Publications

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Hydrokinetic energy from flowing water in open channels has the potential to support local electricity needs with lower regulatory or capital investment than impounding water with more conventional means. MOU agencies involved in federal hydropower development have identified the need to better understand the opportunities for hydrokinetic (HK) energy development within existing canal systems that may already have integrated hydropower plants. This document provides an overview of the main considerations, tools, and assessment methods, for implementing field tests in an open-channel water system to characterize current energy converter (CEC) device performance and hydrodynamic effects. It describes open channel processes relevant to their HK site and perform pertinent analyses to guide siting and CEC layout design, with the goal of streamlining the evaluation process and reducing the risk of interfering with existing uses of the site. This document outlines key site parameters of interest and effective tools and methods for measurement and analysis with examples drawn from the Roza Main Canal, in Yakima, WA to illustrate a site application.

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The technology performance level (TPL) assessments can be applied at all technology development stages and associated technology readiness levels (TRLs). Even, and particularly, at low TRLs the TPL assessment is very effective as it, holistically, considers a wide range of WEC attributes that determine the techno-economic performance potential of the WEC farm when fully developed for commercial operation. The TPL assessment also highlights potential showstoppers at the earliest possible stage of the WEC technology development. Hence, the TPL assessment identifies the technology independent “performance requirements.” In order to achieve a successful solution, the entirety of the performance requirements within the TPL must be considered because, in the end, all the stakeholder needs must be achieved. The basis for performing a TPL assessment comes from the information provided in a dedicated format, the Technical Submission Form (TSF). The TSF requests information from the WEC developer that is required to answer the questions posed in the TPL assessment document.

The Wave - SPARC project developed the Technology Performance Level (TPL) assessment procedure based on a rigorous Systems Engineering exercise. The TPL assessment allows a whole system evaluation of Wave Energy Conversion Technology by measuring it against the requirements determined through the Systems Engineering exercise. The TPL assessment is intended to be useful in technology evaluation; in technology innovation; in allocation of public or priva te investment, and; in making equipment purchasing decisions. This Technical Submission Form (TSF) serves the purpose of collecting relevant and complete information, in a technology agnostic way, to allow TPL assessment s to be made by third party assessor s. The intended usage of this document is that the organization or people that are performing the role of developers or promoters of a particular technology will use this form to provide the information necessary for the organization or people who are perf orming the assessor role to use the TPL assessment.

A motivation for undertaking this stakeholder requirements analysis and Systems Engineering exercise is to document the requirements for successful wave energy farms to facilitate better design and better design assessments. A difficulty in wave energy technology development is the absence to date of a verifiable minimum viable product against which the merits of new products might be measured. A consequence of this absence is that technology development progress, technology value, and technology funding have largely been measured, associated with, and driven by technology readiness, measured in technology readiness levels (TRLs). Originating primarily from the space and defense industries, TRLs focus on procedural implementation of technology developments of large and complex engineering projects, where cost is neither mission critical nor a key design driver. The key deficiency with the TRL approach in the context of wave energy conversion is that WEC technology development has been too focused on commercial readiness and not enough on the stakeholder requirements and particularly economic viability required for market entry.

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This document presents the revised Technology Performance Level (TPL) assessment methodology. There are three parts to this revised methodology 1) the Stakeholder Needs and Assessment Guidance (this document), 2) the Technical Submission form, 3) the TPL scoring spreadsheet. The TPL assessment is designed to give a technology neutral or agnostic assessment of any wave energy converter technology. The focus of the TPL is on the performance of the technology in meeting the customer’s needs. The original TPL is described in [1, 2] and those references also detail the critical differences in the nature of the TPL when compared to the more widely used technology readiness level (TRL). (Wave energy TRL is described in [3]). The revised TPL is particularly intended to be useful to investors and also to assist technology developers to conduct comprehensive assessments in a way that is meaningful and attractive to investors. The revised TPL assessment methodology has been derived through a structured Systems Engineering approach. This was a formal process which involved analyzing customer and stakeholder needs through the discipline of Systems Engineering. The results of the process confirmed the high level of completeness of the original methodology presented in [1] (as used in the Wave Energy Prize judging) and now add a significantly increased level of detail in the assessment and an improved more investment focused structure. The revised TPL also incorporates the feedback of the Wave Energy Prize judges.

Three different ways of combining scores are used in the revised formulation. These are arithmetic mean, geometric mean and multiplication with normalisation. Arithmetic mean is used when combining scores that measure similar attributes, e.g. used for combining costs. The arithmetic mean has the property that it is similar to a logical OR, e.g. when combining costs it does not matter what the individual costs are only what the combined cost is. Geometric mean and Multiplication are used when combining scores that measure disparate attributes. Multiplication is similar to a logical AND, it is used to combine ‘must haves.’ As a result, this method is more punitive than the geometric mean; to get a good score in the combined result it is necessary to have a good score in ALL of the inputs. e.g. the different types of survivability are ‘must haves.’ On balance, the revised TPL is probably less punitive than the previous spreadsheet, multiplication is used sparingly as a method of combining scores. This is in line with the feedback of the Wave Energy Prize judges.

Permafrost-dominated coastlines in the Arctic are rapidly disappearing. Arctic coastal erosion rates in the United States have doubled since the middle of the twentieth century and appear to be accelerating. Positive erosion trends have been observed for highly-variable geomorphic conditions across the entire Arctic, suggesting a major (human-timescale) shift in coastal landscape evolution. Unfortunately, irreversible coastal land loss in this region poses a threat to native, industrial, scientific, and military communities. The Arctic coastline is vast, spanning more than 100,000 km across eight nations, ten percent of which is overseen by the United States. Much of area is inaccessible by all-season roads. People and infrastructure, therefore, are commonly located near the coast. The impact of the Arctic coastal erosion problem is widespread. Homes are being lost. Residents are being dispersed and their villages relocated. Shoreline fuel storage and delivery systems are at greater risk. The U.S. Department of Energy (DOE) and Sandia National Laboratories (SNL) operate research facilities along some of the most rapidly eroding sections of coast in the world. The U.S. Department of Defense (DOD) is struggling to fortify coastal radar sites, operated to ensure national sovereignty in the air, against the erosion problem. Rapid alterations to the Arctic coastline are facilitated by oceanographic and geomorphic perturbations associated with climate change. Sea ice extent is declining, sea level is rising, sea water temperature is increasing, and permafrost state is changing. The polar orientation of the Arctic exacerbates the magnitude and rate of the environmental forcings that facilitate coastal land area loss. The fundamental mechanics of these processes are understood; their non-linear combination poses an extreme hazard. Tools to accurately predict Arctic coastal erosion do not exist. To obtain an accurate predictive model, a coupling of the influences of evolving wave dynamics, thermodynamics, and sediment dynamics must be developed. The objective of this document is to present the state-of-the-science and outline the key steps for creation of a framework that will allow for improved prediction of Arctic coastal erosion rates. This is the first step towards the quantification of coastal hazards that will allow for sustainable planning and development of Arctic infrastructure.

Capabilities and functions are hierarchical structures (i.e. taxonomies) that are used in a systems engineering framework to identify complimentary requirements for the system: what the system must do to achieve what it must be. In the case of capabilities, the taxonomy embodies the list of characteristics that are desired, from the perspective of the stakeholders, for the system to be successful. In terms of the functions, the hierarchy represents the solution agnostic (i.e. independent of specific design embodiments) elements that are needed to meet the stakeholder requirements. This paper will focus on the development of the functions. The functions define the fundamental elements of the solution that must be provided in order to achieve the mission and deliver the capabilities. They identify the behaviors the farm must possess, i.e. the farm must be able to generate and deliver electricity from wave power. High-level functions are independent of the technology or design used to implement the function. However, detailed functions may begin to border on specific design choices. Hence a strong effort has been made to maintain functions that are design agnostic.

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This paper will introduce the Systems Engineering process utilized by the Structured Innovation team. Following the process this paper will present two key elements derived from the analysis of a successful Wave Energy Farm: the capabilities and the functions. The capabilities are the goals of a Wave Energy Farm as determined from a condensed list of the stakeholder needs. The functions are the activities or behaviors performed by the Wave Energy Farm in order to achieve the capabilities (i.e. the goals). At the framework level, both the functions and the capabilities remain independent of the design and these will be used to develop the list of requirements for a WEC in future work.

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A modified version of an indust ry standard wave modeling tool was evaluated, optimized, and utilized to investigate model sensitivity to input parameters a nd wave energy converter ( WEC ) array deployment scenarios. Wave propagation was investigated d ownstream of the WECs to evaluate overall near - and far - field effects of WEC arrays. The sensitivity study illustrate d that wave direction and WEC device type we r e most sensitive to the variation in the model parameters examined in this study . Generally, the changes in wave height we re the primary alteration caused by the presence of a WEC array. Specifically, W EC device type and subsequently their size directly re sult ed in wave height variations; however, it is important to utilize ongoing laboratory studies and future field tests to determine the most appropriate power matrix values for a particular WEC device and configuration in order to improve modeling results .

A modified version of an indust ry standard wave modeling tool, SNL - SWAN, was used to perform model simulations for hourly initial wave conditio ns measured during the month of October 2009. The model was run with an array of 50 wave energy converters (WECs) and compared with model runs without WECs. Maximum changes in H s were found in the lee of the WEC array along the angles of incident wave dire ction and minimal changes were found along the western side of the model domain due to wave shadowing by land. The largest wave height reductions occurred during observed typhoon conditions and resulted in 14% decreases in H s along the Santa Cruz shoreline . Shoreline reductions in H s were 5% during s outh swell wave conditions and negligible during average monthly wave conditions.

The numerical model, SWAN (Simulating WAves Nearshore) , was used to simulate wave conditions in Kaneohe Bay, HI in order to determine the effects of wave energy converter ( WEC ) devices on the propagation of waves into shore. A nested SWAN model was validated then used to evaluate a range of initial wave conditions: significant wave heights (H s ) , peak periods (T p ) , and mean wave directions ( MWD) . Differences between wave height s in the presence and absence of WEC device s were assessed at locations in shore of the WEC array. The maximum decrease in wave height due to the WEC s was predicted to be approximately 6% at 5 m and 10 m water depths. Th is occurred for model initiation parameters of H s = 3 m (for 5 m water depth) or 4 m (10 m water depth) , T p = 10 s, and MWD = 330deg . Subsequently, bottom orbital velocities were found to decrease by about 6%.

The goal s of this study were to develop tools to quantitatively characterize environments where wave energy converter ( WEC ) devices may be installed and to assess e ffects on hydrodynamics and lo cal sediment transport. A large hypothetical WEC array was investigated using wave, hydrodynamic, and sediment transport models and site - specific average and storm conditions as input. The results indicated that there were significant changes in sediment s izes adjacent to and in the lee of the WEC array due to reduced wave energy. The circulation in the lee of the array was also altered; more intense onshore currents were generated in the lee of the WECs . In general, the storm case and the average case show ed the same qualitative patterns suggesting that these trends would be maintained throughout the year. The framework developed here can be used to design more efficient arrays while minimizing impacts on nearshore environmen ts.

A n indust ry standard wave modeling tool was utilized to investigate model sensitivity to input parameters and wave energy converter ( WEC ) array deploym ent scenarios. Wave propagation was investigated d ownstream of the WECs to evaluate overall near - and far - field effects of WEC arrays. The sensitivity study illustrate d that b oth wave height and near - bottom orbital velocity we re subject to the largest pote ntial variations, each decreas ed in sensitivity as transmission coefficient increase d , as number and spacing of WEC devices decrease d , and as the deployment location move d offshore. Wave direction wa s affected consistently for all parameters and wave perio d was not affected (or negligibly affected) by varying model parameters or WEC configuration .

This guidance document provide s the reader with an overview of the key environmental considerations for a typical offshore wind coastal location and the tools to help guide the reader through a thoro ugh planning process. It will enable readers to identify the key coastal processes relevant to their offshore wind site and perform pertinent analysis to guide siting and layout design, with the goal of minimizing costs associated with planning, permitting , and long - ter m maintenance. The document highlight s site characterization and assessment techniques for evaluating spatial patterns of sediment dynamics in the vicinity of a wind farm under typical, extreme, and storm conditions. Finally, the document des cribe s the assimilation of all of this information into the conceptual site model (CSM) to aid the decision - making processes.

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This document describes the marine hydrokinetic (MHK) input file and subroutines for the Sandia National Laboratories Environmental Fluid Dynamics Code (SNL-EFDC), which is a combined hydrodynamic, sediment transport, and water quality model based on the Environmental Fluid Dynamics Code (EFDC) developed by John Hamrick [1], formerly sponsored by the U.S. Environmental Protection Agency, and now maintained by Tetra Tech, Inc. SNL-EFDC has been previously enhanced with the incorporation of the SEDZLJ sediment dynamics model developed by Ziegler, Lick, and Jones [2-4]. SNL-EFDC has also been upgraded to more accurately simulate algae growth with specific application to optimizing biomass in an open-channel raceway for biofuels production [5]. A detailed description of the input file containing data describing the MHK device/array is provided, along with a description of the MHK FORTRAN routine. Both a theoretical description of the MHK dynamics as incorporated into SNL-EFDC and an explanation of the source code are provided. This user manual is meant to be used in conjunction with the original EFDC [6] and sediment dynamics SNL-EFDC manuals [7]. Through this document, the authors provide information for users who wish to model the effects of an MHK device (or array of devices) on a flow system with EFDC and who also seek a clear understanding of the source code, which is available from staff in the Water Power Technologies Department at Sandia National Laboratories, Albuquerque, New Mexico.

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