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.

A case study of digital-twin-modelling analysis on power-plant-performance optimizations

China Energy’s National Institute of Clean-and-Low-Carbon Energy(NICE)is developing a Power Plant Smart Management(PPSM)platform that employs digital-twin technology to undertake techno-economic modelling analysis on China Energy’s existing coal-fired power-plant units and explore cost-effective solutions to improve those plant units’thermal efficiencies and operating performance.This paper presents a case study of PPSM on a 320-MWe coal-fired thermal power-plant unit,demonstrating how the digital-twin technology was employed to explore and analyse optimization solutions.Various optimization solutions and their cost-effectiveness were assessed using the digital-twin-modelling analysis;the results indicated the optimization solutions are expected to improve the plant unit’s operating efficiency and reduce its current electricity-generation coal consumption by up to 3.5 g/kWh standard coal equivalent(sce),worth annual fuel-cost savings of approximately 4 million RMB for a single unit or 8 million RMB for the two identical 320-MWe units that the power plant currently operates.The digital twin was also employed to assess the power-plant unit’s operating economics during both summer and winter.In summer,when the unit operates in electricity-generation-only mode,the unit’s operating thermal efficiency could drop by up to 6%points following the grid demand of load changes from 100%maximum continuous rating(MCR)down to 30% MCR,resulting in an~45 RMB/MWh increase of electricity-generation cost.In winter,when the unit operates in combined heat and power(CHP)cogeneration mode,for the same boiler load,the CHP operation increases the plant unit’s operating profit with increasing district-heating duty,although the relative profit gain from the CHP cogeneration could start to decrease when the district-heating steam-extraction flow increases to a certain point that varies depending on the market prices of heat and electricity,while the fuel cost was found to be equivalent to~50% of the unit’s total CHP income cogenerated from its electricity and district heat outputs.

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.

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.

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 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℃.

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.

Parallelization and I/O Performance Optimization of a Global Nonhydrostatic Dynamical Core Using MPI

The Global-Regional Integrated forecast System(GRIST)is the next-generation weather and climate integrated model dynamic framework developed by Chinese Academy of Meteorological Sciences.In this paper,we present several changes made to the global nonhydrostatic dynamical(GND)core,which is part of the ongoing prototype of GRIST.The changes leveraging MPI and PnetCDF techniques were targeted at the parallelization and performance optimization to the original serial GND core.Meanwhile,some sophisticated data structures and interfaces were designed to adjust flexibly the size of boundary and halo domains according to the variable accuracy in parallel context.In addition,the I/O performance of PnetCDF decreases as the number of MPI processes increases in our experimental environment.Especially when the number exceeds 6000,it caused system-wide outages(SWO).Thus,a grouping solution was proposed to overcome that issue.Several experiments were carried out on the supercomputing platform based on Intel x86 CPUs in the National Supercomputing Center in Wuxi.The results demonstrated that the parallel GND core based on grouping solution achieves good strong scalability and improves the performance significantly,as well as avoiding the SWOs.

Development and Application of Decision Support Model for the Performance Optimization of Office Buildings Based on Grasshopper

With the expansion of the office building area,the energy consumption of office buildings is growing.High⁃performance building design contributes to energy saving and the development of green buildings.However,there is a lack of high⁃performance building tools and the workflow is often time⁃consuming.The building performance simulation,multiple objective optimizations,and the decision support model are the new approaches of high⁃performance building design.This paper proposes a newly developed decision support model,a high⁃performance building decision model named HPBuildingDSM,which integrates the building performance simulation,building performance multiple objective optimizations,building performance sampling,and parameter sensitivity analysis to design high⁃performance office buildings.In this research,the HPBuildingDSM was operated to search for the desirable office building design results with low⁃energy and high⁃quality daylighting performances.The simulated results had better daylighting performance and lower energy consumption,whose UDI100-2000 was 37.94%and annual energy consumption performance was 76.28 kWh/(m2·a),indicating a better building performance than the optimized results in the previous case study.

Optimal Performance Analysis of a Three-Terminal Thermoelectric Refrigerator with Ideal Tunneling Quantum Dots

The model of a three-terminal thermoelectric refrigerator with ideal tunneling quantum dots is established. It consists of a cavity connected to two quantum dots embedded between two electron reservoirs at different temperatures and chemical potentials. According to the Landauer formula the expressions for the heat current, the cooling rate and the coefficient of performance （COP） are derived analytically. The performance characteristic curves of the cooling rate versus the coefficient of performance are plotted with numerical calculation. The optimal regions of the cooling rate and the COP are determined. Moreover, we optimize the cooling rate and the COP with respect to the position of energy level of the right quantum dot, respectively. The influence of the width of energy level and the temperature ratio on performance of the three-terminal thermoelectric refrigerator is analyzed. Lastly, when the width of energy level is small enough, the optimal performance of the refrigerator is discussed in detail.

Satellite constellation design with genetic algorithms based on system performance

Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optical system by taking into account the system tasks（i.e., target detection and tracking）. We then propose a new non-dominated sorting genetic algorithm（NSGA） to maximize the system surveillance performance. Pareto optimal sets are employed to deal with the conflicts due to the presence of multiple cost functions. Simulation results verify the validity and the improved performance of the proposed technique over benchmark methods.

Discrete Optimization on Unsteady Pressure Fluctuation of a Centrifugal Pump Using ANN and Modified GA

Pressure fluctuation due to rotor-stator interaction in turbomachinery is unavoidable,inducing strong vibration in the equipment and shortening its lifecycle.The investigation of optimization methods for an industrial centrifugal pump was carried out to reduce the intensity of pressure fluctuation to extend the lifecycle of these devices.Considering the time-consuming transient simulation of unsteady pressure,a novel optimization strategy was proposed by discretizing design variables and genetic algorithm.Four highly related design parameters were chosen,and 40 transient sample cases were generated and simulated using an automatic program.70%of them were used for training the surrogate model,and the others were for verifying the accuracy of the surrogate model.Furthermore,a modified discrete genetic algorithm(MDGA)was proposed to reduce the optimization cost owing to transient numerical simulation.For the benchmark test,the proposed MDGA showed a great advantage over the original genetic algorithm regarding searching speed and effectively dealt with the discrete variables by dramatically increasing the convergence rate.After optimization,the performance and stability of the inline pump were improved.The efficiency increased by more than 2.2%,and the pressure fluctuation intensity decreased by more than 20%under design condition.This research proposed an optimization method for reducing discrete transient characteristics in centrifugal pumps.

Energy and exergy analyses and optimizations for two-stage TEC driven by two-stage TEG with Thomson effect

Based on the non-equilibrium thermodynamics and energy and exergy analyses,a thermodynamic model of two-stage thermoelectric(TE)cooler(TTEC)driven by two-stage TE generator(TTEG)(TTEG-TTEC)combined TE device is established with involving Thomson effect by fitting method of variable physical parameters of TE materials.Taking total number of TE elements as constraint,influences of number distributions of TE elements on three device performance indictors,that is,cooling load,maximum COP and maximum exergetic efficiency,are analyzed.Three number distributions of TE elements are optimized with three maximum performance indictors as the objectives,respectively.Influences of hot-junction temperature of TTEG and coldjunction temperature of TTEC on optimization results are analyzed,and difference between optimization results corresponding to three performance indicators are studied.Optimal performance intervals and optimal variable intervals are provided.Influences of Thomson effect on three general performance indicators,three optimal performance indicators and optimal variables are comparatively discussed.Thomson effect reduces three general performance indicators and three optimal performance indicators of device.When hot-and cold-junction temperatures of TTEG and TTEC are 450,305,325 and 295 K,respectively,Thomson effect reduced maximum cooling load,maximum COP and maximum exergetic efficiency from 9.528 W,9.043×10^(-2)and2.552%to 6.651 W,6.286×10^(-2)and 1.752%,respectively.

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.

A Review on Cable-driven Parallel Robots

Cable-driven parallel robots(CDPRs) are categorized as a type of parallel manipulators. In CDPRs, flexible cables are used to take the place of rigid links. The particular property of cables provides CDPRs several advantages, including larger workspaces, higher payload-to-weight ratio and lower manufacturing costs rather than rigid-link robots. In this paper, the history of the development of CDPRs is introduced and several successful latest application cases of CDPRs are presented. The theory development of CDPRs is introduced focusing on design, performance analysis and control theory. Research on CDPRs gains wide attention and is highly motivated by the modern engineering demand for large load capacity and workspace. A number of exciting advances in CDPRs are summarized in this paper since it is proposed in the 1980 s, which points to a fruitful future both in theory and application. In order to meet the increasing requirements of robot in different areas, future steps foresee more in-depth research and extension applications of CDPRs including intelligent control, composite materials, integrated and reconfigurable design.