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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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All sources NASA NTRS DOE OSTI

DOE OSTI2025

Modular Autonomous Experimentation for Biological Applications (Full Report)

The Modular Autonomous Research System (MARS) was developed to address the pressing need for faster, more reliable, and more adaptable scientific discovery. Traditional experimentation is limited by manual labor, long cycle times, and fragmented data streams, which constrain the ability to explore complex chemical and materials design spaces. To overcome these limitations, we created an integrated, modular platform that combines laboratory robotics, diverse measurement instruments, and a central data infrastructure with artificial intelligence–driven decision-making. The system links liquid handling robots, robotic arms, and optical plate readers into a closed loop where experiments are executed automatically, data is analyzed in real time, and subsequent experimental conditions are adaptively chosen to maximize information gain. Over the course of the project, MARS was validated on two primary test cases—spectroscopic metal–ligand binding assays and peptide-directed mineralization—which highlighted the system’s ability to handle uncertainty and variability in experimental measurements. To further demonstrate modularity and extensibility, we also established additional testbeds in electrochemistry for catalyst discovery and electrolyte formulation for advanced batteries. The results show that MARS can reliably conduct autonomous campaigns with minimal human intervention, adapt to distinct scientific domains, and provide a scalable model for future self-driving laboratories. This work establishes new capabilities for modular, uncertainty-aware automation and directly supports the need for advanced, data-driven research platforms capable of accelerating discovery across a wide range of scientific and national security missions.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1942

Two-Dimensional Potential Flow Past an Ordinary Thick Wing Profile

This report deals with the development of a method which gives a lucid and convenient solution of the flow conditions in the vicinity of a common, thick airfoil section wherein the thickness of the profile is taken into account. The method consists in making the airfoil the streamline in a parallel flow by disposing on its mean line certain source and vortex distributions the fields of which are superposed on the parallel flow. These distributions of singularities are secured for the generalized Karman-Trefftz profile by means of conformal transformation from the flow about a circle. Five different distribution functions are afforded for the density of superposition, which combine in a specified manner to the necessary distributions of singularity and represent a generalized Karman-Trefftz profile in parallel flow. For these profiles the speed for each of the five distributions is then computed independently of the angle of attack.

Keune, F.↗

DOE OSTI2020

System and method for monitoring power consumption to detect malware

A system and method (referred to as the system) detects malware, viruses, and/or malicious activity by generating a direct current source power consumption profile by causing a monitored device to execute a fully automated recurrent software operation. The system receives by an automated detection system, the direct current source power consumption profile generated by an intelligent power sensor and generates by a detection engine, a power security profile that identifies suspicious code by profiling direct current consumed by monitored type devices. The system executes a detection engine remote from the monitored device that identifies an infected device.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Livewire Data Platform-A Solution for Energy Efficient Mobility Systems (EEMS) Data Sharing

This presentation is an overview of progress made on the Livewire Data Platform since June 2021. Livewire is a publicly accessible platform for sharing energy efficiency and mobility research data funded by DOE's Vehicle Technologies Office. Core services and platform capabilities of Livewire include: Free, secure data storage; Access management that allows data owners to control who sees their data; Data collection and preservation; Quality characterization; Detailed access and download metrics; and Increased visibility of projects and data.

energy efficiency mobility↗

DOE OSTI2022

Generating and Analyzing Program Call Graphs using Ontology

Call graph or caller-callee relationships have been used for various kinds of static program analysis, performance analysis and profiling, and for program safety or security analysis such as detecting anomalies of program execution or code injection attacks. However, different tools generate call graphs in different formats, which prevents efficient reuse of call graph results. In this paper, we present an approach of using ontology and resource description framework (RDF) to create knowledge graphs for specifying call graphs to facilitate the construction of full-fledged and complex call graphs of computer programs, realizing more interoperable and scalable program analyses than conventional approaches. We create a formal ontology-based specification of call graph information to capture concepts and properties of both static and dynamic call graphs so different tools can collaboratively contribute to more comprehensive analysis results. Our experiments show that ontology enables merging of call graphs generated from different tools and flexible queries using a standard query interface. Index Terms—Callgraph, ontology, knowl

Dorta, E.↗

DOE OSTI2019

Generative Adversarial Network for Synthetic Time Series Data Generation in Smart Grids

The availability of fine grained time series data is a pre-requisite for research in smart-grids. While data for transmission systems is relatively easily obtainable, issues related to data collection, security and privacy hinder the widespread public availability/accessibility of such datasets at the distribution system level. This has prevented the larger research community from effectively applying sophisticated machine learning algorithms to significantly improve the distribution-level accuracy of predictions and increase the efficiency of grid operations. Synthetic dataset generation has proven to be a promising solution for addressing data availability issues in various domains such as computer vision, natural language processing and medicine. However, its exploration in the smart grid context remains unsatisfactory. Previous works have tried to generate synthetic datasets by modeling the underlying system dynamics: an approach which is difficult, time consuming, error prone and often times infeasible in many problems. In this work, we propose a novel data-driven approach to synthetic dataset generation by utilizing deep generative adversarial networks (GAN) to learn the conditional probability distribution of essential features in the real dataset and generate samples based on the learned distribution. To evaluate our synthetically generated dataset, we measure the maximum mean discrepancy (MMD) between real and synthetic datasets as probability distributions, and show that their sampling distance converges. To further validate our synthetic dataset, we perform common smart grid tasks such as k-means clustering and short-term prediction on both datasets. Experimental results show the efficacy of our synthetic dataset approach: the real and synthetic datasets are indistinguishable by solely examining the output of these tasks.

14 SOLAR ENERGY↗

DOE OSTI2016

Enhanced Passive Cooling for Waterless-Power Production Technologies

Recent advances in the literature and at SNL indicate the strong potential for passive, specialized surfaces to significantly enhance power production output. Our exploratory computational and experimental research indicates that fractal and swirl surfaces can help enable waterless-power production by increasing the amount of heat transfer and turbulence, when compared with conventional surfaces. Small modular reactors, advanced reactors, and non-nuclear plants (e.g., solar and coal) are ideally suited for sCO2 coolant loops. The sCO2 loop converts the thermal heat into electricity, while the specialized surfaces passively and securely reject the waste process heat in an environmentally benign manner. The resultant, integrated energy systems are highly suitable for small grids, rural areas, and arid regions.

14 SOLAR ENERGY↗

NASA NTRSDate not supplied

How Autonomous Intelligent Systems Can Facilitate Earth-independent Medical Care: Going Beyond Telepresence

During the last decade, teleoperated robotic systems have extended humans’ sensorimotor competence to digitally fly beyond the physical barrier of distance and scale and thus transmit sensorimotor skills of the human through direct communication. Telepresence capabilities have enabled tele-physical remote access at small scales thanks to telerobotic mediums. Although the concept was initially motivated by space applications, such technologies quickly have expanded into the medical domain and resulted in teleoperated medical robots, including telerobotic surgical systems (such as the da Vinci surgical system). Effective telepresence fundamentally depends on an agile, reliable, and secure communication medium that can transmit real-time information between the operator and a remote device. However, direct telepresence may not be achievable for long-duration exploration spaceflight missions. Thus, autonomous systems and local intelligence represent potential solutions to the aforementioned issues. One example solution employs demonstration systems which enable learning from the pre-captured inputs of a skilled human operator. These will be computationally modeled and later probabilistically replicated toward the completion of remote physical tasks when direct telepresence is not viable - such as under communication blackout conditions. In other words, trained autonomous systems (e.g., robots) can perform remote operations that mimic the physical performance of experts during remote operations/training. Beyond learning the physics of the task, autonomous agents can also be used to conduct algorithmic decision-making that mimics the higher-level cognition of the expert. Thus, using an autonomous system, pre-trained cognitive and manipulation-based skills can be leveraged (acquired during pre-mission events) to produce digital twins of an intelligent operator. Such systems can be used for the real-time conduction of intricate tasks in complex and unstructured environments. Such systems will operationalize “cognitive digital twins” and can expand the reach of human cognition and manipulation through the power of data-driven learning from demonstration algorithms. This system category will be discussed as a fully autonomous operation in this talk. In addition to the above, we will also propose and discuss the possibility of partial-automation using remote intelligence and remote sensing. In contrast to full automation, partial automation can close the loop through a local operator equipped with augmented sensory awareness through wearable systems. Such technologies will allow the local operator to conduct delicate tasks while being guided using sensory augmentation and being monitored to gauge her/his level of cognitive focus and performance. The difference with the previous category is that a remote human will conduct the task. Further, rather than making a digital twin of human cognition, we will augment the control inputs of the local human to match those of the skilled expert operator who is not accessible in real-time. Going beyond classic telepresence and thus approaching intelligent telepresence, our vision is that autonomous agents will eventually enable the safe, consistent and efficient delivery of complex, remote and smart medical care during space exploration across operators in an Earth-independent fashion. We will discuss our collective vision from NASA and MERIIT@NYU lab in this talk.

Telepresence↗

DOE OSTI2024

Beyond Binary: Automated PLC Memory Forensics through RGB Image Analysis and Deep Learning

The introduction of Industry 4.0 and the evolution of industrial control systems (ICS) to adopt Internet-based technologies enhanced productivity, but have inadvertently increased their vulnerability to cyber-based malicious attacks. When an ICS system is compromised, security analysts need to identify the root cause quickly to start the recovery process and develop mitigation strategies to safeguard against future instances. Memory forensics is critical in the analysis process to ascertain what occurred. To date, approaches to analyze the persistent memory in ICS devices are limited, and almost nonexistent for volatile memory. This paper proposes an automated methodology, COMA, for PLC memory dump analysis using computer vision and deep learning techniques. Specifically, COMA converts the sequences of bytes in a PLC memory dump to RGB pixels and creates a deep learning model that learns the underlying patterns and features of pre-labeled forensic artifacts in images and segments them into distinct regions. COMA then uses the trained model to automatically segment new memory images and extract forensic artifacts. We evaluate COMA on a Schneider Electric Modicon M221 PLC involving two cyber-based attack scenarios: (i) code injection and (ii) code modification. The empirical results show that COMA can successfully detect attack artifacts in memory dumps in both scenarios.

Asmar Awad, Rima↗

DOE OSTI2024

Mesh Computing Remote Automatic Workflow

The software suite uses a microservice architecture using Docker and `docker-compose`. The microservices are as follows: 1. User interface. This interface is written in JavaScript using the Svelte framework. It exposes form elements and a 3D visualizer to prompt the user through the definition of microstructure parameters, and setting parameters for mesh generation and refinement. 2. Mesh generator. This is a container running the Python package for DREAM3D to generate a voxelized mesh that represents a microstructure defined by the user in the interface. 3. Cubit runner. This is a secure shell protocol tool that makes the submitting the DREAM mesh to an HPC instance and starts to run Cubit shell commands to smooth the grain boundaries with its `sculpt` library, applies user-defined boundary node sets, and bundles and returns the simulation-ready meshes and input files as a zipped directory.

Harris, BrennanKay↗

DOE OSTI2023

Cybersecurity Value-at-Risk Framework

As more variable renewable energy sources are added to the grid, the role of hydropower as a reliable baseline and firming resource is growing more critical. However, the U.S hydropower fleet is not fully prepared to face modern issues such as cybersecurity threats. Hydropower accounts for 37% of U.S. utility-scale renewable electricity but is challenged by diverse infrastructure and legacy devices that predate modern security practices. While new cybersecurity solutions cannot simply be added to current hydropower generation and operation technologies, custom cybersecurity assessments can reveal system-specific threats and risk probabilities and identify mitigating enhancements.

cybersecurity valuation methodology↗

DOE OSTI2018

Analysis of Design Constraints and System Impact of DER Cryptographic Module

In designing a security module for inverter communications in a DER environment, it is critical to consider the impact of the additional security on the environment as well as what types of security is required for the various messages that must pass from the inverter to and from a utility. Also, since cyber security is more than just preventing an unauthorized user from viewing data, mechanisms for proving identity and ensuring that data cannot be altered without such a modification being discovered are needed. This is where the security principles of confidentiality, integrity, and availability come into play. For different types of communications, these different security principles may be important or not needed at all. Furthermore, the cost and constraints for applying cryptography for securing DER communications must be considered to help determine what is feasible within this environment and what will be the impact and cost of applying common cryptographic protections to inverter communications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

End-User Security: A Cornerstone of Defense-in-Depth Cybersecurity Solutions [Slides]

This report will go over the background on the importance of cybersecurity awareness as part of end user security and defense in depth, the review of Fermilab’s upgraded cybersecurity awareness platform, and discuss on how Fermilab’s Cybersecurity Team is evaluating the effectiveness of the upgraded awareness platform.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2018

Analysis of Design Constraints and System Impact of DER Cryptographic Module

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

A Domain-Specific Language for High-Consequence Control Software

While most software development for control systems is directed at what the system is supposed to do (i.e., function), high-consequence controls must account for what the system is not supposed to do (i.e., safety, security and reliability requirements). A Domain Specific Language (DSL) for high-consequence digital controls is proposed. As with similar tools for the design of controls, the DSL will have plug-in modules for common controller functions. However, the DSL will also augment these modules with attendant "templates" that aid in the proof of safety, security and reliability requirements, not available in current tools. The object is to create a development methodology that makes construction of high-assurance control systems as easy as controls that are designed for function alone.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Titan on the Red (advanced AI / ML system) [Slides]

Born out of a need to make LANL Weapons Program archival material available to scientists and engineers. Material is the end result of decades of consolidation of mini-libraries and mini archives at LANL. Latest consolidation brought together LANL’s digital archives and physical archives. This houses over 75 years of nuclear weapons research, designs, procedures, videos, photos, and other reports. For the Titan on the Red machine learning project, the system must be able to automatically extract metadata from digitized documents, perform natural-language search, enforce security classification and NTK protocols, be expandable to ingest various data stores (Online Vault, PDMLink, shared drives, SharePoint, etc.), and utilize commercial, public domain, and LANL ontologies.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2021

2021 LLNL Nuclear Science and Security Summer Internship Program

The Lawrence Livermore National Laboratory (LLNL) Nuclear Science and Security Summer Internship Program (NS 3 IP) is designed to give graduate students an opportunity to come to LLNL for 8–10 weeks of hands-on research. Students conduct research under the supervision of a staff scientist, attend a weekly lecture series, interact with other students, and present their work in poster format at the end of the program. Students also have the opportunity to meet staff scientists one-on-one, participate in LLNL facility tours (e.g., the National Ignition Facility and Center for Accelerator Mass Spectrometry), and gain a better understanding of the various science programs at LLNL. Due to the travel and access restrictions imposed by the COVID-19 pandemic, the 2021 NS 3 IP was organized as an “all-virtual” internship program. With LLNL’s extensive institutional support, students accessed the laboratory’s cyberinfrastructure through a secure virtual desktop environment and all seminars, mentor interactions, summer presentations, and laboratory tours were performed remotely. While this virtual internship format did not allow for hands-on laboratory research projects, both the interns and their mentors constructed creative research projects that maximized student exposure to nuclear science research that is relevant to DTRA and LLNL interests in nuclear security. We anticipate a return to an in-person internship format for the summer of 2022.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

FY22 Laboratory Directed Research and Development Annual Report

The Laboratory Directed Research and Development (LDRD) program yields foundational scientific research and development (R&D) essential to growing SRNL’s core competencies, in alignment with SRNL’s Strategic Plan to provide long-term benefits to the Department of Energy (DOE), the National Nuclear Security Administration (NNSA), and other customers and stakeholders. Five strategic goals are outlined in SRNL’s strategic plan: 1) Provide applied science and engineering for EM’s active clean-up sites and LM’s post closure management sites; 2) Provide science-based solutions for gaps identified in nonproliferation strategic vision and support the government in actives impacting national security; 3) Lead ST&E as the central technical authority for processing tritium loaded reservoirs and support production of plutonium pits; 4) Align science and energy security programs by focusing modern modeling, simulation, and data analytics tools on materials engineering and performance applications; 5) Build a workforce for the future.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗