Search results for: mathematical models.

Authors: Saber Saati Mohtadi, Ali Payan, Azizallah Kord

Abstract:

One of the most important issues in multi-criteria decision analysis (MCDA) is to determine the weights of criteria so that all alternatives can be compared based on the collective performance of criteria. In this paper, one of popular methods in data envelopment analysis (DEA) known as common weights (CWs) is used to determine the weights in MCDA. Two frontiers named ideal and anti-ideal frontiers, instead of ideal and anti-ideal alternatives, are defined based on two new proposed CWs models. Ideal and antiideal frontiers are more flexible than that of alternatives. According to the optimal solutions of these two models, the distances of an alternative from the ideal and anti-ideal frontiers are derived. Then, a relative distance is introduced to measure the value of each alternative. The suggested models are linear and despite weight restrictions are feasible. An example is presented for explaining the method and for comparing to the existing literature.

Keywords: Anti-ideal frontier, Common weights (CWs), Ideal frontier, Multi-criteria decision analysis (MCDA)

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Authors: Zdeňka Rendlová

Abstract:

This paper presents the mathematical description of the high-speed rotating system taking into account the influence of internal and external damping. The mathematical model is obtained by using the finite element method. The analyzed system is an automotive turbocharger understood as a rotor-bearing system. The circular cross-section shaft is equipped with one compressor wheel, one turbine wheel and is supported by two floating ring bearings. Based on the model, the dynamical analysis of a turbocharger is performed and stability conditions are evaluated.

Keywords: External damping, internal damping, journal bearing, stability, turbocharger.

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Authors: Tahseen A. Jilani, Aqil S. Burney, C. Ardil

Abstract:

Time series models have been used to make predictions of academic enrollments, weather, road accident, casualties and stock prices, etc. Based on the concepts of quartile regression models, we have developed a simple time variant quantile based fuzzy time series forecasting method. The proposed method bases the forecast using prediction of future trend of the data. In place of actual quantiles of the data at each point, we have converted the statistical concept into fuzzy concept by using fuzzy quantiles using fuzzy membership function ensemble. We have given a fuzzy metric to use the trend forecast and calculate the future value. The proposed model is applied for TAIFEX forecasting. It is shown that proposed method work best as compared to other models when compared with respect to model complexity and forecasting accuracy.

Keywords: Quantile Regression, Fuzzy time series, fuzzy logicalrelationship groups, heuristic trend prediction.

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Authors: R. Damaševičius

Abstract:

System-level design based on high-level abstractions is becoming increasingly important in hardware and embedded system design. This paper analyzes meta-design techniques oriented at developing meta-programs and meta-models for well-understood domains. Meta-design techniques include meta-programming and meta-modeling. At the programming level of design process, metadesign means developing generic components that are usable in a wider context of application than original domain components. At the modeling level, meta-design means developing design patterns that describe general solutions to the common recurring design problems, and meta-models that describe the relationship between different types of design models and abstractions. The paper describes and evaluates the implementation of meta-design in hardware design domain using object-oriented and meta-programming techniques. The presented ideas are illustrated with a case study.

Keywords: Design patterns, meta-design, meta-modeling, metaprogramming.

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Authors: K. Benhabib, X. Pierens, V-D Nguyen, G. Mimanne

Abstract:

The main hypothesis of the dynamics of solid phase microextraction (SPME) is that steady-state mass transfer is respected throughout the SPME extraction process. It considers steady-state diffusion is established in the two phases and fast exchange of the analyte at the solid phase film/water interface. An improved model is proposed in this paper to handle with the situation when the analyte (atrazine) is in contact with colloid suspensions (carboxylate latex in aqueous solution). A mathematical solution is obtained by substituting the diffusion coefficient by the mean of diffusion coefficient between analyte and carboxylate latex, and also thickness layer by the mean thickness in aqueous solution. This solution provides an equation relating the extracted amount of the analyte to the extraction a little more complicated than previous models. It also gives a better description of experimental observations. Moreover, the rate constant of analyte obtained is in satisfactory agreement with that obtained from the initial curve fitting.

Keywords: Pesticide, SPME methods, polyacrylate, steady state.

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Authors: Changqiao Wu, Guoqing Ding, Xin Chen

Abstract:

In this paper, ways of modeling dynamic measurement systems are discussed. Specially, for linear system with single-input single-output, it could be modeled with shallow neural network. Then, gradient based optimization algorithms are used for searching the proper coefficients. Besides, method with normal equation and second order gradient descent are proposed to accelerate the modeling process, and ways of better gradient estimation are discussed. It shows that the mathematical essence of the learning objective is maximum likelihood with noises under Gaussian distribution. For conventional gradient descent, the mini-batch learning and gradient with momentum contribute to faster convergence and enhance model ability. Lastly, experimental results proved the effectiveness of second order gradient descent algorithm, and indicated that optimization with normal equation was the most suitable for linear dynamic models.

Keywords: Dynamic system modeling, neural network, normal equation, second order gradient descent.

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Authors: D Sudheer Reddy, N Gopal Reddy, P V Radhadevi, J Saibaba, Geeta Varadan

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Smoothing or filtering of data is first preprocessing step for noise suppression in many applications involving data analysis. Moving average is the most popular method of smoothing the data, generalization of this led to the development of Savitzky-Golay filter. Many window smoothing methods were developed by convolving the data with different window functions for different applications; most widely used window functions are Gaussian or Kaiser. Function approximation of the data by polynomial regression or Fourier expansion or wavelet expansion also gives a smoothed data. Wavelets also smooth the data to great extent by thresholding the wavelet coefficients. Almost all smoothing methods destroys the peaks and flatten them when the support of the window is increased. In certain applications it is desirable to retain peaks while smoothing the data as much as possible. In this paper we present a methodology called as peak-wise smoothing that will smooth the data to any desired level without losing the major peak features.

Keywords: smoothing, moving average, peakwise smoothing, spatialdensity models, planar shape models, wavelets.

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Authors: K. M. Tan, A. Anvar, T.F. Lu

Abstract:

A sophisticated simulator provides a cost-effective measure to carry out preliminary mission testing and diagnostic while reducing potential failures for real life at sea trials. The presented simulation framework covers three key areas: AUV modeling, sensor modeling, and environment modeling. AUV modeling mainly covers the area of AUV dynamics. Sensor modeling deals with physics and mathematical models that govern each sensor installed onto the AUV. Environment model incorporates the hydrostatic, hydrodynamics, and ocean currents that will affect the AUV in a real-time mission. Based on this designed simulation framework, custom scenarios provided by the user can be modeled and its corresponding behaviors can be observed. This paper focuses on the accuracy of the simulated data from AUV model and environmental model derived from a developed AUV test-bed which was jointly upgraded by DSTO and the University of Adelaide. The main contribution of this paper is to experimentally verify the accuracy of the proposed simulation framework.

Keywords: Autonomous Underwater Vehicle (AUV), simulator, framework, robotics, maritime robot, modeling.

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Authors: Sean Paulsen, Michael Casey

Abstract:

In this work, we present a self-supervised pretraining framework for transformers on functional Magnetic Resonance Imaging (fMRI) data. First, we pretrain our architecture on two self-supervised tasks simultaneously to teach the model a general understanding of the temporal and spatial dynamics of human auditory cortex during music listening. Our pretraining results are the first to suggest a synergistic effect of multitask training on fMRI data. Second, we finetune the pretrained models and train additional fresh models on a supervised fMRI classification task. We observe significantly improved accuracy on held-out runs with the finetuned models, which demonstrates the ability of our pretraining tasks to facilitate transfer learning. This work contributes to the growing body of literature on transformer architectures for pretraining and transfer learning with fMRI data, and serves as a proof of concept for our pretraining tasks and multitask pretraining on fMRI data.

Keywords: Transfer learning, fMRI, self-supervised, brain decoding, transformer, multitask training.

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Authors: Fadi Awawdeh, O. Alsayyed

Abstract:

New exact three-wave solutions including periodic two-solitary solutions and doubly periodic solitary solutions for the (2+1)-dimensional asymmetric Nizhnik-Novikov- Veselov (ANNV) system are obtained using Hirota's bilinear form and generalized three-wave type of ansatz approach. It is shown that the generalized three-wave method, with the help of symbolic computation, provides an e¤ective and powerful mathematical tool for solving high dimensional nonlinear evolution equations in mathematical physics.

Keywords: Soliton Solution, Hirota Bilinear Method, ANNV System.

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Authors: Madhukar Nagare, Pankaj Dutta, Amey Kambli

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Perishable goods constitute a large portion of retailer inventory and lose value with time due to deterioration and/or obsolescence. Retailers dealing with such goods required considering the factors of short shelf life and the dependency of sales on inventory displayed in determining optimal procurement policy. Many retailers follow the practice of using two bins - primary bin sales fresh items at a list price and secondary bin sales unsold items at a discount price transferred from primary bin on attaining certain age. In this paper, mathematical models are developed for primary bin and for secondary bin that maximizes profit with decision variables of order quantities, optimal review period and optimal selling price at secondary bin. The demand rates in two bins are assumed to be deterministic and dependent on displayed inventory level, price and age but independent of each other. The validity of the model is shown by solving an example and the sensitivity analysis of the model is also reported.

Keywords: Retail Inventory, Perishable Products, Two Bin, Profitable Sales.

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Authors: Nisa Özge Önal, Kamil Karaçuha, Göksu Hazar Erdinç, Banu Bahar Karaçuha, Ertuğrul Karaçuha

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The study aims to use a mathematical approach with the fractional calculus which is developed to have the ability to continuously analyze the factors related to the children’s height development. Until now, tracking the development of the child is getting more important and meaningful. Knowing and determining the factors related to the physical development of the child any desired time would provide better, reliable and accurate results for childcare. In this frame, 7 groups for height percentile curve (3th, 10th, 25th, 50th, 75th, 90th, and 97th) of Turkey are used. By using discrete height data of 0-18 years old children and the least squares method, a continuous curve is developed valid for any time interval. By doing so, in any desired instant, it is possible to find the percentage and location of the child in Percentage Chart. Here, with the help of the fractional calculus theory, a mathematical model is developed. The outcomes of the proposed approach are quite promising compared to the linear and the polynomial method. The approach also yields to predict the expected values of children in the sense of height.

Keywords: Children growth percentile, children physical development, fractional calculus, linear and polynomial model.

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Authors: Mahmoud M. Tash

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The present study was carried out to investigate the effect of alloying elements and thermo-mechanical treatment (TMT) i.e. hot rolling and forging with different reduction ratios on the hardness (HV) and impact toughness (J) of heat-treated low alloy steels. An understanding of the combined effect of TMT and alloying elements and by measuring hardness, impact toughness, resulting from different heat treatment following TMT of the low alloy steels, it is possible to determine which conditions yielded optimum mechanical properties and high strength to weight ratio. Experimental Correlations between hot work reduction ratio, hardness and impact toughness for thermo-mechanically heat treated low alloy steels are analyzed quantitatively, and both regression and mathematical hardness and impact toughness models are developed.

Keywords: Hot Forging, hot rolling, heat treatment, hardness (hv), impact toughness (j), microstructure, low alloy steels.

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Authors: Rossella Arcucci, Luisa D’Amore, Simone Celestino, Giuseppe Scotti, Giuliano Laccetti

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This work is the first dowel in a rather wide research activity in collaboration with Euro Mediterranean Center for Climate Changes, aimed at introducing scalable approaches in Ocean Circulation Models. We discuss designing and implementation of a parallel algorithm for solving the Variational Data Assimilation (DA) problem on Graphics Processing Units (GPUs). The algorithm is based on the fully scalable 3DVar DA model, previously proposed by the authors, which uses a Domain Decomposition approach (we refer to this model as the DD-DA model). We proceed with an incremental porting process consisting of 3 distinct stages: requirements and source code analysis, incremental development of CUDA kernels, testing and optimization. Experiments confirm the theoretic performance analysis based on the so-called scale up factor demonstrating that the DD-DA model can be suitably mapped on GPU architectures.

Keywords: Data Assimilation, Parallel Algorithm, GPU architectures, Ocean Models.

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Authors: Salah I. Al-Mously, Marai M. Abousetta

Abstract:

This paper predicts the effect of the user-s hand-hold position on the Total Isotropic Sensitivity (TIS) of GSM900/1800 mobile phone antennas of realistic in-use conditions, where different semi-realistic mobile phone models, i.e., candy bar and clamshell, as well as different antenna types, i.e., external and internal, are simulated using a FDTD-based platform. A semi-realistic hand model consisting of three tissues and the SAM head are used in simulations. The results show a considerable impact on TIS of the adopted mobile phone models owing to the user-s hand presence at different positions, where a maximum level of TIS is obtained while grasping the upper part of the mobile phone against head. Maximum TIS levels are recorded in talk position for mobile phones with external antenna and maximum differences in TIS levels due to the hand-hold alteration are recorded for clamshell-type phones.

Keywords: FDTD, mobile phone, phantoms, TIS.

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Authors: Hamid Tavakolipour, Mohsen Mokhtarian, Ahmad Kalbasi Ashtari

Abstract:

In this study, the effect of various drying methods (sun drying, shade drying and industrial drying) on final moisture content, shell splitting degree, shrinkage and color change were studied. Sun drying resulted higher degree of pistachio nuts shell splitting on pistachio nuts relative other drying methods. The ANOVA results showed that the different drying methods did not significantly effects on color change of dried pistachio nut. The results illustrated that pistachio nut dried by industrial drying had the lowest moisture content. After the end of drying process, initially, the experimental drying data were fitted with five famous drying models namely Newton, Page, Silva et al., Peleg and Henderson and Pabis. The results indicated that Peleg and Page models gave better results compared with other models to monitor the moisture ratio’s pistachio nut in industrial drying and open sun (or shade drying) methods, respectively.

Keywords: Industrial drying, Modeling, Pistachio, quality properties, Traditional drying.

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Authors: Hans H. Diel

Abstract:

A computer model of Quantum Theory (QT) has been developed by the author. Major goal of the computer model was support and demonstration of an as large as possible scope of QT. This includes simulations for the major QT (Gedanken-) experiments such as, for example, the famous double-slit experiment. Besides the anticipated difficulties with (1) transforming exacting mathematics into a computer program, two further types of problems showed up, namely (2) areas where QT provides a complete mathematical formalism, but when it comes to concrete applications the equations are not solvable at all, or only with extremely high effort; (3) QT rules which are formulated in natural language and which do not seem to be translatable to precise mathematical expressions, nor to a computer program. The paper lists problems in all three categories and describes also the possible solutions or circumventions developed for the computer model.

Keywords: Computability, Foundation of Quantum Mechanics, Measurement Process, Modeling.

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Authors: Toshihiko Matsuka

Abstract:

One problem in evaluating recent computational models of human category learning is that there is no standardized method for systematically comparing the models' assumptions or hypotheses. In the present study, a flexible general model (called GECLE) is introduced that can be used as a framework to systematically manipulate and compare the effects and descriptive validities of a limited number of assumptions at a time. Two example simulation studies are presented to show how the GECLE framework can be useful in the field of human high-order cognition research.

Keywords: artificial intelligence, category learning, cognitive modeling, radial basis functions.

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Authors: J. Dumrongsak, A. M. Savill

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Non-premixed turbulent combustion Computational Fluid Dynamics (CFD) has been carried out in a simplified methanefuelled coaxial jet combustor employing Large Eddy Simulation (LES). The objective of this study is to evaluate the performance of LES in modelling non-premixed combustion using a commercial software, FLUENT, and investigate the effects of the grid density and chemistry models employed on the accuracy of the simulation results. A comparison has also been made between LES and Reynolds Averaged Navier-Stokes (RANS) predictions. For LES grid sensitivity test, 2.3 and 6.2 million cell grids are employed with the equilibrium model. The chemistry model sensitivity analysis is achieved by comparing the simulation results from the equilibrium chemistry and steady flamelet models. The predictions of the mixture fraction, axial velocity, species mass fraction and temperature by LES are in good agreement with the experimental data. The LES results are similar for the two chemistry models but influenced considerably by the grid resolution in the inner flame and near-wall regions.

Keywords: Coaxial jet, reacting LES, non-premixed combustion, turbulent flow.

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Authors: Hafizuddin W. Yussof, Syamsutajri S. Bahri, Adam P. Harvey

Abstract:

Strong anion exchange resins with QN+OH-, have the potential to be developed and employed as heterogeneous catalyst for transesterification, as they are chemically stable to leaching of the functional group. Nine different SIERs (SIER1-9) with QN+OH-were prepared by suspension polymerization of vinylbenzyl chloridedivinylbenzene (VBC-DVB) copolymers in the presence of n-heptane (pore-forming agent). The amine group was successfully grafted into the polymeric resin beads through functionalization with trimethylamine. These SIERs are then used as a catalyst for the transesterification of triacetin with methanol. A set of differential equations that represents the Langmuir-Hinshelwood-Hougen- Watson (LHHW) and Eley-Rideal (ER) models for the transesterification reaction were developed. These kinetic models of LHHW and ER were fitted to the experimental data. Overall, the synthesized ion exchange resin-catalyzed reaction were welldescribed by the Eley-Rideal model compared to LHHW models, with sum of square error (SSE) of 0.742 and 0.996, respectively.

Keywords: Anion exchange resin, Eley-Rideal, Langmuir-Hinshelwood-Hougen-Watson, transesterification.

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Authors: Zhidong Zhang, Zhi-Chao Zhang

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This study examined the mental health and behavioral problems in early adolescence with the instrument of Achenbach System of Empirically Based Assessment (ASEBA). The purpose of the study was stratified sampling method was used to collect data from 1975 participants. Multiple regression models and hierarchical regression models were applied to examine the relations between the background variables and internalizing problems, and the ones between students’ performance and internalizing problems. The results indicated that several background variables as predictors could significantly predict the anxious/depressed problem; reading and social study scores could significantly predict the anxious/depressed problem. However the class as a hierarchical macro factor did not indicate the significant effect. In brief, the majority of these models represented that the background variables, behaviors and academic performance were significantly related to the anxious/depressed problem.

Keywords: Behavioral problems, anxious/depression problems, empirical-based assessment, hierarchical modeling.

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Authors: B. Sir, M. Podhoranyi, S. Kuchar, T. Kocyan

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Rainfall runoff models play important role in hydrological predictions. However, the model is only one part of the process for creation of flood prediction. The aim of this paper is to show the process of successful prediction for flood event (May 15 – May 18 2014). Prediction was performed by rainfall runoff model HEC–HMS, one of the models computed within Floreon+ system. The paper briefly evaluates the results of automatic hydrologic prediction on the river Olše catchment and its gages Český Těšín and Věřňovice.

Keywords: Flood, HEC-HMS, Prediction, Rainfall – Runoff.

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Authors: E. Al Daoud

Abstract:

In many cases, theoretical treatments are available for models for which there is no perfect physical realization. In this situation, the only possible test for an approximate theoretical solution is to compare with data generated from a computer simulation. In this paper, Monte Carlo tools are used to study and compare the elementary particles models. All the experiments are implemented using 10000 events, and the simulated energy is 13 TeV. The mean and the curves of several variables are calculated for each model using MadAnalysis 5. Anomalies in the results can be seen in the muons masses of the minimal supersymmetric standard model and the two Higgs doublet model.

Keywords: Feynman rules, hadrons, Lagrangian, Monte Carlo, simulation.

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Authors: Khaing Win Mar, Thinn Thu Naing

Abstract:

Nowadays, precipitation prediction is required for proper planning and management of water resources. Prediction with neural network models has received increasing interest in various research and application domains. However, it is difficult to determine the best neural network architecture for prediction since it is not immediately obvious how many input or hidden nodes are used in the model. In this paper, neural network model is used as a forecasting tool. The major aim is to evaluate a suitable neural network model for monthly precipitation mapping of Myanmar. Using 3-layerd neural network models, 100 cases are tested by changing the number of input and hidden nodes from 1 to 10 nodes, respectively, and only one outputnode used. The optimum model with the suitable number of nodes is selected in accordance with the minimum forecast error. In measuring network performance using Root Mean Square Error (RMSE), experimental results significantly show that 3 inputs-10 hiddens-1 output architecture model gives the best prediction result for monthly precipitation in Myanmar.

Keywords: Precipitation prediction, monthly precipitation, neural network models, Myanmar.

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Authors: Dragos Nicolae VIZIREANU

Abstract:

One of the main image representations in Mathematical Morphology is the 3D Shape Decomposition Representation, useful for Image Compression and Representation,and Pattern Recognition. The 3D Morphological Shape Decomposition representation can be generalized a number of times,to extend the scope of its algebraic characteristics as much as possible. With these generalizations, the Morphological Shape Decomposition 's role to serve as an efficient image decomposition tool is extended to grayscale images.This work follows the above line, and further develops it. Anew evolutionary branch is added to the 3D Morphological Shape Decomposition's development, by the introduction of a 3D Multi Structuring Element Morphological Shape Decomposition, which permits 3D Morphological Shape Decomposition of 3D binary images (grayscale images) into "multiparameter" families of elements. At the beginning, 3D Morphological Shape Decomposition representations are based only on "1 parameter" families of elements for image decomposition.This paper addresses the gray scale inter frame interpolation by means of mathematical morphology. The new interframe interpolation method is based on generalized morphological 3D Shape Decomposition. This article will present the theoretical background of the morphological interframe interpolation, deduce the new representation and show some application examples.Computer simulations could illustrate results.

Keywords: 3D shape decomposition representation, mathematical morphology, gray scale interframe interpolation

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Authors: Isaac K. E. Ampomah, Seong-Bae Park, Sang-Jo Lee

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Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.

Keywords: Deep neural models, natural language inference, recognizing textual entailment, sentence-to-sentence relation.

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Authors: M. Tahani, G. Karimi

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In this paper after reviewing some previous studies, in order to optimize the above knee prosthesis, beside the inertial properties a new controlling parameter is informed. This controlling parameter makes the prosthesis able to act as a multi behavior system when the amputee is opposing to different environments. This active prosthesis with the new controlling parameter can simplify the control of prosthesis and reduce the rate of energy consumption in comparison to recently presented similar prosthesis “Agonistantagonist active knee prosthesis". In this paper three models are generated, a passive, an active, and an optimized active prosthesis. Second order Taylor series is the numerical method in solution of the models equations and the optimization procedure is genetic algorithm. Modeling the prosthesis which comprises this new controlling parameter (SEP) during the swing phase represents acceptable results in comparison to natural behavior of shank. Reported results in this paper represent 3.3 degrees as the maximum deviation of models shank angle from the natural pattern. The natural gait pattern belongs to walking at the speed of 81 m/min.

Keywords: Above knee prosthesis, active controlling parameter, ballistic motion, swing phase.

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Authors: Benedek Kovacs, Janos Toth

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Solutions are proposed for the central problem of estimating the reaction rate coefficients in homogeneous kinetics. The first is based upon the fact that the right hand side of a kinetic differential equation is linear in the rate constants, whereas the second one uses the technique of neural networks. This second one is discussed deeply and its advantages, disadvantages and conditions of applicability are analyzed in the mirror of the first one. Numerical analysis carried out on practical models using simulated data, and our programs written in Mathematica.

Keywords: Neural networks, parameter estimation, linear regression, kinetic models, reaction rate coefficients.

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Authors: Danni Cong, Meiping Wu, Hua Mu, Xiaofeng He, Junxiang Lian, Juliang Cao, Shaokun Cai, Hao Qin

Abstract:

Gravity field is of great significance in geoscience, national economy and national security, and gravitational gradient measurement has been extensively studied due to its higher accuracy than gravity measurement. Gravity gradient sensor, being one of core devices of the gravity gradient instrument, plays a key role in measuring accuracy. Therefore, this paper starts from analyzing the working principle of the gravity gradient sensor by Newton’s law, and then considers the relative motion between inertial and non-inertial systems to build a relatively adequate mathematical model, laying a foundation for the measurement error calibration, measurement accuracy improvement.

Keywords: Gravity gradient, accelerometer, gravity gradient sensor, single-axis rotation modulation.

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Authors: Elhameh Narimani, Mikhail Sorin, Philippe Micheau, Hakim Nesreddine

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Numerical models of the heat exchangers in ejector refrigeration system (ERS) were developed and validated with the experimental data. The models were based on the switched heat exchangers model using the moving boundary method, which were capable of estimating the zones’ lengths, the outlet temperatures of both sides and the heat loads at various experimental points. The developed models were utilized to investigate the influence of the primary flow pressure on the performance of an R245fa ERS based on its coefficient of performance (COP) and exergy efficiency. It was illustrated numerically and proved experimentally that increasing the primary flow pressure slightly reduces the COP while the exergy efficiency goes through a maximum before decreasing.

Keywords: Coefficient of performance, ejector refrigeration system, exergy efficiency, heat exchangers modeling, moving boundary method.

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