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DOE OSTI2020

Engine-in-the-Loop Study of a Hierarchical Predictive Online Controller for Connected and Automated Heavy-Duty Vehicles

This paper presents a cohesive set of engine-in-the-loop (EIL) studies examining the use of hierarchical model-predictive control for fuel consumption minimization in a class-8 heavy-duty truck intended to be equipped with Level-1 connectivity/automation. This work is motivated by the potential of connected/automated vehicle technologies to reduce fuel consumption in both urban/suburban and highway scenarios. The authors begin by presenting a hierarchical model-predictive control scheme that optimizes multiple chassis and powertrain functionalities for fuel consumption. These functionalities include: vehicle routing, arrival/departure at signalized intersections, speed trajectory optimization, platooning, predictive optimal gear shifting, and engine demand torque shaping. The primary optimization goal is to minimize fuel consumption, but the hierarchical controller explicitly accounts for other key objectives/constraints, including operator comfort and safe inter-vehicle spacing. This work is experimentally experimentally validated via a sequence of EIL studies intended for evaluating the computational costs and fuel savings associated with these algorithms. These EIL studies involve the closed-loop validation of the proposed control strategies, both individually and combined. These studies show that this hierarchy of algorithms is capable of running online, with the round-trip communication delay inherent in EIL simulation being one of the key factors affecting the EIL results. Moreover, the EIL studies are encouraging, both in terms of the successful hierarchical integration of the underlying algorithms and also in the resulting fuel savings seen in the EIL tests. In particular, the EIL results suggest that an aggressive overall goal of reducing vehicle fuel consumption by 15-20% or more is potentially achievable, especially in urban/suburban scenarios.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

BWR Spent Nuclear Fuel Acquisition and Testing to Support DOE-NE High Burnup Spent Fuel Data Project

The Office of Spent Fuel and Waste Disposition (SFWD) within the US Department of Energy (DOE) Office of Nuclear Energy (NE) established the Spent Fuel and Waste Science and Technology (SFWST) campaign to conduct research and development (R&D) activities related to the storage, transportation, and disposal of spent nuclear fuel (SNF) and high-level radioactive waste. The SFWST program was created within SFWD to address issues of extended or long-term SNF storage and transportation. Some near-term objectives of SFWST are to use a science-based, engineering-driven approach to: Support the enhancement of the technical bases to support the continued safe and secure dry storage of SNF for extended periods; Support the enhancement of the technical bases for retrieving SNF after extended dry storage; Support the enhancement of the technical bases for transporting high burnup (HBU) fuel and transporting low burnup fuel and HBU fuel after dry storage DOE-NE, in partnership with the Electric Power Research Institute, developed the High Burnup Spent Fuel Data Project to perform a large-scale demonstration and laboratory-scale testing of HBU pressurized water reactor (PWR) fuels (exceeding 45 gigawatt-days per metric ton of uranium [GWd/MTU]). Under this project, 25 sister rods—which are rods that have the same design, power histories, and other characteristics—were removed from assemblies at the North Anna Nuclear Power Station and sent to Oak Ridge National Laboratory (ORNL) in January 2016. ORNL performed detailed nondestructive examination (NDE) on all 25 rods. The NDE consisted of visual examinations, gamma and neutron scanning, profilometry and rod length measurements, and eddy current examinations. After completing the NDE, 10 of the sister rods were delivered to Pacific Northwest National Laboratory (PNNL) in a NAC International, Inc. legal-weight truck cask in September 2018 for destructive examination (DE). To date, SFWD work has focused on the PWR fuel that is part of the Sister Rod Test program. No boiling water reactor (BWR) fuel has been tested in the program, and the data needs that were identified for the PWR fuel have not been collected for BWR fuel. The goal to obtain six to nine BWR rods and test them at ORNL will support closing this important data gap. BWR fuel comprises approximately 56% of the total fuel assemblies currently in storage at nuclear power plants in the United States. BWR nuclear fuel and cladding designs and manufacturing are significantly different from PWRs. Differences include the following: BWR fuel pellets are larger than PWR pellets; Variations of Zircaloy-2 (including liners) are used instead of the Zircaloy-4 cladding materials used in PWRs; Clad manufacturing and stress-relief processes are different between PWRs and BWRs; The fuel rod dimensions are different because larger rod diameters and thicker cladding are used in BWRs; BWR fuel typically has lower internal rod pressures and sees vastly different operating conditions than PWR fuel (i.e., two-phase flow); BWR assemblies are “canned,” meaning each assembly is surrounded by a metal fuel channel; BWR cladding is often composed of an inner pure Zr liner that has widely different mechanical properties than the Zircaloy-2 alloy and exhibits a stronger affinity for hydrogen; The BWR SNF generally has more total hydrogen in the cladding/liner than typical PWR fuel; The construction of the PWR and BWR assemblies is vastly different; BWR rods are solidly attached to the assembly nozzles and experience a much different vibration and shock load than PWR rods, which are “floating” within a grid system attached to guide tubes, and the rods sit loosely on the bottom end plates. These numerous differences will affect the way the BWR SNF responds under dry storage preparation processes (e.g., vacuum drying) and during transportation. The results collected in the PWR experimental program must be compared with a subset of similar data collected on BWR SNF to establish a technical basis for whether the larger PWR database is sufficient to bound the BWR SNF end-of-life conditions as is currently assumed for several fuel/clad properties. Changes that occur in both fuel types at HBU could exacerbate any mechanical property differences. As the fuel burnup increases, several changes occur that might affect the performance of the fuel, cladding, and assembly hardware in storage and transportation. These changes include increased cladding corrosion layer thickness, increased cladding hydrogen content, increased cladding creep strains, increased fission gas release, and the formation of the HBU structure at the surface of the fuel pellets. The Nuclear Regulatory Commission (NRC) limits the current maximum rod-averaged burnup to 62 GWd/MTU due to these changes and the lack of data at higher burnups.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Heavy Duty Hydrogen: Reference Station, Fueling Performance Test Device Concepts, and Station Capacity Model (CRADA Final Report)

This project will provide valuable information on (1) reference station design, (2) exploration of design concepts for a fueling performance test device, and (3) modeling of station capacity. The work applies to heavy-duty (HD) hydrogen fueling stations with large dispensing capacity and high flowrates servicing HD hydrogen trucks such as class 8 trucks in long haul applications. Additionally, a 4th area will provide near-real-time verification of fuel quality with on-site hydrogen contaminant detectors (HCDs) for use at both light-duty (LD) and HD stations. The work leverages national lab capabilities including staff and equipment at SNL, NLR, and ANL with collaboration and funding cost share from California agencies (CEC, SCAQMD, and GO-Biz). This project will provide tools and information that lead to more efficient design, acceptance, and commissioning of these larger capacity, higher flowrate stations serving HD applications. The HCD work will benefit both LD and HD stations. Note: The 4th area (HCD) mentioned above continued outside of this CRADA and will not be included in this report. There are no tasks included in this CRADA for HCD and the summary text for it should have been removed prior to this CRADA contract execution but remained in error and is included here for completeness.

08 HYDROGEN↗

NASA NTRS2001

Medium Fidelity Simulation of Oxygen Tank Venting

The item to he cleared is a medium-fidelity software simulation model of a vented cryogenic tank. Such tanks are commonly used to transport cryogenic liquids such as liquid oxygen via truck, and have appeared on liquid-fueled rockets for decades. This simulation model works with the HCC simulation system that was developed by Xerox PARC and NASA Ames Research Center. HCC has been previously cleared for distribution. When used with the HCC software, the model generates simulated readings for the tank pressure and temperature as the simulated cryogenic liquid boils off and is vented. Failures (such as a broken vent valve) can be injected into the simulation to produce readings corresponding to the failure. Release of this simulation will allow researchers to test their software diagnosis systems by attempting to diagnose the simulated failure from the simulated readings. This model does not contain any encryption software nor can it perform any control tasks that might be export controlled.

Sweet, Adam↗

NASA NTRS1988

Summary of aerospace and nuclear engineering activities

The Texas A&M Nuclear and Aerospace engineering departments have worked on five different projects for the NASA/USRA Advanced Design Program during the 1987/88 year. The aerospace department worked on two types of lunar tunnelers that would create habitable space. The first design used a heated cone to melt the lunar regolith, and the second used a conventional drill to bore its way through the crust. Both used a dump truck to get rid of waste heat from the reactor as well as excess regolith from the tunneling operation. The nuclear engineering department worked on three separate projects. The NEPTUNE system is a manned, outer-planetary explorer designed with Jupiter exploration as the baseline mission. The lifetime requirement for both reactor and power-conversion systems was twenty years. The second project undertaken for the power supply was a Mars Sample Return Mission power supply. This was designed to produce 2 kW of electrical power for seven years. The design consisted of a General Purpose Heat Source (GPHS) utilizing a Stirling engine as the power conversion unit. A mass optimization was performed to aid in overall design. The last design was a reactor to provide power for propulsion to Mars and power on the surface. The requirements of 300 kW of electrical power output and a mass of less than 10,000 Rg were set. This allowed the reactor and power conversion unit to fit within the Space Shuttle cargo bay.

Source record↗

DOE OSTI2020

The Port Hope Areas Initiative's Port Granby Project: The Decontamination of Site Equipment from a Low Level Waste Facility - 20330

The Port Hope Area Initiative (PHAI) is the Government of Canada's response to the community recommended solutions for the remediation and safe, long-term management of historic low-level radioactive waste (LLRW) that was the result of uranium and radium processing operations in the Municipality of Port Hope, Ontario from the 1930's to the 1980's. The Eldorado refinery, on the shores of Lake Ontario, began refining radium-226 from pitchblende ore, later transitioning to the refining of uranium ore. Process residues were deposited at the Welcome Waste Management Facility in Port Hope until the mid-1950's switching to the Port Granby Waste Management Facility (PGWMF), which continued to accept waste until the late 1980's. The Port Granby Project involves the construction of a new, secure long-term waste management facility (LTWMF) and supporting infrastructure, and the excavation and relocation of approximately 1,200,000 m{sup 3} of historic low-level radioactive waste and marginally contaminated soil from the PGWMF to the newly constructed LTWMF. Since the project's inception in 2016, the bulk of the material has since been moved to the final long term storage cell. This has taken over 500,000 person-hours and a large amount of one site heavy equipment to accomplish including excavators, bulldozers, rock trucks, baker tanks and vacuum trucks. While many of the pieces of heavy equipment and water management equipment has further work to do on site, several of the sites larger items have already undergone the decontamination process prior to release from the site. This process has posed a serious challenge as the waste has a caustic nature to it and over the last few years it has had time to work its way into the metallic structures and framework of the equipment. Coupled with the tight work spaces and in some cases confined work areas there have been many issues that needed to be addressed to successfully reach a state where items can be free released to the unrestricted limits of the sites license. This ongoing process has led to a more refined decontamination program at the Port Granby Facility and has had a rather high success rate considering the length of time the equipment has been exposed to the waste materials on site. This paper discusses the varied challenges of decontamination faced at a low-level radioactive waste facility with a focus on the following aspects: - Challenges faced regarding different equipment types and the solutions implemented by Wood to address these challenges; - An examination of various decontamination methods employed throughout the project; and - Lessons learned over three years of remediation work and how they impact the decontamination process. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRSDate not supplied

Jet Lag, Sleep Timing, and Sleep Inertia

This chapter explores the causes, consequences, and countermeasures of jet lag, mistimed sleep, and sleep inertia. Jet lag can occur when rapidly crossing multiple time zones (e.g., trans-meridian travel for long-haul pilots). The desynchrony between the body’s biological clock, or circadian rhythm, and the new day-night cycle can lead to indigestion, sleep disturbances, fatigue, and cognitive impairments. Mistimed sleep can also occur within a time zone. In the case of night shiftwork, sleep is displaced to the daytime which leads to poor sleep and increased fatigue at night due to the combination of pressures from the two-process model of sleep regulation: sleep loss (homeostatic pressure); and being awake when the body is promoting sleep (circadian pressure). There is also a third process of sleep regulation called sleep inertia, which refers to the brief period of fatigue and impaired cognitive performance experienced after waking. Sleep inertia can be a fatigue risk for transportation workers who work on-call (e.g., emergency services) or who nap on shift (e.g., long-haul truck drivers) and are required to perform a safety-critical task soon after waking. For each of these fatigue risks, strategic exposure to bright light can be used to help realign sleep timing and to promote alertness.

sleep inertia↗

DOE OSTI2026

Propulsion System Design using a Dual 3-Phase PM Synchronous Reluctance Machine with X-Type Multi-Level GaN Inverter

— A propulsion system design is exhibited here that comprises of an X-type GaN power module based dual multi-level inverter and a PM Synchronous Reluctance motor. With the critical benefits of multi-level operation, namely low common mode EMI noise, low switching loss, low current Total Harmonic Distortion (THD), smoother torque and lower iron loss, this topology targets high system-level efficiency and high power density while using reduced rare earth (RE) elements in its motor. General Motors and Purdue University jointly designed and developed this 800V class, highly scalable traction drive that can deliver 200+ kW of power suitable for C-SUV to truck vehicle class. The 3-phase electric machine design utilizes dual winding with 6 terminals to work in line with the GaN X-type inverter, and results in a tractive solution that ensures higher machine efficiency while reducing cost and uncertainty related to use and availability of heavy rare earth/rare earth elements in its rotor.

MOMEN, FAIZUL [General Motors LLC, Detroit, MI (Un↗

DOE OSTI2023

Development of a Heavy-Duty Electric Vehicle Integration and Implementation (HEVII) Tool

As demand for consumer electric vehicles (EVs) has drastically increased in recent years, manufacturers have been working to bring heavy-duty EVs to market to compete with Class 6-8 diesel-powered trucks. Many high-profile companies have committed to begin electrifying their fleet operations, but have yet to implement EVs at scale due to their limited range, long charging times, sparse charging infrastructure, and lack of data from in-use operation. Thus far, EVs have been disproportionately implemented by larger fleets with more resources. To aid fleet operators, it is imperative to develop tools to evaluate the electrification potential of heavy-duty fleets. However, commercially available tools, designed mostly for light-duty vehicles, are inadequate for making electrification recommendations tailored to a fleet of heavy-duty vehicles. The main challenge is that light-duty tools do not estimate real-time vehicle mass, a factor that has a disproportionate impact on the energy consumption of large commercial vehicles. The Heavy-Duty Electric Vehicle Integration and Implementation (HEVII) tool advances the state of the art in evaluating electrification potential and infrastructure requirements for fleets of commercial vehicles. In this work, the HEVII tool is demonstrated with non-uniformly sampled telematics data from an existing fleet to assess the suitability for electrification of each individual vehicle, determine optimal locations for charging infrastructure to support a fleet of EVs and analyze associated costs. Payload mass is predicted using sparse ground-truth data for all input drive cycles and an initial data analysis is conducted to assess the characteristics driving behaviors and energy consumption of the fleet using an adaptable vehicle model. Battery size requirements are determined by applying a novel charger placement algorithm to maximize routes that are viable for EVs and balance time delays with infrastructure development costs. This work details and demonstrates the different aspects of the HEVII tool, presenting preliminary results from an example use case.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

Mass Detection for Heavy-Duty Vehicles using Gaussian Belief Propagation

Predicting vehicle mass is critical to accurately estimate energy use and emissions of commercial trucks. However, data from vehicle telematics is often not at sufficient temporal resolution or accuracy for use in model-based detection methods. In this work, a new statistical mass prediction technique is described for heavy-duty vehicles that incorporates the use Gaussian Belief Propagation (GBP) for probabilistic inference. Similar to Bayesian inference models, the GBP model typically requires less labeled training data than other contemporary machine learning techniques. First, a factor graph is constructed, and a set of Gaussian belief nodes with associated means and variances are fitted to the training data. To better handle noisy input data, the GBP mass prediction model utilizes a k-nearest factors (kNF) algorithm for probabilistic inference on unseen testing data. The proposed method is compared with a classical weighted k-nearest neighbors (kNN) regressor. This statistical kNF-GBP model works even with low-quantity, low-quality initial training data, while being capable of realtime mass estimation. Unlike the kNN regressor, the GBP model produces a measure of uncertainty with its predictions. The proposed method is validated using curve-sampled driving data collected from multiple cloud-connected Class 8 regional haul diesel trucks. Both the kNN regressor and the kNF-GBP mass prediction model were able to predict payload mass with coefficients of determination above 0.97 with minimal data preprocessing.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

SHOCK TUBE IGNITION STUDIES OF RENEWABLE DIESEL FUELS FOR MEDIUM AND HEAVY-DUTY TRANSPORTATION

Currently extensive research on alternative fuels is being conducted due to their increasing demand to reduce greenhouse emissions. One renewable fuel studied in this work is dimethyl ether (DME) blended with propane(C3H8) as a potential mixture for heavy-duty engines used in semi-trucks. The blend has the potential to drastically reduce particulate and greenhouse gas emissions compared to a conventional diesel engine operating under similar conditions. To develop the use of mixture, one must conduct detailed conceptual and simulation studies before progressing to detail studies in CFD, engine modifications, and live testing. For simulations, accurate high-fidelity chemical kinetic models are necessary. However, the validity of the chemical kinetic mechanism for operating conditions of a heavy-duty mixing-controlled compression (MCCI) engine was widely unknown until recent work presented here and published. In this work, we studied the ignition of DME and propane blends in a shock tube under MCCI engine conditions. Ignition delay time (IDT) gathered behind the reflected shock for DME-propane mixtures for heavy-duty compression ignition (CI) engine parameters. Testing was conducted for undiluted varieties spanning from temperatures of 700 to 1100 K at pressures ranging from 55 to 84 bar for various blends (100% CH3OCH3, 100% C3H8, 60% CH3OCH3/ 40% C3H8) of DME and propane were combusted in synthetic air (21% O2/ 79% N2). Several experiments were conducted at higher pressures (90-120 bar) to improve the model performance and accuracy. The ignition delay times (IDTs) were compared to recent mechanisms, including Aramco3.0, NUIG, and Dames et al. A common trend among the mechanisms was overpredicted experimental IDTs. Further studies were conducted by a sensitivity analysis using the Dames et al. model, and critical reactions sensitive to IDTs of DME-propane mixture near 60 bar are outlined. Chemical analysis was conducted on the NTC region to explain chemical kinetics which is critical for developing MCCI heavy duty engines.

Mohammed, Zuhayr Pasha↗

DOE OSTI2022

Control Oriented Model of Cabin-HVAC System in a Long-Haul Trucks for Energy Management Applications

Super Truck II is a 48V mild hybrid class 8 truck with an all auxiliary loads powered purely by the battery pack. Electric Heating Ventilation and Air Conditioning (HVAC) load is the most prominent battery load during the hotel period, when the truck driver is resting inside the sleeper. For the PACCAR Super Truck II (ST-II) project a 48 V battery system provides the required power during the hotel period. A cabin-HVAC model estimates the electric load on the 48V battery system, allowing the control system to implement an efficient energy management strategy that avoids engine idling during the hotel period. The thermal model accounts for the sun load due to the time of day and the geographic location of the truck during the hotel period. The cabin-HVAC model has two parts. First, a grey box model with two heat exchangers (Condenser and Evaporator) working in unison with refrigerant mass flow rate as an input and HVAC load as an output. Second, a two-node cabin model formulated to estimate the cabin temperature as a function of the Global Horizontal Irradiance (GHI), HVAC load and ambient temperature. The models are calibrated using experimental cabin-HVAC system data as for long-haul class 8 truck (e.g. ST-II). Here, the model simulations show that the overall Root Mean Square Error (RMSE) value of 0.4°C between the experimental and simulated cabin temperature.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

A real-time energy and cost efficient vehicle route assignment neural recommender system

Here, this paper presents a neural network recommender system algorithm for assigning vehicles to routes based on energy and cost criteria. In this work, we applied this new approach to efficiently identify the most cost-effective medium and heavy duty truck (MDHDT) powertrain technology, from a total cost of ownership (TCO) perspective, for given trips. We employ a machine learning based approach to efficiently estimate the energy consumption of various candidate vehicles over given routes, defined as sequences of links (road segments), with little information known about internal dynamics, i.e. using high level macroscopic route information. A complete recommendation logic is then developed to allow for real-time optimum assignment for each route, subject to the operational constraints of the fleet. We show how this framework can be used to (1) efficiently provide a single trip recommendation with a top-k vehicles star ranking system, and (2) engage in more general assignment problems where n vehicles need to be deployed over m (m ≤ n) trips. This new assignment system has been deployed and integrated into the POLARIS. Transportation System Simulation Tool for use in research conducted by the Department of Energy's Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium (SMART, 2024).

Energy consumption↗

DOE OSTI2022

Evaluating Class 6 Delivery Truck Fuel Economy and Emissions Using Vehicle System Simulations for Conventional and Hybrid Powertrains and Co-Optima Fuel Blends

The US Department of Energy’s Co-Optimization of Engine and Fuels Initiative (Co-Optima) investigated how unique properties of bio-blendstocks considered within Co-Optima help address emissions challenges with mixing controlled compression ignition (i.e., conventional diesel combustion) and enable advanced compression ignition modes suitable for implementation in a diesel engine. Additionally, the potential synergies of these Co-Optima technologies in hybrid vehicle applications in the medium- and heavy-duty sector was also investigated. In this work, vehicles system were simulated using the Autonomie software tool for quantifying the benefits of Co-Optima engine technologies for medium-duty trucks. A Class 6 delivery truck with a 6.7 L diesel engine was used for simulations over representative real-world and certification drive cycles with four different powertrains to investigate fuel economy, criteria emissions, and performance. Comparisons were made between ultralow-sulfur diesel and a blend of 25% hexyl hexanoate with diesel. Model validation data were informed by 2019 model year Cummins ISB 6.7 L diesel engine maps and transient validation data in a pre-production hybrid configuration and a direct dyno coupled configuration with diesel fuel and a blend of 25% hexyl hexanoate with diesel.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Identifying rolling resistance and air resistance simultaneously for an electric truck

Accurately estimating rolling and air resistance is essential for predicting the energy consumption of vehicles. This study presents a field-based approach using a rolldown test to simultaneously determine rolling and air resistance coefficients. Unlike prior methods that frequently used simulations or models, we employ a goal programming methodology to improve precision and evaluate the actual vehicle and environmental conditions. Our methodology was tested using a Class 8 Freightliner eCascadia on a surveyed road section, ensuring controlled conditions for data collection. By analyzing the time–velocity relationship across multiple test runs, we derived resistance coefficients for both loaded and unloaded conditions. The study confirms that rolling resistance is largely independent of velocity at low speeds but exhibits a nonlinear dependency at higher speeds. Additionally, road surface conditions, tire condition, axle configuration, aerodynamic properties, and weather conditions significantly impact resistance values, emphasizing the need for real-world testing rather than relying solely on standardized projections. Our results align with existing literature while demonstrating the efficacy of the goal programming approach in refining resistance estimates. This work contributes to improved vehicle energy modeling, offering practical insights for fleet operators and policymakers seeking accurate energy consumption predictions for electric trucks operating under varying environmental conditions.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Recycled Glass Polypropylene Composites from Transportation Manufacturing Waste

In recent years there has been growing interest in developing recycling technologies for composites manufacturing scrap, process waste and end-of-life parts. The focus of this work was to establish processing routes and mechanical property bounds for glass-polypropylene (PP-GF) scrap from the production of parts for truck trailers, automobiles, and rail cars. This study considered PP-GF scrap and demonstrated extrusion-compression molding (ECM) as a viable route for the closed-loop manufacture of composite parts. The results were promising in terms of the strength and modulus retention of the PP-GF recyclate. The tensile strength and modulus was the highest for 50% and 66% recycled content, compared with 100% and 83% recycle content. The flexural strength and modulus of the 100% and 83% recycled compositions was higher than the 66% and 50% recycled content, respectively. The impact energy absorption of the PP-GF recyclate at at all fiber loadings was superior in absorbing energy compared with the incumbent (benchmark) plywood. This work is useful to designers seeking to incorporate recycled materials in their products.

Vaidya, Uday↗

DOE OSTI2026

WM26 Paper Multi-Robot Collaboration for Hazardous Environments

Hazardous nuclear and industrial facilities are rarely designed for robots. Work in these domains demand precise manipulation and robust mobility in cluttered, constrained spaces where off-the-shelf platforms struggle and “one-size-fits-all” machines become costly and complex. Idaho National Laboratory (INL) is developing an autonomous, multi-robot inspection system that coordinates task-specific platforms rather than relying on a single omni-tool robot. An electric truck serves as a power and compute hub for a custom manipulator co-developed with Florida International University (FIU), a commercial mini crawler, a pan–tilt–zoom camera, and a Nexxis Argus LiDAR mapping system. Working in concert, these robots generate spatial, radiation, and temperature maps of the pit environments at the Hanford Waste Tank Farms. These systems will capture visual records and environmental telemetry to allow for analysis post inspection. The system architecture uses Robot Operating System 2 (ROS 2) for publish/subscribe integration, NVIDIA Isaac Sim and Unity for simulation and visualization, and algorithms such as NVBlox to fuse data into unified 3D overlays. This robot-agnostic approach reduces operator burden by enabling autonomy across heterogeneous platforms and lets each robot be used where it is strongest. Having autonomous functions means operators don’t have to fully control multiple different components. The ease of use could allow for more widespread adoption of advanced robotics at waste management sites that see continued use. By coordinating simpler, purpose-built mechanisms, the approach lowers design and manufacturing complexity, reduces capital risk in contaminated settings, and improves controllability for complex inspection and manipulation tasks. We present the architecture, early results, and lessons learned from building and deploying this coordinated multi-robot system, with the goal of accelerating safe, cost-effective adoption of advanced robotics at waste-management sites.

42 - ENGINEERING↗

DOE OSTI2026

SERA: A Hydrogen Infrastructure Capacity Expansion Model

The Scenario Evaluation and Regionalization Analysis (SERA) model is an infrastructure planning optimization model that can guide hydrogen production, delivery, and end-use investment decisions and accelerate the adoption of low-cost hydrogen at scale, whether for fuel cell electric vehicles or non-transportation applications. In this talk, we will review the SERA model objective function as well as the data inputs and outputs. We will also look at a SERA case study identifying potential dispensed costs of hydrogen along major refueling corridors throughout the United States. In addition to the SERA model, Justin will also discuss his recent work for the Office of Manufacturing and Energy Supply Chains on electrolyzer supply chain readiness, and his work for the Hydrogen Fuel Cell Technologies Office and Environmental Protection Agency on the levelized cost of dispensed hydrogen for heavy-duty trucking.

30 DIRECT ENERGY CONVERSION↗