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All sources NASA NTRS DOE OSTI

DOE OSTI2024

Nuclear waste attributes of near-term deployable small modular reactors

The nuclear waste attributes of near-term deployable SMRs were assessed using established nuclear waste metrics, which are the DU mass, SNF mass, volume, activity, decay heat, radiotoxicity, and decommissioning LLW volumes. Metrics normalized per unit electricity generation were compared to a reference large PWR. Three SMRs, VOYGR, Natrium, and Xe-100, were selected because they represent a range of reactor and fuel technologies and are active designs deployable by the decade’s end. The SMR nuclear waste attributes show both some similarities to the PWR and some significant differences caused by reactor-specific design features. The DU mass is equivalent to or slightly higher than the PWR. Back-end waste attributes for SNF disposition vary, but the differences have a limited impact on long-term repository isolation. SMR designs can vary significantly in SNF volume (and thus heat generation density). However, these differences are amenable to design optimization for handling, storage, transportation, and disposal technologies. Nuclear waste attributes from decommissioning vary depending on design and decommissioning technology choices. Given the analysis results in this study and assuming appropriate waste management system and operational optimization, there appear to be no major challenges to managing SMR nuclear wastes compared to the reference PWR.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Deployment of a double scatter system for directional detection of background neutron radiation

The detection of special nuclear materials (SNM) requires the understanding of nuclear signatures that allow the discrimination against background. In particular, understanding neutron background characteristics such as count rates and energies and their correlations with environmental conditions and surroundings of measurement locations is important in enhancing SNM detection capabilities. The Mobile Imager of Neutrons for Emergency Responders (MINER) was deployed for 8 weeks in downtown San Francisco (CA) to study such neutron background characteristics in an urban environment. Of specific interest was the investigation of the impact of surrounding buildings on the neutron background count rates and to answer the question whether buildings act as absorber of neutrons or as sources via the so-called ship effect. MINER consists of 16 liquid scintillator detector elements and can be operated as a neutron spectrometer, as a neutron imager, or simply as a counter of fast neutrons. As expected, the neutron background rate was found to be inversely proportional to the atmospheric pressure. In the energy range where MINER is most sensitive, approximately 1–10 MeV, it was found that the shape of the detected background spectrum is similar to that of a detected fission spectrum, indicating the limited discrimination power of the neutron energy. The similarities between the detected background neutron spectrum and fission sources makes it difficult to discriminate SNM from background based solely on the energies observed. The images produced using maximum likelihood expectation maximization revealed that neutrons preferentially are coming from areas in the environment that have open sky, indicating that the surrounding buildings act as absorbers of neutrons rather than sources as expected by the ship effect. Here, the inherent properties of a neutron scatter camera limit the achievable image quality and the effective deployment to systematically map neutron background signatures due to the low count rate.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

The cost of direct air capture and storage can be reduced via strategic deployment but is unlikely to fall below stated cost targets

Carbon dioxide removal (CDR) is necessary to minimize the impact of climate change by tackling hard-toabate sectors and historical emissions. Direct air capture and storage (DACS) is an important CDR technology, but it remains unclear when and how DACS can be economically viable. Here, we use a bottom-up engineering-economic model together with top-down technological learning projections to calculate plant-level cost trajectories for four DACS technologies. Our analysis demonstrates that the costs of these technologies can plateau by 2050 at around $\$$100-600 t-CO 2 -1 mainly via capital cost reduction through aggressive deployment, but still exceed the optimistic targets defined by countries such as the US (i.e., $\$$100 t-CO 2 -1 ). A further analysis of existing policy mechanisms indicates that strong, project-catered policy support will be required to create market opportunities, accelerate DACS scale-up and lower the costs further. Our work suggests that strategic DACS deployment and operation must be coupled with strong policies to minimise the cost of DACS and maximise the opportunity to make a planet-scale climate impact.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Looking Beyond Bill Savings to Equity in Renewable Energy Microgrid Deployment

Microgrids powered by renewable energy can provide backup power to critical infrastructure during grid outages. These systems can also play an important role in advancing energy justice by providing economic, environmental, health, and resilience benefits for underserved communities. The value of microgrids is often measured by the economic savings and resilience provided, but there are other energy justice factors that should be considered. This paper describes a methodology for quantifying broader costs and benefits including utility bill savings, value of resilience, social cost of carbon, public health costs, and jobs associated with the construction and operation of microgrids. We evaluate these factors at three case study sites and find that including energy justice values in the cost-benefit analysis of microgrids can change investment decisions. When climate, health, resilience, and job creation are considered, cost-optimal microgrids include more renewable generation, leading to a 52-82% reduction in emissions and diesel fuel use. The net present values of the microgrids grow from negative $626,000-843,000 in the diesel only case to $10-16 million in the hybrid microgrid case and $12-19 million in the renewable microgrid case, indicating potential for greater microgrid deployment if energy justice values are incorporated in decision making. However, we also see large increases in capital expenses, which could limit deployment unless accompanied by innovative financing measures. These findings may be useful to communities as they seek to strengthen resilience to natural disasters while also improving public health, meeting climate goals, and providing economic opportunity for residents.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Roadmap for Deployment of Modularized Hydrothermal Liquefaction: Understanding the Impacts of Industry Learning, Optimal Plant Scale, and Delivery Costs on Biofuel Pricing

Hydrothermal liquefaction (HTL) is a promising technology for converting abundant organic wastes into fuels. Previous techno-economic analyses (TEAs) of HTL have been used to estimate the minimum fuel selling price (MFSP) of biofuel products, but these analyses often assume a bespoke plant design where each plant operates under unique process conditions and neglect transportation costs. However, transportation costs must be included in realistic TEAs, and further, a mass-produced fixed-scale modular plant design approach may be more effective than case-by-case plant design, provided that there is sufficient market capacity to benefit from modularization. This study estimates fuel price behavior in the presence of transportation costs and benefits stemming from modular plant design. This analysis indicates that a modular process capable of handling 60 dry tons per day (DTPD) is optimal, resulting in a ~25% reduction in MFSP (from $4.70/GGE, fully upgraded) at complete market feedstock utilization compared with case-by-case design. The associated cost reductions are attributable to learning benefits and modularization. Several HTL deployment “roadmaps” are then explored, with each roadmap consisting of different periods of case-by-case design followed by adoption of a modularized approach. A period of nonmodular industry growth up to market saturation of ~7% followed by implementation of modular plant design strikes a balance between the investment risk and learned cost reductions associated with modular plant design. However, if bespoke plants built during this period of nonmodular growth saturate more than 23% of available feedstock, learned cost reductions are significantly diminished. Here, this study points to the potential benefits of modularized and decentralized waste-to-energy processes when the modularization follows an optimal deployment strategy.

09 BIOMASS FUELS↗

DOE OSTI2025

Light-powered end-to-end neutron detection and imaging with an edge-deployed optical AI chip

Neutron detection is widely used in many applications including nuclear physics, nuclear energy, nuclear technologies and nuclear safeguards. Developing an end-to-end neutron detection and imaging workflow paves way towards fully automated processes for many applications. We implemented an automated workflow for neutron detection experiments which use a solid state image sensor to capture neutron hits as a digital image. We deploy the workflow to an edge-based optical neural network (ONN) to increase the radiation-hardness and lifetime of neutron detection instruments. We present a two-stage neural network framework for detection of neutrons at sub-pixel resolution. The first stage uses a region proposal network to efficiently detect and extract neutron hits from the input camera image. The second stage feeds the extracted hits into a fully connected neural network to predict the sub-pixel hit position. The performance of the two-stage framework is evaluated using the edge-based ONN. The results show that we can achieve above 96% neutron detection accuracy as well as sub-pixel and sub-micron position resolution, while enjoying the advantages of the ONN hardware including radiation-hardness, low energy consumption and high computing speed for integrated edge camera and hardware deployment, when compared with electronic counterparts.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Optical calibration of the SNO+ detector in the water phase with deployed sources

SNO+ is a large-scale liquid scintillator experiment with the primary goal of searching for neutrinoless double beta decay, and is located approximately 2 km underground in SNOLAB, Sudbury, Canada. The detector acquired data for two years as a pure water Cherenkov detector, starting in May 2017. During this period, the optical properties of the detector were measured in situ using a deployed light diffusing sphere, with the goal of improving the detector model and the energy response systematic uncertainties. The measured parameters included the water attenuation coefficients, effective attenuation coefficients for the acrylic vessel, and the angular response of the photomultiplier tubes and their surrounding light concentrators, all across different wavelengths. Finally, the calibrated detector model was validated using a deployed tagged gamma source, which showed a 0.6% variation in energy scale across the primary target volume.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Kicking the can down the road: understanding the effects of delaying the deployment of stratospheric aerosol injection

Abstract Climate change is a prevalent threat, and it is unlikely that current mitigation efforts will be enough to avoid unwanted impacts. One potential option to reduce climate change impacts is the use of stratospheric aerosol injection (SAI). Even if SAI is ultimately deployed, it might be initiated only after some temperature target is exceeded. The consequences of such a delay are assessed herein. This study compares two cases, with the same target global mean temperature of ∼1.5° C above preindustrial, but start dates of 2035 or a ‘delayed’ start in 2045. We make use of simulations in the Community Earth System Model version 2 with the Whole Atmosphere Coupled Chemistry Model version 6 (CESM2-WACCM6), using SAI under the SSP2-4.5 emissions pathway. We find that delaying the start of deployment (relative to the target temperature) necessitates lower net radiative forcing (−30%) and thus larger sulfur dioxide injection rates (+20%), even after surface temperatures converge, to compensate for the extra energy absorbed by the Earth system. Southern hemisphere ozone is higher from 2035 to 2050 in the delayed start scenario, but converges to the same value later in the century. However, many of the surface climate differences between the 2035 and 2045 start simulations appear to be small during the 10–25 years following the delayed SAI start, although longer simulations would be needed to assess any longer-term impacts in this model. In addition, irreversibilities and tipping points that might be triggered during the period of increased warming may not be adequately represented in the model but could change this conclusion in the real world.

Brody, Ezra (ORCID:000900030008681X)↗

DOE OSTI2020

Observation of Reactor Antineutrinos with a Rapidly Deployable Surface-Level Detector

We deploy a small 80 kg antineutrino detector, called MiniCHANDLER, based on a solid plastic scintillator, for nearly three months at a distance of 25 m from a 2.9 GW thermal-power-reactor core at the North Anna Nuclear Generating Station. We report the detection of an antineutrino signal resulting from inverse beta decay at 5.5σ significance with no overburden and minimal shielding. This result also demonstrates that three-dimensional segmentation can be used to significantly improve the signal-to-noise ratio, in this case by a factor of 4. Finally, in addition, this measurement represents an observation of a positron spectrum in a small surface-deployed detector; this observation of reactor antineutrinos is achieved with a mobile neutrino detector mounted in an ordinary small trailer.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

A Simulation Framework to Support the Deployment of Vanadium Redox Flow Batteries

This work presents a simulation framework to support Vanadium Redox Flow Batteries deployment. First, an electrical equivalent model of a commercial three-stack, 20 kW VRFB is implemented to capture the battery dynamics. Next, a topology of a power electronics-based solution to integrate a VRFB is introduced, and the effectiveness of the control system is evaluated through battery charging and discharging operation under varying conditions. The results provide insights into power and voltage levels, demonstrating that the series-connected architecture can alleviate the voltage boost on the interface power electronics converter. Additionally, efficiency and temperature-critical factors for VRFB system deployment-are also analyzed.

Rezende Da Costa Reis Kimpara, Renata [ORNL] (ORCI↗

DOE OSTI2022

Deploying Traffic Smoothing Cruise Controllers Learned from Trajectory Data

Autonomous vehicle-based traffic smoothing con- trollers are often not transferred to real-world use due to challenges in calibrating many-agent traffic simulators. We show a pipeline to sidestep such calibration issues by collecting trajectory data and learning controllers directly from trajectory data that are then deployed zero-shot onto the highway. We construct a dataset of 772.3 kilometers of recorded drives on the I-24. We then construct a simple simulator using the recorded drives as the lead vehicle in front of a simulated platoon consisting of one autonomous vehicle and five human followers. Using policy-gradient methods with an asymmetric critic to learn the controller, we show that we are able to improve average MPG by 11% in simulation on congested trajectories. We deploy this controller to a mixed platoon of 4 autonomous Toyota RAV-4’s and 7 human drivers in a validation experiment and demonstrate that the expected time-gap of the controller is maintained in the real world test. Finally, we release the driving dataset [1], the simulator, and the trained controller at https://github.com/nathanlct/trajectory-training-icra.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

ADDS-EVS: An agent-based deployment decision-support system for electric vehicle services

Rapid and sustainable development of the electric vehicle (EV) industry places the requirement for the plan of EV deployment. For public EV, existing models mainly focus on the charging facility design and fail to capture the multi-modal scenarios. In this work, we develop an agent-based decision-support system for multi-modal electric transits to locate the optimal combinations of key parameters, including the fleet size, the transit schedule, the charging facility design, and the routing strategy. We demonstrate the utilities of our system by simulating public EV services deployed to serve travel needs related to a transportation hub in New York City. To support the decision of the fleet size, we summarize system-level performances including the total satisfied demand, passengers' waiting time, vehicle idling time. The spatial and temporal patterns are extracted to serve a deeper understanding of system dynamics and service quality. Finally, we investigate the interaction between the fleet size design and the routing strategy. The results suggest a necessity of integrating the operation strategies into the planning phase.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Model-Based Framework to Optimize Charger Station Deployment for Battery Electric Vehicles

The development of battery electric vehicles (BEVs) is accelerating due to their environmental advantages over gasoline and diesel-powered vehicles, including a decrease in air pollution and an increase in energy efficiency. The deployment of charging infrastructure will need to increase to keep pace with demand, especially for large commercial vehicles for which few public chargers currently exist. In this paper, a new flexible framework is proposed for optimizing the placement of charging stations for BEVs, within which different physical models and optimization techniques may be used. Furthermore, a set of metrics is suggested to help enforce complex constraints and facilitate direct comparison between different optimization techniques. Unlike many existing charger placement techniques, the proposed method directly considers the historical driving patterns on a vehicle-by-vehicle basis, using transparent models to assess impacts of candidate charger placements, thus improving the explainability of the results. In the developed framework, modeled BEVs are first generated along the road network to mimic historical traffic data and are simulated traveling along a given route according to a simplified vehicle model. During the simulation, the charger placement problem is initially relaxed to allow vehicles to charge at any node along the road network, and vehicle states are tracked to assess areas of high charging demand. Charging stations are then placed based on the results of the relaxed simulation, and suggested placements are evaluated via road network simulation with fixed charger locations. This proposed framework is applied to a sample problem of placing charging stations along five major highway corridors for Class 8 over-the-road electric trucks. A novel mixed integer programming (MIP) formulation is proposed to optimize charger placements based upon the expected charging demand. Constraints were imposed on the final placement results to limit expected wait times at each station and ensure a minimum threshold of trucking routes are viable for BEVs. The results demonstrate the flexibility and potential effectiveness of the developed model-based framework for scalable charger station deployment.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Approaches for Synthesis and Deployment of Controller Models on Automated Vehicles for Car-following in Mixed Autonomy

This paper describes the software design patterns and vehicle interfaces that were employed to transition vehicle controllers from simulation environments to open-road field experiments. The approach relies on a life cycle that utilizes model-based design and code generation, along with agile software development, and both software and hardware-in-the-loop testing, with additional safety margins. Autonomous designs should consider the dynamics of mixed autonomy in traffic to safely operate among humans. The software that provides a vehicle’s behavior intelligence is often developed through simulation, which may have a mismatch between dynamics, or as a result of a reinforcement learning workflow, which may be a black box with challenges to analyze. In each of these cases, it is important to have research interfaces that provide strongly typed data streams accessible to researchers who are not software experts while continuing to satisfy safety and liveness constraints. This paper describes how we design the hardware platform interfaces and software design process for a mixed autonomy traffic experiment with a leader-follower scenario. Controller synthesis for these vehicles requires clearly articulated vehicle interfaces and software design patterns for successful onboard deployment. Testing strategies for such controllers are also described before algorithms are transitioned to full-scale field experiments with safety operators for the vehicles. Testing strategies include software-in-the-loop simulation testing, hardware-in-the-loop simulation, ghost-car testing, and read-only testing in live traffic. With our approach, we were not only able to validate our controller synthesized in scripts and simulation, but also able to scale deployment to multiple vehicles.

Bhadani, Rahul↗

DOE OSTI2026

Pioneer WEC v1 Ocean Deployment

There are seven zip files of data pertaining to the Pioneer WEC v1 ocean deployment.(1-6) Pioneer multiple sensor data "Month Year".zip: contains monthly data from the majority of onboard instruments (e.g., electrical power, mechanical motion, etc.)(7) Pioneer vibration sensor data full 6 months.zip: contains data from the vibration sensor instrument for the full 6 month deployment

Ocean sensing↗

DOE OSTI2022

Optimization Models For Drone Deployment

Model that supports drone deployment. Analysis on speed, package weight, energy consumption, # of drones, and battery replacements. This software developed tools for drone deployment optimization for direct delivery by introducing a new model that presents new insights addressing real-life issues. Specifically, this developed a new mixed-integer programming model with both time windows and battery replacements.

Roni, MohammadS↗

DOE OSTI2025

Deployed quantum link characterization via Bayesian ancilla-assisted process tomography

The development of large-scale quantum networks requires reliable quantum channels, the quality of which can be quantified by the framework of quantum process tomography. Here, in this work, we leverage ancilla-assisted process tomography (AAPT) and Bayesian inference to probe a 1.6 km deployed fiber-optic link. We send one of the two polarization-entangled photons at Alice in one building to Bob in another, exploiting the local qubit as an ancilla system to characterize the corresponding quantum channel. Monitoring over a 24 h period returns a steady process fidelity of 97.6(1)%, while controllable spectral filtering with passbands from 0.025 to 4.38 THz finds fidelities that first increase, and then level off with bandwidth, suggesting both stable operation with time and minimal polarization mode dispersion. To our knowledge, these results represent the first AAPT of a deployed quantum link, revealing a valuable tool for in situ analysis of entanglement-based quantum networks.

Rahman, Arefur [Arizona State Univ., Tempe, AZ (Un↗

DOE OSTI2023

Field Demonstration of the Brick Ontology to Scale up the Deployment of ASHRAE Guideline 36 Control Sequences

Many commercial buildings have a vast network of sensors as part of their building automation systems (BAS) that allows opportunities for energy consumption and cost savings by deploying advanced control sequences. However, this resource is often underutilized since BAS are typically programmed with simple control sequences with limited potential to deliver on these opportunities. The recent availability of ASHRAE Guideline 36 (G36) with advanced HVAC control sequences supports control retrofits in existing buildings to unlock much of the savings potential. However, barriers such as the lack of standard naming convention of building assets and data points, proprietary equipment and BAS, and the inherent uniqueness of buildings and their systems prevent building stakeholders from adopting any “plug-and-play” implementation of G36. Instead, control vendors must often undertake the manual and labor-intensive point mapping process to identify a data stream’s functional and spatial relationship within the HVAC system along with other relevant contexts and map it to the new control sequences. The vendor must carry out the point mapping process in each individual building since the mapping is unlikely to port over to another building. Even for the same building, the point mapping process can occur multiple times if various control vendors implement different control retrofits and/or multiple control retrofits happen over the lifecycle of the building. Then, there is the likelihood that G36 control sequences are programmed uniquely to the building, preventing the same implementation from being reused in another. Therefore, this paper presents a field demonstration of how we leveraged the Brick ontology with BACnet, OpenBuildingControl’s Control Description Language (CDL), and open-source support tools to implement scalable and portable advanced building controls. These tools provide standardized semantic descriptions and relationships of the building’s assets and data points (Brick), standardized communication protocol to read from and write to the building’s BAS (BACnet), and standardized code implementations (CDL) of standardized advanced control strategies (G36). We implemented G36’s hot water supply temperature setpoint reset in a Berkeley, CA building for this field demonstration. This field demonstration aims to show how integrating these tools may streamline the deployment of advanced control sequences such as G36 in a consistent manner regardless of differences found across buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗