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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Performance of hybridized bamboo-carbon fiber reinforced polypropylene composites processed using wet laid technique

The end-of-life vehicles (ELV) regulations motivate hybrid materials usage in automotive industries to optimize properties at reduced cost and increase eco-friendly designs. This research explores hybrid compositions of natural bamboo fiber and synthetic carbon fiber. The goal of hybridization was to synergistically benefit from each constituent– i.e., sustainability, energy absorption and superior damping from bamboo, and high strength and stiffness from carbon fiber. Carbon fibers (CF), bamboo fibers (BF) and polypropylene (PP) fibers were dispersed in water to produce wet-laid hybrid mats. The mats were compression molded into consolidated panels to obtain the hybrid composite(s) (BF-CF-PP). Four formulations with different fiber-resin weight percent were designed and produced including- BF-PP (30/70), BF-PP (50/50), BF-CF-PP (32/8/60), and BF-CF-PP (8/32/60). The effect of (a) fiber length, (b) surface treatment, (c) fiber content, and (d) consolidation pressure on the mechanical properties were examined. The improved mechanical (flexural strength 76.4 MPa, flexural modulus 4.1 GPa, ILSS 12.4 MPa and impact strength 49.9 KJ/m 2 ) and vibrational damping (1.05%) properties showed that the BF-CF-PP (8/32/60) provided higher properties compared to the other variants. The projected properties at various stoichiometric ratios of carbon and bamboo fiber revealed that the properties of hybrid composites could be tailored to produce desirable, cost-effective, and sustainable automotive components.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Discrete ordinates analysis of the forced-flight variance reduction technique in Monte Carlo neutral particle transport simulations

This paper presents mathematical formulations and methods to predict the effect of forced-flight variance reduction on Monte Carlo tally variance and calculation time. This includes deducing biasing operators that are then used to construct a history-score probability density function (HSPDF), which represents all possible Monte Carlo random walks and gives the probability of a Monte Carlo history scoring in a tally from a particular phase-space position. The history-score moment equations (HSMEs), the statistical moments of the HSPDF, are then derived to calculate the statistical behavior of the Monte Carlo tally when forced-flight variance reduction is applied. In addition, the future-time equation (FTE) is derived to predict the Monte Carlo computational time as a result of applying forced-flight variance reduction. The solutions of the HSMEs and FTE can be used to predict Monte Carlo computational cost. Furthermore, this work also describes a discrete ordinates method to solve the forced-flight HSMEs and FTE. Several 1-D and 2-D test problems verify that the derivations are performed and implemented correctly.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Code-verification techniques for the method-of-moments implementation of the magnetic-field integral equation

For computational physics simulations, code verification plays a major role in establishing the credibility of the results by assessing the correctness of the implementation of the underlying numerical methods. In computational electromagnetics, surface integral equations, such as the method-of-moments implementation of the magnetic-field integral equation, are frequently used to solve Maxwell's equations on the surfaces of electromagnetic scatterers. These electromagnetic surface integral equations yield many code-verification challenges due to the various sources of numerical error and their possible interactions. In this paper, we provide approaches to separately measure the numerical errors arising from these different error sources. We demonstrate the effectiveness of these approaches for cases with and without coding errors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Code-verification techniques for the method-of-moments implementation of the combined-field integral equation

Code verification plays an important role in establishing the credibility of computational simulations by assessing the correctness of the implementation of the underlying numerical methods. In computational electromagnetics, the numerical solution to integral equations incurs multiple interacting sources of numerical error, as well as other challenges, which render traditional code-verification approaches ineffective. In this paper, we provide approaches to separately measure the numerical errors arising from these different error sources for the method-of-moments implementation of the combined-field integral equation. Here, we demonstrate the effectiveness of these approaches for cases with and without coding errors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Review of additive manufacturing and densification techniques for the net- and near net-shaping of geometrically complex silicon nitride components

Silicon nitride (Si 3 N 4 ) is an advantageous material due its unique combination of mechanical, thermal, chemical, and electrical properties both at ambient and elevated temperatures. Because of these properties there are a wide range of applications for Si 3 N 4 components. Applications include heat exchangers, environmental barrier coatings, osteointegration scaffolds, radomes, and integrated circuitry. Furthermore, such applications often require geometric complexity for efficient and/or effective operation. However, traditional ceramics processing methods such as hot-pressing or die extrusion are typically limited to simple axis-symmetric shapes. With the advent of additive manufacturing, there has been significant advancement into the forming of geometrically complex Si 3 N 4 components. This review documents additive manufacturing advancements that have demonstrated, or are capable of, fabricating Si 3 N 4 components with complex geometry.

36 MATERIALS SCIENCE↗
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 OSTI2026

Predicting river turbidity in Pine Island Bayou using machine learning techniques coupled with variational mode decomposition

Elevated turbidity levels pose significant public health risks by facilitating the transport of harmful pollutants, including metals, organic compounds, and pathogenic microorganisms into the surface water. These conditions create serious challenges for public recreational water use and drinking water treatment, leading to economic losses and health risks. This study utilizes water monitoring data in Pine Island Bayou, Texas, and develops a Sequence-to-Sequence (S2S) model to predict turbidity using Attention-based Gated Recurrent Units with Encoder-Decoder (AT-GRU-ED) and Long Short-Term Memory (LSTM), coupled with Variational Mode Decomposition (VMD). Compared to the model without VMD, the model demonstrates satisfactory 72-hour turbidity prediction performance, achieving MAEs of 2.60 and 3.29 NTU (reductions of 53% and 58%), RMSEs of 21.08 and 31.49 NTU (reductions of 82% and 80%), and R² values of 0.96 and 0.84 on the validation and test sets, respectively. Feature importance analysis reveals that water temperature is the dominant factor influencing seasonal turbidity patterns, while real-time hourly rainfall significantly contributes to short-term variability. Turbidity typically peaks within 48 hours after rainfall events due to lagged effects from surface runoff and upstream flow. Findings suggest suspending recreational water use and water supply pumping for three days after heavy rainfall can benefit public health and improve water treatment processes. Discharges above 100 m3/s are found to accelerate sediment dilution and transport, reducing turbidity levels more quickly after the peak. In conclusion, the proposed model demonstrates reliable 72-hour turbidity prediction, supporting decision-making for water treatment plant operations and providing early warning for public recreational water use.

Deep learning↗
DOE OSTI2023

Customizable adaptive regularization techniques for B-spline modeling

B-spline models are a powerful way to represent scientific data sets with a functional approximation. However, these models can suffer from spurious oscillations when the data to be approximated are not uniformly distributed. Model regularization (i.e., smoothing) has traditionally been used to minimize these oscillations; unfortunately, it is sometimes impossible to sufficiently remove unwanted artifacts without smoothing away key features of the data set. In this article, we present a method of model regularization that preserves significant features of a data set while minimizing artificial oscillations. Our method varies the strength of a smoothing parameter throughout the domain automatically, removing artifacts in poorly-constrained regions while leaving other regions unchanged. Further, the proposed method selectively incorporates regularization terms based on first and second derivatives to maintain model accuracy while minimizing numerical artifacts. The behavior of our method is validated on a collection of two- and three-dimensional data sets produced by scientific simulations. In addition, a key tuning parameter is highlighted and the effects of this parameter are presented in detail. This paper is an extension of our previous conference paper at the 2022 International Conference on Computational Science (ICCS) (Lenz et al., 2022).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

In-situ electrochemical optical techniques in the investigation of lithium interfacial phenomena with a liquid and a solid-state electrolyte

An in-situ electrochemical optical diagnosis is the key to the investigation of electrode interface during a redox reaction. Because the morphology changes particularly, dendrite formation, dendrite shapes, solid electrolyte interface formation and gas generation can be revealed visually. The challenge of ensuring uniform current density on a flat Li anode in a liquid electrolyte was addressed and uniform Li plating was demonstrated. The dendrite shape change under different reduction current density was discussed. Here the Li dendrite shape change and the performance of Li anodes with a surface lamination of graphite and red phosphate were used as examples to demonstrate the capability of the in-situ optical cell. An in-situ electrochemical optical cell used in the investigation of the increasingly popular solid-state Li batteries has its own challenges. Due to the untransparent nature of a solid-state electrolyte, an optical investigation on a solid-state electrolyte Li battery needs to be done by exposing the cross-section of the cell. In addition, it is very difficult to assemble an optical cell with a brittle and fragile solid-state electrolyte in a glove box. A set of formation and transfer dies, and an optical cell were introduced. The Li dendrite growth at the interface can be observed in a solid-state Li cell.

25 ENERGY STORAGE↗
DOE OSTI2023

Modeling and characterizing the elastodynamic response of octet-truss lattice structures using resonance techniques

Characterization of additively manufactured materials and structures is an ongoing effort for the advanced manufacturing community. This article will investigate an approach using resonant spectral ultrasound (RUS) to measure the effective elastic constants of an Octet Truss lattice and apply the results to continuum bases models representing the lattice regions in different structures. The study is focused on simple lattices structures fabricated from Ti5553 using a laser powder bed fusion process (LBPF). Solid and lattice samples are measured to determine RUS estimates for the elastic properties of the bulk material and the effective elastic properties of the lattice structure. The estimated elastic properties are then incorporated into 3d finite element models using a continuum approximation of the physical AM parts. Comparisons show good agreement between the experimentally measured eigen frequencies of the Ti5553 LBPF parts and the eigen frequencies calculated using the continuum approximations based on the effective RUS elastic properties. The current results suggest there is additional physics and geometrical effects that are accounted for in the RUS continuum approximation of the lattice that are not captured in the full 3d finite element model of the parts utilizing only the base material properties.

36 MATERIALS SCIENCE↗