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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Aeroelastic Stability Assessment Methodology and Application to Slat Noise Treatments on the High-Lift Common Research Model

This work supports a larger effort at NASA to reduce airframe noise and, thus, environmental noise around airports. The leading-edge slat of conventional high-lift systems is a prominent source of airframe noise, and two technologies were previously identified as promising for noise reduction without significant aerodynamic or weight penalty: the slat cove filler (SCF) and the slat gap filler (SGF). NASA fabricated a 10%-scale semispan aircraft model for wind tunnel testing based upon the High-Lift Common Research Model (CRM-HL). NASA embarked on a test campaign with the 10% CRM-HL to determine the noise reduction effectiveness and other performance metrics of 3D SCF and SGF treatments in flow conditions representative of flight. The highly flexible SCF and SGF treatments posed a risk to the model and aeroelastic instability would compromise research integrity, so aeroelastic stability assessment was required. This work briefly describes the 10% CRM-HL, design of the SCF and SGF treatments to the model, and supporting fluid structure interaction (FSI) research. Relations for static and dynamic aeroelastic similitude are then developed for convenience of reference, and simplifications are introduced of particular utility to the cases considered in this study. The similitude relations are employed to assess the stability of the treatments under wind tunnel flow conditions based upon known behaviors from the supporting research. It is shown that the treatments are safe for test under the anticipated wind tunnel flow conditions with significant margins/factors of safety. It is furthermore demonstrated that the SCF is more susceptible to aeroelastic instability than the SGF, which has implications for flight feasibility.

Similitude↗
DOE OSTI2025

Immersive Scientific Visualization of Molten-Salt Reactor Waste Characteristics Using Virtual Reality

Immersive visualization is changing how we explore, communicate, and understand complex scientific systems. In nuclear energy, an area in which data are often multidimensional, time-dependent, and difficult to interpret, virtual reality (VR) represents a powerful and intuitive informational medium. This work introduces a VR-based platform that visualizes the post-shutdown behavior and waste management lifecycle of molten-salt reactors (MSRs), a next-generation reactor type with unique operational and safety characteristics. The platform, built in Unity, is streamed on the Meta Quest 3 headset. It transforms high-fidelity simulation data into an interactive, immersive experience. Users can explore time-dependent reactor characteristics such as nuclide decay, which is a key factor for evaluating reactor waste strategies. The datasets were generated using the MOOSE (Multiphysics Object-Oriented Simulation Environment) framework and then processed through ParaView scripting for smooth integration into Unity. From a visualization standpoint, the platform emphasizes spatial storytelling, temporal exploration, and user-centered interaction. Users can navigate 3D reactor geometries, slice through volumetric data, and manipulate time to observe how physical phenomena evolve. Real-scale rendering and embodied interaction make the experience feel tangible. The interface is designed to be accessible, even to those without nuclear or simulation expertise. This lowers the barrier for stakeholders, policymakers, and the general public, while still supporting expert analysis and collaborative decision-making. This work shows how immersive visualization can function as both a scientific tool and a communication interface. By integrating simulation, processing, and visualization into a cohesive workflow, we offer a scalable framework for immersive scientific storytelling. The modular design supports future extensions to other reactor types and lifecycle stages, from shutdown to long-term storage, making the platform adaptable for both research and outreach.

99 - GENERAL AND MISCELLANEOUS↗
NASA NTRSDate not supplied

Understanding and Verifying Neural Networks

Deep Neural Networks (DNNs) have gained immense popularity in recent times and have widespread use in applications such as image classification, sentiment analysis, speech recognition and also in safety-critical applications such as autonomous driving. However, they suffer limitations such as lack of explainability and robustness which raise safety and security concerns in their usage. Further, the complex structure and large input spaces of DNNs act as an impediment to thorough verification and testing. The SafeDNN project at the Robust Software Engineering (RSE) group at NASA aims at exploring techniques to ensure that systems that use deep neural networks are safe, robust and interpretable. In this talk, I will be presenting our technique Prophecy that automatically infers formal properties of deep neural network models. The tool extracts patterns based on neuron activations as preconditions that imply certain desirable output properties of the model. I would be highlighting case studies that use Prophecy in obtaining explanations for network decisions, understanding correct and incorrect behavior, providing formal guarantees wrt safety and robustness, and debugging neural network models. We have applied the tool on image classification networks, neural network controllers providing turn advisories in unmanned aircrafts, regression models used for autonomous center-line tracking in aircrafts and neural network object detectors

Deep Neural Networks↗
NASA NTRS2017

Prognostics and Health Monitoring: Application to Electric Vehicles

As more and more autonomous electric vehicles emerge in our daily operation progressively, a very critical challenge lies in accurate prediction of remaining useful life of the systemssubsystems, specifically the electrical powertrain. In case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle.Our research approach is to develop a system level health monitoring safety indicator either to the pilotautopilot for the electric vehicles which runs estimation and prediction algorithms to estimate remaining useful life of the vehicle e.g. determine state-of-charge in batteries. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

Prognostics↗
NASA NTRSDate not supplied

Statistical learning framework for safety and failure analysis of a DNN-based autonomous aircraft system

Deep Neural Networks (DNNs) and Machine Learning technology is increasingly used for safety-critical applications in the Aerospace domain. To ensure safe operations, the DNN and the system must undergo rigorous verification and validation, including advanced statistical analyses. Performance and safety of the DNN and system behavior must not only be analyzed for the nominal case, but under numerous off-nominal and failure cases. In this paper we will describe how our statistical learning framework SYSAI can efficiently perform such analyses using the tool’s unique combination of advanced learning modeling and statistical analysis techniques. SYSAI can effectively explore the high-dimensional state and failure space of the system under test; geometrical shape detection of safety regions and boundaries support explainability of the results to the designer. In this paper, we report experiments and results obtained with a vision-based DNN control system (ACT) that is capable of autonomously steering an aircraft down a runway.

Yuning He↗
DOE OSTI2022

A Robust Numerical Treatment of Solid-Phase Diffusion in Pseudo Two-Dimensional Lithium-Ion Battery Models

Solid-phase diffusion in active materials of lithium-ion batteries significantly affects charging and safety-related behavior of lithium-ion batteries. Therefore, it is essential to develop an efficient and robust numerical algorithm for solving solid-phase diffusion equations in physics-based battery models. In this work, we discuss the origins of numerical instabilities that can occur when solving the solid-phase diffusion equations using iterative methods. Then, in order to resolve such issues, we propose a simple numerical treatment to the surface flux term of discretized solid-phase diffusion equations. To demonstrate its numerical robustness, the proposed method is implemented into a pseudo two-dimensional (P2D) physics-based battery model and simulations are conducted at wide ranges of operating conditions. Even with extremely poor initial guesses for the Li+ concentrations of the active materials, computations using the proposed method do not diverge and the their computational speeds are comparable to those with conventional initial guesses. Comprehensive tests of the proposed method are also performed with a dynamic current profile based on US06 driving profile and a multi-stage charging profile with very high initial C-rate (12C).

battery modeling↗
NASA NTRS1979

Performability evaluation of the SIFT computer

Performability modeling and evaluation techniques are applied to the SIFT computer as it might operate in the computational evironment of an air transport mission. User-visible performance of the total system (SIFT plus its environment) is modeled as a random variable taking values in a set of levels of accomplishment. These levels are defined in terms of four attributes of total system behavior: safety, no change in mission profile, no operational penalties, and no economic process whose states describe the internal structure of SIFT as well as relavant conditions of the environment. Base model state trajectories are related to accomplishment levels via a capability function which is formulated in terms of a 3-level model hierarchy. Performability evaluation algorithms are then applied to determine the performability of the total system for various choices of computer and environment parameter values. Numerical results of those evaluations are presented and, in conclusion, some implications of this effort are discussed.

Meyer, J. F.↗
NASA NTRSDate not supplied

Health Monitoring and Prognostics in Li-ion Batteries

Space applications need to overcome a very critical challenge of predicting remaining useful life of its critical systems/subsystems, with batteries being one of them. Batteries, power electronics conditioning system and motors and one of the most critical systems. Similarly in case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. Two approaches are presented with battery prognostics application. The first approach presentation covers a physics based-modeling approach implemented for battery prognostics. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making. A second hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems i.e. batteries is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Batteries↗
NASA NTRSDate not supplied

Health Monitoring and Prognostics in Li-ion Batteries

Space applications need to overcome a very critical challenge of predicting remaining useful life of its critical systems/subsystems, with batteries being one of them. Batteries, power electronics conditioning system and motors and one of the most critical systems. Similarly in case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. Two approaches are presented with battery prognostics application. The first approach presentation covers a physics based-modeling approach implemented for battery prognostics. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making. A second hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems i.e. batteries is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Battery Prognostics↗
DOE OSTI2019

Exploring microscopic driving volatility in naturalistic driving environment prior to involvement in safety critical events—Concept of event-based driving volatility

The sequence of instantaneous driving decisions and its variations, known as driving volatility, prior to involvement in safety critical events can be a leading indicator of safety. Here, this study focuses on the component of “driving volatility matrix” related to specific normal and safety-critical events, named “event-based volatility.” The research issue is characterizing volatility in instantaneous driving decisions in the longitudinal and lateral directions, and how it varies across drivers involved in normal driving, crash, and/or near-crash events. To explore the issue, a rigorous quasi-experimental study design is adopted to help compare driving behaviors in normal vs unsafe outcomes. Using a unique real-world naturalistic driving database from the 2nd Strategic Highway Research Program (SHRP), a test set of 9593 driving events featuring 2.2 million temporal samples of real-world driving are analyzed. This study features a plethora of kinematic sensors, video, and radar spatiotemporal data about vehicle movement and therefore offers the opportunity to initiate such exploration. By using information related to longitudinal and lateral accelerations and vehicular jerk, 24 different aggregate and segmented measures of driving volatility are proposed that captures variations in extreme instantaneous driving decisions. In doing so, careful attention is given to the issue of intentional vs. unintentional volatility. The volatility indices, as leading indicators of near-crash and crash events, are then linked with safety critical events, crash propensity, and other event specific explanatory variables. Owing to the presence of unobserved heterogeneity and omitted variable bias, fixed- and random-parameter discrete choice models are developed that relate crash propensity to unintentional driving volatility and other factors. Statistically significant evidence is found that driver volatilities in near-crash and crash events are significantly greater than volatility in normal driving events. After controlling for traffic, roadway, and unobserved factors, the results suggest that greater intentional volatility increases the likelihood of both crash and near-crash events. A one-unit increase in intentional volatility is associated with positive vehicular jerk in longitudinal direction increases the chance of crash and near-crash outcome by 15.79 and 12.52 percentage points, respectively. Importantly, intentional volatility in positive vehicular jerk in lateral direction has more negative consequences than intentional volatility in positive vehicular jerk in longitudinal direction. Compared to acceleration/deceleration, vehicular jerk can better characterize the volatility in microscopic instantaneous driving decisions prior to involvement in safety critical events. Finally, the magnitudes of correlations exhibit significant heterogeneity, and that accounting for the heterogeneous effects in the modeling framework can provide more reliable and accurate results. The study demonstrates the value of quasi-experimental study design and big data analytics for understanding extreme driving behaviors in safe vs. unsafe driving outcomes.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Optimal CO 2 storage management considering safety constraints in multi-stakeholder multi-site GCS projects: A Markov game perspective

Geological carbon storage (GCS) projects could involve a diverse array of stakeholders or players from public, private, and regulatory sectors, each with different objectives and responsibilities. Given the complexity, scale, and long-term nature of GCS operations, determining whether individual stakeholders can independently optimize their interests — or whether collaborative coalition agreements are needed — remains a central question for effective GCS project planning and management. To access large, high-quality storage resources, future GCS deployment may increasingly occur in geologically connected sites, where shared geological features such as pressure space and reservoir pore capacity can lead to competitive behavior among stakeholders. In this work, we propose a paradigm based on Markov games to quantitatively investigate how different coalition structures affect the goals of stakeholders. We frame this multi-stakeholder multi-site problem as a multi-agent reinforcement learning problem with safety constraints. Our approach enables agents to learn optimal strategies while complying with safety regulations. We present an example where multiple operators are injecting CO 2 into their respective project areas in a geologically connected basin. To address the high computational cost of repeated simulations of high fidelity models, a previously developed surrogate model based on the Embed-to-Control (E2C) framework is employed. Our results demonstrate the effectiveness of the proposed framework in addressing optimal management of CO 2 storage when multiple stakeholders with different objectives and goals are involved.

58 GEOSCIENCES↗
NASA NTRS2013

Development of Large-Scale Spacecraft Fire Safety Experiments

The status is presented of a spacecraft fire safety research project that is under development to reduce the uncertainty and risk in the design of spacecraft fire safety systems by testing at nearly full scale in low-gravity. Future crewed missions are expected to be more complex and longer in duration than previous exploration missions outside of low-earth orbit. This will increase the challenge of ensuring a fire-safe environment for the crew throughout the mission. Based on our fundamental uncertainty of the behavior of fires in low-gravity, the need for realistic scale testing at reduced gravity has been demonstrated. To address this gap in knowledge, a project has been established under the NASA Advanced Exploration Systems Program under the Human Exploration and Operations Mission directorate with the goal of substantially advancing our understanding of the spacecraft fire safety risk. Associated with the project is an international topical team of fire experts from other space agencies who conduct research that is integrated into the overall experiment design. The experiments are under development to be conducted in an Orbital Science Corporation Cygnus vehicle after it has undocked from the ISS. Although the experiment will need to meet rigorous safety requirements to ensure the carrier vehicle does not sustain damage, the absence of a crew removes the need for strict containment of combustion products. The tests will be fully automated with the data downlinked at the conclusion of the test before the Cygnus vehicle reenters the atmosphere. A computer modeling effort will complement the experimental effort. The international topical team is collaborating with the NASA team in the definition of the experiment requirements and performing supporting analysis, experimentation and technology development. The status of the overall experiment and the associated international technology development efforts are summarized.

Life Support↗
NASA NTRS1996

Applications of Formal Methods to Specification and Safety of Avionics Software

This report treats several topics in applications of formal methods to avionics software development. Most of these topics concern decision tables, an orderly, easy-to-understand format for formally specifying complex choices among alternative courses of action. The topics relating to decision tables include: generalizations fo decision tables that are more concise and support the use of decision tables in a refinement-based formal software development process; a formalism for systems of decision tables with behaviors; an exposition of Parnas tables for users of decision tables; and test coverage criteria and decision tables. We outline features of a revised version of ORA's decision table tool, Tablewise, which will support many of the new ideas described in this report. We also survey formal safety analysis of specifications and software.

Hoover, D. N.↗
DOE OSTI2019

Aluminum Clad SNF: Technical Considerations and Challenges for Extended (>50 years) Dry Storage - 1900y

The U.S. Department of Energy (DOE) manages a significant inventory (>13 MTHM) of aluminum clad spent nuclear fuel (ASNF) using a combination of wet and dry storage at three primary sites; Hanford, Idaho National Laboratory (INL), and Savannah River Site (SRS). The majority of this ASNF is generated from U.S. and international research test reactors (RTR), examples include Advanced Test Reactor (ATR) at INL and High-Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL). The Obama Administration suspension of the Yucca Mountain repository, issuance of the January 2013 DOE 'Strategy for the Management and Disposal of Used Nuclear Fuel and High-Level Radioactive Waste,' delays in implementing the 'regionalization' SNF approach recommended in the 1995 'Department of Energy Programmatic Spent Nuclear Fuel Management and Idaho National Engineering Laboratory Environmental Restoration and Waste Management Programs Final Environmental Impact Statement,' and limited ASNF processing capability at SRS H-Canyon means that interim storage has become the de-facto strategy for managing ASNF. The 1995 Idaho Settlement Agreement requires the DOE to transfer all SNF stored at INL to interim dry storage prior to December 31, 2023 and wet storage capacity at the SRS is nearing capacity. This situation was recognized by the DOE Spent Nuclear Fuel Working Group (SNFWG) and at its June 2016 meeting formed a ASNF sub-working group comprised subject matter experts to evaluate this area and provide a report of findings. The ASNF sub-working group met many times over several month period to review the engineering and technical basis of existing and planned DOE ASNF dry storage facilities and additional studies on the behavior of ASNF under different conditions, including during reactor service. The ASNF sub-working group focused its review on RTR SNF. Based on these reviews, the ASNF sub-working group identified five knowledge gaps and technical data needs that should be addressed to ensure viability and safety of extended, greater than 50 years, sealed or vented interim dry storage of ASNF. These five areas include; behavior/chemistry of aluminum oxyhydroxide layer in dry storage, radiolytic gas generation properties of aluminum oxyhydroxide layers, effect of episodic breathing and radiolytic generation of corrosive gases during storage, performance of ASNF in existing dry storage systems, and effects of high temperature (>1000 C) drying on chemistry and behavior of aluminum oxyhydroxide layers. The ASNF sub-working group also recommended development of an action plan to identify technical and engineering activities needed to address the knowledge gaps and technical data needs. Results of the ASNF sub-working group are documented in 2017 report 'Aluminum Clad Spent Nuclear Fuel: Technical Considerations and Challenges for Extended (>50 years) Dry Storage,' DOE-ID/RPT-1575. The INL assembled a team comprised of twenty INL and Savannah River National Laboratory SNF management and subject matter experts to develop this action plan. Over the course of 2 months the team worked to develop the action plan. The plan identified and recommended six technical and engineering tasks needed to address the previously identified knowledge gaps and technical data needs. DOE Environmental Management Office of Technology Development is providing funding to the INL to execute these tasks. This paper will present and discuss the results of the ASNF sub-working group, tasks identified in the action plan, and experimental activities underway at INL and SRNL to address the action plan. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Enabling Stable Cycling of 4.2 V High-Voltage All-Solid-State Batteries with PEO-Based Solid Electrolyte

Poly(ethylene oxide) (PEO)-based solid electrolytes are expected to be exploited in solid-state batteries with high safety. Its narrow electrochemical window, however, limits the potential for high voltage and high energy density applications. In this paper, the electrochemical oxidation behavior of PEO and the failure mechanisms of LiCoO 2 -PEO solid-state batteries are studied. It is found that although for pure PEO it starts to oxidize at a voltage of above 3.9 V versus Li/Li + , the decomposition products have appropriate Li + conductivity that unexpectedly form a relatively stable cathode electrolyte interphase (CEI) layer at the PEO and electrode interface. The performance degradation of the LiCoO 2 -PEO battery originates from the strong oxidizing ability of LiCoO 2 after delithiation at high voltages, which accelerates the decomposition of PEO and drives the self-oxygen-release of LiCoO 2 , leading to the unceasing growth of CEI and the destruction of the LiCoO 2 surface. When LiCoO 2 is well coated or a stable cathode LiMn 0.7 Fe 0.3 PO 4 is used, a substantially improved electrochemical performance can be achieved, with 88.6% capacity retention after 50 cycles for Li 1.4 Al 0.4 Ti 1.6 (PO 4 ) 3 coated LiCoO 2 and 90.3% capacity retention after 100 cycles for LiMn 0.7 Fe 0.3 PO 4 . The results suggest that, when paired with stable cathodes, the PEO-based solid polymer electrolytes could be compatible with high voltage operation.

25 ENERGY STORAGE↗
NASA NTRS2000

Structuring Formal Control Systems Specifications for Reuse: Surviving Hardware Changes

Formal capture and analysis of the required behavior of control systems have many advantages. For instance, it encourages rigorous requirements analysis, the required behavior is unambiguously defined, and we can assure that various safety properties are satisfied. Formal modeling is, however, a costly and time consuming process and if one could reuse the formal models over a family of products, significant cost savings would be realized. In an ongoing project we are investigating how to structure state-based models to achieve a high level of reusability within product families. In this paper we discuss a high-level structure of requirements models that achieves reusability of the desired control behavior across varying hardware platforms in a product family. The structuring approach is demonstrated through a case study in the mobile robotics domain where the desired robot behavior is reused on two diverse platforms-one commercial mobile platform and one build in-house. We use our language RSML (-e) to capture the control behavior for reuse and our tool NIMBUS to demonstrate how the formal specification can be validated and used as a prototype on the two platforms.

Thompson, Jeffrey M.↗
DOE OSTI2020

Benchmark Modeling and Simulation of the FFTF LOFWOS Test #13 Using SAM

The Fast Flux Test Facility (FFTF) was a 400 MW thermal powered, oxide-fueled, liquid sodium cooled test reactor, built to assist development and testing of advanced fuels and materials for fast breeder reactors. In July 1986, a series of unprotected Loss of Flow Without Scram (LOFWOS) transients were performed in FFTF as part of the Passive Safety Testing (PST) program. The LOFWOS Test #13, which was initiated at 50% power and 100% flow with the pump pony motors left off, has been chosen as a benchmark case by IAEA to support collaborative efforts within international partnerships on the validation of simulation tools and models in the area of sodium fast reactor passive safety in an IAEA Coordinated Research Project (CRP), launched in October 2018. The System Analysis Module (SAM) is an advanced and modern system analysis tool under development at Argonne National Laboratory for advanced non-LWR safety analysis. It utilizes the object-oriented application framework MOOSE to leverage the modern software environment and advanced numerical methods. The capabilities of SAM are being extended to enable the transient modeling, analysis, and design of various advanced nuclear reactor systems. To participate the IAEA CRP and enhance the SAM validation base for advanced reactor transient safety analysis, benchmark simulations of the FFTF LOFWOS Test #13 are performed using the SAM code. In this first phase of the validation effort, the thermal-hydraulic behavior of the reactor system is the focus and the reactor kinetics is not considered in the SAM FFTF model. Instead, the results of Argonne’s neutronics calculations are directly used, including the power shape of the active core region and the power history during the transient. The simulation results of FFTF at steady state agreed well with the measured data from the test. During the transient, reasonably good agreement were also obtained. Future work to improve the model will focus on introducing the reactivity predictions into the model, as well as better understanding or resolving the current discrepancies with the measured data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2019

Structural Design of a 1/5th Scale Gravo-Aeroelastically Scaled Wind Turbine Demonstrator Blade for Field Testing

This paper presents the structural design of a 21-meter 1/5th scale SUMR demonstrator (SUMR-D) blade, which was designed to replicate the full-scale behavior of a 104-meter SUMR13 initial blade rated at 13.2 MW. This is a challenging structural design as two competing objectives were required including the traditional safety objectives and novel scaling objectives based on gravo-aeroelastic scaling (GAS) requirements. To satisfy the two competing requirements, the spar cap, shear webs and materials were optimized while also ensuring a manufacturable and cost-effective design solution. Based on the wind turbine operating conditions of the test site, which is the NWTC, the blade was analyzed under the extreme loading case and corresponding safety factors were applied. The final blade model had an acceptable maximum strain, buckling performance, and displayed a safe level of deflection under the extreme loading condition. The flutter speed and fatigue life of the blade were also vetted. The mode shape was analyzed for the installation of the blade sensors. This paper documents the structural design process of SUMR-D to achieve both the safety and scaling objectives. At the end, we provide an acceptable solution to control blade mass, limiting the blade deflection, blade max strain and buckling under extreme loading condition by optimizing spar cap, shear web, and core materials while satisfying scaling requirements.

17 WIND ENERGY↗