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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A coupled thermo-hydro-mechanical model for simulating leakoff-dominated hydraulic fracturing with application to geologic carbon storage

A potential risk of injecting CO2 into storage reservoirs with marginal permeability (≲ 10 mD (1 mD = 10 –15 m 2 )) is that commercial injection rates could induce fracturing of the reservoir and/or the caprock. Such fracturing is essentially fluid-driven fracturing in the leakoff-dominated regime. Recent studies suggested that fracturing, if contained within the lower portion of the caprock complex, could substantially improve the injectivity without compromising the overall seal integrity. Modeling this phenomenon entails complex coupled interactions among the fluids, the fracture, the reservoir, and the caprock. Here, we develop a simple method to capture all these interplays in high fidelity by sequentially coupling a hydraulic fracturing module with a coupled thermal-hydrological-mechanical (THM) model for nonisothermal multiphase flow. The model was made numerically tractable by taking advantage of self-stabilizing features of leakoff-dominated fracturing. The model is validated against the PKN solution in the leakoff-dominated regime. Moreover, we employ the model to study thermo-poromechanical responses of a fluid-driven fracture in a field-scale carbon storage reservoir that is loosely based on the In Salah project's Krechba reservoir. The model reveals complex yet intriguing behaviors of the reservoir-caprock-fluid system with fracturing induced by cold CO 2 injection. We also study the effects of the in situ stress contrast between the reservoir and caprock and thermal contraction on the vertical containment of the fracture. The proposed model proves effective in simulating practical problems on length and time scales relevant to geological carbon storage.

58 GEOSCIENCES↗
DOE OSTI2022

Evaluation of possible reactivation of undetected faults during CO 2 injection

Geologic storage of carbon dioxide can efficiently contribute to reduce greenhouse gas emissions to the atmosphere. Two major hazards of this technology are leakage towards the ground surface and fluid-induced seismicity. While major faults may be detected and avoided during site characterization, undetected subseismic faults could be encountered once injection has started. This paper investigates leakage and reactivation of undetected faults through coupled thermal–hydraulic–mechanical modeling. The simulations are performed using a recently developed sequential simulator, TOUGH-Pylith, that allows to accurately account for the thermodynamics of brine-CO 2 injection mixtures, and to model faults as surfaces of discontinuity using state-of-the-art fault friction laws. The simulator is benchmarked against well-known analytical solutions and subsequently applied to investigate two cases of CO 2 injection injection close to undetected faults under normal and strike-slip faulting regimes. Furthermore, although the scenarios are generic and represent unfavorable conditions, they suggest that leakage could occur and that undetected faults could trigger minor seismic events. Therefore, careful site characterization and continuous monitoring during operations should be always performed.

58 GEOSCIENCES↗
DOE OSTI2022

A physics-informed multi-agents model to predict thermo-oxidative/hydrolytic aging of elastomers

This paper introduces a novel physics-informed multi-agents constitutive model to propose prediction in quasi-static constitutive behavior of cross-linked elastomer and the loss of mechanical performance during environmental aging. The presented model is used to simulate the effect of single-mechanism chemical aging (i.e. thermal-inducedor hydrolytic aging) on the behavior of the material in this hybrid framework. Those environmental single-mechanism damages change the polymer matrix over time due to massive chain scission, chain formations, and changing the arrangement of molecules in the polymer matrix. Here we propose a data-driven super-constrained machine-learned engine to represent damage in the polymer matrix and capture the changes in material behavior, including its inelastic features such as Mullins effect and permanent set in the course of aging. We have simplified the 3D stress–strain tensor mapping problem into a small number of super-constrained 1D mapping problems by means of a sequential order reduction. An assembly of multiple replicated conditional neural-network learning-agents (L-agents) is trained to systematically simplify the high-dimensional mapping problem into multiple 1D problems, each represented by a different type of agent. Our hybrid framework is designed to capture the effect of deformation history, aging time, and aging temperature. The model is validated with respect to a comprehensive set of experiments specifically designed to benchmark model capabilities and also against available data in the literature. Thermodynamic consistency and frame independency have been verified. Besides acceptable predictive abilities, a significant reduction of computational cost to predict behavior at multiple states of deformation is the most significant feature of this model.

42 ENGINEERING↗
DOE OSTI2022

Automated bubble analysis of high-speed subcooled flow boiling images using U-net transfer learning and global optical flow

Capturing and analyzing the bubble dynamics is crucial to improving the understanding of boiling heat transfer mechanisms and predicting boiling heat transfer coefficient and boiling crisis. High speed video (HSV) imaging has been used for decades towards this end. Still, there is no universal approach to quantitatively analyze bubble dynamics from HSV images. In this study, we propose a data-driven post-processing approach to segment, track, and identify wall-attached vapor bubbles from HSV images of the boiling process in subcooled flow conditions. Firstly, we employ a transfer learning framework with a U-Net-based convolution neural network (CNN) architecture to detect and segment bubbles in HSV images of diverse contrast and surface texture using very little data (e.g., 10 images) for training. Then, we evaluate the trained CNN model with 100 ground-truth images, and the validation results show that the model accuracy and precision in detecting the optical footprint of bubbles are higher than 90%. Finally, we suggest a criterion to identify a condensing bubble based on the divergence of the bubble displacement, which is calculated from sequential segmented bubble images using a global optical flow code. Using this combination of machine learning and optical flow, we can identify nucleation sites and track the growth of bubbles nucleating at each site to quantify nucleation site density, nucleation frequency, and other fundamental boiling parameters. The proposed system is validated using results obtained on a special heater, which enables both infrared (IR) thermometry and HSV imaging on a metallic surface. We compare the fundamental boiling parameters obtained by the two different diagnostics. The results show good agreement. In conclusion, the difference between the measurements of nucleation site density, averaged nucleation frequency, and averaged growth time performed with the two techniques is always within ± 20% and mostly ± 10% of the values measured with IR thermometry.

42 ENGINEERING↗
DOE OSTI2021

A predictive discrete-continuum multiscale model of plasticity with quantified uncertainty

Multiscale models of materials, consisting of upscaling discrete simulations to continuum models, are unique in their capability to simulate complex materials behavior. The fundamental limitation in multiscale models is the presence of uncertainty in the computational predictions delivered by them. In this work, a sequential multiscale model has been developed, incorporating discrete dislocation dynamics (DDD) simulations and a strain gradient plasticity (SGP) model to predict the size effect in plastic deformations of metallic micro-pillars. The DDD simulations include uniaxial compression of micro-pillars with different sizes and over a wide range of initial dislocation densities and spatial distributions of dislocations. An SGP model is employed at the continuum level that accounts for the size-dependency of flow stress and hardening rate. Sequences of uncertainty analyses have been performed to assess the predictive capability of the multiscale model. The variance-based global sensitivity analysis determines the effect of parameter uncertainty on the SGP model prediction. The multiscale model is then constructed by calibrating the continuum model using the data furnished by the DDD simulations. A Bayesian calibration method is implemented to quantify the uncertainty due to microstructural randomness in discrete dislocation simulations (density and spatial distribution of dislocations) on the macroscopic continuum model prediction (size effect in plastic deformation). Here, the outcomes of this study indicate that the discrete-continuum multiscale model can accurately simulate the plastic deformation of micro-pillars, despite the significant uncertainty in the DDD results. Additionally, depending on the macroscopic features represented by the DDD simulations, the SGP model can reliably predict the size effect in plasticity responses of the micropillars with below 10% of error.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Creep anisotropy modeling and uncertainty quantification of an additively manufactured Ni-based superalloy

The advantages offered by additive manufacturing over traditional processes has driven a great deal of industrial and academic interest in recent years. However, the process is relatively new and requires additional investigation to become sufficiently mature for wide scale industrial adoption. Electron beam melting powder bed fusion is one technology that has shown promise for fabricating high temperature resistant materials such as nickel based superalloys. The resulting microstructures typically exhibit a strong fiber texture in the build direction giving rise to anisotropic time-dependent deformation behavior. In order to accelerate the qualification of these materials for industrial adoption accurate numerical models are needed for simulating their behavior. In this work a crystal plasticity model including non-Schmid effects is presented for capturing creep anisotropy observed in additively manufactured IN738LC. The model is calibrated via a probabilistic framework where model parameters are treated as random variables. An iterative sequential design strategy is utilized to efficiently identify the probability density of the unknown model parameters. As a case study the model is utilized to investigate the behavior of randomly oriented equiaxed grain clusters sometimes observed embedded in the additively manufactured columnar structure. A synthetic realization is simulated and uncertainty is propagated through to the full-field response. Results indicate that these features are the source of significant creep relaxation and strain accumulation which partially explains observed grain boundary decohesion at these locations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Development, validation, and verification of multi-pass thermo-mechanical welding simulations using the open-source MOOSE framework: NeT TG4 benchmark weldment

This study develops and validates a sequentially coupled thermo-mechanical welding simulation for the three-pass 316L stainless steel NeT TG4 benchmark weldment using the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE) and the Nuclear Engineering Material model Library (NEML). A diffused ellipsoidal heat source was calibrated against thermocouple data and weld macrographs to accurately model the fusion zone geometry and transient thermal fields. Material hardening is represented using the Lemaitre-Chaboche mixed isotropic-kinematic hardening model, while four annealing models - no annealing, single-stage at 1050 °C and 1300 °C, and two-stage at 800 °C/1300 °C - were implemented to assess the impact of annealing models on the accuracy of the predicted welding-induced plasticity, distortions, and residual stresses. The predictions were validated against experimental measurements and benchmarked against results from commercial software, demonstrating that thermo-mechanical MOOSE welding simulations achieve comparable accuracy with enhanced computational efficiency. This work highlights the potential of using open-source finite element frameworks like MOOSE for advanced manufacturing simulations.

Ji, Wendy [Australian Nuclear Science and Technolo↗
DOE OSTI2021

Quantitating Interfraction Target Dynamics During Concurrent Chemoradiation for Glioblastoma: A Prospective Serial Imaging Study

Magnetic resonance image (MRI) guided radiation therapy has the potential to improve outcomes for glioblastoma by adapting to tumor changes during radiation therapy. This study quantifies interfraction dynamics (tumor size, position, and geometry) based on sequential magnetic resonance imaging scans obtained during standard 6-week chemoradiation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

An Interpretable Planning Bot for Pancreas Stereotactic Body Radiation Therapy

Pancreas stereotactic body radiation therapy (SBRT) treatment planning requires planners to make sequential, time-consuming interactions with the treatment planning system to reach the optimal dose distribution. We sought to develop a reinforcement learning (RL)–based planning bot to systematically address complex tradeoffs and achieve high plan quality consistently and efficiently.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Moving Beyond the Standard of Care: Accelerate Testing of Radiation-Drug Combinations

Radiation therapy is a major treatment modality used in > 60% of cancer patients as definitive local treatment for inoperable locoregionally confined tumors and as palliative therapy. Although cytotoxic chemotherapy enhances the effectiveness of treatment, the benefit over radiation therapy alone is modest. There is a need to enhance the effectiveness of local tumor control over what sequentially or concurrently administered cytotoxic chemotherapy provides. Although many biological pathways are known to enhance the effectiveness of radiation therapy, there is currently a paucity of drugs approved for use in combination. Several clinical trials have tested the effectiveness of combining targeted agents or immunotherapies with radiation therapy, but the results of these trials have been negative, likely stemming from the relative lack of preclinical evidence using appropriate experimental standardization or model systems. Accelerating the identification of agents tested in an appropriate clinical context and experimental systems or models would greatly enhance the potential to bring forward early testing of drugs that would not only be safe but also more effective. This article provides an overview of the opportunities and challenges of developing therapeutics to combine with radiation therapy, and some guidance toward preclinical and early clinical testing to improve the chance that advanced phase testing of drug-radiation combinations would be successful in the long term.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Stepwise allomorphic transformations by alkaline and ethylenediamine treatments on bamboo crystalline cellulose for enhanced enzymatic digestibility

Efficient modification and decrystallization of cellulose Iβ in lignocellulosic biomass are potential ways to overcome biomass recalcitrance and facilitates enzymatic degradation. Herein, we have treated moso bamboo and microcrystalline cellulose separately by either alkali or ethylenediamine (EDA) treatments, as well as using both in succession. The pretreatments successfully transformed the initial cellulose Iβ allomorph into cellulose II, III I , and cellulose III II . Crystal structures and crystallinities were determined by Rietveld X-ray diffraction and CP/MAS 13 C NMR. Comparatively, the samples that were sequentially treated by EDA and NaOH had low crystallinity (~50%), larger d-spacings of ~4.4 Å for the hydrophobic (110) planes, and less condensed microfibrillar aggregation. The factors simultaneously contributed to as high enzymatic hydrolysis yield as 89.5% and 91.7% respectively, followed by the yields of mercerized cellulose II and cellulose III II allomorph. Here, the stepwise allomorphic transformations may provide new strategies on improving efficiency of saccharification process and biomass utilization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Continuous recovery of high-purity Kraft lignin from black liquor via simultaneous, liquid-phase acidification and purification

Here, a key advantage of the Sequential Liquid-Lignin Recovery and Purification (SLRP) process for the recovery of lignin from Kraft black liquor is the precipitation of a solvated, “liquid-lignin” phase (vs. the typical solid) during the CO 2 acidification step. Here, we have discovered that this lignin can also be maintained in a solvated state during the H 2 SO 4 acidification step if aqueous acetic acid from the Aqueous Lignin Purification with Hot Agents (ALPHA) process is incorporated into the H 2 SO 4 solution. As the liquid-lignin is converted from its salt form at pH = 9.5 to its acid form at pH = 2.5, the lignin initially solvated with water becomes solvated and thus liquefied with the organic–water solution. Maintaining the lignin in a solvated liquid state has two advantages: (1) the entire SLRP + ALPHA process, from starting black liquor to final lignin product, can be operated continuously; and (2) mass transfer of metals impurities out of the liquid-lignin phase is significantly enhanced, resulting in both a cleaner Kraft lignin product (~2000 ppm Na and 0.5-1.0 % ash) and reduced water washing.

09 BIOMASS FUELS↗
DOE OSTI2021

The exo-β- N -acetylmuramidase NamZ from Bacillus subtilis is the founding member of a family of exo-lytic peptidoglycan hexosaminidases

Endo-β-N-acetylmuramidases, commonly known as lysozymes, are well-characterized antimicrobial enzymes that catalyze an endo-lytic cleavage of peptidoglycan; i.e., they hydrolyze the β-1,4-glycosidic bonds connecting N-acetylmuramic acid (MurNAc) and N-acetylglucosamine (GlcNAc). In contrast, little is known about exo-β-N-acetylmuramidases, which catalyze an exo-lytic cleavage of β-1,4-MurNAc entities from the non-reducing ends of peptidoglycan chains. Such an enzyme was identified earlier in the bacterium Bacillus subtilis, but the corresponding gene has remained unknown so far. We now report that ybbC of B. subtilis, renamed namZ, encodes the reported exo-β-N-acetylmuramidase. A ΔnamZ mutant accumulated specific cell wall fragments and showed growth defects under starvation conditions, indicating a role of NamZ in cell wall turnover and recycling. Recombinant NamZ protein specifically hydrolyzed the artificial substrate para-nitrophenyl β-MurNAc and the peptidoglycan-derived disaccharide MurNAc-β-1,4-GlcNAc. Together with the exo-β-N-acetylglucosaminidase NagZ and the exo-muramoyl-L-alanine amidase AmiE, NamZ degraded intact peptidoglycan by sequential hydrolysis from the non-reducing ends. A structure model of NamZ, built on the basis of two crystal structures of putative orthologs from Bacteroides fragilis, revealed a two-domain structure including a Rossmann-fold-like domain that constitutes a unique glycosidase fold. Thus, NamZ, a member of the DUF1343 protein family of unknown function, is now classified as the founding member of a new family of glycosidases (CAZy GH171). NamZ-like peptidoglycan hexosaminidases are mainly present in the phylum Bacteroidetes and less frequently found in individual genomes within Firmicutes (Bacilli, Clostridia), Actinobacteria, and γ-proteobacteria.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Discovery of amivantamab (JNJ-61186372), a bispecific antibody targeting EGFR and MET

A bispecific antibody (BsAb) targeting the epidermal growth factor receptor (EGFR) and mesenchymal–epithelial transition factor (MET) pathways represents a novel approach to overcome resistance to targeted therapies in patients with non–small cell lung cancer. In this study, we sequentially screened a panel of BsAbs in a combinatorial approach to select the optimal bispecific molecule. The BsAbs were derived from different EGFR and MET parental monoclonal antibodies. Initially, molecules were screened for EGFR and MET binding on tumor cell lines and lack of agonistic activity toward MET. Hits were identified and further screened based on their potential to induce untoward cell proliferation and cross-phosphorylation of EGFR by MET via receptor colocalization in the absence of ligand. After the final step, we selected the EGFR and MET arms for the lead BsAb and added low fucose Fc engineering to generate amivantamab (JNJ-61186372). The crystal structure of the anti-MET Fab of amivantamab bound to MET was solved, and the interaction between the two molecules in atomic details was elucidated. Amivantamab antagonized the hepatocyte growth factor (HGF)-induced signaling by binding to MET Sema domain and thereby blocking HGF β-chain—Sema engagement. The amivantamab EGFR epitope was mapped to EGFR domain III and residues K443, K465, I467, and S468. Furthermore, amivantamab showed superior antitumor activity over small molecule EGFR and MET inhibitors in the HCC827-HGF in vivo model. Based on its unique mode of action, amivantamab may provide benefit to patients with malignancies associated with aberrant EGFR and MET signaling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Human cytochrome P450 17A1 structures with metabolites of prostate cancer drug abiraterone reveal substrate-binding plasticity and a second binding site

Abiraterone acetate is a first-line therapy for castration-resistant prostate cancer. This prodrug is deacetylated in vivo to abiraterone, which is a potent and specific inhibitor of cytochrome P450 17A1 (CYP17A1). CYP17A1 performs two sequential steps that are required for the biosynthesis of androgens that drive prostate cancer proliferation, analogous to estrogens in breast cancer. Abiraterone can be further metabolized in vivo on the steroid A ring to multiple metabolites that also inhibit CYP17A1. Despite its design as an active-site-directed substrate analog, abiraterone and its metabolites demonstrate mixed competitive/noncompetitive inhibition. To understand their binding, we solved the X-ray structures of CYP17A1 with three primary abiraterone metabolites. Despite different conformations of the steroid A ring and substituents, all three bound in the CYP17A1 active site with the steroid core packed against the I helix and the A ring C3 keto or hydroxyl oxygen forming a hydrogen bond with N202 similar to abiraterone itself. The structure of CYP17A1 with 3-keto, 5α-abiraterone was solved to 2.0 Å, the highest resolution to date for a CYP17A1 complex. This structure had additional electron density near the F/G loop, which is likely a second molecule of the inhibitor and which may explain the noncompetitive inhibition. Mutation of the adjacent Asn52 to Tyr positions its side chain in this space, maintains enzyme activity, and prevents binding of the peripheral ligand. Collectively, our findings provide further insight into abiraterone metabolite binding and CYP17A1 function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

EHR-BERT: A BERT-based model for effective anomaly detection in electronic health records

Objective: Physicians and clinicians rely on data contained in electronic health records (EHRs), as recorded by health information technology (HIT), to make informed decisions about their patients. The reliability of HIT systems in this regard is critical to patient safety. Consequently, better tools are needed to monitor the performance of HIT systems for potential hazards that could compromise the collected EHRs, which in turn could affect patient safety. In this paper, we propose a new framework for detecting anomalies in EHRs using sequence of clinical events. This new framework, EHR-Bidirectional Encoder Representations from Transformers (BERT), is motivated by the gaps in the existing deep-learning related methods, including high false negatives, sub-optimal accuracy, higher computational cost, and the risk of information loss. EHR-BERT is an innovative framework rooted in the BERT architecture, meticulously tailored to navigate the hurdles in the contemporary BERT method; thus, enhancing anomaly detection in EHRs for healthcare applications.Methods: The EHR-BERT framework was designed using the Sequential Masked Token Prediction (SMTP) method. This approach treats EHRs as natural language sentences and iteratively masks input tokens during both training and prediction stages. This method facilitates the learning of EHR sequence patterns in both directions for each event and identifies anomalies based on deviations from the normal execution models trained on EHR sequences.Results: Extensive experiments on large EHR datasets across various medical domains demonstrate that EHR-BERT markedly improves upon existing models. It significantly reduces the number of false positives and enhances the detection rate, thus bolstering the reliability of anomaly detection in electronic health records. This improvement is attributed to the model’s ability to minimize information loss and maximize data utilization effectively.Conclusion: EHR-BERT showcases immense potential in decreasing medical errors related to anomalous clinical events, positioning itself as an indispensable asset for enhancing patient safety and the overall standard of healthcare services. The framework effectively overcomes the drawbacks of earlier models, making it a promising solution for healthcare professionals to ensure the reliability and quality of health data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Rates and reversibility of CO 2 hydrogenation on Cu-based catalysts

Kinetics of reaction pathways involved in the conversion of CO 2 to methanol and CO on Cu/ZnO/Al 2 O 3 are resolved using in situ chemical titration, steady-state kinetic measurements, and mathematical formalisms for reversibility to probe salient species governing methanol selectivity and yield during CO 2 hydrogenation. Across a range of H 2 :CO 2 = 1:1 to H 2 :CO 2 = 80.5:1, active site density determined from in situ chlorine uptake remained invariant; hence, observed trends in rates can be interpreted as only arising from reaction kinetics and not from changing active site density. Kinetic and thermodynamic contributions to rates are decoupled to evaluate forward and reverse rates of methanol synthesis and reverse water-gas shift (RWGS) reactions. These kinetic analyses show that the forward rates of methanol synthesis exhibit persistent first order dependence on hydrogen pressure and are inhibited by water more significantly than the forward rates of RWGS. In contrast, the reverse rates of methanol synthesis and RWGS are both inhibited by H 2 . Consequently, without any modifications to the Cu/ZnO/Al 2 O 3 catalyst formulation, methanol selectivity can be increased to > 80% by increasing inlet H 2 partial pressure and methanol yield can be enhanced by ~20% by adding water adsorbents even under conditions far from equilibrium. Here, the kinetic treatments presented herein demonstrate a dearth of H* species during catalysis, provide thermodynamic constraints precluding sequential RWGS and CO hydrogenation as the pathway for methanol synthesis, reveal P H2 and P H2O as salient in determining methanol selectivity and yield by impacting both the forward and reverse rates of CO 2 hydrogenation on Cu/ZnO/Al 2 O 3 , and explicate the fundamentals of novel sorption-enhanced methanol synthesis, which not only alleviates equilibrium constraints but also alters the intrinsic rate at which the system approaches equilibrium.

42 ENGINEERING↗