Wave Sequential Data Assimilation in Support of Wave Energy Converter Power Prediction

Integration of renewable power sources into grids remains an active research and development area, particularly for less developed renewable energy technologies such as wave energy converters (WECs). WECs are projected to have strong early market penetration for remote communities, which serve as natural microgrids. Hence, accurate wave predictions to manage the interactions of a WEC array with microgrids is especially important. Recently developed, low-cost wave measurement buoys allow for operational assimilation of wave data at remote, site specific locations where real-time data have previously been unavailable. We present the development and assessment of a wave modeling framework with real-time data assimilation capabilities for WEC power prediction. The availability of real-time wave spectra from low-cost wave measurement buoys allows for operational data assimilation with the ensemble Kalman filter technique within a hybrid modeling procedure whereby physics-based numerical wave models are combined with data-driven error models that aim to capture the discrepancy in prescribed boundary conditions. With that aim, measured wave spectra are assimilated for combined state and parameter estimation while taking into account model and observational errors. The analysis allows for more accurate and precise wave characteristic predictions at the locations of interest. Initial deployment data obtained offshore Yakutat, Alaska, indicated that measured wave data from one buoy that were assimilated into the wave modeling framework resulted in improved forecast skill in comparison to traditional numerical forecasts

Reducing variability in the cost of energy of ocean energy arrays

Variability in the predicted cost of energy of an ocean energy converter array is more substantial than for other forms of energy generation, due to the combined stochastic action of weather conditions and failures. If the variability is great enough, then this may influence future financial decisions. This paper provides the unique contribution of quantifying variability in the predicted cost of energy and introduces a framework for investigating reduction of variability through investment in components. Following review of existing methodologies for parametric analysis of ocean energy array design, the development of the DTOcean software tool is presented. DTOcean can quantify variability by simulating the design, deployment and operation of arrays with higher complexity than previous models, designing sub-systems at component level. A case study of a theoretical floating wave energy converter array is used to demonstrate that the variability in levelised cost of energy (LCOE) can be greatest for the smallest arrays and that investment in improved component reliability can reduce both the variability and most likely value of LCOE. A hypothetical study of improved electrical cables and connectors shows reductions in LCOE up to 2.51% and reductions in the variability of LCOE of over 50%; these minima occur for different combinations of components.The research leading to this publication is part of the DTOceanPlus project which has received funding from the EuropeanUnion's Horizon 2020 research and innovation programme under grant agreement No 785921. Funding was also received from the European Community's Seventh Framework Programme for the DTOcean Project (grant agreement No. 608597). The contribution of Sandia National Laboratories was funded by the U.S. Department of Energy's Water Power Technologies Office. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. The image of the RM3 device, in Fig. 7, was reproduced with the permission of Sandia National Laboratorie

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Flow and turbulence in an industrial/suburban roughness canopy

A field study conducted to investigate the flow and turbulence structure of the urban boundary layer (UBL) over an industrial/suburban area is described. The emphasis was on morning and evening transition periods, but some measurements covered the entire diurnal cycle. The data analysis incorporated the dependence of wind direction on morphometric parameters of the urban canopy. The measurements of heat and momentum fluxes showed the possibility of a constant flux layer above the height z 48 2H, wherein the Monin-Obukhov Similarity Theory (MOST) is valid; here H is the averaged building height. For the nocturnal boundary layer, the mean velocity and temperature profiles obeyed classical MOST scaling up to 3c 0. 5\u39b( 3c 6H), where \u39b is the Obukhov length scale, beyond which stronger stratification may disrupt the occurrence of constant fluxes. For unstable and neutral cases, MOST scaling described the mean data well up to the maximum measured height 3c 6H. Available MOST functions, however, could not describe the measured turbulence structure, indicating the influence of additional governing parameters. Alternative turbulence parameterizations were tested, and some were found to perform well. Calculation of integral length scales for convective and neutral cases allowed a phenomenological description of eddy characteristics within and above the urban canopy layer. The development of a significant nocturnal surface inversion occurred only on certain days, for which a criterion was proposed. The nocturnal UBL exhibited length scale relationships consistent with the evening collapse of the convective boundary layer and maintenance of buoyancy-affected turbulence overnight. The length and velocity scales so identified are useful in parameterizing turbulent dispersion coefficients in different diurnal phases of the UBL

Spatial Environmental Assessment Tool (SEAT): A Modeling Tool to Evaluate Potential Environmental Risks Associated with Wave Energy Converter Deployments

Wave energy converter (WEC) arrays deployed in coastal regions may create physical disturbances, potentially resulting in environmental stresses. Presently, limited information is available on the nature of these physical disturbance or the resultant effects. A quantitative Spatial Environmental Assessment Tool (SEAT) for evaluating the potential effects of wave energy converter (WEC) arrays on nearshore hydrodynamics and sediment transport is presented for the central Oregon coast (USA) through coupled numerical model simulations of an array of WECs. Derived climatological wave conditions were used as inputs to the model to allow for the calculation of risk metrics associated with various hydrodynamic and sediment transport variables such as maximum shear stress, bottom velocity, and change in bed elevation. The risk maps provided simple, quantitative, and spatially-resolved means of evaluating physical changes in the vicinity of a hypothetical WEC array in response to varying wave conditions. The near-field risk of sediment mobility was determined to be moderate in the lee of the densely spaced array, where the potential for increased sediment deposition could result in benthic habitat alteration. Modifications to the nearshore sediment deposition and erosion patterns were observed near headlands and topographic features, which could have implications for littoral sediment transport. The results illustrate the benefits of a risk evaluation tool for facilitating coastal resource management at early market marine renewable energy sites

Modeling and predicting power from a WEC array

This study presents a numerical model of a WEC array. The model will be used in subsequent work to study the ability of data assimilation to support power prediction from WEC arrays and WEC array design. In this study, we focus on design, modeling, and control of the WEC array. A case study is performed for a small remote Alaskan town. Using an efficient method for modeling the linear interactions within a homogeneous array, we produce a model and predictionless feedback controllers for the devices within the array. The model is applied to study the effects of spectral wave forecast errors on power output. The results of this analysis show that the power performance of the WEC array will be most strongly affected by errors in prediction of the spectral period, but that reductions in performance can realistically be limited to less than 10% based on typical data assimilation based spectral forecasting accuracy levels

Levels and genotypes of Salmonella and levels of Escherichia coli in frozen ready-to-cook chicken and turkey products in England tested in 2020 in relation to an outbreak of S. Enteritidis

Frozen reformulated (FR) breaded chicken products have previously been implicated in causing human salmonellosis. A multi-country Salmonella enterica serovar Enteritidis outbreak involving several strains with >400 reported human cases in the UK occurred in 2020. Initially S. Infantis was detected in one sample from a case home but S. Enteritidis was then also isolated using a S. Enteritidis specific PCR in combination with isolation via a Craigie-tube. This prompted a survey to examine the presence and levels of Salmonella and E. coli in ready-to-cook FR poultry products in England in 2020. From a total of 483 samples, including two from cases' homes, Salmonella was detected in 42 chicken samples, these originated from six out of 53 production plants recorded. Salmonella detection was associated with elevated levels of generic E. coli (OR = 6.63). S. Enteritidis was detected in 17 samples, S. Infantis in 25, S. Newport in four and S. Java, S. Livingstone and S. Senftenberg in one each. The highest levels of Salmonella were 54 MPN/g for S. Infantis and 28 MPN/g for S. Enteritidis; 60% of the Salmonella-positive samples had <1.0 MPN/g. S. Enteritidis was detected together with S. Infantis in five samples and with S. Livingstone in one. Where S. Enteritidis was detected with other Salmonella, the former was present at between 2 and 100-fold lower concentrations. The Salmonella contamination was homogeneously distributed amongst chicken pieces from a single pack and present in both the outer coating and inner content. The S. Enteritidis were all outbreak strains and detected in six products that were linked to four production plants which implicated a Polish origin of contamination. Despite S. Infantis being most prevalent in these products, S. Infantis from only two contemporaneous human cases in the UK fell into the same cluster as isolates detected in one product. Except for one human case falling into the same cluster as one of the S. Newport strains from the chicken, no further isolates from human cases fell into clusters with any of the other serovars detected in the chicken samples. This study found that higher E. coli levels indicated a higher probability of Salmonella contamination in FR chicken products. The results also highlight the importance of recognising co-contamination of foods with multiple Salmonella types and has provided essential information for detecting and understanding outbreaks where multiple strains are involved

Levels and genotypes of Salmonella and levels of Escherichia coli in frozen ready-to-cook chicken and turkey products in England tested in 2020 in relation to an outbreak of S. Enteritidis

Frozen reformulated (FR) breaded chicken products have previously been implicated in causing human salmonellosis. A multi-country Salmonella enterica serovar Enteritidis outbreak involving several strains with >400 reported human cases in the UK occurred in 2020. Initially S. Infantis was detected in one sample from a case home but S. Enteritidis was then also isolated using a S. Enteritidis specific PCR in combination with isolation via a Craigie-tube. This prompted a survey to examine the presence and levels of Salmonella and E. coli in ready-to-cook FR poultry products in England in 2020. From a total of 483 samples, including two from cases' homes, Salmonella was detected in 42 chicken samples, these originated from six out of 53 production plants recorded. Salmonella detection was associated with elevated levels of generic E. coli (OR = 6.63). S. Enteritidis was detected in 17 samples, S. Infantis in 25, S. Newport in four and S. Java, S. Livingstone and S. Senftenberg in one each. The highest levels of Salmonella were 54 MPN/g for S. Infantis and 28 MPN/g for S. Enteritidis; 60% of the Salmonella-positive samples had <1.0 MPN/g. S. Enteritidis was detected together with S. Infantis in five samples and with S. Livingstone in one. Where S. Enteritidis was detected with other Salmonella, the former was present at between 2 and 100-fold lower concentrations. The Salmonella contamination was homogeneously distributed amongst chicken pieces from a single pack and present in both the outer coating and inner content. The S. Enteritidis were all outbreak strains and detected in six products that were linked to four production plants which implicated a Polish origin of contamination. Despite S. Infantis being most prevalent in these products, S. Infantis from only two contemporaneous human cases in the UK fell into the same cluster as isolates detected in one product. Except for one human case falling into the same cluster as one of the S. Newport strains from the chicken, no further isolates from human cases fell into clusters with any of the other serovars detected in the chicken samples. This study found that higher E. coli levels indicated a higher probability of Salmonella contamination in FR chicken products. The results also highlight the importance of recognising co-contamination of foods with multiple Salmonella types and has provided essential information for detecting and understanding outbreaks where multiple strains are involved

Metabolic syndrome and neurometabolic asymmetry of hippocampus in adult bonnet monkeys

OBJECTIVE: Obesity is associated with the insulin resistance metabolic syndrome, postulated to be mediated by stress-induced alterations within the hypothalamic-pituitary-adrenal (HPA) axis. In adult bonnet macaques we examined relationships between components of the metabolic syndrome, hippocampal neurometabolic asymmetry, an indicator of negative affect, and juvenile cerebrospinal fluid (csf) corticotropin-releasing factor (CRF) levels obtained after stress exposure associated with maternal food insecurity and in controls. METHODS: Eleven adult male monkeys (seven with early life stress) who had undergone csf-CRF analyses as juveniles had magnetic resonance spectroscopic imaging (MRSI) of bilateral hippocampus, morphometry (body mass index, BMI; sagittal abdominal diameter, SAD) and determination of fasting plasma glucose and insulin as adults. Neurometabolite ratios included N-acetyl-aspartate as numerator (NAA; a marker of neuronal integrity) and choline (Cho; cell turnover) and creatine (Cr; reference analyte) as denominators. RESULTS: Elevated juvenile csf-CRF levels positively predicted adult BMI and SAD and were associated with right > left shift of NAA ratio within the hippocampus. Adult visceral obesity and insulin level correlated with right > left shift in hippocampal NAA concentrations, controlling for age and denominator. CONCLUSION: Juvenile csf-CRF levels, a neuropeptide associated with early life stress, predict adult visceral obesity and hippocampal asymmetry supporting the hypothesis that metabolic syndrome in adults may be related to early life stress. Furthermore, this study demonstrates asymmetrical hippocampal alterations related to obesity