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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Hydrogen for Maritime Applications

The maritime industry is investigating a number of fuel options for reducing emissions, including liquefied natural gas (LNG), biofuels, and electrical drive systems powered by batteries and/or hydrogen-fueled fuel cells. Hydrogen-fueled ships offer the potential to significantly reduce, if not eliminate, regulated and unregulated pollutants in maritime applications. Argonne National Laboratory conducted preliminary comparisons of the total cost of ownership (TCO) of several classes of ships to determine how fuel cell technology compares to the current diesel technology, what advancements are needed for hydrogen fuel cell technology to be competitive in the future, and what applications may be appropriate for introducing fuel cells into the maritime industry. These studies included feeder container ships, harbor tugboats, river pushboats, and auto/passenger ferries. For this study, TCO was defined to include the cost of fuel, propulsion system, and fuel storage system, the levelized cost of propulsion/auxiliary engines, and the cost of annual maintenance and consumables. It did not include the cost of the vessel frame or other components, aside from the propulsion system, that the fuel cell and diesel ships have in common. A 10% internal rate of return (IRR) was applied to the initial capital investment and an installation cost factor of 20% was applied to the capital cost. The capital cost of each component (e.g., engine, fuel tank, motor, etc.) was amortized over a period of 20 years, except for the fuel cell system, which was amortized over 6 or 10 years depending on ship class. The initial comparisons for container ships indicate that fuel costs are by far the dominant contributor to the TCO. With the current low cost of low-sulfur marine gasoil (LSMGO) and relatively high cost of hydrogen, it is difficult for hydrogen to compete with LSMGO in container ship applications. The large energy demand for container ships also favors the use of the higher volumetric energy density LSMGO fuel, especially for longer voyages. The space required to store enough hydrogen for the same journey is larger than that needed to store diesel fuels and can reduce the available cargo carrying and revenue generating space available on the ship.

08 HYDROGEN↗
DOE OSTI2022

Driving Zn-MnO2 grid-scale batteries: A roadmap to cost-effective energy storage

Highlights Zn-MnO 2 batteries promise safe, reliable energy storage, and this roadmap outlines a combination of manufacturing strategies and technical innovations that could make this goal achievable. Approaches such as improved efficiency of manufacturing and increasing active material utilization will be important to getting costs as low as $100/kWh, but key materials innovations that facilitate the full 2-electron capacity utilization of MnO 2 , the use of high energy density 3D electrodes, and the promise of a separator-free battery with greater than 2V potential offer a route to batteries at $50/kWh or less. Abstract Large-scale energy storage is certain to play a significant, enabling role in the evolution of the emerging electrical grid. Battery-based storage, while not a dominant form of storage today, has opportunity to expand its utility through safe, reliable, and cost-effective technologies. Here, secondary Zn–MnO 2 batteries are highlighted as a promising extension of ubiquitous primary alkaline batteries, offering a safe, environmentally friendly chemistry in a scalable and practical energy dense technology. Importantly, there is a very realistic pathway to also making such batteries cost-effective at price points of $50/kWh or lower. By examining manufacturing examples at the Zn–MnO 2 battery manufacturer Urban Electric Power, a roadmap has been created to realize such low-cost systems. By focusing on manufacturing optimization through reduced materials waste, scalable manufacturing, and effective materials selection, costs can be significantly reduced. Ultimately, though, coupling these approaches with emerging research and development advances to enable full capacity active materials utilization and battery voltages greater than 2V are likely needed to drive costs below a target of $50/kWh. Reaching this commercially important goal, especially with a chemistry that is safe, well-known, and reliably effective stands to inject Zn–MnO 2 batteries in the storage landscape at a critical time in energy storage development and deployment. Graphical abstract

Spoerke, Erik D. (ORCID:0000000322862560)↗
DOE OSTI2021

Component Modeling, Co-Optimization, and Trade-Space Evaluation (FY2021 Annual Progress Report)

This project is intended to support the development of new traction drive systems that meet the targets of 100 kW/L for power electronics and 50 kW/L for electric machines with reliable operation to 300,000 miles. To meet these goals, new designs must be identified that make use of state-of-the-art and next-generation electronic materials and design methods. Designs must exploit synergies between components, for example converters designed for high-frequency switching using wide band gap devices and ceramic capacitors. This project includes: (1) a survey of available technologies; (2) the development of design tools that consider the converter volume and performance; (3) exercising the design software to evaluate performance gaps and predict the impact of certain technologies and design approaches, i.e. GaN semiconductors, ceramic capacitors, and select topologies; and (4) building and testing hardware prototypes to validate models and concepts. Early instantiations of the design tools enable co-optimization of the power module and passive elements and provide some design guidance; later instantiations will enable the co-optimization of inverter and machine. Prototype testing begins with evaluation of simpler conversion topologies (i.e. the half-bridge boost converter) and progresses with fabrication of prototype inverter drives.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

How structural differences influence cross-model consistency: An electric sector case study

Multiple models are often employed to describe a range of possible outcomes for one or more scenarios, yielding insights into causal relationships and their uncertainties. Electric sector capacity expansion scenarios are a common topic of such efforts due to the economic influence of the electric sector, but model results typically span a broad solution space despite efforts to harmonize input assumptions, making decision implications difficult to discern. This study investigates the relationship between input harmonization and cross-model scenario consistency under disparate electric sector scenarios. We compare cross-model consistency between two state-of-the-art electric sector capacity expansion models (GCAM-USA and ReEDS) for six electric sector scenarios comprising alternate assumptions about fossil fuel resource availability, technology innovation, and long-term economy-wide transitions under four harmonization configurations varying model representations of electricity demand, fuel prices, renewable resources, and capacity retirements. These comparisons reveal that cross-model consistency can vary across scenarios under a given harmonization configuration, suggesting that harmonization efforts must often be scenario-specific if comparable cross-model consistency is desired. Model structural differences can hinder consistency, and the impact of these differences can depend on the scenario. Ultimately, thorough harmonization can reveal insights into cross-model consistency, which can be used to tighten uncertainty bounds and improve the decision-making implications of multi-model activities.

Cohen, Stuart↗
DOE OSTI2024

Connected and Learning Based Optimal Freight Management for Efficiency

The management of the future heterogenous fleet is a complex decision-making problem. The heterogenous fleet is emerging as decarbonization technologies are deployed by fleets toward lowering the freight operation emissions in Medium and Heavy-duty vehicles. Traditionally, in fleets characterized by a homogeneous Diesel Internal Combustion Engine (ICE) powertrain, the process of fleet planning and operational optimization unfolds sequentially without the necessity to account for powertrain and vehicle-specific characteristics during dispatch decisions. Fleets with trucks less than 5 years old tend to maintain stable vehicle efficiency with minimal operational reliability risks for fleet managers. However, the landscape changes with the incorporation of emerging powertrain technologies, which lack extensive operational data and service experiences. This includes technologies like hybrid, Electric, Fuel Cell, or alternative fuel ICE. Operational decisions for fleets featuring heterogeneous powertrain technologies and facing limited access to alternative fueling and charging stations become intricate, requiring careful consideration and optimization at each dispatch. The difference in efficiency characteristics of emerging technologies, their range limitations, and the restricted availability of charging/alternative fueling infrastructure, coupled with sensitivity to driving conditions (e.g., EV range reduction in low temperatures) and their impact on component aging (such as batteries), become pivotal factors influencing the reliable and efficient freight transportation. To make the path toward low emission freight transportation efficient and reliable, an AI-assisted fleet management software is developed in this project to help fleet managers in optimizing both adoption of emerging powertrain decarbonization, connected and automated technologies and also operating the fleet after such technologies are deployed as schematically. Freight transportation requirements are different depending on the cargos to be shipped, customer requirements and regions of operations. This further highlights the need for software and digital solutions to tailor deployment and operation of emerging powertrain, connectivity, and automation technologies toward the specific fleet operation requirements. The fleet management optimizer was also integrated with a model of the fleet to simulate the operation of the fleet over 1 year of the baseline fleet operation (250,000+ shipments) indicating the significance of day-to-day variations on emissions and energy consumption of a freight transportation fleet. The results demonstrate ≥20% improvement in freight efficiency in terms of WTW CO2 per ton-mile of cargo shipments while all fleet operation constraints are enforced, and the cost (CapEx and OpEx) is minimized.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Evaluating Energy Efficiency Opportunities from Connected and Automated Vehicle Deployments Coupled with Shared Mobility in California

Connected and Automated Vehicles (CAVs) can be considered to be a disruptive transportation technology, with the potential to significantly improve overall transportation system efficiency; however, CAVs may increase induce vehicle miles traveled (VMT) and bring on greater energy consumption. Further, shared mobility is another disruptive transportation event that is reshaping our travel patterns. The primary goal of this project was to extensively collect data from vehicles and associated infrastructure equipped with CAV technologies from both real-world experiments and simulation studies mainly deployed in California, and develop a comprehensive framework for evaluating energy efficiency opportunities from large-scale (e.g., statewide) introduction of CAVs and a wide deployment of shared mobility systems in a variety of scenarios. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance, and energy efficiency, a unique mesoscopic simulation-based model was developed for mobility and energy efficiency evaluation considering these disruptive transportation technologies. As a complement to existing studies on nationwide evaluation of CAVs’ energy impacts, this project was focused on data collection efforts and CAV applications under congested traffic environments that are frequently experienced on a massive scale across the major metropolitan areas in California. Extensive real-world data collection supplemented with simulation studies were conducted to cover a variety of CAV and shared mobility scenarios, particularly on scenarios less-explored in the existing research. Another key component of this project was to consider the interaction between different CAV technologies and shared mobility models, and the compound effect on energy efficiency. A comprehensive modeling suite was developed to quantify the impact of new mobility technologies on travel behavior and traffic performance. The developed modeling framework includes an energy intensity module, mode choice module and activity generation module that are integrated into an agent-based BEAM simulation platform to perform impact analysis based on a variety of scenarios. In addition, the RouteE model has been upgraded to incorporate the impact of CAVs on traffic flow, VMT and energy intensity, using micro-simulation data collected from both freeways and urban arterials. A novel fundamental influencing factor (FIF) mode choice model was developed to link CAV and shared mobility components with travel behaviors, and adapted into the BEAM-centered model framework. A statewide energy inventory was constructed under various CAV technology deployment scenarios by incorporating datasets and models for predicting vehicle market share and vehicle usage, which are tightly associated with the penetration of shared mobility systems. Based applying this modeling suite to a calibrated network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes. The statewide analysis based on the National Household Travel Survey (NHTS) sample data is consistent with the findings from the Riverside network and validate the developed clustering-prediction modeling methodology. The outcomes from this project will help close the knowledge gap on recognizing the potential performance and energy impacts of a broad deployment of CAV and shared mobility technologies across a wide range of roadway infrastructure with varying levels of congestion. Results from this project: 1) will support policymakers in steering CAV development and deployment towards an energy favorable direction; 2) reduce uncertainties in estimating energy saving opportunities from new mobility technologies and services; 3) increase the confidence of CAV technology investors both on the infrastructure side (i.e., transportation agencies) and on the vehicle side (i.e., OEMs); and 4) expedite the deployment of energy-efficient CAV and shared mobility applications.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Estimate long-term impact on battery degradation by considering electric vehicle real-world end-use factors

Many estimates of battery capacity degradation are based on accelerated lab tests that involve charge-discharge cycles or rely on data or electrochemical modeling. These methods are reasonable for technology benchmarking but rarely consider real-world end-use factors. To address this issue, this study develops the Battery Run-down under Electric Vehicle Operation (BREVO) model. It links the driver's travel pattern to physics-based battery degradation and powertrain energy consumption models. The model simulates the impacts of charging behavior, charging rate, driving patterns, and multiple energy management modules on battery capacity degradation. It finds that, over a 10-year timespan, firstly, for a random driver situated in the New England area, daily direct-current fast charging (60 kW) could lead to up to 22% less battery capacity when compared to daily Level-1 charging (1.8 kW). Second, the battery thermal management system can delay battery degradation by approximately 0.5% in the New England area. Third, warmer ambient temperatures enhance BEV battery usage. The model indicates that the battery capacity in the Los Angeles area is 6% higher than that in the New England area. The BREVO model provides crucial information for consumers and BEV manufacturers on range anxiety, BEV battery design, and decision support of battery warranty.

25 ENERGY STORAGE↗
DOE OSTI2021

A 10-Gb/s Driver/Receiver ASIC and Optical Modules for Particle Physics Experiments

We present the design and test results of a Drivers and Limiting AmplifierS ASIC operating at 10 Gbps (DLAS10) and three Miniature Optical Transmitter/Receiver/Transceiver modules (MTx+, MRx+, and MTRx+) based on DLAS10. DLAS10 can drive two Transmitter Optical Sub-Assemblies (TOSAs) of Vertical Cavity Surface Emitting Lasers (VCSELs), receive the signals from two Receiver Optical Sub-Assemblies (ROSAs) that have no embedded limiting amplifiers, or drive a VCSEL TOSA and receive the signal from a ROSA, respectively. Each channel of DLAS10 consists of an input Continuous Time Linear Equalizer (CTLE), a four-stage limiting amplifier (LA), and an output driver. The LA amplifies the signals of variable levels to a stable swing. The output driver drives VCSELs or impedance-controlled traces. DLAS10 is fabricated in a 65 nm CMOS technology. The die is 1 mm x 1 mm. DLAS10 is packaged in a 4 mm x 4 mm 24-pin quad-flat no-leads (QFN) package. DLAS10 has been tested in MTx+, MRx+, and MTRx+ modules. Both measured optical and electrical eye diagrams pass the 10 Gbps eye mask test. Furthermore, the input electrical sensitivity is 40 mVp-p, while the input optical sensitivity is -12 dBm. The total jitter of MRx+ is 29 ps (P-P) with a random jitter of 1.6 ps (RMS) and a deterministic jitter of 9.9 ps. Each MTx+/MTRx+ module consumes 82 mW/ch and 174 mW/ch, respectively.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Manipulating high-temperature superconductivity by oxygen doping in Bi2Sr2CaCu2O8+δ thin flakes

Abstract Harnessing the fascinating properties of correlated oxides requires precise control of their carrier density. Compared to other methods, oxygen doping provides an effective and more direct way to tune the electronic properties of correlated oxides. Although several approaches, such as thermal annealing and oxygen migration, have been introduced to change the oxygen content, a continuous and reversible solution that can be integrated with modern electronic technology is much in demand. Here, we report a novel ionic field-effect transistor using solid Gd-doped CeO2 as the gate dielectric, which shows a remarkable carrier-density-tuning ability via electric-field-controlled oxygen concentration at room temperature. In Bi2Sr2CaCu2O8+δ (Bi-2212) thin flakes, we achieve a reversible superconductor–insulator transition by driving oxygen ions in and out of the samples with electric fields, and map out the phase diagram all the way from the insulating regime to the over-doped superconducting regime by continuously changing the oxygen doping level. Scaling analysis indicates that the reversible superconductor–insulator transition for the Bi-2212 thin flakes follows the theoretical description of a two-dimensional quantum phase transition. Our work provides a route for realizing electric-field control of phase transition in correlated oxides. Moreover, the configuration of this type of transistor makes heterostructure/interface engineering possible, thus having the potential to serve as the next-generation all-solid-state field-effect transistor.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

AI-Enabled Robots for Automated Nondestructive Evaluation and Repair of Power Plant Boilers. Final Report

Boiler failure could cause loss of life and safety issues, cost hundreds of thousands of dollars in equipment repairs, property damage and production losses, and drive up the cost of electric power. Boiler maintenance is challenging and risky for inspectors working on scaffolding in confined hazardous spaces inside of a boiler and sometimes the space is hard to access. The operation is also time-consuming due to the large area of vertical structures for inspection and the tremendous effort needed for scaffolding. Recently, the use of robotics (e.g., drones and crawlers) in power plants for maintenance is growing rapidly. However, the existing robotics solutions show two notable technological gaps: no live repair capability, and no Artificial Intelligence (AI) for smart autonomy. The objective of this project is to develop an integrated autonomous robotic platform that is equipped with compact non-destructive evaluation (NDE) sensors to perform live inspection, operates onboard repair devices to perform live repair, and uses AI for intelligent data fusion and predictive analysis for automated and smart spatiotemporal inspection, analysis and repair of the furnace walls in coal-fired boilers. The approach to achieve the objective includes developing NDE sensors with signal processing techniques, designing and evaluating repair devices for robots based on fusion and solid-state technologies, and an autonomous robotic platform that can attach to and navigate on boiler furnace walls using magnetic drive tracks. The robot is also powered by AI to automate data gathering (e.g., 3D mapping and damage localization) and predictive analysis. This project has advanced the state-of-the-art by providing technological breakthroughs including compact NDE and repair tools for robots, AI capabilities for smart autonomy, and a robotic platform for automated boiler maintenance. This project has great potential to result in significant benefits including limiting or eliminating the need to send operators to assess difficult-to-access or hazardous areas, enabling automated live inspection and repair, avoiding time consuming scaffolding (especially for partial maintenance during unplanned outage), collecting comprehensive and well-organized data smartly, and avoiding or limiting the need for onsite or remote piloting technicians. The impacts can be tremendous in terms of the time and cost savings, reducing the risk for human operators, and increasing boiler reliability, usability, and efficiency. In addition, by developing the new technologies on the autonomous inspection and repair robot, by involving multiple undergraduate and graduate students working together with the faculty members on this project, and by generating knowledge and building up collaborations with industrial partners, this effort will significantly update the education capabilities, support long-term fundamental research, and maintain the leadership of Colorado School of Mines and Michigan State University in energy fields.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Experimental driving performance evaluation of battery-powered medium and heavy duty all-electric vehicles

As the push to reduce air pollutants and carbon dioxide for global warming continues, there is increasing interest in the development and deployment of battery electric vehicles for heavy-duty applications as one of the critical and challenging steps of a broader effort to fully electrify the transportation sector. This study evaluated and quantified the energy consumption rates and energy regeneration rates for three heavy-duty battery electric vehicles (a step van, a yard tractor, and a class 8 truck) using driving cycle tests with a heavy-duty vehicle chassis dynamometer. Information in this area is currently very limited, and yet it is critical to understanding how effective heavy-duty battery electric vehicles will be for a wide range of applications as the electrification of the heavy-duty vehicle market continues to expand. Energy consumption rates ranged from 0.37 and 2.71 kWh/km for most vehicle/cycle combinations. On a diesel liter energy-equivalent basis, the heavy-duty vehicles had approximately 3 to 6 times better energy consumption rates compared to comparable diesel vehicles, with weight normalized energy consumption rates also comparing favorably to the current and future greenhouse gas/fuel economy standards for equivalent diesel vehicles. Based on battery capacities ranging from 80 to 215 kWh, the vehicle ranges varied from 38.18 km to 171.2 km, with ranges of 115.9 km to 171.2 km for most types of driving and lower ranges of 38.18 to 42.20 km for sustained grade cycles. Overall, the advantage of improved energy consumption rates coupled with improvements that can be expected for battery technology and vehicle range, provide promise for expanded use of heavy-duty vehicles going into the future.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Nonlinear saturation of reverse shear Alfvén eigenmodes induced by kinetic electrons

A strong nonlinear saturation mechanism of the reverse shear Alfvén eigenmode (RSAE) induced by thermal electrons is observed in gyrokinetic $\delta$$f$ particle-in-cell simulation. This nonlinear effect occurs at moderate mode amplitude, $\delta$$B$ ⟂ /$B$ < 10 -3 , and is associated with the electron parallel streaming along the perturbed magnetic field lines, the term v $\|$ $\frac{δΒ_{⟂}}{B}$ ∙ ∇$\delta$$f$, in the electron drift-kinetic equation. In the case of an n = 4 RSAE, where n is the toroidal mode number, this magnetic fluttering nonlinearity leads to strong mode coupling and broadening of the saturation spectrum. The nonlinear components, which are highly damped through the interaction between thermal electrons and the parallel electric field, modifies the RSAE mode structure and suppresses energetic particle drive significantly. The split-weight scheme (Chen and Parker 2007 J. Comput. Phys. 220 839–55) is used to simulate kinetic electrons.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Is the Adoption of Electric Vehicles (EVs) and Solar Photovoltaics (PVs) Interdependent or Independent? An Integrated EVs-PVs Modeling Framework

Transportation and residential energy consumption account for more than one-half of the overall energy consumption in the United States. Adoption of electric vehicles (EVs) can play a key role in decarbonizing the transportation sector, while the adoption of renewable energy sources (e.g., solar photovoltaics or PVs) could bring similar benefits to the residential energy sector and in turn support transport electrification. Although the market share for both EVs and PVs continue to grow, both these emerging technologies are deployed rather disjoint without considering the existence of potential similarities among users who own (or aspire to own) EVs and PVs. This might be due to lack of understanding of the behavioral interdependence in consumer preferences towards these technologies. To fill this gap in knowledge, this study utilizes data from the 2018 WholeTraveler Transportation Behavior Study to develop an integrated model system that explores EV and PV adoption behaviors. A structural equations model (SEM) is employed that incorporates direct effects as well as error correlations among the adoption behaviors for EVs and PVs. Model results indicate that the adoption behavior for both these technologies is indeed interconnected and significantly influenced by attitudes, values and personality traits. Findings from this research suggest that incentives (e.g., subsidies) that drive 'bundled' adoption of EVs and PVs could accelerate the adoption of both these sustainable technologies. The results highlights the need to consider transport and building energy efficient technology adoption behavior in a single integrated structure.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2023

Accelerating Battery Simulations by Using High Performance Computing and Opportunities with Machine Learning

The US Department of Energy (DOE) estimates that the battery pack cost of $60 per kilowatt-hour while increasing the driving range to over 300 miles and vehicle charging under 15 min or less would enable mass penetration of electric vehicles in the USA by 2030. These projections are based on the currently available high-density cell chemistry combined with a system level design and optimized electrodes. Electrodes for current state-of-the-art lithium-ion cell technology are fabricated from electrode slurry comprising of active material, polymeric binder, and conductive diluent such as carbon black that are coated on metal current collectors such as copper and aluminum. Given these challenging requirements for development of electrical energy storage devices for future transportation needs, a predictive simulation capability which can accelerate design by considering performance and safety implications of different geometry, materials, and chemistry choices is required. In this chapter, we discuss our state-of-the-art three-dimensional modeling framework, providing examples on battery performance and safety simulations. We also present approaches to use machine learning in the context of large datasets that are becoming available.

Allu, Srikanth↗
DOE OSTI2023

EVI-X Updates and National Charging Assessment Report

With the support of DOE's Vehicle Technologies Office and the DOE/DOT Joint Office (JO), NREL has applied the EVI-X modeling suite to conduct a National Electric Vehicle Infrastructure Needs Assessment. This report considers a 2030 scenario in which 30-42 million light-duty electric vehicles are on the road (including plug-in hybrids). We consider the needs of vehicles used for typical daily driving, drivers without access to residential charging, corridor charging supporting long-distance travel, and ride-hailing electrification. We find that a national network of 28 million charging ports (including public and private infrastructure) will be necessary in our mid adoption scenario. This result is framed as a conservative estimate as the assumed costs include charging equipment and installation but exclude the cost of grid upgrades and distributed energy resources.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2025

Numerical investigation of magneto-inertial fusion targets magnetized by dynamic enforcement of helical current flow

Magnetized Liner Inertial Fusion (MagLIF) targets require premagnetization to reduce thermal conduction losses from the laser-heated fuel to the liner material during implosion. MagLIF targets are typically magnetized by external magnetic field coils which are technologically limited to providing <20 T axial magnetic fields in the fusion fuel. We present a novel target design, AutoMag-D, which employs dynamic enforcement of helical current in the liner, resulting in >20 T axial magnetic field in the fuel region prior to implosion without the need of external magnetic field coils. These self-magnetizing liners are made of helically oriented alternating electrically conductive material and electrically insulating material surrounded by a thin, conductive outer radial layer. As the liner is pulsed with a ∼ 20 MA, ∼ 100 ns rise time drive current from Z, the outer layer of conductive material is shocked and intensely Joule heated, causing it to melt, vaporize, and turn to plasma. This allows the magnetic drive field to diffuse radially inward which dynamically enforces current flow in the helical conduction paths in the liner and produces axial magnetic field in the fuel region prior to implosion of the inner liner surface. AutoMag-D liner designs do not require dielectric breakdown of electrically insulating material (as in traditional auto-magnetizing helical liners) and do not require helical return current geometries (as in dynamic screw pinches). We present results from three-dimensional radiation-magnetohydrodynamic simulations of MagLIF implosions employing AutoMag-D liner designs. Simulated AutoMag-D targets demonstrate improved fuel conditions and thermonuclear yield compared to traditional MagLIF.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Best Practices for Grid Communications

As the grid evolves, the communications architecture will need to evolve with it. That architecture affords a structured means by which the evolving complexities of the modern electric grid can be managed. This document provides best practices that can be implemented in the grid of today and evolve towards the grid and grid architecture of the future. The evolving grid and its control communications increasingly rely on commercial communications providers and a variety of technologies, from wireless (e.g., 5G, microwave, Wi-Fi) to wireline (fiber, copper) to radio communications (P25, other repeater-based systems), and all these communications systems rely on electric power. A reliable and resilient grid must account for this complex set of interdependencies in its planning activities, especially those involving restoration and recovery. The participation of all relevant parties in both planning and exercising of plans can prevent unexpected conditions that impede the reliable operation and recovery of the grid. Best practices for grid communications include using a Network Management System to document the operational state, define and monitor baselines, detect changes, and accelerate response to abnormalities. If transitioning from SONET to IP/packet-based systems, translating grid requirements into communications requirements for latency, bandwidth and throughput, IP packet delay variation, packet loss, and availability should inform and drive technology planning and selection as well as that communication system’s Quality of Service (QoS) policies and Service Level Agreements (SLAs). Secure and reliable timing is another key component of a reliable and resilient grid that can operate through adverse events. A trusted internal NTP configuration, an integrated and diverse timing delivery system, optimizing the timing architecture based on the transport technologies of the communications system, and using established standards can deliver the level of timing accuracy required by a range of time-sensitive power system applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Vehicle Powertrain Simulation Accuracy for Various Drive Cycle Frequencies and Upsampling Techniques

As connected and automated vehicle technologies emerge and proliferate, lower frequency vehicle trajectory data is becoming more widely available. In some cases, entire fleets are streaming position, speed, and telemetry at sample rates of less than 10 seconds. This presents opportunities to apply powertrain simulators such as the National Renewable Energy Laboratory's Future Automotive Systems Technology Simulator to model how advanced powertrain technologies would perform in the real world. However, connected vehicle data tends to be available at lower temporal frequencies than the 1-10 Hz trajectories that have typically been used for powertrain simulation. Higher frequency data, typically used for simulation, is costly to collect and store and therefore is often limited in density and geography. This paper explores the suitability of lower frequency, high availability, connected vehicle data for detailed powertrain simulation. A large data set of 1 Hz trajectories is used to quantify the accuracy loss when simulating energy consumption for conventional, hybrid, and battery electric powertrains using less than 1 Hz data. Techniques to upsample lower frequency drive cycle data in order to increase accuracy are also explored. Median energy consumption errors when simulating energy consumption for a 1/10 Hz trajectory are found to be 3-6% when compared to 1 Hz trajectories. Applying upsampling and interpolation techniques are shown to reduce the simulation errors by roughly 50%. The findings in this work can guide connected vehicle data collection specifications and processing techniques applied when using collected data for powertrain simulation.

ADVANCED PROPULSION SYSTEMS↗