Data Assimilation by Stochastic Ensemble Kalman Filtering to Enhance Turbulent Cardiovascular Flow Data From Under-Resolved Observations

We propose a data assimilation methodology that can be used to enhance the spatial and temporal resolution of voxel-based data as it may be obtained from biomedical imaging modalities. It can be used to improve the assessment of turbulent blood flow in large vessels by combining observed data with a computational fluid dynamics solver. The methodology is based on a Stochastic Ensemble Kalman Filter (SEnKF) approach and geared toward pulsatile and turbulent flow configurations. We describe the observed flow fields by a mean value and its covariance. These flow fields are combined with forecasts obtained from a direct numerical simulation of the flow field. The method is validated against canonical pulsatile and turbulent flows. Finally, it is applied to a clinically relevant configuration, namely the flow downstream of a bioprosthetic valve in an aorta phantom. It is demonstrated how the 4D flow field obtained from experimental observations can be enhanced by the data assimilation algorithm. Results show that the presented method is promising for future use with in vivo data from 4D Flow Magnetic Resonance Imaging (4D Flow MRI). 4D Flow MRI returns spatially and temporally averaged flow fields that are limited by the spatial and the temporal resolution of the tool. These averaged flow fields and the associated uncertainty might be used as observation data in the context of the proposed methodology.

An Ensemble Kalman Filter approach to assess the effects of hydrological variability, water diversion, and meteorological forcing on the total phosphorus concentration in a shallow reservoir

Total phosphorus (TP) is a vitally important water quality index in shallow reservoirs and is closely connected with hydrological variability, anthropogenic water diversion and meteorological forcing. However, it is still unclear to what extent the TP concentration in a complex shallow reservoir system attributes to each type of forcing. To resolve this issue, this study proposed a TP concentration contribution index (TPI) to assess the contribution of each forcing, using the data assimilation (DA) method, the Ensemble Kalman Filter (EnKF), which was applied in the shallow Yuqiao Reservoir, China. The EnKF model was conducted based on the Vollenweider model and logistic regression models with datasets of 1989-2015. The results showed that human-originated activities forcing (water diversion) contributed the maximum TPI (40%), followed by hydrological variability forcing (37%). Finally, meteorological forcing (air temperature and wind included) only accounted for 23%. Furthermore, the seasonal analyses also showed that the TPI of hydrological variability dominated in spring and winter, with 65% and 73% respectively. However, the contributions of meteorological forcing (air temperature and wind) accounted for a larger proportion of 63% and 57% in summer and autumn. The benefit of our EnKF model denoises the Gaussian noise contained in observation and simulation, which offers a chance to isolate and identify even a minor driving factor (i.e., meteorological forcing) from a complex river and lake system with limited data. The study provides a method to assess the influence of direct and indirect forcing on TP concentration in shallow reservoirs from a quantitative perspective. Thus, it may serve as a useful tool for water quality management in water-receiving systems.

Adaptive forecasting of phytoplankton communities

The global proliferation of harmful algal blooms poses an increasing threat to water resources, recreation and ecosystems. Predicting the occurrence of these blooms is therefore needed to assist water managers in making management decisions to mitigate their impact. Evaluation of the potential for forecasting of algal blooms using the phytoplankton community model PROTECH was undertaken in pseudo-real-time. This was achieved within a data assimilation scheme using the Ensemble Kalman Filter to allow uncertainties and model nonlinearities to be propagated to forecast outputs. Tests were made on two mesotrophic lakes in the English Lake District, which differ in depth and nutrient regime. Some forecasting success was shown for chlorophyll a, but not all forecasts were able to perform better than a persistence forecast. There was a general reduction in forecast skill with increasing forecasting period but forecasts for up to four or five days showed noticeably greater promise than those for longer periods. Associated forecasts of phytoplankton community structure were broadly consistent with observations but their translation to cyanobacteria forecasts was challenging owing to the interchangeability of simulated functional species.

Drug product immobilization in recycled polyethylene/polypropylene reclaimed from municipal solid waste: experimental and numerical assessment

Recently, there has been a growing interest in identifying suitable routes for the disposal of pharmaceutical wastes. This study investigates the potential of matrix materials composed of recycled polyethylene/polypropylene reclaimed from municipal solid wastes at immobilizing pharmaceutical solid wastes. Diclofenac (DF) drug product was embedded in boards of recycled plastic material, and leaching in water was assessed at various temperatures. DF concentrations were determined by high-performance liquid chromatography and revealed a maximum leachable fraction of 4% under accelerated conditions of 70°C, and less than 0.3% following 39 days of exposure at 20°C. The Ensemble Kalman Filter was employed to characterize the leaching behavior of DF. The filter verified the occurrence of leaching through diffusion, and was successful in predicting the leaching behavior of DF at 50°C and 70°C.

A Nonlinear Framework of Delayed Particle Smoothing Method for Vehicle Localization under Non-Gaussian Environment

In this paper, a novel nonlinear framework of smoothing method, non-Gaussian delayed particle smoother (nGDPS), is proposed, which enables vehicle state estimation (VSE) with high accuracy taking into account the non-Gaussianity of the measurement and process noises. Within the proposed method, the multivariate Student's t-distribution is adopted in order to compute the probability distribution function (PDF) related to the process and measurement noises, which are assumed to be non-Gaussian distributed. A computation approach based on Ensemble Kalman Filter (EnKF) is designed to cope with the mean and the covariance matrix of the proposal non-Gaussian distribution. A delayed Gibbs sampling algorithm, which incorporates smoothing of the sampled trajectories over a fixed-delay, is proposed to deal with the sample degeneracy of particles. The performance is investigated based on the real-world data, which is collected by low-cost on-board vehicle sensors. The comparison study based on the real-world experiments and the statistical analysis demonstrates that the proposed nGDPS has significant improvement on the vehicle state accuracy and outperforms the existing filtering and smoothing methods.