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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Commercial Building Technology Demonstration (Final Scientific/Technical Report)

The background for this project was a unique and innovative concept that had been proposed and demonstrated by enVerid Systems, reducing the energy consumption in HVAC systems in commercial and public buildings by up to 50% at peak loads. The concept involves selective scrubbing of gas contaminants from indoor air as a practical substitute for outside air ventilation, the latter being the conventional practice today. This scrubbing enables recycling of most of the indoor air and significantly decreasing the amount of outside air, resulting in a reduced HVAC load and commensurate energy savings for the facility. Key to our approach is low cost scrubbing of all gas contaminants from indoor air using novel, efficient and regenerable sorbent materials. The total solution, labeled HLR (HVAC Load Reduction) is implemented by means of compact modules that can be retrofitted onto virtually any building, and utilizes novel sorbents that have been developed and brought to scale production by enVerid. Although the technology had been deployed and demonstrated, the US commercial building and HVAC markets, famously conservative with respect to innovation and new technologies, had yet to embrace the idea or the product. The overarching goal of the project was to help overcome market hesitancy and enable widespread adoption, by creating a critical mass of success stories across different regions and building types, all under oversight and verification of the DOE and NREL, to ensure credibility and accuracy. The project, overall, was a resounding success. Several installations were deployed and tested comprehensively for the impact on both energy consumption and indoor air quality, and results were not only excellent, but quantitatively in line with expectations. A total number of eight (8) installations were attempted, with 2 aborted due to building issues. Of the six successful installations, however, complete M&V was performed by NREL on 2 (Miami and NYC) and partial analysis on a third site (ArcBest), and these three, along with their results, were therefore presented from a financial and reporting standpoint. Although the number of case studies fell short of the original target of 10, we nevertheless had excellent demonstrations in vastly different climate zones: Southeast, Northeast and Midwest, and in very different building types. This included a modern high-rise office building in New York City and a University Campus health center in Miami, and a mid-rise high-density office in Arkansas. The cooling load reduction was shown to be significant, averaging 37% during the final cooling season in the Miami site. In the NYC site, the cooling energy savings were generally less, at around 12%, as expected. While this is a lower number for average/total savings, two comments are important to note: (i) the outside airflow (OA) reduction was not consistent during the data collection period, at least in part understating the attainable savings through incorrect measurement of the “high energy” reference state; and (ii) the peak impact in NYC is almost certainly as high as Miami, with critical ramifications for peak load reduction and equipment downsizing ability. Importantly, the measured energy savings in all locations were related to the outdoor climate conditions and the actual achievable reduction in outside air, very much in line with models and calculations; this leaves us with increased confidence in our understanding of the interaction between the technology and the building, and our ability to project the impact of this innovation in the future, through broad adoption. Simply put: the energy savings and load reduction impact of the HLR solution has been confirmed quantitatively. The results also confirmed the air quality objective. This may be even more significant, in that air quality is more complex and harder to measure and quantify, and the performance of the HLR as an air scrubber is the linchpin of the entire solution. In all sites, indoor air quality met or exceeded the required parameters, in terms of achieving the target (low) levels of indoor pollutants. Furthermore, the concentrations of outdoor sourced pollutants were markedly lower, as expected, mostly due to the reduction in outside air intake although in part also due to the scrubbing. The success was not immediate nor easy, and early challenges with the systems, especially (but not only) around controls and communication, caused delays in commissioning and undermined some of our M&V schedules. The good news is no fundamental issues were encountered and the number of such glitches was declining quickly by the end of the project. Other challenges had to do with coordinating the control of the building OA dampers, which the HLR needs to have in order to deliver the load reduction, but which the facility manager controls regularly and independently. This is not necessarily a problem for normal HLR operation but interferes with accurate M&V, where switching OA settings back and forth on a precise timetable is essential for clean data. The project provided valuable experience in retrofitting buildings that had been originally designed to operate with less recirculation and more outside air, and some key learnings related to the cyclical regeneration of the sorbents in the operational environment of the building. Some of these learnings gave already been incorporated into ongoing improvements in the design of the HLR module as well as the software that is used to control its operation. The cost and time of retrofit installations is much better understood; so is the test and balance procedure of the building that determines the available range of outside air reduction, which in turn determines the amount of load reduced. We had also implemented at least one annual cycle of sorbent cartridge replacement in the field, after which we tested the used sorbent to assess the rate of degradation of sorbent potency – an important factor affecting the long-term economics of HLR deployments. A notable shortfall of the project, other than the fact that the number of sites was below target, was that we had not (yet) completed testing to measure and verify (M&V) the winter energy savings. This is primarily due to late start on the NYC sites (Miami does not portend any heating savings associated with reduced outside air), combined with the issues of OA damper control. It is by no means suggested that such savings cannot be captured, only that the M&V with respect to winter in NYC has been incomplete. The value of this project and its interest to the general public are hard to overstate. The case studies created from Miami and especially from NYC have, without a doubt, opened up the interest of the broad HVAC industry to HLR in the past year. Major manufacturer reps have signed up to represent and promote the product, and dozens of top tier engineering consultants have introduced HLR as a base of design in upcoming construction and renovation projects – precisely the overarching goal of this project. These would not have happened without the highly visible installation in NYC and its ultimately satisfied customer. Furthermore, the DOE itself issued a report in late 2017 analyzing hundreds of candidate technologies for reducing energy consumption in HVAC; remarkably, the budding HLR solution ranked among the top 3(!); no doubt, its status as a viable and significant contender on this list is informed by the success of these demonstrations. And furthermore, in the wake of these demos, several major energy utilities, led by ConEdison in NYC, have endorsed the HLR solution and now routinely provide substantial cash rebates for customers choosing to incorporate the HLR in their buildings and reap the benefit of the reduced peak load as well as reduced cumulative energy demand. Last but not least, in January of 2019, as this report was being written, the HLR module won the AHR Expo Innovation Award for Green Buildings and, remarkably, the prestigious and only Product of the Year 2019, being selected by an independent professional panel of judges from AHR and ASHRAE. Given the confirmation of the enormous energy saving potential of HLR in widescale deployment, as well as ancillary but significant benefits in terms of equipment downsizing and indoor air quality, the benefit of this effort to the public speaks for itself. Accelerating adoption of HLR, without dependence on subsidies and driven by the private sector, is well under way. There is still much work to be done in terms of educating the HVAC and construction ecosystems and improving the product’s robustness and simplicity, to further facilitate the adoption of this technology and the capture of its potential value. But the American public will greatly benefit both directly and indirectly in the immediate future and beyond.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles

Optimal engine operation during a transient driving cycle is the key to achieving greater fuel economy, engine efficiency, and reduced emissions. In order to achieve continuously optimal engine operation, engine calibration methods use a combination of static correlations obtained from dynamometer tests for steady-state operating points and road and/or track performance data. As the parameter space of control variables, design variable constraints, and objective functions increases, the cost and duration for optimal calibration become prohibitively large. In order to reduce the number of dynamometer tests required for calibrating modern engines, a large-scale simulation-driven machine learning approach is presented in this work. A parallel, fast, robust, physics-based reduced-order engine simulator is used to obtain performance and emission characteristics of engines over a wide range of control parameters under various transient driving conditions (drive cycles). We scale the simulation up to 3,906 nodes of the Theta supercomputer at the Argonne Leadership Computing Facility to generate data required to train a machine learning model. The trained model is then used to predict various engine parameters of interest, and the results are compared with those predicted by the engine simulator. Our results show that a deepneural-network-based surrogate model achieves high accuracy: Pearson product-moment correlation values larger than 0.99 and mean absolute percentage error within 1.07% for various engine parameters such as exhaust temperature, exhaust pressure, nitric oxide, and engine torque. Once trained, the deep-neural-network-based surrogate model is fast for inference: it requires about 16 mu s for predicting the engine performance and emissions for a single design configuration compared with about 0.5 s per configuration with the engine simulator. Moreover, we demonstrate that transfer learning and retraining can be leveraged to incrementally retrain the surrogate model to cope with new configurations that fall outside the training data space.

deep learning↗
DOE OSTI2026

Deep Learning and Photogrammetric Reconstruction for Automated Crack Detection and Dimensional Measurement in Mining Operations

Surface crack detection and dimensional measurement at active mining sites present significant safety and operational challenges. Manual inspection methods are labor-intensive, spatially incomplete, and expose personnel to hazardous environments, while existing automated approaches have been developed primarily for concrete civil infrastructure and have not been validated on the complex, variable surfaces characteristic of mining environments. This dissertation presents an automated pipeline that integrates deep learning semantic segmentation with Structure-from-Motion photogrammetry to detect surface cracks and measure their aperture, length, and vertical displacement from standard RGB imagery acquired during routine Uncrewed Aerial Vehicle (UAV) survey operations, without requiring additional sensor hardware or manual measurement. The pipeline combines a U-Net architecture with an EfficientNet-B0 encoder, pretrained on the SDNET2018 concrete crack dataset and fine-tuned on a mining-specific dataset spanning laboratory concrete specimens, coal refuse impoundment embankments, and post-blast limestone quarry benches. Photogrammetric reconstruction is performed using COLMAP Structure-from-Motion and Multi-View Stereo, with crack segmentation masks projected into the reconstructed point cloud to enable three-dimensional vertical displacement measurement through local plane fitting and bimodal surface detection. The pipeline was validated across 36 controlled laboratory specimens at three imaging distances and four vertical displacement levels, achieving aperture measurement RMSE of 0.047 cm and R² of 0.954, and vertical displacement RMSE of 0.140 cm and R² of 0.966, against independent caliper measurements. Field application at a coal refuse impoundment in southwestern Pennsylvania detected 71 crack components across the embankment crest, with a dominant longitudinal crack exhibiting aperture values reaching 28 cm and a 95th percentile vertical displacement of 35.53 cm, consistent in magnitude and spatial distribution with simultaneously acquired LiDAR-derived estimates. Application across four post-blast limestone quarry bench datasets in California successfully characterized blast-induced fracture networks at ground sampling distances ranging from 0.59 to 1.23 cm/pixel, with detected crack geometries physically consistent with observable surface conditions at each site. The results demonstrate that deep learning-based crack detection and photogrammetric measurement can be integrated into routine UAV inspection workflows at mining sites, providing repeatable, scalable, and quantitative crack characterization across surface types, crack scales, and displacement magnitudes not previously addressed in the literature. The pipeline requires no dedicated surveying equipment beyond the UAV platforms already deployed at mine sites for survey and monitoring purposes, supporting practical adoption within existing operational workflows.

Crack detection, Dimensional Measurement↗
NASA NTRS2004

Distributed operations as applied in a large multi-instrument space mission: lessons learned from the Cassini-Huygens Program

Launched in 1997, the Cassini-Huygens Mission sent the largest interplanetary spacecraft ever built in the service of science. Carrying a suite of 12 scientific instruments and an atmospheric entry probe, this complex spacecraft to explore the Saturn system may not have gotten off the ground without undergoing significant design changes and cost reductions.

distributed↗
NASA NTRS2018

Flight Systems Integration & Test: Lessons Learned for Future Success

This paper offers a comprehensive view of flight system integration and test (I&T) lessons learned related to mishaps and close calls, focusing on what can be done to improve the I&T process to avoid recurrence. Specific areas within the realm of I&T that are covered in this paper include: I&T team communication and training; design of flight and ground systems for I&T; planning and scheduling; configuration management and process documentation; ground support equipment and tools; cleanrooms and contamination; mechanical integration, handling, and deployments; electrical integration and electrostatic discharge; functional testing and troubleshooting; environmental testing and facilities; and launch site operations. To illustrate "real-world" lessons learned for I&T, examples from throughout the history of the U.S. space program are presented. Also presented are some best practices for I&T that can help mitigate mishaps and close calls on the ground or during flight.

Integration & Test↗
DOE OSTI2025

Potential of deep learning methods to enhance satellite-based monitoring of nuclear power plants focusing on remote operation evaluations

The anticipated expansion of the nuclear industry and the deployment of new nuclear reactors (200 + GW of new nuclear capacity by 2050) require the development of monitoring systems that align with safety and security concerns, providing enhanced evaluation capabilities. A remote monitoring system using satellites and deep learning techniques was evaluated for its ability to detect anomalies and capture various features of nuclear reactors independently of the conditions on the ground. Satellite images of current operational and under-construction nuclear power plants were collected from Google Earth Pro as a surrogate database. Subsequently, five datasets were created from the collected images. Transfer learning technique was used for several classification tasks utilizing VGG16, ResNet50V2, Xception, DenseNet121, and MobileNetV2 pre-trained models. In the first task, the capability of the monitoring system to detect abnormal conditions or processes in a nuclear power plant was investigated. In the second task, the ability to capture operational features remotely was examined. As an example, for the purposes of this study, these features included classifying reactors based on type, power range, or onsite condition. Several evaluation metrics were used to compare the performance of the pre-trained models and the overall monitoring system. Here, the evaluation results demonstrated that deep learning techniques and pre-trained models applied to satellite images have the potential to facilitate further and expand capabilities in monitoring systems to assess plant operation details.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

FORGE: Summer Internship Experience

The presentation summarizes my summer internship experience with the Fluids Group at Fermilab. During the internship, I learned about the maintenance and operation of water pumps and other equipment that support important facility systems. I worked alongside fluids technicians and gained hands-on experience with maintaining machinery, installing pipes, refilling water, cleaning RF cavities, and observing new water-pump construction for PIP-II. My projects included repairing an air dryer, learning how heat exchangers cool low-conductivity water, and cleaning the copper cooling rings used in RF cavities. Overall, this experience helped me better understand the work processes of technicians and the importance of practical technical knowledge for engineers.

Uribe, Anthony [Unlisted, US, IL]↗
NASA NTRS2017

Monitoring of the Atmosphere on the International Space Station with the Air Quality Monitor

During the early years of human spaceflight, short duration missions allowed for monitoring of the spacecraft environment to be performed via archival sampling, in which samples were returned to Earth for analysis. With the construction of the International Space Station (ISS) and the accompanying extended mission durations, the need for enhanced, real-time monitors became apparent. The Volatile Organic Analyzer (VOA) operated on ISS for 7 years, where it assessed trace volatile organic compounds in the cabin air. The large and fixed-position VOA was eventually replaced with the smaller Air Quality Monitor (AQM). Since March 2013, the atmosphere of the U.S. Operating Segment (USOS) has been monitored in near real-time by a pair of AQMs. These devices consist of a gas chromatograph (GC) coupled with a differential mobility spectrometer (DMS) and currently target detection list of 22 compounds. These targets are of importance to both crew health and the Environmental Control and Life Support Systems (ECLSS) on ISS. Data is collected autonomously every 73 hours, though the units can be controlled remotely from mission control to collect data more frequently during contingency or troubleshooting operations. Due to a nominal three-year lifetime on-orbit, the initial units were replaced in February 2016. This paper will focus on the preparation and use of the AQMs over the past several years. A description of the technical aspects of the AQM will be followed by lessons learned from the deployment and operation of the first set of AQMs. These lessons were used to improve the already-excellent performance of the instruments prior to deployment of the replacement units. Data trending over the past several years of operation on ISS will also be discussed, including data obtained during a survey of the USOS modules. Finally, a description of AQM use for contingency and investigative studies will be presented.

Wallace William T.↗
DOE OSTI2021

Non-intrusive data-driven model reduction for differential–algebraic equations derived from lifting transformations

In this paper we present a non-intrusive data-driven approach for model reduction of nonlinear systems. The approach considers the particular case of nonlinear partial differential equations (PDEs) that form systems of partial differential–algebraic equations (PDAEs) when lifted to polynomial form. Such systems arise, for example, when the governing equations include Arrhenius reaction terms (e.g., in reacting flow models) and thermodynamic terms (e.g., the Helmholtz free energy terms in a phase-field solidification model). Using the known structured form of the lifted algebraic equations, the approach computes the reduced operators for the algebraic equations explicitly, using straightforward linear algebra operations on the basis matrices. The reduced operators for the differential equations are inferred from lifted snapshot data using operator inference, which solves a linear least squares regression problem. The approach is illustrated for the nonlinear model of solidification of a pure material. The lifting transformations reformulate the solidification PDEs as a system of PDAEs that have cubic structure. The operators of the lifted system for this solidification example have affine dependence on key process parameters, permitting us to learn a parametric reduced model with operator inference. Numerical experiments show the effectiveness of the resulting reduced models in capturing key aspects of the solidification dynamics.

42 ENGINEERING↗
DOE OSTI2021

Neighborhood Keeper Program Review

Dragos provided INL access to their Neighborhood Keeper platform, which contains simulated data, sample reports given to utilities, and access to anonymized program data that is sent from utilities to the cloud. Dragos has specifically asked INL to review the available data and respond to the following questions: 1) What detections or combinations of detections are most useful? 2) What additional analysis would benefit the electricity subsector? 3) How could Neighborhood Keeper reporting be modified to benefit the electricity subsector? 4) What lessons learned from the Cybersecurity for the Operational Technology Environment (CyOTE) program could be provided? 5) Based on INL’s experience with utilities, are there lessons learned about data sharing that could be provided? 6) Are there recommendations that could benefit the electricity subsector?

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1998

NASA/FAA/NCAR Supercooled Large Droplet Icing Flight Research: Summary of Winter 1996-1997 Flight Operations

During the winter of 1996-1997, a flight research program was conducted at the NASA-Lewis Research Center to study the characteristics of Supercooled Large Droplets (SLD) within the Great Lakes region. This flight program was a joint effort between the National Aeronautics and Space Administration (NASA), the National Center for Atmospheric Research (NCAR), and the Federal Aviation Administration (FAA). Based on weather forecasts and real-time in-flight guidance provided by NCAR, the NASA-Lewis Icing Research Aircraft was flown to locations where conditions were believed to be conducive to the formation of Supercooled Large Droplets aloft. Onboard instrumentation was then used to record meteorological, ice accretion, and aero-performance characteristics encountered during the flight. A total of 29 icing research flights were conducted, during which "conventional" small droplet icing, SLD, and mixed phase conditions were encountered aloft. This paper will describe how flight operations were conducted, provide an operational summary of the flights, present selected experimental results from one typical research flight, and conclude with practical "lessons learned" from this first year of operation.

Miller, Dean↗
NASA NTRS2022

Environmental Testing of the OVEN System for Lunar Water Extraction and Prospecting

Introduction: The presence of water ice in permanently shadowed regions on the lunar surface [1] may enable a sustained human presence on the Moon with minimal need for consumables. However, in order to develop a long term utilization plan that includes the usage of in-situ water we must first understand the abundance, stratigraphy and distribution of this re-source. Multiple space agencies currently have plans for lunar water prospecting missions. The Optimized Volatile Extraction Node (OVEN) was designed for water prospecting missions that require samples to be weighed, sealed, and heated as the means of determining water concentration. This method of water quantification necessitates a fair amount of automation, so a rigorous environmental test program was performed in order to build confidence in the performance of the OVEN design. The work presented here describes the OVEN environmental test program as well as ongoing efforts to improve on the design. Vibration: The OVEN participated in two rounds of random vibration tests. The first test was a stand-alone test performed at the Energy Systems Test Area of the Johnson Space Center. The second test was an integrated test with the mobile platform developed for the Resource Prospector project. The OVEN survived both tests without damage, but the tests did provide valuable lessons learned with regards to specific operations. Thermal Vacuum: The OVEN was successfully demonstrated at a temperature range of -50 to 75 C in a thermal vacuum chamber. The need to heat motor gearboxes at lower temperatures was predetermined so this test program was completed by implementing a method of gearbox heating that used the existing circuitry within the motors. Dust: A custom dynamometer was built in order to determine the torque required to move the various mechanisms within the OVEN at a range of temperatures. The OVEN system was coated with lunar dust simulant in order to determine mechanism torques under a worst-case operating condition. Sublimation: Sublimation losses within the OVEN were quantified through a series of test configurations, including an integrated test in a thermal vacuum chamber at Glenn Research Center [2]. Current Work: The OVEN subsystem is currently not a component of any existing prospecting missions, but work continues that will take the lessons learned from previous environmental tests and improve on the design in order to be considered for future prospecting opportunities. References: [1] Colaprete, A., Schultz, P., Heldmann, J., Wooden, D., Shirley, M., Ennico, K., ... & Sollitt, L. (2010). Detection of water in the LCROSS ejecta plume. science, 330(6003), 463-468. [2] Kleinhenz, J., Smith, J., Roush, T., Colaprete, A., Zacny, K., Paulsen, G., ... & Paz, A. (2018). Volatiles Loss from water bearing regolith simulant at Lunar Environments. In Earth and Space 2018: Engineering for Extreme Environments (pp. 454-466). Reston, VA: American Society of Civil Engineers.

A Paz↗
NASA NTRS2014

Design Visualization Internship Overview

This is a report documenting the details of my work as a NASA KSC intern for the Summer Session from June 2nd to August 8th, 2014. This work was conducted within the Design Visualization Group, a Contractor staffed organization within the C1 division of the IT Directorate. The principle responsibilities of the KSC Design Visualization Group are the production of 3D simulations of NASA equipment and facilities for the purpose of planning complex operations such as hardware transportation and vehicle assembly. My role as an intern focused on aiding engineers in using 3D scanning equipment to obtain as-built measurements of NASA facilities, as well as using CATIA and DELMIA to process this data. My primary goals for this internship focused on expanding my CAD knowledge and capabilities, while also learning more about technologies I was previously unfamiliar with, such as 3D scanning. An additional goal of mine was to learn more about how NASA operates, and how the U.S. Space Program operates on a day-to-day basis. This opportunity provided me with a front-row seat to the daily maneuvers and operations of KSC and NASA as a whole. Each work day, I was able to witness, and even take part of, a small building block of the future systems that will take astronauts to other worlds. After my experiences this summer, not only can I say that my goals have been met, but also that this experience has been the highlight of my experience in higher education.

Telescience↗
DOE OSTI2020

Sampling Low-Dimensional Markovian Dynamics for Preasymptotically Recovering Reduced Models from Data with Operator Inference

This work introduces a method for learning low-dimensional models from data of high-dimensional black-box dynamical systems. The novelty is that the learned models are exactly the reduced models that are traditionally constructed with classical projection-based model reduction techniques. Thus, the proposed approach learns models that are guaranteed to have the well-studied properties of reduced models known from model reduction, without requiring full knowledge of the governing equations and without requiring the operators of the high-dimensional systems. The key ingredient is a new data sampling scheme to obtain re-projected trajectories of high-dimensional systems that correspond to Markovian dynamics in low-dimensional subspaces. The exact recovery of reduced models from these re-projected trajectories is guaranteed pre-asymptotically under certain conditions for finite amounts of data and for a large class of systems with polynomial nonlinear terms. Numerical results demonstrate that the low-dimensional models learned with the proposed approach match reduced models from traditional model reduction up to numerical errors in practice. In conclusion, the numerical results further indicate that low-dimensional models fitted to re-projected trajectories are predictive even in situations where models fitted to trajectories without re-projection are inaccurate and unstable.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

BioSentinel: Leading the Way for Deep Space CubeSat Missions

Flagship science missions are not alone in Deep Space thanks to BioSentinel, a 6U spacecraft launched on Artemis-1. BioSentinel is one of the longest operating CubeSats beyond cislunar space. The subsystems and COTS components of the BioSentinel bus are a template for future deep space missions, and the lessons learned from over a year of operations will enable improved performance for the next missions. BioSentinel achieved its unprecedented performance for an SLS secondary payload due to preparation, planning, and a robust design. Pre-launch antenna and interface testing with both DSN and ESA confirmed command and data pathways and allowed for operational flexibility in the critical early hours post-deployment. Mission Operations simulations prior to launch identified potential risks and primed operators to respond in flight, preparing the team to react quickly to successfully detumble the spacecraft and enter a power-positive state. The spacecraft would not have survived without the inclusion of the trailblazing 3D-printed composite cold gas propulsion system. The non-standard tank geometry enabled efficient use of the limited space available in the CubeSat, as well as the capability to detumble the spacecraft and manage momentum, while providing sufficient margin to execute potential delta-V maneuvers. The Iris radio has operated for over 18 months with no significant issues. Initial Iris performance estimates have been accurate throughout the mission. BioSentinel continues to collect data on thermal conditions and to validate our performance models with real-world knowledge. We have received exemplary support from our DSN partners. Following the conclusion of the primary science mission, the Linear Energy Transfer (LET) Spectrometer continued to collect solar and galactic radiation data from its location in heliocentric orbit. The free space dataset offered by the BioSentinel LET is a valuable source of data for model validation and future mission planning. As the spacecraft travels farther from Earth it is poised to provide longitudinally distributed measurements of solar particle events during solar maximum. The lessons learned from BioSentinel suggest key areas to enhance performance. The ability to upload modified flight software can increase the stability of memory management. Additional heaters in the propulsion system design have already proven successful on the Starling mission. Streamlining mission operations can reduce costs, increase data return, and better utilize DSN time. Enhancements such as these will facilitate reliable, long-duration deep space exploration using the proven BioSentinel 6U CubeSat bus.

BioSentinel↗
DOE OSTI2024

Integration of scanning probe microscope with high-performance computing: Fixed-policy and reward-driven workflows implementation

The rapid development of computation power and machine learning algorithms has paved the way for automating scientific discovery with a scanning probe microscope (SPM). The key elements toward operationalization of the automated SPM are the interface to enable SPM control from Python codes, availability of high computing power, and development of workflows for scientific discovery. Here, we build a Python interface library that enables controlling an SPM from either a local computer or a remote high-performance computer, which satisfies the high computation power need of machine learning algorithms in autonomous workflows. We further introduce a general platform to abstract the operations of SPM in scientific discovery into fixed-policy or reward-driven workflows. Furthermore, our work provides a full infrastructure to build automated SPM workflows for both routine operations and autonomous scientific discovery with machine learning.

47 OTHER INSTRUMENTATION↗
NASA NTRS2002

Mars mission science operations facilities design

A variety of designs for Mars rover and lander science operations centers are discussed in this paper, beginning with a brief description of the Pathfinder science operations facility and its strengths and limitations. Particular attention is then paid to lessons learned in the design and use of operations facilities for a series of mission-like field tests of the FIDO prototype Mars rover. These lessons are then applied to a proposed science operations facilities design for the 2003 Mars Exploration Rover (MER) mission. Issues discussed include equipment selection, facilities layout, collaborative interfaces, scalability, and dual-purpose environments. The paper concludes with a discussion of advanced concepts for future mission operations centers, including collaborative immersive interfaces and distributed operations. This paper's intended audience includes operations facility and situation room designers and the users of these environments.

Ground Data System (GDC)↗