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Journal Articles
Accepted Manuscript
Article Type: Research-Article
J. Comput. Nonlinear Dynam.
Paper No: CND-20-1233
Published Online: March 27, 2021
Abstract
This manuscript investigates the coupled nonlinear Hirota-Maccari system with the help of using an analytical approach which is the extended sinh-Gordon equation expansion method (ShGEEM). Complex, solitary and singular periodic travelling solutions are successfully gained to the nonlinear model considered. The constraint conditions that validate the existence of the reported soliton solutions are also given in a detailed manner. The 2D, 3D, and the contour graphs to some of the obtained solutions are presented via several computational programs. These simulations present deeper investigations about the wave distributions of the coupled nonlinear Hirota-Maccari system.
Proceedings Papers
Proc. ASME. LEMP2020, JSME 2020 Conference on Leading Edge Manufacturing/Materials and Processing, V001T07A008, September 3, 2020
Paper No: LEMP2020-8626
Abstract
Confocal probes are widely employed in many industrial fields due to the depth-sectioning effect. The author’s group has also proposed a chromatic confocal probe employing a mode-locked femtosecond laser source which can realize an axial resolution of 30 nm and a measurement range of 40 μm Efforts have also been made to improve the thermal stability of the developed femtosecond laser chromatic confocal probe so that the probe can be applied for long-term displacement measurement or surface profile measurement. Meanwhile, surface profile measurement has not been carried out by using the developed femtosecond laser chromatic confocal probe. For the verification of the performance of developed probe in profile measurement, in this paper, an experimental setup is built and a basic experiment is carried out. By using the probe with further improved thermal stability, the measurement of a sample surface profile is carried out. In this paper, the development of the experimental setup with the femtosecond laser chromatic confocal probe, as well as the results of the surface profile measurements, is presented.
Proceedings Papers
Proc. ASME. LEMP2020, JSME 2020 Conference on Leading Edge Manufacturing/Materials and Processing, V001T07A002, September 3, 2020
Paper No: LEMP2020-8501
Abstract
Angle sensors based on the laser autocollimation are often employed to evaluate surface profiles of a target of interest. The authors have developed a femtosecond laser angle sensor, in which a spectrometer or an optical spectrum analyzer with a single-mode fiber is employed as the photodetector for simultaneous capturing of the multiple optical modes. In this paper, the concept of the femtosecond laser angle sensor is applied to evaluate the surface profile of a target of interest. An optical setup is designed in such a way that each mode in the spectrum of the mode-locked femtosecond is utilized as the laser beam to measure the local slope of a measurement target at each different point to evaluate the surface profile. Some basic experiments are carried out by using the developed optical setup with a mode-locked femtosecond laser source to evaluate basic performances of the developed optical setup as an optical angle sensor.
Journal Articles
Article Type: Research Papers
J. Thermal Sci. Eng. Appl. February 2021, 13(1): 011016.
Paper No: TSEA-20-1052
Published Online: June 30, 2020
Abstract
The heating furnace is an essential oilfield facility for surface gathering, treatment, and transportation, so the energy consumption level of an oilfield is directly affected by its operational efficiency. In this paper, the thermal efficiency, exhaust gas temperature, external surface temperature, excess air coefficient, and load rate of a heating furnace are taken as energy efficiency evaluation indexes. By improving game theory, the objective and subjective weights are combined to determine the final weights of each index. On this basis, the grey TOPSIS method is used to establish the energy efficiency evaluation model of an oilfield heating furnace, which is to comprehensively evaluate the energy consumption of the heating furnace by calculating the closeness degree between its actual and ideal operational states. Finally, the effectiveness of the energy efficiency evaluation model is verified by taking an actual oilfield as an example, the results show that the weight sequence of the indexes is thermal efficiency, exhaust gas temperature, external surface temperature, load rate, and excess air coefficient. In addition, the relative closeness of the heating furnace is mostly concentrated between 0.5 and 0.7, which shows that the efficiency is low. The weak link of energy consumption is analyzed, and the corresponding improvement measures are put forward.
Proceedings Papers
Proc. ASME. POWER2019, ASME 2019 Power Conference, V001T08A003, July 15–18, 2019
Paper No: POWER2019-1875
Abstract
In order to explore the operation and maintenance characteristics of important auxiliary machines in large-scale power plant coal-fired boilers, a running state assessment model for auxiliary equipment is established. In this paper, taking the complex variability of the operating conditions of thermal power equipment into consider, auto encoder model combined with fuzzy synthetic is proposed. Based on the residual of the model results and the actual power plant operation data, combined with the fuzzy evaluation model to establish a state assessment model, and analyze the actual situation of the induced draft fan of the power plant, to make a real-time assessment of operation status. The evaluation results show the advantages of the state assessment strategy proposed in this paper, and it can reflect the deterioration of the induced draft fan status in time, providing guidance for the operation and maintenance of the equipment.
Proceedings Papers
Proc. ASME. MSEC2019, Volume 1: Additive Manufacturing; Manufacturing Equipment and Systems; Bio and Sustainable Manufacturing, V001T02A029, June 10–14, 2019
Paper No: MSEC2019-2869
Abstract
The growing resource shortage and environmental concerns have forced mankind to develop and utilize renewable energy sources. The penetration of solar photovoltaic (PV) power in the electricity market has been increasing over the past few decades due to its low construction costs, zero pollution nature, and enormous support from governments. However, the intermittency and randomness of PV power also cause huge grid fluctuations which limit its integration in the system. An accurate forecasting of solar PV power generation and optimization of operation and maintenance (O&M) management are essential for further development of the solar PV farms. A great number of related researches have been done in recent years. A review of PV power generation forecasting techniques together with their brief applications on the optimization of O&M management is presented in this paper. Machine learning methods are thought to be the most suitable at the present stage because of their ease of implementation and their capability in processing non-linear, complex data sets. Typical forecasting accuracy measures are summarized and further applications of PV power forecasting on the O&M management are also presented.
Journal Articles
Article Type: Research-Article
ASME J. Risk Uncertainty Part B. June 2019, 5(2): 021005.
Paper No: RISK-18-1028
Published Online: April 17, 2019
Abstract
The uncertain free vibration analysis of engineering structures with the consideration of nonstochastic spatially dependent uncertain parameters is investigated. A recently proposed concept of interval field is implemented to model the intrinsic spatial dependency of the uncertain-but-bounded system parameters. By employing the appropriate discretization scheme, evaluations of natural frequencies for engineering structures involving interval fields can be executed within the framework of the finite element method. Furthermore, a robust, yet efficient, computational strategy is proposed such that the extreme bounds of natural frequencies of the structure involving interval fields can be rigorously captured by performing two independent eigen-analyses. Within the proposed computational analysis framework, the traditional interval arithmetic is not employed so that the undesirable effect of the interval overestimation can be completely eliminated. Consequently, both sharpness and physical feasibility of the results can be guaranteed to a certain extent for any discretized interval field. The plausibility of the adopted interval field model, as well as the feasibility of the proposed computational scheme, is clearly demonstrated by investigating both academic-sized and practically motivated engineering structures.
Proceedings Papers
Proc. ASME. SMASIS2018, Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies, V002T08A003, September 10–12, 2018
Paper No: SMASIS2018-7960
Abstract
Bayesian statistics is a quintessential tool for model validation in many applications including smart materials, adaptive structures, and intelligent systems. It typically uses either experimental data or high-fidelity simulations to infer model parameter uncertainty of reduced order models due to experimental noise and homogenization of quantum or atomistic behavior. When heterogeneous data is available for Bayesian inference, open questions remain on appropriate methods to fuse data and avoid inappropriate weighting on individual data sets. To address this issue, we implement a Bayesian statistical method that begins with maximizing entropy. We show how this method can weight heterogeneous data automatically during the inference process through the error covariance. This Maximum Entropy (ME) method is demonstrated by quantifying uncertainty in 1) a ferroelectric domain structure model and 2) a finite deforming electrostrictive membrane model. The ferroelectric phase field model identifies continuum parameters from multiple density functional theory calculations. In the case of the electrostrictive membrane, parameters are estimated from both mechanical and electric displacement experimental measurements.
Journal Articles
Article Type: Research-Article
J. Energy Resour. Technol. February 2019, 141(2): 022001.
Paper No: JERT-17-1463
Published Online: September 26, 2018
Abstract
Control quality of an once-through boiler’s water-fuel ratio (WFR) and main-steam temperature are heavily influenced by the control quality of the once-through boiler’s intermediate point enthalpy (IPE), and it is also related to the economic and stable operation of the a once-through boiler. In order to control the IPE in a better way and to increase boiler efficiency, an improved model of IPE control system was built in this paper, matlab/simulink is used to build the IPE control system model based on a 600 MW supercritical unit, and the mechanism model of the control object is built in the same time. The feedforward of the feed-water temperature is brought to this model to increase the control rate. The control method of amendments to the amount of coal and the control method of amendments to the amount of feed-water are combined by the means of fuzzy control to solve the problem of the contradiction of the responding speed of the IPE and the separation interface’s stability of the steam-water separator. The simulation results show that the improved control method has better control effect and higher boiler efficiency was obtained as well.
Proceedings Papers
Proc. ASME. MSEC2018, Volume 3: Manufacturing Equipment and Systems, V003T02A048, June 18–22, 2018
Paper No: MSEC2018-6333
Abstract
Corrective maintenance and preventive maintenance are two common maintenance strategies used in the wind farms, whose drawbacks are obvious and significant. Opportunistic maintenance strategy takes advantage of the dependencies existing among the wind turbine components and implement combined maintenance actions to reduce the huge downtime cost. The opportunistic maintenance strategy for wind turbines has made a great progress, as well as the strategy considering imperfect and condition-based maintenance. However, existing maintenance strategy researches are usually concerned with the maintenance itself and the effects of power generation are barely considered. Nowadays, a current research trend in manufacturing system is the integration of maintenance and production planning. In this paper, the effects of power generation on opportunistic maintenance strategy for wind turbines considering reliability are researched. The opportunistic maintenance reliability threshold is not constant and depends on the real-time power generation. Numerical examples are used to illustrate the economical advantages of this proposed strategy over traditional opportunistic maintenance strategy. Moreover, the optimal maintenance combination is also provided.
Journal Articles
Article Type: Research-Article
ASME J of Nuclear Rad Sci. October 2018, 4(4): 041005.
Paper No: NERS-17-1148
Published Online: September 10, 2018
Abstract
Shanghai Nuclear Engineering Research and Design Institute (SNERDI) has been studying seismic risk analysis for nuclear power plant for a long time, and completed seismic margin analysis for several plants. After Fukushima accident, seismic risk has drawn an increasing attention worldwide, and the regulatory body in China has also required the utilities to conduct a detailed analysis for seismic risk. So, we turned our focus on a more intensive study of seismic probabilistic safety assessment (PSA/PRA) for nuclear power plant in recent years. Since quantification of seismic risk is a key part in Seismic PSA, lots of efforts have been devoted to its research by SNERDI. The quantification tool is the main product of this research, and will be discussed in detail in this paper. First, a brief introduction to Seismic PSA quantification methodology is presented in this paper, including fragility analysis on system or plant level, convolution of seismic hazard curves and fragility curves, and uncertainty analysis as well. To derive more accurate quantification results, the binary decision diagram (BDD) algorithm was introduced into the quantification process, which effectively reduces the deficiency of the conventional method on coping with large probability events and negated logic. Finally, this paper introduced the development of the seismic PSA quantification tool based on the algorithms discussed in this paper. Tests and application have been made for this software based on a specific nuclear power plant seismic PSA model.
Proceedings Papers
Proc. ASME. IMECE2017, Volume 4B: Dynamics, Vibration, and Control, V04BT05A040, November 3–9, 2017
Paper No: IMECE2017-71059
Abstract
In this study, the uncertain free vibration of structures with hybrid (random and fuzzy) parameters is investigated. The non-deterministic Young’s modulus of structural elements is modeled as random variables while the uncertain mass density is represented by fuzzy parameters with associated membership functions. By implementing the α -sublevel strategy, the stochastic fuzzy eigenvalue problem is rigorously transformed into a series of stochastic interval analysis of natural frequencies. In this context, the Unified Interval Stochastic Sampling (UISS) is adopted such that the statistical characteristics of the upper and lower bounds of natural frequencies at each α -sublevel can be calculated. Subsequently, the membership functions of mean and standard deviations are respectively established for the extreme bounds of eigenvalues. Benefiting from the stochastic samples offered by the UISS method, the probability profiles of the uncertain natural frequencies at each α -sublevel can be identified by utilizing either parametric or nonparametric statistical inference methods. The accuracy and effectiveness of the proposed approach is demonstrated by analyzing a practically motivated 3D structure with comparison to the results obtained by the conventional Monte Carlo simulation method.
Proceedings Papers
Proc. ASME. SMASIS2017, Volume 1: Development and Characterization of Multifunctional Materials; Mechanics and Behavior of Active Materials; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies, V001T08A013, September 18–20, 2017
Paper No: SMASIS2017-3919
Abstract
The Maximum Entropy (ME) method is shown to provide a new approach for quantifying model uncertainty in the presence of complex, heterogeneous data. This is important in model validation of a variety of multifunctional constitutive relations. For example, multifunctional materials contain field-coupled material parameters that should be self-consistent regardless of the measurement. A classical example is piezoelectricity which may be quantified from charge induced by stress or strain induced by an electric field. The proposed tools provide new statistical information to address measurement discrepancies, guide model development, and catalyze materials discovery for data fusion problems. The error between the model outputs and heterogeneous data is quantified and used to formulate a second moment constraint within the entropy functional. This leads to an augmented likelihood function that weights each individual set of data by its respective variance and covariance between each data set. As a first step, the method is evaluated on a piezoelectric ceramic to illustrate how the covariance matrix influences piezoelectric parameter estimation from heterogeneous electric displacement and strain data.
Proceedings Papers
Proc. ASME. ICONE25, Volume 4: Nuclear Safety, Security, Non-Proliferation and Cyber Security; Risk Management, V004T14A028, July 2–6, 2017
Paper No: ICONE25-66973
Abstract
Seismic risk of nuclear power plant has drawn increasing attention after Fukushima accident. An intensive study has been carried out in this paper, including sampling of component and structure fragility based on Monte Carlo method, fragility analysis on system or plant level, convolution of seismic hazard curves and fragility curves. To derive more accurate quantification results, the binary decision diagram (BDD) algorithm was introduced into the quantification process, which effectively reduces the deficiency of the conventional method on coping with large probability events and negated logic. Seismic Probabilistic Safety Analysis (PSA/PRA) quantification software was developed based on algorithms discussed in this paper. Tests and application has been made for this software with a specific nuclear power plant seismic PSA model. The results show that this software is effective on seismic PSA quantification.
Proceedings Papers
Proc. ASME. ICONE25, Volume 4: Nuclear Safety, Security, Non-Proliferation and Cyber Security; Risk Management, V004T14A015, July 2–6, 2017
Paper No: ICONE25-66436
Abstract
Zero-suppressed Binary Decision Diagram (ZBDD) algorithm is an advanced method in fault tree analysis, which is developing quickly in recent years and being used in the development of the Probabilistic Safety Assessment (PSA) Quantification Engine. This algorithm converts a fault tree to a ZBDD structure, solves the minimal cut sets and calculates the top node unavailability. The ordering of the basic events and logical gates is the core technique of the ZBDD algorithm, which determines the efficiency of the ZBDD conversion and the size of the ZBDD structure. A variable ordering method based on the structure of the fault tree is developed in this paper, which gives a better basic events order by compressing the fault tree; meanwhile, the method offers a logical gates order. The nodes order derived from this method can accelerate the ZBDD conversion obviously.
Proceedings Papers
Proc. ASME. ICONE25, Volume 4: Nuclear Safety, Security, Non-Proliferation and Cyber Security; Risk Management, V004T14A016, July 2–6, 2017
Paper No: ICONE25-66442
Abstract
Minimal Cut-sets (MCS) post processing is an important part of the Probabilistic Safety Assessment (PSA) analysis. Two alternative post processing methods have been described in this paper based on traditional and Zero-suppressed Binary Decision Diagram (ZBDD) methods. The first post processing method, named the Quick MCS Post Processing Method, obtains MCS results directly from the cut-sets list using a quick sort method according to a certain set of sorting rules. The alternative post processing method, named the ZBDD Based MCS Post Processing Method, obtains MCS results from the ZBDD structure which encodes the MCS, using the ZBDD algorithm according to the post processing rules. Tests show that both of the two methods can derive the accurate MCS post processing results.
Proceedings Papers
Proc. ASME. POWER2017-ICOPE-17, Volume 2: I&C, Digital Controls, and Influence of Human Factors; Plant Construction Issues and Supply Chain Management; Plant Operations, Maintenance, Aging Management, Reliability and Performance; Renewable Energy Systems: Solar, Wind, Hydro and Geothermal; Risk Management, Safety and Cyber Security; Steam Turbine-Generators, Electric Generators, Transformers, Switchgear, and Electric BOP and Auxiliaries; Student Competition; Thermal Hydraulics and Computational Fluid Dynamics, V002T09A001, June 26–30, 2017
Paper No: POWER-ICOPE2017-3077
Abstract
Nowadays, the management level and information construction of wind power industry are still relatively backward, for example, the existing maintenance models for wind farm are much too single, and corrective maintenance strategy is the most commonly used, which means that maintenance measures are initiated only after a breakdown occurs in the system. Moreover, the wind farm spare parts management is out-dated, no practical and accurate spares demand assessment method is available. In order to enrich the choices of maintenance methods and eliminate the subjective influence in the demand analysis of spare parts, a spare parts demand prediction method for wind farm based on periodic maintenance strategy considering combination of different maintenance models for wind farms is proposed in this paper, which consists of five major steps, acquire the reliability functions of components, establish the maintenance strategy, set the maintenance parameters, maintenance strategy simulation and spare parts demand prediction. The discrete event simulation method is used to solve the prediction model, and results demonstrate the operability and practicality of the proposed demand forecasting method, which can provide guidance for the actual operation and maintenance of wind farms.
Journal Articles
Journal:
Journal of Solar Energy Engineering
Article Type: Technical Briefs
J. Sol. Energy Eng. August 2017, 139(4): 044502.
Paper No: SOL-16-1006
Published Online: June 8, 2017
Abstract
Parabolic trough solar concentrating technology is a new and clean way to replace the conventional fossil fuel technology to generate steam for heavy oil recovery in oilfield. A computational model was constructed with simulated direct normal irradiance from nearby similar climate locations. Different system configurations were analyzed with the model, such as with single- and dual-loop, with and without heat storage system. Finally, several solar field configurations with different collector field layouts were compared by the cost of unit generated steam. Results show that using heat storage can effectively improve the stability of steam production, and in a certain oilfield, an optimum steam production amount and optimum heat storage time (HST) exist for lowest steam cost. The methods and results in the paper provide useful suggestions for the implementation of a solar thermal oilfield steam production system.
Proceedings Papers
Proc. ASME. IMECE2016, Volume 6B: Energy, V06BT08A044, November 11–17, 2016
Paper No: IMECE2016-65668
Abstract
Studies show that fusion modeling can improve the forecasting accuracy of wind power. Fusion modeling is the process of selective use of information from individual forecasting models. The reasonable evaluation of the individual models is the premise and basis of model optimization so that the individual models with high forecasting accuracy can be selected to establish the fusion model. Because the results of a single index model evaluation may not be comprehensive, the multi-index fusion evaluation method based on maximizing deviations and subjective correction is proposed. The method is applied to the selection of short-term wind power forecasting models. Firstly, this method establishes the individual model base of wind power forecasting model. Secondly, it establishes the more comprehensive evaluation index system. Thirdly, it combines maximizing deviations with the subjective correction coefficient to determine the comprehensive weight of each model, which is used to calculate the fusion evaluation value and get the evaluation order to achieve the model optimization. Finally, based on five years of data from a wind power plant in Shanxi Province, the validated experiments by multiple sets of forecasting data have been done using MATLAB in this paper. The simulation results demonstrate that the evaluation based on the proposed fusion evaluation method is more comprehensive and stable compared to evaluation using a single index. More importantly, it can effectively guide the model optimization with simple operating steps.
Proceedings Papers
Proc. ASME. OMAE2015, Volume 9: Ocean Renewable Energy, V009T09A065, May 31–June 5, 2015
Paper No: OMAE2015-41666
Abstract
With the demand of renewable energy due to the pressure from environmental pollution and global warming, the wind industry has been growing rapidly over the past decades during which the offshore wind innovation is becoming more and more attractive because of vast offshore wind resources. In this paper, a novel tapered-column semi-submersible floating foundation with economical mooring system is developed to support 6MW wind turbine in South China Sea. The coupled aero-hydro-servo-structural analysis is done in GH-Bladed software to obtain the global dynamic response of the whole floating wind turbine. By combining the derived wind loads with the wave and current induced loads, the global performances of the foundation and attached mooring system under both extreme conditions and normal operation conditions are analyzed in AQWA software. The result reveals that the floating foundation complies with the design standard and meets requirements of wind turbine. This novel patent-pending floating foundation with tapered columns is proved as a successful design with high material efficiency and good seakeeping performance. Also a reliable and efficient design methodology of floating foundation based on optimal cost is provided in this paper which can be used as design reference of floating foundations.