Automated algorithm for impact force identification using cosine similarity searching

© 2018 A similarity searching technique is adopted to identify the impact force applied on a rectangular carbon fibre-epoxy honeycomb composite panel. The purpose of this study is to simultaneously identify both the location and magnitude of an unknown impact using the measured dynamic response collected by only a single piezoelectric sensor. The algorithm assumes that a set of impact forces are concurrently applied on a set of pre-defined locations. However, the magnitude of all the impact forces except one is considered to be zero. The impact force at all potential locations is then reconstructed through an l2-norm-based regularisation via two strategies: even-determined approach and under-determined approach. In an even-determined approach, the reconstruction process is performed independently for each pair of sensor and potential impact location. However, in an under-determined approach, the captured vibration signal is the superposition of the responses of the simultaneous ‘assumed’ impacts at the potential locations. Using either approach, a reconstructed impact force is obtained for each potential impact location. The reconstructed impact forces at spurious locations are expected to have zero magnitude as no impact has actually occurred at these locations. However, there might be some non-zero reconstructed impact forces at spurious locations. Therefore, it is worth designing an automated algorithm capable of detecting the most probable location. Cosine similarity searching is adopted to measure the intensity of the relationship between the reconstructed forces and an impact-like signal with various scale parameters. The largest value of cosine among all reconstructed forces corresponds to the most probable impact location. The results illustrate successful identification of the impact force location and magnitude for both even-determined and under-determined approaches

Inverse Dynamics Problems for a Sustainable Future

Inverse dynamics problems and associated aspects are all around us in everyday life but are commonly overlooked and/or not fully comprehended [...]</jats:p

Automated Operational Modal Analysis of a Cable-Stayed Bridge

© 2017 American Society of Civil Engineers. Automated techniques for analyzing the dynamic behavior of full-scale civil structures are becoming increasingly important for continuous structural health-monitoring applications. This paper describes an experimental study aimed at the identification of modal parameters of a full-scale cable-stayed bridge from the collected output-only vibration data without the need for any user interactions. The work focuses on the development of an automated and robust operational modal analysis (OMA) algorithm, using a multistage clustering approach. The main contribution of the work is to discuss a comprehensive case study to demonstrate the reliability of a novel criterion aimed at defining the hierarchical clustering threshold to enable the accurate identification of a complete set of modal parameters. The proposed algorithm is demonstrated to work with any parametric system identification algorithm that uses the system order n as the sole parameter. In particular, the results from the covariance-driven stochastic subspace identification (SSI-Cov) methods are presented

Hollow Fiber Bioreactor Technology for Tissue Engineering Applications

Hollow fiber bioreactors are the focus of scientific research aiming to mimic physiological vascular networks and engineer organs and tissues in vitro. The reason for this lies in the interesting features of this bioreactor type, including excellent mass transport properties. Indeed, hollow fiber bioreactors allow limitations to be overcome in nutrient transport by diffusion, which is often an obstacle to engineer sizable constructs in vitro. This work reviews the existing literature relevant to hollow fiber bioreactors in organ and tissue engineering applications. To this purpose, we first classify the hollow fiber bioreactors into 2 categories: cylindrical and rectangular. For each category, we summarize their main applications both at the tissue and at the organ level, focusing on experimental models and computational studies as predictive tools for designing innovative, dynamic culture systems. Finally, we discuss future perspectives on hollow fiber bioreactors as in vitro models for tissue and organ engineering applications. </jats:p

Enhanced cosmic-ray flux toward zeta Persei inferred from laboratory study of H3+ - e- recombination rate

The H3+ molecular ion plays a fundamental role in interstellar chemistry, as it initiates a network of chemical reactions that produce many interstellar molecules. In dense clouds, the H3+ abundance is understood using a simple chemical model, from which observations of H3+ yield valuable estimates of cloud path length, density, and temperature. On the other hand, observations of diffuse clouds have suggested that H3+ is considerably more abundant than expected from the chemical models. However, diffuse cloud models have been hampered by the uncertain values of three key parameters: the rate of H3+ destruction by electrons, the electron fraction, and the cosmic-ray ionisation rate. Here we report a direct experimental measurement of the H3+ destruction rate under nearly interstellar conditions. We also report the observation of H3+ in a diffuse cloud (towards zeta Persei) where the electron fraction is already known. Taken together, these results allow us to derive the value of the third uncertain model parameter: we find that the cosmic-ray ionisation rate in this sightline is forty times faster than previously assumed. If such a high cosmic-ray flux is indeed ubiquitous in diffuse clouds, the discrepancy between chemical models and the previous observations of H3+ can be resolved.Comment: 6 pages, Nature, in pres

Scoping carbon dioxide removal options for Germany–What is their potential contribution to Net-Zero CO2_{2}?

In its latest assessment report the IPCC stresses the need for carbon dioxide removal (CDR) to counterbalance residual emissions to achieve net zero carbon dioxide or greenhouse gas emissions. There are currently a wide variety of CDR measures available. Their potential and feasibility, however, depends on context specific conditions, as among others biophysical site characteristics, or availability of infrastructure and resources. In our study, we selected 13 CDR concepts which we present in the form of exemplary CDR units described in dedicated fact sheets. They cover technical CO2 removal (two concepts of direct air carbon capture), hybrid solutions (six bioenergy with carbon capture technologies) and five options for natural sink enhancement. Our estimates for their CO2 removal potentials in 2050 range from 0.06 to 30 million tons of CO2, depending on the option. Ten of the 13 CDR concepts provide technical removal potentials higher than 1 million tons of CO2 per year. To better understand the potential contribution of analyzed CDR options to reaching net-zero CO2 emissions, we compare our results with the current CO2 emissions and potential residual CO2 emissions in 2050 in Germany. To complement the necessary information on technology-based and hybrid options, we also provide an overview on possible solutions for CO2 storage for Germany. Taking biophysical conditions and infrastructure into account, northern Germany seems a preferable area for deployment of many concepts. However, for their successful implementation further socio-economic analysis, clear regulations, and policy incentives are necessary

Earlier snowmelt may lead to late season declines in plant productivity and carbon sequestration in Arctic tundra ecosystems

Arctic warming is affecting snow cover and soil hydrology, with consequences for carbon sequestration in tundra ecosystems. The scarcity of observations in the Arctic has limited our understanding of the impact of covarying environmental drivers on the carbon balance of tundra ecosystems. In this study, we address some of these uncertainties through a novel record of 119 site-years of summer data from eddy covariance towers representing dominant tundra vegetation types located on continuous permafrost in the Arctic. Here we found that earlier snowmelt was associated with more tundra net CO2 sequestration and higher gross primary productivity (GPP) only in June and July, but with lower net carbon sequestration and lower GPP in August. Although higher evapotranspiration (ET) can result in soil drying with the progression of the summer, we did not find significantly lower soil moisture with earlier snowmelt, nor evidence that water stress affected GPP in the late growing season. Our results suggest that the expected increased CO2 sequestration arising from Arctic warming and the associated increase in growing season length may not materialize if tundra ecosystems are not able to continue sequestering CO2 later in the season.Peer reviewe

Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)