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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Anisotropic Thermally Conductive Composites Enabled by Acoustophoresis and Stereolithography

Opportunities to improve thermal management in electronic devices are currently hindered by processing constraints that limit thermal conductivity in polymer-matrix composites. Active patterning of filler particles is a promising route to improve conductivity while retaining processability by improving particle contact density and directing heat along optimized pathways. Here, we employ acoustic patterning to align and compact filler particles into stripes during stereolithographic 3D printing. This approach produces polymer-based composite materials with highly efficient embedded heat transport pathways which reach 95 vol% particle utilization (relative to the parallel conduction upper limit). These composites exhibit anisotropic thermal conductivity up to 300% higher than unpatterned composites, with in-plane anisotropy ratios of up to 350%. Combining this high conductivity with 3D printing enables materials with engineered heat networks that optimize transport from hot spots to heatsinks while maintaining low viscosity for fast particle patterning and for infiltration around electronic components. Finally, numerical simulations of acoustic assembly of particles with varied geometry, when compared to experimentally characterized particle packing, illuminate pathways for further improving conductivity by optimizing particle geometry for alignment and stacking of particles with maximum contact surface area.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Machine Learning Modeling for Accelerated Battery Materials Design in the Small Data Regime

Abstract Machine learning (ML)‐based approaches to battery design are relatively new but demonstrate significant promise for accelerating the timeline for new materials discovery, process optimization, and cell lifetime prediction. Battery modeling represents an interesting and unconventional application area for ML, as datasets are often small but some degree of physical understanding of the underlying processes may exist. This review article provides discussion and analysis of several important and increasingly common questions: how ML‐based battery modeling works, how much data are required, how to judge model performance, and recommendations for building models in the small data regime. This article begins with an introduction to ML in general, highlighting several important concepts for small data applications. Previous ionic conductivity modeling efforts are discussed in depth as a case study to illustrate these modeling concepts. Finally, an overview of modeling efforts in major areas of battery design is provided and several areas for promising future efforts are identified, within the context of typical small data constraints.

Sendek, Austin D.↗
DOE OSTI2024

Controlling the Phase Transformation of Alumina for Enhanced Stability and Catalytic Properties

Current transition alumina catalysts require the presence of significant amounts of toxic, environmentally deleterious dopants for their stabilization. Herein, we report a simple and novel strategy to engineer transition aluminas to withstand aging temperatures up to 1200 °C without inducing the transformation to low-surface-area α-Al 2 O 3 and without requiring dopants. By judiciously optimizing the abundance of dominant facets and the interparticle distance, we can control the temperature of the phase transformation from θ-Al 2 O 3 to α-Al 2 O 3 and the specific surface sites on the latter. These specific surface sites provide favorable interactions with supported metal catalysts, leading to improved metal dispersion and greatly enhanced catalytic activity for hydrocarbon oxidation. Further, the results presented herein not only provide molecular-level insights into the critical factors causing deactivation and phase transformation of aluminas but also pave the way for the development of catalysts with improved activity for catalytic hydrocarbon oxidation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Calibration of hyperelastic constitutive models: the role of boundary conditions, search algorithms, and experimental variability

The calibration of hyperelastic constitutive models of soft tissue and tissue surrogates is often treated as an exercise in curve-fitting to the average experimental response, and many of the complicating factors such as experimental boundary conditions and data variability are ignored. Here, we focus on three questions that arise in this area: the ramifications of ignoring the experimental boundary conditions, the use of local optimizers, and the role of data variability. Using data from a uniaxial extension experiment on a tissue surrogate, we study how these three factors affect the calibration of isotropic hyperelastic constitutive models. Our results show that even with the simplest of constitutive models, it is necessary to look beyond a "good fit" to the average.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Ce- and Ni-Codoped Double PrBaMn 2 O 5 Perovskite as a Ceramic SOFC Anode

This study explores the efficacy of cerium introduction, both on its own and in combination with nickel, into PrBaMn 2 O 5+δ (PBM) structures to enhance solid oxide fuel cell (SOFC) anodes. We synthesized Pr 1–x Ce x BaMn 2 O 5+δ compositions for x values of 0.05 (PrCe5) and 0.1 (PrCe10), as well as a nickel-doped variant (PrCe5Ni), assessing their performance under H 2 – 3% H 2 O reducing conditions pertinent to SOFC anode operations. Our findings reveal that the PrCe5 composition exhibits a thermal expansion coefficient (TEC) that not only improves upon that of the Ce-free counterpart but also aligns closely with the TEC standards of prevalent SOFC electrolytes. A notable advancement was achieved with the application of a 3.5 μm gadoliniadoped ceria (GDC) buffer layer through physical vapor deposition, effectively mitigating chemical interactions between PBM-based anodes and yttria-stabilized zirconia (YSZ) electrolytes, a concern highlighted by in situ neutron diffraction analyses. Electrochemical impedance spectroscopy, conducted over 220 h in a H 2 –3% H 2 O atmosphere at 750 °C, demonstrated that the optimal 3.5 μm thickness of the GDC buffer layer significantly minimizes the area-specific resistance (ASR) degradation rate to 0.002 Ω cm 2 /h, markedly outperforming both thinner (1 μm) and thicker (8 μm) GDC layers, which showed higher degradation rates of 0.15–0.2 Ω cm 2 /h due to the diffusion of Ba ions or delamination. Moreover, cerium doping fosters superior microstructural stability and obviates barium diffusion, thereby suggesting an enhanced durability of the doped anodes over their lifespan. Integrating nickel into the PrCe5 structure halved the ASR to 0.5 Ω cm 2 at 750 °C, situating it well within the ideal performance range for SOFC anodes. Furthermore, the enhancement brought about by simultaneously doping PBM with cerium and nickel, which fundamentally relies on the critical contributions of defect chemistry and crystal structure, highlights the significance of these fields in creating sophisticated materials for energy related applications.

25 ENERGY STORAGE↗
DOE OSTI2018

Rheological Investigation on the Microstructure of Fuel Cell Catalyst Inks

We present a rheological investigation of fuel cell catalyst inks. The effects of ink parameters, which include carbon black-support structure, Pt presence on carbon support (Pt-carbon), and ionomer (Nafion) concentration, on the ink microstructure of catalyst inks were studied using rheometry in combination with ultrasmall-angle X-ray scattering (USAXS) and dynamic light scattering (DLS). Dispersions of a high-surface-area carbon (HSC), or Ketjen black type, demonstrated a higher viscosity than Vulcan XC-72 carbon due to both a higher internal porosity and a more agglomerated structure that increased the effective particle volume fraction of the inks. The presence of Pt catalyst on both the carbon supports reduced the viscosity through electrostatic stabilization. For carbon-only dispersions (without Pt), the addition of ionomer up to a critical concentration decreased the viscosity due to electrosteric stabilization of carbon agglomerates. However, with Pt-carbon dispersions, the addition of ionomer showed contrasting behavior between Vulcan and HSC supports. In the Pt-Vulcan dispersions, the effect of ionomer addition on the rheology was qualitatively similar to Vulcan dispersions without Pt. The Pt-HSC dispersions showed an increased viscosity with ionomer addition and a strong shear-thinning nature, indicating that Nafion likely flocculated the Pt-HSC aggregates. These results were verified using DLS and USAXS. Further, the observations of the effect of ionomer:carbon ratio and a comparison between carbons of different surface areas provided insights on the microstructure of the catalyst ink corresponding to the optimized I/C ratio for fuel cell performance reported in the literature.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Synthesis of Nitrogen and Sulfur Codoped Nanoporous Carbons from Algae: Role in CO 2 Separation

Nitrogen and sulfur codoped and completely renewable carbons were synthesized from two types of algae, Spirulina Platensis and Chlorella Vulgaris, without any additional nitrogen fixation reaction. The type of activation agents, char-forming temperature, activation agent-to-char ratio, and activation temperature were all varied to optimize the reaction conditions for this synthesis. The maximum Brunauer–Emmett–Teller surface area and total pore volumes of the carbons were 2685 m 2 /g and 1.4 cm 3 /g, respectively. The nitrogen and sulfur contents of the carbons were in the range of 0.9–5.69 at. % and 0.05–0.2 at. %, respectively. The key nitrogen functionalities were pyridinic, amino, and pyridonic/pyrrolic groups, whereas the key sulfur functionalities were S–C, O–S–C, and SO x groups. CO 2 adsorption isotherms were measured at 273, 298, and 313 K, and the ideal adsorbed solution theory was employed to calculate the selectivity of adsorption of CO 2 over N 2 and simulate binary adsorption isotherms. The adsorption results demonstrated that the CO 2 adsorption amount and the heat of CO 2 adsorption were higher for carbons with higher nitrogen content, confirming the influence of nitrogen functionality in CO 2 adsorption. The overall results suggested that these algae-derived renewable carbons can serve as potential adsorbents for CO 2 separation from N 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Internal transmission coefficient in charges carrier generation layer of graphene/Si based solar cell device

Internal transmission profile in charges carrier generation layer of graphene/Si based solar cell has been explored theoretically. Photovoltaic device was constructed from graphene/Si heterojunction forming a multilayer stuck with Si as generation layer. The graphene/Si sheet was layered on ITO/glass wafer then coated by Al forming Ohmic contact with Si. Photon incident propagate from glass substrate to metal electrode and assumed that there is no transmission in Al layer. The wavelength range spectra used in this calculation was 200 – 1000 nm. It found that transmission intensity in the generation layer show non-linear behavior and partitioned by few areas which related with excitation process. According to this information, it may to optimize the photons absorption to create more excitation process by inserting appropriate material to enhance optical properties in certain wavelength spectra because of the exciton generation is strongly influenced by photon absorption.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Enhancement of Optical Emission and Ion Currents in a Laser Produced Silicon Plasma by Femtosecond Laser-Induced Periodic Surface Structuring

Laser-induced periodic surface structuring is a powerful technique for modifying the surface properties of solids. Using ultrashort pulses from a Ti:Sapphire laser (800 nm, 100 fs) we have fabricated nanoscale order laser-induced periodic surface structures (LIPSS) on a silicon (100) surface. The LIPSS patterns are found to be strongly dependent on the laser pulse energy, state of polarization, number of shots delivered on the target, and the ambient pressure. The role of laser wavelength is studied by using the second harmonic output (400 nm) also for fabrication. Large area (5x4 mm2) surface structuring has been carried out at atmospheric pressure after optimizing the laser parameters at 800 nm. The patterned silicon surface shows a substantial reduction in the reflection of incident light over a wide range of wavelengths, in comparison to plain silicon. Moreover, when used for laser induced breakdown spectroscopy (LIBS) experiments, enhanced optical emission and ion current have been observed from the patterned surface. This indicates that coupling of laser energy to the surface is enhanced in the case of patterned silicon. While spectral lines emitted by doubly ionized silicon have been observed from the patterned surface, the plain surface gives lines from singly ionized silicon only. A discernible enhancement has been measured in both optical (50-90%) and electrical (34%) signals from the plasma generated on the patterned surface. These results confirm that LIPSS is a versatile method for enhancing the coupling of laser energy on to irradiated solid surfaces.

ion currents, optical emission, laser produced sil↗
DOE OSTI2014

New Effective Multithreaded Matching Algorithms

Matching is an important combinatorial problem with a number of applications in areas such as community detection, sparse linear algebra, and network alignment. Since computing optimal matchings can be very time consuming, several fast approximation algorithms, both sequential and parallel, have been suggested. Common to the algorithms giving the best solutions is that they tend to be sequential by nature, while algorithms more suitable for parallel computation give solutions of less quality. We present a new simple 1 2 -approximation algorithm for the weighted matching problem. This algorithm is both faster than any other suggested sequential 1 2 -approximation algorithm on almost all inputs and also scales better than previous multithreaded algorithms. We further extend this to a general scalable multithreaded algorithm that computes matchings of weight comparable with the best sequential algorithms. The performance of the suggested algorithms is documented through extensive experiments on different multithreaded architectures.

matching, parallel algorithms↗
DOE OSTI2024

Stability-Constrained Learning for Frequency Regulation in Power Grids With Variable Inertia

The increasing penetration of converter-based renewable generation has resulted in faster frequency dynamics, and low and variable inertia. As a result, there is a need for frequency control methods that are able to stabilize a disturbance in the power system at timescales comparable to the fast converter dynamics. This paper proposes a combined linear and neural network controller for inverter-based primary frequency control that is stable at time-varying levels of inertia. We model the time-variance in inertia via a switched affine hybrid system model. We derive stability certificates for the proposed controller via a quadratic candidate Lyapunov function. We test the proposed control on a 12-bus 3-area test network, and compare its performance with a base case linear controller, optimized linear controller, and finite-horizon Linear Quadratic Regulator (LQR). Our proposed controller achieves faster mean settling time and over 50% reduction in average control cost across 100 inertia scenarios compared to the optimized linear controller. Unlike LQR which requires complete knowledge of the inertia trajectories and system dynamics over the entire control time horizon, our proposed controller is real-time tractable, and achieves comparable performance to LQR.

data-driven control↗
DOE OSTI2026

Analytical Models of Frequency and Voltage in Large-Scale All-Inverter Power Systems

Low-order frequency response models for power systems have a decades-long history in optimization and control problems such as unit commitment, economic dispatch, and wide-area control. With a few exceptions, these models are built upon the Newtonian mechanics of synchronous generators, assuming that the frequency dynamics across a system are approximately homogeneous, and assume the dynamics of nodal voltages for most operating conditions are negligible, and thus are not directly computed at all buses. As a result, the use of system frequency models results in the systematic underestimation of frequency minimum nadir and maximum RoCoF, and provides no insight into the reactive power-voltage dynamics. This paper proposes a low-order model of both frequency and voltage response in grid-forming inverter-dominated power systems. The proposed model accounts for spatial-temporal variations in frequency and voltage behavior across a system and as a result, demonstrates the heterogeneity of frequency response in future renewable power systems. Electromagnetic transient (EMT) simulations are used to validate the utility, accuracy, and computational efficiency of these models, setting the basis for them to serve as fast, scalable alternatives to EMT simulation, especially when dealing with very large-scale systems, for both planning and operational studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Distributed Neural Representation for Reactive In Situ Visualization

Implicit neural representations (INRs) have emerged as a powerful tool for compressing large-scale volume data. This opens up new possibilities for in situ visualization. However, the efficient application of INRs to distributed data remains an underexplored area. Here, in this work, we develop a distributed volumetric neural representation and optimize it for in situ visualization. Our technique eliminates data exchanges between processes, achieving state-of-the-art compression speed, quality and ratios. Our technique also enables the implementation of an efficient strategy for caching large-scale simulation data in high temporal frequencies, further facilitating the use of reactive in situ visualization in a wider range of scientific problems. We integrate this system with the Ascent infrastructure and evaluate its performance and usability using real-world simulations.

Wu, Qi↗
DOE OSTI2020

Prediction of the development of islet autoantibodies through integration of environmental, genetic, and metabolic markers

The Environmental Determinants of the Diabetes in the Young (TEDDY) study has prospectively followed, from birth, children at increased genetic risk of type 1 diabetes. We evaluated the potential of machine learning to identify new biomarkers that predict imminent (within 6 months) development of persistent islet autoantibodies to insulin, GAD or IA-2 in TEDDY participants through integration of time-invariant risk factors with time-varying metabolomics. The predictive modeling was initiated with over 220 potential biomarkers; through ensemble-based feature evaluation, the optimal model included 42 biomarkers, returning a cross-validated receiver operating characteristic area under the curve of 0.74. The model identified a principal set of 20 time-invariant markers, including 16 single nucleotide polymorphisms and two HLA-DR genotypes, gestational age, and exposure to a prebiotic formula. Integration of the metabolome identified 22 high-priority metabolites and lipids, including adipic acid and ceramide d42:0, that predicted development of islet autoantibodies, dependent upon the time horizon. The majority (86%) of metabolites that predicted development of islet autoantibodies belonged to 3 pathways: lipid oxidation, phospholipase A2 signaling, and pentose phosphate pathway. TEDDY data suggest that these metabolic processes may play a role in triggering islet autoimmunity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

CASL Institutional Computing Report, Calendar Year 2012

CASL, the Consortium for Advanced Simulation of Light water reactors (or LWRs) is a multi-institutional, $22-25M per year, 5-year project lead by Oak Ridge National Laboratory. This project is one of DOE’s Energy Innovation Hubs and started late in the last quarter of FY10. As part of this joint work with 10 different institutions, senior LANL management agreed to provide 10% of all Institutional Computing resources at Los Alamos to the project for the life of the project. This is part of an in-kind institutional contribution LANL is making to the CASL effort. Similar contributions have been made by all national laboratory partners in CASL (ORNL, LANL, INL, and SNL)1. CASL is a highly visible and extremely valuable project for Los Alamos, with line of sight visibility and attention from the Secretary of Energy and high-impact results delivered to the nuclear energy industry. It is considered to be a model for such large programs with multiple partners, as well as a model for future simulation hubs defined by DOE. At the time of this writing, LANL leads three areas in the project. Thermal Hydraulics Methods (THM, Mark Christon, CCS-2), Materials Optimization and Performance (MPO, Chris Stanek, MST-8), and Validation and Uncertainty Quantification (VUQ, Brian Williams, CCS-6). This report summarizes the use of Institutional Computing (IC) resources by the CASL project, for calendar year 2013.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Requirements for Robust DERMS Control and Cyber Attack Scenarios

A Distribution Management System (DMS) is responsible for monitoring and maintaining the health of a distribution grid. Common DMS applications include fault location isolation and service restoration, on-line power flow (OLPF), volt/VAR optimization (VVO), and switch order management. These systems were developed primarily in the area of top-down, unidirectional power flow, with large centralized generating plants moving electricity through the transmission grid and into the distribution grid from and thence to consumers. The recent and ongoing addition of distributed energy resources (DER) to the grid, including rooftop and community solar and wind, means that power is now flowing into the distribution grid from the edge, which complicates the functioning of the DMS. To ameliorate this problem, DMS use distributed energy resource management systems (DERMS) to control DER such as solar inverters. DERMS comprise software management tools that allow management and near real-time control of an array of DER. DERMS control devices and grid services including smart inverters, capacitor banks, on-load tap changes, voltage regulators, and customer loads.

14 SOLAR ENERGY↗
DOE OSTI2022

Predicting Switchgrass Biomass Yields Using a Spectral Vegetation Index Derived from Multispectral Satellite Imagery

Successful scaling of perennial bioenergy crop production requires a landscape design that optimizes the benefits of finite lands for people, communities, and environments. Utilizing marginal areas is the key to sustainable bioenergy crop production (Ssegane et al., 2015, 2016). Marginal areas are often small-sized lands and unevenly distributed across the agricultural landscape (Ssegane et al., 2016); thus, a systematic, semi-automated remote sensing method is needed as an effective means of estimating bioenergy crop yields across landscapes. Argonne National Laboratory (Argonne) is currently developing a tool, Scaling Up Perennial Bioenergy Economics and Ecosystem Services Tool (SUPERBEEST), to identify marginal agricultural lands and quantify environmental and economic effects of perennial bioenergy crop production systems. The tool aims to provide users a path to foster the sustainable and productive integration of bioenergy crops in the Midwestern agricultural landscape. Reliable, cost-effective, and timely estimation of bioenergy crop yields using remote sensing would help calculate and track the success of integrated bioenergy crops in the landscape for those communities. Argonne previously conducted feasibility studies for estimating biomass yields for bioenergy feedstock, corn and perennial grass using spectral vegetation indices (SVIs)1 derived from optical imagery (Hamada et al., 2015, 2021). In both studies, SVIs, more specifically those sensitive to plant chlorophyll or nitrogen contents, showed potential for estimating or predicting biomass yields with a correlation of determination (R2) ranging from 0.54 to 0.96, indicating a value for further investigation as a viable means of quantifying bioenergy feedstock production across large landscapes. Thus, the goal of this study is to evaluate the feasibility of use of SVIs as a means of estimating or predicting switchgrass biomass yields at harvest using publicly available multispectral satellite imagery. The feasibility analysis was performed using four study areas of mature switchgrass located in Virginia. Objectives are to (1) examine the SVIs and establish their relationships with switchgrass biomass yields at harvest, (2) develop a parsimonious image processing model for predicting at-harvest yields by applying the relationships with the most promising spectral index and (3) map switchgrass yields predicted by the image processing model across the study sites. The calibration to field data will rely on switchgrass biomass yields determined by the baling method, representing a potential challenge to the analysis but an important practical aspect for future applications. With this research design, the study aimed to gain insights into enabling remote sensing-based estimation of bioenergy crop yields in a reliable, cost- effective, and timely manner across large, heterogeneous landscapes.

09 BIOMASS FUELS↗