner. The pore pressure field, however, will not be known

Development of a mathematical model for 'Hayward' kiwifruit softening in the supply chain : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Food Technology at Massey University, New Zealand

Fruit loss is a major concern to the kiwifruit industry as it incurs a high cost to monitor and remove over soft or rotten fruit to meet export standards. Kiwifruit is exposed to various temperature scenarios due to different packhouse cooling practices, and temperature control is difficult to maintain throughout the supply chain. Fruit pallet temperatures are wirelessly monitored in the supply chain. This time temperature data provides valuable rich information which could be used to predict kiwifruit quality. In the laboratory, green ‘Hayward’ kiwifruit were exposed to industry coolchain scenarios to investigate their influence on fruit firmness in subsequent storage. Cooling rate and storage temperature were identified to affect fruit firmness and chilling injury development significantly, where accelerated softening and increased chilling injury development was observed in late storage (> 100 d) when fruit were cooled directly to 0 °C. However, when fast cooled fruit were stored at 2 °C instead of 0 °C, low incidence of chilling injury was observed. The influence of cooling rate and storage temperature on kiwifruit quality suggests that industry should focus on the management practices adopted by packhouses in order to maintain acceptable quality after long term storage. A proportion of the firmness data results were used to develop a mechanistic style mathematical model of kiwifruit softening. Kiwifruit softening was mathematically described based on the correlation with starch degradation, breakdown of cell wall structure, and a description of the incidence of chilling injury development during storage. The model inputs consist of solely commonly collected at-harvest attributes: firmness, dry matter and soluble solids content and time-temperature data. Applying at-harvest attributes as model inputs enabled a capability to predict different softening curves as influenced by fruit maturity, and grower line differences. The developed model demonstrated promising softening prediction with mean absolute errors (MAE) between 0.8 to 2.1 N when fruit were exposed to fluctuating temperatures and cooling profiles. A logistic model was used to estimate the proportion of chilling injured fruit. Based on the given time temperature information, the logistic model was able to predict the proportion of chilling injured fruit reasonably well (R2 = 0.735). This chilling injury prediction was subsequently used to adjust the softening prediction during the late storage period (>100 d). Model validation was performed using the remaining data, identifying a lack of fit in both the rapid (MAE of 20.8 N) and gradual (MAE of 8.0 N) softening phase. The lack of fit in the rapid softening phase is proposed to be explained by the presence of an initial lag phase in softening which the developed model is unable to predict. The magnitude of firmness associated with starch content and cell wall integrity heavily influenced the lack of fit in the gradual softening phase. Fixing the initial amount of firmness associated to cell wall integrity to be constant for all maturities and grower lines improved the softening prediction. Overall, this thesis contributes to the challenge of predictively modelling kiwifruit quality in the supply chain. However, there are still many opportunities for improvement including introducing the influence of: variation within the same batch; fruit maturity on chilling injury development; ethylene in the environment; pre-harvest management practices and extending the model to have more focus on high temperature conditions such as those experienced in the marketplace. Conducting studies on: the effect of curing on kiwifruit; using non-destructive techniques to provide information to help define model parameters for prediction; effect of high temperature exposure on kiwifruit softening are possible opportunities that may contribute to enable better prediction of kiwifruit quality in the supply chain in the future