Optimal operating conditions to maximize the net power of polymer electrolyte membrane fuel cells: The stack-system interface of a commercial 18 kW module

A new, experimental method based on air flow rate rather than current is presented to optimize operating parameters for the stacks and systems of proton exchange membrane fuel cells (PEMFCs) for maximizing their net power. This approach is illustrated for a commercial 18 kW PEMFC module. The impact of contamination pressure drop across the cathode air filter is also investigated on the compressor behavior. It is further shown that a 4V reduction in the compressor voltage reduces its power consumption by 9.1%. Using the 3D graphs of the power-pressure-flow data, it is found that the stack pressure of 180 kPaa is superior to the higher tested pressures as it enhances the net power by 7.0 and 13.7% at different conditions. Application of the present study will lead to the development of PEMFCs with higher power output by optimizing stack pressure, stoichiometry and air flow to properly deliver the system design specifications.     

Removing data and using metafounders alleviates biases for all traits in Lacaune dairy sheep predictions

Bias in dairy genetic evaluations, when it exists, has to be understood and properly addressed. The origin of biases is not always clear. We analyzed 40 yr of records from the Lacaune dairy sheep breeding program to evaluate the extent of bias, assess possible corrections, and emit hypotheses on its origin. The data set included 7 traits (milk yield, fat and protein contents, somatic cell score, teat angle, udder cleft, and udder depth) with records from 600,000 to 5 million depending on the trait, ～1,900,000 animals, and ～5,900 genotyped elite artificial insemination rams. For the ～8% animals with missing sire, we fit 25 unknown parent groups. We used the linear regression method to compare “partial” and “whole” predictions of young rams before and after progeny testing, with 7 cut-off points, and we obtained estimates of their bias, (over)dispersion, and accuracy in early proofs. We tried (1) several scenarios as follows: multiple or single trait, the “official” (routine) evaluation, which is a mixture of both single and multiple trait, and “deletion” of data before 1990; and (2) several models as follows: BLUP and single-step genomic (SSG)BLUP with fixed unknown parent groups or metafounders, where, for metafounders, their relationship matrix gamma was estimated using either a model for inbreeding trend, or base allele frequencies estimated by peeling. The estimate of gamma obtained by modeling the inbreeding trend resulted in an estimated increase of inbreeding, based on markers, faster than the pedigree-based one. The estimated genetic trends were similar for most models and scenarios across all traits, but were shrunken when gamma was estimated by peeling. This was due to shrinking of the estimates of metafounders in the latter case. Across scenarios, all traits showed bias, generally as an overestimate of genetic trend for milk yield and an underestimate for the other traits. As for the slope, it showed overdispersion of estimated breeding values for all traits. Using multiple-trait models slightly reduced the overestimate of genetic trend and the overdispersion, as did including genomic information (i.e., SSGBLUP) when the gamma matrix was estimated by the model for inbreeding trend. However, only deletion of historical data before 1990 resulted in elimination of both kind of biases. The SSGBLUP resulted in more accurate early proofs than BLUP for all traits. We considered that a snowball effect of small errors in each genetic evaluation, combined with selection, may have resulted in biased evaluations. Improving statistical methods reduced some bias but not all, and a simple solution for this data set was to remove historical records.     

基于VMD和改进TCN的短期光伏发电功率预测

光伏发电功率存在波动性,且光伏出力易受各种气象特征影响,传统TCN网络容易过度强化空间特性而弱化个体特性。针对上述问题,文中提出一种基于VMD和改进TCN的短期光伏发电功率预测模型。通过VMD将原始光伏发电功率时间序列分解为若干不同频率的模态分量,将各个模态分量以及相对应的气象数据输入至改进TCN网络进行建模学习。利用中心频率法确定VMD的最优分解模态分解个数。在传统TCN预测模型的基础上,使用DropBlock正则化取代Dropout正则化以达到抑制卷积层中信息协同的效果,并引入注意力机制自主挖掘并突出关键气象输入特征的影响,量化各气象因素对光伏发电的影响,从而提高预测精度。以江苏省某光伏电站真实数据为例进行仿真实验,结果表明所提预测方法的RMSE为0.62 MW,MAPE为2.03%。     

Local scour at offshore windfarm monopile foundations: A review

In this article, current research findings of local scour at offshore windfarm monopile foundations are presented. The scour mechanisms and scour depth prediction formulas under different hydrodynamic conditions are summarized, including the current-only condition, wave-only condition, combined wave-current condition, and complex dynamic condition. Furthermore, this article analyzes the influencing factors on the basis of classical equations for predicting the equilibrium scour depth under specific conditions. The weakness of existing researches and future prospects are also discussed. It is suggested that future research shall focus on physical experiments under unsteady tidal currents or other complex loadings. The computational fluid dynamics-discrete element method and artificial intelligence technique are suggested being adopted to study the scour at offshore windfarm foundations.     

基于多通道的ICU脑血管疾病死亡风险预测模型

死亡风险预测指根据病人临床体征监测数据来预测未来一段时间的死亡风险。对于ICU病患，通过死亡风险预测可以有针对性地对病人做出临床诊断，以及合理安排有限的医疗资源。基于临床使用的MEWS和Glasgow昏迷评分量表，针对ICU病人临床监测的17项生理参数，提出一种基于多通道的ICU脑血管疾病死亡风险预测模型。引入多通道概念应用于BiLSTM模型，用于突出每个生理参数对死亡风险预测的作用。采用Attention机制用于提高模型预测精度。实验数据来自MIMIC [Ⅲ]数据库，从中提取3?080位脑血管疾病患者的16?260条记录用于此次研究，除了六组超参数实验之外，将所提模型与LSTM、Multichannel-BiLSTM、逻辑回归（logistic regression）和支持向量机（support vector machine, SVM）四种模型进行了对比分析，准确率Accuracy、灵敏度Sensitive、特异性Specificity、AUC-ROC和AUC-PRC作为评价指标，实验结果表明，所提模型性能优于其他模型，AUC值达到94.3%。     

Finite-time adaptive fuzzy control for a class of output constrained nonlinear systems with dead-zone

The article investigates the finite-time adaptive fuzzy control for a class of nonlinear systems with output constraint and input dead-zone. First, by skillfully combining the barrier Lyapunov function, backstepping design method, and finite-time control theory, a novel adaptive state-feedback tracking controller is constructed, and the output constraint of the nonlinear system is not violated. Second, the fuzzy logic system is used to approximate unknown function in the nonlinear system. Third, the finite-time command filter is introduced to avoid the problem of “complexity explosion” caused by repeated differentiations of the virtual control signal in conventional backstepping control schemes. Meanwhile, a new saturation function is added in the compensating signal for filter error to improve control accuracy. Finally, based on Lyapunov stability analysis, all the signals of the closed-loop are proved to be semi-globally uniformly ultimately bounded, and the tracking error converges to a small neighborhood region of the origin in a finite time. A simulation example is presented to demonstrate the effectiveness for the proposed control scheme.     

Comprehensive numerical investigations on the thermal performance of heat recirculating micro combustor with pin fins for micro-thermal voltaic system applications

Aiming at improving the relatively low energy output and energy conversion efficiency of the micro-thermal voltaic (MTPV) system, an innovative heat recirculating micro combustor with pin fins is designed. The effects of pin fins arrangement, hydrogen/air equivalent ratio on the energy output and performance of CHMC, HMCP and HMCI are compared and investigated. The result shows that when the V_in is 6 m/s and Φ is 1.0, the emitter power of CHMC is 72.76W, and that of HCMP and HCMI micro combustor are 75.99W and 76.35W. and the emitter efficiency of CHMC, HCMP and HCMI is 41.93%, 43.26% and 44.01%. HMCI has better energy output capability compared with CHMC and HMCP. Even though, HMCI brings a higher pressure drop, it is within the acceptable range. When the V_in is 6 m/s, the pressure drop from the pin fins only accounts for 26.4% of the total pressure drop for HMCI. Through the study of equivalent ratio, it is found that HMCI has good adaptability in different equivalent ratio range. This work provides new ideas for the development of MTPV system in the future.     

Experimental study and model predictions on helium release in an enclosure with single or multiple vents

This paper presents experiments performed at Canadian Nuclear Laboratories (CNL) to examine the dispersion behaviour of helium in a polycarbonate enclosure that was representative of a residential parking garage. The purpose was to gain a better understanding of the effect of buoyancy- or wind-driven natural ventilation on hydrogen dispersion behaviour. Although hydrogen dispersion studies have been reported extensively in the literature, gaps still exist in predictive methods for hazard analysis. Helium, a simulant for hydrogen, was injected near the centre of the floor with a flow rate ranging from 5 to 75 standard litres per minute through an upward-facing nozzle, resulting in an injection Richardson number ranging between 10^?1 and 10². The location of the nozzle varied from the bottom of the enclosure to near the ceiling to examine the impact of the nozzle elevation on the development of a stratified layer in the upper region of the enclosure. When the injection nozzle was placed at a sufficiently low elevation, the vertical helium profile always consisted of a homogenous layer at the top overlaying a stratified layer at the bottom. To simulate outdoor environmental conditions, a fan was placed in front of each vent to examine the effect of opposing or assisting wind on the dispersion. The helium transients in the uniform layer predicted with analytical models were in good agreement with the measured transients for most tests. Model improvements are required for adequately predicting transients with primarily stratified profiles or strong opposing wind.     

Predictive environmental hydrogen embrittlement on fracture toughness of commercial ferritic steels with hydrogen-modified fracture strain model

In this work, a practical numerical model with few parameters was proposed for the prediction of environmental hydrogen embrittlement. The proposed method adopts hydrogen enhanced plasticity-based mechanism in a fracture strain model to describe hydrogen embrittlement. Fracture toughness degradation of three commercial steels SA372J70, AISI4130 and X80 in high pressure hydrogen environment were investigated. Firstly, governing equations for hydrogen distribution and material damage evolution was established. Hydrogen enhanced localized flow softening effect was coupled within fracture strain dependency on stress triaxiality. Then, the numerical implementation and identification process of model parameters was described. Model parameters of the investigated steels were determined based on experiment results from literatures. Finally, with the calibrated model, fracture toughness reduction of the steels was predicted in a wide range of hydrogen pressure. The prediction results were compared with experimental results. Reasonable accuracy was reached. The proposed method is an attempt to reach balance between physical accurate prediction and engineering practicality. It is promising to provide a simplified numerical tool for the design and fit for service evaluation of hydrogen storage vessels.