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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Human-in-the-Loop Motion Control of a Two-DOF Hydraulic Backhoe Powered by the Hybrid Hydraulic Electric Architecture (HHEA)

Abstract The Hybrid Hydraulic-Electric Architecture (HHEA) combines the respective power density and control advantages of hydraulic and electric actuation to save energy for off-road vehicles. It uses a set of selectable common pressure rails to transmit the majority of power and electric actuation to modulate that power. As it is critical that off-road vehicles can perform tasks dexterously and exactly as commanded by the operator, the switchings between discrete pressure rails pose a potential challenge for smooth and precise motion. A control strategy consisting of a backstepping nominal control and least norm transition control has previously been developed to address this issue. It has been tested on 1 degree of freedom (DOF) testbeds where known trajectories were able to be tracked precisely. This paper presents the implementation of the HHEA motion control strategy on a 2-DOF backhoe operated by a human operator via a 2-DOF joystick. Unlike previous studies, the duty cycle is unknown beforehand and the decision to change pressure rails is taken in real-time. The efficacy of the motion control strategy has been validated experimentally. Several strategies to improve the user interface: control in workspace coordinates, pressure feedback, and velocity field-based task specification, have also been implemented and demonstrated to make operating the multiple DOF, HHEA actuated machine more intuitive to novice operators.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

DICE-gas turbine compound reheat combined cycle

Coal-fired Direct Injection Carbon Engine – Gas Turbine (DICE-GT) Compound-Reheat Combined Cycle (CRCC), is a combined cycle power plant comprising a multiplicity of coal-fired reciprocating internal combustion engines (RICE), which is commonly referred to by the acronym DICE (Direct Injection Carbon Engine); a natural gas-fired gas turbine (turbine and combustor); a heat recovery steam generator (HRSG); a steam turbine generator; and an integrally geared and intercooled centrifugal air compressor. Finally, the concept uses coal-water slurry/fluid in the DICE and natural gas (or hydrogen) in the gas turbine for unmatched efficiency (well above 50% net LHV) and modularity (block sizes of 120, 240 and 360 MW) with easy adaptability to carbon capture and sequestration with minimal additional cost, complexity and performance hit.

42 ENGINEERING↗
DOE OSTI2023

Hydroxyl radical‐driven transformations of bisphenol A and 2,4‐dinitroanisole: Experimental and computational analysis

Abstract This study used experimental and computational analysis to investigate the advanced oxidation of bisphenol A (BPA) and 2,4‐dinitroanisole (DNAN). The pseudo first‐order reaction rate constants depended on the molar peroxide ratio and were between 0.13 and 0.28 min −1 for BPA and between 0.018 and 0.032 min −1 for DNAN. The kinetic differences appear to be due in part to the energy requirements for oxidation, which depended on the reaction mechanism but were typically lower for BPA than they were for DNAN. Density functional theory (DFT) was used to develop transformation pathways that included experimentally‐detected byproducts. The most energetically favored pathway for BPA oxidation begins with the formation of hydroxylated derivatives, while for DNAN, the most energetically favorable degradation pathway begins with the substitution of the methoxy group. Overall, these findings demonstrate the power of combining experimental and computational tools to reveal transformation mechanisms during water treatment. Practitioner Points Advanced oxidation transformations for two emerging water pollutants, bisphenol A and dinitroanisole, was investigated. The observed reaction kinetics depended on molar peroxide ratio in a manner that is in keeping with previous findings. Density functional theory‐based analysis revealed reaction energy requirements and degradation pathways.

Engineering↗
DOE OSTI2022

A Study on Efficient Reinforcement Learning Through Knowledge Transfer

Although Reinforcement Learning (RL) algorithms have made impressive progress in learning complex tasks over the past years, there are still prevailing short-comings and challenges. Specifically, the sample-inefficiency and limited adaptation across tasks often make classic RL techniques impractical for real-world applications despite the gained representational power when combining deep neural networks with RL, known as Deep Reinforcement Learning (DRL). Recently, a number of approaches to address those issues have emerged. Many of those solutions are based on smart DRL architectures that enhance single task algorithms with the capability to share knowledge between agents and across tasks by introducing Transfer Learning (TL) capabilities. Here this survey addresses strategies of knowledge transfer from simple parameter sharing to privacy preserving federated learning and aims at providing a general overview of the field of TL in the DRL domain, establishes a classification framework, and briefly describes representative works in the area.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Power-Capping Metric Evaluation for Improving Energy Efficiency in HPC Applications

With high-performance computing systems now running at exascale, optimizing power-scaling management and resource utilization has become more critical than ever. This paper explores runtime power-capping optimizations that leverage integrated CPU-GPU power management on architectures like the NVIDIA GH200 superchip. We evaluate energy-performance metrics that account for simultaneous CPU and GPU power-capping effects by using two complementary approaches: speedup-energy-delay and a Euclidean distance-based multi-objective optimization method. By targeting a mostly compute-bound exascale science application, the Locally Self-Consistent Multiple Scattering (LSMS), we explore challenging scenarios to identify potential opportunities for energy savings in exascale applications, and we recognize that even modest reductions in energy consumption can have significant overall impacts. Our results highlight how GPU task-specific dynamic power-cap adjustments combined with integrated CPU-GPU power steering can improve the energy utilization of certain GPU tasks, thereby laying the groundwork for future adaptive optimization strategies.

Patrou, Maria [ORNL] (ORCID:0000000339754638)↗
DOE OSTI2023

Identifying limitations in screening high-throughput photocatalytic bimetallic nanoparticles with machine-learned hydrogen adsorptions

The Sabatier principle is of fundamental importance to computational catalyst discovery, saving researchers time and expense by predicting catalytic activity in silico at scale. However, as polycrystalline and nanoscale catalysts increasingly dominate industry, computational screening tools must be adapted to these uses. In this work, we demonstrate the effectiveness of computational adsorption energy screening in nanocatalysis by comparing a multisite adsorption energy prediction workflow against a large experimental dataset of hydrogen evolution activities over bimetallic nanoparticles. Comparing 16 million hydrogen adsorption energy predictions with the hydrogen evolution activity of 5300 experiments across 84 monometallic and bimetallic systems, we discover that favorable adsorption energies are a necessary condition for experimental activity, but other factors often determine trends in practice. Finally, about half of the bimetallic search space can be excluded from experimental screens using hydrogen adsorption predictions, but this method may become significantly more powerful when combined with other screening tools.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Economic and environmental performance of biomass gasification for renewable natural gas production in the context of the U.S. natural gas supply

Bioenergy technologies offer potential for reducing greenhouse gas (GHG) emissions. One such promising technology is biomass gasification, which is the conversion of biomass into renewable natural gas (RNG) for use with a natural gas combined-cycle power generation system. However, the associated economic and emission effects need to be better understood to enable optimal decision-making and avoid missed opportunities for enhancing efficiency and increasing system circularity. This analysis explores opportunities to (1) decarbonize natural-gas-based systems and (2) leverage the extensive US natural gas infrastructure to mobilize biomass resources to achieve environmental and economic benefits. Here, in this analysis, the research team used a spatially explicit biomass logistics model (integrated with relevant biomass availability, technoeconomic analysis, and life cycle assessment information) to simulate economically optimal biomass allocation for RNG production and use for decarbonization in the United States. Results show that the United States has the potential to produce 9203 million GJ of RNG within the expected range of $\$$12–30/GJ. Further analyses tested the overall RNG production system's sensitivity to economic and emissions parameters of nine different processes. The sensitivity analysis results indicate that the median carbon abatement cost of RNG is most sensitive to changes in emissions associated with conversion processes and land use changes. These findings provide a deeper understanding of RNG's economic and emission potential for decision-making and guiding future research.

09 BIOMASS FUELS↗
DOE OSTI2026

Harnessing citizen science to contextualize adaptation mechanism discovery

Species occupying broad geographic regions have evolved multiple mechanisms to regulate phenological characteristics, enabling adaptations to diverse native habitats. By developing computer vision AI to process citizen science observations across native habitats over North America, we uncovered a consistent latitudinal trend of earlier flowering at higher latitudes in warm-season perennial grasses. To explore the underlying mechanisms of adaptation, we conducted common garden experiments with one species (switchgrass) and discovered the opposite latitudinal flowering-time trend. Integration of differential plasticity of GI-Hd1-FTL1 haplotypes of flowering time regulatory genes, haplotype range, and local environmental profiles found that observations from native habitats capture only part of the genotype-environment-phenotype spectrum established in common garden experiments, therefore reconciling the discrepancy. Two mechanisms emerged as key forces shaping current haplotype ranges and influencing future shifts. Our study highlights the power of combining citizen science observations with designed experiments to uncover mechanisms of adaptation across spatiotemporal scales.

FTL1↗
DOE OSTI2024

Divide and conquer: Learning chaotic dynamical systems with multistep penalty neural ordinary differential equations

Forecasting high-dimensional dynamical systems is a fundamental challenge in various fields, such as geosciences and engineering. Neural Ordinary Differential Equations (NODEs), which combine the power of neural networks and numerical solvers, have emerged as a promising algorithm for forecasting complex nonlinear dynamical systems. However, classical techniques used for NODE training are ineffective for learning chaotic dynamical systems. In this work, we propose a novel NODE-training approach that allows for robust learning of chaotic dynamical systems. Here, our method addresses the challenges of non-convexity and exploding gradients associated with underlying chaotic dynamics. Training data trajectories from such systems are split into multiple, non-overlapping time windows. In addition to the deviation from the training data, the optimization loss term further penalizes the discontinuities of the predicted trajectory between the time windows. The window size is selected based on the fastest Lyapunov time scale of the system. Multi-step penalty(MP) method is first demonstrated on Lorenz equation, to illustrate how it improves the loss landscape and thereby accelerates the optimization convergence. MP method can optimize chaotic systems in a manner similar to least-squares shadowing with significantly lower computational costs. Our proposed algorithm, denoted the Multistep Penalty NODE, is applied to chaotic systems such as the Kuramoto-Sivashinsky equation, the two-dimensional Kolmogorov flow, and ERA5 reanalysis data for the atmosphere. It is observed that MP-NODE provide viable performance for such chaotic systems, not only for short-term trajectory predictions but also for invariant statistics that are hallmarks of the chaotic nature of these dynamics.

Chaotic dynamical systems↗
DOE OSTI2022

Creative absorber design and optimization for CO 2 capture with aqueous piperazine

The Texas Carbon Management Program is preparing a Front-End Engineering Design (FEED) of the PZASTM (Piperazine with the Advanced Stripper TM ) for CO 2 capture at a natural gas combined cycle power plant in West Texas. This paper develops the absorber design for the capture system and studies the operational strategies of the absorber in response to ambient temperature swing. The design uses 2 absorber trains, and each train integrates absorption and a water-wash section in one column. Each column has a cross-section area of 174 m 2 and consists of 3 sections of MellapakPlus® 252Y structured packing. Packing heights for each section are 3, 4.9, and 2.7 m from top to bottom. A pump-around intercooler applied at the bottom bed circulates solvent and eliminates the direct contact cooler. The baseline CO 2 removal rate is 90% and will vary between 78% and 90% with ambient temperature. The system uses air cooling to eliminate cooling water usage. The water balance in the system will be managed by operating the water wash at a high circulation rate (1300 kg/h) to condense water during the night and make up for water loss during the day. Finally, the proposed design reduces the capital cost of the absorber column by minimizing the packing height and eliminating the direct contact cooler (DCC), and improves the energy cost by lowering the pump-around intercooling temperature and increasing the pump-around rate.

03 NATURAL GAS↗
DOE OSTI2022

Process design of the piperazine advanced stripper for a 460 MW NGCC

The Texas Carbon Management Program is conducting a Front-End Engineering Design (FEED) for Piperazine with the Advanced Stripper™ (PZAS™) at a natural gas combined cycle power plant in west Texas. Here, this paper presents the design of the advanced stripper system with an emphasis on the selection of packing type, packing height, column diameter, and heat exchanger size, demonstrates the effect of ambient temperature swing on the regeneration energy requirement, and investigates two alternate process configurations that provide better energy performance than PZAS™. The design uses 2 stripper trains, and each train uses two cold cross exchangers, one hot cross exchanger, a stripper column, a CO 2 exchanger, and a condenser. Each column consists of two sections of random packing with RSR 2 packing at the top and RSR 3 packing at the bottom, with a total packed height of 7.5 m. The cold exchanger was designed with an NTU of 6, the hot exchanger with an NTU of 2, and the CO 2 exchanger with an NTU of 5. At 0.4 rich loading and 0.2 lean loading, this design resulted in a heat duty of 2.85 GJ/tonne CO 2 . The heat duty can vary up to 3.1 GJ/tonne CO 2 due to lower rich loading at higher summer temperature, but the absolute steam requirement is 8–15% lower in the summer. An alternate design, PZAS with hot rich bypass, is proposed, which can reduce the equivalent work of stripping by 1.5–3.5% compared to PZAS at low heat exchanger size, due to a reduction in stripper driving force. Using a hot rich bypass increases the vapor fraction of the total rich bypass stream from 0.3 to 1 mol% compared to using the warm rich bypass, which also contributes to a reduction in lost work in the column. PZAS with hot rich bypass outperforms PZAS in energy requirement below a packing height of 15 m, but fails to do so above 15 m of packing due to a column pinch that results in little change in LMTD of the column. PZAS with hot rich bypass has the potential to minimize capital cost in addition to reducing energy requirement as it requires less packing to strip CO 2 than PZAS and requires only one cross exchanger with 8 heat transfer units.

03 NATURAL GAS↗
DOE OSTI2024

Heterogeneous oxidation of hydrogen-natural gas blends in a safe, clean, and efficient burner design

The growth of hydrogen as an alternative clean fuel for fulfilling thermal energy needs of multiple economic sectors globally requires access to reliable, safe, energy efficient, emission free combustion technology. The work described in this short communication shows the applicability of a novel heterogeneous combustion design in utilizing a wide range of hydrogen blended methane concentrations to cleanly and safely generate thermal energy. The utilization of a simple first principles design approach along with engineered materials yielded a fuel-flexible hybrid infrared-convection burner design capable of lowering NOx emissions by more than 95% while simultaneously improving the heat transfer efficiency by 15% with a wide range of hydrogen concentrations. Here, the safety and performance of the combustion design was demonstrated in an burner at capacities of up to 12,000 Btu/h in producing temperatures of 900 °C. The atmospheric burner concept was integrated in a cooktop configuration with combined thermal power rating of 30,000 Btu/h and operated with hydrogen blended natural gas.

08 HYDROGEN↗
DOE OSTI2021

X-ray absorption spectroscopy of trivalent Eu, Gd, Tb, and Dy chlorides and oxychlorides

Growing interest in next generation molten salt nuclear reactors necessitates a fundamental understanding of the chemical and physico-chemical properties of fission products, including elements of the lanthanide series. In this study, we probed coordination environment of Eu, Gd, Tb, and Dy in their chloride salts with both laboratory and synchrotron scale X-ray absorption spectroscopy (XAS). Here the aerobic melting was carried out with LnCl 3 ·nH 2 O, where Ln is Eu, Gd, Tb, and Dy, and yielded predominant formation of oxychlorides (LnOCl), that was confirmed by extended X-ray absorption fine structure (EXAFS) analysis and X-ray diffraction (XRD). Density functional theory (DFT) and time-dependent density functional theory (TDDFT) computations were performed to obtain ground state structures, lattice parameters, and to simulate L 3 -edge XANES (X-ray absorption near edge structure) spectra of the LnCl 3 ·nH 2 O and LnOCl. A blue shift, initially predicted by our calculations, was also observed for the experimental Ln L 3 -edge XANES spectra of the hydrated trichlorides vs oxychlorides, highlighting the power of combining the predictive ability of theory with experiment to elucidate the properties of the compounds of interest. The blue shift is indicative of structural stabilization of LnCl 3 ·nH 2 O due to higher symmetry compared with LnOCl. To the best of knowledge, this is a first study providing the framework for fundamental insights into the structure of Ln trichlorides and oxychlorides in support of future characterization of lanthanides in the molten salt environment.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Correlative analysis of structure and chemistry of Li x FePO 4 platelets using 4D-STEM and X-ray ptychography

Lithium iron phosphate (Li x FePO 4 ), a cathode material used in rechargeable Li-ion batteries, phase separates upon de/lithiation under equilibrium. The interfacial structure and chemistry within these cathode materials affects Li-ion transport, and therefore battery performance. Here, correlative imaging of Li x FePO 4 was performed using four-dimensional scanning transmission electron microscopy (4D-STEM), scanning transmission X-ray microscopy (STXM), and X-ray ptychography in order to analyze the local structure and chemistry of the same particle set. Over 50,000 diffraction patterns from 10 particles provided measurements of both structure and chemistry at a nanoscale spatial resolution (16.6–49.5 nm) over wide (several micron) fields-of-view with statistical robustness. Li x FePO 4 particles at varying stages of delithiation were measured to examine the evolution of structure and chemistry as a function of delithiation. In lithiated and delithiated particles, local variations were observed in the degree of lithiation even while local lattice structures remained comparatively constant, and calculation of linear coefficients of chemical expansion suggest pinning of the lattice structures in these populations. Partially delithiated particles displayed broadly core–shell-like structures, however, with highly variable behavior both locally and per individual particle that exhibited distinctive intermediate regions at the interface between phases, and pockets within the lithiated core that correspond to FePO 4 in structure and chemistry. The results provide insight into the Li x FePO 4 system, subtleties in the scope and applicability of Vegard’s law (linear lattice parameter-composition behavior) under local versus global measurements, and demonstrate a powerful new combination of experimental and analytical modalities for bridging the crucial gap between local and statistical characterization.

25 ENERGY STORAGE↗
DOE OSTI2021

Detrital U-Pb zircon and 40 Ar/ 39 Ar muscovite geochronology from Middle Pennsylvanian strata in the Anadarko Basin, Texas Panhandle, USA

The Late Mississippian-Permian Anadarko Basin formed in Texas and Oklahoma, USA as the result of inversion of Neoproterozic and Cambrian rift structures. Subsidence was driven by flexural loading of the Amarillo-Wichita Uplift, and this uplift may represent the easternmost element of the Ancestral Rocky Mountains system. The northwestern part of this basin has generally been interpreted to have been filled by sediment derived from the Ancestral Front Range Uplift, ~475 km to the northwest during the early stages of basin filling. We test this model using U-Pb detrital zircon and 40 Ar/ 39 Ar detrital muscovite results from three subsurface samples of the Morrow B sandstone in the northwestern part of the Anadarko Basin. We provide a new maximum depositional age of 310.9 ± 4.9 Ma that indicates the age of the Morrow B to be no older than late Atokan to early Desmoinesian Age (Moscovian), ~10 Myr younger than previously interpreted. In contrast to some previous interpretations, we propose that the most likely source for the sediment in the Morrow B is the Amarillo Uplift to the south. Detrital zircon and detrital muscovite data have age peaks at 900–1300 Ma, 1370 Ma and 1600–1800 Ma corresponding to derivation from Grenville, Granite-Rhyolite and Yavapai-Mazatzal basement provinces, respectively. A dominant detrital zircon peak at 1370 Ma suggests that Mesoproterozoic granites in the Amarillo Uplift were exposed by Middle Pennsylvanian time, and detritus eroded from the Amarillo Uplift dominated the lowstand sediment budget of the Texas Panhandle during this time; small volumes of sediment were likely sourced from the Ancestral Front Range. In conclusion, this study presents the first detrital geochronology data from the subsurface Anadarko Basin and the first detrital muscovite data from late Paleozoic southwestern Laurentia. The results presented here highlight the interpretive power of combined detrital zircon and muscovite datasets.

58 GEOSCIENCES↗
DOE OSTI2025

On the effectiveness of neural operators at zero-shot weather downscaling

Machine-learning (ML) methods have shown great potential for weather downscaling. These data-driven approaches provide a more efficient alternative for producing high-resolution weather datasets and forecasts compared to physics-based numerical simulations. Neural operators, which learn solution operators for a family of partial differential equations, have shown great success in scientific ML applications involving physics-driven datasets. Neural operators are grid-resolution-invariant and are often evaluated on higher grid resolutions than they are trained on, i.e., zero-shot super-resolution. Given their promising zero-shot super-resolution performance on dynamical systems emulation, we present a critical investigation of their zero-shot weather downscaling capabilities, which is when models are tasked with producing high-resolution outputs using higher upsampling factors than are seen during training. To this end, we create two realistic downscaling experiments with challenging upsampling factors (e.g., 8x and 15x) across data from different simulations: the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit. While neural operator-based downscaling models perform better than interpolation and a simple convolutional baseline, we show the surprising performance of an approach that combines a powerful transformer-based model with parameter-free interpolation at zero-shot weather downscaling. We find that this Swin-Transformer-based approach mostly outperforms models with neural operator layers in terms of average error metrics, whereas an Enhanced Super-Resolution Generative Adversarial Network-based approach is better than most models in terms of capturing the physics of the ground truth data. We suggest their use in future work as strong baselines.

17 WIND ENERGY↗
DOE OSTI2025

Python Library for Monte Carlo Simulations with Ab Initio and Machine-Learned Interatomic Potentials

There is a growing need in the simulation community for software that provides a transparent, reproducible, usable, and extensible (TRUE) Monte Carlo (MC) simulation framework employing energies from ab initio methods and machine-learning interatomic potentials (MLIPs). We introduce a Python library (ASE-MC) that adds Monte Carlo functionality to the Atomic Simulation Environment (ASE) package. Now, we can combine the powerful tools used to build systems and perform ab initio and MLIP in ASE with MC simulation algorithms to sample the configurational space with a concise Python script. After presenting the design philosophy, we demonstrate the flexibility of our approach using selected examples. These example simulations include liquid water described with a message-passing MLIP in the canonical and isothermal–isobaric ensembles, sampling the characteristic dihedral angle of biphenyl and comparing an MLIP to first-principles calculations, and a grand canonical Monte Carlo simulation of ammonia adsorption on Pt(111). These examples showcase the main features of the software, which include flexibility in the choice of ab initio or MLIP engine, ab initio or MLIP grand canonical MC with cavity bias insertions and deletions, the ability to add custom MC moves to the move set, and how users can condense complex MC workflows into a single Python script. Finally, this library serves as a framework for reproducible Monte Carlo simulations, facilitating easy reproduction of the work and application to new systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Vacancy defect control of colossal thermopower in FeSb2

Abstract Iron diantimonide is a material with the highest known thermoelectric power. By combining scanning transmission electron microscopic study with electronic transport neutron, X-ray scattering, and first principle calculation, we identify atomic defects that control colossal thermopower magnitude and nanoprecipitate clusters with Sb vacancy ordering, which induce additional phonon scattering and substantially reduce thermal conductivity. Defects are found to cause rather weak but important monoclinic distortion of the unit cell P n n m → P m . The absence of Sb along [010] for high defect concentration forms conducting path due to Fe d orbital overlap. The connection between atomic defect anisotropy and colossal thermopower in FeSb 2 paves the way for the understanding and tailoring of giant thermopower in related materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗