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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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39 records · Page 3
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DOE OSTI2025

Vitrification Testing of HLW with High Phosphate

Projections of the number of high level waste (HLW) canisters to be produced in the Hanford Tank Waste Treatment and Immobilization Plant (WTP) (e.g., [1]) are based upon the inventory of the tank wastes, the anticipated performance of the sludge treatment processes, and current understanding of the capability of the borosilicate glass waste form. The WTP HLW melter design, unlike earlier Department of Energy (DOE) melter designs, incorporates a glass bubbler system. The bubblers create active glass pool mixing and thereby improve heat and mass transfer and glass melting rate. The WTP HLW melters each have a glass surface area of 3.75 m 2 and depth of ~1.1 m. The two melters in the HLW facility together are designed to produce up to 7.5 MT of glass per day at 100% availability. Further increases in HLW waste processing rates can potentially be achieved by optimization of the feed and glass formulations, increasing the melter operating temperature above 1150⁰C, and by increasing the waste loading in the glass product. Increasing the waste loading also has the added benefit of decreasing the number of canisters for storage.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Regulatory Spike Testing of RPP-WTP LAW and HLW Glasses for Compliance with Land Disposal Restrictions, VSL-03R3760-1, Rev. 1

The United States Department of Energy’s (DOE’s) Hanford site is the current location of storage of about 50 million gallons of mixed waste. This waste is stored in underground tanks at the Hanford site and is both a listed and characteristic waste, as defined in 40 CFR Part 261, and a dangerous waste according to Chapter 173-303 of the Washington Administrative Code (WAC). The waste is also subject to the Land Disposal Restrictions (LDR) (40 CFR Part 268 and WAC 173-303-140). The River Protection Project - Waste Treatment Plant (RPP-WTP) will provide DOE with a means for treating this waste by vitrification (for subsequent disposal). The tank waste will be partitioned into low and high activity fractions, which will then be vitrified respectively into Immobilized Low Activity Waste (ILAW) and Immobilized High Level Waste (IHLW) products. The ILAW product will be disposed of in an engineered facility on the Hanford site while the IHLW product will be directed to the national deep geological disposal facility for high level nuclear waste. The ILAW and IHLW products must meet a variety of requirements with respect to protection of the environment.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Traffic Control via Connected and Automated Vehicles (CAVs): An Open-Road Field Experiment with 100 CAVs

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. Also called “phantom jams” or “stop-and-go waves,” these instabilities are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system, referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment, the MegaVanderTest (MVT), leveraged a heterogeneous fleet of 100 longitudinally controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this article. The MegaController is a hierarchical control architecture that consists of two main layers. The upper layer is called the Speed Planner and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock onboard sensors. The Speed Planner ingests live data feeds provided by third parties as well as data from our own control vehicles and uses both to perform the speed assignment. The architecture of the Speed Planner allows for the modular use of standard control techniques, such as optimal control, model predictive control (MPC), kernel methods, and others. The architecture of the local controller allows for the flexible implementation of local controllers. Corresponding techniques include deep reinforcement learning (RL), MPC, and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers or only some. Likewise, control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars to electronic selection of adaptive cruise control (ACC) setpoints in others. The proposed architecture technically allows for the combination of all possible settings proposed previously, that is {Speed Planner algorithms} × {local Vehicle Controller algorithms} × {full or partial sensing} × {torque or speed control}. As a result, most configurations were tested throughout the ramp up to the MegaVandertest (MVT).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗