Experimental Hardware Setup Research Articles

An Intelligent Dual-Axis Solar Tracking System for Remote Weather Monitoring in the Agricultural Field

Agriculture is a critical domain, where technology can have a significant impact on increasing yields, improving crop quality, and reducing environmental impact. The use of renewable energy sources such as solar power in agriculture has gained momentum in recent years due to the potential to reduce the carbon footprint of farming operations. In addition to providing a source of clean energy, solar tracking systems can also be used for remote weather monitoring in the agricultural field. The ability to collect real-time data on weather parameters such as temperature, humidity, and rainfall can help farmers make informed decisions on irrigation, pest control, and other crop management practices. The main idea of this study is to present a system that can improve the efficiency of solar panels to provide constant power to the sensor in the agricultural field and transfer real-time data to the app. This research presents a mechanism to improve the arrangement of a photovoltaic (PV) array with solar power and to produce maximum energy. The proposed system changes its direction in two axes (azimuth and elevation) by detecting the difference between the position of the sun and the panel to track the sun using a light-dependent resistor. A testbed with a hardware experimental setup is designed to test the system’s capability to track according to the position of the sun effectively. In the end, real-time data are displayed using the Android app, and the weather data are transferred to the app using a GSM/WiFi module. This research improves the existing system, and results showed that the relative increase in power generation was up to 52%. Using intelligent artificial intelligence techniques with the QoS algorithm, the quality of service produced by the existing system is improved.

Human neural network activity reacts to gravity changes in vitro.

During spaceflight, humans experience a variety of physiological changes due to deviations from familiar earth conditions. Specifically, the lack of gravity is responsible for many effects observed in returning astronauts. These impairments can include structural as well as functional changes of the brain and a decline in cognitive performance. However, the underlying physiological mechanisms remain elusive. Alterations in neuronal activity play a central role in mental disorders and altered neuronal transmission may also lead to diminished human performance in space. Thus, understanding the influence of altered gravity at the cellular and network level is of high importance. Previous electrophysiological experiments using patch clamp techniques and calcium indicators have shown that neuronal activity is influenced by altered gravity. By using multi-electrode array (MEA) technology, we advanced the electrophysiological investigation covering single-cell to network level responses during exposure to decreased (micro-) or increased (hyper-) gravity conditions. We continuously recorded in real-time the spontaneous activity of human induced pluripotent stem cell (hiPSC)-derived neural networks in vitro. The MEA device was integrated into a custom-built environmental chamber to expose the system with neuronal cultures to up to 6 g of hypergravity on the Short-Arm Human Centrifuge at the DLR Cologne, Germany. The flexibility of the experimental hardware set-up facilitated additional MEA electrophysiology experiments under 4.7 s of high-quality microgravity (10-6 to 10-5 g) in the Bremen drop tower, Germany. Hypergravity led to significant changes in activity. During the microgravity phase, the mean action potential frequency across the neural networks was significantly enhanced, whereas different subgroups of neurons showed distinct behaviors, such as increased or decreased firing activity. Our data clearly demonstrate that gravity as an environmental stimulus triggers changes in neuronal activity. Neuronal networks especially reacted to acute changes in mechanical loading (hypergravity) or de-loading (microgravity). The current study clearly shows the gravity-dependent response of neuronal networks endorsing the importance of further investigations of neuronal activity and its adaptive responses to micro- and hypergravity. Our approach provided the basis for the identification of responsible mechanisms and the development of countermeasures with potential implications on manned space missions.

Active Disturbance Rejection Control Using Artificial Neural Network for Dual-Active-Bridge-Based Energy Storage System

The dual-active-bridge (DAB) converter has become a popular isolated solution to integrate energy storage systems (ESSs) and dc microgrids (MGs). However, constant power loads (CPLs) and pulsed power loads (PPLs) may reduce system damping and cause voltage oscillations in DAB converter-based ESSs (DAB-ESSs). An artificial neural network-based active disturbance rejection control (ANN-ADRC) is proposed to regulate constant output voltage quickly and accurately under different operating conditions. First, the ADRC controller is designed based on the small-signal modeling of the DAB-ESSs. Feedforward compensation and uncertainty estimations of the extended state observer (ESO) help to improve the dynamic performance and to reduce the number of current sensors. Then, after satisfying the conditions of stability analysis, the parameters of the ADRC controller are selected automatically via ANN. The ANN is trained with two inputs (ADRC controller parameters) and two outputs (performance indicators of the ADRC controller). The well-trained ANN can be used as a surrogate model to obtain the optimal solution of the objective function easily and quickly. The proposed ANN-ADRC algorithm with selected parameters is implemented and validated on the hardware experimental setup. The experimental results illustrate that the proposed controller can achieve fast dynamic performance under various operating conditions.

An Adaptive-Iterative Nonlinear Interference Cancellation in Time-Varying Full-Duplex Channels

In this paper, first, the sensitivity of traditional Full-Duplex (FD) communication systems to time variation in the self-interference (SI) channel is demonstrated via performance analysis. It is seen that conventional schemes are not capable of providing efficient operation regarding practical concerns such as spectral efficiency, SI channel aging and learning accuracy, and the contamination of signal-of-interest (SoI). Then, in regard to the aforementioned concerns, a practical FD operation together with a novel iterative SoI-contamination-aware digital SI cancellation (DSIC) algorithm that relieves the sensitivity against time variation considerably is proposed. The proposed scheme, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Turbo</i> DSIC, is practical in terms of training and computational complexity thanks to its two-stage structure such that complex nonlinear parameter learning (NPL) is conducted very rarely in the first stage, and linear processing is performed adaptively at symbol-rate in the second stage, where SoI is iteratively estimated at each data block and eliminated from the received signal to improve SI channel estimation accuracy. Finally, the performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Turbo</i> DSIC, under hardware impairments and time-varying (TV) propagation, is evaluated via both computer simulation environment and actual experimental hardware setup in terms of bit error rate (BER) and achievable information rate (AIR) metrics.

Design analysis and experimental validation of a multi‐level inverter for vehicle to home application

AbstractRobust and economic inverting devices are needed with the increasing practice of direct current (DC) sources. This paper proposes a compact multi‐level inverter topology for Vehicle to Home (V2H) application under different load conditions. A reduced number of switches with a single DC source (EV battery) is used to generate the 11‐level of output voltage and current waveform. The proposed topology is quite simple in structure and ensures the increase of two levels with each additional switch. The complete power loss analysis is performed for the proposed inverter. The MATLAB/Simulink is used to perform the simulation, and the results are validated through the experimental hardware setup designed. The comparison with the existing multi‐level inverter (MLI) topologies and literature shows the significant acceptability and reliability of the proposed multi‐level inverter. The operation of the proposed MLI under various loading conditions has expressed robustness for V2H application. The simulated and experimental data confirm that both results have a good agreement in terms of THD and output power. The output voltage levels of the proposed inverter could be flexibly varied by employing different numbers of switching capacitors.

Control of parallelized bioreactors I: dynamic scheduling software for efficient bioprocess management in high-throughput systems

The shift towards high-throughput technologies and automation in research and development in industrial biotechnology is highlighting the need for increased automation competence and specialized software solutions. Within bioprocess development, the trends towards miniaturization and parallelization of bioreactor systems rely on full automation and digital process control. Thus, mL-scale, parallel bioreactor systems require integration into liquid handling stations to perform a range of tasks stretching from substrate addition to automated sampling and sample analysis. To orchestrate these tasks, the authors propose a scheduling software to fully leverage the advantages of a state-of-the-art liquid handling station (LHS) and to enable improved process control and resource allocation. Fixed sequential order execution, the norm in LHS software, results in imperfect timing of essential operations like feeding or Ph control and execution intervals thereof, that are unknown a priori. However, the duration and control of, e.g., the feeding task and their frequency are of great importance for bioprocess control and the design of experiments. Hence, a software solution is presented that allows the orchestration of the respective operations through dynamic scheduling by external LHS control. With the proposed scheduling software, it is possible to define a dynamic process control strategy based on data-driven real-time prioritization and transparent, user-defined constraints. Drivers for a commercial 48 parallel bioreactor system and the related sensor equipment were developed using the SiLA 2 standard greatly simplifying the integration effort. Furthermore, this paper describes the experimental hardware and software setup required for the application use case presented in the second part.

Consensus enhanced droop control strategy for islanding mode multi converter system

Under the gradual expansion of the new energy distributed generation, the islanding multi converter microgrid system plays a great role. The stability of voltage and frequency of multi converter systems in islanding mode and the coordination of power allocation among many converter systems have become vital research points. Focusing on the reactive power sharing problem of multi converter system for different line impedances and load disturbances, this paper proposes a consensus enhanced droop control strategy to achieve power sharing and voltage and frequency stability, and the mechanism and steady-state performance of the proposed control strategy are analyzed and studied. In this work, a multi converter system simulation model composed of six inverters is built in the software PLECS, which verifies that the consensus enhanced droop control scheme can make up for the shortcomings of the traditional droop control. Moreover, the hardware experimental setup of three-phase inverter system is further conducted to validate the simulation results. Under load disturbance, the control effects of consensus enhanced droop control proposed in this paper are analyzed and studied, and the good dynamic characteristics of consensus enhanced droop control strategy are verified. Finally, the high robustness of the proposed coordinated control strategy is further illustrated by the jump of unbalanced load. This paper clarifies the effectiveness of the proposed control strategy based on consensus algorithm in solving the reactive power sharing problem of different line impedances and load disturbances around the island mode multi converter system. Furthermore, the work in the paper can ensure effective reactive power sharing under various working conditions with high robustness.

An Open-Source, Fully Customizable 5-Choice Serial Reaction Time Task Toolbox for Automated Behavioral Training of Rodents

The 5-choice serial reaction time task (5-CSRTT) is a behavioral test often used to study visuospatial attention and impulsiveness in rodents. The task requires animals to allocate attention to a horizontal array of five small apertures equipped with light sources and, within a limited time window, nose-poke one illuminated target aperture to get a food reward at the food magazine located in the opposite wall of the chamber. The task considers behavioral control measures such as response accuracy and reaction times and allows to infer selective attention and impulsivity. Task difficulty can be controlled by modifying the stimulus duration and task design in general. Commercially available apparatus usually consists of an experimental chamber and particular software to specify task parameters, but due to fixed hard- and software, they pose many limitations on changes in the general experimental design and specific task requirements and the related data output. This article explains a fully customizable alternative based on an easy-to-use single-board microcontroller and standard electrotechnical components, an open-access Arduino script, and a Matlab-toolbox for hardware control and behavioral task specifications, respectively. The toolbox includes an optional staircase procedure, enabling automated behavioral training. The complete hardware setup, which can be installed in customized chambers, and the freely adaptable software encourage non-standardized task and chamber design. The design of the system and the open-source code for hardware control and experimental setup are described.

An Open-Source, Fully Customizable 5-Choice Serial Reaction Time Task Toolbox for Automated Behavioral Training of Rodents.

The 5-choice serial reaction time task (5-CSRTT) is a behavioral test often used to study visuospatial attention and impulsiveness in rodents. The task requires animals to allocate attention to a horizontal array of five small apertures equipped with light sources and, within a limited time window, nose-poke one illuminated target aperture to get a food reward at the food magazine located in the opposite wall of the chamber. The task considers behavioral control measures such as response accuracy and reaction times and allows to infer selective attention and impulsivity. Task difficulty can be controlled by modifying the stimulus duration and task design in general. Commercially available apparatus usually consists of an experimental chamber and particular software to specify task parameters, but due to fixed hard- and software, they pose many limitations on changes in the general experimental design and specific task requirements and the related data output. This article explains a fully customizable alternative based on an easy-to-use single-board microcontroller and standard electrotechnical components, an open-access Arduino script, and a Matlab-toolbox for hardware control and behavioral task specifications, respectively. The toolbox includes an optional staircase procedure, enabling automated behavioral training. The complete hardware setup, which can be installed in customized chambers, and the freely adaptable software encourage non-standardized task and chamber design. The design of the system and the open-source code for hardware control and experimental setup are described.

IThing: Designing Next-Generation Things With Battery Health Self-Monitoring Capabilities for Sustainable IIoT

An accurate and reliable technique to predict rechargeable battery health proves helpful in battery-operated, low-resourced industrial IoT devices. The existing data-driven battery health prediction techniques often require a comparatively large amount of computational power for predicting the State of Health (SOH) and the Remaining Useful Life (RUL) due to most methods being feature-heavy. Further, there are very limited works for battery RUL prediction in IoT nodes. To address this issue, this paper presents a unique IoT-based sensor node framework, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iThing</i> , to predict the on-board battery SOH and RUL with the least computational and memory load. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iThing</i> automatically extracts the voltage and time-based health indicators, which is then fed to the random learning algorithm-based methods with good learning performance for SOH and RUL prediction. The proposed Extreme Learning Machine (ELM) network provides SOH prediction with 0.0054 Root Mean Square Error (RMSE), and 0.0024 Mean Absolute Error (MAE). Random Vector Functional Link (RVFL) neural network predicted the RUL with 0.0282 RMSE and 0.021 MAE. The proposed method has been tested on three different battery datasets with varying charging policies with high accuracy. The models have been deployed successfully on an experimental hardware setup, proving its eligibility for real-time IIoT applications.

S-Connected Ladder Fashion Multilevel Topology With Reduced Device Count

Advancements in the field of multilevel converters (MLCs) recently have contributed to diverse electrical field applications such as electric vehicles, hybrid energy storage system, and smart grid. In the earlier stages of the basic MLC structure, a large number of switching devices were employed to obtain the replica of the sine wave. This article focuses on reducing the count of switching devices to half the quantity for a higher number of levels in the MLC structure. Proportionally, overall cost and operational complexity in power semiconductor switches get reduced remarkably providing stable operation. In this article, a new topology has been implemented from the knowledge of conventional converters and existing MLCs derived from literature. The proposed topology is designed with a reduced number of both switching devices and dc sources compared to the existing hybrid type and conventional converter structures. Its corresponding topology and mode of operations during each level are studied and the performances are also analyzed. Switching components&#x2019; requirement, switching state table and voltage stress, and total harmonic distortion (THD) values of the proposed topology were evaluated. The output five-level voltage has been generated using both MATLAB simulation and hardware experimental setup. The proposed topology with reduced device components exhibits its superior characteristics in all aspects.

Systematic design of multi-objective enhanced genetic algorithm optimized fractional order PID controller for sensorless brushless DC motor drive

PurposeThe puspose of this paper, a novel systematic design of fractional order proportional integral derivative (FOPID) controller-based speed control of sensorless brushless DC (BLDC) motor using multi-objective enhanced genetic algorithm (EGA). This scheme provides an excellent dynamic and static response, low computational burden, the robust speed control.Design/methodology/approachThe EGA is a meta-heuristic-inspired algorithm for solving non-linearity problems such as sudden load disturbances, modeling errors, power fluctuations, poor stability, the maximum time of transient processes, static and dynamic errors. The conventional genetic algorithm (CGA) and modified genetic algorithm (MGA) are not very effective in solving the above-mentioned problems. Hence, a multi-objective EGA optimized FOPID (EGA-FOPID) controller is proposed for speed control of sensorless BLDC motor under various conditions such as constant load conditions, varying load conditions, varying set speed (Ns) conditions, integrated conditions and controller parameters uncertainty.FindingsThis systematic design of the multi-objective EGA-FOPID controller is implemented in MATLAB 2020a with Simulink models for optimal speed control of the BLDC motor. The overall performance of the EGA-FOPID controller is observed and evaluated for computational burden, time integral performance indexes, transient and steady-state characteristics. The hardware experiment results confirm that the proposed EGA-FOPID controller can precisely change the BLDC motor speed is desired range with minimal effort.Research limitations/implicationsThe conventional real time issues such as nonlinearity characteristics, poor controllability and stability.Practical implicationsIt is clearly evident that out of these three intelligent controllers, the EGA optimized FOPID controller gives enhanced performance by minimizing the time domain parameters, performance Indices error and convergence time. Also, the hardware experimental setup and the results of the proposed EGA-FOPID controller are presented.Originality/valueIt shows the effectiveness of the proposed controllers is completely verified by comparing the above three intelligent optimization algorithms. It is clearly evident that out of these three intelligent controllers, the EGA optimized FOPID controller gives enhanced performance by minimizing the time domain parameters, performance Indices error and convergence time. Also, the hardware experimental setup and the results of the proposed EGA-FOPID controller are presented.

New Asymmetrical Modular Multilevel Inverter Topology With Reduced Number of Switches

In this article, a new single-phase multilevel inverter is introduced with a reduced number of power switches and reduced voltage stress on power switches. The proposed topology consists of four input dc sources and nine semiconductor switches (eight unidirectional and one bidirectional switch). The topology can be used for asymmetrical voltage source configuration to generate seventeen voltage levels. The extended topology is constructed by a series connection of the topology circuit to produce higher voltage levels with less voltage stress on the switches without modifying the existing structure. Comparison is made with traditional and recently introduced topologies based on the number of power switches, dc sources, total blocking voltage of switches, and gate driver circuits, to prove the proposed topology’s superiority. A simple nearest level modulation has been deployed as the switching scheme. Validation on the viability of the proposed topology has been carried out through simulation and hardware experimental setup.

A Novel Switched-Capacitor Multilevel Inverter Topology for Energy Storage and Smart Grid Applications

The recent advancement in the application of the internet of things in the smart grid has led to an industrial revolution in the power industry. The Industry 4.0 revolution has already set in, allowing computers to interact for an efficient and intelligent approach in solving smart grid issues. multilevel inverters (MLIs) are an integral part of the smart grid system for integrating the distributed generation sources and storage energy systems into the smart grid. It attracted attention in industrial applications as they can handle high power and high voltage with an inherent feature of superior output voltage waveform quality. Moreover, its variant, the switched-capacitor MLI (SCMLI), has the added benefit of lesser DC supply requirement. In this paper, a switched-capacitor multilevel inverter topology has been proposed, which can operate in symmetric and asymmetric mode. The proposed SCMLI generate thirteen and thirty-one level output voltages for symmetric and asymmetric selection of DC voltage sources, respectively. The proposed SCMLI has a smaller number of switching devices for a given output voltage level as compared to other recently proposed topologies. A thorough comparison is presented with the recently proposed topologies on several parameters, including cost function. To validate the proposed topology, symmetric and asymmetric cases were simulated using Matlab® 2018a and the results were verified using an experimental hardware setup.

Experimental rig investigation of a direct interconnection technique for airborne wind energy systems

Airborne wind energy is a new approach to reach the stronger and more consistent winds at higher altitudes. In this paper, the interconnection of pumping mode airborne wind energy systems inside an energy farm is investigated. An experimental rig hardware setup has been designed and built to model an AWE farm in small scale. A direct interconnection system has been developed and examined on the experimental test rig. The direct interconnection technique (DIT) is a new method developed for the interconnection of marine wind energy systems within an energy farm, without requiring offshore-based power electronic converters. DIT relocates power electronic converters from the offshore site to the shore substation by interconnecting marine generators directly to a common bus. This method makes possible significant improvements to the economy and reliability of marine renewable energy systems. In this paper, for the first time, the direct interconnection technique is investigated experimentally for physically emulated pumping mode airborne wind energy systems. The construction of the experimental rig hardware setup is described, and the laboratory test results for the direct interconnection of airborne wind energy systems are presented and discussed.

Design and Implementation of a Hybrid Real-Time State of Charge Estimation Scheme for Battery Energy Storage Systems

In order to maximize the operating flexibility and optimize the system performance of a battery energy storage system (BESS), developing a reliable real-time estimation method for the state of charge (SOC) of a BESS is one of the crucial tasks. In practice, the accuracy of real-time SOC detection can be interfered with by various factors, such as battery’s intrinsic nonlinearities, working current, temperature, and aging level, etc. Considering the feasibility in practical applications, this paper proposes a hybrid real-time SOC estimation scheme for BESSs based on an adaptive network-based fuzzy inference system (ANFIS) and Coulomb counting method, where a commercially available lead-acid battery-based BESS is used as the research target. The ANFIS allows effective learning of the nonlinear characteristics in charging and discharging processes of a battery. In addition, the Coulomb counting method with an efficiency adjusting mechanism is simultaneously used in the proposed scheme to provide a reference SOC for checking the system reliability. The proposed estimating scheme was first simulated in a Matlab software environment and then implemented with an experimental hardware setup, where an industrial-grade digital control system using DS1104 as the control kernel and dSPACE Real-Time Interface (RTI) interface were used. Results from both simulation and experimental tests verify the feasibility and effectiveness of the proposed hybrid SOC estimation algorithm.

Demand Response Model f or Duck Curve o n Pv Dominated System u sing Support Vector Machines Based Multistage Modelling

Solar Photovoltaic (PV) generation systems have a less Levelized cost of electricity (LCoE). As such, when solar energy is available, the demand response is scheduled in such a way that maximum utilization of solar energy is practised.But the power generation from a solar PV system is highly uncertain and unpredictable due to irregular solar irradiation. Also, the power generation is limited to a time fraction of a day.The impact of these negative traits in a power system is studied with the help of an analytical curve called “Duck curve”. “Solar Duck curve” is a graphical representation of time scaled imbalances between a SPV generation to peak demand. A steep or rugged part in a duck curve indicates sudden shortcoming of SPV generation with respect to the peak demand. Hence, during this period, the loads are shifted between solar PV sources and the main grid with respect to the insufficiency of solar power from peak demand. The proposed system is a machine learning-based multistage demand response system for meeting demand response of a SPV dominant duck curve. The model has four layers/stages.The primary layer is used to analyse the behaviour of the duck curve with the help of a Support Vector regression algorithm and the second layer is used for determining theoperating parameters based on the economic constraints imposed. The third layer is a demand response model based on the previous layer, and the fourth layer is aadaptive signal-processing model used to improve the stability of the system.The obtained demand response model is updated continuously in an adaptive manner so as to improve the stability of the system.A hardware experimental setup is made with eighteen numbers of 24V/2kW interconnected solar PV real-time system which is used for validating and analysing the method.

Predictive Torque Control and Capacitor Balancing of a SiC-Based Dual T-Type Drive System

This paper proposes a new approach for the capacitor balancing of a dual three-level T-type converter based on silicon carbide discrete semiconductors. This study is performed while the converter feeds an open-end induction motor. The model predictive control (MPC) scheme is developed to balance the dc-link capacitors and to control the machine torque simultaneously. The proposed technique for MPC reduces the number of redundant switching states that are used in computations without affecting the operating voltage vectors. This reduces the computational time substantially. In addition, the proposed control strategy mitigates the weighting factor tuning problem of capacitor balancing in addition to the conventional MPC cost function. MATLAB simulation results for the proposed drive system under different case studies are presented. Hardware experimental setup for the proposed converter is built, tested, and verified. A comparison between experimental and simulation results is presented. It is observed that the theoretical as well as the experimental results are in full agreement.

Efficient algorithms for real‐time monitoring of transmission line parameters and their performance with practical synchrophasors

Accurate transmission line parameters are important for many applications that ensure reliable operation of a power system. The traditional theoretical calculations and offline measurements are widely used approaches obtaining line parameters, but they do not allow tracking of the parameters that change with the environmental factors and load conditions. Synchrophasor-based real-time transmission line parameter monitoring algorithms can track the changing parameters. In this study, two novel line parameter estimation algorithms: a lump parameter model and a distributed parameter model are proposed. The performance of the new algorithms are evaluated under various operating conditions using a real-time digital simulator, and compared with six existing algorithms in terms of both accuracy and computational efficiency. The algorithms were also tested and compared using synchrophasor data obtained from a hardware experimental setup. Furthermore, application of the algorithms to an actual 230 kV transmission line is demonstrated. Finally, the sensitivity of the estimated parameters to bias errors in the measurements is analysed.

Simulation and experimental study of the sensor emitting frequency for ultrasonic tomography system in a conducting pipe

Ultrasonic tomography techniques provide flow visualization capability, non-invasively and non-intrusively, to enhance the understanding of complex flow processes. There is limited ultrasonic research in tomography imaging systems in the tomogram analysis of fluid flow in a conducting pipe because of a high acoustic impedance mismatch, which means that very little ultrasonic energy can be transmitted through the interface. The majority of industrial pipelines are constructed from metallic composites. Therefore, the development and improvement of ultrasonic measurement methods to accommodate a stainless steel pipe are proposed in this paper. Experimental and simulation distribution studies of the ultrasonic emitting frequency in acrylic versus stainless steel pipes were studied, measured and analyzed. During the simulation, ultrasonic transducers were placed on the surface of the investigated pipe to inspect the ultrasonic sensing field. The distribution of the sound wave acoustic pressure was simulated based on the physical dimensions and parameters of the actual experimental hardware set-up. We developed ultrasonic acoustic models using the finite element method with COMSOL software, and experiments were carried out to validate the simulation results. Finally, by performing the static phantoms tests, a feasibility study of ultrasonic tomography system was presented to investigate the void fraction of liquid column inside a stainless steel pipe.