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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Enhancing Security and Resiliency in Operational Technology Environments Through Network Slicing and Federated Learning

The growing convergence of Information Technology (IT) and Operational Technology (OT) within Industry 4.0 environments has introduced new demands on industrial network infrastructure. As cyber-physical systems become increasingly interconnected, ensuring the secure, timely, and efficient exchange of critical data is essential. This thesis explores how network slicing, a method of creating isolated virtual network segments, can be applied within OT environments to address challenges such as latency, security, and resource allocation. The first research question addressed in this thesis is: How can OT networks take advantage of NFV and SDN technology to become cyber resilient? This study examines the operational, security, and architectural implications of introducing network slicing into traditionally static OT infrastructures such as Industrial Control Systems (ICS) and SCADA. Through simulated deployments and case studies, the research demonstrates how slicing enables better isolation between critical and non-critical services, thereby improving response time, throughput, and security in sensitive environments. The second question considers: How to dynamically implement network slicing and take advantage of network resources towards integrating decentralized machine learning? In response, this thesis proposes a framework that combines Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Federated Learning (FL) to enable real-time analytics while maintaining data locality. The proposed approach reduces the burden on centralized infrastructure and minimizes privacy risks by supporting on-site training of models across distributed OT nodes, coordinated through dynamically allocated network slices. The third focus explores: How slicing helps to increase the resiliency of OT networks through the orchestration of a dynamic DMZ? To answer this, the thesis presents a method for creating and managing Dynamic Demilitarized Zones (DMZs) using network slicing. This enables flexible and automated isolation of sensitive subsystems during threat scenarios or high-risk operations. Coupled with intelligent orchestration and containerized security services, the dynamic DMZ significantly enhances the system's ability to respond to cyber incidents without halting production. Ultimately, this thesis contributes a comprehensive architecture that blends network slicing with machine learning, secure segmentation, and automation, paving the way for resilient, adaptive, and intelligent OT environments. Performance evaluations across multiple scenarios show improvements in system reliability, threat response time, model accuracy, and resource utilization, providing a strong foundation for future industrial automation systems.

Rodiles Delgado, Brian G↗
NASA NTRS2011

Science Operations Development for Field Analogs: Lessons Learned from the 2010 Desert RATS Test

Desert Research and Technology Studies (Desert RATS) is a multi-year series of hardware and operations tests carried out annually in the high desert of Arizona on the San Francisco Volcanic Field. Conducted since 1997, these activities are designed to exercise planetary surface hardware and operations in conditions where long-distance, multi-day roving is achievable. Such activities not only test vehicle subsystems through extended rough-terrain driving, they also stress communications and operations systems and allow testing of science operations approaches to advance human and robotic surface capabilities.

Eppler, D. B.↗
NASA NTRS2011

Transient Plume Model Testing Using LADEE Spacecraft Attitude Control System Operations

We have learned it is conceivable that the Neutral Mass Spectrometer on board the Lunarr Atmosphere Dust Environment Explorer (LADEE) could measure gases from surface-reflected Attitude Control System (ACS) thruster plume. At minimum altitude, the measurement would be maximized, and gravitational influence minimized ("short" time-of-flight (TOF) situation) Could use to verify aspects of thruster plume modeling Model the transient disturbance to NMS measurements due to ACS gases reflected from lunar surface Observe evolution of various model characteristics as measured by NMS Species magnitudes, TOF measurements, angular distribution, species separation effects

Woronowicz, Michael↗
DOE OSTI2020

Data-Driven Operator Theoretic Methods for Global Phase Space Learning

In this work, we propose to apply the recently developed Koopman operator techniques to explore the global phase space of a nonlinear system from time-series data. In particular, we address the problem of identifying various invariant subsets of a phase space from the spectral properties of the associated Koopman operator constructed from time-series data. Moreover, in the case when the system evolution is known locally in various invariant subspaces, then a phase space stitching result is proposed that can be applied to identify a global Koopman operator. A biological system, bistable toggle switch and a second-order nonlinear system example are considered to illustrate the proposed results. The construction of this global Koopman operator is very helpful in experimental works as multiple experiments can't be run at the same time starting from several initial conditions.

Nandanoori, Sai Pushpak↗
DOE OSTI2021

Mixed Precision Fermi-Operator Expansion on Tensor Cores from a Machine Learning Perspective

Here we present a second-order recursive Fermi-operator expansion scheme using mixed precision floating point operations to perform electronic structure calculations using tensor core units. A performance of over 100 teraFLOPs is achieved for half-precision floating point operations on Nvidia’s A100 tensor core units. The second-order recursive Fermi-operator scheme is formulated in terms of a generalized, differentiable deep neural network structure, which solves the quantum mechanical electronic structure problem. We demonstrate how this network can be accelerated by optimizing the weight and bias values to substantially reduce the number of layers required for convergence. We also show how this machine learning approach can be used to optimize the coefficients of the recursive Fermi-operator expansion to accurately represent the fractional occupation numbers of the electronic states at finite temperatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Learning Only on Boundaries: A Physics-Informed Neural Operator for Solving Parametric Partial Differential Equations in Complex Geometries

Recently, deep learning surrogates and neural operators have shown promise in solving partial differential equations (PDEs). However, they often require a large amount of training data and are limited to bounded domains. In this work, we present a novel physics-informed neural operator method to solve parameterized boundary value problems without labeled data. By reformulating the PDEs into boundary integral equations (BIEs), we can train the operator network solely on the boundary of the domain. This approach reduces the number of required sample points from $O(N^d)$ to $O(N^{d-1}$), where $d$ is the domain’s dimension, leading to a significant acceleration of the training process. Additionally, our method can handle unbounded problems, which are unattainable for existing physics-informed neural networks (PINNs) and neural operators. Finally, our numerical experiments show the effectiveness of parameterized complex geometries and unbounded problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Lessons from commissioning of the cryogenic system for the Short-Baseline Neutrino Detector at Fermilab

Results from commissioning and first year of operations of the cryogenic system of the Short-Baseline Neutrino Detector (SBND) and its membrane cryostat installed at the Fermi National Accelerator Laboratory are described. The SBND detector is installed in a 200 m$^3$ membrane cryostat filled with liquid argon, which serves both as target and as active media. For the correct operation of the detector, the liquid argon must be kept in very stable thermal conditions while the contamination of electronegative impurities must be consistently kept at the level of small fractions of parts per billion. The detector is operated in Booster Neutrino Beams (BNB) at Fermilab for the search of sterile neutrinos and measurements of neutrino-argon cross sections. The cryostat and the cryogenic systems also serve as prototypes for the much larger equipment to be used for the LBNF/DUNE experiment. Since its installation in 2018-2023 and cooldown in spring of 2024, the cryostat and the cryogenic system have been commissioned to support the detector operations. The lessons learned through installation, testing, commissioning, cooldown, and initial operations are described.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Lessons from commissioning of the cryogenic system for the Short-Baseline Neutrino Detector at Fermilab

Results from commissioning and first year of operations of the cryogenic system of the Short-Baseline Neutrino Detector (SBND) and its membrane cryostat installed at the Fermi National Accelerator Laboratory are described. The SBND detector is installed in a 200 m3 membrane cryostat filled with liquid argon, which serves both as target and as active media. For the correct operation of the detector, the liquid argon must be kept in very stable thermal conditions while the contamination of electronegative impurities must be consistently kept at the level of small fractions of parts per billion. The detector is operated in Booster Neutrino Beams (BNB) at Fermilab for the search of sterile neutrinos and measurements of neutrino-argon cross sections. The cryostat and the cryogenic systems also serve as prototypes for the much larger equipment to be used for the LBNF/DUNE experiment. Since its installation in 2018-2023 and cooldown in spring of 2024, the cryostat and the cryogenic system have been commissioned to support the detector operations. The lessons learned through installation, testing, commissioning, cooldown, and initial operations are described.

Geynisman, Michael [Fermilab]↗
NASA NTRS2005

Atmospheric/Space Environment Support Lessons Learned Regarding Aerospace Vehicle Design and Operations

In modern government and aerospace industry institutions the necessity of controlling current year costs often leads to high mobility in the technical workforce, "one-deep" technical capabilities, and minimal mentoring for young engineers. Thus, formal recording, use, and teaching of lessons learned are especially important in the maintenance and improvement of current knowledge and development of new technologies, regardless of the discipline area. Within the NASA Technical Standards Program Website http://standards.nasa.gov there is a menu item entitled "Lessons Learned/Best Practices". It contains links to a large number of engineering and technical disciplines related data sets that contain a wealth of lessons learned information based on past experiences. This paper has provided a small sample of lessons learned relative to the atmospheric and space environment. There are many more whose subsequent applications have improved our knowledge of the atmosphere and space environment, and the application of this knowledge to the engineering and operations for a variety of aerospace programs.

Vaughan, William W.↗
DOE OSTI2020

Scalable Wind Turbine Generator Bearing Fault Prediction Using Machine Learning: A Case Study

Operation and maintenance (O&M) costs for wind turbines pose a risk to competitiveness and asset owners. With machine-learning technologies and digitalization rapidly maturing, the wind industry is actively investigating these new technologies to optimize O&M practices and reduce costs. This paper reviews recent work on machine-learning approaches to generator bearing failure prediction and presents a relevant real-world case study through a collaboration between the National Renewable Energy Laboratory and Envision Digital Corporation. In the case study, we evaluate the performance of representative machine-learning algorithms for predicting wind turbine generator bearing failures. Operational supervisory control and data acquisition data from one wind power plant was used to train and test the machine-learning models. The investigated data channels are chosen based on whether physically they reflect the failed generator bearing conditions and the component historical usage, including both environmental and operational conditions. Benefits and drawbacks of different methods are identified.

generator bearing failures↗
NASA NTRS2011

A Summary of the Rendezvous, Proximity Operations, Docking, and Undocking (RPODU) Lessons Learned from the Defense Advanced Research Project Agency (DARPA) Orbital Express (OE) Demonstration System Mission

The Guidance, Navigation, and Control (GN&C) Technical Discipline Team (TDT) sponsored Dr. J. Russell Carpenter, a Navigation and Rendezvous Subject Matter Expert (SME) from NASA's Goddard Space Flight Center (GSFC), to provide support to the Defense Advanced Research Project Agency (DARPA) Orbital Express (OE) rendezvous and docking flight test that was conducted in 2007. When that DARPA OE mission was completed, Mr. Neil Dennehy, NASA Technical Fellow for GN&C, requested Dr. Carpenter document his findings (lessons learned) and recommendations for future rendezvous missions resulting from his OE support experience. This report captures lessons specifically from anomalies that occurred during one of OE's unmated operations.

Dennehy, Cornelius J.↗
DOE OSTI2020

An Edge-Cloud Integrated Solution for Buildings Demand Response Using Reinforcement Learning

Buildings, as major energy consumers, can provide great untapped demand response (DR) resources for grid services. However, their participation remains low in real-life. One major impediment for popularizing DR in buildings is the lack of cost-effective automation systems that can be widely adopted. Existing optimization-based smart building control algorithms suffer from high costs on both building-specific modeling and on-demand computing resources. To tackle these issues, this paper proposes a cost-effective edge-cloud integrated solution using reinforcement learning (RL). Beside RL’s ability to solve sequential optimal decision-making problems, its adaptability to easy-to-obtain building models and the off-line learning feature are likely to reduce the controller’s implementation cost. Using a surrogate building model learned automatically from building operation data, an RL agent learns an optimal control policy on cloud infrastructure, and the policy is then distributed to edge devices for execution. Simulation results demonstrate the control efficacy and the learning efficiency in buildings of different sizes. A preliminary cost analysis on a 4-zone commercial building shows the annual cost for optimal policy training is only 2.25% of the DR incentive received. Results of this study show a possible approach with higher return on investment for buildings to participate in DR programs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRSDate not supplied

NASA Delta Airlines Technical Interchange Meeting

The Digital Information Sub-project will hold a Technical Interchange Meeting (TIM) with Delta Airlines to discuss potential collaboration opportunities as well as discuss upcoming NASA-led demonstrations. This interchange meeting will solicit feedback from Delta for consideration of the sub-project in upcoming development and to encourage future participation and collaboration. Additionally, Delta will provide insight into airline operations to allow NASA to learn more about airline operations.

Digital Information Platform↗
NASA NTRS1992

SOCAP: Lessons learned in applying SIPE-2 to the military operations crisis action planning domain

This report describes work funded under the DARPA Planning and Scheduling Initiative that led to the development of SOCAP (System for Operations Crisis Action Planning). In particular, it describes lessons learned in applying SIPE-2, the underlying AI planning technology within SOCAP, to the domain of military operations deliberate and crisis action planning. SOCAP was demonstrated at the U.S. Central Command and at the Pentagon in early 1992. A more detailed report about the lessons learned is currently being prepared. This report was presented during one of the panel discussions on 'The Relevance of Scheduling to AI Planning Systems.'

Desimone, Roberto↗
NASA NTRS2010

Improving the Operations of the Earth Observing One Mission via Automated Mission Planning

We describe the modeling and reasoning about operations constraints in an automated mission planning system for an earth observing satellite - EO-1. We first discuss the large number of elements that can be naturally represented in an expressive planning and scheduling framework. We then describe a number of constraints that challenge the current state of the art in automated planning systems and discuss how we modeled these constraints as well as discuss tradeoffs in representation versus efficiency. Finally we describe the challenges in efficiently generating operations plans for this mission. These discussions involve lessons learned from an operations model that has been in use since Fall 2004 (called R4) as well as a newer more accurate operations model operational since June 2009 (called R5). We present analysis of the R5 software documenting a significant (greater than 50%) increase in the number of weekly observations scheduled by the EO-1 mission. We also show that the R5 mission planning system produces schedules within 15% of an upper bound on optimal schedules. This operational enhancement has created value of millions of dollars US over the projected remaining lifetime of the EO-1 mission.

Chien, Steve A.↗