Energy production based on fossil fuel reserves is largely responsible for carbon emissions, and hence global warming. The planet needs concerted action to reduce fossil fuel usage and to implement carbon mitigation measures. Ocean energy has huge potential, but there are major interdisciplinary problems to be overcome regarding technology, cost reduction, investment, environmental impact, governance, and so forth. This article briefly reviews ocean energy production from offshore wind, tidal stream, ocean current, tidal range, wave, thermal, salinity gradients, and biomass sources. Future areas of research and development are outlined that could make exploitation of the marine renewable energy (MRE) seascape a viable proposition; these areas include energy storage, advanced materials, robotics, and informatics. The article concludes with a sustainability perspective on the MRE seascape encompassing ethics, legislation, the regulatory environment, governance and consenting, economic, social, and environmental constraints. A new generation of engineers is needed with the ingenuity and spirit of adventure to meet the global challenge posed by MRE.
In 2005, the US passed the Energy Policy Act of 2005 mandating the construction and operation of a high-temperature gas reactor (HTGR) by 2021. This law was passed after a multiyear study by national experts on what future nuclear technologies should be developed. As a result of the Act, the US Congress chose to develop the so-called Next-Generation Nuclear Plant, which was to be an HTGR designed to produce process heat for hydrogen production. Despite high hopes and expectations, the current status is that high temperature reactors have been relegated to completing research programs on advanced fuels, graphite and materials with no plans to build a demonstration plant as required by the US Congress in 2005. There are many reasons behind this diminution of HTGR development, including but not limited to insufficient government funding requirements for research, unrealistically high temperature requirements for the reactor, the delay in the need for a “hydrogen” economy, competition from light water small modular light water reactors, little utility interest in new technologies, very low natural gas prices in the US, and a challenging licensing process in the US for non-water reactors.
With the popularization of the Internet, permeation of sensor networks, emergence of big data, increase in size of the information community, and interlinking and fusion of data and information throughout human society, physical space, and cyberspace, the information environment related to the current development of artificial intelligence (AI) has profoundly changed. AI faces important adjustments, and scientific foundations are confronted with new breakthroughs, as AI enters a new stage: AI 2.0. This paper briefly reviews the 60-year developmental history of AI, analyzes the external environment promoting the formation of AI 2.0 along with changes in goals, and describes both the beginning of the technology and the core idea behind AI 2.0 development. Furthermore, based on combined social demands and the information environment that exists in relation to Chinese development, suggestions on the development of AI 2.0 are given.
This paper summarizes the development of hydro-projects in China, blended with an international perspective. It expounds major technical progress toward ensuring the safe construction of high dams and river harnessing, and covers the theorization of uneven non-equilibrium sediment transport, inter-basin water diversion, giant hydro-generator units, pumped storage power stations, underground caverns, ecological protection, and so on.
A future smart grid must fulfill the vision of the Energy Internet in which millions of people produce their own energy from renewables in their homes, offices, and factories and share it with each other. Electric vehicles and local energy storage will be widely deployed. Internet technology will be utilized to transform the power grid into an energy-sharing inter-grid. To prepare for the future, a smart grid with intelligent periphery, or smart GRIP, is proposed. The building blocks of GRIP architecture are called clusters and include an energy-management system (EMS)-controlled transmission grid in the core and distribution grids, micro-grids, and smart buildings and homes on the periphery; all of which are hierarchically structured. The layered architecture of GRIP allows a seamless transition from the present to the future and plug-and-play interoperability. The basic functions of a cluster consist of ① dispatch, ② smoothing, and ③ mitigation. A risk-limiting dispatch methodology is presented; a new device, called the electric spring, is developed for smoothing out fluctuations in periphery clusters; and means to mitigate failures are discussed.
The research roots of 19fluorine (19F) magnetic resonance imaging (MRI) date back over 35 years. Over that time span, 1H imaging flourished and was adopted worldwide with an endless array of applications and imaging approaches, making magnetic resonance an indispensable pillar of biomedical diagnostic imaging. For many years during this timeframe, 19F imaging research continued at a slow pace as the various attributes of the technique were explored. However, over the last decade and particularly the last several years, the pace and clinical relevance of 19F imaging has exploded. In part, this is due to advances in MRI instrumentation, 19F/1H coil designs, and ultrafast pulse sequence development for both preclinical and clinical scanners. These achievements, coupled with interest in the molecular imaging of anatomy and physiology, and combined with a cadre of innovative agents, have brought the concept of 19F into early clinical evaluation. In this review, we attempt to provide a slice of this rich history of research and development, with a particular focus on liquid perfluorocarbon compound-based agents.
This paper presents findings from an investigation of the large-scale construction solid waste (CSW) landslide that occurred at a landfill at Shenzhen, Guangdong, China, on December 20, 2015, and which killed 77 people and destroyed 33 houses. The landslide involved 2.73×106 m3 of CSW and affected an area about 1100?m in length and 630?m in maximum width, making it the largest landfill landslide in the world. The investigation of this disaster used a combination of unmanned aerial vehicle surveillance and multistage remote-sensing images to reveal the increasing volume of waste in the landfill and the shifting shape of the landfill slope for nearly two years before the landslide took place, beginning with the creation of the CSW landfill in March, 2014, that resulted in the uncertain conditions of the landfill’s boundaries and the unstable state of the hydrologic performance. As a result, applying conventional stability analysis methods used for natural landslides to this case would be difficult. In order to analyze this disaster, we took a multistage modeling technique to analyze the varied characteristics of the landfill slope’s structure at various stages of CSW dumping and used the non-steady?flow?theory to explain the groundwater seepage problem. The investigation showed that the landfill could be divided into two units based on the moisture in the land: ① a front uint, consisted of the landfill slope, which had low water content; and ② a rear unit, consisted of fresh waste, which had a high water content. This structure caused two effects—surface-water infiltration and consolidation seepage that triggered the landslide in the landfill. Surface-water infiltration induced a gradual increase in pore water pressure head, or piezometric head, in the front slope because the infiltrating position rose as the volume of waste placement increased. Consolidation seepage led to higher excess pore water pressures as the loading of waste increased. We also investigated the post-failure soil dynamics parameters of the landslide deposit using cone penetration, triaxial, and ring-shear tests in order to simulate the characteristics of a flowing slide with a long run-out due to the liquefaction effect. Finally, we conclude the paper with lessons from the tens of catastrophic landslides of municipal solid waste around the world and discuss how to better manage the geotechnical risks of urbanization.
Starting with the Ertan arch dam (240 m high, 3300 MW) in 2000, China successfully built a total of seven ultra-high arch dams over 200 m tall by the end of 2014. Among these, the Jinping I (305 m), Xiaowan (294.5m), and Xiluodu (285.5 m) arch dams have reached the 300 m height level (i.e., near or over 300 m), making them the tallest arch dams in the world. The design and construction of these 300 m ultra-high arch dams posed significant challenges, due to high water pressures, high seismic design criteria, and complex geological conditions. The engineering team successfully tackled these challenges and made critical breakthroughs, especially in the area of safety control. In this paper, the author summarizes various key technological aspects involved in the design and construction of 300?m ultra-high arch dams, including the strength and stability of foundation rock, excavation of the dam base and surface treatment, dam shape optimization, safety design guidelines, seismic analysis and design, treatment of a complex foundation, concrete temperature control, and crack prevention. The experience gained from these projects should be valuable for future practitioners.
This study provides a definition for urban big data while exploring its features and applications of China’s city intelligence. The differences between city intelligence in China and the “smart city” concept in other countries are compared to highlight and contrast the unique definition and model for China’s city intelligence in this paper. Furthermore, this paper examines the role of urban big data in city intelligence by showing that it not only serves as the cornerstone of this trend as it also plays a core role in the diffusion of city intelligence technology and serves as an inexhaustible resource for the sustained development of city intelligence. This study also points out the challenges of shaping and developing of China’s urban big data. Considering the supporting and core role that urban big data plays in city intelligence, the study then expounds on the key points of urban big data, including infrastructure support, urban governance, public services, and economic and industrial development. Finally, this study points out that the utility of city intelligence as an ideal policy tool for advancing the goals of China’s urban development. In conclusion, it is imperative that China make full use of its unique advantages—including using the nation’s current state of development and resources, geographical advantages, and good human relations—in subjective and objective conditions to promote the development of city intelligence through the proper application of urban big data.
HPR1000 is an advanced nuclear power plant (NPP) with the significant feature of an active and passive safety design philosophy, developed by the China National Nuclear Corporation. On one hand, it is an evolutionary design based on proven technology of the existing pressurized water reactor NPP; on the other hand, it incorporates advanced design features including a 177-fuel-assembly core loaded with CF3 fuel assemblies, active and passive safety systems, comprehensive severe accident prevention and mitigation measures, enhanced protection against external events, and improved emergency response capability. Extensive verification experiments and tests have been performed for critical innovative improvements on passive systems, the reactor core, and the main equipment. The design of HPR1000 fulfills the international utility requirements for advanced light water reactors and the latest nuclear safety requirements, and addresses the safety issues relevant to the Fukushima accident. Along with its outstanding safety and economy, HPR1000 provides an excellent and practicable solution for both domestic and international nuclear power markets.
In recent years, risk analysis techniques have proved to be a useful tool to inform dam safety management. This paper summarizes the outcomes of three themes related to dam risk analysis discussed in the Benchmark Workshops organized by the International Commission on Large Dams Technical Committee on “Computational Aspects of Analysis and Design of Dams.” In the 2011 Benchmark Workshop, estimation of the probability of failure of a gravity dam for the sliding failure mode was discussed. Next, in 2013, the discussion focused on the computational challenges of the estimation of consequences in dam risk analysis. Finally, in 2015, the probability of sliding and overtopping in an embankment was analyzed. These Benchmark Workshops have allowed a complete review of numerical aspects for dam risk analysis, showing that risk analysis methods are a very useful tool to analyze the risk of dam systems, including downstream consequence assessments and the uncertainty of structural models.
A historical review of in-vessel melt retention (IVR) is given, which is a severe accident mitigation measure extensively applied in Generation III pressurized water reactors (PWRs). The idea of IVR actually originated from the back-fitting of the Generation II reactor Loviisa VVER-440 in order to cope with the core-melt risk. It was then employed in the new deigns such as Westinghouse AP1000, the Korean APR1400 as well as Chinese advanced PWR designs HPR1000 and CAP1400. The most influential phenomena on the IVR strategy are in-vessel core melt evolution, the heat fluxes imposed on the vessel by the molten core, and the external cooling of the reactor pressure vessel (RPV). For in-vessel melt evolution, past focus has only been placed on the melt pool convection in the lower plenum of the RPV; however, through our review and analysis, we believe that other in-vessel phenomena, including core degradation and relocation, debris formation, and coolability and melt pool formation, may all contribute to the final state of the melt pool and its thermal loads on the lower head. By looking into previous research on relevant topics, we aim to identify the missing pieces in the picture. Based on the state of the art, we conclude by proposing future research needs.
After the first concrete was poured on December 9, 2012 at the Shidao Bay site in Rongcheng, Shandong Province, China, the construction of the reactor building for the world’s first high-temperature gas-cooled reactor pebble-bed module (HTR-PM) demonstration power plant was completed in June, 2015. Installation of the main equipment then began, and the power plant is currently progressing well toward connecting to the grid at the end of 2017. The thermal power of a single HTR-PM reactor module is 250 MWth, the helium temperatures at the reactor core inlet/outlet are 250/750 °C, and a steam of 13.25 MPa/567 °C is produced at the steam generator outlet. Two HTR-PM reactor modules are connected to a steam turbine to form a 210 MWe nuclear power plant. Due to China’s industrial capability, we were able to overcome great difficulties, manufacture first-of-a-kind equipment, and realize series major technological innovations. We have achieved successful results in many aspects, including planning and implementing R&D, establishing an industrial partnership, manufacturing equipment, fuel production, licensing, site preparation, and balancing safety and economics; these obtained experiences may also be referenced by the global nuclear community.
In 2011, the Chinese Academy of Sciences launched an engineering project to develop an accelerator-driven subcritical system (ADS) for nuclear waste transmutation. The China Lead-based Reactor (CLEAR), proposed by the Institute of Nuclear Energy Safety Technology, was selected as the reference reactor for ADS development, as well as for the technology development of the Generation IV lead-cooled fast reactor. The conceptual design of CLEAR-I with 10 MW thermal power has been completed. KYLIN series lead-bismuth eutectic experimental loops have been constructed to investigate the technologies of the coolant, key components, structural materials, fuel assembly, operation, and control. In order to validate and test the key components and integrated operating technology of the lead-based reactor, the lead alloy-cooled non-nuclear reactor CLEAR-S, the lead-based zero-power nuclear reactor CLEAR-0, and the lead-based virtual reactor CLEAR-V are under realization.
The pressurized water reactor CAP1400 is one of the sixteen National Science and Technology Major Projects. Developed from China’s nuclear R&D system and manufacturing capability, as well as AP1000 technology introduction and assimilation, CAP1400 is an advanced large passive nuclear power plant with independent intellectual property rights. By discussing the top design principle, main performance objectives, general parameters, safety design, and important improvements in safety, economy, and other advanced features, this paper reveals the technology innovation and competitiveness of CAP1400 as an internationally promising Gen-III PWR model. Moreover, the R&D of CAP1400 has greatly promoted China’s domestic nuclear power industry from the Gen-II to the Gen-III level.
This paper presents an overview of the current status of the development of the smart grid in Great Britain (GB). The definition, policy and technical drivers, incentive mechanisms, technological focus, and the industry's progress in developing the smart grid are described. In particular, the Low Carbon Networks Fund and Electricity Network Innovation Competition projects, together with the rollout of smart metering, are detailed. A more observable, controllable, automated, and integrated electricity network will be supported by these investments in conjunction with smart meter installation. It is found that the focus has mainly been on distribution networks as well as on real-time flows of information and interaction between suppliers and consumers facilitated by improved information and communications technology, active power flow management, demand management, and energy storage. The learning from the GB smart grid initiatives will provide valuable guidelines for future smart grid development in GB and other countries.
The rise of big data has led to new demands for machine learning (ML) systems to learn complex models, with millions to billions of parameters, that promise adequate capacity to digest massive datasets and offer powerful predictive analytics (such as high-dimensional latent features, intermediate representations, and decision functions) thereupon. In order to run ML algorithms at such scales, on a distributed cluster with tens to thousands of machines, it is often the case that significant engineering efforts are required—and one might fairly ask whether such engineering truly falls within the domain of ML research. Taking the view that “big” ML systems can benefit greatly from ML-rooted statistical and algorithmic insights—and that ML researchers should therefore not shy away from such systems design—we discuss a series of principles and strategies distilled from our recent efforts on industrial-scale ML solutions. These principles and strategies span a continuum from application, to engineering, and to theoretical research and development of big ML systems and architectures, with the goal of understanding how to make them efficient, generally applicable, and supported with convergence and scaling guarantees. They concern four key questions that traditionally receive little attention in ML research: How can an ML program be distributed over a cluster? How can ML computation be bridged with inter-machine communication? How can such communication be performed? What should be communicated between machines? By exposing underlying statistical and algorithmic characteristics unique to ML programs but not typically seen in traditional computer programs, and by dissecting successful cases to reveal how we have harnessed these principles to design and develop both high-performance distributed ML software as well as general-purpose ML frameworks, we present opportunities for ML researchers and practitioners to further shape and enlarge the area that lies between ML and systems.
To date, the Three Gorges Project is the largest hydro junction in the world. It is the key project for the integrated water resource management and development of the Changjiang River. The technology of the project, with its huge scale and comprehensive benefits, is extremely complicated, and the design difficulty is greater than that of any other hydro project in the world. A series of new design theories and methods have been proposed and applied in the design and research process. Many key technological problems regarding hydraulic structures have been overcome, such as a gravity dam with multi-layer large discharge orifices, a hydropower station of giant generating units, and a giant continual multi-step ship lock with a high water head.
Additive manufacturing (AM) permits the fabrication of functionally optimized components with high geometrical complexity. The opportunity of using porous infill as an integrated part of the manufacturing process is an example of a unique AM feature. Automated design methods are still incapable of fully exploiting this design freedom. In this work, we show how the so-called coating approach to topology optimization provides a means for designing infill-based components that possess a strongly improved buckling load and, as a result, improved structural stability. The suggested approach thereby addresses an important inadequacy of the standard minimum compliance topology optimization approach, in which buckling is rarely accounted for; rather, a satisfactory buckling load is usually assured through a post-processing step that may lead to sub-optimal components. The present work compares the standard and coating approaches to topology optimization for the MBB beam benchmark case. The optimized structures are additively manufactured using a filamentary technique. This experimental study validates the numerical model used in the coating approach. Depending on the properties of the infill material, the buckling load may be more than four times higher than that of solid structures optimized under the same conditions.
Molecular imaging (MI) can provide not only structural images using traditional imaging techniques but also functional and molecular information using many newly emerging imaging techniques. Over the past decade, the utilization of nanotechnology in MI has exhibited many significant advantages and provided new opportunities for the imaging of living subjects. It is expected that multimodality nanoparticles (NPs) can lead to precise assessment of tumor biology and the tumor microenvironment. This review addresses topics related to engineered NPs and summarizes the recent applications of these nanoconstructs in cancer optical imaging, ultrasound, photoacoustic imaging, magnetic resonance imaging (MRI), and radionuclide imaging. Key challenges involved in the translation of NPs to the clinic are discussed.
This paper collects and synthesizes the technical requirements, implementation, and validation methods for quasi-steady agent-based simulations of interconnection-scale models with particular attention to the integration of renewable generation and controllable loads. Approaches for modeling aggregated controllable loads are presented and placed in the same control and economic modeling framework as generation resources for interconnection planning studies. Model performance is examined with system parameters that are typical for an interconnection approximately the size of the Western Electricity Coordinating Council (WECC) and a control area about 1/100 the size of the system. These results are used to demonstrate and validate the methods presented.