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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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DOE OSTI2023

Quantification of 238U Holdup using Iterative, Point-Cloud-Based Compton Imaging

U holdup has impacts on several aspects of operation: Worker dose, Criticality safety, Safeguards, Outage planning. Generalized Geometry Holdup (GGH) currently estimates U mass within a high uncertainty band (± 50%). This method seeks to improve this uncertainty via quantitative Compton imaging.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Blended fuel property analysis of butyl-exchanged polyoxymethylene ethers as renewable diesel blendstocks

Methyl-terminated polyoxymethylene ethers (MM-POMEs), having the formula CH 3 O-(CH 2 O)n-CH 3 (n = 3-6), are a class of oxygenates with desirable diesel-like fuel properties including high cetane number and low soot formation. However, their low energy density and high water-solubility present barriers to their adoption. Both concerns were recently addressed by our research group by synthesizing a mixture of POME structures having butyl end-groups and n = 1-6, termed B*POME1-6. B*POME1-6 maintained the advantageous properties of the parent MM-POMEs, and exhibited improved energy density and most notably, dramatically decreased water solubility. For evaluation against a set of criteria for a blended diesel blendstock, a 20 vol% blend of B*POME1-6 with a base diesel fuel was investigated here. Oxidation stability, cetane number, sooting tendency, lubricity and conductivity were improved in the B*POME1-6 blend compared with the base diesel, while also maintaining the flash point, cloud point, energy density, viscosity, and boiling point requirements. The B*POME1-6 product demonstrated a synergistic blending behavior at 10 vol% and a linear blending behavior at 20-30 vol% blends, in agreement with similar POME blends at comparable blend levels. Finally, common environmental and toxicity models performed on B*POME1-6 component molecules suggested they have a greater propensity to partition into the water compartment compared to a common diesel surrogate, but with a lower tendency to bioaccumulate.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Angle Assessment for Upper Limb Rehabilitation: A Novel Light Detection and Ranging (LiDAR)-Based Approach

The accurate measurement of joint angles during patient rehabilitation is crucial for informed decision making by physiotherapists. Presently, visual inspection stands as one of the prevalent methods for angle assessment. Although it could appear the most straightforward way to assess the angles, it presents a problem related to the high susceptibility to error in the angle estimation. In light of this, this study investigates the possibility of using a new approach to angle calculation: a hybrid approach leveraging both a camera and LiDAR technology, merging image data with point cloud information. This method employs AI-driven techniques to identify the individual and their joints, utilizing the cloud-point data for angle computation. The tests, considering different exercises with different perspectives and distances, showed a slight improvement compared to using YOLO v7 for angle calculation. However, the improvement comes with higher system costs when compared with other image-based approaches due to the necessity of equipment such as LiDAR and a loss of fluidity during the exercise performance. Therefore, the cost–benefit of the proposed approach could be questionable. Nonetheless, the results hint at a promising field for further exploration and the potential viability of using the proposed methodology.

Klein, Luan C. (ORCID:000000016306574X)↗
DOE OSTI2024

Mono-Ether and Alcohol Bioblendstocks to Reduce the Fuel Penalty of Mixing Controlled Compression Ignition (MCCI) Engine Aftertreatment

An integrated approach utilizing catalysis experiments, process systems engineering, fuel property modeling, and engine testing was utilized in this project to optimize the production process and composition of a #2 diesel bioblendstock produced from ethanol consisting primarily of long-chain mono-ethers. The primary objective of the project was to determine the composition and to design the production process for a bioblendstock for #2 diesel fuel with > 50% reduction in greenhouse gas emissions relative to conventional diesel fuel. The desired bioblendstock needed to be blendable with #2 diesel fuel at > 5 vol. % while still meeting ASTM D975 diesel fuel specification properties and achieving improvements in fuel properties: increased cetane number, decreased sooting, and reduced pour point and cloud point temperatures. At the same time, it needed to reduce the fuel energy penalty associated with MCCI engine aftertreatment resulting in improved system efficiency. The findings of the current project demonstrate that primary objective of the work has been met, i.e., to determine the composition and to design the production process for a bioblendstock for #2 diesel fuel with > 50% reduction in greenhouse gas emissions relative to conventional diesel fuel. Additionally, the results indicate that the property objectives (increased cetane number, reduced pour and cloud points, and reduced sooting propensity) for the designed bioblendstock composition have also been met. Engine testing performed has also confirmed that the increased reactivity of the bioblendstock can be used to improve catalyst heating operation and to reduce the fuel penalty associated with this operation mode. The fuel property results demonstrate that >5 vol.% blending is easily achieved while meeting the ASTM D975 #2 diesel fuel property specifications tested in this work, as this was achieved for a blend with 43 vol. % of the bioblendstock. The results provide a foundation for future work to scaleup the catalytic production process designed in this work, with many of the challenges and areas for improvement being identified in this work to enable economic production with low GHG lifecycle emissions.

09 BIOMASS FUELS↗
DOE OSTI2024

Fuel property evaluation of unique fatty acid methyl esters containing β-hydroxy esters from engineered microorganisms

Unique fatty acid methyl esters (FAME) containing ..beta..-hydroxy esters were produced using an engineered microorganism by glucose fermentation. This study investigated the properties of the unique FAME mixture both neat and in blends with conventional diesel, as well as properties of ..beta..-hydroxy esters. The unique FAME blend contained relatively shorter-chain FAME (average fatty acid chain carbon number 14.6) with 58 % monounsaturated fatty acids and 9 % saturated and monounsaturated ..beta..-hydroxy acid chains. The unique FAME had significantly lower distillation T90 (321 °C versus 352 °C) and higher cetane number (56.7 versus 52) compared to soy biodiesel. Cloud points were within method repeatability. Unexpectedly (because of the lack of methylene-interrupted double bonds), the unique FAME had low oxidation stability (1.5 h) as determined by Rancimat induction period. Stability could be improved through addition of commonly used antioxidants. We speculate that monounsaturated ..beta..-hydroxy FAME may be the source of this instability. Blends with conventional diesel up to 50 vol% showed similar kinematic viscosity (within method repeatability) as blends of conventional FAME. The unique FAME had no effect on distillation T90 even at the 80% blend level. A 30 vol% blend into conventional diesel had a Rancimat induction period of only 2 h, very nearly the same as the neat unique FAME sample. The addition of antioxidants produced blends of acceptable stability. Based on an assessment of the properties of individual ..beta..-hydroxy FAME molecules, they have higher boiling point, higher cloud point, lower cetane number, and potentially lower storage stability than analogous FAME not having the ..beta..-hydroxy group. Removing them from the fuel product in the production process may result in a biodiesel product with superior properties to what is on the market today.

09 BIOMASS FUELS↗
DOE OSTI2021

Quality control and crop characterization framework for multi-temporal UAV LiDAR data over mechanized agricultural fields

Recent developments in remote sensing are enabling automatic, high resolution, and non-destructive survey of agriculture fields, providing the key basis for advancing plant breeding. Among the used remote sensing modalities, LiDAR has attracted wide attention for its ability to directly provide accurate 3D information. Despite the increasing utilization of LiDAR technology in phenotyping, there is still a lack of effective quality control strategies, in particular, quality control of LiDAR data collected on a multi-temporal basis. This study proposes a targetless framework for multi-temporal LiDAR data quality control and crop characterization in mechanized agricultural fields. Features extracted from the fields – terrain patches and row/alley locations – are utilized for evaluating the vertical and planimetric relative accuracy of the point clouds. Row/alley locations in the field are automatically identified from the point clouds based on the assumption that higher point density and/or higher elevation correspond to plant locations. The performance of the proposed quality control strategies is evaluated using multi-temporal datasets collected in agricultural fields of different sizes, orientation, crops, and growth stages. The result shows that the net vertical and planimetric discrepancies between multi-temporal point clouds are ±3 cm and ±8 cm, respectively. While the former reflects the actual accuracy of the point clouds, the latter is a combined effect of the LiDAR point cloud accuracy, rasterization artifacts, crop type, growth pattern, and wind condition during data acquisition. In terms of row and alley detection, the result shows that the proposed strategy achieves high performance and can deal with different planting orientation, crop types, growth stages, canopy cover, and planting density. In conclusion, this study presents a quality control framework for multi-temporal LiDAR data. Finally, the row and alley detection leads to automated extraction of plots, and hence facilitates the use of remotely sensed data for automated phenotyping.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Evaluation of Techniques for Vegetation Removal from UAV-Based Photogrammetric DSM near WIPP Land Withdrawal Act Boundary, NM

The Waste Isolation Pilot Plant is located within New Mexico's karst landscape and is the United States' only deep geologic radioactive waste repository which isolates transuranic waste from defense activities underground in a bedded salt formation. Characteristic karst topography in this region is made up of material (such as limestone) that can be dissolved by water over a long period of time, and includes features such as springs, surface streams, sinking streams, caves, and sinkholes. Karst features can potentially impact the integrity of the waste repository in the future and compromise WIPP's performance, particularly when coupled with incompatible land-use activities within the Land Withdrawal Act (LWA) boundary as well as increased water withdrawals outside the LWA. In order to better capture the ground surface topography to support hydrology modeling efforts, the U.S. DOE has enlisted FIU to develop a high-resolution digital elevation model (DEM). In a previous field study, a high-resolution digital surface model (DSM) of an adjacent representative site, Basin 6, was developed by photogrammetry using aerial images captured with unmanned aerial vehicles. The DSM generated was based on point clouds representing the entire landscapes, including points of terrain, vegetation and infrastructure. These above-ground features need to be removed to create a DSM/DEM with good representation of the bare ground. In recent years, many techniques including both pre- and post-processing of DSMs, were developed for vegetation removal with varied success. This research focuses on applying and comparing various vegetation removal methods using point clouds generated of the Basin 6 pilot study area. The 3D RGB-based point cloud classification using Python scripts was a modification of the procedure by Themistocleous (2019). The method using Liblas classifies the point cloud three-dimensionally, whereas the method by Themistocleous (2019) is two-dimensional. The preliminary result of the method using Liblas, however, still had a large amount of vegetation remaining compared to the Pix4D Machine Learning method. The vegetation layer remains highly visible in the DEMs processed using the vegetation indices as opposed to the Pix4D Machine Learning method, which produces a DEM where mostly bare ground is seen. Further modifications to the input parameters are required to improve the vegetation vs. bare ground classification using this approach. The next step will be to improve the method using Liblas and to also test other vegetation removal methods found in the literature review to compare them against the two described here.

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

Point cloud-based diffusion models for the Electron-Ion Collider

At high-energy collider experiments, generative models can be used for a wide range of tasks, including fast detector simulations, unfolding, searches of physics beyond the Standard Model, and inference tasks. In particular, it has been demonstrated that score-based diffusion models can generate high-fidelity and accurate samples of jets or collider events. This work expands on previous generative models in three distinct ways. First, our model is trained to generate entire collider events, including all particle species with complete kinematic information. We quantify how well the model learns event-wide constraints such as the conservation of momentum and discrete quantum numbers. We focus on the events at the future Electron-Ion Collider, but we expect that our results can be extended to proton-proton and heavy-ion collisions. Second, previous generative models often relied on image-based techniques. The sparsity of the data can negatively affect the fidelity and sampling time of the model. We address these issues using point clouds and a novel architecture combining edge creation with transformer modules called Point Edge Transformers. Third, we adapt the foundation model OmniLearn, to generate full collider events. This approach may indicate a transition toward adapting and fine-tuning foundation models for downstream tasks instead of training new models from scratch.

Araz, Jack Y. [Stony Brook Univ., NY (United State↗
DOE OSTI2020

Vision-Guided Automation and Assistance

Robotic automation and assistance is an excellent way to increase efficiency and safety when working in a potentially hazardous workspace. However, many traditional automation approaches rely on the precise placement of items to be manipulated. The scope of such automated tasks is also limited to the manipulation of one type of object since there is no way to distinguish between different objects. The addition of a vision pipeline, where robot vision is used to localize and recognize objects, can allow the robot to perform more complex tasks in a variable workspace. In my vision pipeline, a depth camera is used to generate a 3D point cloud of the workspace. Objects to be manipulated are isolated in the point cloud by filtering out shared surfaces and using Euclidean cluster extraction to group clusters of points. The locations of the objects are then estimated from the point cloud of the isolated objects. The localization of objects with robot vision allows for variable workspaces, reducing the overhead associated with requiring all objects to be precisely placed in the workspace. For object recognition, deep learning is used to train a neural network that classifies objects from the raw point clouds. More complex tasks that require multiple objects to be manipulated differently can be achieved through the vision pipeline. Through its ability to visualize the workspace, robot vision greatly improves efficiency and safety of automated and assistive tasks.

42 ENGINEERING↗