Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Strain improvement”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Structural analysis of the NifL-NifA complex reveals the molecular basis of anti-activation of nitrogen fixation gene expression in Azotobacter vinelandii

Understanding the molecular basis of regulated nitrogen (N 2 ) fixation is essential for engineering N 2 -fixing bacteria that fulfill the demand of crop plants for fixed nitrogen, reducing our reliance on synthetic nitrogen fertilizers. In Azotobacter vinelandii and many other members of Proteobacteria, the two-component system comprising the anti-activator protein (NifL) and the Nif-specific transcriptional activator (NifA)controls the expression of nif genes, encoding the nitrogen fixation machinery. The NifL-NifA system evolved the ability to integrate several environmental cues, such as oxygen, nitrogen, and carbon availability. The nitrogen fixation machinery is thereby only activated under strictly favorable conditions, enabling diazotrophs to thrive in competitive environments. While genetic and biochemical studies have enlightened our understanding of how NifL represses NifA, the molecular basis of NifA sequestration by NifL depends on structural information on their interaction. Here, we present mechanistic insights into how nitrogen fixation is regulated by combining biochemical and genetic approaches with a low-resolution cryo-electron microscopy (cryo-EM) map of the oxidized NifL-NifA complex. Our findings define the interaction surface between NifL and NifA and reveal how this interaction can be manipulated to generate bacterial strains with increased nitrogen fixation rates able to secrete surplus nitrogen outside the cell, a crucial step in engineering improved nitrogen delivery to crop plants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Gene targets for engineering osmotolerance in Caldicellulosiruptor bescii

Abstract Background Caldicellulosiruptor bescii , a promising biocatalyst being developed for use in consolidated bioprocessing of lignocellulosic materials to ethanol, grows poorly and has reduced conversion at elevated medium osmolarities. Increasing tolerance to elevated fermentation osmolarities is desired to enable performance necessary of a consolidated bioprocessing (CBP) biocatalyst. Results Two strains of C. bescii showing growth phenotypes in elevated osmolarity conditions were identified. The first strain, ORCB001, carried a deletion of the FapR fatty acid biosynthesis and malonyl-CoA metabolism repressor and had a severe growth defect when grown in high-osmolarity conditions—introduced as the addition of either ethanol, NaCl, glycerol, or glucose to growth media. The second strain, ORCB002, displayed a growth rate over three times higher than its genetic parent when grown in high-osmolarity medium. Unexpectedly, a genetic complement ORCB002 exhibited improved growth, failing to revert the observed phenotype, and suggesting that mutations other than the deleted transcription factor (the fruR/cra gene) are responsible for the growth phenotype observed in ORCB002. Genome resequencing identified several other genomic alterations (three deleted regions, three substitution mutations, one silent mutation, and one frameshift mutation), which may be responsible for the observed increase in osmolarity tolerance in the fruR / cra -deficient strain, including a substitution mutation in dnaK , a gene previously implicated in osmoresistance in bacteria. Differential expression analysis and transcription factor binding site inference indicates that FapR negatively regulates malonyl-CoA and fatty acid biosynthesis, as it does in many other bacteria. FruR/Cra regulates neighboring fructose metabolism genes, as well as other genes in global manner. Conclusions Two systems able to effect tolerance to elevated osmolarities in C. bescii are identified. The first is fatty acid biosynthesis. The other is likely the result of one or more unintended, secondary mutations present in another transcription factor deletion strain. Though the locus/loci and mechanism(s) responsible remain unknown, candidate mutations are identified, including a mutation in the dnaK chaperone coding sequence. These results illustrate both the promise of targeted regulatory manipulation for osmotolerance (in the case of fapR ) and the challenges (in the case of fruR/cra ).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Building a Computational and Experimental Rapid Response Pipeline to Counter the Coronavirus Disease 2019 Outbreak and Emerging Biothreats

The COVID-19 pandemic underscored the promise of monoclonal antibody-based prophylactic and therapeutic drugs, especially where protective candidates can be rapidly identified or developed for emerging biothreats and escape variants. Current cutting-edge technologies for this purpose still rely on pathogen-exposed convalescent volunteers and a large screening effort to find a proverbial needle in a haystack. Computational design of protective antibodies based on pre-existing templates skips those requirements and allows for greater control over the breadth and target epitope, while also co-optimizing for potency and developability or other biophysical characteristics. We approached this problem by building and expanding an in vitro experimental rapid antibody production and characterization pipeline to support development of an autonomous, closed loop, active learning software system based on structural simulation and ground truth experimental data to design and evaluate antibody antigen interactions. Starting from early in the pandemic, we targeted SARS-CoV-2, the causative agent of COVID-19, by re-purposing neutralizing antibodies against SARS-CoV-1 that had been identified in the wake of that outbreak in the early 2000’s. We successfully re-targeted three different anti-SARS-CoV-1 antibodies to neutralize SARS-CoV-2 in vitro, where the antibodies were generated externally and tested through conventional binding and neutralization assays internally or with collaborators. As antibodies were identified from the blood of humans infected with SARS-CoV-2, we shifted to collaborate with academic partners to develop improved versions of their human-derived antibodies. This work reached its most important stage in rapid response to the emergence of the Omicron variant of concern (VOC) in late 2021. In a matter of weeks, enabled by on demand innovation to our screening pipeline, we computationally designed and experimentally characterized derivative antibodies of COV2-2130, one of two antibodies from Vanderbilt that form the basis of the AstraZeneca Evusheld prophylactic drug product. This drug product suffers a serious loss of efficacy against Omicron BA.1 and BA.1.1, the first Omicron strains. Due to tight integration of computational design and experimental evaluation, we were able to identify a pair of designs with potent neutralization of the main targets Omicron BA.1 and BA.1.1; but also the earlier Delta variant, and subsequent Omicron strains including BA.2, BA.4, BA.5, and BA.2.75, demonstrating that our multi-target design process can, by its nature, produce robust antibody designs that strictly improve over the parental antibody. These results, recognized by a 2022 Director’s Science and Technology award, have enabled the follow-on GUIDE program, to commence in FY23. While earlier design campaigns were substantially outsourced, we have engineered better and faster processes internally to better compliment, calibrate, and speed computational designs. As part of the follow-on GUIDE program, we will stand up a rapid and high-throughput antibody production and characterization facility staffed with the expertise and capabilities to foster our current collaboration across PLS and ENG as well as other partnerships toward computational design of biologics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Nannochloropsis oceanica IMET1 and its bacterial symbionts for carbon capture, utilization, and storage: biomass and calcium carbonate production under high pH and high alkalinity

ABSTRACT To combat the increasing levels of carbon dioxide (CO 2 ) released from the combustion of fossil fuels, microalgae have emerged as a promising strategy for biological carbon capture, utilization, and storage. This study used a marine microalgal strain, Nannochloropsis oceanica IMET1, which thrives in high CO 2 concentrations. A high-pH, high-alkalinity culture was designed for CO 2 capture through algal biomass production as well as permanent sequestration through calcium carbonate (CaCO 3 ) precipitation. This was accomplished by timed pH elevation and the addition of sodium bicarbonate to cultures of N. oceanica grown at lab scale (1 L) and pilot scale (500 L) with 10% and 5% CO 2 , respectively. Our data showed that 0.02 M NaHCO 3 promoted algal growth and that sparging cultures with ambient air after 12 days raised pH and created favorable CaCO 3 formation conditions. At the 1 L scale, we reached 1.52 g L −1 biomass after 12 days and an extra 9.3% CO 2 was captured in the form of CaCO 3 precipitates. At the 500 L pilot scale, an extra 60% CO 2 was captured (Day 40) with a maximum CO 2 capture rate of 63.2 g m −2 day −1 (Day 35). Bacterial communities associated with the microalgae were dominated by two novel Patescibacteria. Functional analysis revealed that genes for several plant growth-promotion traits (PGPTs) were enriched within this group. The microalgal-bacterial coculture system offers advantages for enhanced carbon mitigation through biomass production and simultaneous precipitation of recalcitrant CaCO 3 for long-term CO 2 storage. IMPORTANCE Capturing carbon dioxide (CO 2 ) released from fossil fuel combustion is of the utmost importance as the impacts of climate change continue to worsen. Microalgae can remove CO 2 through their natural photosynthetic pathways and are additionally able to convert CO 2 into a stable, recalcitrant form as calcium carbonate (CaCO 3 ). We demonstrate that microalgae-based carbon capture systems can be greatly improved with high pH and high alkalinity by providing optimal conditions for carbonate precipitation. Our results with the microalga, Nannochloropsis oceanica strain IMET1, show an extra 9.3% CO 2 captured as CaCO 3 at the 1 L scale and an extra 60% CO 2 captured at the 500 L (pilot) scale. Our optimized system provides a novel approach to capture CO 2 through two mechanisms: (i) as organic carbon within microalgal biomass and (ii) as inorganic carbon stored permanently in the form of CaCO 3.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Adjoint-Based Inversion of Geodetic Data for Sources of Deformation and Strain

An adjoint-based formulation leads to a particularly efficient approach for inverting geodetic measurements for the source of the deformation. Specifically, the quantities necessary to iteratively improve the fit to the observations can be computed with just three forward calculations, one to obtain the current residuals, another to solve the adjoint problem, and a third to compute the step length. An inversion algorithm utilizing the adjoint-based gradient is applied to a set of Interferometric Synthetic Aperture Radar (InSAR) data gathered between 2016 and 2018 over the Tulare Basin in California's Central Valley. Because the measured deformation is due to groundwater withdrawal, a penalty function is included in the inversion to avoid placing aquifer volume change in locations that are far from any documented wells. The solution of the inverse problem provides estimates of aquifer compaction that provide a match to the observed range changes while honoring the well data. The solution indicates an average aquifer volume loss of 2.17 km 3 /year over the two year period from January 2016 to January 2018, encompassing one drought year (2016) and one wet year (2017). Finally, this magnitude of lost volume is compatible with the 3.1 km 3 /year decrease in water volume for the entire Central Valley, estimated from GRACE satellite gravity data.

58 GEOSCIENCES↗
DOE OSTI2024

Bridging length scales in organic mixed ionic–electronic conductors through internal strain and mesoscale dynamics

Understanding the structural and dynamic properties of disordered systems at the mesoscale is crucial. This is particularly important in organic mixed ionic-electronic conductors (OMIECs), which undergo significant and complex structural changes when operated in an electrolyte. Here, in this study, we investigate the mesoscale strain, reversibility and dynamics of a model OMIEC material under external electrochemical potential using operando X-ray photon correlation spectroscopy. Our results reveal that strain and structural hysteresis depend on the sample's cycling history, establishing a comprehensive kinetic sequence bridging the macroscopic and microscopic behaviours of OMIECs. Furthermore, we uncover the equilibrium and non-equilibrium dynamics of charge carriers and material-doping states, highlighting the unexpected coupling between charge carrier dynamics and mesoscale order. These findings advance our understanding of the structure-dynamics-function relationships in OMIECs, opening pathways for designing and engineering materials with improved performance and functionality in non-equilibrium states during device operation. Understanding mesoscale structure and dynamics in organic mixed ionic-electronic conductors is crucial. Mesoscale strain kinetics and structural hysteresis have been studied, and they uncover the coupling between charge carrier dynamics and mesoscale order in organic mixed ionic-electronic conductors.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Atomistic determination of Peierls barriers of dislocation glide in nickel

The Peierls barrier measures the lattice resistance to dislocation glide in crystalline solids. We use the nudged elastic band (NEB) method to calculate the Peierls barriers for screw and edge dislocation glide in a face-centered cubic (FCC) metal of Ni. The minimum energy paths (MEPs) across single or sequential Peierls barriers are determined under shear loading. The NEB results show the decreasing Peierls barrier with increasing shear stress, giving the Peierls stress at which the Peierls barrier vanishes. The effects of boundary condition and system size on Peierls barriers are studied by comparing strain- and stress-controlled NEB results. Furthermore, the free-end NEB methods are applied to determine MEPs with improved computational efficiency. The NEB results are also used to evaluate the energetic driving force of dislocation glide, which is consistent with that determined from the Peach-Koehler force. The accuracy of the present NEB results based on an empirical interatomic potential is assessed by comparison with a machine-learning potential. This work demonstrates the robust and efficient quantification of Peierls barriers to dislocation glide in an FCC metal, and it lays a solid foundation for the atomistic determination of Peierls barriers in compositionally complex alloys with the FCC structure in future studies.

42 ENGINEERING↗
DOE OSTI2022

The role of lattice dynamics in ferroelectric switching

Reducing the switching energy of ferroelectric thin films remains an important goal in the pursuit of ultralow-power ferroelectric memory and logic devices. Here, we elucidate the fundamental role of lattice dynamics in ferroelectric switching by studying both freestanding bismuth ferrite (BiFeO 3 ) membranes and films clamped to a substrate. We observe a distinct evolution of the ferroelectric domain pattern, from striped, 71° ferroelastic domains (spacing of ~100 nm) in clamped BiFeO 3 films, to large (10’s of micrometers) 180° domains in freestanding films. By removing the constraints imposed by mechanical clamping from the substrate, we can realize a ~40% reduction of the switching voltage and a consequent ~60% improvement in the switching speed. Our findings highlight the importance of a dynamic clamping process occurring during switching, which impacts strain, ferroelectric, and ferrodistortive order parameters and plays a critical role in setting the energetics and dynamics of ferroelectric switching.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Final Technical Report

This research project aims to understand the structure and properties of hidden intermediates in amorphous oxides grown by atomic layer deposition (ALD), and apply what is learned to achieve predictive phase and property control in the ALD synthesis of amorphous oxide thin films. The one-year renewal is to further this understanding to understand how amorphous TiO2 thin films by ALD turn into crystalline phase in correlation to the presence of medium range ordering (MRO) and other impurities. This fundamental study is expected to produce a predictive synthesis science for amorphous thin films with substantially improved uniformity and thereby achieve higher stability as a new surface coating strategy used for electrochemical catalysis. In this final technical report, we summarize the key achievements in this project to meet the research goals. This includes (1) a new approach to achieve homogeneous nanometer-scale amorphous coating leading to a new record of lifetime for photoelectrochemical hydrogen fuel generation; (2) A advancement in scanning transmission electron microcopy (STEM) that led to new understanding of intermediate phase in amorphous films; and (3) a computational model for understanding the local structure ordering in amorphous TiO2 films. In addition, this project also yielded relevant technical innovations including a development of bioresorbable zinc primary batteries that can self-degrade after depletion; and a ferroelectric membrane that provides active dendrites suppression to substantially improve the lifetime of rechargeable batteries. Collaboration with Lam Research also yield a new understanding of how built-in strain influences the amorphous films’ quality, bringing direct impacts to semiconductor manufacturing industry.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Demonstrate embedding of sensors in a relevant microreactor component

This report describes the first successful embedding of sensors in stainless steel pipe specimens and a miniature hex block to support structural health monitoring of microreactor components during electrically heated testing in non-nuclear testbed facilities. Sensors were embedded with an ultrasonic additive manufacturing process that uses a combination of downward pressure and an ultrasonic scrubbing motion to bond stainless steel foils to underlying materials. After placing sensors within cavities that have been machined to the size of the sensors, the ultrasonic bonding process fills the gaps around the sensors and embeds them within the part. Both Type K thermocouples and spatially distributed fiber-optic strain sensors were successfully embedded within two pipe specimens and a miniature hex block. After the fiber-optic sensors were embedded, they were interrogated and found to have minimal signal attenuation within the embedded region—a significant improvement compared to previous trials. This is likely because of the low-bend-loss fiber that was used, which greatly reduces microbending losses that occur after the embedding process. In addition to showing minimal signal attention, the fibers also showed adequate compressive strain, which is a strong indication that the sensors are physically embedded in the parts as opposed to simply being loosely contained inside the machined cavities. The embedded fibers were able to capture the strain induced in one of the pipe specimens while the pipe was actively heated with a moving heat source.

42 ENGINEERING↗
DOE OSTI2022

Bioconversion of Thermochemical Intermediates

Thermochemical (TC) biofuels production via both pyrolysis and hydrothermal liquefaction produces aqueous waste streams, typically with organic compounds at concentrations of -50-100 g/L. These streams represent a wastewater treatment cost and carbon loss for the TC biorefinery, but the concentration range for these compounds is ideal for bioconversion. To that end, the Bioconversion of Thermochemical Intermediates (BTI) project is developing advanced analytics and engineered microbes to convert these waste streams to co-products, with the overall aim of improving the economics and carbon conversion efficiency of TC biorefining. To date, we have primarily focused on development of advanced analytical chemistry approaches to fully characterize TC aqueous streams and engineering of Pseudomonas putida for conversion of non-conventional substrates, including methylated phenolics, cyclic ketones, furans, and C1-C3 light oxygenates, into atom-efficient products. Two primary challenges are the rapid deployment of aqueous-compatible analytics to changing upstream conditions and dealing with the toxicity of the feed streams to engineered microbes. The project efforts have resulted in engineered strains of P. putida able to consume 90% of the organic compounds in aqueous waste streams from catalytic fast pyrolysis, more than 300-fold toxicity tolerance improvements in P. putida, and carbon closures exceeding 90% for TC wastewater streams across multiple processing technologies.

BASIC BIOLOGICAL SCIENCES,BIOMASS FUELS↗
DOE OSTI2022

Grain boundary strain as a determinant of localized sink efficiency

The opportunity to achieve radiation tolerance in crystalline materials hinges on understanding the structure and response of grain boundary sinks to irradiation. A common descriptor of grain boundary efficiency as a defect sink is the denuded zone, which is a defect free zone adjacent to the grain boundary dictated by its ability to absorb radiation induced defects. This descriptor is often used at the mesoscale, which requires an averaging of absorption events. In this paper, we resolve the defect sink efficiency as a function of interfacial strain with respect to grain boundary character, and correlate high levels of grain boundary strain to an enhanced absorption efficiency. Here, we also introduce a key relationship between localized strain in proximity with the grain boundary sink and the variation absorption efficiency associated with these regions, revealing the pitfalls of averaging absorption events along a grain boundary, and presenting a path forward toward improved models for denuded zones and localized grain boundary absorption phenomena.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Massively distributed fiber strain sensing using Brillouin lasing

Brillouin based distributed fiber sensors present a unique set of characteristics amongst fiber sensing architectures. They are able to measure absolute strain and temperature over long distances, with high spatial resolution, and very large dynamic range in off-the-shelf fiber. However, Brillouin sensors traditionally provide only modest sensitivity due to the weak dependence of the Brillouin frequency on strain and the high signal to noise ratio required to identify the resonance’s peak frequency to within a small fraction of its linewidth. Recently, we introduced a technique which substantially improves the precision of Brillouin fiber sensors by exciting a series of lasing modes in a fiber loop cavity that experience Brillouin amplification at discrete locations in the fiber. The narrow-linewidth and high intensity of the lasing modes enabled ultra-low noise Brillouin sensors with large dynamic range. However, our initial demonstration was only modestly distributed: measuring strain at 40, non-contiguous positions along a 400 m fiber. In this work, we greatly extend this methodology to enable fully distributed sensing at 1000 contiguous locations along 3.5 km of fiber—an order of magnitude increase in sensor count and range. This highly-multiplexed Brillouin fiber laser sensor provides a strain noise as low as 34 nɛ/√Hz and we analyze the limiting factors in this approach.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Applications of Nickelate perovskites for neuromorphic computing from electronic structure and Machine Learning

While the limit of Moore's law is presently being reached with current microelectronic technologies, we need to develop new paradigms that overcome this limitation. In that respect, neuromorphic computing is a concept that emulates the neural behavior and response of the human brain, and it has been recognized as a promising alternative approach. In this research project, we will perform multi-fidelity scale bridging to explore the potential use of materials with metal to insulator transition for neuromorphic applications. In particular, rare earth nickelates are promising for such purposes, as the transition in these materials is quite sensitive to a broad set of different external stimuli. Our multi-fidelity approach will bridge the high-fidelity electronic structure calculations with classical potentials. We will bridge dynamical mean field theory with a classical atomistic representation via a deep learning force field. The neural network is trained with energies, charges, and forces obtained by accurate electronic structure theories based on Dynamical Mean Field Theory. The configurational space is generated from known crystal phases, ab initio molecular dynamics with exchange-correlation functionals corrected with the Hubbard model, disordered phases with different concentrations of oxygen vacancies, and nonsymmetrical positions and induced strain by grain interfaces or contact with a substrate. Strategies to train the model with a reduced number of training examples are obtained from active learning methods, and new structures for improving the learning process are generated by using machine learning autoencoders. This classical potential will be validated through a diversity of electronic structure methods and represents an important step to combine the flexibility and accuracy of first-principles with the speed of classical potentials. The generated multi-fidelity surrogate model will be used to understand the role of strain, oxygen vacancies, proton doping, the variation of the crystal phase, substrate effects, vibrational effects as the octahedral rotation, grain boundaries and defect effects on the response of a Metal to Insulator Transition (MIT) in correlated materials. Long time and large-scale simulations will help understand the role of different stimuli to control the hysteresis of the MIT, as it has been experimentally suggested. Selected configurations will be analyzed with higher-level theories to provide an accurate electronic description and to study how the orbitals and charges are rearranged under different conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Harnessing Heterologous Bacterial Two-Component Systems as Biosensors to Address Challenges in Fermentation Scale-Up

Scaling up bacterial fermentation from bench to industrial scale often results in unpredictable performance losses, possibly in part due to changes in microenvironmental conditions such as pH. To investigate this, we developed a suite of pH-sensitive biosensors from bacterial two-component systems (TCSs) that provide a dynamic, fluorescent readout in response to extracellular pH changes. TCSs consist of a periplasmic sensor histidine kinase (HK) that, in response to an extracellular stimulus, autophosphorylates intracellularly and subsequently transfers the phosphate to a cognate response regulator (RR) that modulates transcription of target genes. We utilized three pH-responsive TCSs (referred to here as CVJ1, CVJ30, and CVJ79) and linked their output to GFP. This was achieved by placing the RR promoter upstream of GFP or by constructing a chimeric RR composed of the native receiver domain and the DNA-binding domain of another well-characterized RR with a defined promoter. All components - HK, RR (native or chimeric), and GFP under its corresponding promoter - were cloned into a broad-host-range plasmid. Sensors were validated in Escherichia coli and Pseudomonas putida, including the muconic acid-producing strain P. putida TL207. All three biosensors successfully reported pH, with fluorescence (normalized to optical density) correlating strongly with media pH. Among the native sensors, CVJ79 showed the most robust performance while CVJ1 also performed best in its native form; CVJ30 exhibited improved functionality as a chimera, suggesting that modular RR design can enhance compatibility in some heterologous hosts. Further, CVJ79 was activated by alkaline conditions, while CVJ30 responded to acidic environments. Notably, CVJ1 was induced by high pH in wild-type E. coli and P. putida, but low pH in TL207. The observed differences in sensor activation between strains - particularly the divergent response of CVJ1 - suggest that host-specific regulatory pathways may influence how cells perceive and adapt to pH stress. Moving forward, these biosensors can be used to guide the rational design of more robust strains, optimize process conditions in real time, and inform strategies to minimize physiological heterogeneity during scale-up. Integrating these tools into high-throughput screening and bioreactors will be a key step toward improving predictability and performance in industrial bioprocesses.

09 BIOMASS FUELS↗
DOE OSTI2024

Multifaceted effects of ring fusion on the stability of charged dialkoxyarene redoxmers

Due to their almost unlimited scalability, redox flow batteries can make versatile and affordable energy storage systems. Redox active materials (redoxmers) in these batteries largely define their electrochemical performance, including the life span of the battery that depends on the stability of charged redoxmers. Here, in this study, we examine the effects of expanding the pi-system in the arene rings on the chemical stability of dialkoxyarene redoxmers that are used to store positive charge in RFBs. When 1,4-dimethoxybenzene is pi-extended to 1,4 dimethoxynaphthalene, a lower redox potential, improved kinetic stability, and longer cycling life are observed. However, when an additional ring is fused to make 9,10-dimethoxyanthracene, the radical cation undergoes rapid O -dealkylation possibly due to increased steric strain that drives methoxy out of the arene plane thus breaking the pi-conjugation with O 2p orbitals. On the other hand, the planar structure of 1,4-dimethoxynaphthalene may facilitate second -order reactions of radical cations leading to their neutralization in the bulk. Our study suggests that extending the pi-system changes reactivity in multiple (sometimes, opposite) ways, so lowering the oxidation potential through pi-conjugation to improve redoxmer stability should be pursued with caution.

25 ENERGY STORAGE↗
DOE OSTI2020

A Hybrid AI/ML and Computational Mechanics Based Approach for Time-Series State and Fatigue Life Estimation of Nuclear Reactor Components

Environmental fatigue modeling is a complex problem due to multiple failure modes and their intermixing. The failure modes are function of various underlying causes in addition to the corrosive effect of reactor coolant environment. Some of the major causes are time-dependence of material associated with cyclic loading, load sequence effect associated with random/variable amplitude loading, effect of strain amplitude and rates, effect of varying temperature (along both temporal and spatial directions) and the effect of mean strain and stress. The nonlinear intermixing of failure modes associated with above mentioned causing parameters makes the environmental fatigue modeling is a challenging task. Because of this challenge, fatigue is traditionally being modeled based on experimental data. However, test based empirical approach often requires hundreds of fatigue tests to model the above-mentioned intermixing failure causes even for a single material system. The problem is further exaggerated for reactor component made from multi-material systems such as made from both carbon and stainless-steel base metals and their similar and dissimilar metal welds. With the difficulty of conducting hundreds of fatigue tests to capture the above-mentioned intermixing failure causes, fatigue modeling approaches often depends on empirical models based on limited available test data such as available through ASME code and NUREG 6909. However, these limited test-data-based models may not be enough to accurately predict the life of reactor components. Accurate prediction of life of reactor component would become a necessity, particularly when the license of the reactors to be extended for long-term-operation (LTO) that is for well beyond its original design life of 40 years. The requirement of extending the license of reactor under LTO requires hundreds of fatigue tests to be conducted to understand the mechanism associated with the above-mentioned interdependent failure causes. However, conducting large number of fatigue tests is not a feasibility due to the cost involved. To address this issues Argonne National Laboratory (ANL) with the sponsorship of DOE Light Water Reactor Sustainability (LWRS) program trying to develop a hybrid predictive modeling approach. This is based on limited experiment-data, Artificial-intelligence (AI) – Machine-Learning (ML) - Deep-Learning (DL) based techniques and Multiphysics-computational-mechanics based modeling tools. The hybrid approach not-only can improve the accuracy of the existing stress analysis and fatigue modeling approach but also can reduce the over-dependency on test-based approach. Towards this goal following are some of the major contributions based on ANL’s FY-20 environmental fatigue modeling activities: 1) A cyclic plasticity material model database for 82/182 dissimilar metal weld, which can be readily shared with US nuclear industry and regulatory agency on request. 2) A well validated analytical modeling methodology to perform cycle-by-cycle stress prediction under both constant amplitude fatigue loading and variable amplitude fatigue loading (with load-sequence effect). 3) An AI/ML/DL based methodology to predict unmeasurable cyclic strain based on other available sensor signals. This type of approach can be used for estimating strain in real reactor components from other sensor readings. 4) An AI/ML based approach to improve the US capability on environmental fatigue testing. This is by improving ANL’s existing environmental fatigue testing capacity to conduct ASME required strain-controlled tests (by controlling strain amplitudes and its rate), while not measuring the strain (due to the difficulty of placing an extensometer in a narrow autoclave in a PWR-water-test system). 5) A simulation and experiment based probabilistic modeling methodology for time-series fatigue state and life estimation of reactor metal such as dissimilar metal weld.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗