Using advanced X-ray spectroscopy to reveal molecular level insights into water treatment
Not Available
36 MATERIALS SCIENCE↗
SEARCH · Engineering Papers
Results for “ethane”
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.
A numerical study of process complexity in permafrost dominated regions
Numerical modeling of permafrost dynamics requires adequate representation of atmospheric and surface processes, a reasonable parameter estimation strategy, and site-specific model development. The three main research objectives of the study are: (i) to propose a novel methodology that determines the required level of surface process complexity of permafrost models by conducting parameter sensitivity and calibration, (ii) to design and compare three numerical models of increasing surface process complexity, and (iii) to calibrate and validate the numerical models at the Yakou catchment on the Qinghai-Tibet Plateau as an exemplary study site. The calibration was carried out by coupling the Advanced Terrestrial Simulator (numerical model) and PEST (calibration tool). Simulation results showed that (i) A simple numerical model that considers only subsurface processes can simulate active layer development with the same accuracy as other more complex models that include surface processes. (ii) Peat and mineral soil layer permeability, Van Genuchten alpha, and porosity are highly sensitive. (iii) Liquid precipitation aids in increasing the rate of permafrost degradation. (iv) Deposition of snow insulated the subsurface during the thaw initiation period. We have developed and released an integrated code that couples the numerical software ATS to the calibration software PEST. The numerical model can be further used to determine the impacts of climate change on permafrost degradation.
Calibration↗
The effects of surface and particle properties on adhesion in humid environments using the enhanced centrifuge method
Not Available
Stevenson, Caralyn A.↗
Deep operator network surrogate for phase-field modeling of metal grain growth during solidification
A deep operator network (DeepONet) has been constructed that generates accurate representations of phase-field model simulations for evolving two dimensional metal grain morphology growing from melt. These representations serve as lower resolution, computationally efficient stand-ins for quick parameter space exploration of solutions to the the Allen-Cahn equations that dictate the phase-field model simulations. The experimental target for the phase-field model is a uranium casting system cooling a 434 g uranium charge from a maximum temperature of 1400° C at an average rate of 30° C / min , traversing the crystallographic phases of the pure metal. Experimental parameters inform the phase-field model, whose higher resolution computational model solutions are used to train the DeepONet in a given parameter space with the aim of developing a faster, more efficient method for predicting the solidifying metal's microstructure at different potential experimental values. The final DeepONet generates high accuracy, lower resolution predictions with cumulative relative approximation error over all timesteps of less than 0.5%, while ensuring solutions remain within physically feasible ranges. Further, these relative error values are comparable with other state-of-the-art DeepONet models for microstructure evolution, while significantly reducing the amount of training data required. Training a convolutional neural network simultaneously with the DeepONet, enforcing realistic values at the complex metal grain boundaries, and mathematically encoding boundary conditions into the structure of the DeepONet improved prediction accuracy and computational efficiency over a standard DeepONet model.
36 MATERIALS SCIENCE↗
Physics-guided neural networks with engineering domain knowledge for hybrid process modeling
As neural networks are more frequently used to solve problems in science and engineering, the methods used to incorporate scientific knowledge into these networks are becoming increasingly complex. Here, this work breaks down these complicated techniques into a set of basic strategies which can easily be applied to diverse situations. Several novel neural networks are built using the categories laid out in this work. These networks are tested on simulated data from a continuous stirred tank reactor (CSTR) model to evaluate the advantages provided by each network. The three points demonstrated in this work are: (1) architectural hybrid models can speed up convergence and reduce the amount of data necessary to train a model; (2) adding a physics-guided loss function can improve model generalization and make models more physically consistent; (3) using physics-guided initialization and transfer learning improves accuracy and speeds up convergence, but can harm generalizability if used incorrectly.
42 ENGINEERING↗
Effects of input gradient regularization on neural networks time-series forecasting of thermal power systems
This study proposes using neural networks, specifically gated recurrent unit (GRU), long-short-term memory (LSTM), and transformer networks, to improve control strategies in a 450 MW coal-fired power plant. However, neural networks face issues of becoming overly dependent on just a few variables to make predictions, which negatively impacts control decisions that rely on the model to determine the value of all manipulated variables. The paper introduces regularization techniques, including noise injection and input gradient regularization, during the training phase. Here, the work presents novel contributions in adapting neural networks to control industrial systems and applying regularization techniques from computer vision to industrial process control. Results demonstrate the effectiveness of input gradient regularization in reducing model dependence on subsets of variables, emphasizing the balance between fidelity and controllability. Further exploration is recommended, including the development of recurrent transformers, closed-loop control testing, and a sensitivity analysis on computer models to provide further insight.
20 FOSSIL-FUELED POWER PLANTS↗
Hyperplane decision trees as piecewise linear surrogate models for chemical process design
Recent trends in chemical engineering research point towards an increasing reliance on data-driven modeling approaches. Neural networks, for instance, have proven to be accurate when data is plentiful and high-dimensional, but in many cases, they require computationally-intensive training procedures. Here, in this work, we describe hyperplane decision trees (HT) as a highly expressive and low-compute machine learning model architecture. These models are locally linear and have linear decision boundaries, resulting in a piecewise linear model of the data. This property allows them to be converted into mixed-integer linear constraints which can be globally optimized. Our open-source PyTorch implementation of this method is a fast, flexible, and accessible way to build accurate piecewise linear models of data.
Decision trees↗
Mode I tensile fracture behavior of adhesively-bonded metal–metal, metal–CFRP, and CFRP–CFRP bi-material combinations analyzed by size effect method
Understanding the adhesive and interfacial fracture is important for developing and achieving better adhesive jointing in bi-materials, the location which becomes less vulnerable in engineered structure components. Still, to characterize the fracturing behavior of various adhesively-bonded materials, it was shown in this work that the Mode I fracture energies estimated from conventional methods (e.g., work-of-fracture, (modified) compliance calibration method, (modified) beam theory, etc.) can be strongly affected by adherent thickness, adhesive bond length, and material type. Consequently, this hindered the proper understanding of fracturing in adhesive jointing of bi-materials since the estimated fracture energies can exhibit unreasonable difference among various material combinations, thus leading to the confusion in the literature due to the unfair comparison on these non-objective results estimated by leveraging conventional methods on the specimens with different geometries. This work compared size effect method with conventional methods on the calculation of the Mode I fracture energies of metal-metal, metal-CFRP, and CFRP-CFRP material combinations via Double Cantilever Beam (DCB) tests. The results showed that the estimated fracture energies of various material combinations are not dependent on the specimen geometries. This aspect allowed the fair comparison particularly on the interfacial fracturing between metal/adhesive and CFRP/adhesive, and the difference was further explained and correlated with the damage morphology on the material surface after failure identified through three-dimensional profilometer.
36 MATERIALS SCIENCE↗
Significant slowdown of plasma-optimized surface energy deactivation by vacuum sealing for efficient adhesive bonding
This work proposes an approach to minimize surface energy deactivation of plasma-treated metal and carbon fiber-reinforced polymer (CFRP) surfaces by vacuum sealing. Plasma treatments enhance adhesive wettability on post-treated surfaces for adhesive joints, but the treated surfaces deactivate quickly in air. The surface energy of aluminum alloy AA6061 and carbon fiber-reinforced polymer-polyamide (CFRP-PA66) optimally treated by a blown air plasma instrument returns to the original surface energy within one hour. Vacuum sealing of AA6061 and CFRP-PA66 reduced the surface energy deactivation over 7 days by at least 230 times and 970 times compared to in air. Double Cantilever Beam (DCB) tests performed on adhesively-bonded AA6061/CFRP-PA66 joints showed that the total energy release and energy dissipation before failure of plasma-treated and vacuum-sealed materials was up to 60% more than plasma-treated materials without vacuum sealing and up to 125% more than non-plasma-treated materials.
42 ENGINEERING↗
Plasma surface modification coupled with thermal and step-over distance effects on significant fracture improvement of adhesively-bonded metal-$\mathrm{CFRTP}$ dissimilar materials
Here, this work proposes an approach to improve the interfacial bonding between carbon-fiber-reinforced thermoplastic polymers (CFRTP) and thermoset adhesive by using the surface modification with the combination of air plasma, thermal, and step-over effects. Thanks to the proper heating which causes the exposure of carbon fibers on the CFRTP surface, both polymer matrix and these exposed carbon fibers can have improved bonding with adhesive due to air plasma treatment. By conducting the Double Cantilever Beam (DCB) tests performed on adhesively-bonded AA5052/CFRP-PA66 dissimilar joints, it was shown in this work that the average Mode I specific fracture energy can be improved up to about 180% compared to non-treated counterparts. The foregoing improvement can be further increased by considering the step-over distance of the surface treatment, which triggers the tortuous damage path in the failure process of adhesively-bonded structures. By manipulating this treatment parameter, often overlooked in the literature, and other parameters (i.e., treatment speed, nozzle tip-to-surface distance, etc.) investigated in this study, the average Mode I specific fracture energy of adhesively-bonded AA5052/CFRP-PA66 dissimilar joints can be improved reaching up to about 410% compared to non-treatment counterparts.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Engineering metalloproteinase inhibitors: tissue inhibitors of metalloproteinases or antibodies, that is the question
Not provided.
Biochemistry & Molecular Biology↗
Computational materials assessment of the D/Li-stripping neutron source as a prototypical facility for fusion materials testing
As the US fusion materials community awaits the selection and design of a fusion prototypical neutron source (FPNS), a risk reduction exercise has been conducted to (i) provide an updated materials performance evaluation using state-of-the-art computational materials modeling, (ii) expand on legacy analysis based on pure Fe to other relevant fusion structural materials types, and (iii) ensure that materials response under FPNS operational conditions is consistent with referential fusion reactor conditions. The current paper describes the efforts undertaken to assemble a comprehensive computational methodology that includes neutronics, primary damage calculations, atomistic simulations of displacement cascades, chemical inventory evolution calculations, and a computational thermodynamic analysis of emerging phases during irradiation. Our work extends existing studies in pure Fe to reduced-activation ferritic/martensitic steels, tungsten, silicon carbide, and vanadium alloys. We focus on the single-beam deuteron/lithium-stripping neutron source behind the IFMIF-DONES concept, which we assess against ITER, two DEMO designs, and an ideal pure 14-MeV flux. Our analysis indicates that, within standard uncertainties inherent to the models employed, the DONES concept adequately captures fusion conditions in the four materials analyzed. Our work is intended as a comprehensive irradiation damage analysis of fusion-representative neutron sources, to be used for further neutron source evaluation and fusion facility operation.
Marian, Jaime [Univ. of California, Los Angeles, C↗
From gamma ray line signals of dark matter to the LHC
We explore the relationship between astrophysical gamma-ray signals and LHC signatures for a class of phenomenologically successful secluded dark matter models, motivated by recent evidence for a ~130@?GeV gamma-ray line. We consider in detail scenarios in which interactions between the dark sector and the standard model are mediated by a vev-less scalar field @f, transforming as an N-plet (N >@?3) under SU(2)_L. Since some of the component fields of @f carry large electric charges, loop induced dark matter annihilation to @c@c and @cZ can be enhanced without the need for non-perturbatively large couplings, and without overproduction of continuum gamma-rays from other final states. We discuss prospects for other experimental tests, including dark matter-nucleon scattering and production of @f at the LHC, where future searches for anomalous charged tracks may be sensitive. The first LHC hints could come from the Higgs sector, where loop corrections involving @f lead to significantly modified h ->@?@c@c and h@?->@?@cZ branching ratios.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Testing the parametric model for self-interacting dark matter using matched halos in cosmological simulations
Here, we systemically evaluate the performance of the self-interacting dark matter (SIDM) halo model proposed in Ref.Yang et al. (2023) with matched halos from high-resolution cosmological CDM and SIDM simulations. The model incorporates SIDM effects along mass evolution histories of CDM halos and it is applicable to both isolated halos and subhalos. We focus on the accuracy of the model in predicting halo density profiles at z = 0 and the evolution of maximum circular velocity. We find the model predictions agree with the simulations within 10%–50% for most of the simulated (sub)halos, 50%–100% for extreme cases. This indicates that the model effectively captures the gravothermal evolution of the halos with very strong, velocity-dependent self-interactions. For an example application, we apply the model to study the impact of various SIDM scenarios on strong lensing perturber systems, demonstrating its utility in predicting SIDM effects for small-scale structure analyses. Our findings confirm that the model is an effective tool for mapping CDM halos into their SIDM counterparts.
Yang, Daneng [Chinese Academy of Sciences, Nanjing↗
Cost optimization of low-salt-rejection reverse osmosis
Low-salt-rejection reverse osmosis (LSRRO) is an emerging membrane-based desalination technology for concentrating brines with potentially lower energy consumption and cost than thermally driven processes. We assess the technoeconomic performance of LSRRO by using an optimization model that minimizes the levelized cost of water (LCOW). We develop detailed models for the RO module and other equipment using WaterTAP (Water treatment Technoeconomic Assessment Platform), an open-source simulation and optimization software for water treatment. We improve upon previous LSRRO cost optimization studies by optimizing the salt permeability for each stage, which has significant implications for technoeconomic performance. We present the cost-optimal design, operation, and performance for three cases: 35 g/L TDS feed at 70% recovery, 70 g/L at 55%, and 125 g/L at 35%. The cost-optimal LCOW and specific energy consumption (SEC) for these cases are 0.70, 1.89, and 7.41 $/m 3 and 3.9, 8.4, and 30.4 kWh/m 3 , respectively. For each case, we use sensitivity analyses to provide guidance on key factors that impact economic viability. We also present the cost-optimal LCOW and SEC for feed concentrations between 5 and 220 g/L TDS and recoveries between 30 and 90%. So, we compare LSRRO to other high-salinity desalination technologies and find that it may be cost-competitive below feed concentrations of 125 g/L.
54 ENVIRONMENTAL SCIENCES↗
Minimally invasive healing of bone implant-cement interfaces by aerogel cement and remote heating
Not Available
Lavelle, Cole↗
Operando Analyses of Solar Fuels Light Absorbers and Catalysts
Operando synchrotron radiation photoelectron spectroscopy in the tender X-ray energy range has been used to obtain information on the energy-band relations of semiconductor and metal-covered semiconductor surfaces while in direct contact with aqueous electrolytes under potentiostatic control. The system that was investigated consists of highly doped Si substrates that were conformally coated with ~70 nm titania films produced by atomic-layer deposition. TiO 2 /electrolyte and Si/TiO 2 /Ni/electrolyte interfaces were then analyzed by synchrotron radiation photoelectron spectroscopy. The PES data allows for determination of the flat-band position and identification of potential regions in which Fermi level pinning, depletion, or accumulation occurred. Operando X-ray absorption spectroscopy (XAS) techniques were additionally used to investigate the properties of heterogeneous electrocatalysts for the oxygen-evolution reaction. Operando XAS including the pre-edge, edge and EXAFS regions allowed the development of a detailed picture of the catalysts under operating conditions, and elucidated the changes in the physical and electronic structure of the catalyst that accompanied increases in the applied potential. Specifically, XAS data, combined with DFT studies, indicated that the activity of the electrocatalyst correlated with the formation of Fe dopant sites in γ-NiOOH.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗