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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Adapting Traditional Hazards Analysis Methods to Address Cyber Risks

Traditional hazards analysis (HA) methods, originally developed to address physical and operational risks, often fall short when it comes to identifying and mitigating cyber threats. These cyber threats pose unique and evolving risks to critical infrastructure and industrial control systems (ICS). This report explores the integration of Cyber-Informed Engineering (CIE) principles into existing HA methods to enhance their ability to address cyber-induced risks. CIE provides organizations with a practical, cost-effective approach to closing the gap between traditional HA methods and the need for cyber risk mitigation. By leveraging existing safety processes and controls, CIE allows users to examine and mitigate cyber vulnerabilities without overhauling existing HA methods. This report identifies areas where HA and CIE naturally align and where their approaches diverge. It emphasizes how CIE principles can be used to adapt HA methods, broadening their scope to include cyber risks and enabling the mitigation of cyber- induced impacts alongside traditional hazards and failure scenarios. This report examines how CIE can be applied across various HA methods—such as Hazard and Operability Studies (HAZOP), Probabilistic Risk Assessment (PRA), Failure Modes and Effects Analysis (FMEA), Systems-Theoretic Process Analysis (STPA), Hazard and Consequence Analysis for Digital Systems (HAZCADS), and Layers of Protection Analysis (LOPA). It provides strategies for integrating CIE to strengthen the identification, assessment, and mitigation of cyber-induced risks. The findings offer a structured entry point for organizations to embed CIE concepts into hazards and safety analyses, as well as broader engineering processes, ultimately supporting the design and operation of a more resilient infrastructure.

42 ENGINEERING↗
DOE OSTI2025

Adapting Traditional Hazards Analysis Methods to Address Cyber Risks

Traditional hazards analysis (HA) methods, originally developed to address physical and operational risks, often fall short when it comes to identifying and mitigating cyber threats. These cyber threats pose unique and evolving risks to critical infrastructure and industrial control systems (ICS). This report explores the integration of Cyber-Informed Engineering (CIE) principles into existing HA methods to enhance their ability to address cyber-induced risks. CIE provides organizations with a practical, cost-effective approach to closing the gap between traditional HA methods and the need for cyber risk mitigation. By leveraging existing safety processes and controls, CIE allows users to examine and mitigate cyber vulnerabilities without overhauling existing HA methods. This report identifies areas where HA and CIE naturally align and where their approaches diverge. It emphasizes how CIE principles can be used to adapt HA methods, broadening their scope to include cyber risks and enabling the mitigation of cyber- induced impacts alongside traditional hazards and failure scenarios. This report examines how CIE can be applied across various HA methods—such as Hazard and Operability Studies (HAZOP), Probabilistic Risk Assessment (PRA), Failure Modes and Effects Analysis (FMEA), Systems-Theoretic Process Analysis (STPA), Hazard and Consequence Analysis for Digital Systems (HAZCADS), and Layers of Protection Analysis (LOPA). It provides strategies for integrating CIE to strengthen the identification, assessment, and mitigation of cyber-induced risks. The findings offer a structured entry point for organizations to embed CIE concepts into hazards and safety analyses, as well as broader engineering processes, ultimately supporting the design and operation of a more resilient infrastructure.

42 - ENGINEERING↗
DOE OSTI2024

Effects of input gradient regularization on neural networks time-series forecasting of thermal power systems

This study proposes using neural networks, specifically gated recurrent unit (GRU), long-short-term memory (LSTM), and transformer networks, to improve control strategies in a 450 MW coal-fired power plant. However, neural networks face issues of becoming overly dependent on just a few variables to make predictions, which negatively impacts control decisions that rely on the model to determine the value of all manipulated variables. The paper introduces regularization techniques, including noise injection and input gradient regularization, during the training phase. Here, the work presents novel contributions in adapting neural networks to control industrial systems and applying regularization techniques from computer vision to industrial process control. Results demonstrate the effectiveness of input gradient regularization in reducing model dependence on subsets of variables, emphasizing the balance between fidelity and controllability. Further exploration is recommended, including the development of recurrent transformers, closed-loop control testing, and a sensitivity analysis on computer models to provide further insight.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Internship Presentation: Reactor System Facility Modification to Detect Compromised Human Machine Interfaces

This study presents a multi-layered Industrial Control System (ICS)/Operational Technology (OT) security architecture aimed at detecting and mitigating compromised Human Machine Interface (HMI) and Instrumentation & Control (I&C) systems within the Flowing Autoclave System (FAS) at Idaho National Laboratory (INL). The approach combines network security solutions, hash-based algorithms, and blockchain technologies to verify system integrity and provide an immutable record of network activity. This integrated three-pronged strategy enhances the detection of system compromises, enabling preemptive action before significant damage occurs.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2025

SECURED: Simulator-Enhanced Control and Understanding of Reactor systems for cyber-Event Defense

The study discusses a learning approach for analyzing cyber-events in reactor systems using integrated hardware and personal computer simulator models. Key points include the rise in cyber-attacks and their sophistication in industrial control systems (ICS), the necessity for awareness, understanding, resource allocation, and preparation to combat these threats, and the digital transformation of old and new nuclear plants, increasing their exposure to cyber threats. It highlights the cyber vulnerabilities of advanced reactor systems, which rely on digital instrumentation and control for operations and safety functions, making them susceptible to cyber-attacks. The approach involves demonstrating reactor system plant ICS cyber-attacks under various operational conditions utilizing tools like simulator models and hardware-based kits. A strategic solution approach tailored to critical infrastructure is emphasized, along with community engagement for public and government support, adopting effective learning approaches, and the preparation for anticipated future challenges. The presentation concludes with a call to action to address challenges, leverage opportunities, and advance through lesson learning in cybersecurity for nuclear energy systems.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Reactor System Facility Modification to Detect Compromised Human Machine Interfaces

This study focuses on a multi-layered Industrial Control System (ICS)/Operational Technology (OT) security architecture to aid in the discovery and mitigation of compromised Human Machine Interface (HMI)/Instrumentation & Control (I&C) based systems for modifying a prototypical reactor condition test facility called the Flowing Autoclave System (FAS) at Idaho National Laboratory (INL). This is achieved through a three-layered combination of network security solutions, hash-based algorithms, and blockchain technologies. Hash algorithms are mathematical functions used to generate a predetermined set of fixed-length values. They are widely used in computer security to verify the integrity of system information and data, both on a local network and the wider internet. Even small amounts of unauthorized system modification will cause the hash algorithm to output a set of characters that deviate significantly from its original value. Assisting secure hash functions, blockchain technology is a secure and distributed technology used to provide an immutable set of records replicated on all devices within a decentralized network. Blockchain offers a cost-effective solution to detect system compromise by providing a traceable breadcrumb trail of all network activity and data modification happening on a system. If both are used in conjunction with network monitoring tools, the integration of this three-pronged approach can become an asset in detecting suspected system compromises before any real damage can occur.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

National Security Programs - Cyber: MMAREJBLIGE – Modular Multi Agent Grid Emulation for Joined Breakdowns in Linked Generative Emulations - 23-0644

Modular Multi Agent Grid Emulations for Joined Breakdowns in Linked Generative Emulations (MMAREJBLIGE) introduces an agent-based modeling framework into real-time cyber-physical emulation to achieve a context-aware environment that introduces operator/attacker/external-condition variability to improve emulation fidelity and testing rigor. We detail our agent framework design, internal communication via message passing, and time synchronization, as well as the individual components of the system. We include a brief analysis of several scenarios run on a real-time, hardware-in-the-loop, Industrial Control Systems (ICS) test-bed which include normal operation, physical disruption, disruption with mitigation, and disruption with mitigation during a cyber denial-of-service (DOS) attack.

42 ENGINEERING↗
DOE OSTI2024

LLM integration into EPICS

The utilization of large language models (LLMs) such as ChatGPT has seen a remarkable increase in various fields over the past few years. These models have demonstrated their versatility and capability in understanding and generating human-like text, making them invaluable tools in numerous applications. In this project, we explore the integration of a LLM into the Experimental Physics and Industrial Control System (EPICS). The primary focus of this integration is to employ the LLM for advanced image processing and spatial analysis on images obtained from the beamlines. By leveraging the capabilities of the LLM, we aim to enhance the accuracy and efficiency of image interpretation, enabling more precise data analysis and decision-making within the EPICS framework. This integration not only showcases the potential of LLMs in scientific and industrial applications but also sets the stage for future advancements in automated control systems.

Adams, Ethan↗
DOE OSTI2026

Building Nuclear-Specific Cybersecurity Expertise in Higher Education

The rapid digitalization of nuclear power plants (NPPs) and the deployment of advanced and small modular reactors (A/SMRs) have expanded the cybersecurity attack surface within the nuclear sector. This evolution introduces unique challenges beyond those faced in general information technology (IT), operational technology (OT) and industrial control system (ICS) security, due to nuclear power’s regulatory rigor, safety-critical nature, and operational needs. A pressing workforce gap persists; cybersecurity graduates typically lack nuclear-specific context and retraining them for industry readiness requires 12–18 months, creating a significant burden. This paper addresses this gap by defining the domains of knowledge that nuclear cybersecurity specialists must master, spanning cybersecurity, nuclear engineering, OT/ICS security, and regulatory governance. We propose a curricular framework integrating technical, regulatory, and applied learning components to accelerate workforce readiness. Our approach builds on existing findings that current curricula inadequately integrate nuclear engineering and cybersecurity, shifting the discourse from why specialization is needed to what knowledge must be taught. The recommendations have implications for workforce development and long-term resilience of the nuclear energy sector.

99 - GENERAL AND MISCELLANEOUS↗
NASA NTRS1980

Microprocessor systems for industrial process control

Six computers operate synchronously and are interconnected by three independent data buses. Processors control one subsystem. Some can control buses to transfer data at 1 megabit per second. Every 2.5 msec each processor examines list of things to do during next interval. This spacecraft control system could be adapted for controlling complex industrial processes.

Lesh, F. H.↗
DOE OSTI2025

Precursor Analysis Report: Blackmatter Ransomware Attack on New Cooperative 2021

The BlackMatter Ransomware Attack on New Cooperative 2021 Precursor Analysis Report leverages publicly available information about the New Cooperative cyber attack and catalogs anomalous observables for each technique employed in the attack. This analysis is based upon the methodology of the Cybersecurity for the Operational Technology Environment (CyOTE) program. The BlackMatter ransomware was first identified in July 2021 and is reported to have infected more than 50 corporations around the world. , The Iowa-based grain cooperative, New Cooperative, was impacted by the BlackMatter ransomware on or before 18 September 2021. The adversary likely resided on New Cooperative’s networks for 15 days prior to encrypting its network and demanding New Cooperative pay $5.9 million in ransom by 25 September to unlock systems and prevent 1 terabyte (TB) of sensitive data from being publicly released. It is not clear if New Cooperative paid the ransom. The full impact of the ransomware attack is not known; however, according to New Cooperative’s general manager, the attack caused the company’s automated processes to revert back to processes used in the 1970s. , As of 6 October, only 50 percent of New Cooperative’s operations were utilizing automated processes. The company took eight weeks to rebuild the entire network and information technology (IT) systems from the ground up, which puts the date of fully recovery around 13 November. Researchers and analysts identified 20 unique techniques utilized during the attack with a total of 404 observables using MITRE ATT&CK® for Industrial Control Systems. The CyOTE program assesses observables accompanying techniques used prior to the triggering event to identify opportunities to detect malicious activity. If observables accompanying the attack techniques are perceived and investigated prior to the triggering event, earlier comprehension of malicious activity can take place. Seventeen of the identified techniques used during the New Cooperative cyber attack were precursors to the triggering event. Analysis identified 360 observables associated with these precursor techniques, 284 of which were assessed to have an increased likelihood of being perceived in the 15 days preceding the triggering event. The response and comprehension time could have been reduced if the observables had been identified earlier. The information gathered in this report contributes to a library of observables tied to a repository of artifacts, data sources, and technique detection references for practitioners and developers to support the comprehension of indicators of attack. Asset owners and operators can use these products if they experience similar observables or to prepare for comparable scenarios.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

Network Slicing for Federated Learning in Operational Technology Environment

Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA) environments are essential to modern infrastructure, facing challenges in ensuring low-latency, high-throughput communication while mitigating cyber threats. This paper presents a framework integrating Federated Learning (FL) and network slicing with Quality of Service (QoS) to enable real-time monitoring without disrupting OT operations. Leveraging digital twin technology and Network Function Virtualization (NFV), the architecture supports predictive analytics and Industry 4.0 requirements. FL facilitates decentralized model training, preserving data privacy and scalability, though it introduces potential throughput constraints. Network slicing addresses this by creating dedicated virtualized segments optimized for performance and security. Advanced fault tolerance at the container and instance levels enhances system reliability. The proposed architecture ensures high throughput, low latency, and secure orchestration for real-time anomaly detection in OT networks. Performance evaluations validate its efficiency in throughput, deployment, and learning accuracy, providing a robust foundation for future ICS automation and data-driven decision-making.

Delgado, Brian G. Rodiles [University of Texas at ↗
NASA NTRS2008

Performance of High Temperature Operational Amplifier, Type LM2904WH, under Extreme Temperatures

Operation of electronic parts and circuits under extreme temperatures is anticipated in NASA space exploration missions as well as terrestrial applications. Exposure of electronics to extreme temperatures and wide-range thermal swings greatly affects their performance via induced changes in the semiconductor material properties, packaging and interconnects, or due to incompatibility issues between interfaces that result from thermal expansion/contraction mismatch. Electronics that are designed to withstand operation and perform efficiently in extreme temperatures would mitigate risks for failure due to thermal stresses and, therefore, improve system reliability. In addition, they contribute to reducing system size and weight, simplifying its design, and reducing development cost through the elimination of otherwise required thermal control elements for proper ambient operation. A large DC voltage gain (100 dB) operational amplifier with a maximum junction temperature of 150 C was recently introduced by STMicroelectronics [1]. This LM2904WH chip comes in a plastic package and is designed specifically for automotive and industrial control systems. It operates from a single power supply over a wide range of voltages, and it consists of two independent, high gain, internally frequency compensated operational amplifiers. Table I shows some of the device manufacturer s specifications.

Patterson, Richard↗
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↗
DOE OSTI2026

CONTROL AND DATA ACQUISITION IN A CYBER-PHYSICAL MIDSTREAM TESTBED

This thesis presents the development of a laboratory-scale cyber–physical midstream pipeline testbed designed to address this gap and support research in industrial control systems security. The platform integrates pumps, valves, sensors, programmable logic controllers (PLCs), and a human–machine interface (HMI) to emulate the monitoring and control architecture of real pipeline operations. The physical process is implemented as a closed-loop liquid circulation system designed to replicate flow behavior characteristic of midstream pipeline infrastructure. The testbed enables real-time data acquisition of key process variables, including flow rate and pressure facilitating the generation of datasets representative of normal pipeline operation. A threat model encompassing common ICS attack vectors was developed, including sensor spoofing, command injection, false data injection, denial-of-service attacks, and relay manipulation. Multiple attack scenarios were implemented and evaluated to demonstrate how cyber intrusions targeting sensors, actuators, networks, and software propagate into measurable physical consequences in pipeline flow and pressure. The developed platform serves as a practical, cost-effective environment for experimentation, education, and future cybersecurity research in midstream pipeline systems.

42 ENGINEERING↗
DOE OSTI2026

Embedded EPICS server for PowerPMAC motion controllers

An embedded server layer of Experimental Physics and Industrial Control System (EPICS) for PowerPMAC motion controllers has been developed and deployed at two undulator beamlines of the National Institute of General Medical Sciences and the National Cancer Institute (GM/CA) Structural Biology Facility at the Advanced Photon Source (APS). This compact, open source solution makes the power and versatility of PowerPMAC motion controls directly accessible to distributed EPICS clients. At GM/CA the system controls about 200 servo and stepper motors — both encoded and unencoded — and multiple digital and analog I/O accessories. The server stack comprises two sublayers: a lower-level driver and database that communicates directly with PowerPMAC, and a facility-specific soft sublayer built on top. The paper describes installing EPICS on PowerPMAC, the implementation of both layers and client examples, including on-the-fly scanning.

EPICS↗
DOE OSTI2026

EPICS for small-scale laboratories with Python soft IOCs

While the Experimental Physics and Industrial Control System (EPICS) is widely used at large laboratories for slow controls and instrumentation, the deployment of a full EPICS installation can be difficult, with a steep learning curve to new users. Taking advantage of the pythonSoftIOC module, we developed an EPICS slow controls implementation for Jefferson Lab's Hall B cryotarget written entirely in Python and based on software IOCs that communicate with instruments over Ethernet. Here, this system ran successfully, interfacing with Jefferson Lab's full EPICS network, and we offer it as an example of the capabilities of pythonSoftIOC to build lightweight, yet robust and flexible instrumentation platforms that would be easily adapted for use at a small-scale laboratory. University groups can use these examples to build complete slow controls systems, from device communication to data archiving and display, using open-source, mature EPICS tools and student-friendly Python as an alternative to expensive and proprietary systems such as LabVIEW.

Computing↗