Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “transference number”

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 1,513 records · Page 84
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Heterobimetallic multi-site concerted proton electron transfer (MS-CPET) promotes coordination-induced O–H bond weakening

Coordination-induced bond weakening of X–H bonds (X = O, N, C) has been observed in a number of low-valent transition metal compounds. However, the impact of an appended electron reservoir on the bond dissociation free energy of the O–H bond (BDFE O–H ) of a substrate bound to a d 0 metal is poorly understood. To gain insight into the ability of separated deprotonation and oxidation sites to decrease the BDFE O–H during proton-coupled electron transfer (PCET) reactions, a bimetallic system in which the sites of proton and electron loss are two distinct metal sites is described. Herein, the interconversion of tris(phosphinoamide) Zr/Co complexes HO–Zr(MesNP i Pr 2 ) 3 CoCN t Bu and O$≡$Zr(MesNP i Pr 2 ) 3 CoCN t Bu via hydrogen atom addition/abstraction was studied. Since the Zr center remains in the d 0 Zr IV state throughout these transformations, the electron transfer process is mediated by the appended redox-active Co 0/I center. A series of open-circuit potential (OCP) measurements on the HO–Zr(MesNP i Pr 2 ) 3 CoCN t Bu and O$≡$Zr(MesNP i Pr 2 ) 3 CoCN t Bu complexes was performed, from which the BDFE O–H was found to be 64 ± 1 kcal mol −1 . The BDFE O–H value was further verified through a series of stoichiometric H atom transfer reactions, stoichiometric protonation/deprotonation reactions, and computational studies.

Chemistry↗
NASA NTRS1989

Pressure and heat transfer investigation of a modified NASP baseline configuration at M = 6

A cooperative NASA Langley-Boeing investigation was conducted in the Langley eight-Foot High Temperature Tunnel to obtain hypersonic pressure and heat transfer data. In this investigation a large scale (1/20), modified version of the National Aero-Space Plane configuration known as the 'Government Baseline' was tested at a nominal Mach number of 6; at two Reynolds numbers (0.6 and 1.6 million per foot); and at angles of attack from about 0 to 15 deg. There were several purposes for the investigation: to provide a windward and leeward pressure and heat transfer data base for a realistic configuration for verification of computational methods, to provide these data for a large-scale model, and to provide these data for true temperature conditions because of concern about data from low temperature tunnels.

Reubush, David E.↗
NASA NTRS1975

Calculation of the flow on a blunted cone at high angle of attack

A new technique for calculating the entire flow-field on spherically blunted circular cones at high angles of attack and high Reynolds numbers is described. The calculations are based on a single-layer system of three-dimensional parabolic equations which are approximations to the full steady Navier-Stokes equations. Initial conditions at the sphere-cone tangency plane are provided by using an inviscid time-dependent solution added to a viscous nonsimilar boundary layer solution. Calculated results are compared with experimental heat transfer and pressure data for a 15 deg half-angle cone with a 1-in. spherical nose at 15 deg angle of attack. The free-stream Mach number is 10.6, and the free-stream Reynolds number is 1,200,000 per foot. Excellent agreement between the calculated and experimental data for both pressure and heat transfer is obtained.

Lubard, S. C.↗
DOE OSTI2023

Separation of volatile organic contaminants from water using a direct-contact dehumidifier: An experimental study and modeling

A packed-bed, direct-contact dehumidifier (PB-DCD) has been developed to selectively condense water from a gaseous mixture of organic contaminants. In this process, hot air is humidified by mixing with wastewater vapor in a static mixer and is dehumidified by subcooled fresh water passing through the PB-DCD in a counter-current configuration. The goal of this study is to understand the relationship between various operating parameters and how they affect separation of isopropyl alcohol (IPA) from the gaseous mixture with the purpose of expanding the investigation to other contaminants in the future. The process has been successfully used to separate IPA from wastewater. Different parameters including the concentration of IPA in water, feed temperature, air-to-vapor ratio, and cooling-water-to-vapor mass ratio, as well as residence time were examined to determine their influence on contaminant separation and clean water recovery rate. The Buckingham Pi theorem was applied to reduce the number of studying variables by generating non-dimensional groups. Experiments were performed to determine the relationship between the non-dimensional parameters and model mass transfer in the system. Mass transfer coefficients for multicomponent gaseous mixture condensation in a packed-bed direct contact condenser column are calculated and a modified correlation of mass transfer is developed. The parameters of the mass transfer model are obtained from operating conditions of the PB-DCD, stream conditions and thermodynamic properties. A modified Sherwood correlation is developed to predict IPA separation from a gaseous mixture consisting of air, water, and IPA. The modified Sherwood correlation is applied to successfully predict the Sherwood number with a mean absolute error equal to 6.6 %. According to the experimental results, the maximum IPA separation achieved was 76.3 %, which corresponds to a water recovery rate of 58.7 %.

42 ENGINEERING↗
DOE OSTI2021

A large-scale comparison of Artificial Intelligence and Data Mining (AI&DM) techniques in simulating reservoir releases over the Upper Colorado Region

In recent years, the Artificial Intelligence and Data Mining (AI&DM) models have become popular tools in assisting various aspects of reservoir operation. However, the practical uses are still rarely reported. Comparison experiment of many AI&DM models over a large number of reservoir cases is particularly valuable to help reservoir operators first examine the usefulness and transferability of different AI&DM models, and then identify the most stable and reliable AI&DM model in assist of various decision-making processes. In this study, a total of 12 AI&DM models with different parameterizations and simulation scenarios are comprehensively tested out and compared in simulating the controlled reservoir outflows of 33 reservoir cases over the Upper Colorado Region, United States. Results show that the Random Forecast and the Long-Short-Term-Memory model could consistently derive the best statistical performance than other models under the baseline simulation scenario. The employed AI&DM models could obtain satisfactory statistical interquartile ranges (25–75%) between [0.6–0.9], [0.3–0.8], and [0.2–0.8], for CORR, NSE, and KGE measurements, respectively, and [1.5–6.5], [–15 to 20], and [0.5–8.5] for the normalized RMSE, PBIAS and RSR measurements, respectively. Results also show Multi-Layer Perceptron model and Extreme Gradient Boosting Tree Algorithm produced more stable and superior performance than other models under more complex input scenarios. We also found that the performance of different AI&DM models are closely relevant to the reservoir elevations, sizes, and functionalities. Discussions were made about the sensitivity of AI&DM models’ parameterizations and the key advantages of AI&DM models over the rule-based reservoir models. We further identify that the main advantage of AI&DM models is the flexibility in designing input structures, whereas the rule-based simulation model is rather limited. Future studies were suggested regarding the best way reservoir operators and researchers could use, select, and apply different AI&DM models in simulating reservoir releases under different natural and modeling environments. Finally, this comparison study also serves as a reference and a piece of groundwork for further promoting the practical uses of AI&DM models in assisting reservoir operation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Fano Resonance in CO 2 Reduction Catalyst Functionalized Quantum Dots

Molecular catalyst functionalized semiconductor quantum dots (QDs) are a promising modular platform for developing novel hybrid photocatalysts. The interaction between adsorbed catalyst vibrations and the QD electron intraband absorption can influence the photophysical properties of both the QD and the catalysts and potentially their photocatalysis. In CdSe QDs functionalized by the CO 2 reduction catalyst, Re(CO) 3 (4,4’-bipyridine-COOH)Cl, we observe that the transient Fano resonance signal resulting from coupling of the catalyst CO stretching mode and the QD conduction band electron mid-IR intraband absorption appears on an ultrafast time scale and decays with the electron population, irrespective of the occurrence of photoreduced catalysts. The Fano asymmetry factor increases with an increase in the adsorbed catalyst number and a decrease in QD sizes. The latter can be attributed to an enhanced charge transfer interaction between the more strongly quantum-confined QD conduction band and catalyst LUMO levels. These results provide a more in-depth understanding of interactions in excited QD-catalyst hybrid photocatalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Molecular catalyst coordinatively bonded to organic semiconductors for selective light-driven CO 2 reduction in water

The selective photoreduction of CO 2 in aqueous media based on earth-abundant elements only, is today a challenging topic. Here we present the anchoring of discrete molecular catalysts on organic polymeric semiconductors via covalent bonding, generating molecular hybrid materials with well-defined active sites for CO 2 photoreduction, exclusively to CO in purely aqueous media. The molecular catalysts are based on aryl substituted Co phthalocyanines that can be coordinated by dangling pyridyl attached to a polymeric covalent triazine framework that acts as a light absorber. This generates a molecular hybrid material that efficiently and selectively achieves the photoreduction of CO 2 to CO in KHCO 3 aqueous buffer, giving high yields in the range of 22 mmol g -1 (458 μmol g -1 h -1 ) and turnover numbers above 550 in 48 h, with no deactivation and no detectable H 2 . The electron transfer mechanism for the activation of the catalyst is proposed based on the combined results from time-resolved fluorescence spectroscopy, in situ spectroscopies and quantum chemical calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Physics-informed neural network with transfer learning (TL-PINN) based on domain similarity measure for prediction of nuclear reactor transients

Nuclear reactor safety and efficiency can be enhanced through the development of accurate and fast methods for prediction of reactor transient (RT) states. Physics informed neural networks (PINNs) leverage deep learning methods to provide an alternative approach to RT modeling. Applications of PINNs in monitoring of RTs for operator support requires near real-time model performance. However, as with all machine learning models, development of a PINN involves time-consuming model training. Here, we show that a transfer learning (TL-PINN) approach achieves significant performance gain, as measured by reduction of the number of iterations for model training. Using point kinetic equations (PKEs) model with six neutron precursor groups, constructed with experimental parameters of the Purdue University Reactor One (PUR-1) research reactor, we generated different RTs with experimentally relevant range of variables. The RTs were characterized using Hausdorff and Fréchet distance. We have demonstrated that pre-training TL-PINN on one RT results in up to two orders of magnitude acceleration in prediction of a different RT. The mean error for conventional PINN and TL-PINN models prediction of neutron densities is smaller than 1%. We have developed a correlation between TL-PINN performance acceleration and similarity measure of RTs, which can be used as a guide for application of TL-PINNs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Quantifying dislocation-type defects in post irradiation examination via transfer learning

The quantitative analysis of dislocation-type defects in irradiated materials is critical to materials characterization in the nuclear energy industry. The conventional approach of an instrument scientist manually identifying any dislocation defects is both time-consuming and subjective, thereby potentially introducing inconsistencies in the quantification. This work approaches dislocation-type defect identification and segmentation using a standard open-source computer vision model, YOLO11, that leverages transfer learning to create a highly effective dislocation defect quantification tool while using only a minimal number of annotated micrographs for training. This model demonstrates the ability to segment both dislocation lines and loops concurrently in micrographs with high pixel noise levels and on two alloys not represented in the training set. Inference of dislocation defects using transmission electron microscopy on three different irradiated alloys relevant to the nuclear energy industry are examined in this work with widely varying pixel noise levels and with completely unrelated composition and dislocation formations for practical post irradiation examination analysis. Code and models are available at https://github.com/idaholab/PANDA.

36 MATERIALS SCIENCE↗
DOE OSTI2022

FINETUNA: fine-tuning accelerated molecular simulations

Abstract Progress towards the energy breakthroughs needed to combat climate change can be significantly accelerated through the efficient simulation of atomistic systems. However, simulation techniques based on first principles, such as density functional theory (DFT), are limited in their practical use due to their high computational expense. Machine learning approaches have the potential to approximate DFT in a computationally efficient manner, which could dramatically increase the impact of computational simulations on real-world problems. However, they are limited by their accuracy and the cost of generating labeled data. Here, we present an online active learning framework for accelerating the simulation of atomic systems efficiently and accurately by incorporating prior physical information learned by large-scale pre-trained graph neural network models from the Open Catalyst Project. Accelerating these simulations enables useful data to be generated more cheaply, allowing better models to be trained and more atomistic systems to be screened. We also present a method of comparing local optimization techniques on the basis of both their speed and accuracy. Experiments on 30 benchmark adsorbate-catalyst systems show that our method of transfer learning to incorporate prior information from pre-trained models accelerates simulations by reducing the number of DFT calculations by 91%, while meeting an accuracy threshold of 0.02 eV 93% of the time. Finally, we demonstrate a technique for leveraging the interactive functionality built in to Vienna ab initio Simulation Package (VASP) to efficiently compute single point calculations within our online active learning framework without the significant startup costs. This allows VASP to work in tandem with our framework while requiring 75% fewer self-consistent cycles than conventional single point calculations. The online active learning implementation, and examples using the VASP interactive code, are available in the open source FINETUNA package on Github.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Electrophotocatalytic perfluoroalkylation by LMCT excitation of Ag(II) perfluoroalkyl carboxylates

Molecular Ag(II) complexes are superoxidizing photoredox catalysts capable of generating radicals from redox-reticent substrates. In this work, we exploited the electrophilicity of Ag(II) centers in [Ag(bpy) 2 (TFA)][OTf] and Ag(bpy)(TFA) 2 (bpy, 2,2′-bipyridine; OTf, CF 3 SO 3 – ) complexes to activate trifluoroacetate (TFA) by visible light–induced homolysis. The resulting trifluoromethyl radicals may react with a variety of arenes to forge C(sp 2 )–CF 3 bonds. This methodology is general and extends to other perfluoroalkyl carboxylates of higher chain length (R F CO 2 – ; R F , CF 2 CF 3 or CF 2 CF 2 CF 3 ). The photoredox reaction may be rendered electrophotocatalytic by regenerating the Ag(II) complexes electrochemically during irradiation. Electrophotocatalytic perfluoroalkylation of arenes at turnover numbers exceeding 20 was accomplished by photoexciting the Ag(II)–TFA ligand-to-metal charge transfer (LMCT) state, followed by electrochemical reoxidation of the Ag(I) photoproduct back to the Ag(II) photoreactant.

Science & Technology - Other Topics↗
DOE OSTI2021

Initial use of Nek5000/Cardinal to improve closure models in Pronghorn

Heat transfer coefficient closure models for pebble bed reactors are built using a data-driven approach by leveraging the capabilities of an Evolutionary Algorithm entitled Particle Swarm Optimization (PSO). In the present work, the Computational Fluid Dynamics code nekRS was used in order to collect the high-fidelity flow data for a core with 1,568 pebbles. To characterize the heat transfer, multiple concentric regions were considered to extract the physical quantities of interest, e.g./ the Reynolds number. The PSO algorithm is employed as part of an inverse problem targeting determine what are the coefficients for a Nusselt number correlation to match the collected data. Such correlation should follow any given format that is defined a priori. Finally, two correlations are proposed, one with an implicit dependence on the pebbles’ wall temperatures and another expressed as a fully explicit correlation depending on the flow conditions and the position within the core. Anyway, given the generic nature of the proposed approach, correlations following different formats could be tested. Preliminary results for the high-fidelity simulation of a fast MSR core are presented. The target Reynolds number is currently 20K, with the expectation that this will increase, pending the availability of further computational resources. These simulations will be used to inform lower fidelity models, including a coarse CFD turbulence model in Pronghorn. Additionally, they will serve as a reference for the RANS models in Nek5000/NekRS.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Accelerated Basin De-Inventory Maximum Fissile Estimate

The Accelerated Basin De-inventory (ABD) program increases processing of Spent Nuclear Fuel (SNF) in H-Canyon to accelerate the closure of L Basin. The dissolved SNF will be dispositioned to Sludge Batches that will be processing in the Defense Waste Processing Facility and converted to glass. The fissile glass loading directly impacts the amount of glass canisters made. This memorandum provides an estimated maximum quantity of total fissile expected to be transferred to a Sludge Batch to support increasing the fissile glass loading and thus reducing the number of canisters created across the lifetime of the ABD mission.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Microphysics regimes due to haze–cloud interactions: cloud oscillation and cloud collapse

Abstract. It is known that aqueous haze particles can be activated into cloud droplets in a supersaturated environment. However, haze–cloud interactions have not been fully explored, partly because haze particles are not represented in most cloud-resolving models. Here, we conduct a series of large-eddy simulations (LESs) of a cloud in a convection chamber using a haze-capable Eulerian-based bin microphysics scheme to explore haze–cloud interactions over a wide range of aerosol injection rates. Results show that the cloud is in a slow microphysics regime at low aerosol injection rates, where the cloud responds slowly to an environmental change and droplet deactivation is negligible. The cloud is in a fast microphysics regime at moderate aerosol injection rates, where the cloud responds quickly to an environmental change and haze–cloud interactions are important. More interestingly, two more microphysics regimes are observed at high aerosol injection rates due to haze–cloud interactions. Cloud oscillation is driven by the oscillation of the mean supersaturation around the critical supersaturation of aerosol due to haze–cloud interactions. Cloud collapse happens under weaker forcing of supersaturation where the chamber transfers cloud droplets to haze particles efficiently, leading to a significant decrease (collapse) in cloud droplet number concentration. One special case of cloud collapse is the haze-only regime. It occurs at extremely high aerosol injection rates, where droplet activation is inhibited, and the sedimentation of haze particles is balanced by the aerosol injection rate. Our results suggest that haze particles and their interactions with cloud droplets should be considered, especially in polluted conditions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1972

Discrete sonic jets used as boundary-layer trips at Mach numbers of 6 and 8.5

The effect of discrete three-dimensional sonic jets used to promote transition on a sharp-leading-edge flat plate at Mach numbers of 6 and 8.5 and unit Reynolds numbers as high as 2.5 x 100,000 per cm in the Langley 20-inch hypersonic tunnels is discussed. An examination of the downstream flow-field distortions associated with the discrete jets for the Mach 8.5 flow was also conducted. Jet trips are found to produce lengths of turbulent flow comparable to those obtained for spherical-roughness-element trips while significantly reducing the downstream flow distortions. A Reynolds number based upon secondary jet penetration into a supersonic main flow is used to correlate jet-trip effectiveness just as a Reynolds number based upon roughness height is used to correlate spherical-trip effectiveness. Measured heat-transfer data are in agreement with the predictions.

Stone, D. R.↗
NASA NTRS1972

Heat transfer and pressure distributions at M equals 8 on 0.029 scale models of the Viking entry vehicle

An investigation in the Langley Research Center Mach-8 Variable Density Hypersonic Tunnel was made of the pressure distributions and heat transfer rate distributions on two 0.029 scale Viking Entry Vehicle models. Comparable ranges of test Reynolds number were exercised for the two tests between run conditions around 4 million and conditions of about 1.6 million. At angles of attack less than 20 degrees the pressure ratio distribution referenced to stagnation pressure appeared invariant with Reynolds number. Increasing angle of attack results in a flatter distribution of both the windward and leeward pressure distributions; in addition, the stagnation point shifted into the windward plane. A subsequent rise in the heating rate profile on the leeward side with further increase in angle of attack is attributed to boundary layer natural transition to turbulent flow. Schlieren photographs were taken for flow field visualization and to correct model angle of attack.

Faye-Petersen, R.↗
NASA NTRS1974

Weak incident shock interactions with Mach 8 laminar boundary layers

Weak shock-wave interactions with boundary layers on a flat plate were investigated experimentally in Mach 8 variable-density tunnel for plate-length Reynolds numbers. The undisturbed boundary layers were laminar over the entire plate length. Pressure and heat-transfer distributions were obtained for wedge-generated incident shock waves that resulted in pressure rises ranging from 1.36 to 4.46 (both nonseparated and separated boundary-layer flows). The resulting heat-transfer amplifications ranged from 1.45 to 14. The distributions followed established trends for nonseparated flows, for incipient separation, and for laminar free-interaction pressure rises. The experimental results corroborated established trends for the extent of the pressure rise and for certain peak heat-transfer correlations.

Kaufman, L. G., II↗