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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 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Investigation of catalytic pathways and separations for lignin breakdown into monomers and fuels

An analysis of the energy and material consumption for three different lignin conversion pathways has been carried out in order to assess their relative promise. Recommendations to allow for improved performance based on the current state of the art have been made, with particular focus on the use of heterogeneous nanocatalysts. Lignin is currently burned for heat and power in industrial processes, but its aromaticity and high energy content make it a good source for fuels and chemical feedstocks. The conversion of lignin to value added products has been the subject of a number of excellent reviews, and it is upon this literature as well as recent publications that the processes were selected. The analysis includes consideration of the thermodynamics of conversion, the technical feasibility of each process, and the energy and material balance. For each of the processes, improvements to future research activities have been suggested to move the processes from the bench-scale to large-scale demonstrations. This analysis is not meant to be comprehensive, in terms of process, feedstock, or selected criteria. However, this work illustrates a path forward following pre-established goals of thermodynamic efficiency, selectivity, and feasibility.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Cloud Based Analytical Framework for Synchrophasor Data Analysis

The paper presents initial results from the development of a cloud-based, Big Data analysis platform for power systems. The computational pipeline uses the Apache Spark framework running in an OpenStack cloud infrastructure. A real-world phasor measurement unit (PMU) dataset has been used to carry out the analysis. Actual examples of power system events detection using synchrophasor data are presented. It has been shown that applications of the cloud based computing environment and the Apache Spark framework enable a significant increase in the computational efficiency of large-scale PMU data analysis.

PMU, Machine Learning, Big Data, SPARK, Event Dete↗
DOE OSTI2026

Effect of Sample Mass, Confinement, and Preheating Time on the Thermal Response of LLM‐105: Experiments and Kinetic Analysis

Various small-scale experiments were performed to provide data for developing a model to predict the thermal response of LLM-105 over a wide range of conditions. The thermal decomposition of LLM-105 was studied as a function of sample mass, confinement of volatile products, and preheating time in both isothermal and ramped heating experiments. The thermal decomposition of LLM-105 is a two-step process, as shown by the two exothermic peaks in the heat flow profiles, which were fitted to two nth-order autocatalytic reaction models with a similar activation energy of ∼289 kJ/mol. The magnitude and shape of these peaks varied with sample mass and confinement. Increasing sample mass enhanced the second exotherm with respect to the first one, while increasing the level of confinement promoted a transition from a sublimation-dominated regime towards thermal decomposition. The effect of LLM-105 particle size on the rate of weight loss was evident for open-pan experiments, where bigger particles sublimed at lower temperatures than smaller particles. Thermal response and solid residue composition of LLM-105 samples were analyzed following preheating for different durations. Longer preheating times caused a shift of the second exotherm to lower temperatures and a decrease in the reaction enthalpy, confirming that LLM-105 decay is a consecutive reaction mechanism, probably autocatalytic. In conclusion, the kinetic model derived from ramped experiments was validated against the measured LLM-105 fraction remaining and the enthalpy remaining of the solid residue as a function of preheating times and showed good agreement.

Chemistry - Chemical explosives↗
DOE OSTI2020

A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence

In the present study, we investigate different data-driven parameterizations for large eddy simulation of two-dimensional turbulence in the a priori settings. These models utilize resolved flow field variables on the coarser grid to estimate the subgrid-scale stresses. We use data-driven closure models based on localized learning that employs a multilayer feedforward artificial neural network with point-to-point mapping and neighboring stencil data mapping, and convolutional neural network fed by data snapshots of the whole domain. The performance of these data-driven closure models is measured through a probability density function and is compared with the dynamic Smagorinksy model (DSM). The quantitative performance is evaluated using the cross-correlation coefficient between the true and predicted stresses. We analyze different frameworks in terms of the amount of training data, selection of input and output features, their characteristics in modeling with accuracy, and training and deployment computational time. We also demonstrate computational gain that can be achieved using the intelligent eddy viscosity model that learns eddy viscosity computed by the DSM instead of subgrid-scale stresses. Here, we detail the hyperparameters optimization of these models using the grid search algorithm.

42 ENGINEERING↗
DOE OSTI2025

New capabilities for rapid depletion analysis of pebble-bed reactors in SCALE

The SCALE Leap-In for Cores at Equilibrium (SLICE) method leverages capabilities available in the SCALE modeling and simulation suite to facilitate rapid estimation of equilibrium core inventories for flowing-pebble reactor systems in support of a variety of fuel cycle applications. New capabilities that compliment the SLICE method have been developed for the ORIGAMI interface to ORIGEN in SCALE to facilitate rapid depletion calculations for flowing-pebble systems. New features include a more generalized and flexible means of specifying interpolation dimensions, the ability to evaluate pebbles moving through user-defined “axial zones” in each of the pebble’s multiple passes through the core, and the treatment of differential velocities across radial channels (representing both pebble-to-pebble and wall-to-pebble friction effects). ORIGAMI thus provides an efficient user interface to define a pebble’s path and irradiation histories of its multiple passes through the core to calculate the pebble’s time-dependent inventories, which can be useful in various applications domains such as safeguards, criticality safety, and disposal analyses. The consistency of new ORIGAMI approach was verified against the solution obtained using the SLICE method with ORIGEN-ARP, showing excellent agreement.

Skutnik, Steven E. [Oak Ridge National Laboratory ↗
DOE OSTI2025

Novel artificial neural network model for instantaneous power losses and operational efficiency mapping of MW-scale vanadium redox flow battery for improved technoeconomic analysis

A novel data-driven, machine-learning-based method for modeling the instantaneous power losses of a distribution-sited 2 MW/8MWh vanadium redox flow battery (VRFB), a grid-scale electrochemical storage technology, is introduced and compared against benchmark empirical modeling approaches, including symmetric and asymmetric models, as well as a recent convex hull modeling approach. The novel loss modeling method introduces several advantages over the benchmark models and over simplistic efficiency estimates, the most significant of which is that the model can accurately reflect the stepwise and non-linear parasitic losses associated with the duty cycles of mechanical auxiliary systems like pump motor drives and blower fans. Residuals of the models are compared; the proposed data driven model features significantly improved accuracy over the benchmark models. The model's coefficient of determination is also improved relative to that of the benchmark models. Furthermore, a novel method for visualization of operational efficiency of the grid-scale storage technology is introduced. To demonstrate the benefits of the novel data-driven method for modeling the VRFB, the benchmark models and the proposed models are embedded into an Open DSS distribution network model to study two applications of the grid-scale electrical storage system: load leveling for grid support and energy arbitrage. This article demonstrates that the accuracy of the instantaneous power loss model significantly impacts the understanding of the state of charge of the VRFB. In turn, the accuracy of the efficiency modeling of the VRFB impacts the understanding of the potential economic value and technical benefits to the distribution network operators. In conclusion, the presented power loss modeling approach is, therefore, highly relevant for utility-stakeholders, battery asset owners, system engineers, system designers, and financial planners interested in evaluating or optimizing the operation of grid-scale VRFBs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

Long-Gradient Separations Coupled with Selected Reaction Monitoring for Highly Sensitive, Large Scale Targeted Protein Quantification in a Single Analysis

Long-gradient separations coupled to tandem MS were recently demonstrated to provide a deep proteome coverage for global proteomics; however, such long-gradient separations have not been explored for targeted proteomics. Herein, we investigate the potential performance of the long-gradient separations coupled with selected reaction monitoring (LG-SRM) for targeted protein quantification. Direct comparison of LG-SRM (5 h gradient) and conventional LC-SRM (45 min gradient) showed that the long-gradient separations significantly reduced background interference levels and provided an 8- to 100-fold improvement in LOQ for target proteins in human female serum. Based on at least one surrogate peptide per protein, an LOQ of 10 ng/mL was achieved for the two spiked proteins in non-depleted human serum. The LG-SRM detection of seven out of eight endogenous plasma proteins expressed at ng/mL or sub-ng/mL levels in clinical patient sera was also demonstrated. A correlation coefficient of >0.99 was observed for the results of LG-SRM and ELISA measurements for prostate-specific antigen (PSA) in selected patient sera. Further enhancement of LG-SRM sensitivity was achieved by applying front-end IgY14 immunoaffinity depletion. Besides improved sensitivity, LG-SRM offers at least 3 times higher multiplexing capacity than conventional LC-SRM due to ~3-fold increase in average peak widths for a 300-min gradient compared to a 45-min gradient. Therefore, LG-SRM holds great potential for bridging the gap between global and targeted proteomics due to its advantages in both sensitivity and multiplexing capacity.

Shi, Tujin↗
DOE OSTI2019

A Scale-Dynamic Network Abstraction Approach for Traffic Analysis

The necessary trade-off between accuracy and efficiency of various traffic simulation tools depends on the resolution of the roadway network. While a detailed high-resolution road network could better represent connectivity of real-world road network and ensure an accurate simulation result, a low-resolution network omitting trivial details could reduce computational burden and improve the computational efficiency of traffic simulation. This study focuses on dynamically adjusting the roadway network resolution during the process of simulation-based dynamic traffic assignment (SBDTA) with the objective to expedite both the simulation and the assignment within SBDTA. We propose to divide the whole time horizon into several periods dynamically and to Abstract the network for each period separately. The result showed that half of the CPU time for both the simulation and the assignment is saved, while the network performance remains consistent or near consistent compared with the scenario using the most detailed network.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

Threshold-based queuing system for performance analysis of cloud computing system with dynamic scaling

Cloud computing is promising technology to manage and improve utilization of computing center resources to deliver various computing and IT services. For the purpose of energy saving there is no need to unnecessarily operate many servers under light loads, and they are switched off. On the other hand, some servers should be switched on in heavy load cases to prevent very long delays. Thus, waiting times and system operating cost can be maintained on acceptable level by dynamically adding or removing servers. One more fact that should be taken into account is significant server setup costs and activation times. For better energy efficiency, cloud computing system should not react on instantaneous increase or instantaneous decrease of load. That is the main motivation for using queuing systems with hysteresis for cloud computing system modelling. In the paper, we provide a model of cloud computing system in terms of multiple server threshold-based infinite capacity queuing system with hysteresis and noninstantanuous server activation. For proposed model, we develop a method for computing steady-state probabilities that allow to estimate a number of performance measures.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Simulated performance of biomass gasification based combined power and refrigeration plant for community scale application

Thermal performance analysis and sizing of a biomass gasification based combined power and refrigeration plant (CPR) is reported in this study. The plant is capable of producing 100 kWe of electrical output while simultaneously producing a refrigeration effect, varying from 28-68 ton of refrigeration (TR). The topping gas turbine cycle is an indirectly heated all-air cycle. A combustor heat exchanger duplex (CHX) unit burns producer gas and transfer heat to air. This arrangement avoids complex gas cleaning requirements for the biomass-derived producer gas. The exhaust air of the topping GT is utilized to run a bottoming ammonia absorption refrigeration (AAR) cycle via a heat recovery steam generator (HRSG), steam produced in the HRSG supplying heat to the generator of the refrigeration cycle. Effects of major operating parameters like topping cycle pressure ratio (r{sub p}) and turbine inlet temperature (TIT) on the energetic performance of the plant are studied. Energetic performance of the plant is evaluated via energy efficiency, required biomass consumption and fuel energy savings ratio (FESR). The FESR calculation method is significant for indicating the savings in fuel of a combined power and process heat plant instead of separate plants for power and process heat. The study reveals that, topping cycle attains maximum power efficiency of 30%in pressure ratio range of 8-10. Up to a certain value of pressure ratio the required air flow rate through the GT unit decreases with increase in pressure ratio and then increases with further increase in pressure ratio. The capacity of refrigeration of the AAR unit initially decreases up to a certain value of topping GT cycle pressure ratio and then increases with further increase in pressure ratio. The FESR is found to be maximized at a pressure ratio of 9 (when TIT=1100°C), the maximum value being 53%. The FESR is higher for higher TIT. The heat exchanger sizing is also influenced by the topping cycle pressure ratio and GT-TIT.

09 BIOMASS FUELS↗
DOE OSTI2017

A mixed-scale dense convolutional neural network for image analysis

Significance Popular neural networks for image-processing problems often contain many different operations, multiple layers of connections, and a large number of trainable parameters, often exceeding several million. They are typically tailored to specific applications, making it difficult to apply a network that is successful in one application to different applications. Here, we introduce a neural network architecture that is less complex than existing networks, is easy to train, and achieves accurate results with relatively few trainable parameters. The network automatically adapts to a specific problem, allowing the same network to be applied to a wide variety of different problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

MELCOR Analysis of OSU Multi-Application Small Light Water Reactor (MASLWR) Experiment

A multi-application small light water reactor (MASLWR) conceptual design was developed by Oregon State University (OSU) with emphasis on passive safety systems. The passive containment safety system employs condensation and natural circulation to achieve the necessary heat removal from the containment in case of postulated accidents. Containment condensation experiments at the MASLWR test facility at OSU are modeled and analyzed with MELCOR, a system-level reactor accident analysis computer code. The analysis assesses its ability to predict condensation heat transfer in the presence of noncondensable gas for accidents where high-energy steam is released into the containment. This work demonstrates MELCOR’s ability to predict the pressure-temperature response of the scaled containment. Our analysis indicates that the heat removal rates are underestimated in the experiment due to the limited locations of the thermocouples and applies corrections to these measurements by conducting integral energy analyses along with CFD simulation for confirmation. Furthermore, the corrected heat removal rate measurements and the MELCOR predictions on the heat removal rate from the containment show good agreement with the experimental data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗