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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Streamlining Pre-Launch Support for Missions with Highly Variable Trajectories

As the Goddard Space Flight Center's (GSFC) Flight Dynamics Facility (FDF) continues to ensure mission success in support of Human Space Flight (HSF) and Launch Vehicle (LV) missions, it continues to improve on different areas of mission support. The FDF processes numerous different types of trajectory profiles, and, for each trajectory, the FDF provides multiple acquisition data vectors to the Space Network (SN) White Sands Complex (WSC) for further processing and uplinking to the SN's Tracking and Data Relay Satellites (TDRSs), tracking the vehicle and maintaining communications throughout required support. Some trajectories target a specific orbit plane through Right Ascension of the Ascending Node (RAAN), or yaw, steering, which can yield to high variability across the launch window. Because of this, there can be a large number of trajectory profiles to process and generate acquisition data for that ensure TDRS is accurately pointing to the launch vehicle. Processing many trajectory profiles by FDF and WSC increases the complexity and level of effort associated with support. FDF employs a legacy boundary on the range difference between trajectories, which determines the number of discrete trajectory cases required to maintain communication with TDRS over the full launch window. An analysis was performed to determine a new boundary, taking into consideration current support requirements. The results show the boundary can be expanded beyond the currently employed legacy value without an impact on communication. In doing so, the number of trajectory profiles processed can be reduced, reducing the complexity and level of effort required, with no impact to mission safety.

Flight Operations↗
NASA NTRSDate not supplied

Assessing Change in Aspen Extent in Northern Yellowstone National Park

The trophic cascade among wolves, elk, and aspen has influenced the landscape of Yellowstone National Park. Aspen promote greater biodiversity and have been indirectly affected by the 1926 removal and 1995 reintroduction of wolves in the park. Partnered with Yellowstone National Park, Utah State University, and the University of Wisconsin–Stevens Point, NASA DEVELOP analyzed the change in aspen stand extent from 1954 to 2021 over the elk wintering range that occurs in the northern part of Yellowstone and southern Montana. Focusing on 113 stands corresponding with belt transects monitored annually since 1999, the team georeferenced and digitized 1954 historical aerial imagery to determine aspen stand extent. From 1986 to 2019, the team processed Landsat 5 Thematic Mapper (TM) and Sentinel-2 Multispectral Instrument (MSI) imagery for the entire elk wintering range using a random forest model to classify landcover types. Outputs were refined using a phenological approach that distinguishes between deciduous and evergreen landcover by differencing summer and fall vegetation indices. Finally, the team conducted a similar analysis for 2021, utilizing both Landsat 8 Operational Land Imager (OLI) and Sentinel-2 MSI imagery. The team again focused on the stands associated with belt transects for this analysis to compare the beginning and end of the study period. Preliminary results indicate a slight decline over time. These results expand the understanding of the role of wolves on aspen in the Yellowstone ecosystem and inform future rewilding decisions within the park and beyond.

Vanessa Bailey↗
DOE OSTI2014

Structural Design Considerations for Tubular Power Tower Receivers Operating at 650 Degrees C: Preprint

Research of advanced power cycles has shown supercritical carbon dioxide power cycles may have thermal efficiency benefits relative to steam cycles at temperatures around 500 - 700 degrees C. To realize these benefits for CSP, it is necessary to increase the maximum outlet temperature of current tower designs. Research at NREL is investigating a concept that uses high-pressure supercritical carbon dioxide as the heat transfer fluid to achieve a 650 degrees C receiver outlet temperature. At these operating conditions, creep becomes an important factor in the design of a tubular receiver and contemporary design assumptions for both solar and traditional boiler applications must be revisited and revised. This paper discusses lessons learned for high-pressure, high-temperature tubular receiver design. An analysis of a simplified receiver tube is discussed, and the results show the limiting stress mechanisms in the tube and the impact on the maximum allowable flux as design parameters vary. Results of this preliminary analysis indicate an underlying trade-off between tube thickness and the maximum allowable flux on the tube. Future work will expand the scope of design variables considered and attempt to optimize the design based on cost and performance metrics.

14 SOLAR ENERGY↗
DOE OSTI2021

Development of a Deep Learning Model for Predicting the Drag Coefficients of Spherical and Non-Spherical Particles,

There is yet to be a well-established drag model for non-spherical particles required in a particle-laden flow that could cover a wide range of sphericities. This talk will explore the development of a general drag model for non-spherical particles by applying deep learning using available experimental data available in the literature. The integration of several raw experimental measurements from different sources and research directions allows the training of robust Artificial Intelligence and Machine Learning (ML) models. Neural networks are an ML approach inspired by the inner biological workings of the brain. This work aims to develop a Deep Neural Network (DNN) that predicts drag coefficient values with the ability to adapt appropriately to unseen data. Given the limited number of data points available and the variance found within the data collected from various sources, challenges may arise when looking to train the model. Our study tests and implements various model regularization techniques and assesses different loss and activation functions for the proposed DNN. The proposed model considers a broader range of features other than sphericity and Reynold number. These features include density ratio, solid volume fraction, lengthwise and crosswise sphericity, and more. Furthermore, we present the features that play a significant role in predicting different drag coefficients through feature importance. Within the investigated parameter ranges in this study, the following conclusions can be achieved and summarized below: • An improved drag coefficient model can be developed by considering more features such as, aspect ratio, lengthwise sphericity, crosswise sphericity, and density ratio. • DNN model can predict better results compared to traditional methods using MAE metric. • The proposed model addresses data challenges such as limited data and extreme data points through expanded feature-set and regularization. • Three major features that mostly affect the drag coefficient were identified from a feature importance analysis.

Presa-Reyes, Maria↗
DOE OSTI2022

Neutrons in MINERvA and the Antineutrino Multi-Neutron Production Cross Section

Neutrinos are among a handful of particles in the Standard Model of particle physics whose properties have not yet been fully measured. Neutrino oscillations, discovered in the late 1990s, still have yet to be fully characterized. Early evidence suggests that neutrinos and antineutrinos may undergo neutrino oscillations differently. So, multiple collaborations are building and operating kTon-scale particle detectors to try to measure this CP violation in the neutrino sector of the Standard Model. Measuring neutrino and antineutrino energies is essential to characterizing oscillation phenomena. But neutrons interfere with (anti)neutrino energy measurements because they are hard to detect in (anti)neutrino detectors. Furthermore, there are very few measurements of neutrons produced by (anti)neutrinos in the energy range of interest to accelerator-based oscillation experiments. The MINERvA collaboration initially tried to fill this gap by measuring the rate of neutron-induced activity i n plastic. The data deviated significantly from leading model predictions, but it could not be determined which models needed to be tuned. MINERvA’s first neutron counting result also cannot be compared to future models because it was not suitable for unsmearing. This thesis first expands upon MINERvA’s first foray into neutron detection at higher antineutrino energy. Neutron detection efficiency is enhanced by advances in analysis algorithms. Then, an unsmeared cross section for producing two or more neutrons is measured. Existing antineutrino interaction models again over-predict the rate of neutron production by antineutrinos. Preliminary neutron production rate measurements in MINERvA’s iron and lead targets suggest that models are also over-predicting neutron production in heavier nuclei.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Accelerating detector simulations with Celeritas: profiling and performance optimizations

Celeritas is a GPU-optimized MC particle transport code designed to meet the growing computational demands of next-generation HEP experiments. It provides efficient simulation of EM physics processes in complex geometries with magnetic fields, detector hit scoring, and seamless integration into Geant4-driven applications to offload EM physics to GPUs. Recent efforts have focused on performance optimizations and expanding profiling capabilities. This paper presents some key advancements, including the integration of the Perfetto system profiling tool for detailed performance analysis and the development of track-sorting methods to improve computational efficiency.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Multi-system analysis of offshore geologic carbon storage: a review of open-source data science solutions

Geologic carbon storage projects are maturing worldwide and the footprint of deployment in the offshore is expanding. At present, there are ten projects in operation or that have been completed, more than 50 in construction and development, and dozens of characterization studies completed or underway. Offshore geologic carbon storage offers potential benefits over onshore geologic carbon storage. These offshore projects are generally remote in location, distant from population centers, and avoid complicated pore space rights while having abundant prospective storage potential. Some offshore fields targeted for carbon storage have comparatively fewer prior borehole penetrations except for areas that have been explored for petroleum production, minimizing potential issues such as pressure interference and infrastructure impacts. Yet offshore geologic carbon storage projects face distinctive technical and economic challenges, such as seafloor geohazards (e.g., seabed instability), expensive maritime transport, and meteorological-oceanographic conditions that can damage infrastructure and impact operations. Analytical capabilities and improved computational speeds have advanced engineering, earth and energy sciences in the wake of the arrival of modern data science over the last decade. These advancements have created an opportunity for integrated, multi-systems modeling approaches utilizing artificial intelligence and machine learning that are no longer limited by computational issues. Analytical tools developed alongside this advancement in data science can be leveraged to calibrate the potential advantages and challenges of carbon storage operations in the offshore. New methods and approaches that incorporate data science to analyze multiple aspects of engineered and natural systems can provide insights that complement the characterization and onsite engineering that traditional commercial and operational software addresses. These new methods and approaches can potentially improve the outcome of energy operations and carbon storage. Providing multi-system, science-driven data analytics enhances the knowledge base that offshore developers, operators, and regulatory bodies may draw from to improve offshore site selection and operational efficiency. Here, we provide a brief synopsis of geologic carbon storage efforts to date, an overview of the engineered and natural systems involved in offshore geologic carbon storage, and a review of publicly available, open-source, offshore and/or carbon storage related data- and science-driven tools developed by 2010 or later that are suitable for screening and assessing regions for offshore geologic carbon storage.

artificial intelligence↗
DOE OSTI2014

Strain-Based Design Methodology of Large Diameter Grade X80 Linepipe

Continuous growth in energy demand is driving oil and natural gas production to areas that are often located far from major markets where the terrain is prone to earthquakes, landslides, and other types of ground motion. Transmission pipelines that cross this type of terrain can experience large longitudinal strains and plastic circumferential elongation as the pipeline experiences alignment changes resulting from differential ground movement. Such displacements can potentially impact pipeline safety by adversely affecting structural capacity and leak tight integrity of the linepipe steel. Planning for new long-distance transmission pipelines usually involves consideration of higher strength linepipe steels because their use allows pipeline operators to reduce the overall cost of pipeline construction and increase pipeline throughput by increasing the operating pressure. The design trend for new pipelines in areas prone to ground movement has evolved over the last 10 years from a stress-based design approach to a strain-based design (SBD) approach to further realize the cost benefits from using higher strength linepipe steels. This report presents an overview of SBD for pipelines subjected to large longitudinal strain and high internal pressure with emphasis on the tensile strain capacity of high-strength microalloyed linepipe steel. The technical basis for this report involved engineering analysis and examination of the mechanical behavior of Grade X80 linepipe steel in both the longitudinal and circumferential directions. Testing was conducted to assess effects on material processing including as-rolled, expanded, and heat treatment processing intended to simulate coating application. Elastic-plastic and low-cycle fatigue analyses were also performed with varying internal pressures. Proposed SBD models discussed in this report are based on classical plasticity theory and account for material anisotropy, triaxial strain, and microstructural damage effects developed from test data. The results are intended to enhance SBD and analysis methods for producing safe and cost effective pipelines capable of accommodating large plastic strains in seismically active arctic areas.

03 NATURAL GAS↗
DOE OSTI2020

RAMSeS: Rapid Analysis of Mission Software Systems

Over the past few decades, software has become ubiquitous as it has been integrated into nearly every aspect of society, including household appliances, consumer electronics, industrial control systems, public utilities, government operations, and military systems. Consequently, many critical national security questions can no longer be answered convincingly without understanding software, including its purpose, its capabilities, its flaws, its communication, or how it processes and stores data. As software continues to become larger, more complex, and more widespread, our ability to answer important mission questions and reason about software in a timely way is falling behind. Today, to achieve such understanding of third-party software, we rely predominantly on the ability of reverse engineering experts to manually answer each particular mission question for every software system of interest. This approach often requires heroic human effort that nevertheless fails to meet current mission needs and will never scale to meet future needs. The result is an emerging crisis: a massive and expanding gap between the national security need to answer mission questions about software and our ability to do so. Sandia National Laboratories has established the Rapid Analysis of Mission Software Systems (RAMSeS) effort, a collaborative long-term effort aimed at dramatically improving our nation’s ability to answer mission questions about third-party software by growing an ecosystem of tools that augment the human reverse engineer through automation, interoperability, and reuse. Focusing on static analysis of binary programs, we are attempting to identify reusable software analysis components that advance our ability to reason about software, to automate useful aspects of the software analysis process, and to integrate new methodologies and capabilities into a working ecosystem of tools and experts. We aim to integrate existing tools where possible, adapt tools when modest modifications will enable them to interoperate, and implement missing capability when necessary. Although we do hope to automate a growing set of analysis tasks, we will approach this goal incrementally by assisting the human in an ever-widening range of tasks.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

STAR-FORMATION ACTIVITY IN THE NEIGHBORHOOD OF W–R 1503-160L STAR IN THE MID-INFRARED BUBBLE N46

In order to investigate star-formation (SF) processes in extreme environments, we have carried out a multi-wavelength analysis of the mid-infrared bubble N46, which hosts a WN7 Wolf–Rayet (W–R) star. We have used {sup 13}CO line data to trace an expanding shell surrounding the W–R star containing about five condensations within the molecular cloud associated with the bubble. The W–R star is associated with a powerful stellar wind having a mechanical luminosity of ∼4 × 10{sup 37} erg s{sup 1}. A deviation of the H -band starlight mean polarization angles around the bubble has also been traced, indicating the impact of stellar wind on the surroundings. The Herschel temperature map shows a temperature range of ∼18–24 K toward the five molecular condensations. The photometric analysis reveals that these condensations are associated with the identified clusters of young stellar objects, revealing ongoing SF process. The densest among these five condensations (peak N(H{sub 2}) ∼9.2 × 10{sup 22} cm{sup 2} and A{sub V} ∼ 98 mag) is associated with a 6.7 GHz methanol maser, an infrared dark cloud, and the CO outflow, tracing active massive SF within it. At least five compact radio sources (CRSs) are physically linked with the edges of the bubble, and each of them is consistent with the radio spectral class of a B0V–B0.5V-type star. The ages of the individual infrared counterparts of three CRSs (∼1–2 Myr) and a typical age of WN7 W–R star (∼4 Myr) indicate that the SF activities around the bubble are influenced by the feedback of the W–R star.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1995

A hybrid neurocomputing/numerical strategy for nonlinear structural analysis

A hybrid neurocomputing/numerical strategy is presented for geometrically nonlinear analysis of structures. The strategy combines model-free data processing capabilities of computational neural networks with a Pade approximants-based perturbation technique to predict partial information about the nonlinear response of structures. In the hybrid strategy, multilayer feedforward neural networks are used to extend the validity of solutions by using training samples produced by Pade approximations to the Taylor series expansion of the response function. The range of validity of the training samples is taken to be the radius of convergence of Pade approximants and is estimated by setting a tolerance on the diverging approximants. The norm of residual vector of unbalanced forces in a given element is used as a measure to assess the quality of network predictions. To further increase the accuracy and the range of network predictions, additional training data are generated by either applying linear regression to weight matrices or expanding the training data by using predicted coefficients in a Taylor series. The effectiveness of the hybrid strategy is assessed by performing large-deflection analysis of a doubly-curved composite panel with a circular cutout, and postbuckling analyses of stiffened composite panels subjected to an in-plane edge shear load. In all the problems considered, the hybrid strategy is used to predict selective information about the structural response, namely the total strain energy and the maximum displacement components only.

Szewczyk, Z. Peter↗
DOE OSTI2026

Analysis of Slow Spill Data for Mu2e

The Mu2e experiment requires a constant, relatively low intensity muon beam to produce data with high clarity, which can be achieved using slow extraction. Slow spills/extractions in the Delivery Ring involve contracting and expanding the stable region, which is bordered by the separatrix, of the beam pipe. While this does lower the beam intensity, it is very inconsistent. To help mitigate future inconsistencies, data from many trial spills (some including various magnet impulses to influence the beam intensity) was examined. This involved cutting low quality spills that have abnormal peak and integrated intensities, as well as spills with unusually low magnet ramping. Then, the remaining spills in the datasets were analyzed for trends within spills and across many spills. The findings from this analysis were then given to the FAN-C team to help them develop their simulations, as well as provide training data for their machine learning models that will use beam and impulse data to apply corrective impulses during future slow extractions.

Osborn, Thomas [Purdue U., West Lafayette]↗
DOE OSTI2015

Next Generation Workload Management System For Big Data on Heterogeneous Distributed Computing

The Large Hadron Collider (LHC), operating at the international CERN Laboratory in Geneva, Switzerland, is leading Big Data driven scientific explorations. Experiments at the LHC explore the fundamental nature of matter and the basic forces that shape our universe, and were recently credited for the discovery of a Higgs boson. ATLAS and ALICE are the largest collaborations ever assembled in the sciences and are at the forefront of research at the LHC. To address an unprecedented multi-petabyte data processing challenge, both experiments rely on a heterogeneous distributed computational infrastructure. The ATLAS experiment uses PanDA (Production and Data Analysis) Workload Management System (WMS) for managing the workflow for all data processing on hundreds of data centers. Through PanDA, ATLAS physicists see a single computing facility that enables rapid scientific breakthroughs for the experiment, even though the data centers are physically scattered all over the world. The scale is demonstrated by the following numbers: PanDA manages O(10 2 ) sites, O(10 5 ) cores, O(10 8 ) jobs per year, O(10 3 ) users, and ATLAS data volume is O(10 17 ) bytes. In 2013 we started an ambitious program to expand PanDA to all available computing resources, including opportunistic use of commercial and academic clouds and Leadership Computing Facilities (LCF). The project titled 'Next Generation Workload Management and Analysis System for Big Data' (BigPanDA) is funded by DOE ASCR and HEP. Extending PanDA to clouds and LCF presents new challenges in managing heterogeneity and supporting workflow. The BigPanDA project is underway to setup and tailor PanDA at the Oak Ridge Leadership Computing Facility (OLCF) and at the National Research Center "Kurchatov Institute" together with ALICE distributed computing and ORNL computing professionals. Our approach to integration of HPC platforms at the OLCF and elsewhere is to reuse, as much as possible, existing components of the PanDA system. Finally, we will present our current accomplishments with running the PanDA WMS at OLCF and other supercomputers and demonstrate our ability to use PanDA as a portal independent of the computing facilities infrastructure for High Energy and Nuclear Physics as well as other data-intensive science applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

On the Partial-Wave Analysis of Mesonic Resonances Decaying to Multiparticle Final States Produced by Polarized Photons

Meson spectroscopy is going through a revival with the advent of high statistics experiments and new advances in the theoretical predictions. The Constituent Quark Model (CQM) is finally being expanded considering more basic principles of field theory and using discrete calculations of Quantum Chromodynamics (lattice QCD). These new calculations are approaching predictive power for the spectrum of hadronic resonances and decay modes. It will be the task of the new experiments to extract the meson spectrum from the data and compare with those predictions. The goal of this report is to describe one particular technique for extracting resonance information from multiparticle final states. The technique described here, partial wave analysis based on the helicity formalism, has been used at Brookhaven National Laboratory (BNL) using pion beams, and Jefferson Laboratory (Jlab) using photon beams. In particular this report broaden this technique to include production experiments using linearly polarized real photons or quasi-real photons. This article is of a didactical nature. We describe the process of analysis, detailing assumptions and formalisms, and is directed towards people interested in starting partial wave analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1975

Analysis of bonded joints

A refined elastic analysis of bonded joints which accounts for transverse shear deformation and transverse normal stress was developed to obtain the stresses and displacements in the adherends and in the bond. The displacements were expanded in terms of polynomials in the thicknesswise coordinate; the coefficients of these polynomials were functions of the axial coordinate. The stress distribution was obtained in terms of these coefficients by using strain-displacement and stress-strain relations. The governing differential equations were obtained by integrating the equations of equilibrium, and were solved. The boundary conditions (interface or support) were satisfied to complete the analysis. Single-lap, flush, and double-lap joints were analyzed, along with the effects of adhesive properties, plate thicknesses, material properties, and plate taper on maximum peel and shear stresses in the bond. The results obtained by using the thin-beam analysis available in the literature were compared with the results obtained by using the refined analysis. In general, thin-beam analysis yielded reasonably accurate results, but in certain cases the errors were high. Numerical investigations showed that the maximum peel and shear stresses in the bond can be reduced by (1) using a combination of flexible and stiff bonds, (2) using stiffer lap plates, and (3) tapering the plates.

Srinivas, S.↗
DOE OSTI2021

Coordinated Modeling of Electric Grid and Natural Gas Network Operations

Presentation based upon full report of a Colorado case study and coordination framework, which is available at https://www.nrel.gov/docs/fy20osti/77096.pdf. JISEA analysts Brian Sergi, Omar Guerra, and Bri-Mathias Hodge will present on recent JISEA work on coordination between the natural gas and electricity sectors. Power and gas are becoming increasingly interdependent but weren't designed to function together. How does greater coordination impact system operations with different levels of solar and wind penetrations? This free JISEA presentation will take place in a Webinar on Tuesday, May 11, 2021 at 12 p.m.: Learn how greater coordination between the natural gas and electricity sectors impacts system operations with different levels of solar and wind penetrations. Presentation information includes: Historical and projected data for natural gas consumption and power generation in the United States as demonstrated by 2014 East Coast Polar Vortex, 2021 Texas Winter Storm Uri. Showing coupling points, FERC identified need for better coordination, different levels of coordination (decision making and optimization control), coordination framework. Case study on Colorado Front Range, ramping requirements and gas nominations, real-time dispatch (June and December scenarios), results of analysis of impacts of coordination on unserved load. Total real-time gas offtakes by node, impacts on unserved gas, effect on CO2 emissions. Discussions on clusions drawn from Colorado Case study expanded co-simulation via HELICS, objectives of the HELICS+ natural gas use case, modeling of hydrogen blending, techno economic assessment of blending, and blending impacts on energy content and pressure.

analysis↗
NASA NTRS2000

A Primer In Advanced Fatigue Life Prediction Methods

Metal fatigue has plagued structural components for centuries, and it remains a critical durability issue in today's aerospace hardware. This is true despite vastly improved and advanced materials, increased mechanistic understanding, and development of accurate structural analysis and advanced fatigue life prediction tools. Each advance is quickly taken advantage of to produce safer, more reliable more cost effective, and better performing products. In other words, as the envelop is expanded, components are then designed to operate just as close to the newly expanded envelop as they were to the initial one. The problem is perennial. The economic importance of addressing structural durability issues early in the design process is emphasized. Tradeoffs with performance, cost, and legislated restrictions are pointed out. Several aspects of structural durability of advanced systems, advanced materials and advanced fatigue life prediction methods are presented. Specific items include the basic elements of durability analysis, conventional designs, barriers to be overcome for advanced systems, high-temperature life prediction for both creep-fatigue and thermomechanical fatigue, mean stress effects, multiaxial stress-strain states, and cumulative fatigue damage accumulation assessment.

Halford, Gary R.↗
NASA NTRS2002

NASA GRC Fatigue Crack Initiation Life Prediction Models

Metal fatigue has plagued structural components for centuries, and it remains a critical durability issue in today's aerospace hardware. This is true despite vastly improved and advanced materials, increased mechanistic understanding, and development of accurate structural analysis and advanced fatigue life prediction tools. Each advance is quickly taken advantage of to produce safer, more reliable, more cost effective, and better performing products. In other words, as the envelope is expanded, components are then designed to operate just as close to the newly expanded envelope as they were to the initial one. The problem is perennial. The economic importance of addressing structural durability issues early in the design process is emphasized. Tradeoffs with performance, cost, and legislated restrictions are pointed out. Several aspects of structural durability of advanced systems, advanced materials and advanced fatigue life prediction methods are presented. Specific items include the basic elements of durability analysis, conventional designs, barriers to be overcome for advanced systems, high-temperature life prediction for both creep-fatigue and thermomechanical fatigue, mean stress effects, multiaxial stress-strain states, and cumulative fatigue damage accumulation assessment.

Arya, Vinod K.↗