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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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66 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Explanation and Application of the Evolving Contact Traction Fields in Shallow Foundation Systems

The present paper provides a qualitative discussion of the evolution of contact traction fields beneath rigid shallow foundations resting on granular materials. A phenomenological similarity is recognized in the measured contact traction fields of rigid footings and at the bases of sandpiles. This observation leads to the hypothesis that the stress distributions are brought about by the same physical phenomena, namely the development of arching effects through force chains and mobilized intergranular friction. A set of semi-empirical equations are suggested for the normal and tangential components of this contact traction based on past experimental measurements and phenomenological assumptions of frictional behaviors at the foundation system scale. These equations are then applied to the prescribed boundary conditions for the analysis of the settlement, resistance, and stress fields in supporting granular materials beneath the footing. A parametric sensitivity study is performed on the proposed modelling method, highlighting solutions to the boundary-value problems in an isotropic, homogeneous elastic half-space.

42 ENGINEERING↗
DOE OSTI2021

Laboratory Study of the Torus Instability Threshold in Solar-relevant, Line-tied Magnetic Flux Ropes

Coronal mass ejections (CMEs) occur when long-lived magnetic flux ropes (MFRs) anchored to the solar surface destabilize and erupt away from the Sun. This destabilization is often described in terms of an ideal magnetohydrodynamic instability called the torus instability. It occurs when the external magnetic field decreases sufficiently fast such that its decay index, ${n}_{}=-z\,\partial (\mathrm{ln}{B}_{})/\partial z$, is larger than a critical value, $n\gt {n}_{\mathrm{cr}}^{}$, where ${n}_{\mathrm{cr}}^{}=1.5$ for a full, large aspect ratio torus. However, when this is applied to solar MFRs, a range of conflicting values for ${n}_{\mathrm{cr}}^{}$ is found in the literature. To investigate this discrepancy, we have conducted laboratory experiments on arched, line-tied flux ropes and applied a theoretical model of the torus instability. Our model describes an MFR as a partial torus with foot points anchored in a conducting surface and numerically calculates various magnetic forces on it. This calculation yields better predictions of ${n}_{\mathrm{cr}}^{}$ that take into account the specific parameters of the MFR. Here, we describe a systematic methodology to properly translate laboratory results to their solar counterparts, provided that the MFRs have a sufficiently small edge safety factor or, equivalently, a large enough twist. After this translation, our model predicts that ${n}_{\mathrm{cr}}^{}$ in solar conditions falls near ${n}_{\mathrm{cr}}^{\mathrm{solar}}\sim 0.9$ and within a larger range of ${n}_{\mathrm{cr}}^{\mathrm{solar}}\sim (0.7,1.2)$, depending on the parameters. The methodology of translating laboratory MFRs to their solar counterparts enables quantitative investigations of CME initiation through laboratory experiments. These experiments allow for new physics insights that are required for better predictions of space weather events but are difficult to obtain otherwise.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

State Machine Operation of Complex Systems

Operation of complex systems which depend on one or more other systems with many process variables often operate in more than one state. For each state there may be a variety of parameters of interest, and for each of these, one may require different alarm limits, different archiving needs, and have different critical parameters. Relying on operators to reliably change 10s-1000s of parameters for each system for each state is unreasonable.Not changing these parameters results in alarms being ignored or disabled, critical changes missed, and/or possible data archiving problems.To reliably manage the operation of complex systems, such as cryomodules (CMs), Fermilab is implementing state machines for each CM and an over-arching state machine for the PIP-II superconducting linac (SCL). The state machine transitions and operating parameters are stored/restored to/from a configuration database. Proper implementation of the state machines will not only ensure safe and reliable operation of the CMs, but will help ensure reliable data quality. A description of PIP-II SCL, details of the state machines, and lessons learned from limited use of the state machines in recent CM testing will be discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

State Machine Operation of Complex Systems

Operation of complex systems which depend on one, or more, sub-systems with many process variables often operate in more than one state. For each state there may be a variety of parameters of interest, and for each of these, one may require different alarm limits, different archiving needs, and have different critical parameters. Relying on operators to reliably change 102-105 parameters for each system for each state is unreasonable. Not changing these parameters results in alarms being ignored or disabled, critical changes missed, and/or data archiving inefficiencies. To reliably manage the operation of complex systems, such as cryomodules (CMs), Fermilab is implementing state machines for each CM and an over-arching state machine for the PIP-II superconducting LINAC (SCL). The state machine transitions and operating parameters are stored/restored to/from a configuration database. Proper implementation of the state machines will not only ensure safe and reliable operation of the CMs, but will help ensure reliable data quality. A description of PIP-II SCL, details of the state machines, and plans for imminent use the state machines for CM testing will be discussed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Novel Preprocessing Techniques for Enhancing Flowability of Miscanthus

Purpose-grown energy crops possess significant potential as promising feedstocks for biofuel and biochemical production. However, similar to other biomass feedstocks, their utilization has been hindered by handling and feeding challenges such as clogging and segregation, which prevent biorefineries from operating at full capacity. These challenges arise primarily due to particle attributes including irregular shape, high flexibility, and high compressibility, leading to interlocking, high compaction, and significant friction under stress consolidation. To enhance operational reliability, it is crucial to focus on preprocessing techniques to address these issues, alongside accurately characterizing the mechanical and physical properties and flow performance of these feedstocks. This study investigates novel preprocessing techniques, including pelletization and torrefaction, to fundamentally alter these particle attributes and improve flowability. Miscanthus, an herbaceous energy crop, was the primary focus of this study. Samples were processed using a Forest Concepts Crumbler rotary shear system and an oscillating multi-stack screen bed to achieve two nominal (baseline) particle sizes: 6mm and 2mm. The study evaluated the efficiency and energy consumption of size reduction, revealing that while 2mm particles required more energy, they exhibited better flowability and higher yield strength compared to 6mm particles. Shear and compression tests assessed the material's shear strength, internal friction, and elastic modulus, while wedge-shaped hopper tests measured the critical arching distance and mass flow rate. The results provide valuable insights into the mechanical properties and processing efficiency of Miscanthus, contributing to the development of a preprocessing framework that enhances energy efficiency and material flowability in bioenergy production. However, further preprocessing and development are required to optimize this framework fully.

09 - BIOMASS FUELS↗
DOE OSTI2024

OC6 Phase IV: Validation of CFD Models for Stiesdal TetraSpar Floating Offshore Wind Platform

ABSTRACT With only a few floating offshore wind turbine (FOWT) farms deployed anywhere in the world, FOWT technology is still in its infancy, building on a modicum of real‐world experience to advance the nascent industry. To support further development, engineers rely heavily on modeling tools to accurately portray the behavior of these complex systems under realistic environmental conditions. This reliance creates a need for verification and validation of such tools to improve reliability of load and dynamic response prediction and analysis capabilities of FOWT systems. The Offshore Code Comparison Collaboration, Continued with Correlation and unCertainty (OC6) project was created under the framework of the International Energy Agency to address this need and considers a three‐sided verification and validation between engineering level models, computational fluid dynamics (CFD), and experimental results. In this paper, a novel floating offshore wind platform, the Stiesdal TetraSpar, is simulated using CFD under the load conditions defined by Phase IV of the OC6 project. The comparison of these CFD results against the experimental results demonstrated the ability to predict the platform response to waves when imposing the measured wave signals as input. Although validation versus experiment was largely successful, the damping behavior was impacted by uncertainties likely originating from the mooring system and sensor umbilical cable. This extensive comparison effort with multiple CFD practitioners offers insight into best practices to achieve reliable results.

17 WIND ENERGY↗
DOE OSTI2020

A Generalized Stress Inversion Approach With Application to Residual Stress Estimation

In this work develop a generalized stress inversion technique (or the generalized inversion method) capable of recovering stresses in linear elastic bodies subjected to arbitrary cuts. Specifically, given a set of displacement measurements found experimentally from digital image correlation (DIC), we formulate a stress estimation inverse problem as a partial differential equation-constrained optimization problem. We use gradient-based optimization methods, and we accordingly derive the necessary gradient and Hessian information in a matrix-free form to allow for parallel, large-scale operations. By using a combination of finite elements, DIC, and a matrix-free optimization framework, the generalized inversion method can be used on any arbitrary geometry, provided that the DIC camera can view a sufficient part of the surface. We present numerical simulations and experiments, and we demonstrate that the generalized inversion method can be applied to estimate residual stress.

42 ENGINEERING↗
DOE OSTI2021

Up-Tower Investigation of Main Bearing Cage Slip and Loads

This paper described the operational conditions of an SRB in the main bearing position of a three-point mount wind turbine drivetrain. A commercial wind turbine was instrumented to measure load characteristics on the outer ring of the bearing, which were used to examine the roller load characteristics qualitatively and the cage slip quantitatively in a range of operational states. Further, a simple analytic model estimated the bearing loads and was used as input conditions to a three-dimensional finite element model. The finite element roller load results were compared to the measurements and contact stresses were also described.

17 WIND ENERGY↗
DOE OSTI2020

Prognosis of Wind Turbine Gearbox Bearing Failures Using SCADA and Modeled Data

Predictive maintenance and condition monitoring systems for wind turbines have seen increased adoption to minimize downtime, reducing operation and maintenance costs. On today’s wind power plants, the integrated supervisory control and data acquisition (SCADA) system provides low- frequency operational data that can be leveraged to quantify a wind turbine’s health. The aim of this study is to utilize machine-learning techniques to predict axial cracking failures in wind turbine gearbox bearings up to 1 month ahead of time. The failures are assumed to have occurred when the investigated bearing was replaced. While current SCADA systems show the overall condition of a wind turbine, often they do not allow for the investigation of specific gearbox bearings’ health. To enrich bearing fault signatures, additional data are computed through physics-based models using gearbox design information. Based on SCADA data, modeled data, and bearing failure log data from an actual wind plant, the performances of different machine-learning models on unseen data are then evaluated using industry-standard metrics such as precision, recall, and F1 score. Results show the overall system performance enhancement in predicting bearing failure when modeled data are included with SCADA data. The reduction in terms of false alarms is about 50%, and improvement in terms of precision and F1 score is about 33% and 12% respectively, based on the best modeling case in this study.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2021

Machine Learning for Gearbox Fault Prediction by Using Both Scada and Modeled Data

This presentation outlines the work in the paper titled "Prognostics of Wind Turbine Gearbox Bearing Failures Using SCADA and Modeled Data" published by the PHM Society and presented at its 2020 annual conference. It is accessible at https://papers.phmsociety.org/index.php/phmconf/article/download/1292/862. The technical work is on machine learning approaches for prognostics for gearbox faults. The methodology combines SCADA time series data and physics domain modeling data, derived from the models developed by the NREL team, as inputs to machine learning models to predict gearbox bearing failures with one month lead time. Based on SCADA data, modeled data, and bearing failure log data from an actual wind plant, the performances of different machine-learning models on unseen data are then evaluated using industry-standard metrics such as precision, recall, and F1 score, and AUC (area under receiver operating characteristic curve). Results show the overall system performance enhancement in predicting bearing failure when modeled data are included with SCADA data. The reduction in terms of false alarms is about 50%, and improvement in terms of precision, and F1 score, and AUC is about 33%, and 12%, and 6% respectively, based on the best modeling case in this study.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2022

Investigation of Multiple Data Streams for Gearbox Bearing Fault Prediction Through Machine-Learning Models

Operations and maintenance (O&M) cost of wind plant accounts up to 30% of total energy cost, which can be reduced through continuous monitoring and successfully detecting incipient wind turbine failures. To accomplish this, condition monitoring and predictive maintenance systems are being implemented in wind industry to support O&M decision making. A wide range of approaches for condition monitoring and fault prediction have been developed. These approaches generally use historical data of wind turbines collected by Supervisory Control and Data Acquisition (SCADA) system to identify patterns that lead to failure. These SCADA data show the overall condition of a wind turbine and can be leveraged to detect when the turbine's performance is degrading and to identify if a fault is developing. However, it becomes challenging to predict the failure of a specific wind turbine gearbox bearing, because the SCADA data are often not directly linked to the component. To bridge the gap, we have investigated features calculated from SCADA data using physics-based models and the gearbox design over the years. The damaged metric we used in the physics domain is frictional energy. Combining these physics domain variables with SCADA data as inputs to various machine learning models for gearbox bearing fault prediction, we have demonstrated the benefits of leveraging both physics and data domain models. It was an attempt to improve frictional-energy-based damage metric by adding data domain inputs, as we had learned that the frictional-energy-based damage metric alone is not sufficient to single out failed bearings from healthy. As condition monitoring data (either vibration or oil debris data) has become available at more and more wind plants, we would like to evaluate whether by adding the condition monitoring data can help further improve the performance of frictional-energy-based damage metric for gearbox bearing fault prediction. Both cases by modeling through various machine learning algorithms are discussed in this study along with some observations.

fault prediction↗
DOE OSTI2023

Wind Turbine Drivetrain Reliability Research - Gearbox Bearing Axial Cracking Failure Mode Example

The U.S. Department of Energy's National Renewable Energy Laboratory and Argonne National Laboratory have been conducting wind turbine drivetrain (formerly gearbox) reliability research for many years. Although the drivetrain focus has not changed, detailed projects are adjusted every few years based on dynamic needs seen in the field across the wind industry. This webinar will walk through the research methodology by using wind turbine gearbox bearing axial cracking failure mode as an example. The detailed steps include: 1) top failure mode identification based on actual failure data collected from project partners, 2) bench-top testing to identify possible contributing factors and formulate a damage metric, 3) physics domain modeling and validation through testing, 4) reliability assessment and prognosis based on the physics domain model and data domain inputs, and further enhancement through machine learning algorithms, using actual wind plant operational and failure event data. Hopefully, the presented work is of interest to the IISE community, and some members can apply their expertise to wind turbine and plant applications, helping enhance wind power generation technology advancement and its broader deployment.

axial cracking↗