Application of the IHT to inform defensive strategies for the protection of NPP I&C systems.
Abstract not provided.
Rowland, Michael Terence↗
SEARCH · Engineering Papers
Results for “Application Abstraction”
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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Hidden Markov Model Application for Unsupervised Clustering of Microseismic Events at CO2 Injection Site Reveals Hidden Zones of Weakness.
Abstract not provided.
Willis, Rachel↗
Application of the AI2 Climate Emulator to E3SMv2's Global Atmosphere Model, With a Focus on Precipitation Fidelity
Abstract Can the current successes of global machine learning‐based weather simulators be generalized beyond 2‐week forecasts to stable and accurate multiyear runs? The recently developed AI2 Climate Emulator (ACE) suggests this is feasible, based upon 10‐year simulations with a network trained on output from a physics‐based global atmosphere model using a grid spacing of approximately 110 km and forced by a repeating annual cycle of sea‐surface temperature. Here we show that ACE, without modification, can be trained to emulate another major atmospheric model, EAMv2, run at a comparable grid spacing for at least 10 years with similarly small climate biases—a prerequisite to wider applicability. With an analysis that combines multiple temporal, spatial, and frequency domain perspectives, we show that ACE faithfully represents the spatiotemporal structure of EAMv2 precipitation and related variables. Finally, we show that a pretrained ACE network is able to adapt to a new global climate model simulation data set with 10 fewer training steps than when starting from random initialization, all while still maintaining low levels of climate bias. Further analysis of these fine‐tuning experiments reveal ACE's intriguing ability to interpolate between distinct global climate models.
Duncan, James P. C.↗
Hardware, methodology and applications of 2 + D backscatter Mössbauer spectroscopy with simultaneous x-ray and γ-ray detection
Abstract A unique method is presented for the acquisition and analysis of 57 Fe backscatter Mössbauer spectra with simultaneous detection of the resonant 14.4 keV γ -rays and the characteristic 6.4 keV x-rays, using a custom-built multi-parameter analyser constructed on the basis of commercial analogue to digital converters and high-speed digital latches. The system allows for the simultaneous registration of Doppler-modulation velocities and photon energies, with up to 4096 and 8192 digital channels respectively. This arrangement is in contrast to most related systems, which detect at a single narrow energy window per detector. Samples of arbitrary atomic structure, morphology and surface topography can be studied without altering the setup or the analysis procedure, provided that the samples are at least micrometre sized. The hardware and software that are used to acquire data with minimal dead time are described and the custom and self-contained methods for post-measurement energy discrimination, background correction and velocity-axis folding are discussed. The data are fit using a general Hamiltonian model for the nuclear energy levels of 57 Fe and a quantum mechanical description of the angular momentum coupling is utilised, with consideration of the crystalline and chemical disorder of the sample under examination. Three examples of distinct magnetic systems, with thicknesses ranging from 5 μ m to 6 mm, that were studied using this method are presented, these are: an amorphous CoFeB-based ribbon with ultra-soft coercivity for high-frequency applications, magnetically hard Nd-Fe-B thick films on Si substrates, examined in both as-deposited and annealed states, and a sample from the nickel-rich iron meteorite NWA 6259 that contains the atomically ordered, elevated coercivity, L 1 0 phase of FeNi, tetrataenite. The wide applicability and usefulness of this method is thus demonstrated on three distinct sample morphologies that required little to no surface preparation prior to examination.
Engineering↗
Middleware for a Heterogeneous CAV Fleet
This paper introduces CAN to ROS, a model-based code generation tool used in development, testing, and deployment of a heterogeneous fleet of vehicles with robotic sensing in ROS. Code generation supports two main features: (1) self-configuration for deployment in a heterogeneous vehicle fleet, and (2) quick iteration for testing and development of reading vehicle sensors and robotic control. This tool features the ability to detect the vehicle it is in and regenerate and rebuild itself at runtime to provide the proper two-way bridge between ROS and the sensed on-board vehicle sensor network. Code generation relies on a per-model defined JSON to map a CAN database (DBC) to the desired ROS topic names and message types. The live ROS publishing of CAN messages allows for instant feedback, and the code regeneration allows for adjustments in DBC or vehicle JSON to iteratively hone in on new vehicle signals. Generated ROS nodes are written in C++ for runtime use in lightweight embedded computers. This has been tested in vehicles from three different Original Equipment Manufacturers (OEMs), and can be extended to support a wide array of vehicles. By using a unifying ROS specification, a heterogeneous set of vehicles can be unified into a fleet with abstracted model-specific details; this opens the door for developing cross-model software applications for vehicle control, connected vehicle applications, or fleet monitoring systems.
42 ENGINEERING↗
¡Viva la mitochondria!: harnessing yeast mitochondria for chemical production
ABSTRACT The mitochondria, often referred to as the powerhouse of the cell, offer a unique physicochemical environment enriched with a distinct set of enzymes, metabolites and cofactors ready to be exploited for metabolic engineering. In this review, we discuss how the mitochondrion has been engineered in the traditional sense of metabolic engineering or completely bypassed for chemical production. We then describe the more recent approach of harnessing the mitochondria to compartmentalize engineered metabolic pathways, including for the production of alcohols, terpenoids, sterols, organic acids and other valuable products. We explain the different mechanisms by which mitochondrial compartmentalization benefits engineered metabolic pathways to boost chemical production. Finally, we discuss the key challenges that need to be overcome to expand the applicability of mitochondrial engineering and reach the full potential of this emerging field.
Biotechnology & Applied Microbiology↗
Trigger-based Incremental Data Processing with Unified Sync and Async Model
In recent years, more and more applications in the cloud have needs to process large-scale on-line datasets, which evolve over time as new entries are added and existing entries are modified. Several programming frameworks, such as Percolator and Oolong, are proposed for such incremental data processing and can achieve efficient processing with an event-driven abstraction. However, these frameworks are inherently asynchronous, leaving the heavy burden of managing synchronization to applications' developers, which further significantly restricts their usabilities. In this study, we propose a trigger-based incremental computing framework in the cloud, called Domino, with both synchronous and asynchronous mechanisms to coordinate parallel triggers. With this new framework, both synchronous and asynchronous applications can be seamlessly developed. Use cases and extensive evaluation results confirm that it can deliver sufficient performance, and also is easy to use for incremental applications in large-scale distributed computing.
97 MATHEMATICS AND COMPUTING↗
Offshore application of landslide susceptibility mapping using gradient-boosted decision trees: a Gulf of Mexico case study
Abstract Among natural hazards occurring offshore, submarine landslides pose a significant risk to offshore infrastructure installations attached to the seafloor. With the offshore being important for current and future energy production, there is a need to anticipate where future landslide events are likely to occur to support planning and development projects. Using the northern Gulf of Mexico (GoM) as a case study, this paper performs Landslide Susceptibility Mapping (LSM) using a gradient-boosted decision tree (GBDT) model to characterize the spatial patterns of submarine landslide probability over the United States Exclusive Economic Zone (EEZ) where water depths are greater than 120 m. With known spatial extents of historic submarine landslides and a Geographic Information System (GIS) database of known topographical, geomorphological, geological, and geochemical factors, the resulting model was capable of accurately forecasting potential locations of sediment instability. Results of a permutation modelling approach indicated that LSM accuracy is sensitive to the number of unique training locations with model accuracy becoming more stable as the number of training regions was increased. The influence that each input feature had on predicting landslide susceptibility was evaluated using the SHapely Additive exPlanations (SHAP) feature attribution method. Areas of high and very high susceptibility were associated with steep terrain including salt basins and escarpments. This case study serves as an initial assessment of the machine learning (ML) capabilities for producing accurate submarine landslide susceptibility maps given the current state of available natural hazard-related datasets and conveys both successes and limitations.
Dyer, Alec S. (ORCID:0000000219813904)↗
A Multirange Vehicle Speed Prediction With Application to Model Predictive Control-Based Integrated Power and Thermal Management of Connected Hybrid Electric Vehicles
Abstract Connectivity and automated driving technologies have opened up new research directions in the energy management of vehicles which exploit look-ahead preview and enhance the situational awareness. Despite this advancement, the vehicle speed preview that can be obtained from vehicle-to-vehicle/infrastructure (V2V/I) communications is often limited to a relatively short time-horizon. The vehicular energy systems, specifically those of the electrified vehicles, consist of multiple interacting power and thermal subsystems that respond over different time-scales. Consequently, their optimal energy management can greatly benefit from long-term speed prediction beyond that available through V2V/I communications. Accurately extending the look-ahead preview, on the other hand, is fundamentally challenging due to the dynamic nature of the traffic environment. To address this challenge, we propose a data-driven multirange vehicle speed prediction strategy for arterial corridors with signalized intersections, providing the vehicle speed preview for three different ranges, i.e., short-, medium-, and long-range. The short-range preview is obtained by V2V/I communications. The medium-range preview is realized using a neural network (NN), while the long-range preview is predicted based on a Bayesian network (BN). The predictions are updated in real-time based on the current state of traffic and incorporated into a multihorizon model predictive control (MH-MPC) for integrated power and thermal management (iPTM) of connected vehicles. The results of design and evaluation of the performance of the proposed data-informed MH-MPC for iPTM of connected hybrid electric vehicles (HEVs) using traffic data for real-world city driving are reported.
Automation & Control Systems↗
Application of Information Harm Triangle to inform defensive strategies for the protection of NPP I&C systems.
Abstract not provided.
Rowland, Michael Terence↗
Application of GEM (Geologic Element Motion) to Heave Modeling of Cast Blasting, Mineral and Iron Ore Blasting.
Abstract not provided.
Preece, Dale -ext↗
Applications of Coatings for the Mitigation and Repair of Stress Corrosion Cracking on Spent Nuclear Fuel Canisters.
Abstract not provided.
Maguire, Makeila↗
Microengineering Pressure Sensor Active Layers for Improved Performance
Abstract Pressure sensors play an integral role in a wide range of applications, such as soft robotics and health monitoring. In order to meet this demand, many groups microengineer the active layer—the layer that deforms under pressure and dictates changes in the output signal—of capacitive, resistive/piezoresistive, piezoelectric, and triboelectric pressure sensors in order to improve sensor performance. Geometric microengineering of the active layer has been shown to improve performance parameters such as sensitivity, dynamic range, limit of detection, and response and relaxation times. There are a wide range of implemented designs, including microdomes, micropyramids, lines or microridges, papillae, microspheres, micropores, and microcylinders, each offering different advantages for a particular application. It is important to compare the techniques by which the microengineered active layers are designed and fabricated as they may provide additional insights on compatibility and sensing range limits. To evaluate each fabrication method, it is critical to take into account the active layer uniformity, ease of fabrication, shape and size versatility and tunability, and scalability of both the device and the fabrication process. By better understanding how microengineering techniques and design compares, pressure sensors can be targetedly designed and implemented.
Ruth, Sara Rachel Arussy↗
Ion Exchange Gels Allow Organic Electrochemical Transistor Operation with Hydrophobic Polymers in Aqueous Solution
Conjugated-polymer-based organic electrochemical transistors (OECTs) are being studied for applications ranging from biochemical sensing to neural interfaces. While new polymers that interface digital electronics with the aqueous chemistry of life are being developed, the majority of high-performance organic transistor materials are poor at transporting biologically relevant ions. Here, the operating mode of an organic transistor is changed from that of an electrolyte-gated organic field-effect transistor (EGOFET) to that of an OECT by incorporating an ion exchange gel between the active layer and the aqueous electrolyte. This device works by taking up biologically relevant ions from solution and injecting more hydrophobic ions into the active layer. Using poly[2,5-bis(3-tetradecylthiophen-2-yl) thieno[3,2-b]thiophene] as the active layer and a blend of an ionic liquid, 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide, and poly(vinylidene fluoride-co-hexafluoropropylene) as the ion exchange gel, four orders of magnitude improvement in device transconductance and a 100-fold increase in kinetics are demonstrated. The ability of the ion-exchange-gel OECT to record biological signals by measuring the action potentials of a Venus flytrap is demonstrated. These results show the possibility of using interface engineering to open up a wider palette of organic semiconductors as OECTs that can be gated by aqueous solutions.
Bischak, Connor G.↗
Thermodynamic Stability and Site‐Specific Distribution of Graphitic and Pyridinic Nitrogen in Graphene Moiré on Ru(0001)
Abstract Graphene‐like materials are of interest for large‐scale hydrogen storage applications due to their lightweight, durable, and scalable properties. Nitrogen‐doping minimizes kinetic limitations in diffusion and recombination on surfaces, however, the role of graphitic nitrogen (GN) and pyridinic nitrogen (PN) is not well understood. Nitrogen‐doped graphene is synthesized on Ru(0001) using chemical vapor deposition (CVD) of pyridine and ion irradiation. Scanning tunneling microscopy (STM), x‐ray photoelectron spectroscopy (XPS), and density functional theory (DFT) are used to identify the structure, location, and thermodynamic stability of nitrogen species within the graphene moiré. CVD of pyridine results in a low nitrogen concentration (<0.1at%), while the post‐growth nitrogen ion irradiation allows us to increase the concentration further. The concentration of GN and PN is controlled by varying the ion dose and annealing temperature. Comparison of measured and simulated STM images of GN and PN yield an excellent agreement, allowing us to confidently establish that GN is preferentially located near the center of the Atop region, while PN is located in the valley region of the graphene moiré. This report explicitly confirms the site assignments and provides a foundation for the site synthesis and analysis of structural and electronic properties that drive the reactivity of N‐doped graphene.
Gedara, Buddhika S. A. [Physical and Computational↗
Engineering of Self‐Aggregation‐Resistant MnO 2 Heterostructure with A Built‐in Field for Enhanced High‐Mass‐Loading Energy Storage
Abstract Although MnO 2 has been intensively investigated for energy storage, further applications are limited by van der Waals force‐triggered self‐aggregation that always leads to poorly exposed active sites and compromised reaction dynamics, especially under high‐mass‐loading conditions. Herein, by synergistically coupling interfacial modulation with the Kirkendall effect, this work achieves in situ topological structure reorganization of MnOOH toward the high‐aspect‐ratio MnO 2 heterostructure (Heter‐MnO 2 ) with fully exposed active sites, which is ready to assemble into self‐supporting high‐mass‐loading film (30 mg cm −2 ) with restrained self‐aggregation. Theoretical calculation and dynamics analysis results demonstrate the generation of the built‐in field within the heterostructure, thus enhancing the electronic‐transfer and ionic‐adsorption/transport rates. As such, the 30 mg cm −2 Heter‐MnO 2 electrode achieves a superior areal capacitance of 4762 mF cm −2 at 1 mA cm −2 and a sound rate performance (79% at 100 mA cm −2 ) comparable to those of low‐mass‐loading/thin‐film electrodes. As a proof of concept, the fabricated planar interdigital quasi‐solid‐state symmetric micro‐supercapacitor (MSC) based on the Heter‐MnO 2 electrode can deliver a remarkable areal capacitance of 181 mF cm −2 and a considerable volumetric energy density of 10.3 mWh cm −3 . This methodology highlights the promise of surface/interface chemistry modulation for the configuration of easy‐to‐integrate hierarchical nanostructures to better meet practical energy applications.
Chemistry↗
Solvent‐Free Self‐Assembly for Scalable Preparation of Highly Crystalline Mesoporous Metal Oxides
Abstract Mesoporous metal oxides (MMOs) have been demonstrated great potential in various applications. Up to now, the direct synthesis of MMOs is still limited to the solvent induced inorganic‐organic self‐assembly process. Here, we develop a facile, general, and high throughput solvent‐free self‐assembly strategy to synthesize a series of MMOs including single‐component MMOs and multi‐component MMOs (e.g., doped MMOs, composite MMOs, and polymetallic oxide) with high crystallinity and remarkable porous properties by grinding and heating raw materials. Compared with the traditional solution self‐assembly process, the avoidance of solvents in this method not only greatly increases the yield of target products and synthesis efficiency, but also reduces the environmental pollution and the consumption of cost and energy. We believe the presented approach will pave a new avenue for scalable production of advanced mesoporous materials for various applications.
Xiong, Hailong↗
Prediction by Convolutional Neural Networks of CO 2 /N 2 Selectivity in Porous Carbons from N 2 Adsorption Isotherm at 77 K
Abstract Porous carbons are an important class of porous materials with many applications, including gas separation. An N 2 adsorption isotherm at 77 K is the most widely used approach to characterize porosity. Conventionally, textual properties such as surface area and pore volumes are derived from the N 2 adsorption isotherm at 77 K by fitting it to adsorption theory and then correlating it to gas separation performance (uptake and selectivity). Here the N 2 isotherm at 77 K was used directly as input (representing feature descriptors for the porosity) to train convolutional neural networks to predict gas separation performance (using CO 2 /N 2 as a test case) for porous carbons. The porosity space for porous carbons was explored for higher CO 2 /N 2 selectivity. Porous carbons with a bimodal pore‐size distribution of well‐separated mesopores (3–7 nm) and micropores (<2 nm) were found to be most promising. This work will be useful in guiding experimental research of porous carbons with the desired porosity for gas separation and other applications.
Wang, Song↗