Multiple Operating Conditions Research Articles

Development of Flac3d software computing platform based on cloud computing technology

With the continuous improvement of the requirements for numerical simulation in engineering at home and abroad, the numerical simulation of geotechnical engineering has become more and more refined and complicated. Flac3d is one of the widely used numerical simulation software in the field of geotechnical engineering. In order to solve the problem that Flac3d is slow in calculation and occupies too much computing resources, the Flac3d software computing platform is developed based on the bottle framework and D3.js framework. The platform is fully functional and includes auxiliary modules such as billing module, file sharing module and security module. Users only need to input the computing resources and storage resources to automatically create a virtual server, and use the pre-installed Flac3d software in the server. The platform greatly reduces the computational time of the Flac3d, and the calculation rate is particularly improved under multiple operating conditions. Compared with general supercomputing centers, although the cloud computing platform is slightly weaker in hardware, its energy consumption is relatively low, and it is not limited by geographic location, and can form a larger scale.

Impact of Various Operating Conditions on Simulated Emissions-Based Stop Penalty at Signalized Intersections

Sustainability has become one of the most important goals when optimizing traffic signals. This goal is achieved through utilizing various objective functions to reduce sustainability metrics (e.g., fuel consumption and emissions). However, most available objective functions do not distinguish between the reduction mechanism of various types of emissions. Further, such functions do not consider the compound impact of multiple operational conditions (e.g., road gradient) influencing emissions on the optimized signal plans. This study derives a new Environmental Performance Index representing a surrogate measure for emission estimates that can be used as an objective function in signal timings optimization to reduce emissions under various operational conditions. The Environmental Performance Index is a linear combination of delays and stops. The key factor of the Environmental Performance Index is the emissions-based stop penalty, which represents an emission stop equivalency measured in seconds of delay. This study also uses traffic simulation and emission models to investigate the compound impact of several operational conditions on the stop penalty. Results show that the stop penalty varies significantly with all the investigated conditions and that the stop penalty is unique for different types of emissions. These findings may have significant implications on the current practice of sustainable signal timing optimization.

Using rectified linear unit and swish based artificial neural networks to describe noise transfer in a full vehicle context.

Vehicle interior noise is a quality criterion of passenger cars. A considerable amount of resources is used to evaluate and design the acoustic environment with respect to given requirements. The customer's perception in the end-of-line vehicle is the main criterion. Therefore, full vehicle testing is a large part of today's sound comfort development. To increase efficiency, it is desirable to limit the hardware testing to a specific component. A later reassembly of the full vehicle is done virtually using transfer functions. These transfer functions of the substructures can be derived numerically or through measurements. However, full vehicle simulations are still challenging. Hence, transfer functions are typically measured but come with the burden of complex procedures. In this work, the authors propose a machine learning algorithm to reduce the effort for finding suitable transfer models in the automotive context. Artificial neural networks with rectified linear unit and swish activation functions are trained on full vehicle measurements. Multiple operation conditions are used for training. The networks compute spectral system responses and relative sensitivities for the input features. The performance is discussed with respect to the full vehicle validation data. The results indicate an effective procedure to reduce the costs of full-size vehicle measurements.

Summarisation, Simulation and Comparison of Nine Control Algorithms for an Active Control Mount with an Oscillating Coil Actuator

With the further development of the automotive industry, the traditional vibration isolation method is difficult to meet the requirements for wide frequency bands under multiple operating conditions, the active control mount (ACM) is gradually paid attentions, and the control algorithm plays a decisive role. In this paper, the ACM with oscillating coil actuator (OCA) is taken as the object, and the comparative study of the control algorithms is performed to select the optimal one for ACM. Through the modelling of ACM, the design of controller and the system simulations, the force transmission rate is used to compare the vibration isolation performance of the nine control algorithms, which are least mean square (LMS) adaptive feedforward control, recursive least square (RLS) adaptive feedforward control, filtered reference signal LMS (FxLMS) adaptive control, linear quadratic regulator (LQR) optimal control, H2 control, H∞ control, proportional integral derivative (PID) feedback control, fuzzy control and fuzzy PID control. In summary, the FxLMS adaptive control algorithm has the better performance and the advantage of easier hardware implementation, and it can apply in ACMs.

Hydrostatic Bearing Characteristics Investigation of a Spherical Piston Pair with an Annular Orifice Damper in Spherical Pump

The spherical pump is a totally new hydraulic concept, with spherical piston and hydrostatic bearing, in order to eliminate the direct contact between the piston and cylinder cover. In this paper, the governing Reynolds equation under spherical coordinates has been solved and the hydrostatic bearing characteristics are systematically investigated. The operating sensitivities of the proposed spherical hydrostatic bearing, with respect to the piston radius, film beginning angle, film ending angle, film thickness, and temperature, are studied. The load carrying capacity, pressure drop coefficient, stiffness variation of the lubricating films, leakage properties, and leakage flow rates are comprehensively discussed. The related findings provide a fundamental basis for designing the high-efficient spherical pump under multiple operating conditions. Besides, these related results and mechanisms can also be utilized to design and improve other kinds of annular orifice damper spherical hydraulic bearing systems.

Assessment of Gradient Descent Trained Rule-Fact Network Expert System Multi-Path Training Technique Performance

The use of gradient descent training to optimize the performance of a rule-fact network expert system via updating the network’s rule weightings was previously demonstrated. Along with this, four training techniques were proposed: two used a single path for optimization and two use multiple paths. The performance of the single path techniques was previously evaluated under a variety of experimental conditions. The multiple path techniques, when compared, outperformed the single path ones; however, these techniques were not evaluated with different network types, training velocities or training levels. This paper considers the multi-path techniques under a similar variety of experimental conditions to the prior assessment of the single-path techniques and demonstrates their effectiveness under multiple operating conditions.

Research on adaptive cruise control algorithm considering road conditions

As one of the most important ADAS systems, traditional adaptive cruise control is only suitable for relatively simple working conditions such as expressways, and it is difficult to deal with complex traffic environment and road working conditions. For the problem that the traditional cruise control strategy cannot effectively use the terrain to reduce vehicle fuel consumption under slope conditions and when the vehicle enters a small radius curve such as a ramp, the excessively high turning speed will cause the vehicle to skid. This paper proposes an adaptive cruise control system architecture with adaptability to multiple operating conditions. Simulation and real-vehicle test results show that the adaptive control system architecture and optimisation algorithm proposed in this paper have good applicability to road conditions, and improve the economy of the system under ramp conditions and safety under curve conditions.

STUDY ON INSTABILITY MECHANISM AND EVOLUTION MODEL OF PROPELLER TIP VORTICES

The propeller wake dynamics is a fundamental but very complicated fluid mechanics problem. Its complexity comes from its sophisticated vortex system, which keeps evolving in high-speed shear layer flow. The mechanism of propeller wake behaviors such as the evolution from stable regime to unstable regime and the flow phenomenon in a complex operating environment have always been difficult and hot topics in the field of fluid mechanics. From the perspective of engineering applications, propeller wakes are directly related to the macroscopic characteristics of marine structures, a better understanding of the dynamic characteristic of the propeller wake under multiple operating conditions helps to improve the propulsion performance related to vibration, noise, and structure problems and has important practical significance for the design and optimization of next-generation propellers with good comprehensive performance. In this paper, the propeller wake dynamics are analyzed numerically using DDES, LES and NTM methods and experimentally based on PIV flow measurements, and the triggering mechanism of the instability of the propeller wake is revealed. Based on the evolution mechanism of the tip vortex in the uniform inflow, an evolution model of the tip vortices is proposed. The proposed model can accurately reproduce the evolution process of propeller tip vortex, predict the instant and position of tip vortex merging, which is of great significance to the prediction and control of propeller flow noise and the design of propellers with excellent performance.

FEA-based tracking control of flexible body switching dynamic structure

AbstractIn dynamically switched systems with unknown switching signal, the control system is conventionally designed based on the worst switching scenario to ensure system stability. Such conservative design demands excessive control effort in less critical switching configurations. In the case of continuum mechanics systems, such excessive control inputs result in increased structural deformations and resultant modeling uncertainties. These deformations alter differential equations of motion which cripple the task of control. In this paper, a new approach for tracking control of uncertain continuum mechanics multivariable systems undergoing switching dynamics and unknown time delay has been proposed. Control algorithm is constructed based on the mathematical rigid model of the plant and a Common Lyapunov Function (CLF) is proposed upon sliding hyperplane regarding all switching configurations. Considering the model-based nature of sliding mode control (SMC) and inevitable uncertainties induced from modeling simplifications of continuum system or parameter evaluation errors, Finite Element Analysis (FEA) is utilized to approximate total model uncertainties. To obtain robust stability, instead of conventional switching functions in the construction of control law, the control inputs are selected such that system dynamics reside within stability bounds which are calculated based on the Lyapunov asymptotic stability criterion. Therefore, the unwanted chattering issue caused by continuous switching is not observed in control input signals. Eventually, the accuracy of the proposed method has been verified through the student version of ANSYS® mechanical APDL-based simulations and its effectiveness has been demonstrated in multiple operating conditions.

A continuous learning monitoring strategy for multi-condition of nuclear power plant

Safety is the most important feature in the operation of nuclear power plants. developing and perfecting energy markets lead to an unavoidable problem of adjusting the structure of the random forest model to adapt to new situations, such as switching to other steady state operating conditions of nuclear power plants. Another critical challenge is how to effectively learn new knowledge from continuously collected measurements and how to integrate new information into current random forest models. So, this paper proposes a continuous learning monitoring strategy for multi-Condition of nuclear power plant. Next, real data from nuclear power plants are used for modeling and the simulator is used to insert the fault test. Finally, simulation tests show that the model can effectively detect the typical faults of nuclear power plants under multiple operation conditions, and the model can effectively learn new knowledge.

Data-driven fault diagnosis of FW-UAVs with consideration of multiple operation conditions

Fixed-wing Unmanned Aerial Vehicles (FW-UAVs) are intelligent aircrafts. It is of significance to carry out fault diagnosis of FW-UAVs to improve reliability and safety. An entire mission of FW-UAVs contains couple of phases; correspondingly, this paper treats FW-UAVs as multiple operation condition processes. An innovative framework is then proposed for fault diagnosis of FW-UAVs, where the process dynamics, multiple operation conditions, variable data density, and process disturbance are considered. Firstly, augmented matrixes are constructed with the data samples to involve the dynamic characteristic of FW-UAVs. Secondly, a modified DBSCAN algorithm employing Shared Nearest Neighbor based Distance (SNND-DBSCAN) and a K Nearest Neighbor algorithm employing SNND (SNND-KNN) are proposed respectively. They cooperate with each other to realize offline operation condition classification and online recognition. Thirdly, Multiple condition oriented Dynamic KPCA (M-DKPCA) algorithms incorporated with Weighted sliding window denoising (WM-DKPCA) is proposed for fault diagnosis. Finally, the proposed algorithms are tested with real flight data sets in terms of linear and nonlinear faults; and the comparisons between KPCA, DKPCA, M-DKPCA and WM-DKPCA are presented. The results confirm that the multiple condition oriented M-DKPCA and WM-DKPA algorithms are more suitable for fault diagnosis of FW-UAVs; and WSW denoising can indeed improve the fault diagnosis performance.

Community noise from urban air mobility (UAM) and its control by traffic management

The awareness about UAM is amplified by steadily growing numbers of air taxi concepts being announced. In general environmentally friendly by electric propulsion, community noise and en-route noise are still prominent open questions. Several studies for larger UAM aircraft, describing the acoustic characteristics of a variety of potential air taxi concepts, have been performed by the author. Due to the abovementioned multitude of different vehicle concepts and their multiple operational conditions, each of them shows individual sound characteristics. Therefore, further investigations of noise created by air taxi fleets appear to be crucial. Understanding of community noise around vertiports and along air taxi routes will strongly depend on those fleets. In this paper, acoustically different air taxi systems are composing different sets of air taxi fleets, used for air traffic noise simulations. The simulations start with baseline scenarios of equally represented taxi systems on fixed flight paths with several flight levels in a certain air lane. The final fleets are consisting of random air taxi composition with randomly populated flight paths. The results, based on common noise metrics and changes in the number of affected residents, could provide a first indication how to reduce community noise by future UAM traffic management.

Sound source modelling by nonnegative matrix factorization for virtual reality applications

Auralization is a suitable method for subjective noise evaluation of virtual prototypes. A basic requirement is the accurate modelling of the sound sources. This includes a dynamic and parametric description at multiple operating conditions. In the case of wave propagation including flow, such as aircraft or vehicle noise, aeroacoustics or fluid dynamics simulations are practically limited to the acoustic near field due to high computational costs. Especially challenging are simulations of rotating systems, such as fan noise radiation. For better applicability, the proposed method is based on in-situ recordings of flyovers. The processing chain compensates for source position and movement as well as atmospheric and soil damping effects on recorded data. The compensated source signal is decomposed into partial sources in spectro-temporal domain with nonnegative matrix factorization (NMF) and can optionally be enhanced by physically-based source information. The format of the source model obtained is ready to use for dynamic sound synthesis in real-time virtual reality applications.

A new evaluation and prediction model of sound quality of high-speed permanent magnet motor based on genetic algorithm-radial basis function artificial neural network.

Sound quality (SQ) has become an important index to measure the competitiveness of motor products. To better evaluate and optimize SQ, a novelty SQ evaluation and prediction model of high-speed permanent magnet motor (HSPMM) with better accuracy is presented in this research. Six psychoacoustic parameters of A-weighted sound pressure level (ASPL), loudness, sharpness, roughness, fluctuation strength (FS), and perferred-frequency speech interference (PSIL) were adopted to objectively evaluate the SQ of HSPMM under multiple operating conditions and subjective evaluation was also conducted by the combination of semantic subdivision method and grade scoring method. The evaluation results show that the SQ is poor, which will have a certain impact on human psychology and physiology. The correlation between the objective evaluation parameters and the subjective scores is analyzed by coupling the subjective and objective evaluation results. The average error of multiple linear regression (MLR) model is 7.10%. It has good accuracy, but poor stability. In order to improve prediction accuracy, a new predicted model of radial basis function (RBF) artificial neural network was put forward based on genetic algorithm (GA) optimization. Compared with MLR, its average error rate is reduced by 3.16% and the standard deviation is reduced by 1.841. In addition, the weight of each objective parameter was analyzed. The new predicted model has a better accuracy. It can evaluate and optimize the SQ exactly. The research methods and conclusions of this paper can be extended to the evaluation, prediction, and optimization of SQ of other motors.

Critical Parameter Identification of Fuel-Cell Models Using Sensitivity Analysis

Numerical modeling has been a vital tool in proton-exchange-membrane fuel-cell (PEMFC) analysis; however, the predictive capabilities of these models depend on the input physical parameters, several of which are either not experimentally measured or have large scatter in measured values. This article presents an uncertainty propagation-based sensitivity analysis to identify the model parameters that impact the model predictions most. A comprehensive 2-D membrane electrode assembly (MEA) model is used to perform local sensitivity analysis at multiple operating conditions, which encompass the range of environments and operating conditions a cell can encounter. While at lower humidities, cathode kinetics and membrane-ohmic-loss related parameters are crucial, gas transport and porous-media saturation behavior are more important at humidified conditions. Several of these findings are different from previous studies presented in literature. Identifying the crucial parameters helps focus future material and cell optimization studies as well as experimental studies to quantify these parameters with higher accuracy.

Type-2 fuzzy broad learning controller for wastewater treatment process

Affected by multiple operation conditions, wastewater treatment process (WWTP) is a complex industrial process with strong nonlinearity and disturbance. How to enhance the rapid tracking response-ability and robustness of the controller is still a challenge when the operation conditions change. To solve this problem, a type-2 fuzzy broad learning controller (T2FBLC) is proposed in this paper. First, a type-2 fuzzy broad learning system (T2FBLS) is constructed in T2FBLC by replacing nodes in feature window with a group of interval type-2 fuzzy submodules. Then, the proposed T2FBLC can take tracking error as inputs while its outputs acting on WWTP to directly obtain a control law, and the controller makes a quick tracking response in different operation conditions. Second, the weight parameters of T2FBLC are adjusted by using the gradient descent method to ensure the control performance. In this way, the developed T2FBLC can realize online learning to reduce tracking errors. Third, according to the Lyapunov function theory, the stability of control strategy is proved. Finally, benchmark simulation model 1 (BSM1) is adopted to verify the effectiveness of T2FBLC. The experimental results prove the applicability and superior tracking performance of the proposed method.

System Characterization and Adaptive Tracking Control of Quadrotors under Multiple Operating Conditions

This paper presents an adaptive controller design framework with input compensation for quadrotor systems, which deals with different system operating conditions with a uniform update law for the controller parameters. The motivation of the work is to handle the situation that existing adaptive control schemes are either restricted to the system equilibrium as the hover condition or unable to deal with the diverse system uncertainties which cause system interactor matrix and high-frequency gain matrix to change. An adaptive control scheme equipped with an input compensator is constructed to make the system to have a uniform interactor matrix and a consistent pattern of the gain matrix signs over different operating conditions, which are key prior design conditions for model reference adaptive control applied to quadrotor systems. To deal with the uncertain system high-frequency gain matrix, a gain matrix decomposition technique is employed to parametrize an error system model in terms of the gain parameters and tracking errors, for the design of an adaptive parameter update law with reduced system knowledge. It is ensured that all closed-loop system signals are bounded, and the system output tracks a reference output asymptotically despite the system parameter uncertainties and the uncertain offsets at non-equilibrium operating conditions. The proposed scheme expands the capacity of adaptive control for quadrotors to operate at multiple operating conditions in the presence of system uncertainties. Simulation results of a quadrotor with the proposed adaptive control scheme are presented to show the desired system performance.

Modal analysis of Kaplan turbine in Haditha hydropower plant using ANSYS and SolidWorks

In this study, numerical analysis is conducted to investigate the failure modes in Kaplan turbine. All necessary steps for Kaplan turbine failure analysis are presented in this work using the modal analysis computational approach. The modal behaver analysis is carried out on a model of an existing Kaplan turbine blade, which is based on the existing turbine used in Haditha hydropower plant in Iraq. This work investigates the modal behavior of the blade of interest, which aid in predicting structural damage initiation. The Kaplan turbine blade is designed using the commercial software ANSYS and SolidWorks. To simulate the blade in operation, the blade is fixed from one end, and all degrees of freedom are measured. Moreover, the turbine blade is moved and rotated to simulate multiple operational conditions. Both mode shapes and natural frequencies are predicted and analyzed using the two aforementioned commercial software and the numerical formula involving the arrest Lanczos method. It is clear from the results that the natural frequency of the specified mode shape does not match with the natural frequency of the runner blade. Hence, there is no failure due to resonance phenomenon in this specific Kaplan turbine. The future work must investigate other aspect of the failure modes in such turbine, such as unbalance dynamic loading. The Results obtained from this study will help study the different possibilities for detecting the failure of the Kaplan blade by examining the modal behavior of the blade.

Chemoresistive Gas Sensors Based on SnO2 and Sn3O4 Nanobelts to Volatile Organic Compounds Detection: A Comparative Investigation

Introduction Volatile organic compounds (VOCs) are organic chemicals with a relatively high vapor pressure at low temperatures released to the environment from biogenic and anthropogenic sources. Hundreds of VOCs have been identified in the human breath, and many of them act as potential biomarkers for noninvasive diagnosis and monitoring of several diseases [1]. Contrarily, the increasing emission of these hazardous compounds in urban areas, mainly from industry and automotive exhaust systems, has been a critical matter of modern society in terms of safety, environmental pollution, and public health [2].Chemoresistors based on semiconducting metal oxides (SMOx) offer unique opportunities for trace detection of VOCs, considering their compatibility for portable devices, simple design, low-cost, and reduced power consumption [3]. While SnO2 has been the most used material in the active layer of market sensors, n-type Sn3O4 is another possible stoichiometry of tin oxide with emergent interests in the gas sensing field over the last years [4,5]. However, only limited research has reported the response of Sn3O4 nanostructures to VOCs detection.In this work, the sensing properties of single-crystalline Sn3O4 nanobelts towards some relevant volatile organic compounds (ethanol, acetone, and toluene) were investigated comparatively to SnO2 in multiple operating conditions. A comprehensive understanding of the conduction mechanism associated with the performance of the sensors was additionally discussed. Materials and Methods Sn3O4 and SnO2 nanobelts were synthesized in a tube furnace by the carbothermal reduction method following optimized synthesis parameters described in our previous work [4]. The materials were collected from different regions of the growth site and separately dispersed in 1,2-propanediol by sonication. Several drops of the solutions were deposited onto alumina substrates containing interdigitated Pt electrodes on the topside and a Pt heater on the backside. The sensors were dried at 80 °C overnight and then annealed at 350°C for 10 minutes.The devices were inserted into a homemade measurement chamber coupled with a gas mixing system equipped with mass-flow controllers. Several concentrations of the target gases, including ethanol, acetone, and toluene, were introduced into the chamber, and the DC electrical resistances were continually monitored at 200, 250, and 300°C. Synthetic air in dry and humid conditions (5%, 30%, and 70% r.h. at 25°C) was used as the reference gas. A constant gas flow of 200 sccm was maintained during all the experiments. Results and Conclusions The gas sensing measurements performed from 200 to 300°C revealed that the baseline resistance of SnO2 and Sn3O4 sensors changes accordingly with the humidity level in the background. It occurs due to the reducing nature of the water vapor on the surface of tin oxide. After exposure to ethanol, acetone, and toluene, the resistance of both sensors decreases following the gas concentrations. These reducing gas species act as surface donors, which improves the number of free electrons in n-type semiconductors. From the calibration curves, the signals of the SnO2 sensor toward toluene, acetone, and ethanol drop as a result of the increment in the relative humidity over the monitored operating temperatures. The same profile was observed in the Sn3O4 sensors at 200°C and 250°C, but the opposite outcome takes place at 300°C, i.e., higher sensor signals were achieved in humid conditions for the tested VOCs. In addition, the sensors exhibit remarkable selectivity to potential interferents such as H2 and CO in the full range of concentrations and working temperature investigated. Changes in the surface band bending were correlated with electrical resistance data based on a depletion layer model. It allowed obtaining information about the electronic structure as a function of the charge carrier concentration induced by the analytes chemisorption on the sensing layers. To the best of our knowledge, this is the first report on 1D single-crystalline Sn3O4 materials towards VOCs detection, opening up new possibilities for further investigations. References Chan, L.W.; Anahtar, M.N.; Ong, T.H.; Hern, K.E.; Kunz, R.R.; Bhatia, S.N. Engineering synthetic breath biomarkers for respiratory disease. Nat. Nanotechnol. 2020, 15, 792–800. He, C.; Cheng, J.; Zhang, X.; Douthwaite, M.; Pattisson, S.; Hao, Z. Recent advances in the catalytic oxidation of volatile organic compounds: A review based on pollutant sorts and sources. Chem. Rev. 2019, 119, 4471–4568. Lin, T.; Lv, X.; Hu, Z.; Xu, A.; Feng, C. Semiconductor metal oxides as chemoresistive sensors for detecting volatile organic compounds. Sensors (Switzerland) 2019, 19, 233. Suman, P.H.; Longo, E.; Varela, J.A.; Orlandi, M.O. Controlled synthesis of layered Sn3O4 nanobelts by carbothermal reduction method and their gas sensor properties. J. Nanosci. Nanotechnol. 2014, 14, 6662–6668. Suman, P.H.; Felix, A.A.; Tuller, H.L.; Varela, J.A.; Orlandi, M.O. Comparative gas sensor response of SnO2, SnO and Sn3O4 nanobelts to NO2 and potential interferents. Sensors Actuators, B Chem. 2015, 208, 122–127.

Selection and Analysis of Dual-motor Coupling Device Based on Efficiency Power

In response to the needs of electric tractors under multiple operating conditions, a dual-motor power coupling drive system is matched. According to its power transmission requirements, a dual planetary gear train dynamic coupling structure scheme and a multi-planetary gear train dynamic coupling structure scheme are proposed. Through example design, the specific parameters of the two schemes are obtained. The transmission efficiency formulas of the two schemes in multiple modes are deduced, the transmission efficiency in each mode is calculated, and the final transmission scheme of the dual-motor power coupling device is optimized based on the transmission efficiency. It provides a theoretical basis and method for the design and analysis of the tractor power coupling device.