Optimization performance of quantum endoreversible Otto machines with dual-squeezed reservoirs

We consider a quantum endoreversible Otto engine cycle and its inverse operation-Otto refrigeration cycle,employing two-level systems as the working substance and operating in dual-squeezed reservoirs.We demonstrate that the efficiency of heat engines at maximum work output and the coefficient of performance for refrigerators at the maximum c criterion will degenerate toη-=η_(C)/(2-η_(C))andε-=(√9+8ε_(C)-3)/2 when symmetric squeezing is satisfied,respectively.We also investigated the influences of squeezing degree on the performance optimization of quantum Otto heat engines at the maximum work output and refrigerators at the maximum X criterion.These analytical results show that the efficiency of heat engines at maximum work output and the coefficient of performance for refrigerators at the maximum X criterion can be improved,reduced or even inhibited in asymmetric squeezing.Furthermore,we also find that the efficiency of quantum Otto heat engines at maximum work output is lower than that obtained from the Otto heat engines based on a single harmonic oscillator system.However,the coefficient of performance of the corresponding refrigerator is higher.

Design and optimization of fluid lubricated bearings operated with extreme working performances——a comprehensive review

Fluid lubricated bearings have been widely adopted as support components for high-end equipment in metrology,semiconductor devices,aviation,strategic defense,ultraprecision manufacturing,medical treatment,and power generation.In all these applications,the equipment must deliver extreme working performances such as ultraprecise movement,ultrahigh rotation speed,ultraheavy bearing loads,ultrahigh environmental temperatures,strong radiation resistance,and high vacuum operation,which have challenged the design and optimization of reliable fluid lubricated bearings.Breakthrough of any related bottlenecks will promote the development course of high-end equipment.To promote the advancement of high-end equipment,this paper reviews the design and optimization of fluid lubricated bearings operated at typical extreme working performances,targeting the realization of extreme working performances,current challenges and solutions,underlying deficiencies,and promising developmental directions.This paper can guide the selection of suitable fluid lubricated bearings and optimize their structures to meet their required working performances.

Performance optimization of a SERF atomic magnetometer based on flat-top light beam

We explore the impact of pumping beams with different transverse intensity profiles on the performance of the spinexchange relaxation-free(SERF) atomic magnetometers(AMs). We conduct experiments comparing the traditional Gaussian optically-pumped AM with that utilizing the flat-top optically-pumped(FTOP) method. Our findings reveal that the FTOP-based approach outperforms the conventional method, exhibiting a larger response, a narrower magnetic resonance linewidth, and a superior low-frequency noise performance. Specifically, the use of FTOP method leads to a 16% enhancement in average sensitivity within 1 Hz–30 Hz frequency range. Our research emphasizes the significance of achieving transverse polarization uniformity in AMs, providing insights for future optimization efforts and sensitivity improvements in miniaturized magnetometers.

A PID Tuning Approach for Inertial Systems Performance Optimization

In the practice of control the industrial processes, proportional-integral-derivative controller remains pivotal due to its simple structure and system performance-oriented tuning process. In this paper are presented two approaches for synthesis the proportional-integral-derivative controller to the models of objects with inertia, that offer the procedure of system performance optimization based on maximum stability degree criterion. The proposed algorithms of system performance optimization were elaborated for model of objects with inertia second and third order and offer simple analytical expressions for tuning the PID controller. Validation and verification are conducted through computer simulations using MATLAB, demonstrating successful performance optimization and showcasing the effectiveness PID controllers’ tuning. The proposed approaches contribute insights to the field of control, offering a pathway for optimizing the performance of second and third-order inertial systems through robust controller synthesis.

Research on the Optimization of Green Building Performance Based on BIM Technology

With the acceleration of urbanization,the construction industry has developed rapidly worldwide but has also brought serious environmental problems.Traditional architectural design methods often only focus on the function and beauty of the building while ignoring its impact on the environment.In addition,the lack of effective design and construction management methods also led to high resource and energy consumption.To overcome this challenge,the concept of green building came into being.Green buildings emphasize reducing the negative impact of buildings on the environment and improving resource utilization efficiency throughout the entire life cycle.BIM technology provides strong support for achieving this goal.Based on this,starting from the role of BIM technology in green building performance optimization,this article analyzes the optimization of green building performance solutions based on BIM technology in detail to promote the sustainable development of buildings.

Performance optimization on finite-time quantum Carnot engines and refrigerators based on spin-1/2 systems driven by a squeezed reservoir

We investigate the finite-time performance of a quantum endoreversible Carnot engine cycle and its inverse operation-Carnot refrigeration cycle,employing a spin-1/2 system as the working substance.The thermal machine is alternatively driven by a hot boson bath of inverse temperatureβ_(h)and a cold boson bath at inverse temperatureβ_(c)(>βh).While for the engine model the hot bath is constructed to be squeezed,in the refrigeration cycle the cold bath is established to be squeezed,with squeezing parameter r.We obtain the analytical expressions for both efficiency and power in heat engines and for coefficient of performance and cooling rate in refrigerators.We find that,in the high-temperature limit,the efficiency at maximum power is bounded by the analytical valueη_(+)=√sech(2r)(1-η_(C)),and the coefficient of performance at the maximum figure of merit is limited byε_(+)=√sech(2r)(1+ε_(C))/sech(2r)(1+ε_(C))-εC)-1,whereη_(C)=1-β_(h)/β_(c)andε_(C)=β_(h)/(β_(c)-β_(h))are the respective Carnot values of the engines and refrigerators.These analytical results are identical to those obtained from the Carnot engines based on harmonic systems,indicating that the efficiency at maximum power and coefficient at maximum figure of merit are independent of the working substance.

Machine Learning Assisted Design of Natural Rubber Composites with Multi⁃Performance Optimization

Multi⁃performance optimization of tread rubber composites is a key issue of great concern in automotive industry.Traditional experimental design approach via“trial and error”or intuition is ineffective due to mutual inhibition among multiple properties.A“Uniform design⁃Machine learning”strategy for performance prediction and multi⁃performance optimization of tread rubber composites was proposed.The wear resistance,rolling resistance,tensile strength and wet skid resistance were simultaneously optimized.A series of feasible optimization designs were screened via statistical analysis and machine learning analysis,and were experimentally prepared.The verification experiments demonstrate that the optimization design via machine learning analysis meets the optimization requirements of all target performance,especially for Akron abrasion and 60℃tanδ(about 21%and 9%lower than the design targets,respectively)due to the inhibition of mechanical degradation and good dispersion of fillers.

Effect of Sweptback Angle of a Delta Wing on Surface Pressure Distribution at Supersonic Mach Numbers

The purpose of this work is to shed light on the effect of the pivot position on the surface pressure distribution over a 3D wing in different flight conditions.The study is intended to support the design and development of aerospace vehicles where stability analysis,performance optimization,and aircraft design are of primary importance.The following parameters are considered:Mach numbers(M)of 1.3,1.8,2.3,2.8,3.3,and 3.8,angle of incidence(θ)in the range from 5°to 25°,pivot position from h=0.2 to 1.The results of the CFD numerical simulations match available analytical data,thereby providing evidence for the reliability of the used approach.The findings provide valuable insights into the relationship between the surface pressure distribution,the Mach number and the angle of incidence.

Performance Optimization of Agricultural Machinery Monitoring WebGIS System Based on ASP.NET

ASP.NET-based agricultural machinery monitoring WEBGIS is flexible and dynamic,but this flexibility and dynamic characteristics reduce the performance of WEBGIS.Therefore,it is necessary to use built-in optimization features of.NET Framework,some performance optimization techniques in program design and ASP.NET cache technology to reduce the loading of server,and make the designed system work more efficiently.

Optimizing Memory Access Efficiency in CUDA Kernel via Data Layout Technique

Over the past decade, Graphics Processing Units (GPUs) have revolutionized high-performance computing, playing pivotal roles in advancing fields like IoT, autonomous vehicles, and exascale computing. Despite these advancements, efficiently programming GPUs remains a daunting challenge, often relying on trial-and-error optimization methods. This paper introduces an optimization technique for CUDA programs through a novel Data Layout strategy, aimed at restructuring memory data arrangement to significantly enhance data access locality. Focusing on the dynamic programming algorithm for chained matrix multiplication—a critical operation across various domains including artificial intelligence (AI), high-performance computing (HPC), and the Internet of Things (IoT)—this technique facilitates more localized access. We specifically illustrate the importance of efficient matrix multiplication in these areas, underscoring the technique’s broader applicability and its potential to address some of the most pressing computational challenges in GPU-accelerated applications. Our findings reveal a remarkable reduction in memory consumption and a substantial 50% decrease in execution time for CUDA programs utilizing this technique, thereby setting a new benchmark for optimization in GPU computing.

CEE-Gr:A Global Router with Performance Optimization Under Multi-Constraints

A global routing algorithm with performance optimization under multi constraints is proposed,which studies RLC coupling noise,timing performance,and routability simultaneously at global routing level.The algorithm is implemented and the global router is called CEE Gr.The CEE Gr is tested on MCNC benchmarks and the experimental results are promising.

New development in Fe/Co catalysts:Structure modulation and performance optimization for syngas conversion

C1 chemistry is the essence of coal chemistry and natural gas chemistry. Catalytic methods to efficiently convert C1 molecules into fuels and chemicals have been extensively studied. Syngas（CO ＋H_2） conversion is the most important industrial reaction system in C1 chemistry, and Fe and Co catalysts, two major industrial catalysts, have been the focus of fundamental research and industrial application. In the last decade, considerable research efforts have been devoted to discoveries concerning catalyst structure and increasing market demands for olefins and oxygenates. Since the development of efficient catalysts would strongly benefit from catalyst design and the establishment of a new reaction system, this review comprehensively overviews syngas conversion in three main reactions, highlights the advances recently made and the challenges that remain open, and will stimulate future research activities. The first part of the review summarizes the breakthroughs in Fischer-Tropsch synthesis regarding the optimization of activity and stability, determination of the active phase, and mechanistic studies. The second part overviews the modulation of catalytic structure and product selectivity for Fischer-Tropsch to olefins（FTO）. Catalysts designed to produce higher alcohols, as well as to tune product selectivity in C1 chemistry, are described in the third section. Finally, present challenges in syngas conversion are proposed, and the solutions and prospects are discussed from the viewpoint of fundamental research and practical application. This review summarizes the latest advances in the design, preparation, and application of Fe/Co-based catalysts toward syngas conversion and presents the challenges and future directions in producing value-added fuels.

H-infinity performance optimization for networked control systems with limited communication channels

This paper studies the problems of H-infinity performance optimization and controller design for continuous-time NCSs with both sensor-to-controller and controller-to-actuator communication constraints （limited communication channels）. By taking the derivative character of network-induced delay into full consideration and defining new Lyapunov functions, linear matrix inequalities （LMIs）-based H-infinity performance optimization and controller design are presented for NCSs with limited communication channels. If there do not exist any constraints on the communication channels, the proposed design methods are also applicable. The merit of the proposed methods lies in their Jess conservativeness, which is achieved by avoiding the utilization of bounding inequalities for cross products of vectors. The simulation results illustrate the merit and effectiveness of the proposed H-infinity controller design for NCSs with limited communication channels.

Optimization of Vibration and Noise Performance of Permanent Magnet Synchronous Motor for Electric Vehicles

In the design of the motor used for electric vehicles(EVS),vibration and noise problems are often ignored,which reduce the reliability and service life of the motor.In this paper,an interior permanent magnet synchronous motor(IPMSM)with high power density is taken as an example,and its electromagnetic vibration and noise problem is investigated and optimized.Firstly,the factors that generate the electromagnetic force harmonic of IPMSM are analyzed by theoretical derivation.Furthermore,the mode and electromagnetic harmonic distribution of the motor are calculated and analyzed by establishing the electromagnetic-structure-sound coupling simulation model.Then,by combining finite element method(FEM)with modern optimization algorithm,an electromagnetic vibration and noise performance optimization method is proposed in the electromagnetic design stage of the motor.Finally,an IPMSM is optimized by this method for electromagnetic vibration and noise performance.The results of comparison between before and after optimization prove the feasibility of the method.

Mechanical Analysis and Performance Optimization for the Lunar Rover’s Vane-Telescopic Walking Wheel

It is well-known that optimizing the wheel system of lunar rovers is essential.However,this is a difficult task due to the complex terrain of the moon and limited resources onboard lunar rovers.In this study,an experimental prototype was set up to analyze the existing mechanical design of a lunar rover and improve its performance.First,a new vane-telescopic walking wheel was proposed for the lunar rover with a positive and negative quadrangle suspension,considering the complex terrain of the moon.Next,the performance was optimized under the limitations of preserving the slope passage and minimizing power consumption.This was achieved via analysis of the wheel force during movement.Finally,the effectiveness of the proposed method was demonstrated by several simulation experiments.The newly designed wheel can protrude on demand and reduce energy consumption;it can be used as a reference for lunar rover development engineering in China.

Randomized Algorithms for Probabilistic Optimal Robust Performance Controller Design

Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach was given. The randomized algorithms here were based on a property from statistical learning theory known as (uniform) convergence of empirical means (UCEM). It is argued that in order to assess the performance of a controller as the plant varies over a pre-specified family, it is better to use the average performance of the controller as the objective function to be optimized, rather than its worst-case performance. The approach is illustrated to be efficient through an example.

Optimization of Gas Sensing Performance of Nanocrystalline SnO_2 Thin Films Synthesized by Magnetron Sputtering

Tin oxide （SnO2） is one of the most promising transparent conducting oxide materials, which is widely used in thin film gas sensors. We investigate the dependence of the deposition time on structural, morphologicaJ and hydrogen gas sensing properties of SnO2 thin films synthesized by dc magnetron sputtering. The deposited samples are characterized by XRD, SEM, AFM, surface area measurements and surface profiler. Also the H2 gas sensing properties of SnO2 deposited samples are performed against a wide range of operating temperature. The XRD analysis demonstrates that the degree of crystallinity of the deposited SnO2 films strongly depends on the deposition time. SEM and AFM analyses reveal that the size of nanoparticles or agglomerates, and both average and rms surface roughness is enhanced with the increasing deposition time. Also gas sensors based on these SnO2 nanolayers show an acceptable response to hydrogen at various operating temperatures.

Optimization of technical measures for improving high-temperature performance of asphalt-rubber mixture

Asphalt-rubber pavements often become dam-aged in high-temperature regions and appear rutted or wavy, and experience slippage. To improve the high-temperature performance of the asphalt-rubber mixture, technical measurements, such as, the optimal adjustment of gradation, technique of composite modification, and control of compaction were investigated. An optimal adjustment of aggregate gradation based on stone matrix asphalt improves the high-temperature stability of the asphaltrubber mixture significantly. Through composite modifi- cation, the effect of asphalt-rubber modification was enhanced, and the dynamic stability and relative defor- mation indices of the asphalt-rubber mixture were improved significantly. Furthermore, compaction parame- ters had a significant influence on the high-temperature stability of the asphalt-rubber mixture. The rolling times for compacting the asphalt-rubber mixture should be controlled to within 18-20 round-trips at a molding temperature at 180℃; if the rolling time is a 12 round-trip, the compaction temperature of the asphalt-rubber mixture should be controlled between 180 and 190℃.

Attitude control allocation strategy of high altitude airship based on synthetic performance optimization

This paper presents a method for solving the attitude control problem of high altitude airship (HAA) with aerodynamic fin and vectored thruster control. The algorithm is based on the synthetic optimization of dynamic performance and energy consumption of airship. Firstly, according to the system overall configuration, the dynamic model of HAA was established and the HAA linearized model of longitudinal plane motion was obtained. Secondly, using the classic PID control theory, the HAA attitude control system was designed. Thirdly, through analyzing the dynamic performance of airship with fin or vectored thruster control, the synthetic performance index function with different weighting functions was determined. By means of optimizing the obtained performance index function, the attitude control of high altitude airship with good dynamic performance and low energy consumption was achieved. Finally, attitude control allocation strategy was designed for the airship station keeping at an altitude of 22 km. The simulation experiment proved the validity of the proposed algorithm.

Prediction and Optimization Performance Models for Poor Information Sample Prediction Problems

The prediction process often runs with small samples and under-sufficient information.To target this problem,we propose a performance comparison study that combines prediction and optimization algorithms based on experimental data analysis.Through a large number of prediction and optimization experiments,the accuracy and stability of the prediction method and the correction ability of the optimization method are studied.First,five traditional single-item prediction methods are used to process small samples with under-sufficient information,and the standard deviation method is used to assign weights on the five methods for combined forecasting.The accuracy of the prediction results is ranked.The mean and variance of the rankings reflect the accuracy and stability of the prediction method.Second,the error elimination prediction optimization method is proposed.To make,the prediction results are corrected by error elimination optimization method(EEOM),Markov optimization and two-layer optimization separately to obtain more accurate prediction results.The degree improvement and decline are used to reflect the correction ability of the optimization method.The results show that the accuracy and stability of combined prediction are the best in the prediction methods,and the correction ability of error elimination optimization is the best in the optimization methods.The combination of the two methods can well solve the problem of prediction with small samples and under-sufficient information.Finally,the accuracy of the combination of the combined prediction and the error elimination optimization is verified by predicting the number of unsafe events in civil aviation in a certain year.