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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Predicting Elastic Constants of Refractory Complex Concentrated Alloys Using Machine Learning Approach

Refractory complex concentrated alloys (RCCAs) have drawn increasing attention recently owing to their balanced mechanical properties, including excellent creep resistance, ductility, and oxidation resistance. The mechanical and thermal properties of RCCAs are directly linked with the elastic constants. However, it is time consuming and expensive to obtain the elastic constants of RCCAs with conventional trial-and-error experiments. The elastic constants of RCCAs are predicted using a combination of density functional theory simulation data and machine learning (ML) algorithms in this study. The elastic constants of several RCCAs are predicted using the random forest regressor, gradient boosting regressor (GBR), and XGBoost regression models. Based on performance metrics R-squared, mean average error and root mean square error, the GBR model was found to be most promising in predicting the elastic constant of RCCAs among the three ML models. Additionally, GBR model accuracy was verified using the other four RHEAs dataset which was never seen by the GBR model, and reasonable agreements between ML prediction and available results were found. The present findings show that the GBR model can be used to predict the elastic constant of new RHEAs more accurately without performing any expensive computational and experimental work.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Utilization of Synthetic Near-Infrared Spectra via Generative Adversarial Network to Improve Wood Stiffness Prediction

Near-infrared (NIR) spectroscopy is widely used as a nondestructive evaluation (NDE) tool for predicting wood properties. When deploying NIR models, one faces challenges in ensuring representative training data, which large datasets can mitigate but often at a significant cost. Machine learning and deep learning NIR models are at an even greater disadvantage because they typically require higher sample sizes for training. In this study, NIR spectra were collected to predict the modulus of elasticity (MOE) of southern pine lumber (training set = 573 samples, testing set = 145 samples). To account for the limited size of the training data, this study employed a generative adversarial network (GAN) to generate synthetic NIR spectra. The training dataset was fed into a GAN to generate 313, 573, and 1000 synthetic spectra. The original and enhanced datasets were used to train artificial neural networks (ANNs), convolutional neural networks (CNNs), and light gradient boosting machines (LGBMs) for MOE prediction. Overall, results showed that data augmentation using GAN improved the coefficient of determination (R 2 ) by up to 7.02% and reduced the error of predictions by up to 4.29%. ANNs and CNNs benefited more from synthetic spectra than LGBMs, which only yielded slight improvement. All models showed optimal performance when 313 synthetic spectra were added to the original training data; further additions did not improve model performance because the quality of the datapoints generated by GAN beyond a certain threshold is poor, and one of the main reasons for this can be the size of the initial training data fed into the GAN. LGBMs showed superior performances than ANNs and CNNs on both the original and enhanced training datasets, which highlights the significance of selecting an appropriate machine learning or deep learning model for NIR spectral-data analysis. The results highlighted the positive impact of GAN on the predictive performance of models utilizing NIR spectroscopy as an NDE technique and monitoring tool for wood mechanical-property evaluation. Further studies should investigate the impact of the initial size of training data, the optimal number of generated synthetic spectra, and machine learning or deep learning models that could benefit more from data augmentation using GANs.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2005

Aerodynamics of trans-atmopsheric vehicles : a non-dimensional approach

The non-dimensional approach to aerodynamics of trans-atmospheric flight is discussed, which explicitly takes into account the vertical span of the atmosphere as well as atmospheric mass load (atmospheric pressure) at the current flight level. As an example, a simple analytic model of the dynamics of trans-atmospheric flight is considered for both powered boost and non-powered glide of a trans-atmospheric vehicle, and the applicability to more general, numerical models of atmospheric flight is discussed.

powered boost↗
DOE OSTI2021

Fault location in High Voltage Multi-terminal dc Networks Using Ensemble Learning

Precise location of faults for large distance power transmission networks is essential for faster repair and restoration process. High Voltage direct current (HVdc) networks using modular multi-level converter (MMC) technology has found its prominence for interconnected multi-terminal networks. This allows for large distance bulk power transmission at lower costs. However, they cope with the challenge of dc faults. Fast and efficient methods to isolate the network under dc faults have been widely studied and investigated. After successful isolation, it is essential to precisely locate the fault. The post-fault voltage and current signatures are a function of multiple factors and thus accurately locating faults on a multi-terminal network is challenging. In this paper, we discuss a novel data-driven ensemble learning based approach for accurate fault location. Here we utilize the eXtreme Gradient Boosting (XGB) method for accurate fault location. The sensitivity of the proposed algorithm to measurement noise, fault location, resistance and current limiting inductance are performed on a radial three-terminal MTdc network designed in Power System Computer Aided Design (PSCAD)/Electromagnetic Transients including dc (EMTdc).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Event-Based Energy Impact Tracking and Forecasting with Limited Measurements for Rooftop Units

Packaged air conditioning units and heat pumps, also known as rooftop units (RTUs), are responsible for almost 133 billion kWh of electricity usage annually on site for space cooling U.S. commercial buildings. In addition, the use of heat pumps is a trend we expect to accelerate as buildings transition from fossil fuel-based heating to electricity as a key step for decarbonizing the U.S. commercial buildings sector. However, the operation conditions and energy use of RTUs and heat pumps are usually not well monitored as they are not commonly integrated with building automation systems and lack exposed sensing and control points. To fill this gap, this paper proposes a framework for tracking and forecasting energy impacts resulting from degradation of performance and improved performance for unit servicing using limited data. The proposed framework makes use of a constrained dataset, specifically measurements of the outdoor air temperature and the power demand of individual RTUs, to track and forecast changes in energy use associated with changes in performance over various temporal horizons ranging from days to weeks. Following the detection of an RTU fault, performance degradation, or performance improvement, the framework employs a prediction model to assess the cumulative energy impact. We demonstrate the effectiveness of the method with field-collected data for servicing and degradation examples and compare the predicting accuracy of Gradient Boosting Decision Tree (GBDT) Regression models to Support Vector Regression and Linear Regression models. The results show that GBDT achieved the best accuracy for time-series validation datasets for the servicing and degradation cases, and the prediction model was able to track the cumulative energy impacts of events. The proposed framework can inform building owners of the cumulative change in energy usage of RTUs associated with performance degradation, performance improvement, or a fault.

packaged air conditioners, packaged heat pumps, ro↗
DOE OSTI2021

Stability enhancement module for grid-following converters: Hardware implementation and validation

Low-frequency oscillations limit power delivery of voltage-source converter interfaced resources, for example, wind and solar, operating in grid-following control mode. Our prior research identified the instability mechanism as the coupling between real power and voltage at point of common coupling. In turn, a stability enhancement control was proposed to weaken the coupling by introducing a simple voltage-based feedback loop. The stability enhancement control has been validated via electromagnetic transient simulation testbeds. The objectives of this paper are (a) to provide a more insightful explanation of the enhancement module by examining the impedance of the grid-following converter and (b) to verify the performance of the stability module using a hardware testbed. The theoretic examination demonstrates that stability enhancement module effectively reduces the negative resistance effect of $Z_{qq}$ and thus boosts stability. The experimental research leads to a hardware testbed of a grid-following converter with the ability of demonstrating low-frequency oscillations. Finally, the experimental results demonstrate the efficacy of the proposed stability enhancement control and the readiness of commercial realization of the stability enhancement unit.

42 ENGINEERING↗
DOE OSTI2021

Sink Engineering in Photosynthetic Microbes

Photosynthetic organisms must balance harvested light energy (source) with a corresponding capacity to utilize the energy via downstream metabolic processes (sink). Insufficient source energy leads to starvation conditions, while insufficient sink capacity results in an overreduction of the electron transport chain, reduced photosynthetic efficiency, and photodamage. Despite the central importance of source/sink balancing in photosynthesis, our understanding of the mechanisms used to control this process in remains poor across diverse phototrophs. In particular, these balancing processes are essentially unstudied in cyanobacteria and microalgae, which possess distinct needs relative to the multicellular plants that have served as the model organisms for elucidating source/sink pathways. Recent bioengineering efforts have uncovered phenomena that are consistent with a well-developed source/sink sensing machinery in unicellular phototrophs. Furthermore, a deeper understanding of the fundamental mechanisms underlying source/sink dynamics in cyanobacteria and algae may prove to be critical for our capacity to boost photosynthetic efficiencies in these organisms, and enable their use as alternative crop species.

Carbon Fixation↗
DOE OSTI2020

Autonomously Responsive Membranes for Chemical Warfare Protection

Stimuli-responsive materials offer new opportunities to resolve long-standing material challenges and are rapidly gaining pivotal roles in diverse applications. For example, smart protective garments that rapidly transport water vapor and autonomously block chemical threats are expected to enable an effective new paradigm of adaptive personal protection. However, the incorporation of these seemingly incompatible properties into a single responsive system remains elusive. Herein, a bistable membrane that can rapidly, selectively, and reversibly transition from a highly breathable state in a safe environment to a chemically protective state when exposed to organophosphate threats such as sarin is demonstrated. Dynamic response to chemical stimuli is achieved through the physical collapse of an ultrathin copolymer layer on the membrane surface, which efficiently gates transport through membrane pores composed of single-walled carbon nanotubes (SWNTs). The adoption of nanometer-wide SWNTs for ultrafast moisture conduction enables a simultaneous boost in size-sieving selectivity and water-vapor permeability by decreasing nanotube diameter, thereby overcoming the breathability/protection trade-off that limits conventional membrane materials. Adaptive multifunctional membranes based on this platform greatly extend the active use of a protective garment and present exciting opportunities in many other areas including separation processes, sensing, and smart delivery.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Synergistic Use of Pyridine and Selenophene in a Diketopyrrolopyrrole‐Based Conjugated Polymer Enhances the Electron Mobility in Organic Transistors

Abstract To achieve semiconducting materials with high electron mobility in organic field‐effect transistors (OFETs), low‐lying energy levels (the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO)) and favorable molecular packing and ordering are two crucial factors. Here, it is reported that the incorporation of pyridine and selenophene into the backbone of a diketopyrrolopyrrole (DPP)‐based copolymer produces a high‐electron‐mobility semiconductor, PDPPy‐Se. Compared with analogous polymers based on other DPP derivatives and selenophene, PDPPy‐Se features a lower LUMO that can decrease the electron transfer barrier for more effective electron injection, and simultaneously a lower HOMO that, however, can increase the hole transfer barrier to suppress the hole injection. Combined with thermal annealing at 240 °C for thin film morphology optimization to achieve large‐scale crystallite domains with tight molecular packing for effective charge transport along the conducting channel, OFET devices fabricated with PDPPy‐Se exhibit an n‐type‐dominant performance with an electron mobility (μ e ) as high as 2.22 cm 2 V −1 s −1 and a hole/electron mobility ratio (μ h /μ e ) of 0.26. Overall, this study demonstrates a simple yet effective approach to boost the electron mobility in organic transistors by synergistic use of pyridine and selenophene in the backbone of a DPP‐based copolymer.

Liu, Qian↗
DOE OSTI2020

Secondary Ion Mass Spectrometry (SIMS) for Chemical Characterization of Metal Halide Perovskites

Metal halide perovskite (MHP) solar cells have attracted much attention due to the rapidly growing power conversion efficiency that has reached 25.2% in a decade, comparable to established commercial photovoltaic modules. Compositional engineering is one of the most effective methods to boost the performance of MHP solar cells. Further improving the efficiency and the stability of MHP solar cells necessitates good understanding of the chemical–efficiency correlation and the chemical evolution during the degradation of MHP solar cells. In this regard, time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful tool to investigate the chemical aspect of MHPs and has played an important role in advancing the development of MHP optoelectronics. However, up to date, a review that can guide future utilization of ToF-SIMS in the MHP development is missing. In this paper, the capabilities of ToF-SIMS in MHP investigations are summarized and analyzed from simple material synthesis and chemical distribution to more complicated device operation mechanism and stability. The strength of ToF-SIMS in resolving important issues in this field, such as interface composition, ion migration, and degradation in MHP is highlighted. Finally, an outlook with an emphasis on making the utmost of ToF-SIMS in developing MHP devices is provided.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Synergy of Ion Doping and Spiral Array Architecture on Ti 2 Nb 10 O 29 : A New Way to Achieve High-Power Electrodes

Ameliorating electronic/ionic transport and structural stability of electrode materials is important to the development of power-intensive lithium ion batteries. Despite its great potential as a high-power anode, titanium niobium oxide (Ti 2 Nb 10 O 29 , TNO) still underperforms due to its unsatisfactory electronic/ionic conductivity. In this work, a powerful synergistic strategy by combining ion doping and spiral array architecture to boost high-rate performance of TNO is reported. Cr 3+ doped TNO nanoparticles (Cr-TNO) of 5-10 nm intimately grow on a conductive vertical graphene@TiC-C (VGTC) skeleton, forming novel Cr-TNO@VGTC spiral arrays. The unique spiral growth of TNO is achieved due to the confinement effect of VGTC skeleton. Meanwhile, a more open TNO crystal structure with faster ion transfer paths and enhanced structural stability is realized by Cr 3+ doping, demonstrated via density functional theory calculation and in situ synchrotron X-ray diffraction technique. Benefiting from the superior conductive network, enhanced intrinsic electronic/ionic conductivity of Cr-TNO and reinforced structural stability, the Cr-TNO@VTC arrays show prominent high-power performance with a large capacity of 220 mAh g -1 at 40 C (power density of ≈11 kW kg -1 ) and superior durability (91% retention after 500 cycles). This work provides a new path for the construction of widespread high-power electrodes for fast energy storage.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Decreasing the Overpotential of Aprotic Li-CO 2 Batteries with the In-Plane Alloy Structure in Ultrathin 2D Ru-Based Nanosheets

We report the aprotic Li-CO 2 battery is emerging as a promising energy storage technology with the capability of CO 2 fixation and conversion. However, its practical applications are still impeded by the large overpotential. Herein, the general synthesis of a series of ultrathin 2D Ru-M (M = Co, Ni, and Cu) nanosheets by a facile one-pot solvothermal method is reported. As a proof-of-concept application, the representative RuCo nanosheets are used as the cathode catalysts for Li-CO 2 batteries, which demonstrate a low charge voltage of 3.74 V, a small overpotential of 0.94 V, and hence a high energy efficiency of 75%. Ex/in situ studies and density functional theory calculations reveal that the excellent catalytic performance of RuCo nanosheets originates from the enhanced adsorption toward Li and CO 2 during discharge as well as the elevated electron interaction with Li 2 CO 3 during charge by the in-plane RuCo alloy structure. This work indicates the feasibility of boosting the electrochemical performance of Li-CO 2 batteries by in-plane metal alloy sites of ultrathin 2D alloy nanomaterials.

25 ENERGY STORAGE↗
DOE OSTI2022

Practically Accessible All-Solid-State Batteries Enabled by Organosulfide Cathodes and Sulfide Electrolytes

The combination of organic electrode materials and sulfide electrolytes is expected to enable the development of all-solid-state organic batteries featuring high energy density and long-term sustainability. In this work, thiuram hexasulfide is reported as a low-cost and high-capacity organic cathode for solid-state batteries based on sulfide electrolytes, delivering a capacity of ~600 mAh g -1 and retaining 80.8 % capacity after 500 cycles. An electrochemically reversible change of the cathode interface was revealed upon cycling. Full cell displays an oscillating stress change up to 0.6 MPa during cycling, predominated by the anode side. The energy density is 1140 Wh kg -1 at the material level and 376 Wh kg -1 at the electrode level, which are among the best-reported organic cathodes to date. A high areal capacity of 10.4 mAh cm -2 is reached with a high mass loading cathode. A dry-film approach is further explored to manufacture sheet-type cells. Free-standing electrolyte film is merely ~48 μm thick and demonstrates an ultralow areal resistance of 3.9 Ω cm 2 , significantly boosts the cell-level energy density and reduces the cell internal resistance.

25 ENERGY STORAGE↗
DOE OSTI2024

Molecular Fe-N 4 Moieties Coupled with Atomic Co-N 4 Sites Toward Improved Oxygen Reduction Performance

Research on high-efficiency and cost-efficient catalysts for oxygen reduction reaction (ORR) is still a vital but challenging issue for commercializing metal–air batteries. Herein, a single-molecule/atom hybrid catalyst is developed to boost the ORR, in which iron phthalocyanine molecules containing molecular Fe-N 4 moieties couple with atomic Co-N 4 sites on the surface of polyhedral carbon. Density functional theory calculations reveal that face-to-face laminated construction of Fe-N 4 and Co-N 4 in the hybrid catalyst can effectively modulate the electronic structure of active iron atoms and reduce the energy barrier of the rate-determining step for ORR. As a result, this hybrid catalyst demonstrates excellent ORR performance, featuring a half-wave potential of 0.904 V, a peak power density of 238.3 mW cm -2 for zinc–air battery, and outstanding electrocatalytic stability. Here, this work offers a distinctive and robust molecular/atomic engineering approach to creating efficient electrocatalysts, advancing the fields of metal–air batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Investigation of Key Electronic States in Layered Mixed Chalcogenides With a d 0 Transition Metal as Li-Ion Cathodes

Lithium-rich transition metal chalcogenides are witnessing a revival as candidates for Li-ion cathode materials, spurred by the boost in their capacities from transcending conventional redox processes based on cationic states and tapping into additional chalcogenide states. A particularly striking case is Li 2 TiS 3-y Se y , which features a d 0 metal. While the end members are expectedly inactive, substantial capacities are measured when both Se and S are present. Using X-ray absorption spectroscopy, it is shown that the electronic structure of Li 2 TiS 3-y Se y is not a simple combination of the end members. The data confirm previous hypotheses that, in Li 2 TiS 2.4 Se 0.6 , this behavior is underpinned by concurrent and reversible redox of only S and Se, and identify key electronic states. Moreover, wavelet transforms of the extended X-ray absorption fine structure provide direct evidence of the formation of short Se–Se units upon charging. The study uncovers the underpinnings of this intriguing reactivity and highlights the richness of redox chemistry in complex solids.

25 ENERGY STORAGE↗
DOE OSTI2026

A Holistic Stabilization of the Anode in Lithium‐Sulfur Batteries Through a Ternary Alloy Fusion

The anode in lithium–sulfur batteries (LSBs) is plagued by not only inhomogeneous lithium (Li) deposition, but also by parasitic side-reactions. Often, a thick anode (∼400 µm) is used to compensate for these drawbacks, lowering gravimetric and volumetric energy densities. In this work, we demonstrate a ternary alloy anode fabricated via scalable thermal fusion of Li with aluminum (Al) and tellurium (Te) to cohesively address these issues. The Al–Li skeleton serves to homogenize Li deposition as well as reinforce the anode, allowing it to be rolled down to low thicknesses. Te incorporation suppresses the reaction of Li with polysulfides (Li 2 Sn) to form lithium sulfide (Li 2 S) and electrolyte degradation. It can also facilitate the formation of polytellurosulfides (Li 2 Te x Sy), which are far more conducive to Li + -ion diffusion. Even under a lean electrolyte/sulfur (E/S) ratio of 8 µL mg −1 and a low negative/positive (N/P) ratio of 3, capacities over 900 mA h g −1 at a C/5 rate are obtained in cells with Li–Te–Al anodes with a capacity retention of 80% after 70 cycles. In comparison, the baseline lithium-metal anode retains only 35% after 50 cycles. Furthermore, the Li–Te–Al anodes confer a 24% boost to gravimetric energy density at the pouch cell level.

25 ENERGY STORAGE↗