Term Temperature Data Research Articles

Large magnetocaloric effect and magnetic properties of EuRhO3

We report on the structural, magnetic, and magnetocaloric properties of EuRhO3 powders. The oxidation states of Eu and Rh ions were studied using X-ray photoelectron spectroscopy (XPS). It is found that the Eu ions are mainly in the divalent oxidation state while the Rh ions have +4 state. EuRhO3 powders are found to be antiferromagnetic with a second order magnetic transition at Néel temperature (TN = 2.9 K). Analysis of the magnetic susceptibility versus temperature data in terms of the Curie–Weiss law: (χ = C/(T–θW)) for T > TN, yields θW = −3.1 K and effective magnetic moment μeffexp = 7.72 μB, which is close to the theoretical value μefftheo = 7.94 μB. The magnetic entropy change (–ΔSM), was determined by employing the thermodynamic Maxwell's relation. At μ0H = 5 T and near TN, (–ΔSMMax) and relative cooling power (RCP) exhibit large values of 33.7 J/(kg⋅K) and 238 J/kg, respectively. The large magnitude of –ΔSM and RCP show that the EuRhO3 compound could be a potential candidate to be used in cryogenic magnetic refrigeration.

Long term belt conveyor gearbox temperature data analysis – Statistical tests for anomaly detection

Abstract Temperature monitoring is one of the easiest and frequent way to support maintenance procedures in the industry. In this paper, a procedure of long term temperature data processing for anomaly detection is proposed. The data obtained from a commercial SCADA (supervisory control and data acquisition system) covers a couple of months of gearbox operation. During that period, repair action has been performed. The gearbox is part of the belt conveyor drive unit used in a deep underground mine. Due to time-varying load conditions, changing environment (ambient temperature), frequent starting and stopping of the machine simple decision-making rules (as if the temperature is higher than the threshold) are not appropriate. To detect anomaly in the data describing the process, we proposed a procedure based on the statistical testing methodology applied to long term data analysis. From a signal processing perspective, the proposed algorithm leads to data segmentation. As a result, one receives a moment (called the structure break point) where the statistical properties of the signal change. As criteria of segmentation we utilize the classical test statistics of the known statistical tests (parametric Student's t and non-parametric Wilcoxon tests) for equal means of two samples, however, we applied them not the raw signal but to its characteristics (here means and medians calculated for the work shifts). The proposed procedure is very effective for long term temperature data, however, it indicates the shift detected anomaly, not particular samples. Assuming that the degradation process is rather slow and reaction on anomaly takes some time, it is enough in practice to provide information that the machine can work for the next shift. The proposed procedure could be considered as a training process to establish a “threshold” in the statistics domain for complex decision making.

MONTHLY TEMPERATURE PREDICTION BASED ON ARIMA MODEL: A CASE STUDY IN DIBRUGARH STATION OF ASSAM, INDIA

The forecasting of temperature on a seasonal time scales has been attempted by many researchers by different techniques at different time across the globe. It is a challenging task to forecast temperature on monthly and seasonal time scale. In this paper, an attempt has been made to develop a Seasonal Autoregressive Integrated Moving Average (SARIMA) model to long term temperature data of Dibrugarh, Assam, for the period of fifty (50) years (1966-2015). The analysis revels that the best seasonal models which are satisfactory to describe the data are SARIMA(2,1,1)(0,1,1)12 for monthly maximum and SARIMA(2,1,1)(0,1,1)12 for monthly minimum temperature data respectively.

Increasing minimum daily temperatures are associated with enhanced pesticide use in cultivated soybean along a latitudinal gradient in the mid-western United States.

Assessments of climate change and food security often do not consider changes to crop production as a function of altered pest pressures. Evaluation of potential changes may be difficult, in part, because management practices are routinely utilized in situ to minimize pest injury. If so, then such practices, should, in theory, also change with climate, although this has never been quantified. Chemical (pesticide) applications remain the primary means of managing pests in industrialized countries. While a wide range of climate variables can influence chemical use, minimum daily temperature (lowest 24 h recorded temperature in a given year) can be associated with the distribution and thermal survival of many agricultural pests in temperate regions. The current study quantifies average pesticide applications since 1999 for commercial soybean grown over a 2100 km North-South latitudinal transect for seven states that varied in minimum daily temperature (1999–2013) from −28.6°C (Minnesota) to −5.1°C (Louisiana). Although soybean yields (per hectare) did not vary by state, total pesticide applications (kg of active ingredient, ai, per hectare) increased from 4.3 to 6.5 over this temperature range. Significant correlations were observed between minimum daily temperatures and kg of ai for all pesticide classes. This suggested that minimum daily temperature could serve as a proxy for pesticide application. Longer term temperature data (1977–2013) indicated greater relative increases in minimum daily temperatures for northern relative to southern states. Using these longer-term trends to determine short-term projections of pesticide use (to 2023) showed a greater comparative increase in herbicide use for soybean in northern; but a greater increase in insecticide and fungicide use for southern states in a warmer climate. Overall, these data suggest that increases in pesticide application rates may be a means to maintain soybean production in response to rising minimum daily temperatures and potential increases in pest pressures.

Uncertainties and shortcomings of ground surface temperature histories derived from inversion of temperature logs

Analysing borehole temperature data in terms of ground surface history can add useful information to reconstructions of past climates. Therefore, a rigorous assessment of uncertainties and error sources is a necessary prerequisite for the meaningful interpretation of such ground surface temperature histories. This study analyses the most prominent sources of uncertainty. The diffusive nature of the process makes the inversion relatively robust against incomplete knowledge of the thermal diffusivity. Similarly the influence of heat production is small. It turns out that for investigations of the last 1000 to 100000 years the maximum depth of the temperature log is crucial. More than 3000 m are required for an optimal inversion. Reconstructions of the last one or two millennia require only modestly deep logs (>300 m) but suffer severely from noisy data.

Long term changes in diurnal temperature range in Cyprus

Long term temperature data from two stations on the island of Cyprus have been analysed. Increasing trends of approximately 1°C/100 years in the annual mean temperatures are found at both stations. However, the minimum daily temperatures have generally increased at a larger rate than the maximum daily temperatures, resulting in a decrease in the long-term diurnal temperature range. This decrease ranges from −0.5°C/100 years to −3.5°C/100 years, depending on the location. The reduction in the diurnal temperature range is consistent with observations from other parts of the globe, and may indicate that the climate in this region of the globe is part of a larger global climate change that has been occurring over the last century. It is possible that long term changes in greenhouse gas concentrations in the atmosphere are responsible for the long-term annual mean temperature increase. Furthermore, the changes in the diurnal temperature range can possibly be explained by increases in cloud cover and/or tropospheric aerosols. It is possible that part of these changes is caused by local land-use changes, primarily by the increasing urbanization of Cyprus.

Convective and conductive heat transfer in sedimentary basins

AbstractIn the Earth's crust the temperature is largely controlled by heat conduction. However, under some circumstances, the thermal state is disturbed by advection of heat associated with groundwater flow. The corresponding thermal disturbance depends on the water flow velocity (modulus and direction) and therefore thermal data may be used to constrain the pattern of natural fluid flow. In this paper, some models of thermal disturbance induced by convective heat transfer are presented. They are based on the assumption that the water flow is concentrated in thin permeable structures such as aquifer or fault zones. The steady‐state and transient thermal effects associated with such scenarios are computed using a somewhat idealized model which depends on a small number of parameters: flow rate, time, aquifer geometry and thermal parameters of surrounding rocks. In order to extract the conductive and convective components of heat transfer from temperature data and to estimate the corresponding fluid flow rate, it is first necessary to estimate the thermal conductivity field. The problem of the estimation of thermal conductivity in clay‐rich rocks, based on laboratory and in‐situ measurements, is emphasized. Then a method is proposed for the inversion of temperature data in terms of fluid flow. Vertical and lateral variations of thermal conductivity are taken into account and the fluid flow is assumed to be concentrated on a specified surface (2‐D quasi‐horizontal pattern). Thermal effects of the flow are simulated by a distribution of surface heat production which can be calculated and then inverted in terms of horizontal fluid flow pattern.

Temperature dependence of electrical conductivity

It has been shown that the temperature dependence of electrical conductivity for widely different classes of materials can be reproduced by use of the derivative of the Fermi function, G (Ê, T); as the probability density function in place of the Fermi function, f(Ê, T) [1]. The G(Ê, T) function has been found to be applicable to low-dimensional synthetic metallic conductors, semi-conductors, and vitreous insulators. The temperature dependence of electrical conductivity is simulated by curve-fitting the G(Ê, T) function to experimental conductivity versus temperature data in terms of the parameter, E ̂ = E − E F is the energy of the bottom of the conduction band and E F is the Fermi energy. It is shown that Ê, which lies in the band gap region, corresponds to experimentally observable absorption of energy in compounds such as (TMTSF) 2 PF 6, and single crystalline germanium. The quantity Ê can be considered as a sub-band gap activation energy. The derivation of the G(Ê, T) function is briefly discussed. The standard derivation of the Fermi function should yield a cumulative probability function rather than the probability density function.

An urban impact model for changes in the length of the frost free season at selected Canadian stations

A study of long term temperature data for fourteen Canadian cities showed that the length of the annual frost free season has increased by average of 7 days since 1940. Change in individual stations ranged from 26 to -17 days resulting in significant shifts in the mean probable dates for the first and the last frost of the season at most stations. No similar trend was shown in three non-urban control stations. Three urban factors: heat island effect, enhanced cloud cover and the rate of population growth - together accounted for 80% of the explained variance in the phenomenon. A multiple regression model was developed to describe the relationship between the change in the season and the urban factors. It is shown that for any appreciable gains to be made in the length of the season at reasonably predictable changes in temperature, such changes in temperature should be accompanied by a modest increase in cloud cover.

Transport-properties of metallic glasses at high pressures

We report on the pressure and temperature dependence of the electrical resistivity of several amorphous alloys. The pressure range covered extends from 1 bar to 130 kbar. The temperature is varied at constant pressure 0etween 1.3 K and 300 K. Data for several types of amorphous alloys will be presented. The low temperature data will be discussed in terms of superconductivity effects, the high temperature data in terms of a modified Ziman-model.

Toward an Accurate and Efficient Chemical Model for Hydrothermal Brines

Abstract In this paper, we describe the development of a chemical equilibrium model for hydrothermal waters based on the semiempirical equations of aqueous electrolyte solutions recently introduced by Pitzer et al.1 We discuss application of this model to the system, Na/K/Ca/Mg/Cl/SO4/ H2O, at 25°C. Accurate solubility predictions (usually within 5 % of experimental results) can be made for ionic strengths of 0 to 20 m. Comparison with experimental data indicates that a theory parameterized by binary and ternary data can be used to predict solubilities accurately in much more complex mixtures. Mineral solubilities calculated with this model are compared with those calculated from models based on ion pairing. Whereas our predictions are typically within 5 %, the ion pairing models are substantially in error even at low ionic strengths. We are extending our chemical equilibrium programs to include high temperature (T ≃ 200°C). We have assessed the availability of data for modeling hydrothermal waters. In this paper, we discuss the accuracy of existing high temperature data in terms of the binary HCl/H2O system. Our results indicate that below about 150°C, there may be a sufficiently accurate data base to parameterize a hydrothermal water model including all commonly occurring ions. Above 150°C, the data may not be reliable, and appropriate data should be collected for certain key systems.

Adaptation of silage maize varieties under extreme northern growing condions in Finland

Trials with maize varieties from various places in the world were started in 1975. In preliminary trials in 1975, 280 varieties were tested. Between 19 and 23 varieties were selected for ordinary variety tests in 1976—78 at the University farm in Siuntio. Weather conditions, particularly average daily temperatures in 1975 were better than the long term averages, and in 1976—78 far below the average growing conditions. Dry matter yields of the seven harvested silage varieties in 1975 varied between 5.8 and 11.5 tons/ha. In 1976—78 the variation in DM yields was 3.8—8.0 tons/ha among 19—23 varieties. In 1975, 44 varieties out of 280 produced mature seed. Only one variety matured in 1978, but none in 1976—77. The developmental stage of silage maize is primarily determined by ear percentage and secondarily by DM %. In 1975 the average ear % of seven varieties was 49.1 %, in 1978 18.1 % and in 1976—77 only 4.0—5.7 % in DM. As a result of the variety tests promising varieties from Yugoslavia, France and Germany could be found. It can be concluded from the long term temperature data that with very early hybrid varieties a mature grain yield can be harvested twice in ten years. Good quality silage material can be harvested six times in ten years and a satisfactory crop can be obtained eight times in then years. The limiting factor for the growth and development of maize in Finland is the low average temperature of the growing season. Important but less significant is the length of the vegetative period, which is determined by the first killing frost in the fall. The temperature deficit is particularly critical at the beginning of the growing season.

Some Temperature Coefficients for Insect Oviposition

Studies of the effect of temperature have shown that insect oviposition is limited by definite temperature extremes within which the rate of egg laying is accelerated to a maximum and then is retarded by increasingly higher temperatures. Other work has shown that the acceleration of different biological processes may vary in proportion to the temperature or according to more complex relationships. For this reason a quantitative analysis of the relative effect of different temperatures is generally essential in the proper measurement and interpretation of temperature as an environmental factor and also has an important relation to different theories on the nature of the effect of temperature on physiological processes. Since the effect of the same number of degrees difference in temperature may vary in different portions of the temperature gradient, the relations must ordinarily be determined by experimental studies of the relative influence of different intensities or controlled degrees of temperature. The fact that different temperatures may exercise a disproportionate effect shows that serious limitations may be involved in the analysis of variable temperature data in terms of mean temperatures. Curvilinear relations may be obscured and a spurious appearance of linearity may be produced, since in the use of mean temperatures in correlation there is a tacit assumption that temperatures above and below the mean are proportional in effect, as would be expressed by a straight-line graph.