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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

A Legendre–Fourier spectral method with exact conservation laws for the Vlasov–Poisson system

In this study, we present the design and implementation of an L 2 -stable spectral method for the discretization of the Vlasov–Poisson model of a collisionless plasma in one space and velocity dimension. The velocity and space dependence of the Vlasov equation are resolved through a truncated spectral expansion based on Legendre and Fourier basis functions, respectively. The Poisson equation, which is coupled to the Vlasov equation, is also resolved through a Fourier expansion. The resulting system of ordinary differential equation is discretized by the implicit second-order accurate Crank–Nicolson time discretization. The non-linear dependence between the Vlasov and Poisson equations is iteratively solved at any time cycle by a Jacobian-Free Newton–Krylov method. In this work we analyze the structure of the main conservation laws of the resulting Legendre–Fourier model, e.g., mass, momentum, and energy, and prove that they are exactly satisfied in the semi-discrete and discrete setting. The L 2 -stability of the method is ensured by discretizing the boundary conditions of the distribution function at the boundaries of the velocity domain by a suitable penalty term. The impact of the penalty term on the conservation properties is investigated theoretically and numerically. An implementation of the penalty term that does not affect the conservation of mass, momentum and energy, is also proposed and studied. A collisional term is introduced in the discrete model to control the filamentation effect, but does not affect the conservation properties of the system. Numerical results on a set of standard test problems illustrate the performance of the method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

SDYN-GANs: Adversarial learning methods for multistep generative models for general order stochastic dynamics

We introduce adversarial learning methods for data-driven generative modeling of dynamics of nth-order stochastic systems. Our approach builds on Generative Adversarial Networks (GANs) with generative model classes based on stable m-step stochastic numerical integrators. From observations of trajectory samples, we introduce methods for learning long-time predictors and stable representations of the dynamics. Our approaches use discriminators based on Maximum Mean Discrepancy (MMD), training protocols using both conditional and marginal distributions, and methods for learning dynamic responses over different time-scales. We show how our approaches can be used for modeling physical systems to learn force-laws, damping coefficients, and noise-related parameters. Our adversarial learning approaches provide methods for obtaining stable generative models for dynamic tasks including long-time prediction and developing simulations for stochastic systems.

• Artificial intelligence (AI) / machine learning ↗
DOE OSTI2016

An ecology-oriented exploitation mode of groundwater resources in the northern foot of Tianshan Mountain, China

In recent years, ecological degradation caused by irrational groundwater exploitation has been of growing concern in arid and semiarid regions. To address the groundwater-ecological issues, this paper proposes a groundwater-resource exploitation mode to evaluate the tradeoff between groundwater development and ecological environment in the northern Tianshan Mountains, northwest China’s Xinjiang Uygur Autonomous Region. Field surveys and remote sensing studies were conducted to analyze the relation between the distribution of hydrological conditions and the occurrence of ecological types. The results show that there is a good correlation between groundwater depth and the supergene ecological type. Numerical simulations and ecological assessment models were applied to develop an ecology-oriented exploitation mode of groundwater resources. The mode allows the groundwater levels in different zones to be regulated by optimizing groundwater exploitation modes. The prediction results show that the supergene ecological quality will be better in 2020 and even more groundwater can be exploited in this mode. This study provides guidance for regional groundwater management, especially in regions with an obvious water scarcity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A Kinetic Model-Driven Techno-Economic Analysis of Plastic Pyrolysis: Linking Process Dynamics to Economic Viability

This study employs a kinetic model integrated into Aspen Plus to predict pyrolysis product distribution under various conditions. A techno-economic assessment calculated the minimum selling price (MSP) of pyrolysis oil under different operating conditions for the baseline capacity of 100 kta, and across eight processing capacities ranging from 30 to 150 kta. The lowest MSP under the baseline capacity is estimated at $\$$420/ton, which is 33% lower than the 2023 average US crude oil price ($\$$74.6/bbl, equivalent to $\$$634/ton based on the density of pyrolysis oil). Under Monte Carlo simulation, accounting for variability in key economic and technical parameters, the mean MSP is estimated at $\$$1137/ton. The economic viability depends on feedstock price remaining below $\$$320/ton, defining the break-even feedstock price threshold. Sensitivity analysis further identifies capital investment and transportation cost as key economic drivers. Capacities beyond 90 kta show limited economies of scale benefits. Reducing product storage time cuts capital costs by 7% but raises operational risk. Uncertainty analysis suggests the economic feasibility of pyrolysis oil is unlikely to compete with crude oil without policy incentives.

petrochemicals↗
DOE OSTI2023

Computational and Experimental Characterization of the Ligand Environment of a Ni-Oxo Catalyst Supported in the Metal–Organic Framework NU-1000

Heterogeneous catalysts exhibit significant changes in composition due to the influence of operating conditions, and these compositional changes can have dramatic effects on catalytic performance. For traditional bulk metal heterogeneous catalysts, relationships between composition and catalytic operating conditions are well documented. However, the influence of operating conditions on the compositions of single-site heterogeneous catalysts remains largely unresolved. To address this, we report a combined computational and experimental characterization of a Ni oxo catalyst under catalytic hydrogenation conditions. Specifically, pair distribution function (PDF) analysis is combined with ab initio thermodynamic modeling to investigate ligand environments present on a Ni oxo cluster supported in the metal–organic framework NU-1000. Comparisons of the experimentally observed and simulated Ni–O coordination numbers and Ni–O, Ni···Ni, and Ni···Zr distances provide insight into the Ni ligand environment under H 2 (g). These comparisons suggest significant OH and H 2 O content and, further, that different Ni ions within the cluster and/or NU-1000 structure may comprise subtly different numbers of these ligands. Further, the observation of significant H 2 O content under H 2 (g) suggests that the NU-1000 support supplies H 2 O to the cluster. Examples of ligand environments that could lead to the observed PDFs are provided. Furthermore, the combination of simulations and experiments provides new insights into the ligand environment for Ni-NU-1000 catalysts that will be useful for understanding the ligand environments of other single-site Ni catalysts as well.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Process Modeling of Aerosol‐Cloud Interaction in Summertime Precipitating Shallow Cumulus Over the Western North Atlantic

Abstract Process modeling of Aerosol‐cloud interaction (ACI) is essential to bridging gaps between observational analysis and climate modeling of aerosol effects in the Earth system and eventually reducing climate projection uncertainties. In this study, we examine ACI in summertime precipitating shallow cumuli observed during the Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE). Aerosols and precipitating shallow cumuli were extensively observed with in‐situ and remote‐sensing instruments during two research flight cases on 02 June and 07 June, respectively, during the ACTIVATE summer 2021 deployment phase. We perform observational analysis and large‐eddy simulation (LES) of aerosol effect on precipitating cumulus in these two cases. Given the measured aerosol size distributions and meteorological conditions, LES is able to reproduce the observed cloud properties by aircraft such as liquid water content (LWC), cloud droplet number concentration ( N c ) and effective radius r eff . However, it produces smaller liquid water path (LWP) and larger N c compared to the satellite retrievals. Both 02 and 07 June cases are over warm waters of the Gulf Stream and have a cloud top height over 3 km, but the 07 June case is more polluted and has larger LWC. We find that the N a ‐induced LWP adjustment is dominated by precipitation feedback for the 2 June precipitating case and there is no clear entrainment feedback in both cases. An increase of cloud fraction due to a decrease of aerosol number concentration is also shown in the simulations for the 02 June case.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM)

Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM). However, successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large, high-quality training datasets from experiments. In this work, we introduce Construction Zone, a Python package for rapid generation of complex nanoscale atomic structures which enables fast, systematic sampling of realistic nanomaterial structures and can be used as a random structure generator for large, diverse synthetic datasets. Using Construction Zone, we develop an end-to-end machine learning workflow for training neural network models to analyze experimental atomic resolution HRTEM images on the task of nanoparticle image segmentation purely with simulated databases. Further, we study the data curation process to understand how various aspects of the curated simulated data—including simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions—affect model performance across three benchmark experimental HRTEM image datasets. Using our workflow, we are able to achieve state-of-the-art segmentation performance on these experimental benchmarks and, further, we discuss robust strategies for consistently achieving high performance with machine learning in experimental settings using purely synthetic data. Construction Zone and its documentation are available at https://github.com/lerandc/construction_zone.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The emergence of valency in colloidal crystals through electron equivalents

Colloidal crystal engineering of complex, low-symmetry architectures is challenging when isotropic building blocks are assembled. In this study we describe an approach to generating such structures based upon programmable atom equivalents (nanoparticles functionalized with many DNA strands) and mobile electron equivalents (small particles functionalized with a low number of DNA strands complementary to the programmable atom equivalents). Under appropriate conditions, the spatial distribution of the electron equivalents breaks the symmetry of isotropic programmable atom equivalents, akin to the anisotropic distribution of valence electrons or coordination sites around a metal atom, leading to a set of well-defined coordination geometries and access to three new low-symmetry crystalline phases. All three phases represent the first examples of colloidal crystals, with two of them having elemental analogues (body-centred tetragonal and high-pressure gallium), while the third (triple double-gyroid structure) has no known natural equivalent. This approach enables the creation of complex, low-symmetry colloidal crystals that might find use in various technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2015

A novel process route for the production of spherical SLS polymer powders

Currently, rapid prototyping gradually is transferred to additive manufacturing opening new applications. Especially selective laser sintering (SLS) is promising. One drawback is the limited choice of polymer materials available as optimized powders. Powders produced by cryogenic grinding show poor powder flowability resulting in poor device quality. Within this account we present a novel process route for the production of spherical polymer micron-sized particles of good flowability. The feasibility of the process chain is demonstrated for polystyrene e. In a first step polymer microparticles are produced by a wet grinding method. By this approach the mean particle size and the particle size distribution can be tuned between a few microns and several 10 microns. The applicability of this method will be discussed for different polymers and the dependencies of product particle size distribution on stressing conditions and process temperature will be outlined. The comminution products consist of microparticles of irregular shape and poor powder flowability. An improvement of flowability of the ground particles is achieved by changing their shape: they are rounded using a heated downer reactor. The influence of temperature profile and residence time on the product properties will be addressed applying a viscous-flow sintering model. To further improve the flowability of the cohesive spherical polymer particles nanoparticles are adhered onto the microparticles’ surface. The improvement of flowability is remarkable: rounded and dry-coated powders exhibit a strongly reduced tensile strength as compared to the comminution product. The improved polymer powders obtained by the process route proposed open new possibilities in SLS processing including the usage of much smaller polymer beads.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Effect of catalyst concentration on size, morphology and optical properties of silica nanoparticles

Today, nanomaterials play a key role in various fields such as electronics, aerospace, pharmaceuticals and biomedical because of their unique physical, chemical and biological properties which are different from bulk materials. Nano sized silica particles have gained the prominent position in scientific research and have wide applications. The sol-gel method is the best method to synthesize silica nanoparticles because of its potential to produce monodispersed with narrow size distribution at mild conditions. The silica nanoparticles were obtained by hydrolysis of tetraethyl orthosilicate (TEOS) in ethanol act as solvent. The synthesized nanoparticles were characterized by Field Emission Scanning electron Microscope (FE-SEM), UV Spectrometer. The smallest size of silica particles is around 150nm examined by using FE-SEM. The optical properties and band structure was analyzed using UV-visible spectroscopy which is found to be increase by reducing the size of particles. Concentration effect of catalyst on the size, morphology and optical properties were analyzed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Data mining for better material synthesis: The case of pulsed laser deposition of complex oxides

The pursuit of more advanced electronics, and finding solutions to energy needs often hinges upon the discovery and optimization of new functional materials. However, the discovery rate of these materials is alarmingly low. Much of the information that could drive this rate higher is scattered across tens of thousands of papers in the extant literature published over several decades but is not in an indexed form, and cannot be used in entirety without substantial effort. Many of these limitations can be circumvented if the experimentalist has access to systematized collections of prior experimental procedures and results. Here, we investigate the property-processing relationship during growth of oxide films by pulsed laser deposition. To do so, we develop an enabling software tool to (1) mine the literature of relevant papers for synthesis parameters and functional properties of previously studied materials, (2) enhance the accuracy of this mining through crowd sourcing approaches, (3) create a searchable repository that will be a community-wide resource enabling material scientists to leverage this information, and (4) provide through the Jupyter notebook platform, simple machine-learning-based analysis to learn the complex interactions between growth parameters and functional properties. Here, the results allow visualization of growth windows, trends and outliers, which can serve as a template for analyzing the distribution of growth conditions, provide starting points for related compounds and act as a feedback for first-principles calculations. Such tools will comprise an integral part of the materials design schema in the coming decade.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Evaluating Forest Biomass Recovery in South Central Alabama Pine Plantations

Abstract Improved use of forest biomass has been presented as a viable option to satisfy a portion of the demand for sustainable alternative sources of energy. Yet, there are considerable gaps in our understanding related to the efficiencies of current state-of-the-art forest biomass recovery systems. Southern pine plantation biomass stands typically exhibit higher stand densities and smaller-diameter trees than conventional stands, which, in turn, may result in reduced recovery efficiencies. In this study, the impact of new harvest systems for biomass recovery was investigated in typical southern pine plantation biomass harvests. Specifically, spatial and temporal effects on residue distribution were examined following biomass harvest of 14- and 24-year-old loblolly pine plantations. Preharvest total standing biomass for the younger site at 90 t ac–1 (220 t hectare–1) was half that of the older site at 160 t ac–1 (390 t hectare–1). Although the analysis detected no significant temporal effects on residue distributions, the preharvest condition exhibited 100 percent ground cover, whereas postharvest conditions had nearly 20 percent of the area designated as bare. Two of the five residue classifications, light debris and litter-herbaceous, were found to have a significantly higher incidence of occurrence than the other residue classifications on the sites based on a multinomial regression. In general, we found recovery efficiencies for both sites of 80 percent or greater for both methods of determination, by destructive sampling and based on load tickets.

Forestry↗
DOE OSTI2018

Optimization Methods for Evaluating PEV Charging Considering Customer Behavior

Plug-in electric vehicles (PEVs) can be connected to grid through electrical vehicle supply equipment (EVSE) to provide various grid services in addition to battery charging. This paper evaluates loading condition of a distribution system with a medium penetration of PEVs considering different charging strategies and public EVSE availability. A mobility and charging flexibility model is proposed to capture PEVs’ temporal availability and varying charging capability at different parking locations. Detailed trip information extracted from the National Household Travel Survey is used to represent travel and parking patterns. An optimization method is then proposed to coordinate PEV charging to effectively shift load from peak to off-peak hours. One of the prototypical feeders developed by the Pacific Northwest National Laboratory is studied in different scenarios.

Wu, Di↗
DOE OSTI2020

Impact of Aggregated PV on Subsynchronous Torsional Interaction: Preprint

This paper investigates induced oscillation (specifically subsynchronous torsional interactions) in power systems arising from photovoltaic (PV) power modulation. Time domain simulations of a modified IEEE first-benchmark system are presented and analyzed. In the IEEE benchmark system, a PV generator was added at the same bus where the synchronous generator is connected. The impact of different parameters of the PV inverters volt-volt-ampere reactive (volt-var) curve on SSTI is examined. The most severe impact found arises from the time response of the inverters reactive power change. A second-order time response with natural frequency matching the rotor speed deviation oscillation frequency and low-damping ratio quickly drives the system to an unstable region. Although it is highly unlikely that conditions necessary for distributed energy resource-induced oscillations would occur in the field, with knowledge of this vulnerability in mind, inverters' control parameters can be designed to avoid it.

aggregated↗
DOE OSTI2023

Damage to living trees contributes to almost half of the biomass losses in tropical forests

Abstract Accurate estimates of forest biomass stocks and fluxes are needed to quantify global carbon budgets and assess the response of forests to climate change. However, most forest inventories consider tree mortality as the only aboveground biomass (AGB) loss without accounting for losses via damage to living trees: branchfall, trunk breakage, and wood decay. Here, we use ~151,000 annual records of tree survival and structural completeness to compare AGB loss via damage to living trees to total AGB loss (mortality + damage) in seven tropical forests widely distributed across environmental conditions. We find that 42% (3.62 Mg ha −1 year −1 ; 95% confidence interval [CI] 2.36–5.25) of total AGB loss (8.72 Mg ha −1 year −1 ; CI 5.57–12.86) is due to damage to living trees. Total AGB loss was highly variable among forests, but these differences were mainly caused by site variability in damage‐related AGB losses rather than by mortality‐related AGB losses. We show that conventional forest inventories overestimate stand‐level AGB stocks by 4% (1%–17% range across forests) because assume structurally complete trees, underestimate total AGB loss by 29% (6%–57% range across forests) due to overlooked damage‐related AGB losses, and overestimate AGB loss via mortality by 22% (7%–80% range across forests) because of the assumption that trees are undamaged before dying. Our results indicate that forest carbon fluxes are higher than previously thought. Damage on living trees is an underappreciated component of the forest carbon cycle that is likely to become even more important as the frequency and severity of forest disturbances increase.

Zuleta, Daniel↗
DOE OSTI2022

DistGANS- Distributed Generative Adversarial Neural Networks

DistGANs is a Python package to perform distributed training of conditional generative adversarial neural networks for multi-class labeled image data. DistGANs partitionins the training data according to data labels, and enhances scalability by performing a parallel training where multiple generators are concurrently trained, each one of them focusing on a single data label.

Lupo Pasini, Massimiliano [Oak Ridge National Lab.↗
DOE OSTI2019

VOLTTRON/econ-dispatch

Work space for in development Economic Dispatch Application. Multi-purpose open-source control algorithms that will ensure real-time optimal operation, increase electric grid reliability, and lead to the goal of clean, efficient, reliable and affordable next generation building-integrated combine cooling, heating and power (CHP) system.The CHP system could include conventional heating, ventilation, air conditioning (HVAC) systems, distributed generation (DG), local storage (both thermal and electric) and local solar photovoltaic (PV) systems.

Katipamula, Srinivas↗
DOE OSTI2025

Design of an Out-Of-Pile Experimental Facility to Demonstrate the Feasibility of In Situ Thermal Conductivity Measurements of Nuclear Fuels Under Irradiation

There is substantial merit in quantifying nuclear fuel performance under irradiation. At Oak Ridge National Laboratory (ORNL), the MiniFuel irradiation platform has become the primary test vehicle for conducting separate-effects fuel performance irradiation experiments. The MiniFuel experiment is a passively controlled capsule design deployed in the High Flux Isotope Reactor (HFIR) through which fuel performance data is collected post-irradiation. Separate effects fuels irradiation capabilities are being expanded at ORNL by developing instrumented capsule designs that aim to capture fuel performance phenomena in-situ. One such capsule will specifically target fuel specimen thermal conductivity changes as a function of fuel burnup. Due to the complexity of making this measurement on nuclear fuel in-pile, this paper describes the necessary out-of-pile testing conducted on the thermal conductivity capsule (TCC) design. The measurement is ascertained via a thermopile system with heat transferred unidirectionally through a surrogate fuel specimen sandwiched between two conductive materials. The capsules investigated in this study are representative of the in-pile design, with the primary departure from irradiation conditions being the distribution of heat generation within the capsule. In the out-of-pile experiment, an external heater was used to drive heat through the conductive slug materials and into the specimen. This paper expounds the design of the out-of-pile experimental system and the thermal conductivity measurement technique. Predictive models used to determine the sensitivity of the measurement to variables governing thermal contact conductance between the specimen and slug materials and to predict experimental results are also described. Data from the out-of-pile experiment will be used to validate the readiness of the design for insertion into HFIR for irradiation.

Parker, Trevor [ORNL]↗