SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Our research focuses on designing all-reflective phase retarders (RPRs) to generate and maintain circularly polarized (CP) light for NSF OPAL and other multipetawatt laser facilities. Assuming that the input polarization is either s- or p-polarized, RPRs need to be used in an out-of-plane configuration. Here, we will discuss the impact of transporting and focusing optics on CP light.
Amorphous spherical titanium dioxide (TiO{sub 2}) nanoparticles, decorated gold nanoparticles, are shown to be formed under irradiation of commercial TiO{sub 2} nanopowders dispersed in aqueous solution of hydrogen tetrachloroaurate (HAuCl{sub 4}) by second-harmonic nanosecond pulses of an Nd : YAG laser. It is found that these hybrid nanostructures are formed during laser-induced remelting of the initial nanoparticles, stimulated by gold nanoclusters (providing more efficient absorption of visible laser radiation by titanium dioxide) recovered on their surface. The morphology and chemical composition of the newly formed hybrid nanomaterials are investigated in detail by electron microscopy, Raman spectroscopy, and energy-dispersive analysis. It is shown that the average size and number of gold nanoparticles recovered on the TiO{sub 2} surface can be controlled by varying the concentration ratio of the initial nanomaterial and HAuCl{sub 4}. A spectroscopic analysis of light scattering by single hybrid nanoparticles and the results of numerical calculation of the structure of electromagnetic fields near their surface indicate good prospects of these hybrid nanomaterials for various applications in modern optics, optoelectronics, and nanophotonics; for example, for designing chemo- and biosensor platforms and new-generation solar cells. (nanoparticles)
ABSTRACT We introduce the AbacusHOD model and present two applications of AbacusHOD and the AbacusSummit simulations to observations. AbacusHOD is a Halo Occupation Distribution (HOD) framework written in Python that is particle-based, multitracer, highly generalized, and highly efficient. It is designed specifically with multitracer/cosmology analyses for next-generation large-scale structure surveys in mind, and takes advantage of the volume and precision offered by the new state-of-the-art AbacusSummit cosmological simulations. The model is also highly customizable and should be broadly applicable to any upcoming surveys and a diverse range of cosmological analyses. In this paper, we demonstrate the capabilities of the AbacusHOD framework through two example applications. The first example demonstrates the high efficiency and the large HOD extension feature set through an analysis of full-shape redshift-space clustering of BOSS galaxies at intermediate to small scales ($\lt 30\, h^{-1}$ Mpc), assessing the necessity of introducing secondary galaxy biases (assembly bias). We find strong evidence for using halo environment instead of concentration to trace secondary galaxy bias, a result which also leads to a moderate reduction in the ‘lensing is low’ tension. The second example demonstrates the multitracer capabilities of the AbacusHOD package through an analysis of the extended Baryon Oscillation Spectroscopic Survey cross-correlation measurements between three different galaxy tracers: luminous red galaxies, emission-line galaxies, and quasi-stellar objects. We expect the AbacusHOD framework, in combination with the AbacusSummit simulation suite, to play an important role in a simulation-based analysis of the upcoming Dark Energy Spectroscopic Instrument data sets.
The volatile organic compounds (VOCs) in a wide spectrum of categories were identified as biomarkers in aquatic environments, playing an important role in marine and freshwater ecology and global atmospheric chemistry. VOCs released from biofuel have also attracted increasing attention. Although the importance has been recognized, the portable detection and analysis methods of VOC in aquatic systems have not yet been well developed and understood. Here, in this work, we innovatively proposed an intelligent electrochemical sensing approach to classify and quantify VOCs in solution. Utilizing the cyclic voltammetry (CV) method with an ionic liquid (IL)-based electrolyte, we analyzed 50 μL samples of various VOC analytes, including acetic acid (AC), acetone, dimethylformamide (DMF), dimethyl sulfoxide (DMSO), ethanol, formaldehyde, formic acid, methanol, methyl formate (MF), toluene, and a formaldehyde-methanol mixture, along with deionized water (DI water). The generated voltammograms were subsequently analyzed using our uniquely designed and optimized 1-D convolutional neural network (1D-CNN). This deep-learning algorithm achieved a 99.09% accuracy in VOC classification validated through fivefold cross-validation and demonstrated an impressive 94.4% test accuracy for methanol detection within a 10 μL error range. For quantification, the system accurately categorized methanol volumes ranging from 0 to 50 μL in 10 μL increments, achieving a 98.18% accuracy. A notable linear correlation (R2 = 95.56%) was found between max current density at the oxidation peak and methanol volume, with the limit of detection (LOD) at 9.3 μL. Such a sensing method exhibits potential for portability, high accuracy, and generalization in the classification and quantification, ultimately reshaping the realm of VOC analysis in solution.
This paper proposes a new distributed model-free control (MFC) strategy for dynamic voltage control to diminish distribution systems' voltage violations. The objective is to maintain all critical load bus voltages within the acceptable ANSI Range A (+/- 5% of nominal). The distributed MFC strategy, which only requires local voltage measurements from designated load buses, controls online the reactive power generation of available synchronous generator (SG)-based and photovoltaic (PV)-based distributed generators (DGs). The distributed MFC strategy is computationally efficient and does not require modelling of the different system components and disturbances. Time-domain dynamic simulations are conducted for the 21-bus test distribution system fed by multiple DGs to verify the performance of the proposed MFC strategy, and the results are compared against the conventional model-based microgrid voltage stabilizer (MGVS) control strategy. The simulation results show that the distributed MFC strategy provides minimal voltage violations and achieves the dynamic voltage stability of the system under diverse disturbances.
The proliferation of electric vehicles (EVs) brings environmental benefits and technical challenges to power grids. An identification algorithm which can accurately extract individual EV charging profiles out of widely available smart meter measurements has attracted great interests. This paper proposes a non-intrusive identification framework for EV charging profile extraction, which is driven by deep generative models (DGM). First, the proposed DGM is designed as a representation layer embedded into the Markov process and used to model the joint probability distribution of available time-series data. A novel contribution is to approximate posterior distributions by neural networks whose parameters are obtained by variational inference and supervised learning. Second, the EV charging status is inferred from the DGM via dynamic programming. Lastly, the desired EV charging profile can be reconstructed by the rated power of EV models and inferred status. Compared with the benchmark Hidden Markov Models, the proposed framework can better handle noise in data with less computational complexity and better overall accuracy performances with smaller recall. The proposed framework is validated by numerical experiments on the Pecan Street dataset.
Abstract Gross primary production (GPP) by terrestrial ecosystems is a key quantity in the global carbon cycle. The instantaneous controls of leaf‐level photosynthesis are well established, but there is still no consensus on the mechanisms by which canopy‐level GPP depends on spatial and temporal variation in the environment. The standard model of photosynthesis provides a robust mechanistic representation for C 3 species; however, additional assumptions are required to “scale up” from leaf to canopy. As a consequence, competing models make inconsistent predictions about how GPP will respond to continuing environmental change. This problem is addressed here by means of an empirical analysis of the light use efficiency (LUE) of GPP inferred from eddy covariance carbon dioxide flux measurements, in situ measurements of photosynthetically active radiation (PAR), and remotely sensed estimates of the fraction of PAR (fAPAR) absorbed by the vegetation canopy. Focusing on LUE allows potential drivers of GPP to be separated from its overriding dependence on light. GPP data from over 100 sites, collated over 20 years and located in a range of biomes and climate zones, were extracted from the FLUXNET2015 database and combined with remotely sensed fAPAR data to estimate daily LUE. Daytime air temperature, vapor pressure deficit, diffuse fraction of solar radiation, and soil moisture were shown to be salient predictors of LUE in a generalized linear mixed‐effects model. The same model design was fitted to site‐based LUE estimates generated by 16 terrestrial ecosystem models. The published models showed wide variation in the shape, the strength, and even the sign of the environmental effects on modeled LUE. These findings highlight important model deficiencies and suggest a need to progress beyond simple “goodness of fit” comparisons of inferred and predicted carbon fluxes toward an approach focused on the functional responses of the underlying dependencies.
Redox-active colloids (RACs) represent a novel class of energy carriers that exchange electrical energy upon contact. Understanding contact-mediated electron transfer dynamics in RACs offers insights into physical contact events in colloidal suspensions and enables quantification of electrical energy transport in nonconjugated polymers. Redox-based electron transport was directly observed in monolayers of micron-sized RACs containing ethyl-viologen side groups via fluorescence microscopy through an unexpected nonlinear electrofluorochromism that is quantitatively coupled to the redox state of the colloid. Via imaging studies, using this electrofluorochromism, the apparent charge transfer diffusion coefficient D CT of the RAC was easily determined. The visualization of energy transport within suspensions of redox-active colloids was also demonstrated. Our work elucidates fundamental mechanisms of energy transport in colloidal systems, informs the development of next-generation redox flow batteries, and may inspire new designs of smart active soft matter including conductive polymers for applications ranging from electrochemical sensors and organic electronics to colloidal robotics.
SOEC is a promising H 2 generation technology for mitigating climate change. Novel material design and optimization strategies, such as oxygen vacancy chemistries, can enhance SOEC efficiency. In this study, Monte Carlo-ReaxFF and eReaxFF simulations were used to study oxygen vacancies (O v ) and electron migration in BZY20 solid oxide material. Our results shows that O v migrate towards the surface, increasing surface O v concentration by 10%. Yttrium restricts electron mobility and functions as an electron trapping site, while Zr accelerates electron mobility and migration. These insights could improve solid-state electrolytes’ electrochemical performance in renewable energy applications.
We would like to create an open source repository in LANL's github on code written in Julia, in which we implement and extend the data-driven Mori-Zwanzig method for extracting large-scale spatio-temporal structures from data, which we call MZMD. This method is an extension of Dynamic Mode Decomposition (DMD) in which Mori-Zwanzig memory kernels are included into the associated companion matrix. In the code we would like to release, we apply MZMD to a flow over a cylinder with Reynolds number 100 rather than the much larger data set used in the associated manuscript. DMD is used extensively in the fluid dynamics community mainly for extracting large scale spatio-temporal structures (patters) from flow data. This is useful for understanding the key mechanisms that generate certain complex dynamical process relevant in engineering design. In MZMD, we improve upon DMD by adding the Mori-Zwanzig memory kernels, and show this improvement is especially important in strongly nonlinear regions of the flow.
ProteinTuneRL is a framework designed to harness the power of reinforcement learning for advanced protein design. The project enables fine-tuning of generative models to explore and optimize protein sequences with tailored structural and functional properties.
Work continues on a minimally invasive, nitrogen fluorescence gas sheet at the Facility for Rare Isotope Beams (FRIB). A low density gas sheet may be used to observe the 2D transverse beam profile of high intensity, multiple charge state beams with minimal interference. Spatially and temporally correlated profiles are of particular interest in locations where there is significant charge state spread, such as the FRIB linac folding segments. A low-density gas sheet measurement system offers advantages for gas handling in nitrogen sensitive areas, however signal intensity is significantly lower than techniques using higher density gas sheets and jets. This work discusses measurement considerations for photon distributions generated by several spatially separated interaction points and design considerations for a high-sensitivity optics system for handling the expected low signal intensity.
Modern computing platforms whose workloads generate large amounts of network traffic, such as cloud and HPC systems, often suffer from performance bottlenecks associated with the network interface. In order to alleviate the effects of this obstacle, a new generation of accelerators known as ‘SmartNICs’, which are designed to offload low level networking tasks from the processor into the NIC, have emerged.
Growing concerns over the impact of CO 2 emissions from combustion sources on global climate change have prompted numerous research and development projects aimed at developing cost-effective technologies for CO 2 capture. One family of technologies being demonstrated at pilot and full scale globally is postcombustion CO 2 capture (PCCC) systems that employ amine-based solvents. The captured CO 2 can be compressed and permanently stored underground or used for enhanced oil recovery (EOR). The proximity of North Dakota’s lignite-fired fleet of power plants to potential CO 2 storage options creates a unique atmosphere for PCCC within the state. However, the aerosols present in lignite flue gas present a challenge for large-scale PCCC at North Dakota power plants. Aerosols can negatively impact the long-term performance of amine-based solvents for CO 2 capture. Amine-based solvents are volatile, and aerosols provide nucleation sites where amine vapors can condense. Because aerosols cannot be easily captured at the column outlet using conventional technologies, the amine-laden aerosols escape the system and lead to accelerated solvent losses. Moreover, aerosol components can chemically react with amines to form degradation products that can permanently deactivate the amine, cause fouling, and lead to hazardous emissions. Many of the elements that have been shown to catalyze solvent degradation are present in lignite coals and can exacerbate solvent replacement economics. Understanding this issue is critical to the implementation of solvent-based CO 2 capture systems as applied to lignite-fired generation systems. The Energy & Environmental Research Center (EERC) designed and carried out this project to optimize aerosol mitigation technology for PCCC at a lignite-fired power plant. To meet the goal of this project, the following objectives were identified: Determine the effectiveness of a wet electrostatic precipitator (WESP) on collection of aerosols at a low-rank coal-fired power station. Determine the impact of aerosols on the efficiency and degradation products of amine-based carbon capture systems fired with low-rank fuels. Work was conducted at Minnkota Power Cooperative’s (MPC’s) Milton R. Young (MRY) Station Unit 2 using a slipstream of flue gas from the outlet of the plant’s flue gas desulfurization (FGD) unit. To gather initial data for sizing and specifying a WESP for this system, a temporary pilot-scale WESP was rented and installed on-site. Several different conditions were tested to examine the impact of flow rate, voltage, and current on WESP performance. The WESP was effective at removing large particulate (>200 nm) but caused an increase in fine particulate (<75 nm). Fine particulate material at the inlet and outlet of the WESP was collected, analyzed, and showed that crystalline sulfates carried over from the plant’s FGD unit were being converted to fine aerosols and SO 2 was being converted to SO 3 through the WESP. Additionally, the high moisture content of the flue gas stream at this sample location also contributed to an overall increase in aerosol mass under some of the test conditions. Using the results from the rented WESP, the project team installed a smaller-scale WESP upstream of the EERC’s slipstream CO 2 capture system. Flue gas was routed through a pilot-scale FGD unit to remove SO 2 to very low levels (~1 ppm) and then through a direct contact cooler (DCC) to further cool the gas and to remove moisture. The gas exiting the DCC was then routed through the new WESP before passing to the CO 2 absorber columns. Fluor’s amine-based solvent was used to scrub CO 2 from the slipstream through a set of two absorber columns. The rich solvent was regenerated in a stripper column by heating to drive off captured CO 2 . The system operated using a catch-and-release method where the CO 2 was separated to provide data on the process, but the captured CO 2 was released back into the host site stack. Aerosols and sulfur species were measured at multiple locations throughout the pilot-scale system. The inlet FGD and DCC removed much of the particulate matter and gaseous sulfur upstream of the WESP. With this configuration, the WESP achieved >95% particulate capture. The new WESP did not show any of the increases in SO 3 or other aerosol species that had been consistently observed with the larger-scale WESP installed immediately downstream of the plant’s full-scale FGD unit. Particulate samples captured and analyzed from the WESP inlet did not show any presence of crystalline sulfate materials, indicating that the pilot-scale FGD and DCC were efficient at reducing carryover from the plant’s full-scale FGD unit. A set of parametric tests were conducted on the new WESP to assess the impacts of flow rate, voltage, number of online WESP fields, and gas-phase sulfur content on aerosol and sulfur transformations. The results showed that the WESP performed similarly well at all sets of conditions. Sulfur and particulate matter exiting the WESP were further reduced through the absorber column as the amine-based solvent captured some of the residual contaminants. Solvent analysis showed that these species were slowly concentrating in the solvent over the duration of the test. When the WESP was taken offline and the sulfur slip through the FGD allowed to rise, the sulfate content in the solvent rose sharply, showing that the extra FGD and WESP were effective at reducing sulfate and cation uptake. This would be expected to extend amine-based solvent life by slowing the formation of heat-stable salts and other degradation products. This subtask was cofunded through the EERC–U.S. Department of Energy Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by the North Dakota Industrial Commission and MPC.
State of the art concentrating solar power plants generally rely on a two-tank system with a large central tower. These plants employ nitrate salts that are limited to approximately 565°C in practice. A novel system arrangement is proposed using molten salt as the heat transfer and energy storage media in a single thermocline storage tank with an integral heat exchanger to the power cycle. The system has unique features that enable temperatures higher than 565°C while reducing costs. This study explores the considerations for the concept and presents a preconceptual point design for a 5 MWt system capable of generating 100 MWt per day with 12 hours of thermal energy storage. This study also examines the potential operation of the system with nitrate and higher temperature chloride salts. Both salt systems appear promising and future concept maturation is recommended.
For the next multi-megawatt accelerator generation, targets and other beam-intercepting components will face even more severe challenges due to the higher power densities, higher energy, and higher radiation. A comprehensive research and development program must be implemented to address the challenges. International collaborations such as RaDIATE or High Power Targetry Workshop should be leveraged and expanded to accelerate knowledge gain and avoid duplicated efforts. The next generation of high power targets will need novel designs allowing better high heat flux cooling methods, novel materials, advanced simulations, and better instrumentation. Four main R&D tracks were identified and discussed for targets. For electron sources, current state of art and challenges we are facing for key components such as cathodes, guns and injector design for sources R&D are presented for highly polarized electrons and unpolarized high current high brightness beams. For ion sources such as electron cyclotron resonance ion source (ECRIS), laser ion source (LIS), charge breeders, status of development are reviewed, and areas require investment for future R&D are discussed separately. Facilities critical for various source development are recommended.
The general goal of the work was to develop new late metal Ni(II)- and Pd(II)-based catalysts and processes to convert ethylene and other readily available α-olefins via polymerization and oligomerization reactions to an array of new value-added materials with high efficiency. An emphasis was placed on invention of new catalysts and processes guided by rational design and detailed mechanistic studies. These investigations have generated new knowledge which may underpin technological advances directed towards new and more efficient methods for utilizing ethylene and α-olefins derived from ethylene.
Inspection of coal-fired power plants is frequently dangerous, includes difficult places to reach, and can turn expensive due to the downtimes and cost of inspection crew. Robotic systems have shown capabilities to address some of these issues, but most of the current robotic inspection technology in power plants is designed for specific components. Conversely, recent advances in machine vision have empowered aerial platforms for long-range, remotely-controlled, GPS-based inspections of industrial plants. This capability has led to wide spread utilization of aerial robots (commonly termed Drones, UVS or UAS) platforms for inspection in less challenging environments where both collision avoidance, and GPS reception are not significant issues. The challenge in adapting airborne technology for power plant inspection lies in internal structures and the complex network of piping, and distribution systems, which impose significant risks for collision and can hinder the reception and transmission of GPS signals. The current state of the art in aerial inspection technology within the energy sector is controlled via radio control, and utilizes GPS-based navigation, for inspection of large-scale plants such as offshore platforms and wind turbine parks. Nevertheless, close-range and autonomous inspection in the GPS-denied environments of power plants has not yet been achieved, as it requires precise guidance and navigation with real-time situational awareness and obstacle avoidance capabilities. This endeavor introduced the use of rotary wing flying robots, due to their station keeping and vertical take-off capabilities for power plant components inspection. To enable close quarter inspection two methods were used. One method uses the 3D CAD (Three-dimensional Computer-Aided Design) model of the asset to inspect to generate the UAV’s inspection path. To acquire, analyze and process the 3D model, first, the STL file is produced to obtain surface points and vectors normal to the surface. Later, by introducing other variables such as wall offset and a controlled trajectory between each outline and each subsequent layer, the flight path is generated. The proposed framework will generate a path that will pass as close as desired from the surface and navigate in intricate environments. A second method, use advanced manufacturing techniques such as CNC (Computer Numerical Control) and additive manufacturing. Once the inspection flight path is obtained, vision-based navigation systems are employed to have the UAV autonomously tracking the provided trajectory. Finally, Artificial Intelligence-enabled developments are in charge of detecting cracks and corrosion in structural components of power plants. The proposed methods are validated in simulations, laboratory and industrial setups, where it is shown that the developed systems acting together enable close-quarter autonomous aerial inspection and mapping in power plant assets. The system can be further improved by adding more sensors to navigate in different GPS-denied environments, with non-homogeneous lighting conditions, dust and in general situations where vision-based systems may fail.