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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Using phenology to unravel differential soil water use and productivity in a semiarid savanna

Savannas are water-limited ecosystems characterized by two dominant plant types: trees and an understory primarily made up grass. Different phenology and root structures of these plant types complicate how savanna primary productivity responds to changes in water availability. We tested the hypothesis that productivity in savannas is controlled by the temporal and vertical distribution of soil water content (SWC) and differences in growing season length of understory and tree plant functional types. To quantify the relationship between tree, understory, and savanna-wide phenology and productivity, we used PhenoCam and satellite observations surrounding an eddy covariance tower at a semiarid savanna site in Arizona, USA. We distinguished between SWC across two different depth intervals (shallow, <0–30 cm and deep, >30–100 cm). We found that tree greenness increased with SWC at both depths, while understory greenness was only sensitive to the shallower SWC measurements. Onset of ecosystem dormancy, estimated from satellite observations close to the eddy covariance tower, explained more variability in annual gross primary productivity (GPP) than in other phenometrics. Higher SWC led to an extended growing season, caused by delayed dormancy in trees, but the understory showed no evidence of delayed dormancy in wetter periods. We infer that the timing of ecosystem scale dormancy, driven by trees, is important in understanding changes in a savanna's GPP. These findings highlight the important effects of rainfall during the winter. These findings suggest that savanna GPP is conditional on different responses to moisture availability in each of the dominant vegetation components.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Depyrogenation using plasmas: A novel approach for endotoxin deactivation using a dielectric barrier discharge at atmospheric pressure

Developing a low-cost depyrogenation process is vital in extending the medical applicability of polymers that can be used in medicine. We present an overview of the plasma-based depyrogenation literature and address the need to develop a nonthermal plasma-based depyrogenation process for delicate materials such as chitosan. We present a low-cost plasma apparatus to treat chitosan powder in hermetically sealed bags. We decouple the experiments into two parts: depyrogenation experiments for dried standard endotoxin on glass slides and analysis of chitosan modifications through Fourier-transform infrared spectroscopy. We demonstrate depyrogenation efficacy with up to a 4-log reduction in endotoxin levels and discuss minor changes observed in plasma-treated chitosan.

atmospheric pressure plasmas↗
DOE OSTI2021

Quantitative concentration measurements in a turbulent helium jet using rainbow schlieren deflectometry

Rainbow schlieren deflectometry (RSD) is applied to acquire quantitative concentration measurements in a turbulent environment for the first time. RSD methodology is developed and validated using Rayleigh scattering data available in the literature. Experiments were performed in an initially laminar, momentum-driven helium jet injected into ambient air. Full-field measurements are acquired that span the laminar, transition, and fully turbulent regions of the jet. Furthermore, the RSD results are shown to provide accurate measurements in each region and also confirm the self-similar nature of the fully turbulent jet. Analysis shows that the transition from laminar to turbulent flow occurs across an axial span that decreases with increasing Reynolds number. Experiments with a long exposure time were conducted to evaluate the limitations of hardware averaging of the RSD signal in comparison with software averaging.

02 PETROLEUM↗
DOE OSTI2020

Time-Sequenced Flow Field Prediction in an Optima Spark-Ignition Direct-Injection Engine Using Bidirectional Recurrent Neural Network (bi-RNN) with Long Short-Term Memory

To further improve the energy conversion efficiency of internal combustion engine, the transient and complex air flow movement inside the cylinder needs to be better understood and controlled. Although the in-cylinder flow fields are highly stochastic with strong cycle-to-cycle fluctuations, machine learning can still provide an efficient way to learn and regress the complex flow movement process inside the cylinder. In this work, a bidirectional recurrent neural network (bi-RNN) model with long short-term memory was applied to predict the in-cylinder flow fields at different time steps using training data from mull-cycle particle image velocimetry (PIV) measurements. To evaluate the agreement between the true and predicted flow fields, structure and magnitude comparison indices are calculated both globally and locally. The comparison results show that the bi-RNN model can accurately predict the bulk flow and vortex motions from early intake stroke to compression stroke. This work demonstrates that the machine learning model has the potential to predict the underlying dynamics of the interaction between in-cylinder flows and provides a reliable way to improve temporal resolution in PIV flow data to better reveal transient in-cylinder flow features.

Bi-RNN model↗
DOE OSTI2022

Removal of iodine (I- and IO3-) from aqueous solutions using CoAl and NiAl layered double hydroxides

The treatment of radioactive iodine released from nuclear power plants and radiological waste disposal sites is of great concern due to its high mobility and toxicity. In particular, iodide (I-) and iodate (IO3-) are the major iodine species of concern under various pHs and groundwater conditions. Herein, CoAl and NiAl layered double hydroxides (LDHs) were synthesized by a hydrothermal method and investigated to identify the removal mechanisms and efficiencies of both I- and IO3-. Both CoAl and NiAl LDHs exhibited rapid iodine removal processes within 20 min, following the pseudo-second-order model via ion-exchange with parent NO3- anion in the LDHs. The CoAl LDH’s maximum sorption capacities for I- and IO3- were about 1.67 and 2.16 mmol g-1, respectively, whereas for the NiAl LDH, these were about 2.10 and 2.26 mmol g-1, and they followed the Langmuir isotherm model. Interestingly, both the CoAl and NiAl LDHs showed a preferential ion-exchange affinity for IO3- over I-, which was attributed to the structural similarity of the IO3- and NO3- as well as new formation of secondary Co(or Ni)(IO3)2·2H2O phases. In addition, a desorption study indicated that the selectivity order was SO42- = IO3- = OH- > HCO3- > Cl- > NO3- = I- and demonstrated the higher retention of the IO3- than I- anion. This study provides insights into promising iodine sorbents and the different removal mechanisms of I- and IO3- using CoAl and NiAl LDHs.

Kang, Jaehyuk↗
DOE OSTI2020

Toward a scalable robust security-constrained optimal power flow using a proximal projection bundle method

Robust security-constrained optimal power flow (rSCOPF) aims to find the worst-case contingencies of alternating current optimal power flow (ACOPF) in power systems. With the rise of GPU architectures on the upcoming supercomputer architectures, optimization algorithms that rely on sparse linear algebra and indefinite linear systems are becoming increasingly hard to solve efficiently (e.g. interior-point method). To address this we revisit a maximin optimization formulation of the rSCOPF and the single-level mixed-integer semidefinite programming (MISDP) reformulation, which is obtained by taking the Lagrangian relaxation of the inner minimization ACOPF problem. In this paper, we focus on the development of a proximal projection bundle method (PPBM) for solving continuous relaxation node subproblems of the MISDP problem, based primarily on the well-known alternating direction method of multipliers. Cutting planes reminiscent of bundle method ideas are also applied in coordination with updates of the proximal parameter. The cutting-plane method can generate a large number of linear inequalities, leading to a large scale but decomposable quadratic programming (QP) subproblem that is amenable to GPUs. We present the numerical results on the IEEE 30, 57, 118, and 300-bus systems by using our PBMM method. We discuss the main computational bottleneck of our method, which is the time taken to solve each iteration of a QP subproblem instance of the PPBM, and how GPU architectures can accelerate this solution process.

bundle method↗