Layer Parameters Research Articles

Влияние интерфейса на антисимметричное обменное взаимодействие в наноструктурах

The work is dedicated to the study of the peculiarities of spin wave dispersion in metallic magnetic nanostructures of heavy metal/ferromagnet based on PtCo compounds using Mandelstam-Brillouin light scattering techniques. Structural and magnetic characterization of the obtained thin-film nanostructures has been conducted and estimates of the magnitude of the Dzyaloshinskii-Moriya antisymmetric exchange interaction have been made for samples with different magnetic layer parameters. It has been shown that the introduction of an atomically ordered layer of PtCo alloy into the nanostructure allows for effective modeling of the Dzyaloshinskii-Moriya interaction strength, as well as the width of the spin-wave resonance line as shown from the Brillouin light scattering spectra. The results can be utilized for the creation of thin-film materials promising for research either in the fields of magnonics or topological magnetism.

Croplands Quality Evaluation of Whole Tillage Layer Based on the Minimum Data Set in Jilin Province, China

The aim of this study is to accurately evaluate the quality characteristics of whole tillage cropland and deepen the knowledge of sub-tillage soil quality evaluation in Jilin Province, China. In this study, top-tillage and sub-tillage soil samples were collected from 185 maize continuous cropping areas in Jilin Province, and 12 physicochemical indexes (pH, cation exchange capacity (CEC), soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), available nitrogen (AN), available phosphorus (AP), available potassium (AK), sand, silt, and clay) were used to evaluate the whole tillage layer soil quality index (SQI). The results showed that the whole tillage soil physicochemical indexes in Jilin Province were generally above the moderate level, and nutrient contents increased from West to East among the regions. The minimum data set SQI (SQI-MDS) of the top-tillage and sub-tillage layers were 0.22–0.98 (0.46) and 0.23–0.93 (0.55), respectively. The suitable ranges of MDS parameters for reasonable tillage layers were as follows: top-tillage layer SOM ≥ 34.5 g kg−1, 31.5% ≤ sand ≤ 53.5%, AP ≥ 32.1 mg kg−1, and TK ≥ 15.18 g kg−1; sub-tillage layer 31.3% ≤ sand ≤ 51.2%, TN ≥ 1.48 g kg−1, 6.4 ≤ pH ≤ 7.1, and AK ≥ 157.6 mg kg−1. In summary, the SQI and evaluation indexes of the top-tillage and sub-tillage layers in different ecological zones are varied. It is necessary to adjust the evaluation index thresholds in combination with the actual conditions to establish a more accurate evaluation index system of the whole tillage soil quality.

Analysis of the influence of thermal insulation parameters on the effectiveness of enclosing structures and building heating systems

The results of thermal engineering calculations of external multilayer enclosing structures of buildings and the thermal power of the heating system of residential buildings are presented, including calculations of temperature fields and partial pressures of water vapor at various parameters of structural layers and thermal insulation layers. Variants are considered and an assessment is given of the variation of parameters and the different location of thermal insulation in external multilayer fences for changes in temperature fields and partial pressures of water vapor in the fence, as well as for changes in the thermal power of the heating system of the building premises for two climatological design areas. The calculations were performed according to the methodology presented below using a specially developed computer program.

Skin ultrasound for rheumatologists: Technical issues and challenges.

In several rheumatic diseases, the skin is involved and therefore it is an organ of interest which may be investigated by the rheumatologist. Ultrasound imaging has been proposed for assessing multiple skin conditions providing qualitative and quantitative information about different parameters in distinct skin layers. Our aim was to describe with a pictorial essay the main challenges that the ultrasound imaging may encounter when investigating the healthy skin in different body areas. Fourteen healthy subjects were submitted to skin ultrasound. The body areas that were investigated were decided following the 17 modified Rodnan skin score anatomical areas used in systemic sclerosis. Skin ultrasound was performed with a GE Logiq 10 with two probes (20 and 24 MHz). For an optimal quantification of the skin layers, the dermis interfaces either with the epidermis and hypodermis were investigated. The results of the ultrasound analysis are described and summarized in a pictorial essay which shows that some main factors may significantly influence the quality of the images of the skin layers. Specifically, age, scanning position, probe frequency, and the optimal positioning of some anatomical areas, in particular the hands and digits, emerged as factors that may have a relevance in influencing the quality of the imaging of the skin layers. Moreover, some technical suggestions are proposed to help optimizing and standardizing the performance of skin ultrasound assessment in healthy population. The pictorial essay we performed shows that the performance of skin ultrasound in healthy subjects still presents several issues that need to be addressed. The definition of the interface in different body areas is the first step which should be standardized before any measurement of the thickness of the skin layers is performed in rheumatic diseases.

The Impact of Non‐Monolithic Semiconductor Capacitance on Organic Electrochemical Transistors Performance and Design

AbstractThe existing device models for organic electrochemical transistors (OECTs) fail to provide any device design guidelines for optimized performance parameters such as transconductance that are pivotal for the applications OECTs in sensing. Moreover, the current models are based on the questionable assumption of a homogenous organic semiconductor layer, and all predict a linear behavior of the resistance with the OECT channel length. Consequently, the experimentally observed nonlinear resistance behavior in OECTs has been overlooked thus far. Here, an OECT device model is developed that accurately describes the nonlinear behavior of the OECT channel resistance and offers the first guidelines for maximizing transconductance. The model is inherently nonlinear and the nonlinearity stem from the non‐monolithic capacitance of the organic semiconductor layer. Moreover, the model provides a consistent and reliable estimations for the contact resistance in OECTs. The success of the model in accurately describing and providing predictions of the OECT operation by relating the device's geometrical parameters with electrochemical parameters of the semiconductor layer paves the way toward unlocking OECT potentials in diverse applications, from biosensing to neuromorphic computing and flexible electronics.

Gravity data inversion for parameters assessment over geologically faulted structures—A hybrid particle swarm optimization and gravitational search algorithm technique

AbstractInterpreting gravity anomalies caused by fault formations is associated with hydrocarbon systems, mineralized areas and hazardous zones and is the main goal of this research. To achieve an effective and robust model over the geologically faulted structures from gravity anomalies, we present a nature‐inspired hybrid algorithm, which synergizes the physics of the particle swarm optimization and gravitational search algorithm with variable inertia weights. The basic principle of developed particle swarm optimization and gravitational search algorithm method is to synergistically use the exploratory strengths of gravitational search algorithm with the exploitation capacity of particle swarm optimization in order to optimize and enhance the effectiveness by both algorithms. The technique has been tested on synthetic gravity data with varying settings of noises over geologically faulted structure before being applied to field data taken from Ahiri‐Cherla and Aswaraopet master fault present in Pranhita–Godavari valley, India. The optimization process is further refined through normalized Gaussian probability density functions, confidence intervals, histograms and correlation matrices to quantify uncertainty, stability, sensitivity and resolution. When dealing with field data, the true model is never known; in these circumstances, the quality of the outcome can only be inferred from the uncertainty in the mean model. The research utilizes a 68.27% confidence intervals to identify a location where the probability density function is more dominant. This region is then used to evaluate the mean model, which is expected to be more appropriate and closer to the genuine model. Correlation matrices further provide a clear demonstration of the strong connection between layer parameters. The results suggest that particle swarm optimization and gravitational search algorithm is less affected by model parameters and yields geologically more consistent outcomes with little uncertainty in the model, aligning well with the available results. The analysed results show that the method we came up with works well and is stable when it comes to solving the two‐dimensional gravity inverse problem. Future research may involve extending the approach to three‐dimensional inversion problems, with potential improvements in computational efficiency and search accuracy for global optimization methods.

Intelligent Inversion Analysis of Surrounding Rock Parameters and Deformation Characteristics of a Water Diversion Surge Shaft

The water diversion surge shaft is vital for a hydropower station. However, the complex geological properties of the surrounding rock make it challenging to obtain its mechanical parameters. A method combining particle swarm optimization (PSO) and support vector machine (SVM) algorithms is proposed for estimating these parameters. According to the engineering geological background and support scheme, a three-dimensional model of the water diversion surge shaft is established by FLAC3D. An orthogonal test is designed to verify the accuracy of the numerical model. Then, the surrounding rock mechanical parameter database is established. The PSO-SVM intelligent inversion algorithm is used to invert the optimal values of the mechanical parameters of the surrounding rock. The support for excavating the next layer depends on the mechanical parameters of the current rock layer. An optimized design scheme is then compared and analyzed with the original support scheme by considering deformation and plastic characteristics. The research results demonstrate that the PSO-SVM intelligent inversion algorithm can effectively improve the accuracy and efficiency of the inversion of rock mechanical parameters. Under the influence of excavation, the surrounding rock in the plastic zone mainly fails in shear, with maximum deformation occurring in the middle and lower parts of the excavation area. The maximum deformation of the surrounding rock under support with long anchor cables is 0.6 cm less than that of support without long anchor cables and 4.07 cm less than that of support without an anchor. In the direction of the maximum and minimum principal stress, the maximum depth of the plastic zone under the support with long anchor cables is 1.3 m to 2.6 m less than that of the support without long anchor cables and the support without an anchor. Compared with the support without long anchor cables and support without an anchor, the support with long anchor cables can effectively control the deformation of the surrounding rock and limit the development of the plastic zone.

Band offset optimization in MAGeI3 based perovskite solar cells

The study focuses on a complete modelling of perovskite solar cell (PSC) employing methyl ammonium germanium iodide (MAGeI3) as light absorbing material via optimizing device’s conduction band offset (CBO) and valence band offset (VBO), which helps in identifying the suitable transport materials that can be employed for extracting the best photovoltaic parameters for solar cell. The device architecture FTO/TiO2/MAGeI3/CuO/Pd has been considered for the study, which after the input parameter optimization provides a power conversion efficiency (PCE) of 12.33 %. An optimization of the CBO of the device configuration results in a PCE of 13.57 %, indicating that the chosen ETL, TiO2 is suitable for the device configuration. An alternate ETL, IGZO with the required electron affinity of 4.18 eV, when tested for the device shows a PCE of 12.39 %. The VBO optimization of the device configuration FTO/TiO2/MAGeI3/CuO/Pd suggests the inclusion of CZTS as the HTL and further input parameter optimization of the device provides PCE of 16.59 %. The VBO optimization of FTO/IGZO/MAGeI3/CuO/Pd suggests for the inclusion of CZTS as the HTL, and provides an excellent PCE of 14.57 %. Hence, the CBO and VBO optimized device configuration, FTO/IGZO/MAGeI3/CZTS/Pd is again optimized by changing the parameters of perovskite and transport layers and a PCE of 20.54 % is achieved. It is further analysed considering diverse back contact metal and optimum PCE of 20.57 % is attained, with Se as the back metal contact. An estimation of the QE of FTO/IGZO/MAGeI3/CZTS/Se configuration indicates efficient charge generation and collection in visible and NIR wavelength regimes.

CME Arrival Time Prediction Based on Coronagraph Observations and Machine-learning Techniques

The timely and precise prediction of the arrival time of coronal mass ejections (CMEs) is crucial in mitigating their potential adverse effects. In this study, we present a novel prediction method utilizing a deep-learning framework coupled with physical characteristics of CMEs and background solar wind. Time series images from synchronized solar white-light and EUV observations of 156 geoeffective CME events during 2000–2020 are collected for this study, according to the Richardson and Cane interplanetary CME directory and the SOHO/LASCO CME catalog of NASA/CDAW. The CME parameters are obtained from the CDAW website and the solar wind parameters are from OMNI2 website. The observational images are first fed into a convolutional neural network (CNN) to train a regression model as Model A. The results generated by the original CNN are then integrated with 11 selected physical parameters in additional neural network layers of Model B to improve the predictions. Under optimal configurations, Model A achieves a minimum mean absolute error (MAE) of 7.87 hr, whereas Model B yields a minimum MAE of 5.12 hr. During model training, we employed tenfold cross validation to reduce the occasionality of biased data. The average MAE of Model B on 10 folds is 33% lower than that of model A. The results demonstrate that combining the imaging observations with the physical properties of CMEs and background solar wind to train a machine-learning model can benefit the forecasting of CME arrival times.

Going Deeper, Generalizing Better: An Information-Theoretic View for Deep Learning.

Deep learning has transformed computer vision, natural language processing, and speech recognition. However, two critical questions remain obscure: 1) why do deep neural networks (DNNs) generalize better than shallow networks and 2) does it always hold that a deeper network leads to better performance? In this article, we first show that the expected generalization error of neural networks (NNs) can be upper bounded by the mutual information between the learned features in the last hidden layer and the parameters of the output layer. This bound further implies that as the number of layers increases in the network, the expected generalization error will decrease under mild conditions. Layers with strict information loss, such as the convolutional or pooling layers, reduce the generalization error for the whole network; this answers the first question. However, algorithms with zero expected generalization error do not imply a small test error. This is because the expected training error is large when the information for fitting the data is lost as the number of layers increases. This suggests that the claim "the deeper the better" is conditioned on a small training error. Finally, we show that deep learning satisfies a weak notion of stability and provides some generalization error bounds for noisy stochastic gradient decent (SGD) and binary classification in DNNs.

Comparative study on the performance of fresh concrete comprising manufactured sand vs. river sand during full-scale long-distance pumping

The shift from river sand (RS) to manufactured sand (MS) as a fine aggregate in concrete is driven by environmental sustainability concerns. However, negative impacts of MS on concrete workability have raised doubts about its suitability for long-distance pumping in construction projects. This study evaluates the performance of MS concrete compared to RS concrete of the same strength grade during long-distance pumping. A full-scale coiled pipeline with measured length of 348 m was built, twelve batches of C30 and C60 concrete were prepared, and pumping tests were conducted at discharge rates of 2.5–18.9 liter per second (L/s). The workability and rheological parameters were assessed before and after pumping. The measured and predicted pumping pressure were analyzed and calculated based on rheological parameters and lubricating layer (LL) parameters. Results indicate that MS increased the yield stress and decreased the LL thickness index of C30 concrete, while it decreased the viscosity coefficient of C60 concrete. Post-pumping, the change in yield stress was similar for both MS and RS concrete, but MS reduced the plastic viscosity change in C60 concrete. MS did not significantly affect the pumping pressure loss, but influenced the pumping pressure prediction deviation by influencing the thixotropy. The classic pumping pressure prediction model developed by Kapan obviously overestimated the pumping pressure during long-distance pumping, and the deviation was more prominent in high-strength concrete pumping. MS increased the thixotropy of C30 concrete and reduced the prediction deviation of pumping pressure, but had the opposite effect on C60 concrete.

Effect of layered fertilizer strategies on rapeseed (Brassica napus L.) productivity and soil macropore characteristics under mechanical direct-sowing

Layered fertilization parameters affects crop root system configuration and growth distribution, which in turn affects soil pore properties and aggregate structure. Therefore, understanding the spatial distribution of the root system and soil pore space is helpful in choosing a reasonable fertilization ratio in production practice, which ensures high and stable crop yields and at the same time improves fertilizer utilization and soil health. Albeit the impact of layered fertilization ratio at varied rates on soil pore characteristics in the soil profile at a microscale level remains limited. This study quantifies the impacts of layered fertilization ratio under on rapeseed root distribution and soil pore characteristics using the root analysis system and X-ray computed tomography (XCT). A new fertilization pattern that shallow-deep layer synchronized mechanical sowing was using, rotary tillage mixed fertilization in the shallow layer and 10 cm side-deep band fertilization in the deeper layer. It set five ratios of fertilizer amounts between shallow and deep layers as 0:4 (FD), 1:3 (FL), 2:2 (FM), 3:1 (FH), and 4:0 (F0), with non-fertilization (CK) as the control treatment. The results indicated that layered fertilization effectively improved the rape root conformation and spatial distribution, significantly increased total soil porosity and moisture content, and reduced soil bulk density and resistance (P < 0.05). Additionally, root configuration is closely related to soil pore structure and crop yield. Notably, compared with other treatments, the macroscopic porosity, equivalent pore diameter, hydraulic radius and roundness of FM treatment are significantly improved, and it effectively improves the yield and quality of rapeseed. Correlation analysis showed that the root system configuration parameters were negatively correlated with soil bulk density and resistance but positively correlated with soil moisture content, and macropore morphology parameters and rapeseed yield. Our study demonstrate that layered fertilization can improved rape growth and soil macropore porosity, but its effect depends on good tillage macropore characteristics and distribution created by reasonable fertilization ratio, which provided a theoretical basis for high-yield, efficient, green, and sustainable mechanized direct-sowing production of rapeseed.

Detection of morphometric indicators of the soil surface of a grape plantation using spectral bands of satellite images

Introduction. Soils play an important role in the approximately 30-year period of operation of a grape planting, influencing plant growth, their yield and the quality of the grapes. In this study, the morphometric parameters of the surface soil layer of a grape plantation were studied using spectral channels of satellite images.Methodology. The methodology included the use of a “random forest” algorithm to classify soil cover using spectral channels and normalized satellite image indices and analyze the main physicochemical properties of soils. Accuracy was assessed using RMSD and confidence intervals calculated via bootstrapping.Results. The study revealed significant differences in the spectral reflectivity of different site options, which was due to carbonate content, humidity levels and the amount of humus. Areas with high carbonate and moisture content showed higher standard deviation values in the spectral channels. Studying the spectral characteristics of the soil surface makes it possible to effectively classify different areas based on remote sensing data. Analysis of combinations of spectral channels revealed an optimal set of three channels (B12, B11, B8A) with a minimum standard deviation when classifying an image into six soil variants of areas. For classification, a composition of five normalized indices can also be used, but in this case the calculation time increases significantly with a larger standard deviation and a larger confidence interval range. Using machine learning, six distinct soil surface types were segmented, demonstrating the complexity of the field›s soil mosaic. These results are critical for improving vineyard management and productivity

Numerical Analysis of a Two-Layer PCM Based Battery Thermal Management System for Different Material Properties

The design and numerical analysis of the two-layer PCM (Phase Change Material)-based thermal management system for a 18650-type lithium-ion battery have been performed. In relation to simulation, the coefficient of thermal conductivity and melting temperature of the first layer of PCMs are varied. Other parameters are made identical to that of the next layer's parameters in order that the generation of two different layers of PCMs can be attained: PCM-1 and PCM-2. To obtain a more realistic approach in the numerical analysis, the battery thermal model was created in the COMSOL-MATLAB interface using the experimental internal resistance data obtained for 18650 type Li-ion batteries in the literature. While a cheaper and more accessible material with a thermal conductivity of 0.2 W/mK and a melting point of 50 °C was used in the PCM-2 layer, the thermal conductivity was changed to 0.2; 1 and 5 W/mK in the PCM-1 layer; and the melting point was changed to 30, 40 and 50 °C. In this way, for PCM layers with different thickness (tpcm), the system was optimized at two different discharge rates, 5C and 7C. As a result of the numerical analysis, it was determined that the optimum tpcm, kpcm,1 and Tm values for the 5C discharge rate were 2 mm, 0.2 W/mK and 40 °C, respectively; and the optimum tpcm, kpcm,1 and Tm values for the 7C discharge rate were 4 mm, 5 W/mK and 40 °C, respectively.

Point-enhanced convolutional neural network: A novel deep learning method for transonic wall-bounded flows

Low order models can be used to accelerate engineering design processes. Ideally, these surrogates should meet the conflicting requirements of large design space coverage, high accuracy and fast evaluation. Within the context of aerospace applications at transonic conditions, this can be challenging due to the associated non-linearity of the flow regime. Different methods have been investigated in the past to predict the flow-field around shapes such as airfoils or cylinders. However, they usually have reduced spatial resolution, limiting the prediction capabilities within the boundary layer which is of interest for transonic wall-bounded flows. This work proposes a novel Point-Enhanced Convolutional Neural Network (PCNN) method that combines the advantages of the well-established PointNet and convolutional neural network approaches. The PCNN model has relatively low memory requirements in the training process, preserves the spatial correlation in the domain and has the same resolution as a traditional computational method. The architecture is used for the flow-field prediction of civil aero-engine nacelles in which it is demonstrated that the flow features of peak isentropic Mach number (Mis), pre-shock isentropic Mach number and shock location (X/Lnac) are captured within ΔMis = 0.02, ΔMis=0.04, ΔX/Lnac=0.007, respectively. The PCNN model successfully predicts the integral parameters of the boundary layer, in which the incompressible displacement thickness, momentum thickness and shape factor are typically within 5% of the CFD. Overall, the PCNN method is demonstrated for transonic wall-bounded flows for a range of flow physics that include shock waves and shock-induced separation.

Comparison of passive and semi-active piezoelectric transducer damping of cantilever oscillation

The aim of this work is the analysis of passive and semi-active damping methods on the model woven beam with a piezoelectric (PZ) layer. The mathematical model of the PZ layer on cantilever beam is derived. For modeling, real product parameters of PZ layer are used, and through simulation find the optimal values to dampen it. The resistive-inductance passive model, and State-Switch Damping SSD semi-active model are used in this work. The models are processed in the MATLAB Simulink environment and the results of the behavior of the models compared to each other. The program involving passive and semi-active systems, where analyze the behavior of the model for different parameters and the damping capabilities of individual methods are compared. The simulation results show the effectiveness of the semi-active system in comparison with the passive systems, also it shows the passive method is not so effective at low frequency as it in high frequency, and the semi-active method is not optimal for high frequency vibration.

Fatigue life prediction of CFRP flat-fold-flat adhesive joints

Taking the flat-fold-flat (FJF) adhesive joints of carbon fiber reinforced bismaleimide resin-based composite materials as the object, the regularized fatigue life model and residual stiffness model of the one-way plate and the cohesive model parameters of the adhesive layer were fitted experimentally, and based on this, the fatigue life of the adhesive joint was predicted by ABAQUS software. The results show that under specific maximum load conditions, the predicted cracking life of the adhesive layer at the end of the lap joint and the final fatigue life are not greater than the error values of the test results 3% , which has a relatively good calculation accuracy. The cracking life of the adhesive layer at the end of the lap joint is approximately equal to the final fatigue life of the adhesive joint, indicating that the cracking of the adhesive layer at the end of the lap joint accounts for the main part of the total life of the adhesive joint, and the crack propagation life of the adhesive layer can be ignored.

Exploring Wettability: A Key to Optimizing Liquid-Solid Triboelectric Nanogenerators.

Nowadays, the liquid-solid triboelectric nanogenerator (L-S TENG) has gained much attention among researchers because of its ability to be a part of self-powering technology by harvesting ultra-low-frequency vibration in the environment. The L-S TENG works with the principle of contact electrification (CE) and electrostatic induction, in which CE takes place between the solid and liquid. The exact mechanism behind the CE at the L-S interface is still a debatable topic because many physical parameters of both solid and liquid triboelectric layers contribute to this process. In the L-S TENG device, water or solvents are commonly used as liquid triboelectric layers, for which their wettability over the solid triboelectric layer plays a significant role. Hence, this review is extensively focused on the influence of the wettability of solid surfaces on the CE and the corresponding impact on the output performance of L-S TENGs. The present review starts with introducing the L-S TENG, a mechanism that contributes to CE at the L-S interface, the significance of hydrophobic materials/surfaces in TENG devices, and their fabrication methods. Further, the impact of the contact angle over the electron/ion transfer over various surfaces has been extensively analyzed. Finally, the challenges and future prospects of the fabrication and utilization of superhydrophobic surfaces in the context of L-S TENGs have been included. This review serves as a foundation for future research aimed at optimizing the L-S TENG performance and inspiring new approaches in material design and multifunctional energy-harvesting systems.

A deep autoencoder for electric double layer capacitance prediction in electrochemical sensors

This study explores the application of a deep autoencoder neural network to accurately predict the electric double layer capacitance from real-world parameters in binary, asymmetric electrolytes under low concentration conditions. By utilizing a modest simulation-based dataset of just 250 samples, the deep autoencoder neural network model developed in this study effectively predicted the capacitance by learning the critical features and relationships of the electric double layer model and encoding this learned representation into a low-dimensional latent space. From the latent variables, the decoder block of the neural network learned to effectively recreate the high-dimensional input. To enhance the model's robustness, prevent overfitting, and better simulate real-world conditions, noise was incorporated into the training and test data. The model demonstrated strong performance across various conditions, such as ionic size, ionic charge, and surface potential, yielding satisfactory results on both clean and noisy test datasets. A key feature of this approach was the mapping of real-world electric double layer parameters to the latent variables of the model, allowing for direct input of physical parameters to predict the electric double layer capacitance. This research highlights the potential of machine learning techniques to expedite the design and analysis of complex multi-physics systems such as electrochemical sensors by reducing the dependence on extensive domain expertise throughout the design process.

Field investigations on the thermo-mechanical behavior of a partially activated energy pile in Miocene sediments

The City of Vienna, as one of the largest public clients in Austria, initiated a research project to determine the load-bearing behavior of various foundation elements in typical Viennese soils. Numerous large-scale tests were carried out on bored piles, micro piles, anchors, and jet grouted columns. In addition, two energy piles were installed in different soil layers to investigate their behavior under mechanical and thermal loading in each soil layer separately. This paper discusses the energy pile in the Miocene sediments, which consist mainly of silty fine sand and some sandy silt. The energy pile – fully instrumented with strain gauges, extensometers, heat gauges and optical fiber sensors – was loaded for two and a half months. The mechanical load was kept constant throughout the thermal cycles to determine the response of the pile to thermal loads. The measured temperature data were used in numerical back-calculations to determine the thermal parameters of the soil layers and the concrete. Based on the measured strain and deformation data, the deformation behavior of the energy pile due to the thermal load was investigated. Finally, a static pile load test was carried out on the energy pile and the results are compared with those of the conventional reference piles installed in the vicinity, which have not been subjected to thermal loading. The test demonstrated that, after several weeks of cyclic thermal loading, the energy pile exhibited more favorable load-deformation behavior compared to conventional reference piles.