WFIP3 - BLOC site - NREL Scanning Lidar (Streamline XR SN:217) / Reviewed Data
This nacelle mounted LiDAR is a Halo Photonics StreamLine XR Scanning Doppler LiDAR and is rear facing to measure the wake behind the turbine.
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This nacelle mounted LiDAR is a Halo Photonics StreamLine XR Scanning Doppler LiDAR and is rear facing to measure the wake behind the turbine.
This nacelle mounted LiDAR is a Halo Photonics StreamLine XR Scanning Doppler LiDAR and is rear facing to measure the wake behind the turbine.
These data are from a short deployment of the UC Davis Halo Streamline scanning lidar. The scanning lidar was deployed 880 m south of the NEON tower and in the same location as the field profiling lidar that was deployed later in spring 2024.
These data are collected by a Streamline XR Doppler Lidar operated by Lawrence Livermore National Laboratory and deployed at the Tree Tops site (1.5 km South-West of MLBS site).
This dataset contains high-frequency vertical velocity recorded by fixed-point vertical scans done by the Lawrence Livermore National Laboratory Halo Streamline scanning lidar at Tree Tops. The quality control is performed according to the algorithm of Goring & Nikora (2002).
This dataset contains high-frequency vertical velocity recorded by fixed-point vertical scans done by the UC Davis Halo Streamline scanning lidar. The quality control is performed according to the algorithm of Goring & Nikora (2002).
This dataset contains wind profiles retrieved from 6-beam Velocity Azimuth Display (VAD) scans done by a Streamline XR Doppler Lidar operated by Lawrence Livermore National Laboratory and deployed at the Tree Tops site (1.5 km South-West of MLBS site). The wind components (expressed as zonal, meridional and vertical) are retrieved through the algorithm of Paschke et al. (2015). The quality control of the radial wind speed is performed following the algorithm of Foken et al. (2004).
These data are from a deployment of the LLNL Halo Streamline scanning lidar in Summer 2025. The scanning lidar was deployed in a clearing area 880 m south of the NEON tower.
These data are from a deployment of the LLNL Halo Streamline scanning lidar in Summer 2025. The scanning lidar was deployed in a clearing area 880 m south of the NEON tower.
Biofuels are anticipated to enable a shift from fossil fuels for renewable transportation and manufacturing fuels, with biohydrogen considered attractive since it could offer the largest reduction of global carbon budgets. Currently, lignocellulosic biohydrogen production remains inefficient with pretreatments that are heavily fossil fuel-dependent. However, bacteria using alkali-treated biomass could streamline biofuel production while reducing costs and fossil fuel needs. An alkaliphilic bacterium, Halanaerobium hydrogeniformans, is described that is capable of biohydrogen production at levels rivaling neutrophilic strains, but at pH 11 and hypersaline conditions. H. hydrogeniformans ferments a variety of 5- and 6-carbon sugars derived from hemicellulose and cellulose including cellobiose, and forms the end products hydrogen, acetate, and formate. Further, it can also produce biohydrogen from switchgrass and straw pretreated at temperatures far lower than any previously reported and in solutions compatible with growth. Hence, this bacterium can potentially increase the efficiency and efficacy of biohydrogen production from renewable biomass resources.
Training an effective deep learning model to learn ocean processes involves careful choices of various hyperparameters. We leverage DeepHyper’s advanced search algorithms for multiobjective optimization, streamlining the development of neural networks tailored for ocean modeling. The focus is on optimizing Fourier neural operators (FNOs), a data-driven model capable of simulating complex ocean behaviors. Selecting the correct model and tuning the hyperparameters are challenging tasks, requiring much effort to ensure model accuracy. DeepHyper allows efficient exploration of hyperparameters associated with data preprocessing, FNO architecture-related hyperparameters, and various model training strategies. We aim to obtain an optimal set of hyperparameters leading to the most performant model. Moreover, on top of the commonly used mean squared error for model training, we propose adopting the negative anomaly correlation coefficient as the additional loss term to improve model performance and investigate the potential trade-off between the two terms. The numerical experiments show that the optimal set of hyperparameters enhanced model performance in single timestepping forecasting and greatly exceeded the baseline configuration in the autoregressive rollout for long-horizon forecasting up to 30 days. Utilizing DeepHyper, we demonstrate an approach to enhance the use of FNO in ocean dynamics forecasting, offering a scalable solution with improved precision.
This fact sheet presents the most valuable insights and resources from a November 2022 BBWA convening of workforce experts around the topic of Streamlining Career Pathways. The Workshop presented ways to support green building trainers and employers in understanding, developing, and communicating career pathways to their trainees and fellow workforce stakeholders.
In the fast-paced construction world, efficiency isn't just an advantage - it's a necessity. By implementing the Cooperative Construction Contracting Agreement (CCCA), we are transforming how we approach major projects, replacing traditional multistep contracting with a streamlined, one-step process. This innovative contracting approach saves valuable time and resources, enabling us to accelerate delivery across our portfolio of projects. Under the old model, each construction phase required separate competitive solicitations for design, build, and other services. With the CCCA, we've consolidated these steps, working with a trusted partner from start to finish. This results in significant time savings that translate into faster project execution without compromising quality or safety.
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Large-scale deployment of Carbon Capture and Storage (CCS) will require a commensurately large number of sites. Efficient screening methods are needed to create investment assurance and focus efforts on the most promising sites. The problem is similar to petroleum exploration, for which there are well-developed (though seldom published) workflows, including Common Risk Segment (CRS) mapping. In brief, the process requires 1) defining the key play elements; 2) identifying candidate geologic intervals for each; 3) creating fact-based maps for those intervals; 4) determining minimum criteria for the success of each element; 5) reinterpreting the fact-based maps in terms of chance of success; and 6) combining the individual maps to form a composite, basin-scale view of prospectivity. We adapt the CRS process to screening for CO 2 storage sites. Critically, we redefine the process in terms of cost of characterization and development, rather than chance of success. For illustration, we apply the process to the example of the Lower Miocene on the Texas and Louisiana Gulf Coast. We show that the predictions are consistent with historic hydrocarbon production volumes and rates. The power of the CRS method is that it creates a systematic approach to geologic evaluation and translates complex, multidimensional analysis into clear, graphical and easily comprehended business inputs. The results highlight sweet spots and identifies critical risks, suggesting a focus for further data collection and analysis. Furthermore, the method developed here can be applied to both surface and subsurface factors anywhere that there is interest in geologic storage of CO 2 .
Abstract Separation of an individual heavy actinide from other actinides, lanthanides, and coproduced fission products is challenging not only because of their similarity in chemistry but also because the chemistry of heavy actinides is largely unknown. At present, the cation-exchange chromatography with α-hydroxyisobutyric acid (CX-AHIB) method is used to isolate milli- to picogram quantities of heavy actinides (i.e., 249Bk, 252Cf, 254Es, and 257Fm). This method allows simultaneous separation of these actinides; however, isolating a clean individual product with a high yield has proven challenging. The process is also very slow and labor-intensive and requires precise control of various chemical conditions, such as pH, temperature, and AHIB concentration. Developing a separation scheme for heavy actinides requires identifying their unique feature and then harnessing this feature in the separation process design. The unique characteristic of Bk4+ is that it does not adsorb onto anion exchange resin columns, unlike other tetravalent actinides. This article discusses what makes Bk unique and how this discovery led to a new method for separating Bk from adjacent actinides, lanthanides, and coproduced fission products. The method employed two different resin columns in tandem to separate unwanted actinides from 249Bk, followed by fine cleanup of 249Bk. The advantages of the new Bk method over the CX-AHIB method in Bk production are discussed, and the performance and robustness of the proposed method were assessed in two recent production campaigns.