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BOs is Tungsten Carbide structured and crystallizes in the hexagonal P-6m2 space group. The structure is three-dimensional. Os3+ is bonded to six equivalent B3- atoms to form a mixture of distorted corner, edge, and face-sharing OsB6 pentagonal pyramids. All Os–B bond lengths are 2.21 Å. B3- is bonded to six equivalent Os3+ atoms to form a mixture of distorted corner, edge, and face-sharing BOs6 pentagonal pyramids.
Hybrid Power Plants (HPPs) have the potential to increase the value of renewable energy systems and decrease their costs through shared development (e.g., permitting) and infrastructure (e.g., collection system). Prior work has identified potential cost savings and technical and economic performance improvements for solar plus storage plants. However, additional research is needed to understand cost drivers that are specific to hybrid wind plants. Here we analyze the potential for shared infrastructure cost savings at one type of hybrid plant: wind plus solar photovoltaic (PV). To perform this analysis, we developed a new open-source, Python-based cost modeling tool: the Hybrid Balance-of-System (BOS) Systems Engineering model (HybridBOSSE). HybridBOSSE is an extension of NREL's LandBOSSE tool.
Nd(OsB)4 is alpha Pu-derived structured and crystallizes in the tetragonal P4_2/n space group. The structure is three-dimensional. Nd2+ is bonded in a 8-coordinate geometry to eight equivalent Os2- atoms. There are four shorter (3.22 Å) and four longer (3.23 Å) Nd–Os bond lengths. Os2- is bonded in a 4-coordinate geometry to two equivalent Nd2+ and four equivalent B+1.50+ atoms. There are a spread of Os–B bond distances ranging from 2.14–2.19 Å. B+1.50+ is bonded in a 4-coordinate geometry to four equivalent Os2- atoms.
Pr(OsB)2 crystallizes in the orthorhombic Fddd space group. The structure is three-dimensional. Pr3+ is bonded in a 4-coordinate geometry to four equivalent Os+1.50- atoms. All Pr–Os bond lengths are 3.06 Å. Os+1.50- is bonded in a 4-coordinate geometry to two equivalent Pr3+ and four equivalent B atoms. There are two shorter (2.10 Å) and two longer (2.18 Å) Os–B bond lengths. B is bonded to four equivalent Os+1.50- atoms to form a mixture of distorted corner and edge-sharing BOs4 trigonal pyramids.
CeOs2B2 crystallizes in the orthorhombic Fddd space group. The structure is three-dimensional. Ce3+ is bonded in a 4-coordinate geometry to four equivalent Os+1.50- atoms. All Ce–Os bond lengths are 3.01 Å. Os+1.50- is bonded in a 4-coordinate geometry to two equivalent Ce3+ and four equivalent B atoms. There are two shorter (2.09 Å) and two longer (2.17 Å) Os–B bond lengths. B is bonded in a 4-coordinate geometry to four equivalent Os+1.50- atoms.
La(OsB)2 crystallizes in the orthorhombic Fddd space group. The structure is three-dimensional. La3+ is bonded in a 4-coordinate geometry to four equivalent Os+1.50- atoms. All La–Os bond lengths are 3.06 Å. Os+1.50- is bonded in a 4-coordinate geometry to two equivalent La3+ and four equivalent B atoms. There are two shorter (2.11 Å) and two longer (2.18 Å) Os–B bond lengths. B is bonded to four equivalent Os+1.50- atoms to form a mixture of distorted edge and corner-sharing BOs4 trigonal pyramids.
Th(OsB)2 crystallizes in the orthorhombic Fddd space group. The structure is three-dimensional. Th4+ is bonded in a 4-coordinate geometry to four equivalent Os2- atoms. All Th–Os bond lengths are 3.04 Å. Os2- is bonded in a 4-coordinate geometry to two equivalent Th4+ and four equivalent B atoms. There are two shorter (2.10 Å) and two longer (2.18 Å) Os–B bond lengths. B is bonded to four equivalent Os2- atoms to form a mixture of distorted edge and corner-sharing BOs4 trigonal pyramids.
U(OsB)4 crystallizes in the tetragonal I4_1/acd space group. The structure is three-dimensional. U6+ is bonded in a 12-coordinate geometry to twelve equivalent Os+1.50- atoms. There are a spread of U–Os bond distances ranging from 2.93–3.27 Å. Os+1.50- is bonded in a 5-coordinate geometry to three equivalent U6+ and five equivalent B atoms. There are a spread of Os–B bond distances ranging from 2.16–2.28 Å. B is bonded in a 6-coordinate geometry to five equivalent Os+1.50- and one B atom. The B–B bond length is 1.77 Å.
Nd(OsB)2 crystallizes in the orthorhombic Fddd space group. The structure is three-dimensional. Nd3+ is bonded in a 4-coordinate geometry to four equivalent Os+1.50- atoms. All Nd–Os bond lengths are 3.05 Å. Os+1.50- is bonded in a 4-coordinate geometry to two equivalent Nd3+ and four equivalent B atoms. There are two shorter (2.10 Å) and two longer (2.18 Å) Os–B bond lengths. B is bonded to four equivalent Os+1.50- atoms to form a mixture of distorted edge and corner-sharing BOs4 trigonal pyramids.
Gd(OsB)2 crystallizes in the orthorhombic Fddd space group. The structure is three-dimensional. Gd3+ is bonded in a 4-coordinate geometry to four equivalent Os+1.50- atoms. All Gd–Os bond lengths are 3.02 Å. Os+1.50- is bonded in a 4-coordinate geometry to two equivalent Gd3+ and four equivalent B atoms. There are two shorter (2.08 Å) and two longer (2.18 Å) Os–B bond lengths. B is bonded in a 4-coordinate geometry to four equivalent Os+1.50- atoms.
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This software is the world's first integrated background oriented schlieren and machine learning-based leak detection system. The system provides real time visualization of gas leaks and machine learning interpenetration of leak severity. The software is supplemented by SWR-25-177, "gpu_piv (Graphics Processing Unit Accelerated Background Oriented Schlieren Algorithm", also developed by the National Laboratory of the Rockies. SEE DOECODE ID 182832.
This report describes the Offshore Renewables Balance-of-system Installation Tool ("ORBIT"), a new model developed by the National Renewable Energy Laboratory (NREL) to evaluate the balance-of-system (BOS) costs associated with offshore wind projects. In the context of wind energy projects BOS costs encompass all expenses required to construct a project other than the capital expenditures (CapEx) of the turbines and towers, including the procurement costs for all other components (such as substructures, cables, and electrical infrastructure), offshore and land-based construction costs, port costs, site surveying fees, permitting fees, and leasing fees are all categorized as BOS costs. BOS costs significantly contribute to the levelized cost of energy (LCOE) for offshore wind, typically comprising over 50\% of the CapEx for a fixed-bottom offshore wind project and 60\% for a floating project. In addition, technology solutions and installation methods vary drastically between projects as they are impacted by factors such as vessel availability, geographic considerations and site geotechnical conditions. These effects require a model with appropriate fidelity to understand how these costs scale as turbine rating increases and the offshore wind supply chain, particularly offshore construction vessels, is expanded. For offshore wind, cost savings attributed to increased turbine rating are primarily realized through BOS procurement and project installation as fewer substructures and less cable are required. BOS costs represent both a modeling challenge, as well as an opportunity, for project developers to optimize solutions to reduce costs. It is critical to understand how these costs are affected by novel technologies, innovative installation processes, and operational constraints in order identify meaningful cost reductions for offshore wind energy.
Tomographic background oriented Schlieren (Tomo-BOS) imaging measures density or temperature fields in three dimensions using multiple camera BOS projections, and is particularly useful for instantaneous flow visualizations of complex fluid dynamics problems. We propose a new method based on physics-informed neural networks (PINNs) to infer the full continuous three-dimensional (3-D) velocity and pressure fields from snapshots of 3-D temperature fields obtained by Tomo-BOS imaging. The PINNs seamlessly integrate the underlying physics of the observed fluid flow and the visualization data, hence enabling the inference of latent quantities using limited experimental data. In this hidden fluid mechanics paradigm, we train the neural network by minimizing a loss function composed of a data mismatch term and residual terms associated with the coupled Navier–Stokes and heat transfer equations. We first quantify the accuracy of the proposed method based on a two-dimensional synthetic data set for buoyancy-driven flow, and subsequently apply it to the Tomo-BOS data set, where we are able to infer the instantaneous velocity and pressure fields of the flow over an espresso cup based only on the temperature field provided by the Tomo-BOS imaging. Moreover, we conduct an independent PIV experiment to validate the PINN inference for the unsteady velocity field at a centre plane. To explain the observed flow physics, we also perform systematic PINN simulations at different Reynolds and Richardson numbers and quantify the variations in velocity and pressure fields. Furthermore, the results in this paper indicate that the proposed deep learning technique can become a promising direction in experimental fluid mechanics.
Wind power plant sizes, hub heights, and turbine ratings have increased since 2008 to optimize the cost and performance of wind power; however, the limits of these economies of scale remain unclear. Here, we explore how the costs incurred to install turbines at a wind power plant—the balance-of-system (BOS) costs—scale with turbine rating, hub height, and plant size. We also investigate how these changes in BOS costs influence the levelized cost of energy (LCOE). We show that increasing the plant size from 150 to 400 MW could reduce the BOS costs by 21%. We also show that if the foundation costs decreased by 50%, building a wind power plant with 5-MW turbines (having rotor diameters of 166 m and hub heights of 120 m) could decrease the LCOE by 5%. These results could help inform future BOS cost-reduction opportunities and thereby reduce future capital costs for land-based wind power.
Innovations improve technology costs through various kinds of engineering advancements, including changes to materials choices and device or process designs. Understanding how these innovations relate to cost change can reveal aspects of the process of technology evolution, yet developing such understanding is often not possible with a strictly quantitative approach due to data limitations. In this paper we develop a hybrid quantitative-qualitative framework for relating specific innovations to cost change by using the variables in a quantitative technology cost change model as an organizing principle. We demonstrate this framework by applying it to the cost decline in photovoltaic (PV) systems over the last five decades. This framework generates new understanding of a set of innovations that contributed to PV modules’ sustained cost decline and the more modest trends observed in balance-of-system (BOS) costs. The results show the great diversity of innovations that affected PV costs, drawing on wide-ranging fields of expertise within scientific research and practice. We find that there are differences in the characteristics of innovations that reduced the cost of PV modules compared to innovations influencing BOS costs. Numerous module innovations reduced costs by advancing manufacturing tools and processes that improved material quality. Many BOS innovations reduced costs through a combination of component design changes, integration, automation, digitalization, and standardization. Overall, most innovations in our sample affected PV hardware. However, some also target ‘soft technologies’ such as task durations through innovations like fast-track permitting, which require improved collaboration and process streamlining. This framework also provides insight into the nature of knowledge spillovers between technologies. Both module and BOS hardware innovations show the benefits of PV’s position within an ‘ecosystem’ of continuously advancing technologies in many industries, in particular semiconductors and electronics, and also point to the importance of public institutions for accelerating testing, permitting, and training.
Here, we theoretically analyze the effect of density/refractive index gradients on the measurement precision of background-oriented schlieren (BOS) experiments by deriving the Cramer–Rao lower bound (CRLB) for the 2D centroid estimation process. A model is derived for the diffraction limited image of a dot viewed through a medium containing density gradients that includes the effect of the experimental parameters such as the magnification and f-number. It is shown using the model that nonlinearities in the density gradient field lead to blurring of the dot image. This blurring amplifies the effect of image noise on the centroid estimation process, leading to an increase in the CRLB and a decrease in the measurement precision. The ratio of position uncertainties of a dot in the reference and gradient images is shown to be a function of the ratio of the dot diameters and dot intensities. We termed this parameter the amplification ratio (A F ), and a methodology for reporting position uncertainties in tracking-based BOS measurements is proposed. The theoretical predictions of the dot position estimation variance from the CRLB are compared to ray tracing simulations, and agreement is obtained. The uncertainty amplification is also demonstrated on experimental BOS images of flow induced by a spark discharge, where it is seen that regions of high amplification ratio correspond to regions of density gradients. This analysis elucidates the dependence of the position uncertainty on density and refractive index gradient-induced distortion parameters, provides a methodology for accounting its effect on uncertainty quantification and provides a framework for optimizing experiment design.
We propose an improved density integration methodology for Background-Oriented Schlieren (BOS) measurements that overcomes the noise sensitivity of the commonly used Poisson solver. Here, the method employs a weighted least-squares (WLS) optimization of the 2D integration of the density gradient field by solving an over-determined system of equations. Weights are assigned to the grid points based on density gradient uncertainties to ensure that a less reliable measurement point has less effect on the integration procedure. Synthetic image analysis with a Gaussian density field shows that WLS constrains the propagation of random error and reduces it by 80% in comparison to Poisson for the highest noise level. Using WLS with experimental BOS measurements of flow induced by a spark plasma discharge shows a 30% reduction in density uncertainty in comparison to Poisson, thereby increasing the overall precision of the BOS density measurements.