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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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Multimodal sensor fusion framework for residential building occupancy detection
For several years now, smart building energy systems have been a research area of intensive activity. In light of the increasing need for sustainable buildings and energy systems, this trend motivates an increasing need for a solution to reduce carbon dioxide emissions and improve energy efficiency. This work proposes a high-performing and transferable occupancy detection framework that combines sensor data from different data modalities, including time series environmental data (temperature, humidity, and illuminance), image data, and acoustic energy data using ensemble method. To draw out the best prediction performance in each modality, the proposed framework was developed, including various models that were designed to learn the occupancy patterns reflected in the physical data streams. To tackle the time series environmental data, we designed two variants of an occupancy detection spatiotemporal pattern network (Occ-STPN) that performs both feature level and decision level fusion, respectively. We also propose a new metric; the fading memory mean square error (FMMSE), that provides a fair evaluation and penalization of delayed occupancy predictions. Multiple open-sourced datasets, including the Electricity Consumption and Occupancy and the University of California, Irvine's (UCI) building occupancy detection dataset, along with our own real data collected from six different houses, were used to validate the algorithms' performance. The experimental results presented herein break down the performance for each sensing modality, and a detailed analysis of the performance is also discussed.
Polycation ionic liquid tailored PEO-based solid polymer electrolytes for high temperature lithium metal batteries
Poly(ethylene oxide) (PEO)-based polymer electrolytes are promising candidates for solid-state electrolytes in safer, next generation lithium metal batteries. Despite their benefits however, PEO-based electrolyte exhibits highly crystalline ethylene oxide chains that provide poor ionic conductivity and, thus severely limit its practical application. Here, we report the use of hydroxypropyl trimethylammonium bis(trifluoromethane) sulfonimide chitosan salt (HACC-TFSI), which is an amorphous poly(ionic liquid) based biomass chitosan derivative, as a modifier for PEO-based solid polymer electrolytes (SPEs) to address these deficiencies. Hybrid SPEs with HACC-TFSI display enlarged amorphous regions with enhanced ionic conductivity. Interactions between quaternary ammonium cations and TFSI-anions in hybrid SPEs are also found to promote dissociation between Li + and TFSI - , which further increases ionic mobility. Moreover, the electrochemical stability, mechanical strength, and thermal stability of hybrid SPEs are collectively superior to blank SPEs without HACC-TFSI. LiFePO 4 /SPEs/Li full-cells assembled using 10wt% HACC-TFSI in PEO (10%HACC-TFSI/SPEs) electrolyte provide a capacity of 161.3 mAh g -1 and operate with excellent cycle performances at 0.2 C and 60 C. Finally, even when the temperature is increased to 150 C, LiFePO 4 /SPEs/Li cells with 10%HACC-TFSI/SPEs still display remarkable cycle performance with 73% capacity retention after 100 cycles at 1 C rate.
Simultaneous Single Crystal Growth and Segregation of Ni-Rich Cathode Enabled by Nanoscale Phase Separation for Advanced Lithium-Ion Batteries
Here, a novel nanoscale phase separation process has been discovered to promote the growth and segregation of single-crystal LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811). This process occurs directly during high-temperature calcination without significant agglomeration. The key lies in converting transition metal hydroxide (TM(OH) 2 ) precursors with well-controlled morphology into transition metal oxide (TMO) intermediates before reacting them with lithium salt. The nanoscale redistribution of Ni in TMO, resulting from the concurrent formation of spinel and rock salt phases, helps to deagglomerate the clusters of later-formed NMC811 crystals. The as-prepared single-crystal NMC811 is further validated in a 2Ah pouch cell, demonstrating 1,000 stable cycles. The fundamentally new reaction mechanism of single-crystal growth and segregation provides a new direction for large-scale synthesis of a broad range of single crystals for advanced energy storage.
Development of a River Dynamical Core for E3SM to simulate compound flooding on Exascale-class heterogeneous supercomputers
Flooding events pose significant risk to human life, property, and infrastructure. Physically-consistent quantification of altered flood risks in global models requires hyper-resolution (~1 km) or fine flood simulations using two-dimensional (2D) physics schemes, both of which are unavailable in the current generation Earth System Models. Here, in this work, we have developed the River Dynamical Core (RDycore), which is an open-source, 2D shallow water equation (SWE) library for the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM). RDycore uses PETSc and libCEED libraries that allows it to run efficiently on CPUs and GPUs, as well as select a time-integration algorithm at runtime without requiring any code modifications. RDycore achieves spatial error convergence rates for problems with analytical and manufactured solutions similar to those reported previously in the literature, or consistent with the implemented first-order spatial discretization scheme. RDycore's accuracy in predicting flooding for a well-studied dam break problem is comparable to existing SWE models. For a problem with 471 million grid cells, RDycore achieves a speedup of 6.6x and 7.6x on GPUs compared to CPUs when using 320 compute nodes on DOE's Perlmutter and Frontier supercomputers, respectively. The one-way coupling of the RDycore library within E3SM is demonstrated by performing multiple 5-day flooding simulations during Hurricane Harvey driven by five precipitation datasets. The E3SM--RDycore simulations at 30 m spatial resolution accurately simulate maximum water height during the hurricane when benchmarked against a previously published study and achieve a speedup of 15x (Perlmutter) and 21x (Frontier) on GPUs relative to CPUs. The work presented here is the foundational step in providing hardware and algorithmic portability framework for simulating kilometer-scale river dynamics within E3SM.
Towards intelligent emergency control for large-scale power systems: Convergence of learning, physics, computing and control
Here, this paper has delved into the pressing need for intelligent emergency control in large-scale power systems, which are experiencing significant transformations and are operating closer to their limits with more uncertainties. Learning-based control methods are promising and have shown effectiveness for intelligent power system control. However, when they are applied to large-scale power systems, there are multifaceted challenges such as scalability, adaptiveness, and security posed by the complex power system landscape, which demand comprehensive solutions. The paper first proposes and instantiates a convergence framework for integrating power systems physics, machine learning, advanced computing, and grid control to realize intelligent grid control at a large scale. Our developed methods and platform based on the convergence framework have been applied to a large (more than 3000 buses) Texas power system, and tested with 56 000 scenarios. Our work achieved a 26% reduction in load shedding on average and outperformed existing rule-based control in 99.7% of the test scenarios. The results demonstrated the potential of the proposed convergence framework and DRL-based intelligent control for the future grid.
An Open-Source Parallel EMT Simulation Framework
As the integration level of inverter-based resources (IBRs) increases, ensuring the reliable operation of the bulk power systems requires the use of electromagnetic transient (EMT) simulation tools to identify and mitigate system-wide stability risks. Conducting EMT studies for large-scale, IBR-rich grids, however, is challenging due to the inherent computational bottleneck caused by the underlying high-fidelity models and required small time steps. This paper introduces ParaEMT: an open-source, generic EMT simulation framework designed to accelerate simulations by leveraging advanced parallel computational technologies, such as high-performance computers. This paper presents a comprehensive exposition of ParaEMT, covering its modeling library, simulation strategy, framework structure, operational procedures, and auxiliary features, alongside its extensible parallel computational architecture. Notably, ParaEMT is a publicly accessible and modularized framework written in Python, thereby facilitating future development and the integration of new models and algorithms. The accuracy and efficiency of ParaEMT are demonstrated by rigorous validations via multiple case studies.
Analytical small-signal stability analysis of low-inertia power system frequency response considering secondary frequency regulation
Modern power systems are increasingly vulnerable to frequency instability as inverter-based resources (IBRs) replace synchronous machines and reduce system rotational inertia. Existing small-signal frequency stability assessment methods are either computationally intensive, relying on simulation-driven approaches, or lack analytical stability regions that explicitly account for secondary frequency response (SFR). This paper introduces new analytical frameworks that enable evaluation small-signal frequency stability while explicitly incorporating tunable IBR and SFR parameters. Using Kharitonov’s theorem with an overbounding approach, explicit small-signal stability criteria are derived. In addition, based on Białas’ criterion and Hurwitz matrix, analytical stability regions are established to reveal feasible design spaces for SFR and IBR parameters tuning. Extensive Matlab/Simulink-based simulations validate the accuracy and computational efficiency of the proposed methods, demonstrating that coordinated tuning of SFR and IBR parameters can substantially enhance system resilience. By bridging analytical rigor with practical tunability, this work provides an analytical framework for assessing small-signal frequency stability in low-inertia grids, supporting the real-time, scalable, and resilient operation of sustainable power systems.
Carbon nanofiber electrodes without commercial polymer precursors deliver superior electrochemical performance
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Evolution of particulate matter in the post-combustion zone of Zhundong lignite
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Assessment of sulfur trioxide formation due to enhanced interaction of nitrogen oxides and sulfur oxides in pressurized oxy-combustion
Pressurized oxy-combustion is emerging to be one of the best technologies for significantly decreasing the energy penalty for CO 2 capture in coal-fired power plants. However, the higher pressure boosts the formation of acid gases, including SO 3 and NO 2 , which could increase the risk of corrosion. The synergistic promotion of SO 3 and NO 2 formation in pressurized oxy-combustion is kinetically evaluated under representative conditions (1 ~ 30 atm, 600 ~ 1200 °C, NO/SO 2 = 0.1 ~ 5). We begin with a comprehensive mechanism (72 species and 428 reactions), covering nitrogen and sulfur chemistry, relying on GRI-Mech 3.0. This analysis shows that the interaction of SO X and NO X enhances the conversion rates of SO 2 → SO 3 , and this effect is more apparent at elevated pressures and lower temperatures. Mechanism analyses indicate that at elevated pressures, the formation pathways of SO 3 through HOSO 2 + O 2 = SO 3 + HO 2 , and NO 2 through HO 2 + NO = NO 2 + OH, are promoted due to the strong interaction between SO X and NO X . The intermediate between these two reactions is SO 2 + OH + M = HOSO 2 + M, resulting in a strong cycle, that can be expressed by the global reaction NO + SO 2 + O 2 = NO 2 + SO 3 . Finally, a nine-step reduced chemistry is developed and validated to accurately predict the formation of SO 3 in the post-flame region at elevated pressures.
Sustainable marine fuel production through mild hydrotreating of catalytic fast pyrolysis bio-oil
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Rheology and engine performance of very low sulfur fuel oil blended with 10% fast pyrolysis and hydrothermal liquefaction oils in a 2-stroke crosshead engine
The performance and emissions for a downscaled single-cylinder 2-stroke crosshead engine were determined for a very low sulfur fuel oil (VLSFO) when blended with 10 wt.% fast pyrolysis (FP) or hydrothermal liquefaction (HTL) bio-intermediates. The FP and HTL oils were derived from biomass and were observed to contain lower molecular weight (MW) hydrocarbons than neat VLSFO (which was evaluated as a baseline comparison). The addition of either biofuel reduced the overall viscosity of the VLSFO. Aging tests at 50, 90, and 120°C showed that the dynamic viscosity of VLSFO increased with exposure time up to two weeks. Similar trends were observed for the FP and HTL blends, but a pronounced spike in viscosity occurred for these fuels during the early period of exposure. None of the viscosity increases exceeded the operational limits of fuel system pumps. Engine performance studies were conducted under low, medium and high load operational settings. The relative performance of the test fuels was highly dependent on operating condition. In general, the engine results for the three test fuels were similar, but modest improvements in brake thermal efficiency and brake specific fuel consumption were observed, which may be attributed to the heightened reactivity of low molecular weight fraction of the FP and HTL oils.