This paper presents a numerical benchmark study of wave propagation due to a paddle motion using different high-fidelity numerical models, which are capable of replicating the nearly actual physical wave tank testing. A full time series of the measured wave generation paddle motion that was used to generate wave propagation in the physical wave tank will be utilized in each of the models contributed by the participants of International Energy Agency Ocean Energy Systems Task 10, which includes both computational fluid dynamics and smoothed particle hydrodynamics models. The high-fidelity simulations of the physical wave testcase will allow for the evaluation of the initial transient effects from wave ramp-up and its evolution in the wave tank over time for two representative regular waves with varying levels of nonlinearity. Metrics like the predicted wave surface elevation at select wave probes, wave period, and phase-shift in time will be assessed to evaluate the relative accuracy of numerical models versus experimental data within specified time intervals. These models will serve as a guide for modelers in the wave energy community and provide a base case to allow further and more detailed numerical modeling of the fixed Kramer Sphere Cases under wave excitation force wave tank testing.
Abstract Presently, marine energy (ME) deployments are absorbing unsustainable costs and timelines associated with planning and permitting to get projects in the water (up to 25% of total project cost, which is more than double comparable offshore energy projects at approximately 10% of total project costs; Kramer et al. 2020; Peplinski et al. 2021). To overcome this challenge, the Spatial Environmental Assessment Toolkit (SEAT) is in development to provide the highest-quality site characterization and a priori understanding of the potential environmental impacts using numerical modeling tools and available site data to reduce uncertainty. Reduced uncertainty equates to a reduction in resources required for planning and environmental permitting and a more streamlined path to realized commercial-scale projects. In this work, numerical modeling and mapping tools are linked together within SEAT to assess ecosystem impacts due to marine energy installations and evaluate optimal ME array layouts based on meaningful site and ME device physics. Of utmost importance is the ability of ME developers, regulators, and stakeholders to develop ME array layouts that maximize energy production, support environmental benefits and Powering the Blue Economy applications (e.g., coastal resiliency, desalination), and minimize potential undesirable environmental effects. The SEAT is an open-source graphical user interface (GUI) that aggregates numerical model results and spatial receptor data to evaluate the potential risk of change and subsequent impact on the environment being developed for ME. The numerical models can represent the presence of wave, tidal, or river energy converters in their respective environments and evaluate device and array generated site changes in hydrodynamics (e.g., wave fields, water currents), sediment and larval dynamics (e.g., benthic, spawning habitats), and propagation of new acoustic signals (e.g., hearing thresholds). The toolkit of linked models and site-specific receptors will ultimately allow developers to determine optimal designs for MRE deployments that maximize power performance and benefits from changes that promote project resiliency while minimizing the potential for unwanted environmental effects. The SEAT is an integrated communication tool with which regulatory agencies, stakeholders, and industry developers can effectively evaluate the complex information required for the permitting process thereby reducing the time and costs associated with the process.
have provided a fairly detailed account of one approach to model validation and prediction applied to an analysis investigating thermal decomposition of polyurethane foam. A model simulates the evolution of the foam in a high temperature environment as it transforms from a solid to a gas phase. The available modeling and experimental results serve as data for a case study focusing our model validation and prediction developmental efforts on this specific thermal application. We discuss several elements of the ''philosophy'' behind the validation and prediction approach: (1) We view the validation process as an activity applying to the use of a specific computational model for a specific application. We do acknowledge, however, that an important part of the overall development of a computational simulation initiative is the feedback provided to model developers and analysts associated with the application. (2) We utilize information obtained for the calibration of model parameters to estimate the parameters and quantify uncertainty in the estimates. We rely, however, on validation data (or data from similar analyses) to measure the variability that contributes to the uncertainty in predictions for specific systems or units (unit-to-unit variability). (3) We perform statistical analyses and hypothesis tests as a part of the validation step to provide feedback to analysts and modelers. Decisions on how to proceed in making model-based predictions are made based on these analyses together with the application requirements. Updating modifying and understanding the boundaries associated with the model are also assisted through this feedback. (4) We include a ''model supplement term'' when model problems are indicated. This term provides a (bias) correction to the model so that it will better match the experimental results and more accurately account for uncertainty. Presumably, as the models continue to develop and are used for future applications, the causes for these apparent biases will be identified and the need for this supplementary modeling will diminish. (5) We use a response-modeling approach for our predictions that allows for general types of prediction and for assessment of prediction uncertainty. This approach is demonstrated through a case study supporting the assessment of a weapons response when subjected to a hydrocarbon fuel fire. The foam decomposition model provides an important element of the response of a weapon system in this abnormal thermal environment. Rigid foam is used to encapsulate critical components in the weapon system providing the needed mechanical support as well as thermal isolation. Because the foam begins to decompose at temperatures above 250 C, modeling the decomposition is critical to assessing a weapons response. In the validation analysis it is indicated that the model tends to ''exaggerate'' the effect of temperature changes when compared to the experimental results. The data, however, are too few and to restricted in terms of experimental design to make confident statements regarding modeling problems. For illustration, we assume these indications are correct and compensate for this apparent bias by constructing a model supplement term for use in the model-based predictions. Several hypothetical prediction problems are created and addressed. Hypothetical problems are used because no guidance was provided concerning what was needed for this aspect of the analysis. The resulting predictions and corresponding uncertainty assessment demonstrate the flexibility of this approach.
INTRODUCTION Accurately assessing potential far-field environmental impacts due to wave energy converter (WEC) arrays is needed for commercialization of wave energy. One of the barriers to development is how to assess environmental concerns related to the potential effects these arrays will have on the nearand farfield wave climate. In order for projects in the United States to be approved, regulatory agencies must perform an Environmental Assessment proving little to no environmental impact. However, little is known about the environmental impacts of such wave farms as utility-scale WEC arrays have not yet made it to the market. As a result, the environmental impacts of wave farms are largely determined by numerical wave models capable of modeling large areas (i.e., spectral wave models). Therefore a validated, publicly available wave model that accurately predicts the effects due to WEC-arrays is crucial to WEC commercialization Existing spectral wave models are limited in their ability to model WECs. They typically model WECs as obstacles with a constant amount of energy absorption across all frequencies. This approach does not accurately account for the WEC’s performance, which is often tuned maximize energy capture for certain periods or sea states. Sandia National Laboratories has modified the open source spectral wave model Simulation WAves Nearshore [1] (SWAN), to include a validated WEC Module that more realistically models the frequency and sea state dependent energy absorption of WECs. SNL-SWAN is an open source code available for download and use by developers, licensing agencies, and other interested parties. This extended abstract will provide an update on code developments since the initial release of SNL-SWAN v1.0 in Oct, 2014. It will focus on the new model features and modifications that are incorporated in SNL-SWAN v1.1 (planned to be released Fall 2015). The significant modifications for SNL-SWAN Version 1.1 include the following: An output file for power absorbed by each WEC obstacle Directional dependent WEC power extraction Incorporation of a frequency dependent reflection coefficient term An update to transmission coefficients determined by WEC power matrix
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