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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Structural Tuning of Self‐Conductive Polymer as Gas Diffusion Layer for Electrocatalytic Reactions at High Current

Electrocatalytic conversions offer a promising route for sustainable chemical production using renewable energy. Gas diffusion layers (GDLs) enable selective product formation at high current densities but suffer from electrolyte flooding, and polytetrafluoroethylene (PTFE)-based GDLs typically require metal conductive layers, which constrain catalyst development. A recently developed GDL configuration, electropolymerized poly(3,4-ethylenedioxythiophene) (PEDOT)-coated PTFE, demonstrates notable flooding resistance, but suffers from gas diffusion limitations at elevated currents due to limited gas diffusion through the PEDOT layer. Here, different dopants in PEDOT are exploited to modify the physical properties and enhance gas transport. ClO 4 − -doped PEDOT exhibits superior performance due to optimized physical structure, leading to increased gas permeance and faradaic efficiency (FE) for CO production during electrocatalytic CO 2 reduction. Further optimization of coverage and thickness achieved by adjusting charge density led to an optimal configuration at 33 mC cm −2 . This GDL supports various metal electrocatalysts and demonstrates FE CO of > 90% for over 150 h at −200 mA cm −2 using a commercial silver electrocatalyst. This work highlights the importance of GDL engineering in enhancing performance and durability for long-term electrocatalytic processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A GPU Accelerated Mixed‐Precision Finite Difference Informed Random Walker (FDiRW) Solver for Strongly Inhomogeneous Diffusion Problems

In nature, many complex multi‐physics coupling problems exhibit significant diffusivity inhomogeneity, where one process occurs several orders of magnitude faster than others temporally. Simulating rapid diffusion alongside slower processes demands intensive computational resources due to the necessity for small time steps. To address these computational challenges, we have developed an efficient numerical solver named Finite Difference informed Random Walker (FDiRW). In this study, we propose a GPU‐accelerated, mixed‐precision configuration for the FDiRW solver to maximize efficiency through GPU multi‐threaded parallel computation and lower precision computation. Numerical evaluation results reveal that the proposed GPU‐accelerated mixed‐precision FDiRW solver can achieve a 117× speedup over the CPU baseline, while an additional 1.75× speedup is achieved by employing lower precision GPU computation. Notably, for large model sizes, the GPU‐accelerated mixed‐precision FDiRW solver demonstrates strong scaling with the number of nodes used in simulation. When simulating radionuclide absorption processes by porous wasteform particles with a medium‐sized model of 192 × 192 × 192, this approach reduces the total computational time to 10 min, enabling the simulation of larger systems with strongly inhomogeneous diffusivity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Grassmannian diffusion maps based surrogate modeling via geometric harmonics

Abstract A novel surrogate model based on the Grassmannian diffusion maps (GDMaps) and utilizing geometric harmonics (GH) is developed for predicting the response of complex physical phenomena. The method utilizes GDMaps to obtain a low‐dimensional representation of the underlying behavior of physical/mathematical systems with respect to uncertain input parameters. Using this representation, GH, an out‐of‐sample extension technique, is employed to create a global map from the input parameter space to a Grassmannian diffusion manifold. GH is further employed to locally map points on the diffusion manifold onto the tangent space of a Grassmann manifold. The exponential map is then used to project the points in the tangent space onto the Grassmann manifold, where reconstruction of the full solution is performed. The performance of the proposed surrogate model is verified with three examples. The first problem is a toy example used to illustrate the technique. In the second example, errors associated with the various mappings are assessed by studying response predictions of the electric potential of a dielectric cylinder in a homogeneous electric field. The last example applies the method for uncertainty prediction in the strain field evolution in a model amorphous material using the shear transformation zone theory of plasticity.

42 ENGINEERING↗
DOE OSTI2022

Manning condensation in ion exchange membranes: A review on ion partitioning and diffusion models

Here, the rational design of ion exchange membranes (IEMs) is becoming more pertinent as their usage becomes broader and as their staple applications (i.e., electrodialysis, flow batteries, and fuel cells) improve in commercial viability. Such efforts would be catalyzed by an improved fundamental understanding of ion transport in IEMs. This review discusses recent progress in modeling ion partitioning and diffusion in IEMs in an effort to relate IEM performance metrics to fundamental membrane properties over which researchers and membrane manufacturers possess direct and sometimes precise control. Central focus is given to the Donnan-Manning model for ion partitioning and the Manning-Meares model for ion diffusion in IEMs. These two frameworks, which are derived from Manning's counter-ion condensation theory for polyelectrolyte solutions, have been widely used within the IEM literature since their recent introduction. To explore this topic, the mathematical derivation of both models is revisited, followed by a survey of experimental and computational discussions of counter-ion condensation in IEMs. Alternative models which fulfill similar roles in predicting IEM transport properties are compared. This review concludes by highlighting the uniquely favorable positions of the Donnan-Manning and Manning-Meares models and discussing their prospects as leading predictors of IEM partitioning and diffusive properties.

25 ENERGY STORAGE↗
DOE OSTI2021

Predicting Fluid Flow Regime, Permeability, and Diffusivity in Mudrocks from Multiscale Pore Characterisation

In geoenergy applications, mudrocks prevent fluids to leak from temporary (H 2 , CH 4 ) or permanent (CO 2 , radioactive waste) storage/disposal sites and serve as a source and reservoir for unconventional oil and gas. Understanding transport properties integrated with dominant fluid flow mechanisms in mudrocks is essential to better predict the performance of mudrocks within these applications. In this study, small-angle neutron scattering (SANS) experiments were conducted on 71 samples from 13 different sets of mudrocks across the globe to capture the pore structure of nearly the full pore size spectrum (2 nm–5 μm). We develop fractal models to predict transport properties (permeability and diffusivity) based on the SANS-derived pore size distributions. The results indicate that transport phenomena in mudrocks are intrinsically pore size-dependent. Depending on hydrostatic pore pressures, transition flow develops in micropores, slip flow in meso- and macropores, and continuum flow in larger macropores. Fluid flow regimes progress towards larger pore sizes during reservoir depletion or smaller pore sizes during fluid storage, so when pressure is decreased or increased, respectively. Capturing the heterogeneity of mudrocks by considering fractal dimension and tortuosity fractal dimension for defined pore size ranges, fractal models integrate apparent permeability with slip flow, Darcy permeability with continuum flow, and gas diffusivity with diffusion flow in the matrix. This new model of pore size-dependent transport and integrated transport properties using fractal models yields a systematic approach that can also inform multiscale multi-physics models to better understand fluid flow and transport phenomena in mudrocks on the reservoir and basin scale.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Influence of Particle Size on Apparent Diffusivity During Spark Plasma Sintering of Crystalline Powders

Abstract:: A theoretical framework is presented to determine an apparent diffusivity D during powder sintering and further elucidate the underlying interrelation between D and shrinkage behaviors of powders. Furthermore, to eliminate the influence of crystalline defects that are typically present in raw powders, complete crystallization of metallic glass powders is performed to allow the investigation of the influence of particle size solely on powder densification. Furthermore, to validate the framework developed, Ti{sub 40.6}Zr{sub 9.4}Cu{sub 37.5}Ni{sub 9.4}Sn{sub 3.1} crystalline alloy powders constituting particles with different sizes are examined to establish a correlation between D and the sintering behaviors of the powders. The findings show that the value of the apparent diffusivity D increases with the increasing particle size, which accelerates powder densification during spark plasma sintering. Furthermore, the results quantitatively support the argument that particle size can affect atomic diffusion during powder sintering.

36 MATERIALS SCIENCE↗
DOE OSTI2018

A class of efficient difference method for time fractional reaction–diffusion equation

The fractional reaction–diffusion equation has profound physical background and theoretical connotation, and its numerical methods are of great scientific significance and practical value. In this paper, a class of explicit–implicit (E–I) difference method and implicit–explicit (I–E) difference method are proposed for one-dimensional time fractional reaction–diffusion equation. The methods are constructed by combining classical explicit scheme and classical implicit scheme. The existence, uniqueness and convergence of solutions are given for E–I and I–E schemes. Theoretical analysis and numerical experiments show that the E–I and I–E schemes are unconditionally stable, with second-order spatial accuracy and 2−α order temporal accuracy. Compared with the classical implicit difference method with the similar accuracy, the computation time of the presented methods is improved by nearly 41%. It is shown that the E–I difference method and I–E difference method are effective for solving the time fractional reaction–diffusion equation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

ICAT: A numerical scheme to minimize numerical diffusion in advection-dispersion modeling and its application in identifying flow channeling

The advection-dispersion equation for scalar transport is essential for the numerical modeling of many fluid dynamics problems. However, solutions from numerical schemes always suffer from numerical diffusion and/or oscillation. In this study, we develop an Intra-Cell Advection Tracking (ICAT) scheme to minimize numerical diffusion and preserve monotonicity for advection-dispersion modeling. The key idea is to introduce “queues” in each discretized cell, and using a sequential transport rule and a flow distribution mechanism to track the scalar transport in these queues temporally and spatially. The capability and limitations of ICAT are first investigated through three test cases. Compared with the results obtained from other numerical schemes, the results from ICAT show substantially reduced numerical diffusion and agree better with analytical solutions. We also employ ICAT to simulate the transport process of a conservative tracer in a fracture with a highly heterogeneous aperture distribution. We report discrete flow channels in the fracture are better discerned by ICAT than by other numerical schemes, indicating the suitability of ICAT for modeling tracer transport in channelized flow fields.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Performance of fly ash concrete with ferronickel slag fine aggregate against alkali-silica reaction and chloride diffusion

Ferronickel slag (FNS) is an industrial by-product of ferronickel alloy production at a high temperature which can be a promising potential to be used as fine aggregate to produce more sustainable concrete. In this study, the performance of concrete containing ferronickel slag sand and fly ash relating to alkali-silica reaction (ASR) and chloride contamination was investigated. ASR-induced expansion, chloride diffusion resistance, and chloride binding capacity of FNS concrete were determined through concrete prism tests (CPT), accelerated diffusion test, and bulk diffusion test. Thermogravimetric analysis (TGA) was conducted to measure the amount of Portlandite and Friedel's salt in concrete. Concrete with 50 wt% FNS sand as fine natural aggregate replacement and 25 wt% of cement replacement by fly ash showed a remarkable potential to be used not only as a low-carbon concrete with comparable mechanical properties to conventional concrete but also with a better performance against ASR and chloride contamination.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Cs retention and diffusion in C-S-H at different Ca/Si ratio

Cement and concrete are commonly used in the construction of repository sites for radioactive wastes. The correct isolation of those contaminants requires good adsorption and low diffusion rates. Both parameters are highly affected by many factors, such as pH, temperature or composition. The large variability of experimental conditions and formulations makes it extraordinarily difficult to tackle the influence of each of them independently in experimental samples. To this effect, molecular dynamics simulations have been employed in this study to investigate the role of the composition in the capacity to retain Cs and diffusivity of these ions in calcium silicate hydrate (C-S-H) gel pores. The results indicate that the adsorption of Cs ions is worsened at high Ca/Si ratios due to a lower interaction of the cations with the C-S-H surface, while hydrophilicity of the C-S-H nanopore rises, resulting in higher long-range ordering and lower diffusion coefficients of Cs ions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A time-dependent chloride diffusion model for predicting initial corrosion time of reinforced concrete with slag addition

The effect of granulated blast furnace slag (GBFS) addition on the threshold chloride concentration (TCC) for rebar corrosion was investigated. A modified diffusion model, coupled with a time-dependent effective diffusion coefficient and surface chloride concentration, was proposed to predict the chloride profile. The corrosion initiation time was estimated based on the model predictions and the measured TCC. The results indicate that adding GBFS decreases the TCC by lowering the pH value of the pore solution. The evolution of corrosion potential and current density is found to obey a 3-parameter Weibull distribution. MIP tests show that adding GBFS contributes to refinement of pore structure by decreasing the fraction of large capillary pores. The time-dependent model exhibits good predictive strength and helps understand how GBFS addition delays the corrosion initiation by retarding the chloride diffusion, though a lower TCC is obtained.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Nitrogen diffusion in silicate minerals, with implications for nitrogen transport and cycling in the lithosphere

Diffusion laws for nitrogen in orthoclase, quartz, olivine and clinopyroxene were determined using a combination of experimental strategies that included: in-diffusion from C-H-O-N vapor; in-diffusion from molten NH 4 Cl; and ion implantation followed by heating to mobilize N. Most experiments were conducted at pressures near 1 bar, but seven experiments were also run in a piston-cylinder device at P total = 1 GPa. The C-H-O-N-source experiments were “wet” (H 2 O present); those on ion-implanted samples were dry. All N sources were highly enriched in 15 N to avoid spurious 14 N signals resulting from surface contamination. Use of 15 N also enabled depth profiling by nuclear reaction analysis (NRA) using the 15 N(p,αγ) 12 C reaction, which has a detection limit of ~10 ppm. Nitrogen profiles in three samples were characterized by both NRA and SIMS, with similar results.

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

Long carrier diffusion length in two-dimensional lead halide perovskite single crystals

Ruddlesden-Popper (RP) perovskites are two-dimensional semiconductors for high-performance optoelectronic devices. In this work, we report a long in-plane carrier diffusion length in 2D RP perovskite single crystals probed by scanning photocurrent microscopy. Carrier diffusion lengths of 7–14μm are observed when the number of PbI 6 –2 octahedra between organic spacers increases from 1 to 3. Using detailed light intensity and electric-field-dependent photocurrent measurements, we attribute the observed long diffusion length to the dominating dissociated free carrier transport. This is further validated by time-resolved photoluminescence measurements, where the decay lifetime increases in the presence of an electric field. From our experiments, we conclude that the in-plane transport in RP perovskites is efficient because of the partial free carrier generation, which overcomes strong excitonic effects. In conclusion, our results suggest that semiconducting devices fabricated from RP perovskite single crystals can be as efficient as their 3D counterparts.

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

Boron capture stabilizing the diffusion barriers in a two-step Mo-Si-B coated refractory multi-principal element alloy

A Mo-Si-B based coating has been applied on a refractory multi-principal element alloy (RMPEA) using a two-step coating strategy and has endured more than 750 hours of thermal cycling oxidation exposure between room temperature to 1300°C with a minimal weight change. Here, the formation of the Mo 5 SiB 2 diffusion barrier in the coating prevents the inward diffusion of the coating components. Then, boron is captured by the RMPEA, resulting in the formation of a solid solution X 5 SiB 2 (X= W, Mo, Ta, Nb, V) layer and an RMPEA-B boride layer underneath it that acts as the second diffusion barrier.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Conditional distribution estimation of building characteristics with diffusion models for urban energy modeling

Understanding current energy consumption behavior in communities is critical for informing future energy use decisions and enabling efficient energy management. Urban energy models, which are used to simulate these energy use patterns, require large datasets with detailed building characteristics for accurate outcomes. However, such detailed characteristics at the individual building level are often unknown and costly to acquire, or unavailable. Through this work, we propose using a generative modeling approach to generate realistic building attributes to fill in the data gaps and finally provide complete characteristics as inputs to energy models. Our model learns complex, building-level patterns from training on a large-scale residential building stock model containing 2.2 million buildings. We employ a tabular diffusion-based framework that is designed to handle heterogeneous (discrete and continuous) features in tabular building data, such as occupancy, floor area, heating, cooling, and other equipment details. We develop a capability for conditional diffusion, enabling the imputation of missing building characteristics conditioned on known attributes. We conduct a comprehensive validation of our conditional diffusion model, firstly by comparing the generated conditional distributions against the underlying data distribution, and secondly, by performing a case study for a Baltimore residential region, showing the practical utility of our approach. Our work is one of the first to demonstrate the potential of generative modeling to accelerate building energy modeling workflows.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Adaptive Interface-PINNs (AdaI-PINNs) for transient diffusion: Applications to forward and inverse problems in heterogeneous media

We model transient diffusion in heterogeneous materials using a novel physics-informed neural networks framework (PINNs) termed Adaptive interface physics-informed neural networks or AdaI-PINNs (Roy et al. arXiv preprint arXiv:2406.04626, 2024). AdaI-PINNs utilize different activation functions with trainable slopes tailored to each material region within the computational domain, allowing for a fully automated and adaptive PINNs approach to model interface problems with strongly and weakly discontinuous solutions. To enhance its performance in highly heterogeneous transient diffusion systems, we prescribe a suite of robust practices, including appropriate non-dimensionalization of equations, a biased sampling method, Glorot initialization, and the hard enforcement of boundary and initial conditions. Here we evaluate the efficacy of the proposed method on several benchmark forward and inverse problems. Comparative studies on one-dimensional and two-dimensional benchmark problems reveal that the modified AdaI-PINNs outperform its unmodified counterpart, achieving root-mean-square errors that are at least two orders of magnitude better in forward problems. For inverse problems, the maximum errors in the approximated diffusion coefficients by modified AdaI-PINNs are four orders of magnitude better than those of the unmodified version. Additionally, modified AdaI-PINNs demonstrate improved stability in problems with large material mismatches.

42 ENGINEERING↗
DOE OSTI2023

Acceleration of Pd-V intermetallic diffusion by hydrogen

Vanadium-based membranes have great potential for hydrogen purification due to their perfect selectivity, high permeability, and relatively low cost. With appropriate surface cleaning, V efficiently permeates hydrogen at elevated temperature, but performance declines due to its affinity to absorb impurities. Here, the application of palladium thin films maintains a clean surface that catalyzes hydrogen dissociation and recombination. Hydrogen permeation in Pd-V-Pd membranes initially reach theoretical permeability, but declines due to Pd-V interdiffusion. The objective of this work was to quantify the intermetallic diffusion process as a function of temperature and ambient. Pd-V composites were subjected to various annealing treatments and characterized using Auger electron spectroscopy, X-ray diffraction, and energy dispersive X-ray spectroscopy, as well as correlated to measurements of membrane permeability. In an inert environment Pd-V interdiffusion was observable as low as 300 °C, and the diffusion coefficient had an activation energy of 44 kJ/mol. Furthermore, the presence of hydrogen at partial pressures > 10 kPa accelerated interdiffusion six-fold at T = 400 °C. Membrane performance degraded with an activation energy 75 kJ/mol, suggesting that intermetallic diffusion leads to both a loss of catalytic activity and as well as degradation of bulk permeability. These findings provide a baseline for evaluating hydrogen permeable interdiffusion barriers to overcome these challenges.

08 HYDROGEN↗
DOE OSTI2024

High resolution identification and quantification of diffuse deep groundwater discharge in mountain rivers using continuous boat-mounted helium measurements

Discharge of deeply sourced groundwater to streams is difficult to locate and quantify, particularly where both discrete and diffuse discharge points exist, but diffuse discharge is one of the primary controls on solute budgets in mountainous watersheds. The noble gas helium is a unique identifier of deep groundwater discharge because groundwater with long residence times is commonly enriched in helium. In this study, a portable mass spectrometer was used to measure longitudinal variation in dissolved helium concentrations in two mountainous rivers at high spatial resolution not feasible with traditional sampling techniques. Helium profiles were then simulated using a mass-balance model to quantify longitudinal variation in groundwater discharge to the receiving rivers. Results indicate helium concentrations were enriched by multiple orders of magnitude above atmospheric equilibrium in both rivers and that this persisted for up to 18 km below observed pulse inputs in the Colorado River. Helium mass-balance models match observed longitudinal patterns with the exception of sharp initial increases in helium observed in the rivers. Increased longitudinal groundwater discharge rates correspond to mapped geologic structures in both watersheds that likely transport deep geothermal water. Models show variable sensitivity to spatial assignment of input variables representing the groundwater source, illustrating the importance of collecting data from discrete groundwater discharges where possible. The methodology shows promise for field experiments designed to assess air–water exchange rates and to quantify total groundwater discharge from a combination of discrete and diffuse sources.

Deep groundwater↗