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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 OSTI2018

Concurrent implicit spectral deferred correction scheme for low-Mach number combustion with detailed chemistry

We present a parallel multi-implicit time integration scheme for the advection-diffusion-reaction systems arising from the equations governing low-Mach number combustion with complex chemistry. Here, our strategy employs parallelisation across the method to accelerate the serial Multi-Implicit Spectral Deferred Correction (MISDC) scheme used to couple the advection, diffusion, and reaction processes. In our approach, the diffusion solves and the reaction solves are performed concurrently by different processors. Our analysis shows that the proposed parallel scheme is stable for stiff problems and that the sweeps converge to the fixed-point solution at a faster rate than with serial MISDC. We present numerical examples to demonstrate that the new algorithm is high-order accurate in time, and achieves a parallel speedup compared to serial MISDC.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Graphene foam supported LiFePO4 nanosheets composite as advanced cathode for lithium ion batteries

Highlights: • Construct self-supported porous LiFePO{sub 4}/GF composite film. • Porous LiFePO{sub 4}/GF composite film show superior cycling life. • Hierarchical porous nanosheets structure boost fast electron/ion transfer. - Abstract: Smart design of advanced cathode plays a significantly important role for the development of high-performance lithium ion batteries (LIBs). In this work, we synthesized a new kind of LiFePO{sub 4}/Graphene foam (LFP/GF) integrated binder-free cathode via chemical vapor deposition (CVD) + hydrothermal method. The LFP is composed of interconnected LiFePO{sub 4} nanosheets with numerous nanopores of 50–500 nm. High porosity and good adhesion endow LiFePO{sub 4}/GF film superior electrochemical properties with remarkable high-rate capacities, small polarization and stable cycling life. A high-rate capacity of 110 mAh g{sup −1} at 24 C is obtained for the LiFePO{sub 4}/GF composite. Moreover, a high capacity retention of 95% is acquired after 160 cycles. This enhancement is due to the unique integrated porous LiFePO{sub 4} nanosheets and good support of graphene foam providing fast ion/electron transfer. Our work displays a new insight in constructing integrated cathode for application in high-performance LIBs.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2017

Sustainable Energy-Storage Materials from Lignin-Graphene Nanocomposite-Derived Porous Carbon Film

We present a simple, green approach to fabricating porous free-standing carbon films. An alkaline solution of low-cost, renewable lignin and graphene oxide (GO) is cast, followed by simultaneous carbonization and activation. Lignin, which is the least valued product from several biomass processing industries, is an efficient source of carbon when used as an intercalating agent to separate graphene sheets derived from homogeneous GO/lignin nanocomposite films prepared from an aqueous alkaline (KOH) solution. After thermal treatment the GO/lignin films show complete dispersion of reduced GO sheets within amorphous lignin-derived carbon. Furthermore, the presence of KOH in the film produces activated carbon. The resulting activated carbon films display a specific surface area of up to 1744 m2 g 1 and consist of a balance of pore volumes with pore sizes below and above 1 nm. A two-electrode supercapacitor composed of these films in an aqueous electrolyte exhibits near-ideal capacitive behavior at an ultrahigh scan rate of 1 V s 1, while maintaining an excellent specific capacitance of 162 F g 1. Such outstanding performance of renewable carbon as a supercapacitor, in addition to the ease of electrode fabrication from a precursor containing 85 % lignin, offers a novel method for valorization of lignin-rich byproduct streams from biomass processing industries.

25 ENERGY STORAGE↗
DOE OSTI2026

Two-phase flow numerical analysis of electrode geometry for alkaline water electrolyzers

Hydrogen is a promising component of a future energy-secure and efficient economy, but its competitiveness depends on reducing production costs. One strategy is to operate alkaline water electrolyzers at higher current densities to increase output. However, this intensifies performance losses due to gas bubble accumulation, which blocks transport pathways and deactivates electrochemically active surfaces. Enhancing bubble evacuation through electrode design is therefore essential. Previous studies have explored various approaches — such as modifying surface morphology, applying sonication or pressure modulation, and introducing surfactants — but these efforts have addressed a limited range of conditions due to the complexity of two-phase flow and electrode geometries. Experiments have also largely been focused on either cell level improvements, which lack the information necessary to isolate each contributing factor, or on modified geometries that are not relevant to practical cell operation. From a modeling perspective, conventional Eulerian multiphase models do not track the complex gas–liquid interfacial dynamics and often neglect surface tension and contact angle effects, reducing their predictive accuracy. To provide insights on the effects of different electrode geometries on the performance of alklaine water electrolyzers this work employs an immersed boundary volume-of-fluid method to simulate bubble behavior in 3D porous electrodes. Multiple base electrode geometries, typically used in practice, with varying porosity are evaluated under a constant surface gas generation rate. Simulation data is analyzed to quantify electrode gas coverage, bubble size dynamics and other relevant metrics. Results show that porosity strongly influences bubble accumulation on electrode surfaces, with higher porosity reducing gas coverage, and its not strictly dependent on the electrode geometry. However, the electrode’s base geometry significantly affects gas accumulation at the separator gap, independent of porosity. A foam electrode geometry resulted in the lowest gas coverage of all electrodes with a median volumetric gas coverage of 11%, but at the cost of a 70% reduction in active area compared with the largest surface area electrode, while gyroid electrodes showed the best trade-off between gas coverage, particularly at the separator surface, and electrochemically active area. In conclusion, the results highlight the need for holistic electrode design strategies.

Alkaline↗
DOE OSTI2025

Crystal Growth Scale-Up and Stability of RbSr 2 X 5 :Eu Scintillators

The discovery and development of new scintillation materials support national security needs. In these applications, large volumes of scintillator crystals are needed to achieve efficient screening for contraband. Therefore, one important step in the discovery of new scintillator compositions is testing their feasibility for scale-up and their stability. In this work, high-quality Ø22 mm crystals of two new scintillators RbSr 2 Br 5 :Eu and RbSr 2 I 5 :Eu were grown via the Vertical Bridgman method, and their scintillation properties were characterized. Here, the Ø22 mm RbSr 2 I 5 :Eu crystals could be grown with fast translation rates up to 3.5 mm/h. Both RbSr 2 Br 5 :Eu and RbSr 2 I 5 :Eu had high scintillation performance, including light yields of 46,000 and 61,000 ph/MeV, respectively, for Ø22 × 35 mm crystals. Additionally, properties related to physical stability were investigated, including the coefficients of thermal expansion via high-temperature X-ray diffraction (HTXRD) as well as moisture sensitivity. HTXRD confirmed the absence of solid–solid phase transitions and showed that RbSr 2 Br 5 had minimal thermal expansion anisotropy compared to RbSr 2 I 5 and some other inorganic metal halide scintillators, which favors the growth of large-sized crystals.

36 MATERIALS SCIENCE↗
DOE OSTI2020

TeraChem Cloud: A High-Performance Computing Service for Scalable Distributed GPU-Accelerated Electronic Structure Calculations

The encapsulation and commoditization of electronic structure arise naturally as interoperability, and the use of nontraditional compute resources (e.g., new hardware accelerators, cloud computing) remains important for the computational chemistry community. Here, we present TERACHEM CLOUD, a high-performance computing service (HPCS) that offers on-demand electronic structure calculations on both traditional HPC clusters and cloud-based hardware. The framework is designed using off-the-shelf web technologies and containerization to be extremely scalable and portable. Within the HPCS model, users can quickly develop new methods and algorithms in an interactive environment on their laptop while allowing TERACHEM CLOUD to distribute ab initio calculations across all available resources. This approach greatly increases the accessibility of hardware accelerators such as graphics processing units (GPUs) and flexibility for the development of new methods as additional electronic structure packages are integrated into the framework as alternative backends. Cost-performance analysis indicates that traditional nodes are the most cost-effective long-term solution, but commercial cloud providers offer cutting-edge hardware with competitive rates for short-term large-scale calculations. We demonstrate the power of the TERACHEM CLOUD framework by carrying out several showcase calculations, including the generation of 300,000 density functional theory energy and gradient evaluations on medium-sized organic molecules and reproducing 300 fs of nonadiabatic dynamics on the B800-B850 antenna complex in LH2, with the latter demonstration using over 50 Tesla V100 GPUs in a commercial cloud environment in 8 h for approximately $1250.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

What is the Role of Nb in Nickel-Rich Layered Oxide Cathodes for Lithium-Ion Batteries?

Nickel-rich layered metal oxide LiNi 1–y–z Mn y Co z O 2 (1 – y – z ≥ 0.8) materials are the most promising cathodes for next-generation lithium-ion batteries in electric vehicles. However, they lose more than 10% of their capacity on the first cycle, and interfacial/structural instability causes capacity fading. Coating and substitution are possible direct and effective solutions to solve these challenges. In this Letter, Nb coating and Nb substitution on LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) is easily produced through a scalable wet chemistry method followed by sintering from 400 to 800 °C. A Li-free Nb oxide treatment is found to remove surface impurities forming a LiNbO 3 /Li 3 NbO 4 surface coating, to reduce the first capacity loss and to improve the rate performance. Furthermore, Nb substitution stabilizes the structure, as evidenced by less heat evolution on heating, thus providing better long cycling stability with a 93.2% capacity retention after 250 cycles.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Insights from high-fidelity modeling of industrial rotary bell atomization

The global automotive industry sprayed over 2.6 billion liters of paint in 2018, much of which through electrostatic rotary bell atomization, a highly complex process involving the fluid mechanics of rapidly rotating thin films tearing apart into micrometer-thin filaments and droplets. Coating operations account for 65% of the energy usage in a typical automotive assembly plant, representing 10,000s of gigawatt-hours each year in the United States alone. Optimization of these processes would allow for improved robustness, reduced material waste, increased throughput, and significantly reduced energy usage. Here, we introduce a high-fidelity mathematical and algorithmic framework to analyze rotary bell atomization dynamics at industrially relevant conditions. Our approach couples laboratory experiment with the development of robust non-Newtonian fluid models; devises high-order accurate numerical methods to compute the coupled bell, paint, and gas dynamics; and efficiently exploits high-performance supercomputing architectures. These advances have yielded insight into key dynamics, including i) parametric trends in film, sheeting, and filament characteristics as a function of fluid rheology, delivery rates, and bell speed; ii) the impact of nonuniform film thicknesses on atomization performance; and iii) an understanding of spray composition via primary and secondary atomization. These findings result in coating design principles that are poised to improve energy- and cost-efficiency in a wide array of industrial and manufacturing settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

PMU-data-driven Event Classification in Power Transmission Grids

This paper presents an event classification in transmission grids. The convolutional neural network (CNN)-based classifier is proposed to capture the temporal similarity of time-synchronized data stream from phasor measurement units (PMUs). The proposed CNN is trained using Bayesian optimization to search for the best hyperparameters. The effectiveness of the proposed event classification is validated through the real-world dataset from the U.S. transmission grids. This dataset includes line outage, transformer outage, frequency, and oscillation events. The validation process also includes different PMU outputs, such as voltage magnitude, phase angle, current magnitude, frequency, and rate of change of frequency (ROCOF). The results show that ROCOF gives the best classification performance compared to other PMU outputs. In addition, it is shown that the classifier trained with a larger dataset has higher accuracy. Moreover, the superiority of the proposed method is validated through comparison with other state-of-the-art classification methods.

Niazazari, Iman↗
DOE OSTI2018

Optimizing DER Participation in Inertial and Primary-Frequency Response

This paper develops an approach to enable the optimal participation of distributed energy resources (DERs) in inertial and primary-frequency response alongside conventional synchronous generators. Leveraging a reduced-order model description of frequency dynamics, DERs' synthetic inertias and droop coefficients are designed to meet time-domain performance objectives of frequency overshoot and steady-state regulation. Furthermore, an optimization-based method centered around classical economic dispatch is developed to ensure that DERs share the power injections for inertial- and primary-frequency response in proportion to their power ratings. Simulations for a modified New England test-case system composed of ten synchronous generators and six instances of the IEEE 37-node test feeder with frequency-responsive DERs validate the design strategy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

SU-E-T-354: Efficient and Enhanced QA Testing of Linear Accelerators Using a Real-Time Beam Monitor

Purpose: To investigate the feasibility of performing routine QA tests of linear accelerators (Linac) using the Integral Quality Monitoring (IQM) system. The system, consisting of a 1-D sensitivity gradient large area ion-chamber mounted at the collimator, allows automatic collection and analysis of beam data. Methods: The IQM was investigated to perform several QA constancy tests, similar to those recommended by AAPM TG142, of a Linac including: beam output, MLC calibration, beam symmetry, relative dose factor (RDF), dose linearity, output as a function of gantry angle and dose rate. All measurements by the IQM system accompanied a reference measurement using a conventional dosimetry system and were performed on an Elekta Infinity Linac with Agility MLC. The MLC calibration check is done using a Picket-Fence type 2×10cm{sup 2} field positioned at different off-axis locations along the chamber gradient. Beam symmetry constancy values are established by signals from an 4×4cm{sup 2} aperture located at various off-axis positions; the sensitivity of the test was determined by the changes in the signals in response to a tilt in the beam. The data for various square field sizes were used to develop a functional relationship with RDF. Results: The IQM tracked the beam output well within 1% of the reference ion-chamber readings. The Picket-Fence type field test detected a 1mm shift error of one MLC bank. The system was able to detect 2.5% or greater beam asymmetry. The IQM results for all other QA tests were found to agree with the reference values to within 0.5%. Conclusion: It was demonstrated that the IQM system can effectively monitor the Linac performance parameters for the purpose of routine QA constancy tests. With minimum user interactions a comprehensive set of tests can be performed efficiently, allowing frequent monitoring of the Linac. The presenting author’s salary is funded by the manufacturer of the QA device. All the other authors have financial interests with the commercialization of this QA device.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

Machine learning and TDDFT software for stopping power computation

(SF-24-012) Stopping power describes the rate that a material slows radiation particles passing through it and is useful in designing many technologies. Few organizations can perform new measurements, which require significant resources and rare equipment, and all others rely on coarse approximations rendered from pre-existing data. Methods for computing stopping power in new materials, such as time-dependent density functional theory (TD-DFT), have only recently (circa-2015) become available but are too computationally costly to use frequently enough to have a pronounced impact. We have created a method that opens a pathway to computing stopping power without any need for experimental data by combining electronic structure computations and machine learning.

Ward, Logan↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Towards Resolving the Atomization Process of an Idealized Fire Sprinkler with VOF Modeling

Fire suppression modeling depends on accurate characterization of the atomization processes of fire sprinklers.Numerical modeling, particularly the volume of fluid (VOF) method, has been applied to understanding the atomization behavior of an idealized sprinkler geometry consisting of a 9.5 mm inner-diameter cylindrical nozzle and a flat, 25.4 mm diameter disk with a liquid flow rate of 0.87 L/s. The simulations have been performed with an OpenFOAM based VOF solver, using the isoAdvector scheme for interfacial reconstruction. The sheet breakup distance and film thickness were calculated and compared with measurements from a previous study. A mesh refinement study identified the sensitivities in the predicted quantities to mesh resolution. This study enables furtherapplication of the model to simulation of the fully atomized spray.

Engineering↗
DOE OSTI2016

Sediment Resuspension and Transport in Water Distribution Storage Tanks

Computational and experimental studies were conducted to better understand conditions that affect particle resuspension and movement in water storage tanks. Parameters that were investigated included inlet/outlet (I/O) line location and diameter, flow rate, particle size, and filling versus draining cycles. Simulation results showed that smaller particle sizes, higher flow rates, and draining cycles yielded the greatest potential for particle resuspension, which was generally limited to regions near the I/O line. Small‐scale experiments were also performed using different sizes of glass beads and silica sand; the results generally validated the models. Mitigation methods were also presented to reduce the amount of particle resuspension. A pipe that extended from the I/O line into the tank (slightly above the bottom floor) was found to significantly reduce the potential for particle resuspension in both the computational models and experiments.

Ho, Clifford K.↗
DOE OSTI2015

Simplified Predictive Models for CO 2 Sequestration Performance Assessment: Research Topical Report on Task #4 - Reduced-Order Method (ROM) Based Models

Reduced-order models provide a means for greatly accelerating the detailed simulations that will be required to manage CO 2 storage operations. In this work, we investigate the use of one such method, POD-TPWL, which has previously been shown to be effective in oil reservoir simulation problems. This method combines trajectory piecewise linearization (TPWL), in which the solution to a new (test) problem is represented through a linearization around the solution to a previously-simulated (training) problem, with proper orthogonal decomposition (POD), which enables solution states to be expressed in terms of a relatively small number of parameters. We describe the application of POD-TPWL for CO 2 -water systems simulated using a compositional procedure. Stanford’s Automatic Differentiation-based General Purpose Research Simulator (AD-GPRS) performs the full-order training simulations and provides the output (derivative matrices and system states) required by the POD-TPWL method. A new POD-TPWL capability introduced in this work is the use of horizontal injection wells that operate under rate (rather than bottom-hole pressure) control. Simulation results are presented for CO 2 injection into a synthetic aquifer and into a simplified model of the Mount Simon formation. Test cases involve the use of time-varying well controls that differ from those used in training runs. Results of reasonable accuracy are consistently achieved for relevant well quantities. Runtime speedups of around a factor of 370 relative to full- order AD-GPRS simulations are achieved, though the preprocessing needed for POD-TPWL model construction corresponds to the computational requirements for about 2.3 full-order simulation runs. A preliminary treatment for POD-TPWL modeling in which test cases differ from training runs in terms of geological parameters (rather than well controls) is also presented. Results in this case involve only small differences between training and test runs, though they do demonstrate that the approach is able to capture basic solution trends. The impact of some of the detailed numerical treatments within the POD-TPWL formulation is considered in an Appendix.

Mishra, Srikanta↗
DOE OSTI2017

Fault diagnosis and severity estimation in nuclear power plants - 196

Signed Directed Graph (SDG) is a type of fault diagnosis method with a capability to display the complex relationship between plant parameters and reveal the potential danger and the propagation path when a fault occurs in a nuclear plant. However, the fault diagnosis method based on qualitative SDG model inevitably has a number of disadvantages, and some of these disadvantages include its inability to identify reoccurring faults; ambiguous reasoning that can result in low resolution; performance that relies on the accuracy of the model; slow diagnosis rate for large systems and poor real-time diagnosis, among others. In this paper, we propose an SDG fault diagnosis methodology based on Granular Computing Theory, referred to as Granular Computing Signed Directed Graph (GCSDG). This paper shows how knowledge reduction nature of granular computing based fault diagnosis can simplify the decision table, reduce the allocation of resources, scale-down the complexity of the problem and improve the efficiency of data processing. Then, we estimate the severity of the fault using Back Propagation (BP) neural network approach. The proposed methodology was verified by considering certain faults in the primary loop of a PWR, using PCTRAN. The result shows a considerable improvement in fault diagnosis and the severity estimated serves as a useful information for the operator, providing a firm foundation for further intervention. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗