Renewable Energy Laboratory Research Articles

Burdened by renewable energy? A multi-scalar analysis of distributional justice and wind energy in the United States

The transition towards renewable energy is likely to be uneven across social and spatial dimensions. To ensure this transition is equitable and just, energy injustice has become the key framework for analyzing and interpreting the distribution of energy infrastructure. Wind energy development has experienced a significant gap between broad public support for increased development but persistent localized opposition to proposed projects, indicating that wind represents a locally unwanted land use. We present the theoretical argument that although the negative impacts of wind energy infrastructure are less extreme than those posed by other, more toxic, unwanted land uses, their status as a locally unwanted land use will produce similar distributional injustices as have been found throughout the environmental injustice literature. Using data from both the American Community Survey, the U.S. Wind Turbine Database, and the National Renewable Energy Lab we use logistic and Poisson regressions, fixed effects, and temporal lags to evaluate the current landscape of wind energy injustice along the social dimensions of income, race and ethnicity, age, education, labor force participation, and rurality at three spatial scales: between all counties within the contiguous United States, between counties within states with wind energy, and between census tracts within counties with wind energy. We do not find strong evidence of distributional injustice along the lines of race, ethnicity, or low-income. However, we do find evidence of injustice for populations which are younger, less educated, have lower labor force participation, and are more rural.

(Invited) An Experimental and Computational Data Ecosystem for Advancing Materials Research

For the past few decades technological advancement in computer capabilities along with researchers’ desire for more data has combined into a perfect storm of data overload. While data is constantly being generated at an ever-increasing pace, there is also a wealth of historical and potentially useful data residing on researchers’ computers and abandoned hard drives. Over 2.5 quintillion bytes of data is generated every day and over the last two years 90% of all data in the world was generated. These statistics present a daunting problem for both data scientists and materials researchers in general. Not only do we need methods to manage the oncoming flood of materials data, but methods to prepare, aggregate and effectively utilize that data. Over the past decade, we have developed a materials data ecosystem at the National Renewable Energy Lab (NREL) to provide researchers with the ability to manage and explore the vast amounts of data generated both in-house and across multiple materials collaborations. During the course of this work, we have identified several key concepts required for any research data ecosystem, including a ground-up development approach, workflow and metadata capture, easy accessibility, ability to handle large data formats, and integration with other data resources. Any such system needs to initially focus on the acquisition and archiving of raw data from across all expected and available resources, and in most cases, this should be an automated process. It needs to be able to help researchers past traditional work flow barriers and capture metadata and associate that with data easily. All the acquired information must easily be queryable and retrievable at numerous nexus of the process as a technique for supporting both research and data quality control. The resulting data warehouse needs the ability to be filtered and aggregated to feed supplemental research-targeted databases. Due to the possibility of extremely large resources needing to be processed and analyzed, a means for transmitting and providing enclosed work environments for big data should be anticipated and developed. With the advent of large materials repositories and virtual laboratory consortia, such as the Energy Material Networks (EMN), the data ecosystem needs to take advantage of its resources and supply data streams as needed for researchers wanting to contribute to those platforms. Finally, the research data ecosystem needs forward-facing tools and web applications that are intuitive and easy to use. In the end, information garnered from the data ecosystem would complete the research cycle and foment the next round of experimental design and data generation. In this talk we will look at the underlying drivers of our ecosystem design, the design and development of the various elements, and how that work helped drive our development of the data hub, analysis tools and support for the Energy Material Networks (EMN). Acknowledgements: This work was supported by U.S. Department of Energy, under contract under contract DEAC36-08GO28308 to Alliance for Sustainable Energy, LLC, the manager and operator of the National Renewable Energy Laboratory.

Performance of a 3D pendulum tuned mass damper in offshore wind turbines under multiple hazards and system variations

Misaligned wind-wave and seismic loading render offshore wind turbines suffering from excessive bi-directional vibration. However, most of existing research in this field focused on unidirectional vibration mitigation, which is insufficient for research and real application. Based on the authors\' previous work (Sun and Jahangiri 2018), the present study uses a three dimensional pendulum tuned mass damper (3d-PTMD) to mitigate the nacelle structural response in the fore-aft and side-side directions under wind, wave and near-fault ground motions. An analytical model of the offshore wind turbine coupled with the 3d-PTMD is established wherein the interaction between the blades and the tower is modelled. Aerodynamic loading is computed using the Blade Element Momentum (BEM) method where the Prandtl\'s tip loss factor and the Glauert correction are considered. Wave loading is computed using Morison equation in collaboration with the strip theory. Performance of the 3d-PTMD is examined on a National Renewable Energy Lab (NREL) monopile 5 MW baseline wind turbine under misaligned wind-wave and near-fault ground motions. The robustness of the mitigation performance of the 3d-PTMD under system variations is studied. Dual linear TMDs are used for comparison. Research results show that the 3d-PTMD responds more rapidly and provides better mitigation of the bi-directional response caused by misaligned wind, wave and near-fault ground motions. Under system variations, the 3d-PTMD is found to be more robust than the dual linear TMDs to overcome the detuning effect. Moreover, the 3d-PTMD with a mass ratio of 2% can mitigate the short-term fatigue damage of the offshore wind turbine tower by up to 90%.

Early Detection for Li-Ion Batteries Thermal Runaway Based on Gas Sensing

Thermal Runaway of Li-ion batteries is a major safety risk, and the propagation of fire from one cell to the neighboring cells is a huge challenge. Furthermore, the risk of damage to loose cells in the waste and recycling stream can result in catastrophic fires leading to property damage and injury. During storage and transport, batteries from collection centers awaiting recycling are typically housed in metal 55 gallon (0.208 m3) drums. Several accidents resulting in thermal runaway have occurred, without being observed from the outside until gas pressure caused the vessel to rupture. Early and robust detection for thermal runaway events could enable protection and mitigation to reduce the risks. Previous studies have used voltage or current to detect possible thermal runaway events, assuming the cells are assembled into a pack where electrical and thermal measurements are feasible. However, in this case, without any battery management system that measures the current, voltage, and temperature of the individual cells, the only information we can get is from a few sensors at the lid of the drum. To address the challenges of storage and transport, a Li-ion battery thermal runaway early detection method based on gas sensing is developed. To compare the performance of several detection methods, a 0.208 m3 cylindrical storage drum filled with fully charged 18650 cells, is simulated in COMSOL. In the simulation, an 18650 NMC cell, located in the center of the drum, as shown in Fig. 1, triggers a hard internal short circuit and leads to thermal runaway. A single cell thermal runaway can heat neighboring cells and triggers new thermal runaway events, thus causing a chain reaction of more and more thermal runaway cells [1]. A nine-cell system, with three cells in parallel and three cells in series, is simulated. Each battery is spaced 2 mm from each other, and the free space is assumed to be filled with air. The results for temperature evolution of each cell during thermal runaway propagation is shown in Fig. 2. As seen from the figure, the second cell triggers thermal runaway after 710 seconds. The remaining cells quickly enter the thermal runaway state after the second cell. The detection of thermal runaway must be made before this time if corrective action is to be applied. So, this time is used as the benchmark of thermal runaway detection in this study, defined as propagation critical time. To detect possible thermal runaway, temperature sensors are located on the surface of the drum as shown in Fig. 1. From the simulation result, at 710 seconds, the maximum surface temperature change on the drum surface is less than 0.001 °C. In this simulation, conventional surface temperature sensing approach (e.g., Infrared Imaging) cannot detect the thermal runaway event before the propagation critical time of 710s when the second cell starts a thermal runaway event. Extending the model of [2] inside a drum full of 18650 cells, the detection of a single thermal runaway event by CO2 gas sensors is investigated. Li-ion battery vents significant amount of gas during thermal runaway, and due to SEI decomposition reaction at the early stage of thermal runaway [3], CO2 is generated and vented from battery to the drum space. This gas sensing approach then monitors the concentrations of CO2 inside the drum, and cell venting during thermal runaway can be detected when the concentrations of CO2 are high. The CO2 sensor is located at the center of the drum top surface in Fig .1, and the detection threshold of CO2 concentrations for a thermal runaway event is set to be 2000 ppm. Fig. 3 shows the evolution of CO2 concentrations with time. The initial gas venting speed will accelerate the CO2 species transport process, and CO2 propagates to most of the drum space in a short period. The detection can be made at 85 seconds, which is ahead of the propagation critical time 710 seconds. This study shows the faster response of gas sensing comparing to drum surface temperature sensing in a cylindrical drum. With the use of gas sensing approach in large battery storage facilities, the speed of thermal runaway event detection can be improved and precede the propagation of thermal runaway to additional cells. Reference [1] Kim, G-H.; Pesaran, A.; Smith, K. No. NREL/PR-540-43186. National Renewable Energy Lab.(NREL), Golden, CO (United States), 2008. [2] Cai, T.; Stefanopoulou, A.; Siegel, J. In Dynamic Systems and Control Conference, ASME: 2018. [3] Feng, X.; Ouyang, M.; Liu, X.; Lu, L.; Xia, Y.; He, X. Energy Storage Materials 2018, 10, 246-267. Figure 1

(Invited) Progress in Alkaline Membranes and Alkaline Membrane Fuel Cells at the National Renewable Energy Lab

Alkaline membrane fuel cells are of high interest because they offer the ability to use lower cost catalyst and separator materials: two cost limitations of polymer electrolyte fuel cell systems. The performance advances of these systems has been fairly remarkable as power densities around 2 W/cm2 and current densities above 5 A/cm2 have now been demonstrated.(1) Efforts at NREL have focused on the development of novel ionomers for use in electrodes and membranes. However most of the improvements in performance have arisen due to the impact of electrode fabrication and operating conditions. The different advances that have led to improved performance and durability of these systems as well as remaining challenges will be discussed. These systems and the results presented are closely tied to the area of alkaline membrane fuel cells where Shimshon Gottesfeld has been an early champion. Beyond the current research portfolio, the presenter for this talk, a former post-doc under Shimshon Gottesfeld will talk about his interactions with Shimshon, with a specific focus on the area of alkaline membrane fuel cells.

OPTIMALISATION THE USE OF HOUSEHOLD-SCALE WATER PUMP MACHINES WITH THE ARDUINO NANO v3.0 CONTROL SYSTEM

The need of clean water keep increasing, therefore some energy sources that are classified in the renewed energies such as solar energy, wind energy, etc. This research is conducted to analyse usage of water pump engine by using the automatic switches of controller arduino nano v.3.0 and solar energy as the homebased scale pump actuator. The experimental research against the automatic switches usage and solar energy with battery photovoltaic (PV) for water pump actuator electric energy generator is done in the Renewable Energy Laboratory at Mechanical Engineering Department of State university of Makassar. The result of research shows the sun intensity excelsior, hence battery generator output power PV obtained excelsior to also. Maximum current of PV is obtained at 13.35 Central Indonesia Time Zone (local time) 599 Watts. Current or generator output power PV obtained hardly having an effect on to performa pump and water debit yielded. Water debit yielded by pump at 08.00 until Pukul 16.00 is 0,8 Liter/minute. The controller system with this PV generator is able to deliver water to reservoir with head 3.5 meter.

A simulation approach to sizing batteries for integration with net-zero energy residential buildings

Utilities have greatly increased the use of time-of-use (TOU) rate structures for residential customers to more closely match the cost of delivered electricity as well as demand tariffs to incentivize a temporal shift in residential energy use. However current research tends to focus on a particular climate or location. This study analyzes four residences across four different climate zones in Arizona and explores the value of adding battery storage to a net-zero energy (NZE) photovoltaic (PV) system. Using the National Renewable Energy Lab's Building Optimization (BEopt) and System Advisor Model (SAM) tools, this study performs parametric analysis by varying battery size to determine a capacity that produces a maximum net present value (NPV). A sensitivity analysis on three possible future battery price trends is also performed. This study finds the NZE PV systems are only able to mitigate 37–44% of the peak electricity purchases and 4–12% of demand charges due to mismatch between solar potential and on-peak hours. Adding large batteries, 1.5–1.6 times larger than required to meet the annual peak energy purchase requirements, provides a maximum NPV for PV-battery systems at locations with favorable utility rates. We conclude that the best economics are achieved, at both current and expected future battery prices, when batteries are sized to never require replacement during the PV system lifetime.

A study of the wind turbine wake dynamics in the neutral boundary layer using large eddy simulation

Accurate prediction of atmospheric boundary layer (ABL) flow requires great importance for optimization of wind farms in influence by the wake from upstream turbines. In this paper three-dimensional transient turbulent flow combining with actuator-disc model for the 1.5 MW wind turbine designed and optimized using airfoil families of National Renewable Energy Lab over flat terrain through case of a neutral turbulent boundary-layer are validated. Large eddy simulation (LES) was used to potentially provide the kind of high-resolution spatial and temporal information needed to investigate the characteristics of a wind-turbine wake. Furthermore, Spatial and temporal characteristics of the flow are examined using OpenFOAM software (The OpenFOAM Foundation, 2013). After verifying that the code is performing consistently, neutral initial profiles are constructed from two years data analysis of the atmospheric boundary layer (ABL) at met mast of Høvsøre in Denmark and are implemented into Large Eddy Simulation code. In general, the characteristics of the wakes simulated with the proposed large eddy simulation (LES) framework are in good agreement with several wake characteristics, such as the velocity deficit, centreline location, and wake width as metrics for model verification. The results demonstrate that, despite its simplicity, the actuator disc model can give very useful information when developing a wind farm in neutral atmospheric conditions.

Research on Solar Resource Evaluation Method Based on Mathematical Statistics

According to the satellite data provided by the US Renewable Energy Laboratory, the datum of solar resources 25 typical cities in China for 29 years were analyzed by method of principal component analysis and the whole evaluation of this kind of energy was summarized. The indicators for evaluation of solar energy resources, including richness, stability, utilization value and mean of effective sunshine duration, are incorporated into three main parameters. And the score of principal component is considered as the main factor of the resource evaluation. The evaluation and prediction of solar energy in a region are efficient and accurate by this method, which provide guidance for utilization and development of solar energy.

Modeling and Design Method for an Adaptive Wind Turbine Blade With Out-of-Plane Twist

A modeling framework to analyze a wind turbine blade subjected to an out-of-plane transformation is presented. The framework combines aerodynamic and mechanical models to support an automated design process. The former combines the National Renewable Energy Lab (NREL) aerodyn software with a genetic algorithm solver. It defines the theoretical twist angle distribution (TAD) as a function of wind speed. The procedure is repeated for a series of points that form a discrete range of wind speeds. This step establishes the full range of blade transformations. The associated theoretical TAD geometry is subsequently passed to the mechanical model. It creates the TAD geometry in the context of a novel wind turbine blade concept. The blade sections are assumed to be made by additive manufacturing, which enables tunable stiffness. An optimization problem minimizes the difference between the practical and theoretical TAD over the full range of transformations. It does so by selecting the actuator locations and the torsional stiffness ratios of consecutive segments. In the final step, the blade free shape (undeformed position) is found. The model and design support out-of-plane twisting, which can increase energy production and mitigate fatigue loads. The proposed framework is demonstrated through a case study based on energy production. It employs data acquired from the NREL Unsteady Aerodynamics Experiment. A set of blade transformations required to improve the efficiency of a fixed-speed system is examined. The results show up to 3.7% and 2.9% increases in the efficiency at cut-in and rated speeds, respectively.

(Invited) PEM Fuel Cell Catalyst Layer Architectures

The primary PEMFC catalyst layer architecture used since the early 1990’s is a porous dispersed Pt-supported carbon catalyst with recast solid ionomer component forming a continuous (percolating) network through the electrode for proton transport; the carbon facilitating the electrical conduction; the active electrochemical reactions conducted on the metallic Pt sites [1,2]. The thin catalyst layer consists of microscale carbon particles each supporting nanoscale platinum catalyst particles all loosely embedded in a matrix of ionomer creating a porous structure. The fuel cell catalyst layer is a complex structure that facilitates the electrochemical conversion of hydrogen and oxygen; it provides pathways for reactant transport, provides both electrical and proton conductivity pathways. This has been traditionally referred to as the 3-phase boundary of an electrolyte, an electrode, and a gaseous fuel. In addition to the gas-reactant transport to the active site, the result product water must also be managed both in terms of product removal and optimal hydration of the membrane/ionomer providing the proton conductivity. Various methods are used to produce these thin-layer-electrode films including decal transfer (in both H+ and Na+ forms), direct membrane application, catalyst-coated-membranes and catalyst-coated-gas-diffusion-media. However the resulting structures all must result in providing the 3-phase boundary regions for good performance. As ubiquitous as the thin layer electrode structure has been used, it is still an active field of research to properly understand the structure and improve the reactant transport and catalyst utilization. A demonstration of the 3-phase boundary is shown in Figure 1 by high resolution TEM [3]. Platinum nanoparticles are observed attached to the carbon support with a thin layer of surrounding ionomer. However, this observed structure is not consistent through-out the electrode; regions where agglomerates of ionomer occur, including Pt nanoparticles separated from the carbon support. One consortia working to better understand and improve the catalyst layer structure is FC-PAD (Fuel Cell-Performance and Durability) consortia, comprised of five U.S. National Labs: Argonne, Berkeley, Los Alamos, Oak Ridge and National Renewable Energy Lab. Recent results from the FC-PAD consortia include elemental mapping of the electrode layers (see Figure 2). This type of elemental mapping suggests an inhomogeneous distribution in the electrode layer. Such an inhomogeneous distribution suggests that catalyst utilization is not fully optimized. Additionally, the appropriate pore structure in the catalyst layer is also necessary for providing transport paths for reactants to reach reaction sites as well as allow water mobility throughout the catalyst layer. Various methods have also been utilized to map the electrode layer pore-structure including digitizing of microscopy data, MIP and BET analysis. Because performance is largely influenced by the ionomer/catalyst interface within the catalyst layers of a MEA, the “ideal” interface should contain 100% ionomer contact with the platinum nanoparticles to maximize catalyst utilization. In addition, the thickness of ionomer film over the catalyst nanoparticles should be optimal to facilitate gas diffusion and water balance without sacrificing its protonic conductivity. Partial ionomer contact renders a portion of the catalyst to be electrochemically inactive and can also limit proton conduction. However, increasing ionomer content (to increase contact with the catalyst) leads to a thicker ionomer film, which results in an increased gas and water diffusional barrier. Some novel MEA fabrications various developers are exploring include: NSTF (Nano Structure Thin Film) catalyst and electrodesCatalyst support functionalization to control the catalyst/ionomer interfaceStratified catalyst layersIonomer “nanofiber” inclusion in the catalyst layerIonomer-coated MWCNTsHigh temperature membrane electrode assemblies Acknowledgments This research is supported by DOE Fuel Cell Technologies Office, through the Fuel Cell Performance and Durability (FC-PAD) Consortium; Fuel Cells program manager: Dimitrios Papageorgopoulos. References S. Wilson and S. Gottesfeld, Thin- film catalyst layers for polymer electrolyte fuel cell electrodes, J. Appl. Electrochem., 22, 1 (1992)MS Wilson, S Gottesfeld, High Performance Catalyzed Membranes of Ultra‐low Pt Loadings for Polymer Electrolyte Fuel Cells, Journal of the Electrochemical Society, Electrochem. Soc. 1992. Moore, R Borup, KS Reeves, ECS Trans. 3(1), 717-733 (2006) Figure 1

A renewable energy solution for stand-alone power generation: A case study of KhshU Site-Iran

Abstract In order to offer cost-effective options to supersede fossil fuel resources, we developed a research methodology based on techno-economic feasibility analysis. The purpose of this paper is to find off-grid renewable energy solutions, including solar panel, wind turbine and batteries as possible options for zero-emission stand-alone power generation in KhshU Site, a renewable energy laboratory in Iran. The HOMER software, which determines the most cost effective system for a given load by considering incident solar radiation, wind speed, electrical demand profile, and equipment characteristics, was employed. The results revealed that the most economical configuration among different renewable energy systems (RES's) was the PV-battery, which has a total net present cost (NPC) of 8173 US$ and a cost of energy (COE) of 0.546 $/kWh, followed by the hybrid PV/wind/battery. Sensitivity analysis demonstrated that raising discount rates could increase the cost of energy while net present cost of the entire system would be reduced in any inflation rate. The analysis also showed that the wind speed variation between 3 and 6 m/s didn't have a considerable effect on NPC and COE.

Bi-directional vibration control of offshore wind turbines using a 3D pendulum tuned mass damper

Abstract Offshore wind turbines suffer from excessive bi-directional vibrations due to wind-wave misalignment and vortex induced vibrations. However, most of existing research focus on unidirectional vibration attenuation which is inadequate for real applications. The present paper proposes a three dimensional pendulum tuned mass damper (3d-PTMD) to mitigate the tower and nacelle dynamic response in the fore-aft and side-side directions. An analytical model of the wind turbine coupled with the 3d-PTMD is established wherein the interaction between the blades, the tower and the 3d-PTMD is modeled. Aerodynamic loading is computed using the Blade Element Momentum method where the Prandtls tip loss factor and the Glauert correction are considered. JONSWAP spectrum is adopted to generate wave data. Wave loading is computed using Morisons equation in collaboration with the strip theory. Via a numerical search approach, the design formula of the 3d-PTMD is obtained and examined on a National Renewable Energy Lab (NREL) monopile 5 MW baseline wind turbine model under misaligned wind, wave and seismic loading. Dual linear tuned mass dampers (TMDs) deployed in the fore-aft and side-side directions are utilized for comparison. It is found that the 3d-PTMD with a mass ratio of 2 % can improve the mitigation of the root mean square and peak response by around 10 % when compared with the dual linear TMDs in controlling the bi-directional vibration of the offshore wind turbines under misaligned wind, wave and seismic loading.

Tip-Vortex Noise Reduction of a Wind Turbine Using a Winglet

AbstractThe present study investigates the feasibility of using winglet as a novel approach to reduce aero-acoustic noise of wind turbine blades. For this purpose, the National Renewable Energy Lab...

Semi-active control of monopile offshore wind turbines under multi-hazards

Abstract The present paper studies the control of monopile offshore wind turbines subjected to multi-hazards consisting of wind, wave and earthquake. A Semi-active tuned mass damper (STMD) with tunable natural frequency and damping ratio is introduced to control the dynamic response. A new fully coupled analytical model of the monopile offshore wind turbine with an STMD is established. The aerodynamic, hydrodynamic and seismic loading models are derived. Soil effects and damage are considered. The National Renewable Energy Lab monopile 5 MW baseline wind turbine model is employed to examine the performance of the STMD. A passive tuned mass damper (TMD) is utilized for comparison. Through numerical simulation, it is found that before damage occurs, the wind and wave induced response is more dominant than the earthquake induced response. With damage presence in the tower and the foundation, the nacelle and the tower response is increased dramatically and the natural frequency is decreased considerably. As a result, the passive TMD with fixed parameters becomes off-tuned and loses its effectiveness. In comparison, the STMD retuned in real-time demonstrates consistent effectiveness in controlling the dynamic response of the monopile offshore wind turbines under multi-hazards and damage with a smaller stroke.

Modeling of Wind Energy Conversion System using PSCAD/EMTDC

This paper aims to model a complete wind energy conversion system ( WECS ) connected to a grid. The motivation comes from the Distributed Generation System ( DGS ) installed at the Renewable Energy Lab at UMass Lowell. The objective of this work is to develop universal and standardized manufacturer independent textbook models. Manufacturer specific models are more accurate and detailed, but proprietary and non-disclosure agreements become an issue for research purposes. Since the wind turbines installed in the lab are VSWT (Variable Speed Wind Turbine) with Permanent Magnet Synchronous Generators ( PMSG ), so such a turbine system is modeled to represent them in general. PMSG requires very less maintenance and has high efficiency since it doesn't have rotor current and is used without a gearbox. Further, there are two more advantages: first, it has the capability of variable speed control due to the fact that its rotor speed can change in a large range; second, it's excitation system is independent of the grid and require any other excitation source. In addition to the turbine generator, other main components of WECS are also modeled namely: wind source model, wind turbine, permanent magnet synchronous generator and AC/DC/AC control. The equations governing these models are also discussed. The entire generation system is implemented on PSCAD / EMTDC and integrated to a grid and a basic fault analysis is done under different conditions. The proposed model can be used for research purposes on distributed generation issues. This model provides a good software simulation test bed for further research.

Performance prediction of horizontal hydrokinetic energy converter using multiple-cylinder synergy in flow induced motion

Horizontal hydrokinetic energy can be harnessed using Steady Lift Technology (SLT) like turbines or Alternating Lift Technology (ALT) like the VIVACE Converter. Tidal/current turbines with low mechanical losses typically achieve about 30% peak power efficiency, which is equivalent to 50.6% power efficiency over the Betz limit at flow speed nearly 3.0m/s. The majority of flows worldwide are slower than 1.0–1.5m/s. Turbines also require large in-flow spacing resulting in farms of low power-to-volume density. Alternating-lift overcomes these challenges. The purpose of this study is to show that the ALT Converter is a three-dimensional energy absorber that efficiently works in river/ocean currents as slow as 1.0–1.5m/s a range of velocities presently inaccessible to watermills and turbines. This novel converter utilizes flow-induced motions (FIM), which are potentially destructive phenomena for structures, enhances them, and converts hydrokinetic energy to electricity. It was invented in the Marine Renewable Energy Lab (MRELab) and patented through the University of Michigan. MRELab has been studying the effect of passive turbulence control (PTC) to enhance FIMs and to expand their synchronization range for energy harnessing. This study shows that multiple cylinders in proximity can synergistically work and harness more energy than the same number of a single cylinder in isolation. Estimation based on experiments, shows that a 4 PTC-cylinder Converter can achieve 88.6% peak efficiency of the Betz limit at flow speed slower than 1.0m/s and power-to-volume density of 875W/m3 at 1.45m/s. Thus, the Converter can efficiently harness energy from rivers and ocean current as slow as 0.8–1.5m/s, with no upper limit in flow velocity.

A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks

This work proposes a novel fault diagnostic technique for photovoltaic systems based on Artificial Neural Networks (ANN). For a given set of working conditions - solar irradiance and photovoltaic (PV) module's temperature - a number of attributes such as current, voltage, and number of peaks in the current–voltage (I–V) characteristics of the PV strings are calculated using a simulation model. The simulated attributes are then compared with the ones obtained from the field measurements, leading to the identification of possible faulty operating conditions. Two different algorithms are then developed in order to isolate and identify eight different types of faults. The method has been validated using an experimental database of climatic and electrical parameters from a PV string installed at the Renewable Energy Laboratory (REL) of the University of Jijel (Algeria). The obtained results show that the proposed technique can accurately detect and classify the different faults occurring in a PV array. This work also shows the implementation of the developed method into a Field Programmable Gate Array (FPGA) using a Xilinx System Generator (XSG) and an Integrated Software Environment (ISE).

English

The harvesting of wind energy is expected to increase greatly in the future because of its stability, abundance, and renewability in large coastal states such as China. The floating support structure will likely become the major structural form for wind turbines in the future due to its cost advantages when the water depth reaches 50 m. The 5MW wind turbine model from National Renewable Energy Lab (NREL) and the modified tension leg platform model proposed by Harbin Institute of Technology (HIT) were applied to certain sea conditions in the South China Sea in order to consider the effects of external load coupling actions. In this study, the internal force, mooring system force, as well as the acceleration, displacement and velocity of the floating structure of the modified HIT Tension Leg Platform (HIT-TLP) were calculated. During this process, the physical parameters of its tension leg structure at a specific frequency domain were obtained to find the technical reserves for its practical application in the future.

High-throughput Screening of Recalcitrance Variations in Lignocellulosic Biomass: Total Lignin, Lignin Monomers, and Enzymatic Sugar Release.

The conversion of lignocellulosic biomass to fuels, chemicals, and other commodities has been explored as one possible pathway toward reductions in the use of non-renewable energy sources. In order to identify which plants, out of a diverse pool, have the desired chemical traits for downstream applications, attributes, such as cellulose and lignin content, or monomeric sugar release following an enzymatic saccharification, must be compared. The experimental and data analysis protocols of the standard methods of analysis can be time-consuming, thereby limiting the number of samples that can be measured. High-throughput (HTP) methods alleviate the shortcomings of the standard methods, and permit the rapid screening of available samples to isolate those possessing the desired traits. This study illustrates the HTP sugar release and pyrolysis-molecular beam mass spectrometry pipelines employed at the National Renewable Energy Lab. These pipelines have enabled the efficient assessment of thousands of plants while decreasing experimental time and costs through reductions in labor and consumables.