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DOE OSTI2019

Materials data analytics for 9% Cr family steel

A materials data analytics (MDA) methodology was developed in this study to evaluate publicly available information on 9% Cr family steel and to handle nonlinear relationships and the sparsity in materials data for this alloy class. The overarching goal is to accelerate the design process as well as to reduce the time and expense associated with qualification testing of new alloys for fossil energy applications. Data entries in the analyzed data set for 82 iron–base alloy compositions, several processing parameters, and results of tensile mechanical tests selected for this study were arranged in 34 columns by 915 rows. While detailed microstructural information was not available, it is assumed that the compositional space for the 9 to 12% Cr steels is limited such that all data entries have a tempered martensitic microstructure during service. Establishing a hierarchy of first–order trends in the publicly available data requires the MDA to filter out the biases. Complexity of the phase transformations and microstructure evolution in the multicomponent alloys (using 21 chemical elements) with major influence on mechanical properties, leads to inefficiency in direct application of unbiased linear regression across the entire data space. To address the nonlinearity, analyses of tensile data were performed in composition–based clusters. Clusters corresponding to moderately frequent patterns and maximized information gain were further refined by using p–norm distance measures, matching the alloy classification groups adopted by industry. Lastly, the evolutionary method of propagating an ensemble of competing cluster–based models proved to be a viable option in dealing with scarce, multidimensional data.

36 MATERIALS SCIENCE↗

DOE OSTI2024

ZeroCAL: Eliminating Carbon Dioxide Emissions from Limestone’s Decomposition to Decarbonize Cement Production

Limestone (calcite, CaCO 3 ) is an abundant and cost-effective source of calcium oxide (CaO) for cement and lime production. However, the thermochemical decomposition of limestone (~800 °C, 1 bar) to produce lime (CaO) results in substantial carbon dioxide (CO 2(g) ) emissions and energy use, i.e., ~1 tonne [t] of CO 2 and ~1.4 MWh per t of CaO produced. Here, we describe a new pathway to use CaCO 3 as a Ca source to make hydrated lime (portlandite, Ca(OH) 2 ) at ambient conditions (p, T) while nearly eliminating process CO 2(g) emissions (as low as 1.5 mol. % of the CO 2 in the precursor CaCO 3 , equivalent to 9 kg of CO 2(g) per t of Ca(OH) 2 ) within an aqueous flowelectrolysis/ pH-swing process that coproduces hydrogen (H 2(g) ) and oxygen (O 2(g) ). Because Ca(OH) 2 is a zero-carbon precursor for cement and lime production, this approach represents a significant advancement in the production of zero-carbon cement. The Zero CArbon Lime (ZeroCAL) process includes dissolution, separation/recovery, and electrolysis stages according to the following steps: (Step 1) chelator (e.g., ethylenediaminetetraacetic acid, EDTA)-promoted dissolution of CaCO 3 and complexation of Ca 2+ under basic (>pH 9) conditions, (Step 2a) Ca enrichment and separation using nanofiltration (NF), which allows separation of the Ca-EDTA complex from the accompanying bicarbonate (HCO 3 – ) species, (Step 2b) acidity-promoted decomplexation of Ca from EDTA, which allows near-complete chelator recovery and the formation of a Ca-enriched stream, and (Step 3) rapid precipitation of Ca(OH) 2 from the Ca-enriched stream using electrolytically produced alkalinity. These reactions can be conducted in a seawater matrix yielding coproducts including hydrochloric acid (HCl) and sodium bicarbonate (NaHCO 3 ), resulting from electrolysis and limestone dissolution, respectively. Careful analysis of the reaction stoichiometries and energy balances indicates that approximately 1.35 t of CaCO 3 , 1.09 t of water, 0.79 t of sodium chloride (NaCl), and ~2 MWh of electrical energy are required to produce 1 t of Ca(OH) 2 , with significant opportunity for process intensification. This approach has major implications for decarbonizing cement production within a paradigm that emphasizes the use of existing cement plants and electrification of industrial operations, while also creating approaches for alkalinity production that enable cost-effective and scalable CO 2 mineralization via Ca(OH) 2 carbonation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2019

Development Considerations for Implementing a Voice-Controlled Spacecraft System

As computational power and speech recognition algorithms improve, the consumer market will see better-performing speech recognition applications. The cell phone and Internet-related service industry have further enhanced speech recognition applications using artificial intelligence and statistical data-mining techniques. These improvements to speech recognition technology (SRT) may one day help astronauts on future deep space human missions that require control of complex spacecraft systems or spacesuit applications by voice. Though SRT and more advanced speech recognition techniques show promise, use of this technology for a space application such as vehicle/habitat/spacesuit requires careful considerations. This paper provides considerations and guidance for the use of SRT in voice-controlled spacecraft systems (VCSS) applications for space missions, specifically in command-and-control (C2) applications where the commanding is user-initiated. First, current SRT limitations as known at the time of this report are given. Then, highlights of SRT used in the space program provide the reader with a history of some of the human spaceflight applications and research. Next, an overview of the speech production process and the intrinsic variations of speech are provided. Finally, general guidance and considerations are given for the development of a VCSS using a human-centered design approach for space applications that includes vocabulary selection and performance testing, as well as VCSS considerations for C2 dialogue management design, feedback, error handling, and evaluation/usability testing.

Salazar, George↗

DOE OSTI2026

Machine-Protection System for the PIP-II Linear Accelerator: Architecture and Data Processing Schemes

PIP-II at Fermilab features a brand-new, 800 MeV leading-edge Linear Accelerator (Linac) that will enable the Fermilab complex to deliver more than a megawatt of beam power to the Long Base-line Neutrino Facility (LBNF), requiring a robust Machine Protection System (MPS) to prevent beam-induced damage to delicate cryomodules and vacuum components. Signals in various accelerator components, in both analog and digital format, are collected and processed to assest the operating conditions of the accelerator. In the situation a potential damage is foreseen, the beam permit signal will be drop. A critical element of this system is the fast Analog Machine Protection System, a high-bandwidth platform designed for real-time beam loss monitoring. The system utilizes a modular, FPGA-based architecture to digitize signals from beam-sensing devices, such as AC Current Transformers (ACCTs), non-invasive Ring Pickups (RPUs) and beam scrapers. To eliminate both high frequency as well as 60 Hz noise that might be picked up in the industrial environment, data processing schemes beyond regular digital filtering are planned and implemented.

Wu, Jinyuan [Fermilab] (ORCID:0000000344329521)↗

DOE OSTI2026

Revealing the Defect‐Driven Ferroelectric Mechanisms of Aluminum Nitride

Wurtzite III-nitride compounds are CMOS-compatible with widespread industrial interest to exercise ferroelectricity, despite their polar structure being highly resistant to polarization reversal. Here, we induce and tune ferroelectric properties in w-AlN via direct-write ion-beam processing, using nanoscale patterned defect engineering as a post-growth alternative to conventional cation substitution. Nanometric piezoresponse spectroscopy of the focused He + beam patterned defect concentrations in ferroelectric Al 0.92 B 0.08 N measures a localized 10x enhancement in effective piezoresponse and 40% reduction in switching barrier. The irradiation-induced point defects convert piezoelectric AlN into a ferroelectric system with site-saturated nucleation and raise the dielectric susceptibility, switched polarization, and effective piezoelectric coefficient. Enhanced defect-lattice interactions in AlN increase carrier conduction and phonon scattering loss but preserve long-range crystallinity. Here, based on atomistic analysis of nudged elastic band density functional theory calculations and reactive force field simulations, both nitrogen vacancies and defect complexes disrupt bond ordering, facilitating a line-by-line low-barrier switching of pristine AlN.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Farmer characteristics and decision-making: A model for bioenergy crop adoption

We report the commercial development of biofuels and bioproducts depends on whether renewable biomass feedstock is available while not directly competing with the production of food. Farmers are one of the most important stakeholders in the biofuel supply chain and confront a range of uncertainties while entering the bioenergy market. Their decision-making process is extremely complex and rarely purely rational. Modeling farmer behavior requires considering a wide range of individual-level factors, socio-temporal dynamics, institutional settings, and their interactions. These characteristics make agent-based modeling a suitable framework for evaluating such systems. We developed a model to simulate farmer bioenergy crop adoption behavior across a 50-county study region in Nebraska, Kansas, and Colorado. The analysis considers adoption decisions for two bioenergy feedstocks, crop residues and energy crops. We examine the influence of individual and farm characteristics, market structure, social networks, and media influence on farmer adoption decisions. Our results indicate that different factors can have varied impacts on the speed of adoption for the crop residues and energy crops. Identifying levers that have the most impact on grower adoption can inform the design of interventions both from policy and private sector standpoints with important implications for the future the bioenergy industry.

09 BIOMASS FUELS↗

DOE OSTI2015

A Potent, Selective, and Cell-Active Inhibitor of Human Type I Protein Arginine Methyltransferases

Protein arginine methyltransferases (PRMTs) play a crucial role in a variety of biological processes. Overexpression of PRMTs has been implicated in various human diseases including cancer. Consequently, selective small-molecule inhibitors of PRMTs have been pursued by both academia and the pharmaceutical industry as chemical tools for testing biological and therapeutic hypotheses. PRMTs are divided into three categories: type I PRMTs which catalyze mono- and asymmetric dimethylation of arginine residues, type II PRMTs which catalyze mono- and symmetric dimethylation of arginine residues, and type III PRMT which catalyzes only monomethylation of arginine residues. Here, we report the discovery of a potent, selective, and cell-active inhibitor of human type I PRMTs, MS023, and characterization of this inhibitor in a battery of biochemical, biophysical, and cellular assays. MS023 displayed high potency for type I PRMTs including PRMT1, -3, -4, -6, and -8 but was completely inactive against type II and type III PRMTs, protein lysine methyltransferases and DNA methyltransferases. A crystal structure of PRMT6 in complex with MS023 revealed that MS023 binds the substrate binding site. MS023 potently decreased cellular levels of histone arginine asymmetric dimethylation. It also reduced global levels of arginine asymmetric dimethylation and concurrently increased levels of arginine monomethylation and symmetric dimethylation in cells. We also developed MS094, a close analog of MS023, which was inactive in biochemical and cellular assays, as a negative control for chemical biology studies. In conclusion, MS023 and MS094 are useful chemical tools for investigating the role of type I PRMTs in health and disease.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Sulfide Generation by Dominant Halanaerobium Microorganisms in Hydraulically Fractured Shales

ABSTRACT Hydraulic fracturing of black shale formations has greatly increased United States oil and natural gas recovery. However, the accumulation of biomass in subsurface reservoirs and pipelines is detrimental because of possible well souring, microbially induced corrosion, and pore clogging. Temporal sampling of produced fluids from a well in the Utica Shale revealed the dominance of Halanaerobium strains within the in situ microbial community and the potential for these microorganisms to catalyze thiosulfate-dependent sulfidogenesis. From these field data, we investigated biogenic sulfide production catalyzed by a Halanaerobium strain isolated from the produced fluids using proteogenomics and laboratory growth experiments. Analysis of Halanaerobium isolate genomes and reconstructed genomes from metagenomic data sets revealed the conserved presence of rhodanese-like proteins and anaerobic sulfite reductase complexes capable of converting thiosulfate to sulfide. Shotgun proteomics measurements using a Halanaerobium isolate verified that these proteins were more abundant when thiosulfate was present in the growth medium, and culture-based assays identified thiosulfate-dependent sulfide production by the same isolate. Increased production of sulfide and organic acids during the stationary growth phase suggests that fermentative Halanaerobium uses thiosulfate to remove excess reductant. These findings emphasize the potential detrimental effects that could arise from thiosulfate-reducing microorganisms in hydraulically fractured shales, which are undetected by current industry-wide corrosion diagnostics. IMPORTANCE Although thousands of wells in deep shale formations across the United States have been hydraulically fractured for oil and gas recovery, the impact of microbial metabolism within these environments is poorly understood. Our research demonstrates that dominant microbial populations in these subsurface ecosystems contain the conserved capacity for the reduction of thiosulfate to sulfide and that this process is likely occurring in the environment. Sulfide generation (also known as “souring”) is considered deleterious in the oil and gas industry because of both toxicity issues and impacts on corrosion of the subsurface infrastructure. Critically, the capacity for sulfide generation via reduction of sulfate was not detected in our data sets. Given that current industry wellhead tests for sulfidogenesis target canonical sulfate-reducing microorganisms, these data suggest that new approaches to the detection of sulfide-producing microorganisms may be necessary.

04 OIL SHALES AND TAR SANDS↗

DOE OSTI2026

Reliable statistics-based detection and investigation of anomalies in a SMART valve system

Reliable anomaly detection and diagnosis are critical for the safe operation of complex engineered systems. This study presents a unified framework that integrates statistical, model-based, and data-driven techniques for anomaly detection and investigation, demonstrated on SMART valve systems in hybrid energy applications. Four detection methods—mean deviation, seasonal extreme studentized deviate, ARIMA forecasting, and matrix profiling—were implemented and compared. Matrix profiling was particularly effective in revealing subtle deviations and hidden relationships among variables. Anomaly investigation was performed by analyzing variable-level and grouped signal profiles, with system topology incorporated to distinguish primary faults from propagated effects. Grouping signals by type enhanced interpretability, enabling accurate localization of anomalies across multi-dimensional datasets. Experimental results confirmed the framework's capability to consistently detect and isolate anomalies while providing actionable insights into system interdependencies. The proposed methodology offers a robust, interpretable, and scalable solution for condition monitoring, with potential applications in safety-critical domains such as nuclear energy, aerospace, and process industries.

ARIMA models↗

DOE OSTI2017

Highlighting the complexities of a groundwater pilot study during an avian influenza outbreak: Methods, lessons learned, and select contaminant results

The highly pathogenic avian influenza (H5N2) outbreak in the Midwestern United States (US) in 2015 was historic due to the number of birds and poultry operations impacted and the corresponding economic loss to the poultry industry and was the largest animal health emergency in US history. The U.S. Geological Survey (USGS), with the assistance of several state and federal agencies, aided the response to the outbreak by developing a study to determine the extent of virus transport in the environment. The study goals were to: develop the appropriate sampling methods and protocols for measuring avian influenza virus (AIV) in groundwater, provide the first baseline data on AIV and outbreak- and poultry-related contaminant occurrence and movement into groundwater, and document climatological factors that may have affected both survival and transport of AIV to groundwater during the months of the 2015 outbreak. While site selection was expedient, there were often delays in sample response times due to both relationship building between agencies, groups, and producers and logistical time constraints. This study's design and sampling process highlights the unpredictable nature of disease outbreaks and the corresponding difficulty in environmental sampling of such events. The lessons learned, including field protocols and approaches, can be used to improve future research on AIV in the environment. - Highlights: • Historic 2015 avian influenza outbreak resulted in over 50 million birds destroyed. • Study to determine environmental extent of virus transport logistically challenging. • Challenges included communication, relationship building, and safety protocols. • Temperature, precipitation may have favored virus survival, transport to subsurface.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Physics-Informed and Data-Driven Prediction of Residual Stress in Three-Dimensional Machining

Efficient and reliable prediction of machining-induced residual stress (RS) is a key requirement for truly integrated computational materials engineering (ICME). Currently available process modeling approaches, including empirical, analytical, and numerical methodologies lack predictive power and require substantial calibration and validation data. Moreover, most model-based approaches consider only two-dimensional (2D) (i.e., orthogonal), cutting processes. Meanwhile, industrial processes such as milling, turning, and drilling are inherently three-dimensional (3D). The present work attempts to bridge the gap between 2D and 3D through careful consideration of the process physics, including geometric, kinematic, and size-effect constraints to realize robust prediction of how RS develops in 3D machining. Using a novel in-situ experimental technique and digital image correlation (DIC) to determine equivalent Hertzian contact widths, contact pressures, and friction coefficients, the proposed methodology leverages a discretized conversion algorithm that includes multi-pass shakedown effects. This paper presents a semi-analytical model to predict machining-induced RS in 3D turning operations, which are used representatively for 3D processes more generally. Rather than follow a ‘brute force’ 3D FEM approach or conduct countless experiments to train a purely data-driven machine learning algorithm, the proposed approach builds on previous 2D modeling work. Through careful consideration of the process physics, including complex geometry/kinematic considerations of 3D turning, the authors demonstrated an experimentally calibrated approach, as well as validation based on published RS data. Model predictions and previously published measurement data of RS depth profiles for turning of Inconel 718 were compared for a range of process parameters. Correlation between the proposed 3D model and validation data was found to be within the margin of experimental error for most conditions. The proposed model appears to capture the overall behavior of 3D RS depth profiles with acceptable accuracy, particularly the key metrics of near-surface stress, peak stress magnitude and location, as well as overall stress profile depth. This report presents a physics-informed, data-driven approach for efficient calibration of a 2D model for machining-induced RS through DIC analysis of in-situ characterized subsurface displacement fields.

42 ENGINEERING↗

DOE OSTI2021

Carbon fiber synthesis from pitch: Insights from ReaxFF based molecular dynamics simulations

Despite the potential to lowering fabrication costs of pitch-based carbon fiber (CF) compared to polyacrylonitrile-based CF, large scale utilization is currently limited by the complexity in manufacturing. This is mainly due to the lack of consistency between the pitch structure/chemistry and CF properties under different processing conditions. Here, we employed full atomistic simulations based on reactive force fields to investigate the conversion of pitch precursors to CFs. Additionally, it was found that at the carbonization stage, the consumption rates of precursors, gas formations, and solid yields (carbonized CFs) are all dependent on the composition and arrangement of molecular moieties in the precursor. Specifically, precursors with sp 3 carbons in the cata-condensed core area have limited decomposition pathways (i.e., limited reaction sites), which leads to the formation of carbonized CFs with similar compositions. Furthermore, carbonized CFs possess distinct structural characteristics, and finally lead to the formation of CFs with different moduli. By analyzing CF crystallinity and elemental composition, general rationales are proposed for the selection of optimal precursors and processing conditions, and thus shed lights on parameter screening during industrial CF synthesis. For instance, inclusion of sp 3 carbons in cata- condensed rings can be preferred over the presence of sp 3 carbons on side chains.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2024

SPARTAN (Scalable Probabilistic Application Reconfigurable Tensor Autonomous Network)

The technical founder of Ludwig Computing Inc has been competitively selected for support by Cyclotron Road, a U.S. Department of Energy (DOE) Advanced Manufacturing Office (AMO) Lab-Embedded Entrepreneurship Program (LEEP) through an approved merit review process. Ludwig Computing Inc, supported by the U.S. Department of Energy's Advanced Manufacturing Office through the Cyclotron Road program, has investigated the advantages of probabilistic computing for real-world compute-intensive applications. This research adds to the understanding of alternative computing paradigms by exploring a unique hardware-software co-design that integrates quantum computing methods with nature-inspired problem-solving techniques. The project's focus on areas such as combinatorial optimization, graph analytics, and machine learning demonstrates the potential for significant advancements in computational efficiency and performance. By harnessing natural randomness to streamline large circuits into fewer devices, Ludwig's approach enables massive parallelism, potentially offering higher throughput, speed, and energy efficiency compared to conventional hardware solutions. This work benefits the public by paving the way for more efficient computing solutions that could address complex real-world problems while potentially reducing energy consumption in data-intensive industries.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2017

Improvement of Automated POST Case Success Rate Using Support Vector Machines

Zwack, Mathew R.↗

DOE OSTI2018

Integration of optimal operational dispatch and controller determined dynamics for microgrid survivability

We present that the reliability and resilience of the electrical power grids are essential to industry, economy and society. Microgrids that are able to island from the bulk electrical grid are one technology that may vastly improve electrical power service to customer loads. To achieve these improvements, an islanded microgrid should be able to operate through the loss of one of its generators without shedding electrical load. The loss of one generator will typically result in significant additional loads, including transient overloads, being placed on the remaining generators. There is also the possibility of additional generator tripping during these processes (i.e. cascading failures), which would likely result in the collapse of the microgrid. The novelty of our work consists in incorporating dynamic models of generator controllers into a microgrid optimal dispatch formulation with the ultimate goal to avoid operational failures and ensure the “survivability” of all-inverter microgrids to generator loss and transient overloads. The integration of generator and controller dynamics into the optimal dispatch formulation significantly increases the computational complexity. As we develop algorithms to restore speed of the optimization, our method can be readily implemented into a new operational strategy capable of an unprecedented level of reliability against generator contingencies. In addition, we quantitatively illustrate the effect of the survivability constraints on the microgrid operating costs and how the related trade-off between capital and operating costs should be taken into account at the design stage. Finally, the methods developed here also apply to the dispatch of off-grid microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2017

Improvement of Automated POST Case Success Rate Using Support Vector Machines

During early conceptual design of complex systems, concept down selection can have a large impact upon program life-cycle cost. Therefore, any concepts selected during early design will inherently commit program costs and affect the overall probability of program success. For this reason it is important to consider as large a design space as possible in order to better inform the down selection process. For conceptual design of launch vehicles, trajectory analysis and optimization often presents the largest obstacle to evaluating large trade spaces. This is due to the sensitivity of the trajectory discipline to changes in all other aspects of the vehicle design. Small deltas in the performance of other subsystems can result in relatively large fluctuations in the ascent trajectory because the solution space is non-linear and multi-modal [1]. In order to help capture large design spaces for new launch vehicles, the authors have performed previous work seeking to automate the execution of the industry standard tool, Program to Optimize Simulated Trajectories (POST). This work initially focused on implementation of analyst heuristics to enable closure of cases in an automated fashion, with the goal of applying the concepts of design of experiments (DOE) and surrogate modeling to enable near instantaneous throughput of vehicle cases [2]. Additional work was then completed to improve the DOE process by utilizing a graph theory based approach to connect similar design points [3]. The conclusion of the previous work illustrated the utility of the graph theory approach for completing a DOE through POST. However, this approach was still dependent upon the use of random repetitions to generate seed points for the graph. As noted in [3], only 8% of these random repetitions resulted in converged trajectories. This ultimately affects the ability of the random reps method to confidently approach the global optima for a given vehicle case in a reasonable amount of time. With only an 8% pass rate, tens or hundreds of thousands of reps may be needed to be confident that the best repetition is at least close to the global optima. However, typical design study time constraints require that fewer repetitions be attempted, sometimes resulting in seed points that have only a handful of successful completions. If a small number of successful repetitions are used to generate a seed point, the graph method may inherit some inaccuracies as it chains DOE cases from the non-global-optimal seed points. This creates inherent noise in the graph data, which can limit the accuracy of the resulting surrogate models. For this reason, the goal of this work is to improve the seed point generation method and ultimately the accuracy of the resulting POST surrogate model. The work focuses on increasing the case pass rate for seed point generation.

Zwack, Matthew R.↗

DOE OSTI2021

A review of microwave-assisted process intensified multiphase reactors

Microwaves provide alternative heating and allow process intensification due to their rapid, volumetric, and selective nature. Recognizing the central role of multiphase reactors in chemical industry, a recent surge in employing microwaves is observed. We review the recent experimental and modeling investigations of microwave heating of multiphase reactors with emphasis on chemical engineering applications. Here we demonstrate that there is accumulated evidence for improved performance via microwave heating and a clear opportunity for further process intensification. In most of the cases, this improved performance stems from a temperature gradient between two phases. We discuss the ongoing debate on the mechanism by which microwaves affect chemical processes exacerbated by the inability of measuring the temperature distribution in a microwave cavity. We outline recent progress in this direction in monolith reactors and needs for future work. We underscore the lack of detailed modeling and simulation tools even for single-phase systems and emphasize the imperative for multiscale predictive modeling to bridge the experimental-modeling gap. Promising results are shown by a few recently published modeling studies that can predict the experimental measurements in complex multiphase reactors. A combination of experimental and modeling tools can provide a comprehensive picture of the microwave multiphase reactors as well as a means toward scale-up and optimization.

42 ENGINEERING↗

NASA NTRS2008

Friction Stir Welding

Friction stir welding (FSW) is a solid state welding process invented in 1991 at The Welding Institute in the United Kingdom. A weld is made in the FSW process by translating a rotating pin along a weld seam so as to stir the sides of the seam together. FSW avoids deleterious effects inherent in melting and promises to be an important welding process for any industries where welds of optimal quality are demanded. This article provides an introduction to the FSW process. The chief concern is the physical effect of the tool on the weld metal: how weld seam bonding takes place, what kind of weld structure is generated, potential problems, possible defects for example, and implications for process parameters and tool design. Weld properties are determined by structure, and the structure of friction stir welds is determined by the weld metal flow field in the vicinity of the weld tool. Metal flow in the vicinity of the weld tool is explained through a simple kinematic flow model that decomposes the flow field into three basic component flows: a uniform translation, a rotating solid cylinder, and a ring vortex encircling the tool. The flow components, superposed to construct the flow model, can be related to particular aspects of weld process parameters and tool design; they provide a bridge to an understanding of a complex-at-first-glance weld structure. Torques and forces are also discussed. Some simple mathematical models of structural aspects, torques, and forces are included.

Nunes, Arthur C., Jr.↗