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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Beyond Taylor’s hypothesis: a novel volumetric reconstruction of velocity and density fields for variable‑density and shear flows

Abstract This work presents a novel numerical procedure for reconstructing volumetric density and velocity fields from planar laser-induced fluorescence (PLIF) and stereoscopic particle image velocimetry (SPIV) data. This new method is theoretically and practically demonstrated to provide more accurate 3D vortical structures and density fields in high shear flows than reconstruction methods based on the mean convective velocity. While Taylor’s hypothesis of frozen turbulence is commonly applied by using the local mean streamwise velocity, the proposed algorithm uses the measured local instantaneous velocity for data convection. It consists of a step-by-step reconstruction based on a mixed Lagrangian–Eulerian solver that includes the 3D interpolation of scattered flow data and that relaxes the Taylor’s hypothesis by iterative enforcement of the incompressibility constraint on the velocity field. This methodology provides 3D fields with temporal resolution, spatial resolution, and accuracy comparable to that of real 3D snapshots, thus providing a practical alternative to tomographic measurements. The procedure is validated using numerical data of the constant-density channel flow available on the Johns Hopkins University Turbulence Database (JHTDB), showing the accurate reconstruction of the 3D velocity field. The algorithm is applied to an experimental dataset of PLIF and SPIV measurements of a variable-density jet flow, demonstrating its capability to provide 3D velocity and density fields that are more consistent with the Navier–Stokes equations compared to the mean flow convective method. Graphic abstract

42 ENGINEERING↗
DOE OSTI2023

Novel gas exposure system for the controlled exposure of plants to gaseous hydrogen fluoride

Abstract Plants can serve as sensitive bioindicators of the presence of contaminant vapors in the atmosphere. This work describes a novel laboratory-based gas exposure system capable of calibrating plants as bioindicators for the detection and delineation of the atmospheric contaminant hydrogen fluoride (HF) as a preparatory step for monitoring release emissions. To evaluate changes in plant phenotype and stress-induced physiological effects attributed to HF alone, the gas exposure chamber must have additional controls to simulate otherwise optimal plant growth conditions including variables such as light intensity, photoperiod, temperature, and irrigation. The exposure system was designed to maintain constant growth conditions during a series of independent experiments that varied between optimal (control) and stressful (HF exposure) conditions. The system was also designed to ensure the safe handling and application of HF. An initial system calibration introduced HF gas into the exposure chamber and monitored HF concentrations by cavity ring-down spectroscopy for a 48-h period. Stable concentrations inside the exposure chamber were observed after approximately 15 h, and losses of HF to the system ranged from 88 to 91%. A model plant species ( Festuca arundinacea ) was then exposed to HF for 48 h. Visual phenotype stress-induced responses aligned with symptoms reported in the literature for fluoride exposure (tip dieback and discoloration along the dieback transition margin). Fluoride concentrations in exposed tissues compared to control tissues confirmed enhanced fluoride uptake due to HF exposure. The system described herein can be applied to other reactive atmospheric pollutants of interest in support of bioindicator research.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Identification, isolation, and characterization of a novel type of Fukushima-derived microparticle

Abstract In the course of the Fukushima nuclear accident, radionuclides were released in various forms, including so-called radiocesium-bearing microparticles (CsMP). So far, four types of CsMP were described: Type A is smaller in size (< 10 μm), Types B, C, and D are larger (> 100 μm). In this work, we present a novel type of CsMP (proclaimed Type E). Three particles of Type E were extracted from a contaminated blade of grass that was sampled 1.5 km from the Fukushima Daiichi nuclear power plant in late 2011. They were located using autoradiography, isolated using an optical microscope and micromanipulator, and characterized using scanning electron microscopy, energy dispersive x-ray spectroscopy, and low-level gamma-ray spectrometry. Type E CsMPs are 10–20 μm in size and exhibit an unusually low and barely detectable 137 Cs activity of only ≤ 10 mBq per particle. Their brittle and fragile character may indicate a high surface tension.

137Cs↗
DOE OSTI2020

A novel battery scheme: Coupling nanostructured phosphorus anodes with lithium sulfide cathodes

Lithium-ion batteries are approaching their theoretical limit and can no longer keep up with the increasing demands of human society. Lithium-sulfur batteries, with a high theoretical specific energy, are promising candidates for next generation energy storage. However, the use of Li metal in Li-S batteries compromises both safety and performance, enabling dendrite formation and causing fast capacity degradation. Previous studies have probed alternative battery systems to replace the metallic Li in Li-S system, such as a Si/Li2S couple, with limited success in performance. Recently, there is a focus on red P as a favorable anode material to host Li. In this work, we establish a novel battery scheme by utilizing a P/C nanocomposite anode and pairing it with a Li 2 S coated carbon nanofiber cathode. We find that red P anode can be compatible in ether-based electrolyte systems and can be successfully coupled to a Li 2 S cathode. Our proof of concept full-cell displays remarkable specific capacity, rate and cycling performances. We expect our work will provide a useful alternative system and valuable insight in the quest for next generation energy storage devices.

25 ENERGY STORAGE↗
DOE OSTI2020

A novel matching formulation for startup costs in unit commitment

Here we present a novel formulation for startup cost computation in the unit commitment problem (UC). Both our proposed formulation and existing formulations in the literature are placed in a formal, theoretical dominance hierarchy based on their respective linear programming relaxations. Our proposed formulation is tested empirically against existing formulations on large-scale UC instances drawn from real-world data. While requiring more variables than the current state-of-the-art formulation, our proposed formulation requires fewer constraints, and is empirically demonstrated to be as tight as a perfect formulation for startup costs. This tightening can reduce the computational burden in comparison to existing formulations, especially for UC instances with large reserve margins and high penetration levels of renewables.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Novel Rotating Flexure-Test Technique for Brittle Materials with Circular Geometries

The four-point bend test is one of the simplest and often the preferred flexural strength evaluation method for brittle materials. In this loading mode, fracture often initiates from a critical surface (or subsurface) flaw when subjected to a tensile stress state. However, if the critical flaw exists on the compression side of the test specimen, it may not activate to grow a crack and hence the resulting flexural strength will be higher than the true value. The goal of this study is to measure the true flexural strength of a solid or hollow cylindrical brittle specimen by ensuring that failure occurs at its weakest point by rotating along its longitudinal axis, thereby exposing and activating its critical surface flaw during a four-point bend test. A novel test fixture has been designed and fabricated, and the true flexural strengths of cylindrical brittle and quasi-brittle tubular specimens have been measured and compared to existing experimental data obtained through traditional four-point bend tests. In this work, experimental results showcase the orientation dependance on flexural strength for various materials. Additionally, similarities between experimental findings and those available in literature, including observations of fracture surfaces and relationships between surface roughness and material strength, are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Novel Design of Guiding Stress Wave Propagation

Impact loads can induce a series of undesirable physical phenomena including vibration, acoustical shock, perforation, fracture and fragmentation, etc. The energy associated with the impact loads can lead to severe structure damage and human injuries. A design approach which effectively reduces these negative impacts through shock/stress wave diversion is highly needed. In this paper, a computational model which predicts stress wave propagation by considering different beam geometries and configurations is developed. Additionally, a novel concept of wave guide design which modifies the stress wave propagation path without disturbance is also presented. Furthermore, this design approach is not only useful for material property characterization particularly at intermediate or high strain rates, but also allows stress wave propagation in a desired direction as the shock/impact energy can be redistributed in controllable paths. The numerical results are experimentally verified through a Drop-Hopkinson bar apparatus at Sandia National Laboratories.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A novel additive manufacturing compression overmolding process for hybrid metal polymer composite structures

Metal polymer composites combining low density, high strength composites with highly ductile and tough metals have gained traction over the last few decades as lightweight and high-performance materials for industrial applications. However, the mechanical properties are limited by the interfacial bonding strength between metals and polymers achieved through adhesives, welding, and surface treatment processes. In this paper, a novel manufacturing process combining additive manufacturing and compression molding to obtain hybrid metal polymer composites with enhanced mechanical properties is presented. Additive manufacturing enabled deposition of polymeric material with fibers in a predetermined pattern to form tailored charge or preform for compression molding. Here, a grade 300 maraging steel triangular lattice is first fabricated using AddUp FormUp350 laser powder bed system and compression overmolded with additively manufactured long carbon fiber-reinforced polyamide-6,6 (40% wt. CF/PA66) preform. The fabricated hybrid metal polymer composites showed high stiffness and tensile strength. The stiffness and failure characteristics determined from the uniaxial tensile tests were correlated to a finite element model within 20% deviation. Fractographic analyses was performed using microscopy to investigate failure mechanisms of the hybrid structures.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Maximizing machine learning interatomic potential transferability for the discovery of the novel stellated octadecagon Bi18-Pt24 cage structure

Achieving true transferability remains the central challenge for Machine Learning Interatomic Potentials (ML-IAPs) in modeling complex bimetallic nanoclusters across their vast potential energy surfaces. We systematically investigate data selection strategies to optimize the Chebyshev Interaction Model for Efficient Simulation (ChIMES) potential for the Bi-Pt nanoclusters by comparing three innovative sampling methods: Principal Component Analysis (PCA)/k-means (structural diversity), t-distributedStochasticNeighborEmbedding (t-SNE)/k-means (force-space diversity), and hierarchical clustering. Quantitatively, the PCA/k-means strategy proved most effective for global accuracy, yielding the lowest force errors and achieving energy root mean square errors (RMSE) values competitive with Density Functional Theory (DFT), demonstrating excellent accuracy (19.16meV/atom). Structural validation on 34 unique DFT-optimized isomers further confirmed the potential’s high fidelity, with the best model PCA/k-means reproducing structures with an average root mean square deviation (RMSD) of 0.10 Å. However, the t-SNE methods, by maximizing diversity in the force space, demonstrated superior extrapolative power, leading to the more precise prediction of a novel stellated octadecagon Bi18⁢Pt24 cage structure, demonstrating the potential for exploring previously unseen morphologies. Our results establish a clear methodology for strategic data sampling that successfully maximizes ML-IAP transferability, providing an accurate and computationally efficient tool that accelerates the theoretical discovery of complex bimetallic architectures.

Vangheluwe, Raphaël [Université Paris-Saclay, CNRS↗
DOE OSTI2020

A novel improved model for building energy consumption prediction based on model integration

Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design

A novel design optimization approach (ActivO) that employs an ensemble of machine learning algorithms is presented. The proposed approach is a surrogate-based scheme, where the predictions of a weak leaner and a strong learner are utilized within an active learning loop. The weak learner is used to identify promising regions within the design space to explore, while the strong learner is used to determine the exact location of the optimum within promising regions. For each design iteration, exploration is done by randomly selecting evaluation points within regions where the weak learner-predicted fitness is high. The global optimum obtained by using the strong learner as a surrogate is also evaluated to enable rapid convergence once the most promising region has been identified. First, the performance of ActivO was compared against five other optimizers on a cosine mixture function with 25 local optima and one global optimum. In the second problem, the objective was to minimize indicated specific fuel consumption of a compression-ignition internal combustion (IC) engine while adhering to desired constraints associated with in-cylinder pressure and emissions. In this work, the efficacy of the proposed approach is compared to that of a genetic algorithm, which is widely used within the internal combustion engine community for engine optimization, showing that ActivO reduces the number of function evaluations needed to reach the global optimum, and thereby time-to-design by 80%. Furthermore, the optimization of engine design parameters leads to savings of around 1.9% in energy consumption, while maintaining operability and acceptable pollutant emissions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A novel machine learning based identification of potential adopter of rooftop solar photovoltaics

With the proliferation of rooftop solar photovoltaic installations, there is a need to proactively predict consumer potential for solar photovoltaic adoption, for improved electric utility planning and operation. Traditional analytical modeling approaches are limited to a few survey features and a larger part of the survey would remain untouched by the decision model. This article presents a novel, data-driven modeling approach that strategically prunes a large set of consumer profile features using a machine learning framework to train a model for predicting potential solar adoption. The approach utilizes the Gradient Boosting Decision Tree model through a Light Gradient Boosting framework that improves significantly over the poor prediction accuracy of the existing approaches. Model training using focal-loss based supervision is used to overcome the difficulty in identifying the potential adopters that is inherent in conventional data-driven models. In addition, to overcome possible data sparsity in a limited survey sample, a Generative Adversarial Network is presented to create synthetic user samples and its effectiveness on model performance is assessed. A Bayesian optimization approach is used to systematically arrive at the hyperparameters of the proposed model. Validation of the presented approach on a survey data collected by the National Rural Electric Cooperative Association in Virginia in 2018 demonstrates the excellent predictive capability of the machine learning based approach to modeling solar adoption reliably.

14 SOLAR ENERGY↗
DOE OSTI2023

Perspectives on novel refractory amorphous high-entropy alloys in extreme environments

Two new refractory amorphous high-entropy alloys (RAHEAs) within the W–Ta–Cr–V and W–Ta–Cr–V–Hf systems were herein synthesized using magnetron-sputtering and tested under high-temperature annealing and displacing irradiation using in situ Transmission Electron Microscopy. While the 14W-41Ta-26Cr-19V in at.% RAHEA (defined as WTaCrV RAHEA) was found to be unstable under such tests, additions of Hf in this system composing a new quinary 24W-40Ta-18Cr-5V-13Hf in at.% RAHEA (defined as WTaCrVHf RAHEA) was found to be a route to achieve stability both under annealing and irradiation. A new effect of nanoprecipitate reassembling observed to take place within the WTaCrVHf RAHEA under irradiation indicates that a duplex microstructure composed of an amorphous matrix with crystalline nanometer-sized precipitates enhances the radiation response of the system. Further, it is demonstrated that tunable chemical complexity arises as a new alloy design strategy to foster the use of novel RAHEAs within extreme environments. New perspectives for the alloy design and application of chemically-complex amorphous metallic alloys in extreme environments are presented with focus on their thermodynamic phase stability when subjected to high-temperature annealing and displacing irradiation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A novel shallow bore ground heat exchanger for ground source heat pump applications—Model development and validation

A novel ground heat exchanger design, the Underground Thermal Battery (UTB), was proposed as a low-cost alternative to the conventional vertical bore ground heat exchanger (VBGHE) for the application of ground source heat pumps (GSHPs). The UTB is designed to be installed in a shallow borehole (less than 6 m deep) and thus could cost much less than the conventional VBGHE, which usually is installed in vertical bores 60 m deep. By utilizing natural convection of water and phase change materials, the UTB tempers its temperature change in response to thermal loads, which helps improve the efficiency of GSHPs. A one-dimensional (1D) model of the UTB has been developed and validated with the measured performance data of a small-scale UTB, as well as the simulation results of a more detailed three dimensional (3D) numerical model, which accounts for both heat transfer and fluid dynamics in the UTB. Further, this 1D model is computationally much more efficient than the 3D model and thus can simulate the annual performance of the UTB with acceptable computation time. The 1D model has been used to evaluate the performance of the UTB so that it can be compared with the conventional VBGHE.

42 ENGINEERING↗
DOE OSTI2022

Novel solid-infused durable nonwetting surfaces for sustained condensation heat transfer enhancement

Here, in this article, we report novel nonwetting solid-infused surfaces that are easily fabricated using industrially widely adopted methods such as chemical etching or electrodeposition and exhibit sustained dropwise condensation performance durably than superhydrophobic and lubricant-infused surfaces. Solid-infused surfaces demonstrate fourfold larger condensation heat transfer coefficient and 25% better condensation effectiveness when compared to superhydrophobic surface and conventional filmwise condensation. Although the condensation heat transfer coefficient of lubricant-infused surface initially matches that of solid-infused surface, it is shown through durability studies that the wettability characteristic that underpins the performance of lubricant-infused surfaces degrades quickly with time as the lubricant depletes and lubricant-infused surface transitions to superhydrophobic surface. Solid-infused surface, on the other hand, retains its steadfast superior dropwise condensation effectiveness in the long run, overcoming the challenges of droplet pinning, surface inundation, cloaking, and lubricant depletion that eventually deteriorate condensation on conventional nonwetting superhydrophobic and lubricant-infused surfaces.

42 ENGINEERING↗
DOE OSTI2023

Proof-of-concept studies of novel protocols for producing highly pure 48 V from a 48 Cr/ 48 V generator

Here, the quest to improve the quality of nuclear data, such as half-lives, transition yields, and reaction cross-sections, is a shared endeavor among various areas of nuclear science. 48 V is a vanadium isotope for which experimental data on neutron reaction cross-sections is needed. However, traditional isotope production techniques cannot produce 48 V with high enough isotopic purity for some of these measurements. “Isotope harvesting” at the Facility for Rare Isotope Beams (FRIB) is a new isotope production technique that could potentially yield 48 V with the necessary purity for such studies. In this case, 48Cr would be collected and allowed to generate 48 V that can be separated from undecayed 48 Cr to yield highly pure 48 V. Thus, any protocol for producing pure 48 V via isotope harvesting would involve utilizing a separation technique that can effectively separate 48 Cr and 48 V. In this study, the radiotracers 51 Cr and 48 V were used to develop possible radiochemical separation methodologies, which can be translated to obtain high purity 48 V via this novel isotope production method. The developed protocols utilize either ion exchange or extraction chromatographic resins. Separations of 51 Cr and 48 V with AG 1-X8 anion exchange resin respectively resulted in recoveries of 95.6(26)% and 96.2(12)% with radionuclidic purities of 92(2)% and 99(1)%. An even more effective Cr and V separation was obtained with an extraction chromatographic resin (TRU resin) and 10 M HNO 3 loading solution. Here, 51 Cr and 48 V respectively had recoveries of 94.1(28)% and 96.2(13)% with high radionuclidic purities (100(2)% and 100(1)%) in small volumes (8.81(8) mL and 5.39(16) mL). This study suggests that, to maximize the yield and isotopic purity of 48 V, the best production protocol would involve utilizing two separations with TRU resin and 10 M HNO 3 to isolate 48 Cr and purify the generated 48 V.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

Multi-channel Imager Algorithm (MIA): A novel cloud-top phase classification algorithm

The current Geostationary Operational Environmental Satellites (GOES-16 and 17) cloud-top phase classification algorithm is based primarily on empirical thresholds at multiple wavelengths that have varying absorption capabilities for water and ice. The performance of current GOES-16 cloud-top phase product largely depends on the accuracy of the selection of reflectance ratios. Here this study aims at presenting a novel cloud-top phase classification algorithm (the Multi-channel Imager Algorithm, MIA) that provides a more judicious selection of relationships between channels using a supervised K-mean clustering method on multi-channel Red-Green-Blue images. The K-mean clustering method works analogously to how human eyes separate different colors in a microphysical color rendering set of satellite images, which differentiates water, ice and unclassified thin clouds. For water phase, cloud-top temperature information is used to further distinguish supercooled water. To evaluate the performance of the MIA, an extensive comparison with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer, and current GOES-16 cloud-top phase products is conducted, using CALIOP as the benchmark. Compared to the current GOES-16 cloud-top phase product, MIA demonstrates a substantial improvement in phase classification, where hit rate increases from 69% to 76% over the Continental United States and 58% to 66% over the full disk domain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Facilitating enzymatic hydrolysis with a novel guaiacol-based deep eutectic solvent pretreatment

In this work, we established a novel deep eutectic solvent (DES) using lignin-derived guaiacol as hydrogen bond donor (HBD). The sole ChCl/guaiacol system was found to be inefficient for the fractionation of wheat straw (WS), while the incorporation of trace AlCl 3 significantly facilitated the degradation of hemicellulose and lignin, resulting in a complete enzymatic digestibility of the pretreated WS. Further, this study revealed that the DES-degraded lignin was readily precipitated during the washing process, and thus hindered the enzymatic hydrolysis of poplar and bamboo (with hydrolysis yield of 42.03% and 71.67%, respectively). Alkali washing offers a possible approach to remove the precipitated lignin, after which a near 100% hydrolysis yield was also obtained for poplar and bamboo.

59 BASIC BIOLOGICAL SCIENCES↗