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Ensemble Wind Power Prediction Interval with Optimal Reserve Requirement

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Journal of Modern Power systems and Clean Energy 2024年第1期12卷 65-76页

作者： Hamid Rezaie Cheuk Hei Chung Nima Safari the Saskatchewan Power Corporation(SaskPower) ReginaSaskatchewanCanada the department of Electrical and Computer engineering and department of Human Biology University of TorontoTorontoON M5S 3G4Canada the Alberta Electric system Operator(AESO) CalgaryAlbertaCanada

Wind power prediction interval(WPPI)models in the literature have predominantly been developed for and tested on specific case studies.However,wind behavior and characteristics can vary significantly across regions.Thus,a prediction model that performs well in one case might underperform in another.To address this shortcoming,this paper proposes an ensemble WPPI framework that integrates multiple WPPI models with distinct characteristics to improve robustness.Another important and often overlooked factor is the role of probabilistic wind power prediction(WPP)in quantifying wind power uncertainty,which should be handled by operating reserve.Operating reserve in WPPI frameworks enhances the efficacy of WPP.In this regard,the proposed framework employs a novel bi-layer optimization approach that takes both WPPI quality and reserve requirements into account.Comprehensive analysis with different real-world datasets and various benchmark models validates the quality of the obtained WPPIs while resulting in more optimal reserve requirements.

关键词： Ensemble model linear programming operating reserve optimal reserve requirement prediction interval probabilistic prediction renewable integration uncertainty representation wind power prediction(WPP)

A Review on the Mullins Effect in Tough Elastomers and Gels

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Acta Mechanica Solida Sinica 2024年第2期37卷 181-214页

作者： Lin Zhan Shaoxing Qu Rui Xiao MOE Key Lab of Disaster Forecast and Control in engineering College of Mechanics and Construction EngineeringJinan UniversityGuangzhou510632China State Key Laboratory of Fluid Power and Mechatronic system Key Laboratory of Soft Machines and Smart Devices of Zhejiang ProvinceDepartment of Engineering MechanicsZhejiang UniversityHangzhou310027China

Tough elastomers and gels have garnered broad research interest due to their wide-ranging potential applications.However,during the loading and unloading cycles,a clear stress softening behavior can be observed in many material systems,which is also named as the Mullins effect.In this work,we aim to provide a complete review of the Mullins effect in soft yet tough materials,specifically focusing on nanocomposite gels,double-network hydrogels,and multi-network elastomers.We first revisit the experimental observations for these soft materials.We then discuss the recent developments of constitutive models,emphasizing novel developments in the damage mechanisms or network representations.Some phenomenological models will also be briefly introduced.Particular attention is then placed on the anisotropic and multiaxial modeling aspects.It is demonstrated that most of the existing models fail to accurately predict the multiaxial data,posing a significant challenge for developing future anisotropic models tailored for tough gels and elastomers.

关键词： Mullins effect Double-network hydrogels Nanocomposite gels Multi-network elastomers Anisotropic damage model Multiaxial model

Macroporous Directed and Interconnected Carbon Architectures Endow Amorphous Silicon Nanodots as Low‑Strain and Fast‑Charging Anode for Lithium‑Ion Batteries

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Nano-Micro Letters 2024年第5期16卷 333-351页

作者： Zhenwei Li Meisheng Han Peilun Yu Junsheng Lin Jie Yu Guangdong Provincial Key Laboratory of semiconductor Optoelectronic Materials and Intelligent Photonic systems Shenzhen Engineering Lab for Supercapacitor MaterialsSchool of Material Science and EngineeringHarbin Institute of TechnologyShenzhenUniversity TownShenzhen 518055People’s Republic of China Songshan Lake Materials Laboratory Dongguan Dongguan 523808GuangdongPeople’s Republic of China department of Mechanical and Energy engineering Southern University of Science and TechnologyShenzhen 518055People’s Republic of China

Fabricating low-strain and fast-charging silicon-carbon composite anodes is highly desired but remains a huge challenge for lithium-ion batteries.Herein,we report a unique silicon-carbon composite fabricated by uniformly dis-persing amorphous Si nanodots(SiNDs)in carbon nanospheres(SiNDs/C)that are welded on the wall of the macroporous carbon framework(MPCF)by vertical graphene(VG),labeled as MPCF@VG@SiNDs/C.The high dispersity and amor-phous features of ultrasmall SiNDs(~0.7 nm),the flexible and directed electron/Li+transport channels of VG,and the MPCF impart the MPCF@VG@SiNDs/C more lithium storage sites,rapid Li+transport path,and unique low-strain property during Li+storage.Consequently,the MPCF@VG@SiNDs/C exhibits high cycle stability(1301.4 mAh g^(-1) at 1 A g^(-1) after 1000 cycles without apparent decay)and high rate capacity(910.3 mAh g^(-1),20 A g^(-1))in half cells based on industrial electrode standards.The assembled pouch full cell delivers a high energy density(1694.0 Wh L^(-1);602.8 Wh kg^(-1))and an excellent fast-charging capability(498.5 Wh kg^(-1),charging for 16.8 min at 3 C).This study opens new possibilities for preparing advanced silicon-carbon com-posite anodes for practical applications.

关键词： Amorphous Si nanodots Low-strain Fast-charging Lithium-ion batteries

Cognitive impairment in cerebral small vessel disease induced by hypertension

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Neural Regeneration Research 2024年第7期19卷 1454-1462页

作者： Weipeng Wei Denglei Ma Lin Li Lan Zhang department of Pharmacy Xuanwu Hospital of Capital Medical UniversityBeijingChina Beijing Geriatric Medical Research Center Beijing Engineering Research Center for Nervous System DrugsNational Center for Neurological DisordersNational Clinical Research Center for Geriatric DiseasesBeijingChina

Hypertension is a primary risk factor for the progression of cognitive impairment caused by cerebral small vessel disease,the most common cerebrovascular disease.Howeve r,the causal relationship between hypertension and cerebral small vessel disease remains unclear.Hypertension has substantial negative impacts on brain health and is recognized as a risk factor for cerebrovascular disease.Chronic hypertension and lifestyle factors are associated with risks for stro ke and dementia,and cerebral small vessel disease can cause dementia and stroke.Hypertension is the main driver of cerebral small vessel disease,which changes the structure and function of cerebral vessels via various mechanisms and leads to lacunar infarction,leukoaraiosis,white matter lesions,and intracerebral hemorrhage,ultimately res ulting in cognitive decline and demonstrating that the brain is the to rget organ of hypertension.This review updates our understanding of the pathogenesis of hypertensioninduced cerebral small vessel disease and the res ulting changes in brain structure and function and declines in cognitive ability.We also discuss drugs to treat cerebral small vessel disease and cognitive impairment.

关键词： blood-brain barrier cerebral small vessel disease cognitive impairment dementia endothelial dysfunction enlarged perivascular space hypertension lacunar infarction neuroinflammation treatment white matter high signal intensity

AI for tribology:Present and future

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Friction 2024年第6期12卷 1060-1097页

作者： Nian YIN Pufan YANG Songkai LIU Shuaihang PAN Zhinan ZHANG State Key Laboratory of Mechanical system and Vibration Shanghai Jiao Tong UniversityShanghai 200240China School of Mechanical engineering Shanghai Jiao Tong UniversityShanghai 200240China department of Mechanical engineering University of UtahSalt Lake CityUT 84112USA

With remarkable learning capabilities and swift operational speeds,artificial intelligence(AI)can assist researchers in swiftly extracting valuable patterns,trends,and associations from subjective information.Tribological behaviors are characterized by dependence on systems,evolution with time,and multidisciplinary coupling.The friction process involves a variety of phenomena,including mechanics,thermology,electricity,optics,magnetics,and so on.Hence,tribological information possesses the distinct characteristics of being multidisciplinary,multilevel,and multiscale,so that the application of AI in tribology is highly extensive.To delineate the scope,classification,and recent trends of AI implementation in tribology,this review embarks on exploration of the tribology research domain.It comprehensively outlines the utilization of AI in basic theory of tribology,intelligent tribology,component tribology,extreme tribology,bio-tribology,green tribology,and other fields.Finally,considering the emergence of"tribo-informatics"as a novel interdisciplinary field,which combines tribology with informatics,this review elucidates the future directions and research framework of"AI for tribology".In this paper,tribo-system information is divided into 5 categories:input information(I),system intrinsic information(S),output information(O),tribological state information(Ts),and derived state information(Ds).Then,a fusion method among 5 types of tribo-system information and different AI technologies(regression,classification,clustering,and dimension reduction)has been proposed,which enables tribo-informatics methods to solve common problems such as tribological behavior state monitoring,behavior prediction,and system optimization.The purpose of this review is to offer a systematic comprehension of tribo-informatics and to inspire new research ideas of tribo-informatics.Ultimately,it aspires to enhance the efficiency of problem-solving in tribology.

关键词： artificial intelligence(AI) tribology machine learning tribo-informatics AI for tribology

Tuning mechanical behaviors of highly entangled hydrogels with the random distribution of mobile entanglements

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Applied Mathematics and Mechanics(English Edition) 2024年第2期45卷 277-294页

作者： Jinlong LIU Di LU Bin CHEN department of engineering Mechanics Zhejiang UniversityHangzhou 310027China Beijing system Design Institute of Electro-Mechanic engineering Beijing 100854China Intelligent Robotic Research Center Zhejiang LaboratoryHangzhou 310027China Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Hangzhou 311100China

Highly entangled hydrogels exhibit excellent mechanical properties,including high toughness,high stretchability,and low hysteresis.By considering the evolution of randomly distributed entanglements within the polymer network upon mechanical stretches,we develop a constitutive theory to describe the large stretch behaviors of these hydrogels.In the theory,we utilize a representative volume element(RVE)in the shape of a cube,within which there exists an averaged chain segment along each edge and a mobile entanglement at each corner.By employing an explicit method,we decouple the elasticity of the hydrogels from the sliding motion of their entanglements,and derive the stress-stretch relations for these hydrogels.The present theoretical analysis is in agreement with experiment,and highlights the significant influence of the entanglement distribution within the hydrogels on their elasticity.We also implement the present developed constitutive theory into a commercial finite element software,and the subsequent simulations demonstrate that the exact distribution of entanglements strongly affects the mechanical behaviors of the structures of these hydrogels.Overall,the present theory provides valuable insights into the deformation mechanism of highly entangled hydrogels,and can aid in the design of these hydrogels with enhanced performance.

关键词： highly entangled hydrogel constitutive theory entanglement gamma distribution

Stochastic Flexibility Evaluation for Virtual Power Plants by Aggregating Distributed Energy Resources

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CSEE Journal of Power and Energy systems 2024年第3期10卷 988-999页

作者： Siyuan Wang Wenchuan Wu Qizhan Chen Junjie Yu Peng Wang department of Electrical engineering Tsinghua UniversityBeijing 100084China Zhongshan Power Supply Bureau of the Guangdong Power Grid Corporation Zhongshan 528400China Beijing Qingda Gaoke system Control Company Beijing 102208China CSEE IEEE

To manage a large amount of flexible distributed energy resources(DERs)in the distribution networks,the virtual power plant(VPP)is introduced into the industry.The VPP can optimally dispatch these resources in a cluster manner and provide flexibility for the power system operation as a whole.Most existing studies formulate the equivalent power flexibility of the aggregating DERs as deterministic optimization models without considering their uncertainties.In this paper,we introduce the stochastic power flexibility range(PFR)and timecoupling flexibility(TCF)to describe the power flexibility of VPP.In this model,both operational constraints and the randomness of the DERs’output are incorporated,and a combined model and data-driven solution is proposed to obtain the stochastic PFR,TCF,and cost function of VPP.The aggregating model can be easily incorporated into the optimization model for the power system operator or market bidding,considering uncertainties.Finally,a numerical test is performed.The results show that the proposed model not only has higher computational efficiency than the scenario-based methods but also achieves more economic benefits.

关键词： Chance constrained optimization combined model and data-driven stochastic power flexibility virtual power plant

A Reverse Path Planning Approach for Enhanced Performance of Multi-Degree-of-Freedom Industrial Manipulators

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Computer Modeling in engineering & Sciences 2024年第5期139卷 1357-1379页

作者： Zhiwei Lin Hui Wang Tianding Chen Yingtao Jiang Jianmei Jiang Yingpin Chen Key Laboratory of Light Field Manipulation and system Integration Applications in Fujian Province School of Physics and Information EngineeringMinnan Normal UniversityZhangzhou363000China department of Electrical and Computer engineering University of NevadaLas Vegas89154USA College of Material engineering Fujian Agriculture and Forestry UniversityFuzhou350000China

In the domain of autonomous industrial manipulators,precise positioning and appropriate posture selection in path planning are pivotal for tasks involving obstacle avoidance,such as handling,heat sealing,and stacking.While Multi-Degree-of-Freedom(MDOF)manipulators offer kinematic redundancy,aiding in the derivation of optimal inverse kinematic solutions to meet position and posture requisites,their path planning entails intricate multiobjective optimization,encompassing path,posture,and joint motion optimization.Achieving satisfactory results in practical scenarios remains challenging.In response,this study introduces a novel Reverse Path Planning(RPP)methodology tailored for industrial manipulators.The approach commences by conceptualizing the manipulator’s end-effector as an agent within a reinforcement learning(RL)framework,wherein the state space,action set,and reward function are precisely defined to expedite the search for an initial collision-free path.To enhance convergence speed,the Q-learning algorithm in RL is augmented with Dyna-Q.Additionally,we formulate the cylindrical bounding box of the manipulator based on its Denavit-Hartenberg(DH)parameters and propose a swift collision detection technique.Furthermore,the motion performance of the end-effector is refined through a bidirectional search,and joint weighting coefficients are introduced to mitigate motion in high-power joints.The efficacy of the proposed RPP methodology is rigorously examined through extensive simulations conducted on a six-degree-of-freedom(6-DOF)manipulator encountering two distinct obstacle configurations and target positions.Experimental results substantiate that the RPP method adeptly orchestrates the computation of the shortest collision-free path while adhering to specific posture constraints at the target point.Moreover,itminimizes both posture angle deviations and joint motion,showcasing its prowess in enhancing the operational performance of MDOF industrial manipulators.

关键词： Reverse path planning Dyna-Q bidirectional search posture angle joint motion

Depolarization mitigated in ferroelectric Hf_(0.5)Zr_(0.5)O_(2)ultrathin films(<5 nm)on Si substrate by interface engineering

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Journal of Advanced Ceramics 2024年第3期13卷 282-292页

作者： Se Hyun Kim Younghwan Lee Dong Hyun Lee Geun Hyeong Park Hyun Woo Jeong Kun Yang Yong Hyeon Cho Young Yong Kim Min Hyuk Park department of Materials Science and engineering College of EngineeringSeoul National UniversitySeoul 08826Republic of Korea Research Institute of Advanced Materials College of EngineeringSeoul National UniversitySeoul 08826Republic of Korea Inter-university semiconductor Research Center College of EngineeringSeoul National UniversitySeoul 08826Republic of Korea Pohang Accelerator Laboratory Pohang University of Science&TechnologyPohang 790784Republic of Korea

(Hf,Zr)O_(2)offers considerable potential for next-generation semiconductor devices owing to its nonvolatile spontaneous polarization at the nanoscale.However,scaling this material to sub-5 nm thickness poses several challenges,including the formation of an interfacial layer and high trap concentration.In particular,a low-k SiO_(2)interfacial layer is naturally formed when(Hf,Zr)O_(2)films are directly grown on a Si substrate,leading to high depolarization fields and rapid reduction of the remanent polarization.To address these issues,we conducted a study to significantly improve ferroelectricity and switching endurance of(Hf,Zr)O_(2)films with sub-5 nm thicknesses by inserting a TiO_(2)interfacial layer.The deposition of a Ti film prior to Hf_(0.5)Zr_(0.5)O_(2)film deposition resulted in a high-k TiO_(2)interfacial layer and prevented the direct contact of Hf_(0.5)Zr_(0.5)O_(2)with Si.Our findings show that the high-k TiO_(2)interfacial layer can reduce the SiO_(2)/Si interface trap density and the depolarization field,resulting in a switchable polarization of 60.2μC/cm^(2)for a 5 nm thick Hf_(0.5)Zr_(0.5)O_(2)film.Therefore,we propose that inserting a high-k TiO_(2)interfacial layer between the Hf_(0.5)Zr_(0.5)O_(2)film and the Si substrate may offer a promising solution to enhancing the ferroelectricity and reliability of(Hf,Zr)O_(2)grown on the Si substrate and can pave the way for next-generation semiconductor devices with improved performance.

关键词： ferroelectric charge trapping hafnia depolarization