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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Comparative Study of Nonlinear Black-Box Modeling for Power Electronics Converters

With the increasing penetration level of renewable sources and power electronics loads in modern power systems, accurate and computationally efficient models are needed. Black-box model (BBM) could be a useful method in such systems. However, not very extensive research efforts have been made for power electronics BBM so far, and existing works mostly focus on steady-state operation, neglecting the important transient behaviors such as load transients, voltage transients, and faults. This paper presents a comparative study of three commonly used nonlinear BBM approaches for transient behaviors of power electronics converters. Comparison methods are proposed, and the evaluations are conducted under different transients using a grid-connected single-phase photovoltaic inverter. The findings of this study provide valuable references for further feasibility investigations on implementing BBMs in large-scale power electronics-rich power systems.

Qiao, Liang↗
DOE OSTI2022

The response of accident tolerant fuel cladding to LOCA burst testing: A comparative study of leading concepts

Accident tolerant fuel claddings seek to improve safety margins during loss-of-coolant accident (LOCA) scenarios by reducing cladding steam oxidation rates, hydrogen production, delaying or mitigating cladding burst, and reducing cladding deformation. After rapid and extensive progress in recent years, Cr coated Zr-based alloys, ferritic FeCrAl, and SiC/SiC ceramic matrix composites (CMCs) have emerged as the most promising cladding concepts, but there are few studies comparing the accident tolerance of all three concepts simultaneously. Here, in this study, LOCA burst testing has been conducted on Cr high impulse power magnetron sputtered Zry-4 (Cr/Zry-4), C26M (FeCrAl), and SiC/SiC SiGA (CMC). Experimental observations indicate Cr/Zry-4 burst at higher temperatures than Zry-4 however the effectiveness of the coating on mitigating ZrO 2 formation via Cr 2 O 3 formation depended on physical proximity to the burst opening. No dramatic improvement in diametrical strain nor opening geometry was observed. C26M burst at higher temperatures than Zry-4 at lower pressures, exhibiting small amounts of strain and smaller openings, and formed slow-growing Al 2 O 3 regardless of location. The CMCs did not burst or show any macroscopic signs of deformation, all the way up to an internal cladding pressure of 15.2 MPa. High temperature expanding Nb plug testing indicated that CMC room temperature strength may persist up to 1650°C, with an approximately 40% loss of strength at 1900°C. CMC steam oxidation was also compared to reference chemically vapor deposited SiC up to 1700°C, and CMCs were found to oxidize at slightly accelerated rates that were still orders of magnitude lower than bare Zry-4. Projected H 2 generation during LOCA burst testing showed that when accounting for cladding burst exposing inner diameters to steam, Cr coated Zry-4 reduced H 2 generation by a factor of two compared with bare Zry-4, while both C26M and SiC/SiC reduced H 2 generation by orders of magnitude relative to bare Zry-4.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Comparative Study of Redox Mediators for Improved Performance of Li-Oxygen Batteries.

Redox meditators (RMs) are soluble catalysts located in an electrolyte that can improve the energy efficiency (reduced overpotential) and cyclability of Li-oxygen (Li-O-2) batteries. In this work, 20 RMs within a Li-O-2 system with dimethyl sulfoxide and tetraethylene glycol dimethyl ether electrolytes are studied and their electrochemical features such as redox potential, the separation of cathodic and anodic peaks, and their current intensities are measured using cyclic voltammetry (CV) experiments. Six RMs are selected as "primary" choices based on their electrochemical performance, and stability tests are then performed to examine their electrochemical responses after consecutive cycles. Moreover, galvanostatic cycling tests are performed within a Li-O-2 battery system assembled with selected six RMs for real case consistency investigations. It is found that results from CV to galvanostatic cycling tests are consistent for halides and organometallic RMs, where the former exhibit much higher stability. However, the organic RMs show high reversibility in CV but low in battery cycling results. Density functional theory calculations are carried out to gain more understanding of the stability and redox potentials of the RMs. This study provides comparative information to select the most reliable RMs for Li-O-2 batteries along with new fundamental understanding of their electrochemical activity and stability.

cyclic voltammetry↗
DOE OSTI2019

New trends in solar: A comparative study assessing the attitudes towards the adoption of rooftop PV

The factors affecting the adoption of conventional solar PV have been broadly addressed in the recent literature. However, it is still to determine the public's acceptance of innovations of the traditional solar PV architecture. Building applied photovoltaic technology (BAPV) is a technological innovation that can be installed over existing building surfaces. This study compares an evaluation of the conscious and subconscious attitudinal, control and normative beliefs of American homeowners when randomly primed with two brochures depicting the purchasing, installation, and commissioning of solar PV systems, developed according to the characteristics of conventional and an adhesive “plug and play” BAPV system. The survey instrument (N = 400 survey participants) was designed in consonance to the Theory of Planned Behavior (TPB). When comparing the direct measures for each solar PV technology, no significant differences were found. This may indicate that for those unfamiliar with PV technology, placing an adhesive backing module on the roof is standard procedure and does not impact purchasing intentions. The evaluation further showed that unlike the subconscious control beliefs, social norms and attitudes have a significant impact on forming intentions to adopt solar PV. The implications of these findings for strategy, policy and future research are explored.

14 SOLAR ENERGY↗
DOE OSTI2020

Comparative Studies of Optoelectrical Properties of Prominent PV Materials: Halide Perovskite, CdTe, and GaAs

We compare three representative high performance PV materials: halide perovskite MAPbI3, CdTe, and GaAs, in terms of photoluminescence (PL) efficiency, PL lineshape, carrier diffusion, and surface recombination and passivation, over multiple orders of photo-excitation density or carrier density appropriate for different applications. An analytic model is used to describe the excitation density dependence of PL intensity and extract the internal PL efficiency and multiple pertinent recombination parameters. A PL imaging technique is used to obtain carrier diffusion length without using a PL quencher, thus, free of unintended influence beyond pure diffusion. Our results show that perovskite samples tend to exhibit lower Shockley-Read-Hall (SRH) recombination rate in both bulk and surface, thus higher PL efficiency than the inorganic counterparts, particularly under low excitation density, even with no or preliminary surface passivation. PL lineshape and diffusion analysis indicate that there is considerable structural disordering in the perovskite materials, and thus photo-generated carriers are not in global thermal equilibrium, which in turn suppresses the nonradiative recombination. This study suggests that relatively low point-defect density, less detrimental surface recombination, and moderate structural disordering contribute to the high PV efficiency in the perovskite. This comparative photovoltaics study provides more insights into the fundamental material science and the search for optimal device designs by learning from different technologies.

14 SOLAR ENERGY↗
DOE OSTI2021

A Comparative Study on the Effect of Zr, Sn, and Ti on the Crystallization Behavior of Nepheline Glass

In alkali aluminosilicate glasses, additions of 4+ cations like Zr and Ti are often added to promote crystallization. In this study, Zr, Ti, or Sn are progressively substituted for Si in nepheline (NaAlSiO4) glass to determine their impact on the crystallization behavior. For glasses homogeneous on quenching, up to NaAlZr0.075Si0.925O4, NaAlSn0.100Si0.900O4, and NaAlTi0.300Si0.700O4, respectively, crystallization temperatures were investigated by thermal analysis. A subset of compositions was subjected to additional thermal analysis, varying heating rate and particle size, to investigate the subtleties of crystallization behavior. Subsequent heat treatment of glass powders was performed to maximize crystallization, and glass-ceramic microstructure was assessed by optical microscopy and electron microprobe, while crystalline phases were determined by X-ray diffraction. In all cases the major crystalline phase was orthorhombic carnegieite, which accommodated Ti in its structure but not Zr or Sn, and excess 4+ cations formed MO2, i.e., brookite, baddeleyite, and cassiterite, respectively.

Nienhuis-Marcial, Emily T.↗
DOE OSTI2021

Comparative Study on the Machine Learning-Based Prediction of Adsorption Energies for Ring and Chain Species on Metal Catalyst Surfaces

Computation of adsorption and transition state energies for a large number of surface intermediates for numerous active site models pose significant computational overhead in computational screening of catalysts. Machine learning (ML) techniques can be used to predict part of these energies. To predict the energies, ML models need to be fed appropriate metal and species descriptors. For complex surface chemistries, the structures of the intermediate species can vary greatly. In this paper, working with the hydrodeoxygenation of succinic acid on six different metal surfaces, we have studied the effect of linear and non-linear ML models used along with pen-and-paper based species descriptors and two categories of metal descriptors on two different categories of intermediate species: chain and ring. More specifically, our computations include the prediction of chain species when trained on only chain species and also when trained on both chain and ring species. Similar computations were performed for predictions of ring species. In each case, results of linear ML models were compared with kernel based non-linear models. Our results indicate that ring species data does not improve the prediction of chain species. Similarly, chain species data does not improve the prediction of ring species. The use of non-linear ML models, however, did help to minimize the prediction errors compared to the linear models. Furthermore, the study also shows that electronic or adsorption energy based metal descriptors along with bond count based species fingerprints can achieve a mean absolute error (MAE) of less than 0.2 eV for complex chain molecules when used with an appropriate machine learning model.

Adsorption↗
DOE OSTI2019

Rotationally invariant slave-boson and density matrix embedding theory: Unified framework and comparative study on the one-dimensional and two-dimensional Hubbard model [Rotationally invariant slave-boson and density matrix embedding theory: A unified framework and a comparative study on the 1D and 2D Hubbard Model]

We present detailed benchmark ground-state calculations of the one- and two-dimensional Hubbard model utilizing the cluster extensions of the rotationally invariant slave-boson mean-field theory and the density matrix embedding theory. Our analysis shows that the overall accuracy and the performance of these two methods are very similar. Furthermore, we propose a unified computational framework that allows us to implement both of these techniques on the same footing. In conclusion, this provides us with a different line of interpretation and paves the ways for developing systematically distinct generalizations of these complementary approaches.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Comparative study on the overall energy performance between photovoltaic and Low-E insulated glass units

In this report a novel semi-transparent building integrated photovoltaic (BIPV) laminate was developed and introduced in this paper. It was produced by cutting standard mono-crystalline silicon solar cells into small strips and then making electrical connections between each strip before laminating the cells between two layers of glass. The overall energy performance and energy saving potential of the BIPV insulated glass unit (IGU) under real world conditions were identified through a side by side comparative study. Compared to the reference IGU, the BIPV IGU had lower solar heat gain coefficient (SHGC) but much higher U-factor. The average HVAC electricity saving of the BIPV IGU was about 10% relative to the reference IGU. Daylighting measurement and analysis were carried out to evaluate the trade-offs associated with the BIPV IGU between daylight, glare, and lighting energy use. The results indicated that the BIPV IGU is better than the reference IGU in reducing discomfort glare. However, if the most conservative viewpoint near the window is used for the assessment, a lower transmittance BIPV IGU is required to bring the overall discomfort levels below the perceptible level. Lastly, the net energy saving potential associated with the novel BIPV IGU was identified based on the power, thermal and daylighting performance. On average, the BIPV IGU saved 16.8% of the total electricity use of the room. Further studies and improvement on the energy conversion efficiency of solar cells, the optimal transmittance as well as the thermal properties would make this technology more energy-efficient and affordable.

14 SOLAR ENERGY↗
DOE OSTI2025

Deep Learning for Subsurface Flow: A Comparative Study of U‐Net, Fourier Neural Operators, and Transformers in Underground Hydrogen Storage

Subsurface flow research is essential for the sustainable management of natural resources and the environment. Deep learning (DL) has significantly advanced this field by developing efficient and accurate surrogate models to replace computationally expensive physics‐based simulations. These surrogate models are commonly used to predict the spatiotemporal evolution of state variables, such as gas saturation and reservoir pressure, in heterogeneous geological formations. Despite the various DL models applied to this task, there is a lack of studies systematically comparing their performance. This absence of comparative analysis leads to somewhat arbitrary DL model selection in subsurface flow research, resulting in suboptimal performance and potentially inaccurate predictions. To bridge this gap, we conduct a systematic comparison study of three popular DL architectures—U‐Net, Fourier Neural Operators (FNO), and Segmentation Transformer (SETR)—in surrogate modeling of underground hydrogen storage (UHS). We focus on UHS due to its promise of enhancing clean energy resilience and its cyclic operational conditions that represent common scenarios in various subsurface applications. We evaluate the models based on accuracy, training cost, and inference speed. The comparison shows that U‐Net achieves the highest accuracy, followed by SETR and FNO. Despite its lower accuracy, FNO has the highest inference speed. SETR offers competitive accuracy with the least training memory usage, demonstrating the potential of transformers in learning subsurface flow. Our results provide guidance for selecting DL models for surrogate modeling in a wide range of subsurface flow problems.

42 ENGINEERING↗
DOE OSTI2023

A Comparative Study of Deep Learning Models for Fracture and Pore Space Segmentation in Synthetic Fractured Digital Rocks

This study focuses on the comparative study of deep learning (DL) models for pore space and discrete fracture networks (DFNs) segmentation in synthetic fractured digital rocks, specifically targeting low-permeability rock formations, such as shale and tight sandstones. Accurate characterization of pore space and DFNs is critical for subsequent property analysis and fluid flow modeling. Four DL models, SegNet, U-Net, U-Net-wide, and nested U-Net (i.e., U-Net++), were trained, validated, and tested using synthetic datasets, including input and label image pairs with varying properties. The model performance was assessed regarding pixel-wise metrics, including the F1 score and pixel-wise difference maps. In addition, the physics-based metrics were considered for further analysis, including sample porosity and absolute permeability. Particularly, We first simulated the permeability of porous media containing only pore space and then simulated the permeability of porous media with DFNs added. The difference between these two values is used to quantify the connectivity of segmented DFNs, which is an important parameter for low-permeability rocks. The pixel-wise metrics showed that the nested U-Net model outperformed the rest of the DL models in pore space and DFNs segmentation, with the SegNet model exhibiting the second-best performance. Particularly, nested U-Net enhanced segmentation accuracy for challenging boundary pixels affected by partial volume effects. The U-Net-wide model achieved improved accuracy compared to the U-Net model, which indicated the influence of parameter numbers. Similarly, nested U-Net has the closest match to the ground truth of physics-based metrics, including the porosity of pore space and DFNs, and the permeability difference quantifying the connectivity of DFNs. The findings highlight the effectiveness of DL models, especially the U-Net++ model with nested architecture and redesigned skip connections, in accurately segmenting pore spaces and DFNs, which are crucial for pore-scale fluid flow and transport simulation in low-permeability rocks.

Wang, Hongsheng↗
DOE OSTI2019

Comparative Study of the Effect of Magnetic Nanoparticle CoFe{sub 2}O{sub 4} on Fluctuation-Induced Conductivity of Y-123 and Y-358 Superconductors

In this study, a comparative investigation of the electrical conductivity fluctuations in nano-CoFe{sub 2}O{sub 4} added to Y{sub 3}Ba{sub 5}Cu{sub 8}O{sub 18±x} (Y-358) and YBa{sub 2}Cu{sub 3}O{sub 7−d} (Y-123) products is performed. All products were made using the solid-state reaction process. X-ray powder diffraction (XRD) analyses confirmed the formation of different phases. Scanning electron microscope (SEM) analysis shows the reduction of grain size with increasing the quantity of nano-CoFe{sub 2}O{sub 4} particles for both the Y-123 and Y-358 samples. The decrease of grain size is more accentuated in the case of the Y-358 samples compared with the Y-123 ones. The data of conductivity fluctuations are examined in terms of the derivative of the electrical resistivity with respect to temperature and the logarithmic derivative of the conductivity with regard to temperature. The results illustrate the existence of a transition with two stages; intragrain and intergrain. It is demonstrated that the addition of CoFe{sub 2}O{sub 4} nanoparticles affects the electrical conductivity fluctuations, the values of the critical exponents, and the fluctuation dimensionality in YBCO systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

A comparative study of calibration techniques for finite strain elastoplasticity: Numerically-exact sensitivities for FEMU and VFM

Accurate identification of material parameters is crucial for predictive modeling in computational mechanics. Here, the two primary approaches in the experimental mechanics community for calibration from full-field digital image correlation data are known as finite element model updating (FEMU) and the virtual fields method (VFM). In VFM, the objective function is a squared mismatch between internal and external virtual work or power. In FEMU, the objective function quantifies the weighted mismatch between model predictions and corresponding experimentally measured quantities of interest. It is minimized by iteratively updating the parameters of an FE model. While FEMU is seen as more flexible, VFM is commonly used instead of FEMU due to its considerably greater computational expense. However, comparisons between the two methods usually involve approximations of gradients or sensitivities with finite difference schemes, thereby making direct assessments difficult. Hence, in this study, we compare VFM and FEMU in the context of numerically-exact sensitivities obtained through local sensitivity analyses and the application of automatic differentiation software. To this end, we conduct a series of test cases to assess both methods under practical challenges using a finite strain elastoplasticity model.

Automatic differentiation↗
DOE OSTI2023

Genomic Features of Antimicrobial Resistance in Staphylococcus pseudintermedius Isolated from Dogs with Pyoderma in Argentina and the United States: A Comparative Study

Staphylococcus pseudintermedius is the most common opportunistic pathogen in dogs and methicillin resistance (MRSP) has been identified as an emerging problem in canine pyoderma. Here, we evaluated the antimicrobial resistance (AMR) features and phylogeny of S. pseudintermedius isolated from canine pyoderma cases in Argentina (n = 29) and the United States (n = 29). 62% of isolates showed multi-drug resistance. The AMR genes found: mecA, blaZ, ermB, dfrG, catA, tetM, aac(6')-aph(2"), in addition to tetK and lnuA (only found in U.S. isolates). Two point mutations were detected: grlA(S80I)-gyrA(S84L), and grlA(D84N)-gyrA(S84L) in one U.S. isolate. A mutation in rpoB (H481N) was found in two isolates from Argentina. SCCmec type III, SCCmec type V, ΨSCCmec 57395 were identified in the Argentinian isolates; and SCCmec type III, SCCmec type IVg, SCCmec type V, and SCCmec type VII variant in the U.S. cohort. Sequence type (ST) ST71 belonging to a dominant clone was found in isolates from both countries, and ST45 only in Argentinian isolates. This is the first study to comparatively analyze the population structure of canine pyoderma-associated S. pseudintermedius isolates in Argentina and in the U.S. It is important to maintain surveillance on S. pseudintermedius populations to monitor AMR and gain further understanding of its evolution and dissemination.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Crossing The Gap Using Variational Quantum Eigensolver: A Comparative Study

Within the evolving domain of quantum computational chemistry, the Variational Quantum Eigensolver (VQE) has been developed to explore not only the ground state but also the excited states of molecules. In this study, we compare the performance of Variational Quantum Deflation (VQD) and Subspace-Search Variational Quantum Eigensolver (SSVQE) methods in determining the low-lying excited states of $LiH$. Our investigation reveals that while VQD exhibits a slight advantage in accuracy, SSVQE stands out for its efficiency, allowing the determination of all low-lying excited states through a single parameter optimization procedure. We further evaluate the effectiveness of optimizers, including Gradient Descent (GD), Quantum Natural Gradient (QNG), and Adam optimizer, in obtaining $LiH$'s first excited state, with the Adam optimizer demonstrating superior efficiency in requiring the fewest iterations. Moreover, we propose a novel approach combining Folded Spectrum VQE (FS-VQE) with either VQD or SSVQE, enabling the exploration of highly excited states. We test the new approaches for finding all three $H_4$'s excited states. Folded Spectrum SSVQE (FS-SSVQE) can find all three highly excited states near $-1.0$ Ha with only one optimizing procedure, but the procedure converges slowly. In contrast, although Folded spectrum VQD (FS-VQD) gets highly excited states with individual optimizing procedures, the optimizing procedure converges faster.

Chen, I-Chi↗