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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Real-Time GPU-Accelerated OFDR With an Integrated Auxiliary Interferometer

A GPU-accelerated optical frequency domain reflectometry (OFDR) system with an improved integrated auxiliary interferometer is proposed. Unlike conventional approaches that require separate auxiliary interferometers and multiple detection channels, the proposed OFDR system embeds this functionality directly into the signal via an intentional beat component. This enables self-calibration of laser nonlinearity while maintaining a cost-effective hardware configuration. Building on this simplified configuration, the system leverages GPU acceleration with an NVIDIA RTX 4070 Ti to achieve real-time performance, delivering high-throughput signal processing for continuous OFDR interrogation. The signal processing pipeline comprises signal capture, resampling for nonlinearity compensation, and frequency shift computation, all optimized for parallel execution. Hardware benchmarking demonstrates substantial acceleration over CPU implementations, achieving up to a 45× speedup for resampling and frequency shift computations and enabling processing latencies below 30 ms. Thermal response validation is conducted under two complementary scenarios: localized heating using a water bath and cryogenic-temperature conditions using liquid nitrogen. Under localized heating, the system achieves an accuracy of 0.249 °C with a thermal sensitivity of 5.971 GHz/°C, while cryogenic-temperature validation demonstrates a frequency shift response with a sensitivity of 2.383 GHz/°C and an accuracy of 2.04 °C. The high acceleration of the proposed GPU-accelerated OFDR system and its accuracy are achieved by exploiting CUDA-based stride indexing, enabling efficient parallel segmentation and processing of large datasets without additional memory copies. The benchmarking results confirm the robustness, accuracy, and deployability of the proposed OFDR system across a wide temperature range, establishing it as a practical platform for real-time distributed fiber sensing in structurally dynamic environments.

Harb, Salah [Lawrence Berkeley National Laboratory↗
DOE OSTI2020

NCBI’s Virus Discovery Codeathon: Building “FIVE” —The Federated Index of Viral Experiments API Index

Viruses represent important test cases for data federation due to their genome size and the rapid increase in sequence data in publicly available databases. However, some consequences of previously decentralized (unfederated) data are lack of consensus or comparisons between feature annotations. Unifying or displaying alternative annotations should be a priority both for communities with robust entry representation and for nascent communities with burgeoning data sources. To this end, during this three-day continuation of the Virus Hunting Toolkit codeathon series (VHT-2), a new integrated and federated viral index was elaborated. This Federated Index of Viral Experiments (FIVE) integrates pre-existing and novel functional and taxonomy annotations and virus–host pairings. Variability in the context of viral genomic diversity is often overlooked in virus databases. As a proof-of-concept, FIVE was the first attempt to include viral genome variation for HIV, the most well-studied human pathogen, through viral genome diversity graphs. As per the publication of this manuscript, FIVE is the first implementation of a virus-specific federated index of such scope. FIVE is coded in BigQuery for optimal access of large quantities of data and is publicly accessible. Many projects of database or index federation fail to provide easier alternatives to access or query information. To this end, a Python API query system was developed to enhance the accessibility of FIVE.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A novel improved model for building energy consumption prediction based on model integration

Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2015

The Rapid Response Radiation Survey (R3S) Mission Using the HiSat Conformal Satellite Architecture

The Rapid Response Radiation Survey (R3S) experiment, designed as a quick turnaround mission to make radiation measurements in Low Earth Orbit (LEO), will fly as a hosted payload in partnership with NovaWurks using their Hyper-integrated Satlet (HISat) architecture. The need for the mission arises as the Nowcast of Atmospheric Ionization Radiation for Aviation Safety (NAIRAS) model moves from a research effort into an operational radiation assessment tool. Currently, airline professionals are the second largest demographic of radiation workers and to date their radiation exposure is undocumented in the USA. The NAIRAS model seeks to fill this information gap. The data collected by R3S, in addition to the complementary data from a NASA Langley Research Center (LaRC) atmospheric balloon mission entitled Radiation Dosimetry Experiment (RaD-X), will validate exposure prediction capabilities of NAIRAS. The R3S mission collects total dose and radiation spectrum measurements using a Teledyne μDosimeter and a Liulin-6SA2 LED spectrometer. These two radiation sensors provide a cross correlated radiometric measurement in combination with the Honeywell HMR2300 Smart Digital Magnetometer. The magnetometer assesses the Earth's magnetic field in the LEO environment and allows radiation dose to be mapped as a function of the Earth's magnetic shielding. R3S is also unique in that the radiation sensors will be exposed on the outer surface of the spacecraft, possibly making this the first measurements of the LEO radiation environment with bare sensors. Viability of R3S as an extremely fast turnaround mission is due, in part, to the nature of the robust, well-defined interfaces of the conformal satellite HiSat Architecture. The HiSat architecture, which was developed with the support of the Defense Advanced Research Projects Agency's (DARPA's) Phoenix Program, enabled the R3S system to advance from the first concept to delivery of preliminary design review (PDR) level documents in 29 calendar days. The architecture allows for interface complexities between the specific devices and the satellite bus to be resolved in a standardized interface control document (ICD). The ICD provided a readymade framework to interface to the modular satellite bus. This modularity allowed for approximately 90% of the R3S system to be designed and fabricated in two months without constraint of the hosting satellite's development cycle. This paper discusses the development of the R3S experiment as made possible by use of the HiSat architecture. The system design and operational modes of the experiment are described, as well as the experiment interfaces to the HiSat satellite via the user defined adapter (UDA) provided by NovaWurks. This paper outlines the steps taken by the project to execute the R3S mission in the 4 months of design, build, and test. Additionally portrayed is the ground work done at LaRC to posture the organization for a fast response and the process by which the opportunity was identified as aligning with key strategic goals. Finally, a description of the engineering process is provided, including the use of facilitated rapid/concurrent engineering sessions, the associated documentation, and the review process employed.

Miller, Nathanael A.↗
DOE OSTI2022

Constructing high-fidelity halo merger trees in abacussummit

ABSTRACT Tracking the formation and evolution of dark matter haloes is a critical aspect of any analysis of cosmological N-body simulations. In particular, the mass assembly of a halo and its progenitors, encapsulated in the form of its merger tree, serves as a fundamental input for constructing semi-analytic models of galaxy formation and, more generally, for building mock catalogues that emulate galaxy surveys. We present an algorithm for constructing halo merger trees from abacussummit, the largest suite of cosmological N-body simulations performed to date consisting of nearly 60 trillion particles, and which has been designed to meet the Cosmological Simulation Requirements of the Dark Energy Spectroscopic Instrument (DESI) survey. Our method tracks the cores of haloes to determine associations between objects across multiple time slices, yielding lists of halo progenitors and descendants for the several tens of billions of haloes identified across the entire suite. We present an application of these merger trees as a means to enhance the fidelity of abacussummit halo catalogues by flagging and ‘merging’ haloes deemed to exhibit non-monotonic past merger histories. We show that this cleaning technique identifies portions of the halo population that have been deblended due to choices made by the halo finder, but which could have feasibly been part of larger aggregate systems. We demonstrate that by cleaning halo catalogues in this post-processing step, we remove potentially unphysical features in the default halo catalogues, leaving behind a more robust halo population that can be used to create highly accurate mock galaxy realizations from abacussummit.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Hamiltonian learning using machine-learning models trained with continuous measurements

Here, we build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

Closed Loop Guidance Trade Study for Space Launch System Block-1B Vehicle

NASA is currently building the Space Launch System (SLS) Block-1 launch vehicle for the Exploration Mission 1 (EM-1) test flight. The design of the next evolution of SLS, Block-1B, is well underway. The Block-1B vehicle is more capable overall than Block-1; however, the relatively low thrust-to-weight ratio of the Exploration Upper Stage (EUS) presents a challenge to the Powered Explicit Guidance (PEG) algorithm used by Block-1. To handle the long burn durations (on the order of 1000 seconds) of EUS missions, two algorithms were examined. An alternative algorithm, OPGUID, was introduced, while modifications were made to PEG. A trade study was conducted to select the guidance algorithm for future SLS vehicles. The chosen algorithm needs to support a wide variety of mission operations: ascent burns to LEO, apogee raise burns, trans-lunar injection burns, hyperbolic Earth departure burns, and contingency disposal burns using the Reaction Control System (RCS). Additionally, the algorithm must be able to respond to a single engine failure scenario. Each algorithm was scored based on pre-selected criteria, including insertion accuracy, algorithmic complexity and robustness, extensibility for potential future missions, and flight heritage. Monte Carlo analysis was used to select the final algorithm. This paper covers the design criteria, approach, and results of this trade study, showing impacts and considerations when adapting launch vehicle guidance algorithms to a broader breadth of in-space operations.

Von der Porten, Paul↗
DOE OSTI2025

Cyanobacteria and Chloroflexota cooperate to structure light-responsive biofilms

Microbial mats are stratified communities often dominated by unicellular and filamentous phototrophs within an exopolymer matrix. It is challenging to quantify the dynamic responses of community members in situ as they experience steep gradients and rapid fluctuations of light. To address this, we developed a binary consortium using two representative isolates from hot spring mats: the unicellular oxygenic phototrophic cyanobacterium Synechococcus OS-B′ (Syn OS-B′) and the filamentous anoxygenic phototroph Chloroflexus MS-CIW-1 (Chfl MS-1). We quantified the motility of individual cells and entire colonies and demonstrated that Chfl MS-1 formed bundles of filaments that moved in all directions with no directional bias to light. Syn OS-B′ was slightly less motile but exhibited positive phototaxis. This binary consortium displayed cooperative behavior by moving further than either species alone and formed ordered arrays where both species aligned with the light source. No cooperative motility was observed when a nonmotile pilB mutant of Syn OS-B′ was used instead of Syn OS-B′. The binary consortium also produced more adherent biofilm than individual species, consistent with the close interspecies association revealed by electron microscopy. We propose that cyanobacteria and Chloroflexota cooperate in forming natural microbial mats by colonizing new niches and building robust biofilms.

Functional and Systems Biology↗
DOE OSTI2025

Building a quantum computing architecture using 3D superconducting cavities

Quantum computers promise advantages over classical machines for solving certain complex problems, but building processors that truly deliver this advantage remains a central challenge, particularly due to limited coherence times. Three-dimensional superconducting radio-frequency (SRF) cavities offer an attractive platform due to their exceptionally long lifetimes. However, since these harmonic systems require nonlinear elements, such as transmons, for control, additional losses are often introduced. In this talk, I will present a multimode quantum system based on an elliptical SRF cavity hosting two cavity modes weakly coupled to an ancillary transmon circuit. This architecture is carefully engineered to preserve coherence while enabling efficient control. By optimizing the design to mitigate transmon-induced decoherence, we realize single-photon lifetimes of 20.6 ms and 15.6 ms in the two modes, with pure dephasing times exceeding 40 ms. Using sideband interactions and error-resilient protocols, such as measurement-based correction and post-selection, we demonstrate high-fidelity state control, including preparation of Fock states up to N=20 with fidelities above 95% (to our knowledge, the highest reported to date), as well as high-fidelity two-mode entanglement. These results highlight 3D SRF cavities as a robust foundation for qudit-based quantum information processing, harnessing the large Hilbert space of cavity modes. I will conclude by outlining strategies to further enhance coherence in both cavities and ancilla qubits, and discuss pathways toward scaling this architecture into a larger quantum computing platform.

Roy, Tanay [Fermilab]↗
DOE OSTI2022

A process optimization framework for laser direct energy deposition: Densification, microstructure, and mechanical properties of an Fe-Cr alloy

Laser Direct Energy Deposition (DED) is a metal additive manufacturing technique with the ability to fabricate large and complex parts through deposition of metal powders. However, achieving high-density parts and targeted build heights using DED can be challenging due to the large number of highly sensitive process variables. This work proposes a robust fabrication parameter optimization framework to generate process maps for primary parameters in DED, including laser power, scan speed, mass flow rate, hatch spacing, and layer height. Simple single-track experiments were utilized to map out the parameter space, and a combination of geometric criteria for hatch spacing and layer height were proposed to determine parameter sets that achieve both targeted build heights and mitigate porosity formation. Using this framework, specimens with >99 % density and consistent mechanical properties were successfully fabricated over a wide range of process parameters for an Fe-9wt.%Cr (Fe9Cr) alloy, a surrogate for radiation damage-resistant reduced activation ferritic/martensitic (RAFM) steels. Processing these materials using DED is of particular interest in the development of plasma facing components for nuclear fusion applications. The microstructure and mechanical properties of as-printed Fe9Cr were characterized using optical and electron microscopy, X-ray diffraction, and uniaxial tensile tests. As-printed Fe9Cr displayed ~25 % elongation and ultimate tensile strengths of up to 475 MPa which is comparable to similar wrought alloys. Finally, the proposed framework will allow for accelerated DED parameter optimization for novel alloy systems, as well as open the possibility for local microstructure control while simultaneously mitigating defect formation.

42 ENGINEERING↗
DOE OSTI2025

Cyber Conservative Operations

In an era of increasingly sophisticated and pervasive cyber threats, robust cyber resilience strategies are more critical than ever. This paper introduces the concept of Cyber Conservative Operations, a proactive approach designed to assist critical infrastructure owners and operators in managing risk and maintaining resilience in the face of imminent, yet not occurring, cyber events. By leveraging the principles of Cyber-Informed Engineering (CIE) and the energy sector’s practice of conservative operations, Cyber Conservative Operations offer a framework for planning and executing coordinated active defense and resilience-oriented actions ahead of cyber events. This approach aims to minimize the consequences of digitally-enabled hazards and ensure swift recovery from anticipated cyber threats. Cyber Conservative Operations enhance the ability of owners and operators to execute pre-planned defense and resilience actions, thereby reducing the impact of digitally-enabled hazards. These operations are crucial for addressing impacts that cannot be precisely quantified or forecasted and for preparing organizations for rapid recovery from imminent digital threats. The paper begins with a discussion of conservative operations as applied to the bulk power system (BPS), a current mechanism allowing BPS entities to enact defensive operating plans to mitigate impending grid unreliability. Building on this model, we present a concept for implementing cyber conservative operations at asset owner facilities. The paper concludes with two case studies of cyber conservative operations drawn from high-profile cyber events, illustrating the practical application and benefits of this proactive approach.

42 - ENGINEERING↗
NASA NTRS2015

Control and Non-Payload Communications (CNPC) Prototype Radio - Generation 2 Security Architecture Lab Test Report

NASA Glenn Research Center, in cooperation with Rockwell Collins, is working to develop a prototype Control and Non-Payload Communications (CNPC) radio platform as part of NASA Integrated Systems Research Program's (ISRP) Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) project. A primary focus of the project is to work with the FAA and industry standards bodies to build and demonstrate a safe, secure, and efficient CNPC architecture that can be used by industry to evaluate the feasibility of deploying a system using these technologies in an operational capacity. GRC has been working in conjunction with these groups to assess threats, identify security requirements, and to develop a system of standards-based security controls that can be applied to the current GRC prototype CNPC architecture as a demonstration platform. The security controls were integrated into a lab test bed mock-up of the Mobile IPv6 architecture currently being used for NASA flight testing, and a series of network tests were conducted to evaluate the security overhead of the controls compared to the baseline CNPC link without any security. The aim of testing was to evaluate the performance impact of the additional security control overhead when added to the Mobile IPv6 architecture in various modes of operation. The statistics collected included packet captures at points along the path to gauge packet size as the sample data traversed the CNPC network, round trip latency, jitter, and throughput. The effort involved a series of tests of the baseline link, a link with Robust Header Compression (ROHC) and without security controls, a link with security controls and without ROHC, and finally a link with both ROHC and security controls enabled. The effort demonstrated that ROHC is both desirable and necessary to offset the additional expected overhead of applying security controls to the CNPC link.

Communication Networks↗
DOE OSTI2021

In Situ Inference for Earth System Predictability

An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Science Benefits of Onboard Spacecraft Navigation

Primitive bodies (asteroids and comets), which have remained relatively unaltered since their formation, are important targets for scientific missions that seek to understand the evolution of the solar system. Often the first step is to fly by these bodies with robotic spacecraft. The key to maximizing data returns from these flybys is to determine the spacecraft trajectory relative to the target body-in short, navigate the spacecraft- with sufficient accuracy so that the target is guaranteed to be in the instruments' field of view. The most powerful navigation data in these scenarios are images taken by the spacecraft of the target against a known star field (onboard astrometry). Traditionally, the relative trajectory of the spacecraft must be estimated hours to days in advance using images collected by the spacecraft. This is because of (1)!the long round-trip light times between the spacecraft and the Earth and (2)!the time needed to downlink and process navigation data on the ground, make decisions based on the result, and build and uplink instrument pointing sequences from the results. The light time and processing time compromise navigation accuracy considerably, because there is not enough time to use more accurate data collected closer to the target-such data are more accurate because the angular capability of the onboard astrometry is essentially constant as the distance to the target decreases, resulting in better "plane-of- sky" knowledge of the target. Excellent examples of these timing limitations are high-speed comet encounters. Comets are difficult to observe up close; their orbits often limit scientists to brief, rapid flybys, and their coma further restricts viewers from seeing the nucleus in any detail, unless they can view the nucleus at close range. Comet nuclei details are typically discernable for much shorter durations than the roundtrip light time to Earth, so robotic spacecraft must be able to perform onboard navigation. This onboard navigation can be accomplished through a self- contained system that by eliminating light time restrictions dramatically improves the relative trajectory knowledge and control and subsequently increases the amount of quality data collected. Flybys are one-time events, so the system's underlying algorithms and software must be extremely robust. The autonomous software must also be able to cope with the unknown size, shape, and orientation of the previously unseen comet nucleus. Furthermore, algorithms must be reliable in the presence of imperfections and/or damage to onboard cameras accrued after many years of deep-space operations. The AutoNav operational flight software packages, developed by scientists at the Jet Propulsion Laboratory (JPL) under contract with NASA, meet all these requirements. They have been directly responsible for the successful encounters on all of NASA's close-up comet-imaging missions (see Figure !1). AutoNav is the only system to date that has autonomously tracked comet nuclei during encounters and performed autonomous interplanetary navigation. AutoNav has enabled five cometary flyby missions (Table!1) residing on four NASA spacecraft provided by three different spacecraft builders. Using this software, missions were able to process a combined total of nearly 1000 images previously unseen by humans. By eliminating the need to navigate spacecraft from Earth, the accuracy gained by AutoNav during flybys compared to ground-based navigation is about 1!order of magnitude in targeting and 2!orders of magnitude in time of flight. These benefits ensure that pointing errors do not compromise data gathered during flybys. In addition, these benefits can be applied to flybys of other solar system objects, flybys at much slower relative velocities, mosaic imaging campaigns, and other proximity activities (e.g., orbiting, hovering, and descent/ascent).

Autonomy↗
DOE OSTI2022

Technical Performance and Cost Optimization of Unobtrusive Multi-static Serial LiDAR Imager (UMSLI) for Wide-area Surveillance and Identification of Marine Life at Marine Energy Installations

Florida Atlantic University developed an underwater optical monitoring system prototype - Unobtrusive Multi-static Serial LiDAR Imager (UMSLI) - suitable for marine energy full project lifecycle observation (baseline, commissioning, and decommissioning), with an automated real-time classification of marine animals. With precursor 2014 DOE funding (award DE-EE0006787), a prototype UMSLI was demonstrated in a controlled laboratory environment and achieved a TRL 6. This EERE DE-EE0007828 award aimed to both increase the TRL of the UMSLI by improving the technology performance (e.g., increase distance of marine animal target detection capability, add additional species classification capabilities, improve the system performance during the day, etc.) and reduce the cost. The UMSLI presents a novel application of underwater distributed Light Detection And Ranging (LiDAR), an advanced remote sensing method that uses light in the form of laser pulses for an application, this paired with an algorithm provides 360 degrees underwater detection, imaging, and classification of marine life. This solution for underwater monitoring of biota preserves the advantages of traditional optical and acoustic solutions while overcoming many associated disadvantages for marine energy site environmental monitoring, such as difficulties in species detection and classification in low light or night and turbid environments. This new approach is a purposefully designed, reconfigurable adaptation of an existing class 3B laser technology into one UMSLI instrument that can be easily mounted on or around different classes of marine energy equipment, such as devices to capture ocean current energy or wave energy. The system uses relatively low average power, utilizes far-red (> 635nm) laser illumination to be invisible and eye-safe to marine animals, is compact, and cost-effective. The equipment is designed for long-term, maintenance-free operations (i.e., current design is targeting more than 7 days continuous operation), to inherently generate a sparse primary dataset that only includes detected anomalies, and to allow robust real-time automated animal classification and identification with a low data bandwidth requirement. The technology’s overarching goal for application, is a system that can be deployed to collect pre-installation baseline species observations at a proposed marine energy deployment site with minimal post-processing overhead. The envisioned system will also produce high-resolution imagery of marine animals through a wide range of conditions and support automated tracking and notification of the presence of managed animals within established perimeters of marine energy equipment to satisfy deployed marine energy projects’ endangered and threatened species monitoring requirements. Through the current project, we demonstrated the UMSLI prototype in an operational environment and increased the UMSLI’s Technology Readiness Level from 6 to 7. The project resulted in many novel technologies, including: 1) an eye-safe red laser based, low-cost LiDAR system that can detect targets up to 10 meters distance; 2) the GAN-based machine learning underwater LiDAR image enhancement technique (this is the first known application of GAN technique in underwater LiDAR); 3) LiDAR-based real-time automated detection capabilities; and 4) a template matching based automated classification tool. These technologies build a solid foundation for future efforts to develop an extended range electro-optical monitoring system suitable for marine energy deployments. In addition to technology development, a key lesson learned is that addressing regulatory and safety requirements must be front and center in any marine energy monitoring applications.

16 TIDAL AND WAVE POWER↗
NASA NTRSDate not supplied

ISRU Pilot Excavator - Development of Autonomous Excavation Algorithms

The ISRU Pilot Excavator (IPEx) is a Space Technology Mission Directorate (STMD) Game Changing Development (GCD) project to develop a robotic excavator to demonstrate excavation of up to 10 metric tons of lunar regolith. IPEx is based on the Regolith Advanced Surface Systems Operations Robot (RASSOR) excavator developed at NASA Kennedy Space Center (KSC) and utilizes a counter rotating bucket drum concept to balance excavation forces for use on reduced gravity planetary bodies. To take advantage of the counter rotating bucket drum mechanism, work is being done to develop new autonomous excavation strategies and algorithms. Referred to as “Auto-dig”, these algorithms will allow IPEx to excavate, drive, and deliver its target mass of 10 metric tons of lunar regolith during an 11-day mission semi-autonomously. Due to bandwidth and latency constraints teleoperation will be kept to a minimum, with operators periodically confirming and verifying the excavator’s high-level tasks and operations. While the work to develop optimized autodig solutions is ongoing at KSC, early tests have yielded interesting results that have led to the discovery of additional risks that need to be mitigated in autonomous excavation. Testing has also encouraged the development of new software and visualization tools that provide real-time insight into excavation loads during operation, allowing faster development and helping build better intuition to the excavation process. These tools will help in the pursuit to develop fully optimized digging algorithms that are robust enough to handle hazards such as rocks or irregular terrain.

B. C. Buckles↗
DOE OSTI2024

Leveraging design of experiments to build chemometric models for the quantification of uranium (VI) and HNO3 by Raman spectroscopy

Partial least squares regression (PLSR) and support vector regression (SVR) models were optimized for the quantification of U(VI) (10–320 g L −1 ) and HNO 3 (0.6–6 M) by Raman spectroscopy with optimized calibration sets chosen by optimal design of experiments. The designed approach effectively minimized the number of samples in the calibration set for PLSR and SVR by selecting sample concentrations with a quadratic process model, despite complex confounding and covarying spectral features in the spectra. The top PLS2 model resulted in percent root mean square errors of prediction for U(VI), HNO 3 , and NO 3 − of 3.7%, 3.6%, and 2.9%, respectively. PLS1 models performed similarly despite modeling an analyte with a majority linear response (i.e., uranyl symmetric stretch) and another with more covarying vibrational modes (i.e., HNO 3 ). Partial least squares (PLS) model loadings and regression coefficients were evaluated to better understand the relationship between weaker Raman bands and covarying spectral features. Support vector machine models outperformed PLS1 models, resulting in percent root mean square error of prediction values for U(VI) and HNO 3 of 1.5% and 3.1%, respectively. The optimal nonlinear SVR model was trained using a similar number of samples (11) compared with the PLSR model, even though PLS is a linear modeling approach. The generic D-optimal design presented in this work provides a robust statistical framework for selecting training set samples in disparate two-factor systems. This approach reinforces Raman spectroscopy for the quantification of species relevant to the nuclear fuel cycle and provides a robust chemometric modeling approach to bolster online monitoring in challenging process environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Scale-Up and Modeling Efforts Using an Omniphobic Surface Treatment for Mitigating Solids Deposition

Abstract Gas hydrates, waxes and asphaltenes represent some of the most significant flow assurance challenges, especially in subsea lines, where treatment options can be limited. Currently, complete avoidance is the primary strategy for hydrate management, while chemical or mechanical flushing/pigging may be utilized for other solids. Each system must also be treated with individualized solutions, as there have been no proved one-size-fits-all technologies demonstrated to date. As an alternative to constant chemical injection or thermodynamic controls such as insulation or heating, a robust omniphobic surface treatment material has been developed which has been shown in previous studies to significantly reduce the adhesion of flow assurance solids, resulting in lower risk for deposition and plugging of gas hydrates, waxes, and asphaltenes. As part of a Department of Energy study, laboratory scale tests were performed on a variety of apparatuses ranging from micromechanical force (micron scale) to laboratory flow loop testing (meter scale). While the results from these tests have been promising, there can be some disconnects between lab-scale observations and field-scale testing. Several factors can result in discrepancies between small-scale bench-top studies and deployment-scale efforts. These include flow conditions (Reynolds number, flow regime), chemistry (synthetic vs. crude oils, gas composition, and additives), physical differences (pipeline material, size, and geometry), etc. This work will describe the actions taken to better understand the deployment and performance of a material such as this omniphobic surface treatment in field scenarios, and to build confidence for deployment in a production scenario. These efforts include modeling field production scenarios using models developed from lab observations, application of the material to field-scale equipment to better understand application challenges, and expanded survivability and longevity testing. Understanding the bridge between laboratory testing and full-scale deployment allows for better technique and risk mitigation for field scale testing.

03 NATURAL GAS↗