Temperature Uncertainty Research Articles

A temperature-robust level-set approach for eigenfrequency optimization

The optimization of target eigenfrequencies is crucial for several engineering applications, including dynamical systems. Micro-electro-mechanical systems (MEMS) used in time-keeping applications, for example, require exceptional frequency stability. Most eigenfrequency structural optimization methods focus on a deterministic approach, often neglecting potential fluctuations in operational conditions. Among these, temperature variations have long been known to have a detrimental effect on the natural frequencies of a structure. In this work, we show how eigenfrequency optimization can be applied to the field of structural dynamics while minimizing the variance of natural frequencies caused by external temperature uncertainties. To accomplish this, we employ a level-set optimization algorithm, known for its computational efficiency and ability to define crisp interfaces.

Unlock the Thermal Flexibility in Integrated Energy Systems: A Robust Nodal Pricing Approach for Thermal Loads

The thermal flexibility of buildings in integrated energy systems (IES) has great potential to improve the operational economy and wind power consumption. To incentivize the thermal flexibility of buildings under uncertainties, we propose a robust nodal pricing (RNP) model for thermal loads based on the Stackelberg game approach. The RNP model adopts a bilevel framework in which the IES operator plays the leader at the upper level while the thermal load aggregators (TLA) play the followers at the lower level. The IES operator problem optimizes the heat prices and dispatch plan, which is modeled as a two-stage robust optimization problem to address the uncertainties in the renewables and the loads. The TLA problem optimizes the thermal loads of buildings to minimize the energy cost and thermal comfort loss, which is modeled as a distributionally robust chance-constrained optimization problem to address the uncertainty in outdoor temperature. Then, we convert the TLA model into deterministic quadratic programming and prove that Slaters condition holds under a mild assumption. Using it, the TLA model is equivalently converted into Karush-Kuhn-Tucker conditions, leading to a reformulation of a classical two-stage robust optimization model for the RNP model. Case studies compare different pricing methods and verify the superiority of the proposed method.

Enhancing process efficiency through improved temperature measurement: the EMPRESS projects

EMPRESS 2 is a European project to enhance the efficiency of high value manufacturing processes by improving temperature measurement and control capability. This project seeks to address four contemporary thermometry challenges in this sector, and new developments from this and its predecessor project, EMPRESS, will be described:• Below 1000°C many industrial processes require reliable surface thermometry e.g. welding, coating, forging and forming. Conventional non-contact surface thermometry techniques e.g. thermal imaging are prone to large errors (tens of degrees) due to reflected thermal radiation and unknown emissivity. Contact thermometry approaches are prone to similarly large errors. Traceable imaging phosphor thermometry is being developed to overcome these difficulties, and is being combined with quantitative thermography to determine emissivity for thermometry over wide fields of view.• Above 1300°C sensor drift is a significant unaddressed issue for casting, forging and heat treatment, causing large errors. There is a need for more stable sensors and standardisation of at least one new thermocouple type to fill the gap from 1300°C to 1800°C. This is being addressed through improved Pt-Rh thermocouples and optimisation of double-walled mineral insulated, metal sheathed thermocouples by mitigating insulation breakdown and drift effects.• Combustion temperature measurement is very challenging and traceability is almost non-existent; for example, thermocouple measurements of flame temperatures can be in error by hundreds of degrees. A ‘standard flame’ that can be transported to users’ sites has been developed, and is being deployed in several high value manufacturing and industrial applications to a) demonstrate the possibility of reducing flame temperature uncertainties by at least an order of magnitude and b) for the first time to demonstrate in-situ traceability to the International Temperature Scale of 1990 (ITS-90).• Many processes are not amenable to any conventional thermometry techniques due to inaccessibility, ionising radiation, electromagnetic interference, and contamination; here methods based on optical fibres are ideal but there are no traceable calibration techniques for such sensors currently available. A suite of different fibre-optic thermometers and calibration techniques is being developed to address this. In some cases (ionising radiation) darkening of the fibre is a problem, and this is being overcome by the development of novel thermometry approaches based on practical ‘hollow core’ fibres.

Water requirement of Urban Green Infrastructure under climate change

The increased uncertainty of rainfall and high urban temperatures resulting from climate change present challenges for water management in Urban Green Infrastructure (UGI). UGI is an important component of cities, and it plays a crucial role in addressing various environmental issues (e.g., floods, pollutants, heat islands, etc.). Effective water management of UGI is essential to ensure its environmental and ecological benefits in the face of climate change. However, previous studies have not adequately investigated water management strategies for UGI under climate change scenarios. This study aims to estimate the current and future water requirement and effective rainfall (rainwater stored in the soil and plant roots available for plant evapotranspiration) to determine the irrigation requirement of UGI during periods of rainfall deficit under current and future climate conditions.The results indicate that the water requirement for UGI will continue to increase under both RCP4.5 and RCP8.5 climate scenarios, with a larger increase projected under RCP8.5. For instance, the average annual water requirement for UGI in Seoul, South Korea is currently 731.29 mm, and it is projected to increase to 756.45 mm (RCP4.5) and 816.47 mm (RCP8.5) during the period of 2081–2100, assuming low managed water stress condition. In addition, the water requirement of UGI in Seoul is the highest in June (approximately 125–137 mm) and the lowest in December or January (about 5–7 mm). While irrigation is unnecessary in July and August due to sufficient rainfall, other months in Seoul require irrigation when rainfall is insufficient. For example, continuous insufficient rainfall from May to June 2100 and April to June 2081 would need >110 mm (RCP4.5) of irrigation requirement even under high managed water stress condition. The findings of this study provide a theoretical foundation for water management strategies in current and future UGI settings.

Calibration and Uncertainty Quantification for Single-Ended Raman-Based Distributed Temperature Sensing: Case Study in a 800 m Deep Coaxial Borehole Heat Exchanger

Raman-based distributed temperature sensing (DTS) is a valuable tool for field testing and validating heat transfer models in borehole heat exchanger (BHE) and ground source heat pump (GSHP) applications. However, temperature uncertainty is rarely reported in the literature. In this paper, a new calibration method was proposed for single-ended DTS configurations, along with a method to remove fictitious temperature drifts due to ambient air variations. The methods were implemented for a distributed thermal response test (DTRT) case study in an 800 m deep coaxial BHE. The results show that the calibration method and temperature drift correction are robust and give adequate results, with a temperature uncertainty increasing non-linearly from about 0.4 K near the surface to about 1.7 K at 800 m. The temperature uncertainty is dominated by the uncertainty in the calibrated parameters for depths larger than 200 m. The paper also offers insights into thermal features observed during the DTRT, including a heat flux inversion along the borehole depth and the slow temperature homogenization under circulation.

A data-driven rolling optimization control approach for building energy systems that integrate virtual energy storage systems

The virtual energy storage system (VESS) is an innovative and cost-effective technique for coupling building envelope thermal storage and release abilities with the electric and heat power conversion characteristics of an air conditioner; this system provides building energy systems (BESs) with adjustable potentials similar to those of conventional battery energy storage systems (BESSs). However, the VESS is a dynamic system, and uncertainties in the outdoor temperature and solar irradiance are difficult to accurately predict, which impacts the quantification accuracy of VESSs; these characteristics challenge the BES control scheme economy and the thermal comfort of occupants. To solve this crucial issue, a data-driven rolling optimization (RO) control approach for a BES that integrates a VESS is proposed. First, a BES state space model integrating the VESS is created to reflect the VESS adjustable potential and dynamic characteristics. Based on the above model, while aiming at a small BES data sample size, a support vector machine (SVM) is combined with RO to correct the day-ahead quantification errors of the VESS adjustable potential and enhance the economical operation and thermal comfort of the BES that integrates the VESS in uncertain environments. Comparative simulations validate the effectiveness of this VESS modelling and data-driven RO control approach.

Quantification of tissue property and perfusion uncertainties in hyperthermia treatment planning: Multianalysis using polynomial chaos expansion

IntroductionHyperthermia treatment planning (HTP) tools can guide treatment delivery, particularly with locoregional radiative phased array systems. Uncertainties in tissue and perfusion property values presently lead to quantitative inaccuracy of HTP, leading to sub-optimal treatment. Assessment of these uncertainties would allow for better judgement of the reliability of treatment plans and improve their value for treatment guidance. However, systematically investigating the impact of all uncertainties on treatment plans is a complex, high-dimensional problem and too computationally expensive for traditional Monte Carlo approaches.This study aims to systematically quantify the treatment-plan impact of tissue property uncertainties by investigating their individual contribution to, and combined impact on predicted temperature distributions. MethodsA novel Polynomial Chaos Expansion (PCE)-based HTP uncertainty quantification was developed and applied for locoregional hyperthermia of modelled tumours in the pancreatic head, prostate, rectum, and cervix. Patient models were based on the Duke and Ella digital human models. Using Plan2Heat, treatment plans were created to optimise tumour temperature (represented by T90) for treatment using the Alba4D system. For all 25–34 modelled tissues, the impact of tissue property uncertainties was analysed individually i.e., electrical and thermal conductivity, permittivity, density, specific heat capacity and perfusion. Next, combined analyses were performed on the top 30 uncertainties with the largest impact. ResultsUncertainties in thermal conductivity and heat capacity were found to have negligible impact on the predicted temperature ( < 1 × 10−10 °C), density and permittivity uncertainties had a small impact (< 0.3 °C). Uncertainties in electrical conductivity and perfusion can lead to large variations in predicted temperature. However, variations in muscle properties result in the largest impact at locations that could limit treatment quality, with a standard deviation up to almost 6 °C (pancreas) and 3.5 °C (prostate) for perfusion and electrical conductivity, respectively. The combined influence of all significant uncertainties leads to large variations with a standard deviation up to 9.0, 3.6, 3.7 and 4.1 °C for the pancreatic, prostate, rectal and cervical cases, respectively. ConclusionUncertainties in tissue and perfusion property values can have a large impact on predicted temperatures from hyperthermia treatment planning. PCE-based analysis helps to identify all major uncertainties, their impact and judge the reliability of treatment plans.

A cryogenic visual apparatus for solid-liquid equilibrium and the measurement of CH4 + C2H6

In this work, an apparatus was designed to measure the phase equilibrium for cryogenic pure fluids and mixtures. The apparatus features view windows and an internal stirrer, and the solid-liquid equilibrium (SLE) is determined through a combination of visual observation and thermal analysis. The apparatus is capable of operating over a range of temperatures (40–300 K) and pressures (0–5 MPa), and has been validated through the measurement of triple points of various fluids. The standard combined uncertainties of temperature, pressure and mole fraction for the SLE measurements are no greater than ±0.70 K, ±0.012 kPa, and 0.010, respectively. The performance of the apparatus is further demonstrated through the presentation of experimental SLE data for methane-ethane binary mixtures.

Experimental study on soot formation and primary particle size in oxy-combustion ethylene diffusion flames under CO2 substitution for N2

This paper reports an experimental study on the effects of O2 concentration and CO2 concentration on soot volume fraction and primary particle size of flame centre in ethylene laminar co-diffusion flame. The incandescent light was induced by a two-color band method and laser-excited by wavelength 1064 nm. Previous studies have shown that the oxygen concentration affects the soot volume fraction in the flame. However, the effects of O2 concentration on the primary particle size under CO2 atmosphere are unclear and need to be further discussed. Seven flames were optically detected, in which the oxygen concentration of the accompanying gas varied from 21% to 50% by volume. One primary flame was burned in co-flowing ethylene/air. Six ethylene flames were burned in an oxygen-rich O2/N2 or O2/CO2 atmosphere with an oxygen mole fraction from 30% to 50%. The C2H4 flow rate remained constant in all operating conditions. The results show that the SVF observed by the LII method and spectroscopy in the oxygen-enriched flame are in good agreement. Furthermore, the soot's primary particle diameter measured by the time-resolved LII method is in good agreement with the transmission electron microscope image analysis.With the increase of oxygen concentration, the flame height becomes shorter, the luminosity becomes brighter, and the soot yield increases. Compared with an N2 atmosphere with the same oxygen concentration, the soot yield in a CO2 atmosphere was significantly inhibited. In all the flames studied, the SVF along the flame axis showed a similar distribution: it first increased to the maximum value and then decreased rapidly until the flame tip. The maximum SVF on the axis appears between 0.64 and 0.78 based on the normalized height of the visible flame. In all ethylene/(O2/N2) flames, the maximum primary particle diameter and average primary particle diameter increase with oxygen content. For ethylene/(O2/CO2) flame, the maximum diameter of primary particles decreases with the increased oxygen content. The uncertainty of primary diameter is mainly caused by the thermal adaptation coefficient, the lower LII signal of smaller particles and the uncertainty of local flame temperature.

Impacts of Initial Zonal Current Errors on the Predictions of Two Types of El Niño Events

AbstractPerturbations in the thermocline and surface zonal current (ZC) play crucial roles in the evolutions of the eastern Pacific (EP) and central Pacific (CP) El Niño events, respectively. Whereas numerous studies have examined the influence of initial uncertainties in ocean temperature on the predictability of El Niño, only a few studies investigated the impact of the initial ZC. Using an air‐sea coupling model, the conditional nonlinear optimal perturbation (CNOP) approach was employed to investigate the maximum impact of initial ZC errors on the El Niño prediction. The optimal initial ZC errors (denoted as CNOP‐Us) that have the severest impact on the El Niño prediction are found to mainly concentrate in the western and central tropical Pacific. The CNOP‐Us cause larger errors in the CP El Niño prediction than in the EP El Niño prediction. Additionally, CNOP‐Us cause rapid sea surface temperature error growth in spring in the EP El Niño prediction but in summer in the CP El Niño prediction. Dynamically, the former is related to the large uncertainties in the meridional current in spring caused by CNOP‐Us, while the latter is related to the strong ZC errors in summer. According to the distributions of CNOP‐Us, reducing the initial ZC errors in the western and central tropical Pacific may be vital in weakening the predictability barrier phenomena and improving the predictions of El Niño diversity.

The Impact of Meteorological Forcing Uncertainty on Hydrological Modeling: A Global Analysis of Cryosphere Basins

AbstractMeteorological forcing is a major source of uncertainty in hydrological modeling. The recent development of probabilistic large‐domain meteorological data sets enables convenient uncertainty characterization, which however is rarely explored in large‐domain research. This study analyzes how uncertainties in meteorological forcing data affect hydrological modeling in 289 representative cryosphere basins by forcing the Structure for Unifying Multiple Modeling Alternatives (SUMMA) and mizuRoute models with precipitation and air temperature ensembles from the Ensemble Meteorological Data set for Planet Earth (EM‐Earth). EM‐Earth probabilistic estimates are used in ensemble simulation for uncertainty analysis. The results reveal the magnitude, spatial distribution, and scale effect of uncertainties in meteorological, snow, runoff, soil water, and energy variables. There are three main findings. (a) The uncertainties in precipitation and temperature lead to substantial uncertainties in hydrological model outputs, some of which exceed 100% of the magnitude of the output variables themselves. (b) The uncertainties of different variables show distinct scale effects caused by spatial averaging or temporal averaging. (c) Precipitation uncertainties have the dominant impact for most basins and variables, while air temperature uncertainties are also nonnegligible, sometimes contributing more to modeling uncertainties than precipitation uncertainties. We find that three snow‐related variables (snow water equivalent, snowfall amount, and snowfall fraction) can be used to estimate the impact of air temperature uncertainties for different model output variables. In summary, this study provides insight into the impact of probabilistic data sets on hydrological modeling and quantifies the uncertainties in cryosphere basin modeling that stem from the meteorological forcing data.

Toward a Transportable Ca+ Optical Clock with a Systematic Uncertainty of 4.8 × 10 −18

We present the compact long-term nearly continuous operation of a room-temperature ${\mathrm{Ca}}^{+}$-optical-clock setup towards a transportable clock, achieving an overall systematic uncertainty of 4.8 \ifmmode\times\else\texttimes\fi{} ${10}^{\ensuremath{-}18}$ and an uptime rate of 97.8% over an 8-day period. The active liquid-cooling scheme is adopted, combined with precise temperature measurement with 13 temperature sensors both inside and outside the vacuum chamber to ensure the accurate evaluation of the thermal environment for the optical clock. The environmental temperature uncertainty is evaluated as 293.31(0.4) K, corresponding to a blackbody-radiation frequency-shift uncertainty of 4.6 \ifmmode\times\else\texttimes\fi{} ${10}^{\ensuremath{-}18}$, which is reduced more than twice compared to our previous work. Through frequency comparison of the room-temperature ${\mathrm{Ca}}^{+}$ optical clock with a cryogenic ${\mathrm{Ca}}^{+}$ optical clock, the overall uncertainty of the clock comparison is 7.5 \ifmmode\times\else\texttimes\fi{} ${10}^{\ensuremath{-}18}$, including a statistical uncertainty of 4.9 \ifmmode\times\else\texttimes\fi{} ${10}^{\ensuremath{-}18}$ and a systematic uncertainty of 5.7 \ifmmode\times\else\texttimes\fi{} ${10}^{\ensuremath{-}18}$. This work provides a set of feasible implementations for high-precision transportable ion optical clocks.

Design and Implementation of a Multi-Channel Temperature Measurement System Based on LabVIEW

As computer technology and instrument technology advance rapidly, both virtual instrument and Lab-VIEW have significantly grown. LabVIEW is extensively employed in the industrial and academic sectors for developing data acquisition systems, instrument control software, and analysis software. This paper presents the design of a multi-channel temperature measurement system based on a single-chip CPU and LabVIEW. The system consists of analog-to-digital conversion modules, temperature measurement modules, and data acquisition modules. The system uses the Nyquist-Shannon sampling theorem and the advantages of various digital filter types to obtain temperature uncertainty, including the compensation uncertainty of the reference junction. The system is of high quality in measuring accuracy, stability, scalability and other aspects. The complete system was inspected and verified using a superconducting magnetic energy storage device (SMES). The results demonized the ease of construction, practicality and precise regulation of the system. These results also show that temperature measurement devices are less than 0.1°C absolute error and are highly accurate, making them valuable for applications that require precise temperature measurements.

Machine learning-assisted heat transport modelling for full-scale emplacement experiment at Mont Terri underground laboratory

Machine learning (ML)-assisted modelling of deep geological repositories (DGR) is of emerging interest and can help to improve the safe and reliable operation of DGRs as well as the public acceptance. Here, a concept of ML-assisted physical-based 3D heat transport model for the Full-scale Emplacement (FE) experiment performed at Mont Terri Underground Laboratory (URL), is presented. The FE experiment is a 1:1 scale mock-up of a DGR tunnel where heaters simulate emplaced high level waste. ML is applied to sparse sensor data of the water saturation degree in the granulated bentonite material (GBM) yielding a physically and neural network (NN) based surrogate model for the thermal conductivity of the GBM, needed to calculate the temperature evolution in the FE tunnel near-field. In order to investigate the dominant parameters influencing the temperature evolution in the vicinity of the FE experiment, the results of 32 orthogonal test cases have been analysed systematically.For the ML predicted water saturation degree, three NN methods are tested. The Elman NN (with a Pearson's r coefficient of 0.9911, a mean squared error (MSE) of 4.62, and mean absolute error (MAE) of 1.44) operates better than the back propagation (BP) and cascade BP (CBP) NN methods. Results of ML-assisted heat transport calculations are validated with the large experimental dataset of 137 × 10 temperature sensor data points (errors range within 7%). Parameters uncertainty ranges of ±10% for thermal conductivities of the GBM λGBM and bentonite block λblock are analysed and bands of temperature uncertainties are compared with temperature sensor data, which allows sensor data assessment including identification of the faulty sensor data. The ML-assisted physical modelling framework can be applied to future DGRs.

Enhanced multi-state estimation methods for lithium-ion batteries considering temperature uncertainties

Due to their high energy density and minimal emissions, lithium-ion batteries are frequently employed in electric vehicles (EVs). Accurate estimation of the micro-parameters, state of charge (SOC), and state of health (SOH) are a few primary monitoring functions of the battery management system (BMS) to increase the battery's efficiency and safety under various operating conditions. This paper proposes a SOC and SOH co-estimation method by adopting an ensemble empirical mode decomposition method with adaptive noise and an autoencoder (EEMDA) to extract, decompose, and reconstruct the full-scale charging voltage and current data for a dual extended Kalman filter (DEKF) with multi-parameter and time-scale updates for accurate estimation based on a variable forgetting factor limited memory recursive least squares (VFF-LMRLS) method. The VFF-LMRLS method is used to solve the data saturation phenomenon and identify the battery's characteristic micro-parameters based on a proposed dynamic migration second-order resistor-capacitor equivalent circuit model under different operating states. Battery tests are conducted at temperatures ranging from −10 to 50 °C under complex working conditions. Using the VFF-LMRLS method, the effects of different temperatures on the micro-parameters are discussed. The SOC and SOH results of the proposed EEMDA-DEKF method based on the dynamic migration battery model show that the mean absolute error and root mean square error metrics have the least values of 0.0233% and 0.0252%, which signify an optimal performance improvement of 93.26% and 93.66%, respectively, compared to the conventional DEKF method. Based on the experimental results and analyses, the proposed method has a high degree of accuracy and robustness, which makes it feasible for battery monitoring and prognostic BMS applications.

Reliability Assessment of Reinforced Concrete Sewer Pipes under Adverse Environmental Conditions: Case Study for the City of Arlington, Texas

With aging and deterioration of underground sewer utilities, there is an urgent need for a framework to track their reliability over expected lifetime to facilitate asset management decisions like inspection, maintenance, or replacement. This study proposes multilevel, multifailure framework model for reliability evaluation of reinforced concrete sewer pipe (RCSP) considering the effect of adverse environmental factors such as chloride and sulfide induced attacks, uncertainties in different properties, seasonal temperature and humidity variations, and cracking effects. Monte-Carlo simulation with Latin hypercube sampling is chosen for stochastic analysis to reduce the computational burdens and enhance the accuracy. The application of this framework is illustrated for a case study of an RCSP that has been in service for more than 35 years. Based on results, it is shown that at serviceability stage, sulfide wall erosion is redundant relative to other serviceability issues; therefore, it cannot be used solely for serviceability evaluation of RCSP, as previously done in the literature. At the ultimate level, the effect of chloride pitting corrosion dominates and expedites failure rate. It is proven that there is a huge potential for reinforcement brittle failure under the effect of pitting corrosion, which can lead to a catastrophic collapse.

Using hydrogen and oxygen stable isotopes to estimate soil water evaporation loss under continuous evaporation conditions

AbstractThe use of hydrogen and oxygen stable isotopes in estimating soil water evaporation loss under continuous evaporation conditions is crucial for gaining insight into soil water movement processes under different conditions. In this study, via high‐frequency meteorological monitoring and continuous soil water measurements, we investigated the variation of hydrogen and oxygen stable isotopes and soil water fluxes with soil depth and time for soil water at different depths under continuous evaporation conditions. The precipitation isotope δrain and soil water flux changes were determined using the Craig‐Gordon model. It was shown that the isotopic fractionation of soil water in the surface layer 0–30 cm was dominated by a gaseous‐dominated transport process, and that both the δ18O and δD values and evaporative intensity decreased with increasing soil depth. In terms of time dynamics, the evaporation loss of soil water varies continuously with seasons and is the highest during summer. The use of δ18O to quantify the soil water evaporation loss provides greater accuracy than that provided by δD. The relative errors in the evaporation loss calculated based on δ18O and δD were 13% and 34%, respectively. A sensitivity analysis of each parameter indicated that the relative error calculated by the model is primarily determined by temperature and relative humidity uncertainty. The sensitivity analysis reveals the critical evaporation intensity of soil water at various depths from unsteady to steady state evaporation. When the relative humidity changes by 1%, the evaporation loss fraction changes from 0.001 to 0.034. The results of this study are important for quantifying the soil water resources in arid and semi‐arid areas without precipitation using stable isotopes of hydrogen and oxygen.

The Role of Ocean–Atmosphere Interactions in Tropical Cyclone Intensity Predictability

Abstract The negative feedback between tropical cyclone (TC) intensity and sea surface temperature (SST) plays an important role in TC development. In this study, ocean–atmosphere coupled and uncoupled ensemble forecasts are conducted to investigate the dynamics of error growth and predictability of TC intensity in an ocean–atmosphere coupled system. For the TC–ocean coupled system, the TC intensity–SST negative feedback is the essential mechanism to reduce the error growth of TC intensity by two routes, and thereby improves the TC intensity predictability. For the first route (atmosphere-limited route), the TC-induced SST cooling slows the intensification rate of the TC and weakens the final TC intensity, thereby reducing the error growth of TC intensity. In this route, the TC intensity spread is limited by the magnitude of TC intensity, while SST can be regarded as an environmental forcing. For the second route (atmosphere–ocean mutually influenced route), the interaction between the TC intensity spread and SST spread is dominant. The increasing TC intensity spread could lead to an increase in SST cooling spread, and then reduce the TC intensity spread through the negative feedback. In other words, the more (less) intense TC produces stronger (weaker) SST cooling, and thereby limits (enhances) further TC intensification in an ensemble forecast. In the second route, initial ocean temperature uncertainty could suppress the TC intensity spread reduction. Significance Statement Tropical cyclones force the sea surface and can lead to its cooling. This cooled sea surface can then suppress tropical cyclone intensification. The purpose of this study is to better examine the influence of such an interaction between a tropical cyclone and the ocean on tropical cyclone forecasts. We explore how accurately representing the interaction can improve the capacity to forecast tropical cyclone intensity. Given that many weather forecasting centers have considered this interaction in their models, this study should help them to understand and improve their forecasts.

Rare-earth and transition metal ion single-/co-doped double-perovskite tantalate phosphors: Validation of suitability for versatile applications

Novel rare-earth (RE; e.g., europium (Eu³⁺), samarium (Sm³⁺), and praseodymium (Pr³⁺)) and transition metal (TM⁴⁺; e.g., manganese (Mn⁴⁺)) ion single-/co-doped double-perovskite Ca₂InTaO₆ (CITO) phosphors were prepared and investigated with respect to their crystal structure and photoluminescence (PL) properties. Among them, the CITO:Eu³⁺ phosphors were found to exhibit an ultra-high internal PL quantum yield (89.1%) and good thermal stability (78.7% at 423 K relative to the initial value at 303 K). As such, the corresponding packaged white light-emitting diode (LED) was able to display a remarkable color rendering index (CRI; = 91.51@10 mA). Besides, the potential in applications of anti-counterfeiting fields and a novel LED structure based on flexible phosphor-converted films was also studied. Moreover, due to their different thermal quenching, trivalent lanthanide (Ln³⁺)/Mn⁴⁺ co-doped CITO phosphors were designed for optical thermometry based on the luminescence intensity ratio (LIR) between different 4f transitions of various Ln³⁺ ions and ²E_g → ⁴A_2g (Mn⁴⁺) transition. Particularly, the LIR between the ⁴G_5/2 → ⁶H_9/2 and ²E_g → ⁴A_2g peaks of the CITO activated with 5 mol% Sm³⁺ and 0.3 mol% Mn⁴⁺ exhibited the most excellent relative sensitivity (<i>S</i>_r; = 3.80 %·K⁻¹) with beneficial temperature uncertainty of 0.0648 K. Overall, these results are of significance to offer valuable databases for constructing multifunctional high-performance optical platforms using single-/co-doped double-perovskite tantalates.

Optimized deformation monitoring models of concrete dam considering the uncertainty of upstream and downstream surface temperatures

Measured data-driven deformation monitoring models of concrete dams are reliable scientific approaches for analyzing the operational state of dam structures. Considering the complexity of dam structural behavior, the existing models are established on the basis of a series of assumptions, which also contribute to the uncertainty of the analyzing results. In view of the fact that classical deformation monitoring models are insufficient in effectively accounting for the hysteresis effect of upstream and downstream surface temperature on the structural behavior of dams, this research analyzes the construction theory of deformation models and adopts the chi-square distribution to determine the weight of antecedent surface temperature. A model taking the weighted air and water temperature measurements as variables for thermal deformation and accounting for the hysteresis effect of surface temperature is then proposed. Meanwhile, to make up for the weakness of the proposed model to be applied in situations where little recorded water temperature data is available, a variant form of the proposed model is also presented. For further improvements in the prediction performance, a Long Short-Term Memory (LSTM) network-based model optimized by Genetic Algorithm (GA) is introduced. An engineering example demonstrates that the proposed model efficiently lowers the uncertainty aroused by surface temperature, that is, it quantitatively considers the hysteresis effect and, to some extent, separates the temperature component mixed with the hydrostatic component. And the GA-LSTM network significantly promotes the prediction performance of the monitoring model, which provides a new method for dam deformation prediction.