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For presentation at the 2023 FECM/NETL Carbon Management Research Project Review Meeting, Pittsburgh, PA, August 28-September 1, 2023.
The ability to dynamically control the radiative transfer of heat at the nanoscale holds the key to the development of a diverse number of technologies, ranging from nanoscale thermal-management systems to improved thermophotovoltaic devices. Recently, graphene has emerged as an ideal material to achieve this goal, since it can be electrically doped to support surface plasmons, collective oscillations of the conduction electrons. These resonances produce large and spectrally narrow optical cross sections, which dictate the emission and absorption properties of the graphene nanostructure and, thus, the heat that it radiatively exchanges with other objects and the environment. For attainable levels of doping, the plasmons supported by graphene nanostructures naturally lie in the midinfrared part of the spectrum, which is the most relevant wavelength range for radiative heat transfer under realistic temperatures. Furthermore, these resonances are actively tunable, thus providing full dynamic control over the heat transfer. Motivated by this great potential, we present a comprehensive analysis of the temporal evolution of the radiative heat transfer between arrangements of graphene nanodisks, showing that it is possible to exploit the tunability of these structures to obtain actively controlled heat transfer scenarios not possible with conventional passive nanostructures. We report the results of this work provide a framework for achieving fully dynamical control over nanoscale radiative heat transfer and thus provide fundamental insights into this process.
Since 2017, the Worldwide LHC Computing Grid (WLCG) has been working towards enabling token based authentication and authorisation throughout its entire middleware stack. Following the publication of the WLCG Common JSON Web Token (JWT) Schema v1.0 [1] in 2019, middleware developers have been able to enhance their services to consume and validate the JWT-based [2] OAuth2.0 [3] tokens and process the authorization information they convey. Complex scenarios, involving multiple delegation steps and command line flows, are a key challenge to be addressed in order for the system to be fully operational. This paper expands on the anticipated token based workflows, with a particular focus on local storage of tokens and their discovery by services. The authors include a walk-through of this token flow in the RUCIO managed data-transfer scenario, including delegation to FTS and authorised access to storage elements. Next steps are presented, including the current target of submitting production jobs authorised by Tokens within 2021.
The evolution of SARS-CoV-2 virus has resulted in variants likely to be more readily transmitted through respiratory aerosols, underscoring the increased potential for indoor environmental controls to mitigate risk. Use of tight-fitting face masks to trap infectious aerosol in exhaled breath and reduce inhalation exposure to contaminated air is of critical importance for disease control. Administrative controls including the regulation of occupancy and interpersonal spacing are also important, while presenting social and economic challenges. Indoor engineering controls including ventilation, exhaust, air flow control, filtration, and disinfection by germicidal ultraviolet irradiation can reduce reliance on stringent occupancy restrictions. However, the effects of controls-individually and in combination-on reducing infectious aerosol transfer indoors remain to be clearly characterized to the extent needed to support widespread implementation by building operators. We review aerobiologic and epidemiologic evidence of indoor environmental controls against transmission and present a quantitative aerosol transfer scenario illustrating relative differences in exposure at close-interactive, room, and building scales. We identify an overarching need for investment to implement building controls and evaluate their effectiveness on infection in well-characterized and real-world settings, supported by specific, methodological advances. Finally, improved understanding of engineering control effectiveness guides implementation at scale while considering occupant comfort, operational challenges, and energy costs.
Many extreme-scale applications require the movement of large quantities of data to, from, and among leadership computing facilities, as well as other scientific facilities and the home institutions of facility users. These applications, particularly when leadership computing facilities are involved, can touch upon edge cases (e.g., terabyte files) that had not been a focus of previous Globus optimization work, which had emphasized rather the movement of many smaller (megabyte to gigabyte) files. We report here on how automated client-driven chunking can be used to accelerate both the movement of large files and the integrity checking operations that have proven to be essential for large data transfers. In conclusion, we present detailed performance studies that provide insights into the benefits of these modifications in a range of file transfer scenarios.
This work will be presented at the combustion symposium in Milan, Italy.
Real-Twin is a unified, model-agnostic scenario generation tool designed to streamline and standardize the evaluation of emerging mobility technologies. It provides an end-to-end framework that includes robust workflows, integrated tools, and comprehensive metrics to generate, calibrate, and benchmark microscopic traffic simulation scenarios across multiple platforms. Key Features of Real-Twin include: - Unified Scenario Generation: generate transferable, simulation-ready scenarios from heterogeneous data sources using a consistent workflow. - Automated Calibration Workflow: bridges simulation and real-world data, minimizing manual effort and making traffic simulation more accessible to researchers and engineers. - Model-Agnostic Compatibility: supports SUMO, VISSIM, and AIMSUN for cross-platform scenario generation and benchmarking. Enables reliable comparisons and reproducibility across different simulation tools. - Consistent Scenarios across Different Simulators: generate comparable simulation scenarios across different microscopic traffic simulators, providing users the ability to conduct benchmarking and cross-validation that are crucial for ensuring the reliability and reproducibility of simulation results. - Emerging Technology Support: includes a scenario database and pipeline for studying autonomous vehicles (AVs), with planned extensions to CAVs, EVs, and other advanced technologies.
Transfer learning enables the transfer of knowledge gained while learning to perform one task (source) to a related but different task (target), hence addressing the expense of data acquisition and labelling, potential computational power limitations and dataset distribution mismatches. Here, we propose a new transfer learning framework for task-specific learning (functional regression in partial differential equations) under conditional shift based on the deep operator network (DeepONet). Task-specific operator learning is accomplished by fine-tuning task-specific layers of the target DeepONet using a hybrid loss function that allows for the matching of individual target samples while also preserving the global properties of the conditional distribution of the target data. Inspired by conditional embedding operator theory, we minimize the statistical distance between labelled target data and the surrogate prediction on unlabelled target data by embedding conditional distributions onto a reproducing kernel Hilbert space. We demonstrate the advantages of our approach for various transfer learning scenarios involving nonlinear partial differential equations under diverse conditions due to shifts in the geometric domain and model dynamics. Our transfer learning framework enables fast and efficient learning of heterogeneous tasks despite considerable differences between the source and target domains.
This report documents the results of preliminary nuclear criticality and radiation shielding assessments during transfer and dry storage of irradiated Microreactor Applications Research Validation and Evaluation (MARVEL) reactor fuel at Idaho National Laboratory (INL). The assessments focus on transfer casks and storage canisters that are currently in use at INL, which may be compatible with the irradiated MARVEL reactor fuel. The criticality assessments were performed using the radiation transport code MCNP6 with 37 MARVEL reactor fuel elements in various configurations and scenarios. All transfer and storage configurations under dry conditions were below the assumed criticality safety limit of 0.93. Some storage and transfer configurations under wet conditions exceeded the criticality safety limit. This suggests that the appropriate administrative and engineering controls, in addition to reducing the number of MARVEL reactor fuel elements per transfer cask or storage canister, can be expected to ensure criticality safety under all scenarios. The radiation shielding assessments were performed by generating conservative neutron and photon source terms using the ORIGEN module in the SCALE suite of codes. These source spectra were used to estimate the dose equivalent rates using the radiation transport code MCNP6, both on contact and 1 m away from the fuel and transfer casks. The maximum estimated dose equivalent rate of 37 unshielded MARVEL reactor fuel elements on contact is approximately 42000 R/hr. The maximum estimated dose equivalent rates on contact to the ATR transfer cask, HFEF-5 transfer cask, and high load charger were approximately 233 mR/hr, 171 mR/hr, and 201 mR/hr, respectively. This suggests that with the appropriate administrative and engineering controls, all three transfer casks analyzed can be expected to provide sufficient radiation shielding to workers during transfer of irradiated MARVEL reactor fuel. These calculations are performed to support the planning and strategy for the MARVEL project and will demonstrate the technical viability of the different configurations discussed and help identify where engineered or administrative controls may be necessary. A complete criticality safety analysis and radiation shielding analysis, including validation and contingency and accident analysis must be completed by licensed and authorized personnel before any transfer or storage of MARVEL reactor nuclear fuel.
Driver distraction behavior is one of the critical factors in traffic accidents. Thus, advanced driver state detection system has become the focus in the field of intelligent vehicle. However, in practical applications, insufficient samples of driving distraction behaviors bring great challenges to training a personalized behavior distraction detection model for a specific driver. To this end, a novel transformer model based on a transfer learning strategy is proposed in this paper to accurately recognize driver distraction behavior. Inspired by the effect of the transformer network in visual recognition, we firstly present a transformer behavior distraction detection system to identify the behavior categories that cause driver distraction. Then, for the specific driving dataset in practical application scenarios, the transfer learning strategy is introduced into the driver distraction detection model to further train the general transformer network. The effectiveness of the transformer based on the transfer learning strategy is validated compared with other traditional deep learning methods. The results show that the proposed detection method has better generalization ability and higher accuracy.
The thermal and fast-ion transport properties of DIII-D steady-state hybrid discharges with normalized beta β N ≳ 3 are studied at low injected torque and an increased electron to ion temperature ratio T e / T i . Linear stability analysis performed with the TGLF turbulent code indicates that a high-k mode is usually dominant at smaller radii, whereas a low-k mode is usually dominant at larger radii in these plasmas. A reduction in the net injected torque from 8.6 to 4.3 N-m leads to reduced E × B shear and hence, an enhanced turbulence that was observed on the Doppler backscattering diagnostic and was also computed with TGLF. As T e / T i in the core was increased from 0.57 to 0.66 by adding electron cyclotron current drive (ECCD) to these plasmas, higher levels of transport are observed with increased high-k modes indicated by TGLF. The fast-ion transport level varied over an order of magnitude in these discharges depending on whether Alfvén eigenmodes, fishbones, or no instabilities were observed. Hybrid plasmas with fishbones have decreased fast-ion transport, compared to plasmas with Alfvén eigenmodes, since they are resonant with a smaller portion of phase space and their resonance is farther from the wall. This reduction in fast-ion transport with ECCD mitigates the increase in turbulent transport, resulting in higher performance than expected during strong electron heating. Similarly, the lowest fast-ion transport was observed in the low torque plasma, which also led to better than expected performance at this torque value. Finally, the thermal and fast-ion transport changes observed as the torque/rotation and T e / T i are varied indicate possible methods for transferring this scenario to a reactor.
This paper describes an updated biokinetic model for systemic sodium (Na), developed for use in a series of reports by the International Commission on Radiological Protection (ICRP) on occupational intake of radionuclides. In contrast to the ICRP's previous model for intake of radio-sodium by workers, the updated model depicts realistic directions of movement of Na in the body including recycling of activity between blood and tissues. The updated model structure facilitates extension of the baseline transfer coefficients for adults to different age groups and to special exposure scenarios such as transfer of radio-sodium from the mother to the foetus or the nursing infant. Dose coefficients for 22Na and 24Na based on the updated model generally do not differ greatly from those based on the ICRP's previous Na model when both models are connected to the ICRP's latest dosimetry system. The main exception is that the updated model yields roughly twofold higher dose coefficients for endosteal bone surface than does the previous model due to the dosimetrically cautious assumption in the updated model that exchangeable Na in bone resides on bone surface.
The United States Department of Energy (DOE) has long term goals to develop solutions for managing the nation’s spent nuclear fuel (SNF) and high-level waste (HLW) inventory. The Integrated Waste Management (IWM) program under the DOE office of Nuclear Energy (DOE-NE) is employing system-level engineering and analysis principles to inform potential future waste management system architectures. Managing the spent nuclear waste requires the use of system-level analysis software that takes various aspects of the fuel cycle into account like waste generation, on-site/centralized storage, transportation infrastructure, and long-term disposal. The Next Generation System Analysis Model (NGSAM) is an agent-based model that was developed to simulate the transportation and storage of SNF and HLW. As an agent-based model, NGSAM has the capability to detail the interaction and movement of individual components and groups, such as rail cars and casks. The SNF inventory from commercial nuclear reactors is currently in temporary storage at multiple locations spread across the United States. Shipping of SNF from these locations relies on one of three transportation modes: rail, heavy-haul truck, or barge. Out of the three modes identified, rail is generally the most preferred due to the size of the canisters and casks the SNF would be shipped in. However, under some scenarios, a direct rail route might not be readily available to a reactor site or improving the rail infrastructure at shutdown sites might be too cost-prohibitive for utilities to opt for a direct rail transfer. Under such scenarios, using a barge or heavy haul truck to de-inventory the site and transfer the SNF to a nearby intermodal transfer site with adequate rail infrastructure where the payload could be transferred to a rail car might prove to be an attractive option. This work initially presents the various intermodal transportation options that could be used to transfer SNF from reactor sites to rail cars. This is followed by exploring the operational steps in each of these modes to move the SNF from a reactor site and transfer it to a rail car. This work also presents the procedure of implementing the intermodal transfer methodology in NGSAM using various Java methods. Finally, the process times for accomplishing each of the individual steps are furnished. The implementation ideology, assumptions, and future steps are presented in this work.
The Preliminary Remediation Goal (PRG) and Dose Compliance Concentration (DCC) calculators are screening level risk assessment tools that set forth the Environmental Protection Agency’s (EPA) recommended approaches and currently available risk assessment guidance for response actions at Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) sites, commonly known as Superfund. The environmental screening levels derived by the PRG and DCC calculators are used to identify isotopes contributing the highest risk and dose as well as establish preliminary remediation goals. Each calculator has residential gardening and subsistence farmer exposure scenarios that model transfer of contaminants from soil and water into various types of biota (crops and animal products). New publications of human intake rates of biota; farm animal intakes of water, soil, and fodder; and soil to plant interactions require updates be implemented into the PRG and DCC calculators. Recent improvements in the biota modeling for these calculators include newly derived biota intake rates, enhanced soil mass loading factors (MLFs), and more comprehensive soil to plant transfer factors (BV’s) and soil to tissue transfer factors (TFs) for animals. New biota have been added in both the produce and animal products categories that greatly improve the accuracy and utility of the PRG and DCC calculators and encompass greater geographic diversity on a national and international scale.
With recent advances in the fields of satellite imagery and machine learning we now have the ability to develop explainable deep learning models that enhance critical infrastructure analysis. Funded through Idaho National Laboratory’s (INL) Laboratory Directed Research and Development (LDRD) office we are in the process of developing a deep learning model capable of identifying critical infrastructure facilities and embedded features within those facilities. Utilizing current limit of practice techniques in the machine learning areas of explainability and transfer learning our model, once complete, will have the capacity to be used on multiple different imagery data sets and produce results that not only classify critical infrastructure facilities, but also explain why a critical infrastructure facility was classified as a certain type of facility. These advancements eliminate the ‘black box’ approach deep learning models have had in the past, where a user will have to trust the conclusion of a model without understanding what reasoning when into the model’s classification process. They also expand a model’s usefulness, traditionally a deep learning model will have to use the same data set it was originally trained on. Given the long training times of deep learning models this is impractical in a number of scenarios. By utilizing transfer learning advancements, we are eliminating the need to train our model on the same data set it is then run on to classify critical infrastructure facilities. We are also enabling the analysis and classification of data sets that are potentially too small to be divided into a training and testing data set. Once completed this model can provide a foundation to enhanced critical infrastructure analysis, dependency analysis, and potential disaster relief efforts.
Increasing penetration levels of inverter-based distributed energy resources (DERs) in distribution systems are creating challenges in system operation. Currently, wired or proprietary wireless communications are used by advanced distribution management systems (ADMS) to detect the state of the system and control the DERs for optimal operation. Wireless communications are an alternative solution that can enable connectivity between the dispersed assets used in distribution system applications. As demand for the seamless integration of distributed energy resources (DERs) continues to increase, proper networking and communications systems are becoming important to support the DER infrastructure. This report summarizes validation of a private wireless LTE network in a laboratory environment. The purpose of this work is to 1) demonstrate the effectiveness of private wireless LTE communication for grid applications and 2) provide a means of evaluating latency and overall performance of wireless communication in grid applications. First, private wireless LTE communication was validated in a direct transfer trip relaying scenario as an example of one-way communications using grid equipment. This study provided a baseline understanding of the variation in latency for seven different kinds of wireless scenarios shown in Figure A below. As expected, weak (attenuated) wireless signal-based communication yielded the widest ranges in latency. When higher priority was set for the trip signal communication, this resulted in lower latency in communication timing. In the final phase of this project, the private wireless LTE network was validated using two-way communications in an advanced distribution management system (ADMS) testbed which was developed in the ESIF at NREL. The purpose of this was to study the impact of wireless communication on grid performance.
Bayesian inference is used to calibrate a bottom-up home PLC network model with unknown loads and wires at frequencies up to 30 MHz. A network topology with over 50 parameters is calibrated using global sensitivity analysis and transitional Markov Chain Monte Carlo (TMCMC). The sensitivity-informed Bayesian inference computes Sobol indices for each network parameter and applies TMCMC to calibrate the most sensitive parameters for a given network topology. A greedy random search with TMCMC is used to refine the discrete random variables of the network. This results in a model that can accurately compute the transfer function despite noisy training data and a high dimensional parameter space. The model is able to infer some parameters of the network used to produce the training data, and accurately computes the transfer function under extrapolative scenarios.
The Hydrogen Production, Grid Integration, and Scaling for the Future project will inform clean hydrogen production deployment across the planning, procurement, and operation stages of hydrogen production on the grid. Hydrogen production from renewables is a clean source of fuel which is near zero for greenhouse gas emissions and criteria pollutants. The results from this project will inform entities looking to build clean energy projects that produce good paying jobs in manufacturing, installation, maintenance and operation of these facilities. This project provides system characterization examples, multiple configurations, optimizations, and suggested metering and custody transfer points through multiple scenarios and hardware testing of NREL's state-of-the-art 1.25 MW polymer electrolyte membrane (PEM) electrolyzer system to characterize system performance in relevant scenarios. This work also creates a digital twin for emulation in the Advanced Research on Integrated Systems (ARIES) virtual environment and performs hardware-in-the-loop (HIL) testing of pilot-scale, decentralized, and centralized hydrogen systems.