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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

MOSIQS: Persistent Memory Object Storage With Metadata Indexing and Querying for Scientific Computing

Scientific applications often require high-bandwidth shared storage to perform joint simulations and collaborative data analytics. Shared memory pools provide a chance to satisfy such needs. Recently, a high-speed network such as Gen-Z utilizing persistent memory (PM) offers an opportunity to create a shared memory pool connected to compute nodes. However, there are several challenges to use scientific applications on the shared memory pool directly such as scalability, failure-atomicity, and lack of scientific metadata-based search and query. In this paper, we propose MOSIQS, a persistent memory object storage framework with metadata indexing and querying for scientific computing. We design MOSIQS based on the key idea that memory objects on PM pool can live beyond the application lifetime and can become the sharing currency for applications and scientists. MOSIQS provides an aggregate memory pool atop an array of persistent memory devices to store and access memory objects to accelerate scientific computing. MOSIQS uses a lightweight persistent memory key-value store to manage the metadata of memory objects, which enables memory object sharing. To facilitate metadata search and query over millions of memory objects resident on memory pool, we introduce Group Split and Merge (GSM), a novel persistent index data structure designed primarily for scientific datasets. GSM splits and merges dynamically to minimize the query search space and maintains low query processing time while overcoming the index storage overhead. MOSIQS is implemented on top of PMDK. We evaluate the proposed approach on many-core server with an array of real PM devices. Experimental results show that MOSIQS gains a 100% write performance improvement and executes multi-attribute queries efficiently with 2.7× less index storage overhead offering significant potential to speed up scientific computing applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Nested Workflows for Loosely Coupled HPC Simulations

The increasing complexity of modern scientific simulations has given rise to the notion of re-usability as a means to reduce development time and effort. One form of re-usability involves hierarchical modelling, where the code and artifacts that model a physical phenomenon are used as the building blocks for more complex coupled physical systems. This reusability mode allows for improvements in constituents sub-models to impact the overall fidelity of the entire system with minimal effort. This benefit, however, hinges upon the ability to provide a fairly loose coupling across model boundaries, where changes in any sub-model do not result in wholesale changes to other sub-models, or in the structure and details of the code for the entire physical system. In this paper, we present the design, implementation, and case studies for incorporating sub-workflows as building blocks in a framework for loosely coupled high performance simulations. We outline the issues involved in providing lightweight customization points for full workflows that allows their use unchanged in a nested-workflow setting, while providing each sub-workflow with a separate execution context that ensures non-interference with other parts of the simulation. We present several use cases that demonstrate the successful use of the proposed design and its flexibility in accommodating different customization and scaling requirements.

Elwasif, Wael↗
DOE OSTI2022

A Multi-Scale Computational Platform for Predictive Modeling of Corrosion in Al-Steel Joints (Final Report)

The research team proposed to develop innovative multi-scale models to predict corrosion and the resulting mechanical performances in aluminum-steel joints. The methods of joining considered are resistance spot welding, self-piercing riveting, and rivet-welding, all suitable for mass production applications. The multi-scale models integrate high throughput first-principle calculations based on density functional theory (DFT), high throughput calculation of phase diagrams (CALPHAD) modeling, and finite element method (FEM) simulations. These models are to be validated through laboratory experiments. Furthermore, the models are available as open source so as to enable scientists and engineers in the community to adapt and contribute to the development and application. The approaches rely on the research team’s extensive experience on the prediction of properties of individual phases at finite temperatures and variable compositions through DFT calculations, and our broad expertise on dissimilar material joining and their corrosion. The proposed computational framework enables high throughput computations for improved predictions of corrosion and the associated mechanical performance in dissimilar material joints, resulting in significant reduction in computational time needed by the current state-of-the-art methods. With the participation of researchers from three universities, an auto manufacturer, two manufacturing technology/equipment suppliers, and a software developer/vendor, the interdisciplinary research team applies the technical development on both phase-based modeling and laboratory experiments into the automobile body joining processes for validation and technology demonstration. The global cost of corrosion was estimated at about 3.4% of the global GDP in 2013. By using available corrosion control practices, it is estimated a saving between 15-35% of the cost of corrosion. In the U.S., more than $276 billion is spent repairing corrosion damage. Prediction of the corrosion and its impact on performance of the dissimilar material joints is critical for reducing the massive number of the current corrosion-based recalls for automobiles. Thus, the project goal is to develop models to enable predictive maintenance and end-of-life planning of multi-metal joints with risk of corrosion under different conditions such as exposure to high temperatures in summer and salt solutions in winter, quantified through its pH. An academia-industry consortium led by the University of Michigan and including Pennsylvania State University, University of Illinois Urbana-Champaign, University of Georgia, General Motors Company, Livermore Software Technology Corporation, and Optimal Process Technologies, LLC. created multi-scale models for prediction of corrosion in aluminum-steel joint structures such of them used in vehicle subassemblies – chassis and transmission systems. Starting from the first principle calculations, the team developed mathematical and data-driven models to predict the metallic components, which are formed during joining of two metals, for example aluminum and steel - a lightweight multilateral system which is currently used in more than 60% car bodies. These models were used for simulating chemical reactions that are happening when the joining metallic components are exposed to high temperatures and different pH values. The team was able to predict how the corrosion installs on the metallic components and how they lead to a sudden failure of components in cars. Newly developed machine learning algorithms combining Science, Technology, Engineering and Math disciplines, advanced finite element simulation and experimental validations have been integrated in a platform for prediction of the corrosion evolution and prediction the failure of joints under mechanical loadings and fatigue. Moreover, based on machine learning and inverse analysis, the team proposed solutions for designing new metallic alloys less susceptible to corrosion when joining multi-material assembles. An average of 4% error compared with experiments was achieved for the most common joints that are used in vehicle subassemblies.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A generative machine learning model for designing metal hydrides applied to hydrogen storage

Developing new metal hydrides is a critical step toward efficient hydrogen storage in carbon-neutral energy systems. However, existing materials databases, such as the Materials Project, contain a limited number of well-characterized hydrides, which constrains the discovery of optimal candidates. This work presents a framework that integrates causal discovery with a lightweight generative machine learning model to generate novel metal hydride candidates that may not exist in current databases. Using a dataset of 450 samples (270 training, 90 validation, and 90 testing), the model generates 1000 candidates. After ranking and filtering, six previously unreported chemical formulas and crystal structures are identified, four of which are validated by density functional theory simulations and show strong potential for future experimental investigation. Overall, the proposed framework provides a scalable and time-efficient approach for expanding hydrogen storage datasets and accelerating materials discovery.

generative model↗
DOE OSTI2018

Molecular engineered conjugated polymer with high thermal conductivity

Traditional polymers are both electrically and thermally insulating. The development of electrically conductive polymers has led to novel applications such as flexible displays, solar cells, and wearable biosensors. As in the case of electrically conductive polymers, the development of polymers with high thermal conductivity would open up a range of applications in next-generation electronic, optoelectronic, and energy devices. Current research has so far been limited to engineering polymers either by strong intramolecular interactions, which enable efficient phonon transport along the polymer chains, or by strong intermolecular interactions, which enable efficient phonon transport between the polymer chains. However, it has not been possible until now to engineer both interactions simultaneously. We report the first realization of high thermal conductivity in the thin film of a conjugated polymer, poly(3-hexylthiophene), via bottom-up oxidative chemical vapor deposition (oCVD), taking advantage of both strong C=C covalent bonding along the extended polymer chain and strong pi-pi stacking noncovalent interactions between chains. We confirm the presence of both types of interactions by systematic structural characterization, achieving a near-room temperature thermal conductivity of 2.2 W/m.K, which is 10 times higher than that of conventional polymers. In conclusion, with the solvent-free oCVD technique, it is now possible to grow polymer films conformally on a variety of substrates as lightweight, flexible heat conductors that are also electrically insulating and resistant to corrosion.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Colloidal Gelation in Liquid Metals Enables Functional Nanocomposites of 2D Metal Carbides (MXenes) and Lightweight Metals

Nanomaterials dispersed in different media, such as liquids or polymers, generate a variety of functional composites with synergistic properties. In this work, we discuss liquid metals as the nanomaterials' dispersion media. For example, 2D transition-metal carbides and nitrides (MXenes) can be efficiently dispersed in liquid Ga and lightweight alloys of Al, Mg, and Li. We show that the Lifshitz theory predicts strong van der Waals attraction between nanoscale objects interacting through liquid metals. However, a uniform distribution of MXenes in liquid metals can be achieved through colloidal gelation, where particles form self-supporting networks stable against macroscopic phase segregation. This network acts as a reinforcement boosting mechanical properties of the resulting metal-matrix composite. By choosing Mg-Li alloy as an example of ultralightweight metal matrix and Ti 3 C 2 T x MXene as a nanoscale reinforcement, we apply a liquid metal gelation technique to fabricate functional nanocomposites with an up to 57% increase in the specific yield strength without compromising the matrix alloy's plasticity. MXenes largely retain their phase and 2D morphology after processing in liquid Mg-Li alloy at 700 degrees C. The 2D morphology enables formation of a strong semicoherent interface between MXene and metal matrix, manifested by biaxial strain of the MXene lattice inside the metal matrix. Finally, this work expands applications for MXenes and shows the potential for developing MXene-reinforced metal matrix composites for structural alloys and other emerging applications with metal-MXene interfaces, such as batteries and supercapacitors.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Process Integration and Optimization of ICME Carbon Fiber Composites for Vehicle Lightweighting: A Preliminary Development

Process integration and optimization is the key enabler of the Integrated Computational Materials Engineering (ICME) of carbon fiber composites. In this paper, automated workflows are developed for two types of composites: Sheet Molding Compounds (SMC) short fiber composites, and multi-layer unidirectional (UD) composites. For SMC, the proposed workflow integrates material processing simulation, microstructure representation volume element (RVE) models, material property prediction and structure preformation simulation to enable multiscale, multidisciplinary analysis and design. Processing parameters, microstructure parameters and vehicle subframe geometry parameters are defined as the design variables; the stiffness and weight of the structure are defined as the responses. For multi-layer UD structure, this work focuses on the discussion of different design representation methods and their impacts on the optimization performance. Challenges in ICME process integration and optimization are also summarized and highlighted. Two case studies are conducted to demonstrate the integrated process and its application in optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Increasing the composition range of a novel τ 11 -Al 4 Fe 1.7 Si alloy with additions of Mn

Automotive applications need low-cost, lightweight, high-temperature alloys to increase vehicle efficiency. The Al–Fe–Si system provides an opportunity to develop such a material, as it consists of three low-cost elements that are all abundant in nature. Specifically, the τ 11 -Al 4 Fe 1.7 Si ternary intermetallic phase is a high-temperature, lightweight phase with high strength and good corrosion resistance. However, this phase exhibits a narrow compositional range of stability, resulting in undesirable microstructures forming during solidification and processing, limiting its use in potential applications. Density functional theory (DFT) calculations and a thermodynamically-driven experimental approach utilizing diffusion couples were employed to study the effect of Mn on the stability and composition range of τ 11 -Al 4 Fe 1.7 Si. The DFT calculations showed a decrease in the energy of the structure when alloying with Mn. Experimental results confirmed the predictions from the DFT calculations, indicating that alloying with Mn increases the compositional range, and thus the processability of this phase. New phase diagrams and equilibria are proposed by exploring and determining phase boundaries for the τ 11 -Al 4 Fe 1.7 Si phase with Mn.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Tetrabutylammonium cation in a homoleptic environment of borohydride ligands: [(n-Bu){sub 4}N][BH{sub 4}] and [(n-Bu){sub 4}N][Y(BH{sub 4}){sub 4}]

A novel solvent-free dual-cation organic-inorganic derivative, tetrabutylammonium yttrium borohydride (TBAYB), has been prepared for the first time and structurally characterized together with its organic precursor, tetrabutylammonium borohydride (TBAB). Both compounds crystallize in monoclinic unit cells (TBAYB: P2{sub 1}/c, TBAB: P2/c) and they contain [(n-Bu){sub 4}N]{sup +} in a homoleptic environment consisting of BH{sub 4}{sup -} ligands. Presence of large and lightweight Bu{sub 4}N{sup +} cations results in loose packing and low densities of both solids close to 1 g cm{sup -3}. TBAB melts at ca. 130 Degree-Sign C and it decomposes thermally above 160 Degree-Sign C while TBAYB melts at temperature as low as 78 Degree-Sign C, and the melt is stable over an appreciable temperature range of ca. 150 Degree-Sign C. The low melting point of TBAYB is the second lowest among derivatives of yttrium rendering this compound a new ionic liquid above 78 Degree-Sign C. - Graphical abstract: A novel organic-inorganic hybrid material for hydrogen storage, where (n-Bu){sub 4}N{sup +} cation is found in homoleptic environment of BH{sub 4}{sup -} ligands, was synthesised. Highlights: Black-Right-Pointing-Pointer Novel organic-inorganic hybrid material for hydrogen storage was synthesised. Black-Right-Pointing-Pointer (n-Bu){sub 4}N{sup +} cation is found in a homoleptic environment of BH{sub 4}{sup -} ligands. Black-Right-Pointing-Pointer TBAYB derivative is a novel ionic liquid with melting point of 78 Degree-Sign C..

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Corrosion resistant hot melt adhesive to bind metals

Hot melt adhesives (HMAs) play an important role in many industries, and their demand is expected to grow. HMAs don't require any solvents, and their application results in the formation of strong bonds with the substrate upon cooling within seconds. These properties differentiate them from liquid glues and make them preferable for practical application. Currently, commercial HMAs are used in bonding lightweight materials such as paper, polymers, and cartons, and have limited usage in areas necessitating the bonding of heavier objects like metals. Here, in this study, we report a design and testing of versatile platform comprising an ion-coordinating polymer and ionic fillers for performance optimization and understanding of structure–property relationships, enabling the rational design of HMAs with improved adhesion to metal surfaces. All-atom Molecular dynamics (MD) simulations and various characterization methods are used to elucidate the adhesion mechanism in model composite system containing polyethylene oxide mixed with chemically diverse salt particles. The maximum adhesion strength is found in composites with Al(OH) 3 and FeCl 3 fillers. Interestingly, the presence of Al(OH) 3 also provides a multifunctional anticorrosion property as measured electrochemically using the Tafel method. The discovered path to formulations with improved adhesion to metal surfaces constitutes an important step toward advancing HMAs for use in the structural and semi-structural metal work domain.

Adhesive and cohesive forces↗
DOE OSTI2014

Lignin-Derived Carbon Fiber as a Co-Product of Refining Cellulosic Biomass

Lignin by-products from biorefineries has the potential to provide a low-cost alternative to petroleum-based precursors to manufacture carbon fiber, which can be combined with a binding matrix to produce a structural material with much greater specific strength and specific stiffness than conventional materials such as steel and aluminum. The market for carbon fiber is universally projected to grow exponentially to fill the needs of clean energy technologies such as wind turbines and to improve the fuel economies in vehicles through lightweighting. In addition to cellulosic biofuel production, lignin-based carbon fiber production coupled with biorefineries may provide $2,400 to $3,600 added value dry Mg-1 of biomass for vehicle applications. Compared to producing ethanol alone, the addition of lignin-derived carbon fiber could increase biorefinery gross revenue by 30% to 300%. Using lignin-derived carbon fiber in 15 million vehicles per year in the US could reduce fossil fuel consumption by 2-5 billion liters year-1, reduce CO2 emissions by about 6.7 million Mg year-1, and realize fuel savings through vehicle lightweighting of $700 to $1,600 per Mg biomass processed. The value of fuel savings from vehicle lightweighting becomes economical at carbon fiber price of $6.60 kg-1 under current fuel prices, or $13.20 kg-1 under fuel prices of about $1.16 l-1.

09 BIOMASS FUELS↗
DOE OSTI2023

Neural Network Export Package (nexport) v0.1.0

nexport is a lightweight python 3 package which enables neural network developers to export the weights and biases of trained networks to useful file types. It provides I/O support for applications which require specific data types for instantiating neural networks in various inference environments. It was initially developed to support exporting to the data structure used by 'inference-engine', a Fortran inference package.

Welsman, Jordan↗
DOE OSTI2019

Nanostructured polymer films with metal-like thermal conductivity

Due to their unique properties, polymers – typically thermal insulators – can open up opportunities for advanced thermal management when they are transformed into thermal conductors. Recent studies have shown polymers can achieve high thermal conductivity, but the transport mechanisms have yet to be elucidated. Here we report polyethylene films with a high thermal conductivity of 62 Wm –1 K –1 , over two orders-of-magnitude greater than that of typical polymers (~0.1 Wm –1 K –1 ) and exceeding that of many metals and ceramics. Structural studies and thermal modeling reveal that the film consists of nanofibers with crystalline and amorphous regions, and the amorphous region has a remarkably high thermal conductivity, over ~16 Wm –1 K –1 . This work lays the foundation for rational design and synthesis of thermally conductive polymers for thermal management, particularly when flexible, lightweight, chemically inert, and electrically insulating thermal conductors are required.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Nanometal-Interconnected Carbon Conductors (NICCS) for Advanced Electric Machines (RIT Final Technical Report)

Recent advancements in carbon nanotube (CNT) research have enabled lightweight, conductive wires as a transformative technology. Metal-carbon nanotube (CNT) hybrid conductors aim to combine the high conductivity of traditional metals with the low mass and temperature coefficient of resistance (TCR) of carbon nanotubes. The high conductivity of copper makes it a promising candidate to combine with CNTs in a hybrid structure, but there is limited physical and electrical interaction between copper and CNTs. The use of an interfacial layer offers one method of improving the interconnection of a Cu-CNT hybrid conductor. Over the course of this grant, a joule heating-driven chemical vapor deposition (CVD) technique was developed to deposit nanometal seeds throughout a porous, low-density (0.12 g/cm 3 , ~10 tex or mg/m) CNT roving template. Modification of the applied current to the CNT roving allows for the tuning of depositions towards either hot-spot site-specificity or overall uniformity. The effects of temperature, pressure, precursor mass, and the interval of applied current were investigated, demonstrating nanometal depositions ranging from less than 5 % w/w to over 85 % w/w. The versatility of CVD allows for a wide variety of metals to be deposited including copper, titanium, nickel, silver, tungsten, palladium, platinum, ruthenium, rhodium, and iridium. One preferred demonstration involved using platinum depositions to improve the electrical properties of metal-seeded CNTs across all mass loadings studied. Moreover, when the metal-seeded CNT wires were electroplated with copper, densified, and annealed under hydrogen/argon; the result was a Cu-CNT hybrid conductor with the highest conductivities reported to date. The good interconnection of the metal and CNT portions results in a stable conductor. An electrical conductivity of 16-20 MS/m was achieved for multiple Cu-CNT hybrid conductors at 150 °C, which exceeds the program goal for conductivity of greater than 15 MS/m for a 1 m long CNT wire. Overall, the research outcomes from the project showed improvements in CNT wire fabrication from roll-to-roll CVD grown carbon nanotube wires using nanometal interconnects to bridge CNT-to-CNT junctions, thus mitigating network contact resistances. Advanced nanometal interconnected carbon conductors (NICCs) were developed as a means to achieve novel light-weight wiring appropriate for applications that require the conductivity of metallic (i.e., Cu, Al, etc.) wires at elevated temperatures. High conductivity, low TCR electrical conductors such as the nanometal interconnected Cu-CNT hybrids have numerous future applications towards high efficiency motors, generators, and transformers. Specifically, the advanced wires have the ability to operate at lower resistance during conditions for standard electric motors at 150 °C, which would improve the electrical efficiency while lowering energy needs.

36 MATERIALS SCIENCE↗
DOE OSTI2021

An optimized FM-index library for nucleotide and amino acid search

Abstract Background Pattern matching is a key step in a variety of biological sequence analysis pipelines. The FM-index is a compressed data structure for pattern matching, with search run time that is independent of the length of the database text. Implementation of the FM-index is reasonably complicated, so that increased adoption will be aided by the availability of a fast and flexible FM-index library. Results We present AvxWindowedFMindex (AWFM-index), a lightweight, open-source, thread-parallel FM-index library written in C that is optimized for indexing nucleotide and amino acid sequences. AWFM-index introduces a new approach to storing FM-index data in a strided bit-vector format that enables extremely efficient computation of the FM-index occurrence function via AVX2 bitwise instructions, and combines this with optional on-disk storage of the index’s suffix array and a cache-efficient lookup table for partial k-mer searches. The AWFM-index performs exact match count and locate queries faster than SeqAn3’s FM-index implementation across a range of comparable memory footprints. When optimized for speed, AWFM-index is $$\sim $$ ∼ 2–4x faster than SeqAn3 for nucleotide search, and $$\sim $$ ∼ 2–6x faster for amino acid search; it is also $$\sim $$ ∼ 4x faster with similar memory footprint when storing the suffix array in on-disk SSD storage. Conclusions AWFM-index is easy to incorporate into bioinformatics software, offers run-time performance parameterization, and provides clients with FM-index functionality at both a high-level (count or locate all instances of a query string) and low-level (step-wise control of the FM-index backward-search process). The open-source library is available for download at https://github.com/TravisWheelerLab/AvxWindowFmIndex.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

SPRUCE Wood Anatomy of Picea mariana and Larix laricina in SPRUCE Experimental Plots, Marcell Experimental Forest, Minnesota, July 2023

Branch samples were collected in July 2023 to measure wood anatomical traits on two dominant conifer species, Picea mariana and Larix laricina, in a bog forest at the SPRUCE (Spruce and Peatland Responses Under Changing Environments) experiment in northern Minnesota. Anatomical measurements were made on the annual rings of those branches with dates ranging from 2011-2023. Wood anatomical measurements include annual tracheid diameter, tracheid density, cell wall thickness, thickness-to-span-ratio, and conduit lumen fraction in both earlywood and latewood. Wood anatomical samples were prepared using a portable sliding microtome (G.S.L.-1 lightweight microtome, WSL) and a light microscope (Leica DM2500). This dataset spans 2011–2023, with full branch-level coverage for 2020–2023, whereas some thinner branches formed only in recent years and therefore do not contain rings from earlier years. By providing annual, treatment-specific anatomical measurements, this dataset can help quantify structural acclimation to global change, clarify links among phenology, wood formation, and hydraulic traits, and improve predictions of forest growth response under future climates. This dataset contains one data file in comma separate (.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format.

annual tracheid diameter↗
DOE OSTI2021

Machine Learning for Automated Weld Quality Monitoring and Control (CRADA 461)

The emergence of Artificial Intelligence (AI) and Machine Learning (ML) methods may provide an alternative pathway to elicit the desired process-structure-property relationship. To achieve such relationship at an accelerated pace the use of data driven machine learning techniques are employed. This CRADA investigates the use and effectiveness of applying AI/ML techniques to a large dataset with the intention of creating a data-driven model that will predict processing parameters required to join lightweight materials at optimal performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Analysis of the Aeroelastic Dynamics of Lightweight Flexible Variations of the SNL-NRT Turbine

Current trends show that wind turbines are growing in size to meet a rising demand for renewable energy generation, and their upscaled rotors have inherently become more flexible to maintain a proportionally lighter design. This is because larger rotors must be less massive relative to their diameter to minimize the levelized cost of energy (LCOE), which means that blades that are notably less stiff are produced as a result. These structural changes to blades are often reflected in their compromised aeroelastic stability and amplified deformation during operation, which has the potential to decrease the blade’s expected lifetime and the performance of the machine overall. Variations in blade flexibility are also known to influence vortex-wake structures downstream of the turbine, causing patterns of velocity deficit to evolve in ways that affect the performance of other turbines in the farm. This research explores how the increased flexibility of modern utility-scale wind turbine blades influences rotor aeroelastic behavior and interactions with farm flow. High-fidelity simulations of Sandia National Laboratories’ (SNL) National Rotor Testbed (NRT) wind turbine are presented. Flexible variations of the NRT baseline blade are simulated in a variety of realistic operational conditions typically expected at the SNL’s SWiFT facility in Lubbock, Texas. Solutions are then compared to investigate how specific changes to the structural properties of the NRT baseline blade’s design and construction can influence its aeroelastic response at the rotor and the evolution of the turbine’s wake.

Farrell, Alayna (ORCID:000000023555720X)↗