SEARCH · Search NASA
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.
In this Program, a team of industry and academic experts in optical interconnect design have come together to develop optical components and network architectures for the next generation high-bandwidth memory modules for DOE relevant high-performance computing applications.
The U.S. Department of Energy (DOE)’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel-cycle systems. This program has been providing engineering-scale support for the continued development of BISON, a high-fidelity, high-resolution fuel performance tool. Fuel behavior in nuclear reactors is governed by a complex network of mechanisms that interact with various other physics aspects in the reactor system. Any model developed to represent fuel behavior will likely be idealized, resulting in uncertainties when comparing their predictions against the observed data. In Fiscal Year (FY)-23, we initiated the Uncertainty Quantification (UQ) work by using Bayesian methods to establish a level of model trustworthiness and further improve it, with a particular emphasis on TRI-Structural isOtropic (TRISO) nuclear fuel. This year, we further expanded on that UQ work by investigating an approach to quantifying model inadequacy and accounting for lower-length scale (LLS) effects in TRISO silver (Ag) release modeling. Furthermore, we are implementing parallel active learning capabilities to reduce the computational cost (i.e., required computational resources and elapsed time) of performing UQ. Specifically, we utilized The Kennedy O’Hagan framework for Bayesian uncertainty quantification (KOH) to account for model inadequacy in TRISO Ag release predictions made by BISON. The KOH framework represents an improvement over the standard Bayesian framework used in FY-23. Explicitly accounting for model inadequacy in the Bayesian framework helps establish the level of experimental noise uncertainty in the Advanced Gas Reactor (AGR) data. We compared the inverse UQ results obtained from both the standard Bayesian and KOH frameworks in light of the AGR-2/3/4 data, and also compared the predictive UQ results obtained from these two frameworks in light of the AGR-1 data. Next, we investigated the impact of considering LLS effects in the Ag release simulations. We developed an expanded database of LLS simulated effective diffusivities for Ag, covering a wide range of microstructures and temperatures. Using this database, we developed a framework for incorporating LLS effects into the engineering-scale Ag release UQ. We developed both parametric and non-parametric approaches for bridging the length scales. We then investigated the inverse UQ results in light of the AGR-2/3/4 data and the predictive UQ results in light of the AGR-1 data, and compared the LLS-informed approach and the Arrhenius equation, which does not include microstructure information. Finally, we discussed implementing parallel active learning capabilities in the Multiphysics Object Oriented Simulation Environment (MOOSE)/BISON to reduce the computational cost (i.e., computational resources and elapsed time) of Bayesian UQ. For verification purposes, we first tested these new capabil ities on a species interaction problem. We then demonstrated them on the TRISO Ag release application, showing that parallel active learning capabilities can enhance the accuracy of UQ while also substantially reducing the computational cost in comparison to the reference methods developed in FY-23.
This report documents the development and application of two web-based decision-support tools for pump-and-treat (P&T) groundwater remediation systems: PTOLEMY (Pump-and-Treat Optimized Location Evaluation to Maximize Yields) and OPTIMA (Optimization for Pump-and-Treat Implementation, Management, & Assessment). These tools enhance remedy design and management by leveraging advanced computational methods – specifically deep learning and multi-objective optimization – within a user-friendly platform. By integrating data-driven models with established hydrogeological knowledge, PTOLEMY and OPTIMA enable more efficient evaluation of well placement and operational strategies, helping site managers balance multiple remediation objectives under complex conditions. Both tools are implemented as modules within the SOCRATES (Suite Of Comprehensive Rapid Analysis Tools for Environmental Sites) web platform, which provides data access, visualization, and analytics to support remedy optimization across sites in the U.S. Department of Energy Office of Environmental Management complex. PTOLEMY is a rapid screening module designed to identify promising locations for new extraction wells. It employs a multi-channel three-dimensional convolutional neural network (MC3D-CNN) trained on high-fidelity simulation data to predict the relative performance (in terms of contaminant mass recovery) of potential well sites. Through an interactive web interface, PTOLEMY visualizes the probability of high performance across a site, highlighting areas where an extraction well is likely to yield above-threshold contaminant removal over a multi-year period. PTOLEMY’s map-based displays and exportable results support transparent communication of screening analyses. By focusing attention on the most favorable candidate locations, the tool augments traditional engineering judgment and physics-based modeling, providing a data informed basis for subsequent detailed evaluations. OPTIMA is a multi objective optimization module designed to find wellfield layouts and operating schedules that meet various cleanup goals. It quickly evaluates thousands of candidate setups – combinations of well locations, timing, and rates – and returns a small set of best trade-off options for comparison. At its core, OPTIMA uses a U-Net-based surrogate model – a deep-learning emulator of a groundwater flow and transport simulator – to dramatically accelerate scenario evaluations. Coupling this fast surrogate with the NSGA-II (Non-dominated Sorting Genetic Algorithm II) evolutionary algorithm, OPTIMA explores a wide decision space of well locations and schedules to identify Pareto-optimal solutions that trade off key objectives (e.g., minimizing cleanup time, maximizing contaminant mass removal, and minimizing plume extent). The tool outputs a family of optimal configurations and visualizes their trade-offs (Pareto frontiers of cleanup metrics and maps of optimized well placements). Site managers can use these results to understand the range of viable strategies and to select candidate designs for more detailed verification. OPTIMA is currently under active development and not yet fully released; this guide provides early documentation to support planning and gather user feedback.
The frequency decoupling effect of the voltage source converter (VSC)-based high voltage dc (HVDC) transmission makes frequency support unavailable between two ac subsystems interconnected by the HVDC link. Recently, several algorithms have been proposed and demonstrated to improve the ac system stability by implementing inertia emulation (IE) functions in the HVDC VSC stations. However, the capability of the VSC-HVDC to achieve the IE control is not clear. According to the previous research, the IE control strategy has been studied without concerning the impact of the practical control process, which will introduce some time delay into the IE realization. In this paper, a detailed multi-terminal HVDC VSC control module, including the phase-locked loop (PLL) and low pass filter (LPF), is considered in order to analyze the multi-terminal HVDC network impact on the IE performance. The analysis indicates that the frequency performance of the ac network with IE integrated HVDC transmission can be nearly as good as that directly connected with the ac subsystem which involves a high penetration of conventional generation units, except for the short time delay introduced by the VSC control modules. The effectiveness of IE performance will be compromised if the response delay is significant. The simulation results have verified the analysis.
Finding optimal hyperparameters is necessary to identify the best performing deep learning models but the process is costly. In this paper, we applied model-based optimization, also known as Bayesian optimization, using the CANDLE framework implemented on a High-Performance Computing environment. As a use case we selected information extraction from cancer pathology reports using a multi-task convolutional neural network, and hierarchical convolutional attention network to be optimized. We utilized a synthesized text corpus of 8,000 training cases and 2,000 validation cases with four types of clinical task labels including primary cancer site, laterality, behavior, and histological grade. We demonstrated that hyperparameter optimization using the CANDLE framework is a feasible approach with respect to both scalability and clinical task performance.
Abstract Radiation mapping has attracted widespread research attention and increased public concerns on environmental monitoring. Regarding materials and their configurations, radiation detectors have been developed to identify the position and strength of the radioactive sources. However, due to the complex mechanisms of radiation-matter interaction and data limitation, high-performance and low-cost radiation mapping is still challenging. Here, we present a radiation mapping framework using Tetris-inspired detector pixels. Applying inter-pixel padding for enhancing contrast between pixels and neural networks trained with Monte Carlo (MC) simulation data, a detector with as few as four pixels can achieve high-resolution directional prediction. A moving detector with Maximum a Posteriori (MAP) further achieved radiation position localization. Field testing with a simple detector has verified the capability of the MAP method for source localization. Our framework offers an avenue for high-quality radiation mapping with simple detector configurations and is anticipated to be deployed for real-world radiation detection.
Tools are now available that enable measurement electromagnetic radiation (EMR) from active electronics in an item. This radiation may be intended WIFI or cellular network links, for example or unintended such as the switching noise generated by DC-to-DC converters. It would be extremely valuable to have the capability to discriminate between the low-voltage DC-to-DC converters or other digital noise prevalent in most modern electronics, versus the high-voltage DC-to-DC converters used in utility firesets. Previous work performed under a Sandia Laboratory Directed Research and Development (LDRD) project on Charge State Detection using a deep neural network has been continued in this effort. A state-of-the-art supervised machine learning algorithm has not only been extended to discriminate between low and high voltage converters but has been validated in determining a converters make and model.
The ESnet-JLab FPGA Accelerated Transport system is a solution for streaming high-speed scientific measurement data from Data Acquisition Systems (DAQs) to high-performance computing facilties. It is generally compatible with many science workflows, and makes no assumptions about the specifics of any particular experiment. This program (udplbd version 2) implements the control plane for the system. It is responsible for programming network forwarding rules into the data plane (implemented by the hardware designed named udplb, described separately). It also implements the control loop necessary to match up offered workload with available capacity on high-performance compute nodes.
The numerical simulation of quantum circuits is an indispensable tool for development, verification, and validation of hybrid quantum-classical algorithms intended for near-term quantum co-processors. The emergence of exascale high-performance computing (HPC) platforms presents new opportunities for pushing the boundaries of quantum circuit simulation. Here, we present a modernized version of the Tensor Network Quantum Virtual Machine (TNQVM) that serves as the quantum circuit simulation backend in the eXtreme-scale ACCelerator (XACC) framework. The new version is based on the scalable tensor network processing library ExaTN (Exascale Tensor Networks). It provides multiple configurable quantum circuit simulators that perform either an exact quantum circuit simulation via the full tensor network contraction or an approximate simulation via a suitably chosen tensor factorization scheme. Upon necessity, stochastic noise modeling from real quantum processors is incorporated into the simulations by modeling quantum channels with Kraus tensors. By combining the portable XACC quantum programming frontend and the scalable ExaTN numerical processing backend, we introduce an end-to-end virtual quantum development environment that can scale from laptops to future exascale platforms. We report initial benchmarks of our framework, which include a demonstration of the distributed execution, incorporation of quantum decoherence models, and simulation of the random quantum circuits used for the certification of quantum supremacy on Google’s Sycamore superconducting architecture.
Offering an independent humidity management method for buildings, desiccant-coated heat exchangers (DCHXs) are deemed a promising approach to improve the overall energy efficiency of air conditioning (AC) systems. State-of-the-art DCHXs, however, are bound with conventional HX topologies either providing limited desiccant-air interfacial areas or introducing excessive pressure drop penalties. In this paper a novel 3D-printed DCHX concept inspired by the bronchi arrangement of a human lung is introduced to address the shortcomings inherent in existing DCHX designs. The proposed lung-inspired DCHX utilizes two intertwined bicontinuous flow networks enabling highly efficient heat and mass transfer characteristics for augmented adsorption and regeneration processes at low pressure drop penalties. While the first network evenly distributes an incoming air stream through the entire volume of the lung-inspired DCHX, the second network volumetrically splits a cooling water stream within and through the first network. Effects of various parameters including air flow rate, outdoor air humidity ratio, and regeneration temperature on dehumidification performance and energy efficiency of the proposed lung-inspired DCHX were investigated. Experimental results indicated the proposed lung-inspired 3D-printed DCHX outperforms existing DCHX systems by demonstrating an excellent balance between a high volumetric adsorption rate and a low pressure drop penalty. The volumetric adsorption rate of the proposed lung-inspired DCHX technology is 54.8 g/m 3 -s, a more than two-fold improvement compared with state-of-the-art DCHX systems. Additionally, the lung-inspired DCHX offers high thermal energy efficiency of 56% at a low regeneration temperature of 40 °C. Therefore, the proposed lung-inspired 3D-printed DCHX offers a new solid-desiccant-based air dehumidification pathway for next-generation high-performance AC systems.
Control of both the magnitude and the shape of tokamak profiles will be necessary to achieve stable, high-performance plasmas. In order to reject disturbances in real time, feedback-control algorithms rely on accurate real-time knowledge of the plasma state. When diagnostics alone are insufficient, either because they are limited in number or their measurements are too noisy, observers can be used to combine diagnostic data with a response model to provide a better estimation of different plasma properties. An observer has been developed to estimate the electron temperature profile in real time using both diagnostic data from the Thomson scattering system and a model based on the electron heat transport equation describing the evolution of the electron temperature profile. Here, neural network surrogate models are leveraged to help improve the overall model prediction while staying within computation time constraints for real-time use. The observer algorithm is shown in offline tests to produce smooth profiles that are consistent with both the diagnostic data and the electron heat transport equation. When implemented into the real-time plasma control system, this observer will provide valuable information on the electron temperature profile to many potential feedback-control applications.
TCP and the socket abstraction have barely changed over the last two decades, but at the network layer there has been a giant leap from a few megabits to 100 gigabits in bandwidth. At the same time, CPU architectures have evolved into the multicore era and applications are expected to make full use of all available resources. Applications in the data acquisition domain based on the standard socket library running in a Non-Uniform Memory Access (NUMA) architecture are unable to reach full efficiency and scalability without the software being adequately aware about the IRQ (Interrupt Request), CPU and memory affinities. During the first long shutdown of LHC, the CMS DAQ system is going to be upgraded for operation from 2015 onwards and a new software component has been designed and developed in the CMS online framework for transferring data with sockets. This software attempts to wrap the low-level socket library to ease higher-level programming with an API based on an asynchronous event driven model similar to the DAT uDAPL API. It is an event-based application with NUMA optimizations, that allows for a high throughput of data across a large distributed system. This paper describes the architecture, the technologies involved and the performance measurements of the software in the context of the CMS distributed event building.
Direct removal of 99 TcO 4 - from the highly acidic solution of used nuclear fuel is highly beneficial for the recovery of uranium and plutonium and more importantly aids in the elimination of 99 Tc discharge into the environment. However, this task represents a huge challenge given the combined extreme conditions of super acidity, high ionic strength, and strong radiation field. Here in this paper we overcome this challenge using a cationic polymeric network with significant TcO 4 - uptake capabilities in four aspects: the fastest sorption kinetics, the highest sorption capacity, the most promising uptake performance from highly acidic solutions, and excellent radiation-resistance and hydrolytic stability among all anion sorbent materials reported. In addition, this material is fully recyclable for multiple sorption/desorption trials, making it extremely attractive for waste partitioning and emergency remediation. The excellent TcO 4 - uptake capability is elucidated by X-ray absorption spectroscopy, solidstate NMR measurement, and density functional theory analysis on anion coordination and bonding.
Boron-enriched scaffolds have demonstrated unique features and promising performance in the field of catalysis towards the activation of small gas molecules. However, there is still a lack of facile approaches capable of achieving high B doping and abundant porous channels in the targeted catalysts. Here, in this paper, construction of boron- and nitrogen-enriched nanoporous π-conjugated networks (BN-NCNs) was achieved via a facile ionothermal polymerization procedure with hexaazatriphenylenehexacarbonitrile [HAT(CN) 6 ] sodium borohydride as the starting materials. The as-produced BN-NCN scaffolds were featured by high heteroatoms doping (B up to 23 wt. % and N: up to 17 wt. %) and permanent porosity (surface area up to 759 m 2 g -1 mainly contributed by micropores). With the unsaturated bonded B species acting as the active Lewis acid sites and defected N species acting as the active Lewis base sites, those BN-NCNs delivered attractive catalytic performance towards H 2 activation/dissociation in both gaseous and liquid phase, acting as efficient metal-free heterogeneous frustrated Lewis pairs (FLPs) catalysts in hydrogenation procedures.
DynamiteNSM is a free Network Security Monitor (NSM), built on top of several leading, enterprise-grade technologies. The tool provides network and cybersecurity operators with holistic insights into their networks while giving them the ability to deep-dive into lower-level activities. The solution presents powerful dashboards, giving comprehensive view into performance and threat-based metrics. Dynamite-NSM can be easily deployed in different environments including high-speed data centers, small-to-large enterprises, IoT & industrial networks, and even at home. Dynamite-NSM handles massive volumes of network traffic through scalable ingestion and optimized network sensors. The solution includes two key components: the agent and the monitor. The agent analyzes and forwards network events, while the monitor processes incoming events and displays analytic information. The monitor component builds upon the OpenSearch stack and is coupled with network packet inspection by Zeek and Suricata. Dynamite-NSM is designed to be deployed very quickly with minimal configuration. Unlike many other tools, it can be installed and managed with a standalone command-line utility. The system is inherently passive without disruption to the network. There is no need to install agents on every computer, perform network scans, or directly interact with network assets.
Quantum networking continues to encode information in polarization states due to ease and precision. The variable environmental polarization transformations induced by deployed fiber need correction for deployed quantum networking. Here, we present a method for automatic polarization compensation (APC) and demonstrate its performance on a metropolitan quantum network. Designing an APC involves many design decisions as indicated by the diversity of previous solutions in the literature. Our design leverages heterodyne detection of wavelength-multiplexed dim classical references for continuous high-bandwidth polarization measurements used by newly developed multi-axis (non-)linear control algorithm(s) for complete polarization channel stabilization with no downtime. This enables continuous relatively high-bandwidth correction without significant added noise from classical reference signals. We demonstrate the performance of our APC using a variety of classical and quantum characterizations. Finally, we use C-band and L-band APC versions to demonstrate continuous high-fidelity entanglement distribution on a metropolitan quantum network with an average relative fidelity of 0.94 ± 0.03 for over 30 hrs.
In the polymeric material industry, thermosets and related composites have played a substantial role in the production of rubber and plastics. One important subset of these is thermoset composites with carbon reinforcement. The incorporation of carbon fillers and fibers gives polymeric materials improved electrical and mechanical properties, among other benefits. However, the covalently crosslinked network of thermosets presents significant challenges for recycling and reprocessing because of its intractable nature. The introduction of vitrimer materials opens a new avenue to produce biodegradable and recyclable thermosets. Carbon-reinforced vitrimer composites are pursued for high-performance, long-lasting materials with attractive physical properties, the ability to be recycled and processed, and other features that respond uniquely to stimuli. The development of carbon-reinforced vitrimer composites over the last few years is summarized in this article. First, an overview of vitrimers and the methods used to prepare carbon fiber-reinforced vitrimer composites is provided. Because of the vitrimer nature of such composites, reprocessing, healing, and recycling are viable ways to greatly extend their service life; these approaches are thoroughly explained and summarized. The conclusion is our prediction for developing carbon-based vitrimer composites.
The conversion of wastepaper into high-value carbon materials has gained significant attention as a sustainable strategy for energy storage applications. Owing to its low cost, abundance, and intrinsic fibrous structure, wastepaper serves as an attractive precursor for carbon-based electrode materials. In this work, a novel electrode comprising cobalt nanoparticles and Co 3 O 4 embedded in wastepaper-derived porous carbon has been developed for use in a flexible all-solid-state asymmetric supercapacitor. The integration of cobalt nanoparticles and Co 3 O 4 introduces redox-active centres, which enhance the electrochemical activity of the carbon framework. Mean while, the interconnected porous and conductive carbon network enables rapid ion diffusion and efficient electron transport, synergistically improving the overall electrochemical performance. It delivers a high specific capacitance of 61.5 Fg -1 , a power density of 5143 W kg -1 , and an energy density of 9 Wh kg -1 at a current density of 2.5 A/ g. Furthermore, the device maintains outstanding cycling stability, retaining 82.86 % of its capacitance after 10,000 charge-discharge cycles. Practical applicability is confirmed through its ability to power