In this research, a new adaptive control strategy is formulated for the pitch control of wind turbine that may suffer from reduced life owing to extreme loads and fatigue when operated under high ...wind speed and internal structural uncertainties. Specifically, we aim at making a trade-off between the maximum energy captured and the load induced. The adaptive controller is designed to both regulate generator speed and mitigate component loads under turbulent wind field when blade stiffness uncertainties exist. The proposed algorithm is tested on the NREL offshore 5-MW benchmark wind turbine. The control performance is compared with those of the gain scheduled proportional integral (GSPI) control and the disturbance accommodating control (DAC) that are used as baselines. The results show that with the proposed adaptive control the blade root flapwise load can be reduced at a slight expense of optimal power output. Moreover, the blade load mitigation performance under uncertain blade stiffness reduction is improved over the baseline controllers. The control approach developed in this research is general, and can be extended to mitigating loads on other components.
•A new adaptive control is formulated for the pitch control of wind turbine with structural uncertainties.•The controller makes a trade-off between the maximum energy captured and the load induced.•The controller is designed to regulate generator speed and to mitigate component loads under turbulent wind field.•Case studies show that the blade root flapwise load can indeed be reduced at a slight expense of optimal power output.
Advances from immuno-oncology (IO) are changing the standard of care of many types of cancer, and the paradigm of cancer treatments and drug development is being rewritten on a regular basis. ...Moreover, an unprecedented number of new investigational agents and companies are entering the field of IO. As such, it has become challenging for oncology physicians conducting clinical trials, industry veterans developing IO drugs, and even regulators reviewing novel IO agents to keep track of the rapidly evolving landscape. To help the key stake holders in the field understand the latest IO landscape, we sought to present an unbiased, neutral, scientifically curated, and timely updated analysis of all the current IO agents in clinical development and the clinical trials testing these agents. We based our analyses on information collected from numerous trusted and publicly available sources. We have developed two databases. One database tracks 2004 IO agents (940 in clinical stage and 1064 in preclinical stage) against 303 targets, from 864 companies; the other tracks 3042 active clinical trials of these agents with a target enrollment of 577 076 patients. This report provides key analyses of these data. Furthermore, we will discuss a number of important and actionable trends in the current IO landscape: a large number of companies developing agents against the same IO targets; a rapid increase in the number of anti-PD-1/L1 combination studies, many of which are testing the same combinations and following inefficient patterns; and a significant increase in the number of small, investigator-initiated studies. For each of the findings, we speculate the causes and discuss a few initiatives that aim to address some of these challenges. Finally, by making these landscape analyses available, we aspire to inform the cancer community as they seek to strive for efficiencies and innovation while avoiding duplication.
•Fault signature enhancement is developed for gearbox vibration measurement.•Signals are processed through angle-frequency domain synchronous averaging.•Enhanced results are analyzed through feature ...extraction algorithms.•Case studies reveal the effectiveness in enabling feature classification.
Gear fault diagnosis relies heavily on the scrutiny of vibration responses measured. In reality, gear vibration signals are noisy and dominated by meshing frequencies as well as their harmonics, which oftentimes overlay the fault related components. Moreover, many gear transmission systems, e.g., those in wind turbines, constantly operate under non-stationary conditions. To reduce the influences of non-synchronous components and noise, a fault signature enhancement method that is built upon angle-frequency domain synchronous averaging is developed in this paper. Instead of being averaged in the time domain, the signals are processed in the angle-frequency domain to solve the issue of phase shifts between signal segments due to uncertainties caused by clearances, input disturbances, and sampling errors, etc. The enhanced results are then analyzed through feature extraction algorithms to identify the most distinct features for fault classification and identification. Specifically, Kernel Principal Component Analysis (KPCA) targeting at nonlinearity, Multilinear Principal Component Analysis (MPCA) targeting at high dimensionality, and Locally Linear Embedding (LLE) targeting at local similarity among the enhanced data are employed and compared to yield insights. Numerical and experimental investigations are performed, and the results reveal the effectiveness of angle-frequency domain synchronous averaging in enabling feature extraction and classification.
•Multi-response Gaussian process (MRGP) emulation is formulated for model updating.•Adaptive sampling strategy is established to guide the search of unknown parameters.•MRGP meta-model is iteratively ...refined throughout searching.•Case studies demonstrate the high efficiency and accuracy of the new framework.
Finite element model updating utilizing frequency response functions as inputs is an important procedure in structural analysis, design and control. This paper presents a highly efficient framework that is built upon Gaussian process emulation to inversely identify model parameters through sampling. In particular, a multi-response Gaussian process (MRGP) meta-modeling approach is formulated that can accurately construct the error response surface, i.e., the discrepancies between the frequency response predictions and actual measurement. In order to reduce the computational cost of repeated finite element simulations, an adaptive sampling strategy is established, where the search of unknown parameters is guided by the response surface features. Meanwhile, the information of previously sampled model parameters and the corresponding errors is utilized as additional training data to refine the MRGP meta-model. Two stochastic optimization techniques, i.e., particle swarm and simulated annealing, are employed to train the MRGP meta-model for comparison. Systematic case studies are conducted to examine the accuracy and robustness of the new framework of model updating.
Coal fly ash, an industrial by-product, is derived from coal combustion in thermal power plants. It is one of the most complex anthropogenic materials, and its improper disposal has become an ...environmental concern and resulted in a waste of recoverable resources. There is a pressing and ongoing need to develop new recycling methods for coal fly ash. The present review first describes the generation, physicochemical properties and hazards of coal fly ash at the global level, and then focuses on its current and potential applications, including use in the soil amelioration, construction industry, ceramic industry, catalysis, depth separation, zeolite synthesis, etc. Finally, the advantages and disadvantages of these applications, the mode of fly ash utilization worldwide and directions for future research are considered.
The coronavirus disease 2019 (COVID-19) pandemic has caused untold disruption throughout the world. Understanding the mechanisms for transmission of severe acute respiratory syndrome coronavirus-2 ...(SARS-CoV-2) is key to preventing further spread, but there is confusion over the meaning of ‘airborne’ whenever transmission is discussed. Scientific ambivalence originates from evidence published many years ago which has generated mythological beliefs that obscure current thinking. This article collates and explores some of the most commonly held dogmas on airborne transmission in order to stimulate revision of the science in the light of current evidence. Six ‘myths’ are presented, explained and ultimately refuted on the basis of recently published papers and expert opinion from previous work related to similar viruses. There is little doubt that SARS-CoV-2 is transmitted via a range of airborne particle sizes subject to all the usual ventilation parameters and human behaviour. Experts from specialties encompassing aerosol studies, ventilation, engineering, physics, virology and clinical medicine have joined together to produce this review to consolidate the evidence for airborne transmission mechanisms, and offer justification for modern strategies for prevention and control of COVID-19 in health care and the community.
Biogenic volatile organic compounds (BVOCs) can be released from soils to the atmosphere through microbial decomposition of plant residues or soil organic carbon, root emission, evaporation of ...litter‐stored BVOCs, and other physical processes. Soils can also act as a sink of BVOCs through biotic and abiotic uptake. Currently, the source and sink capabilities of soils have not been explicitly accounted for in global BVOC estimates from the terrestrial biosphere. In this review, we summarize the current knowledge of soil BVOC processes and aim to propose a generic framework for modelling soil BVOCs based on current understanding and data availability. To achieve this target, we start by reviewing measured sources and sinks of soil BVOCs and summarize commonly reported compounds. Next, we strive to disentangle the drivers for the underlying biotic and abiotic processes. We have ranked the list of compounds, known to be emitted from soils, based on our current understanding of how each process controls emission and uptake. We then present a modelling framework to describe soil BVOC emissions. The proposed framework is an important step toward initializing modelling exercises related to soil BVOC fluxes. Finally, we also provide suggestions for measurements needed to separate individual processes, as well as explore long‐term and large‐scale patterns in soil BVOC fluxes.
Plain Language Summary
Living plants emit biogenic volatile organic compounds (BVOCs), which have impacts on regional and global climate. However, BVOCs can also be released from fallen leaf litter, plant roots, and soil organic matter, and some compounds are also consumed by soil microbes. In this article, we begin by sorting out the processes that govern soil emissions and uptakes of BVOCs and summarize the current understanding and available data for each process. Furthermore, we propose a generic modelling framework to add soil BVOC‐related processes into the typical structure existing in many ecosystem models. We also provide suggestions for future measurements that would help with model‐data integration.
Key Points
We present biotic and abiotic drivers for soil BVOC emissions and sinks
We summarize the often‐reported compounds and key processes regulating their fluxes
We propose a generic framework for including soil BVOCs in ecosystem models
Multi-objective optimization allows satisfying multiple decision criteria concurrently, and generally yields multiple solutions. It has the potential to be applied to structural damage identification ...applications which are oftentimes under-determined. How to achieve high-quality solutions in terms of accuracy, diversity, and completeness is a challenging research subject. The solution techniques and parametric selections are believed to be problem specific. In this research, we formulate a reinforcement learning hyper-heuristic scheme to work coherently with the single-point search algorithm MOSA/R (Multi-Objective Simulated Annealing Algorithm based on Re-seed). The four low-level heuristics proposed can meet various optimization requirements adaptively and autonomously using the domination amount, crowding distance, and hypervolume calculations. The new approach exhibits improved and more robust performance than AMOSA, NSGA-II, and MOEA/D when applied to benchmark test cases. It is then applied to an active damage interrogation scheme for structural damage identification where solution diversity/completeness and accuracy are critically important. Results show that this approach can successfully include the true damage scenario in the solution set identified. The outcome of this research can potentially be extended to a variety of applications.
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