The Abinitproject: Impact, environment and recent developments
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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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OpenSn is an open-source, massively parallel deterministic radiation transport code for solving the discrete-ordinates ( S N ) form of the Boltzmann transport equation on unstructured, arbitrary polyhedral meshes. It supports high-fidelity simulations involving steady-state, eigenvalue, and adjoint problems for neutral particles (e.g., neutrons, photons, multi-particles), using the multigroup approximation in energy. OpenSn combines angular discretization via discrete ordinates with a discontinuous Galerkin finite element method (DGFEM) in space, enabling accurate resolution of transport physics on arbitrary polyhedral cells, included locally refined spatial grids. It includes multiple angular quadrature types, including locally refined angular quadratures. Written in modern C++ with a Python API, OpenSn runs efficiently on platforms ranging from laptops to supercomputers. The transport sweep algorithm is implemented using a task-based, directed-acyclic-graph (DAG) approach for each angle and supports asynchronous parallelism across thousands of MPI ranks. Group-set aggregation improves compute intensity, and synthetic acceleration techniques (e.g., diffusion synthetic acceleration, second-moment method) enhance solver convergence. OpenSn has been verified on reactor physics problems and demonstrated excellent weak and strong scaling performance on more than 32,768 processes, making it a versatile and robust platform for large-scale transport simulations in complex geometries.
Greenhouse gas emissions from physical permafrost thaw disturbance and subsidence, including the formation and expansion of thermokarst (thaw) lakes, may double the magnitude of the permafrost carbon feedback this century. These processes are not accounted for in current global climate models. Thermokarst lakes, in particular, have been shown to be hotspots for emissions of methane (CH4), a potent greenhouse gas with 32 times more global warming potential than carbon dioxide (CO2) over a 100-year timescale. Here, we synthesize several studies examining CH4 dynamics in a representative first-generation thermokarst lake (Vault Lake, informal name) to show that CH4 production and oxidation potentials vary with depth in thawed sediments beneath the lake. This variation leads to depth-dependent differences in both in situ dissolved CO2:CH4 ratios and net CH4 production responses to additional warming. Comparing CH4 production, oxidation, and flux values from studies at Vault Lake suggests up to 99% of produced CH4 is oxidized and/or periodically entrapped before entering the atmosphere. We summarize these findings in the context of CH4 literature from thermokarst lakes and identify future research directions for incorporating thermokarst lake CH4 dynamics into estimates of the permafrost carbon feedback.
The Mekong River is one of the most biodiverse rivers on the planet. As a result, many communities located near the river and its tributaries rely on fisheries for sustenance as well as economic viability. Along the Mekong River mainstem, there are plans for 11 new hydropower projects with potentially several hundred smaller hydropower projects along the tributaries. Currently, there is little information to guide construction of fish-friendly hydropower systems. Preliminary guidelines for construction on the mainstem Mekong call for safe upstream and downstream fish passage for 95% of target species. To understand the hydraulic conditions at existing dams in the region, Sensor Fish, a small neutrally buoyant autonomous sensor package capable of measuring and collecting in situ motion and force data, were deployed at Nam Ngum Dam in Lao PDR. The Sensor Fish were released through two turbine units—Unit 1, which contains an older 17.5-MW Francis turbine, and Unit 4, which contains a slightly newer 40-MW Francis turbine. Comparisons were made between the results from the two turbine units at Nam Ngum Dam to results for four hydroelectric dams in the U.S. Pacific Northwest for which live fish survival studies had been conducted. Three of the U.S. dams have Francis-turbine units and the other has a fish-friendly Kaplan turbine. Based on the occurrence of severe acceleration events, the survival rate of juvenile salmonids at a dam like Nam Ngum Dam would be expected to between the 11.1% to 54.1% survival rates experienced at the U.S. with Francis turbines and the 97.0% survival rate at the U.S. dam equipped with fish-friendly Kaplan turbines. Between the newer and older turbine units tested at Nam Ngum Dam, the newer Unit 4 had a higher median nadir pressure of 126 kPaA compared to 99 kPaA for Unit 1. Based on a dose-response relationship for juvenile Chinook salmon, these nadir pressures predict probabilities of mortal injury of 5% for Unit 4 and 12% for Unit 1. With respect to both pressure and acceleration, the older 17.5-MW Francis turbine had more severe hydraulic conditions than the newer 40-MW Francis turbine. Francis turbines, like the ones involved in this study, are the most common type of hydropower turbines in the throughout the world. The results from this study can be used to guide the construction of new hydropower structures and make design and operational changes to existing hydropower structures that will improve fish passage conditions.
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Maximum pit sizes were predicted for dilute and concentrated NaCl and MgCl 2 solutions as well as sea-salt brine solutions corresponding to 40% relative humidity (RH) (MgCl 2 -rich) and 76% RH (NaCl-rich) at 25 °C. A quantitative method was developed to capture the effects of various cathode evolution phenomena including precipitation and dehydration reactions. Additionally, the sensitivity of the model to input parameters was explored. Despite one's intuition, the highest chloride concentration (roughly 10.3 M Cl – ) did not produce the largest predicted pit size as the ohmic drop was more severe in concentrated MgCl 2 solutions. Therefore, the largest predicted pits were calculated for saturated NaCl (roughly 5 M Cl – ). Next, it was determined that pit size predictions are most sensitive to model input parameters for concentrated brines. However, when the effects of cathodic reactions on brine chemistry are considered, the sensitivity to input parameters is decreased. Although there was not one main input parameter that influenced pit size predictions, two main categories were identified. Under similar chloride concentrations (similar RH), the water layer thickness (WL), and pit stability product, (i · x) sf , are the most influential factors. When varying chloride concentrations (RH), changes in WL, the brine specific cathodic kinetics on the external surface (captured in the equivalent current density (i eq )), and conductivity (k o ) are the most influential parameters. Finally, it was noted that dehydration reactions coupled with precipitation in the cathode will have the largest effect on predicted pit size, and cause the most significant inhibition of corrosion damage.
Model-based optimization can help improve the indoor thermal comfort and energy efficiency of Heating, Ventilation and Air Conditioning (HVAC) systems. The models used in previous optimization studies either omit the dynamic interaction between indoor airflow and HVAC or are too slow for model-based optimization. To address this limitation, we propose an optimization methodology using coupled simulation of the airflow and HVAC that captures the dynamics of both systems. We implement an optimization platform using the coupled models of a coarse grid Fast Fluid Dynamics model for indoor airflow and Modelica models for HVAC which is linked to the GenOpt optimization engine. Then, we demonstrate the new optimization platform by studying the optimal thermostat placement in a typical office room with a VAV terminal box in the design phase. After validating the model, we perform an optimization study, in which the VAV terminal box is dynamically controlled, and find that our optimization platform can determine the optimal location of thermostat to achieve either best thermal comfort or least energy consumption, or the combined. Finally, the time cost for performing such optimization study is about 6.2 h, which is acceptable in the design phase.
With the intermittency that comes with electricity generation from renewables, utilizing dynamic pricing will encourage the demand-side to respond in a smart way that would minimize the electricity costs and flatten the net electricity demand curve. Determining the optimal dynamic pricing profile that would leverage distributed storage to flatten the curve is a novel idea that needs to be studied. Moreover, the economic feasibility of utilizing distributed electrical energy storage is still not given in the literature. Therefore, in this paper, a novel way of solving a citywide dynamic model using a bilevel programming algorithm is introduced. The problem is developed as a novel non-cooperative Stackelberg game that utilizes air-conditioning systems and electrical storage through the end-users to determine the optimal dynamic pricing profile. The results show that the combined effect of utilizing demand-side air-conditioning systems and distributed storage together can flatten the curve while employing the optimal dynamic pricing profile. An economic study is performed to determine the economic feasibility of 20 different cases with different battery designs and the level of solar penetration. Three metrics were used to evaluate the economic performance of each case: the levelized cost of storage, the levelized cost of energy, and the simple payback period. Most cases had levelized cost of storage values lower than 0.457 $/kWh, which is the lower bound available in the literature. Seven out of 16 cases have a simple payback period shorter than the lifetime of the system (25 years). The case with a 100 MW PV power plant and a battery storage of size 597 MWh, was found to be the most promising case with a simple payback period of 12.71 years for the photovoltaic plant and 19.86 years for the demand-side investments.
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We report that Multi-Agent Systems (MAS) are seen from different areas as one of the paramount trends for the next generation of power systems. Numerous published studies about MAS discuss its utilization in power distribution networks but none focuses on the prior step to restoration and self-healing that is fault section estimation. This paper aims to show how MAS can improve the utilities’ reliability indexes and consumer satisfaction by overcoming the multiple fault section estimation problem. In order to do this, the authors considered using MAS as a means of communication between smart meters. The purpose of smart meters usage is to employ devices that are already present in smart grids, mainly because of their reading and saving data capacity. The proposed method was tested on a radial feeder generated by the authors. The network was built on HYPERSIM, a software platform of OPAL-RT Technologies. The simulation results show that this MAS provides speed, efficiency, and automation for the process of fault section estimation.
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Small-scale coal gasification technology, coupled to a reciprocating engine generator, has the potential for making coal a cost competitive resource for meeting the flexible energy needs and resiliency requirements of utilities across the United States. To maintain grid stability and reliability, electrical generation must be regulated to match the load at the proper voltage and frequency. With the expansion of intermittent sources into the grid, such as wind and solar, frequency and voltage regulation become increasingly important and challenging. This work presents the results of a Front End Engineering Design (FEED) effort to detail the engineering and preliminary economics of a small-scale, air blown, fixed-bed gasification process, operating at near-atmospheric pressure, with gas cleanup to provide syngas and pyrolysis liquid fuels for use in reciprocating engine generators for combined heat and power at the University of Alaska Fairbanks. A very detailed assessment of capital and operating costs allows the evaluation of a levelized cost of electricity of 208.06 $/MWh, which can be reduced through the selling of by-products (steam for heating purposes and pyrolysis liquids to be used for power generation in an existing diesel engine). Here, a combined sensitivity analysis, based on the Monte Carlo approach, has been carried out to evaluate the effects of the uncertainties (capital and operating costs and plant annual availability) on the LCOE.
Wetting conditions and fluid-fluid interfacial properties are essential to understanding oil recovery in the context of low-salinity and smart waterflooding. Many have deemed wettability alteration to be the dominant interaction, while some highlight the importance of fluid-fluid interactions. In some rock-fluid systems, benefits from the use of additives can arise to favor hydrocarbon production. Understanding the balance between the fluid-fluid and rock-fluid interactions when utilizing additives is crucial to evaluate benefits in improved efficiency. Ultimately, this understanding will shed light on recovery mechanisms. Regarding oil recovery, previous studies indicate that the injection of a naphthenic acid (NA) blend dissolved in an injection brine improves oil recovery in Berea Sandstone, particularly under low-salinity conditions. Interfacial rheology results for the brine-oil interface correlated well with the recovery response. This observation was speculated to serve as a potential improved recovery mechanism through oil-trapping suppression. However, the said studies did not evaluate potentially concurrent rock wettability alteration during recovery or the initial wetting condition. The purpose of this work is to determine the production behavior in systems with NAs, namely NA-10 (3-Cyclohexanepropinoic acid), NA-14 (Cyclopentane carboxylic acid), NA-18 (Cyclohexane carboxylic acid), and NA-mix (Naphthenic acid mixture), at different initial wetting conditions under spontaneous imbibition. In addition, we tested the NAs ability to alter wettability in Berea Sandstone and determine each “additive’s” capacity to improve recovery. Here, time-domain nuclear magnetic resonance (TD-NMR), contact angle, and Amott cell tests, i.e. spontaneous imbibition, were used to analyze the influence of the selected naphthenic acids on oil displacement under different initial wetting conditions. Our results indicate that individual naphthenic acids indeed have potential as additives in low-salinity waterflooding for oil-wet systems while negatively impacting recovery in water-wet systems. However, results suggest that the evolution of the interfacial properties and wettability conditions are equally important on the recovery behavior.
Recent advances in selective laser melting (SLM) 3D printing technology allow additive manufacture of lower hybrid current drive (LHCD) RF launchers from a new material, Glenn Research Copper 84 (GRCop-84), a Cr 2 Nb (8 at. % Cr, 4 at. % Nb) precipitation hardened alloy, in configurations unachievable with conventional machining. Tensile strength and thermal conductivity are compared to other competing high conductivity copper alloys for fusion use. Swelling and conductivity at 100 DPA when exposed to the Hanford Fast Flux Test Facility neutron spectrum is predicted in the 1–2 % and 65-70% IACS range, respectively, by comparison to copper alloys with similar precipitate and grain size. Finally, self-similar ion irradiation of a GRCop-84 target at 430 °C to 20 DPA resulted in no noticeable void formation in FIB cross sections or TEM lamella.