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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Preliminary Report on Mission Design and Operations for Critical Events

Mission-critical events are defined in the Jet Propulsion Laboratory s Flight Project Practices as those sequences of events which must succeed in order to attain mission goals. These are dependent on the particular operational concept and design reference mission, and are especially important when committing to irreversible events. Critical events include main engine cutoff (MECO) after launch; engine cutoff or parachute deployment on entry, descent, and landing (EDL); orbital insertion; separation of payload from vehicle or separation of booster segments; maintenance of pointing accuracy for power and communication; and deployment of solar arrays and communication antennas. The purpose of this paper is to report on the current practices in handling mission-critical events in design and operations at major NASA spaceflight centers. The scope of this report includes NASA Johnson Space Center (JSC), NASA Goddard Space Flight Center (GSFC), and NASA Jet Propulsion Laboratory (JPL), with staff at each center consulted on their current practices, processes, and procedures.

Hayden, Sandra C.↗
NASA NTRS2009

Importance of Model Simulations in Cassini In-Flight Mission Events

Simulation environments have been an integral part of Cassini's heritage. From the time of flight software development and testing to the beginning of the spacecraft's extended mission operations, both softsim and hardware-in-the-loop testbeds have played vital roles in verifying and validating key mission events. Satellite flybys and mission-critical events have established the need to model Titan's atmospheric torque, Enceladus' plume density, and other key parametric spacecraft environments. This paper will focus on enhancements to Cassini's Flight Software Development System (FSDS) and Integrated Test Laboratory (ITL) to model key event attributes which establish valid test environments and ensure safe spacecraft operability. Comparisons between simulated to in-flight data are presented which substantiate model validity.

FSDS↗
NASA NTRS2012

Communicating by Doppler: Detecting Spacecraft Dynamics During Critical Maneuvers

Communicating information from spacecraft in deep space utilizes sophisticated techniques of modulations onto a microwave signal carrier. Under most conditions, there is a high success rate of sending commands to the spacecraft and receiving science data acquired by on-board instruments along with health and status information. There are conditions, however, where the signal dynamics are too high and/or the received signal-to-noise ratio is below the receiver threshold. Under these conditions, often by design and sometimes as a result of planned or unplanned critical maneuvers, events (e.g., orbit insertion or descent and landing), safe mode, etc., it becomes highly critical but exceedingly challenging to receive information about the health and dynamical behavior of the spacecraft. The Deep Space Network, being a world-class instrument for Radio Science research, developed openloop receivers, called the Radio Science Receiver, designed to capture the raw incoming electromagnetic signals and associated noise for Radio Science experiments; post data capture digital signal processing extracts the signal carrier for scientific analysis. This receiver provides a high level of configuration flexibility and can be optimized for the various types of experiments. In addition to its scientific utility, it proved to be useful, and in some cases critical, for the support of missions during specific scenarios were the link budget is below the threshold of the tracking receiver to maintain lock or the frequency dynamics are faster than the limits of the tracking receiver. In these cases, the signal carrier is often detected only in the open-loop receiver to provide information on the specific behavior of the spacecraft from the carrier dynamics. This paper describes the utility of the system to support mission-critical events for the three cases of Cassini's Saturn orbit insertion, Huygens Titan landing, and Mars rovers landing.

radio science↗
NASA NTRS2006

Spitzer Pre Launch Mission Operations System - The Road to Launch

Spitzer Space Telescope was launched on 25 August 2003 into an Earth-trailing solar orbit to acquire infrared observations from space. Development of the Mission Operations System (MOS) portion prior to launch was very different from planetary missions from the stand point that the MOS teams and Ground Data System had to be ready to support all aspects of the mission at launch (i.e., no cruise period for finalizing the implementation). For Spitzer, all mission-critical events post launch happen in hours or days rather than months or years, as is traditional with deep space missions. At the end of 2000 the Project was dealt a major blow when the Mission Operations System (MOS) had an unsuccessful Critical Design Review (CDR). The project made major changes at the beginning of 2001 in an effort to get the MOS (and Project) back on track. The result for the Spitzer Space Telescope was a successful launch of the observatory followed by an extremely successful In Orbit Checkout (IOC) and operations phase. This paper describes how the project was able to recover the MOS to a successful Delta (CDR) by mid 2001, and what changes in philosophies, experiences, and lessons learned followed. It describes how projects must invest early or else invest heavily later in the development phase to achieve a successful operations phase.

observatory↗
NASA NTRS2014

Communications During Critical Mission Operations: Preparing for InSight's Landing on Mars

Radio communications with deep space missions are often taken for granted due to the impressively successful records since, for decades, the technology and infrastructure have been developed for ground and flight systems to optimize telemetry and commanding. During mission-critical events such as the entry, descent, and landing of a spacecraft on the surface of Mars, the signal's level and frequency dynamics vary significantly and typically exceed the threshold of the budgeted links. The challenge is increased when spacecraft shed antennas with heat shields and other hardware during those risky few minutes. We have in the past successfully received signals on Earth during critical events even ones not intended for ground reception. These included the UHF signal transmitted by Curiosity to Marsorbiting assets. Since NASA's Deep Space Network does not operate in the UHF band, large radio telescopes around the world are utilized. The Australian CSIRO Parkes Radio Telescope supported the Curiosity UHF signal reception and DSN receivers, tools, and expertise were used in the process. In preparation for the InSight mission's landing on Mars in 2016, preparations are underway to support the UHF communications. This paper presents communication scenarios with radio telescopes, and the DSN receiver and tools. It also discusses the usefulness of the real-time information content for better response time by the mission team towards successful mission operations.

Radio Science↗
NASA NTRS2018

A Plasma Aerocapture and Entry System for Manned Missions and Planetary Deep Space Orbiters

A rapid mission to Mars requires a large change in vehicle velocity upon arrival to establish a stable orbit. This demand is even greater for a Neptune science, requiring many kilometers per second of V. It is clear from past mission studies that a manned Mars mission and deep space planetary orbiters require aerobraking and aerocapture which use aerodynamic drag forces to slow the spacecraft. Aerocapture would enable long term studies of the outer planets and moons that would not be possible with existing braking methodologies. While the ability to utilize these atmospheres to slow down and capture spacecraft would dramatically reduce the cost, launch mass, and travel time, currently planned approaches require significant additional spacecraft mass and risk as the spacecraft must descend deep into the planetary atmosphere in order to produce significant drag on a relatively small aeroshell. The plasma based Magnetoshell being developed in this program holds the potential to perform the desired braking with significantly increased drag and control while dramatically reducing mass. Most importantly, this technology significantly lowers the risk involved with aerocapture thereby making manned planetary missions possible. The fundamental physics of the Magnetoshell is based on demonstrated experimental results. Successful implementation will dramatically decrease radiation exposures, mission risk, launch cost, and launch mass. Implementation of aerobraking by employing a solid deflector or aeroshell as a method for orbit insertion and circularization has been successfully demonstrated in the past, resulting in launch mass savings greater than 50%. In order to reduce the effect of frictional heating and dynamic pressure on the typically fragile aeroshell, or worse solar panels, the braking must be distributed over many orbital passes at a high altitude in the less dense regions of the atmosphere. It can thus take several months for a meter-scale, 1000 kg, aeroshell to execute the many elliptic orbital passes through the atmosphere to achieve the required V. This rather slow method of braking not only reduces frictional heating and dynamic forces, but also avoids unpredictable dynamic behavior due to turbulence, as well as unknown and seasonally variable atmospheric composition and temperature which has led to dangerous, mission-critical events. For exploration class missions such as DRA 5.0 aerocapture and Thermal Protection Systems (TPS) are proposed for breaking at Mars for cargo missions. Traditional aerocapture is considered too risky for manned missions. Even with the enormous mass savings that aerocapture allows, it still requires 80 tons of aeroshell and significantly increased launch mass and propellant. As will be shown, by using Magnetoshells for aerobraking, the DRA 5.0 mission will save 224 metric tons (MT) and greater than $2 B in launch costs. Beyond the dramatic savings for existing mission architectures, a low-mass, risk-free aerocapture system would allow much more rapid missions to Mars and deep space orbiters by allowing direct, faster trajectories. As will be shown, the plasma Magnetoshell Aerobraking, Aerocapture, and Entry System (AAES) not only reduces mass and cost while enabling significant new mission architectures, but also significantly reduces radiation exposures by decreasing trip times.

Manned Missions↗
NASA NTRS2011

Adaptation of G-TAG Software for Validating Touch-and-Go Comet Surface Sampling Design Methodology

The G-TAG software tool was developed under the R&TD on Integrated Autonomous Guidance, Navigation, and Control for Comet Sample Return, and represents a novel, multi-body dynamics simulation software tool for studying TAG sampling. The G-TAG multi-body simulation tool provides a simulation environment in which a Touch-and-Go (TAG) sampling event can be extensively tested. TAG sampling requires the spacecraft to descend to the surface, contact the surface with a sampling collection device, and then to ascend to a safe altitude. The TAG event lasts only a few seconds but is mission-critical with potentially high risk. Consequently, there is a need for the TAG event to be well characterized and studied by simulation and analysis in order for the proposal teams to converge on a reliable spacecraft design. This adaptation of the G-TAG tool was developed to support the Comet Odyssey proposal effort, and is specifically focused to address comet sample return missions. In this application, the spacecraft descends to and samples from the surface of a comet. Performance of the spacecraft during TAG is assessed based on survivability and sample collection performance. For the adaptation of the G-TAG simulation tool to comet scenarios, models are developed that accurately describe the properties of the spacecraft, approach trajectories, and descent velocities, as well as the models of the external forces and torques acting on the spacecraft. The adapted models of the spacecraft, descent profiles, and external sampling forces/torques were more sophisticated and customized for comets than those available in the basic G-TAG simulation tool. Scenarios implemented include the study of variations in requirements, spacecraft design (size, locations, etc. of the spacecraft components), and the environment (surface properties, slope, disturbances, etc.). The simulations, along with their visual representations using G-View, contributed to the Comet Odyssey New Frontiers proposal effort by indicating problems and/or benefits of different approaches and designs.

Mandic, Milan↗
NASA NTRS2016

Dawn Safing Approach to Ceres Re-Design

In mid-2014 the Dawn team finalized preparations for the spacecraft’s approach to Ceres. The design was the product of careful planning that began several years in advance, when the spacecraft left Vesta in September 2012. However, a few months prior to the anticipated start of approach, the spacecraft entered a safe mode. While temporary loss of thrust for ion propulsion missions is usually not mission-critical, it still presents many challenges. The safing event occurred at a time when thrusting to match Ceres’ orbital velocity was most effective, which meant the spacecraft must fly past Ceres before capturing into orbit. The new approach geometry changed the team’s strategy for navigating the spacecraft safely into orbit around Ceres with optical data. In this paper we describe the challenges of navigating the Dawn spacecraft into orbit around Ceres, including the safing event and re-design of the approach architecture.

Grebow, Daniel J.↗
NASA NTRS2016

Dawn Safing Approach to Ceres Re-Design

In mid-2014 the Dawn team finalized preparations for the spacecraft’s approach to Ceres. The design was the product of careful planning that began several years in advance, when the spacecraft left Vesta in September 2012. However, a few months prior to the anticipated start of approach, the spacecraft entered a safe mode. While temporary loss of thrust for ion propulsion missions is usually not mission-critical, it still presents many challenges. The safing event occurred at a time when thrusting to match Ceres’ orbital velocity was most effective, which meant the spacecraft must fly past Ceres before capturing into orbit. The new approach geometry changed the team’s strategy for navigating the spacecraft safely into orbit around Ceres with optical data. In this paper we describe the challenges of navigating the Dawn spacecraft into orbit around Ceres, including the safing event and re-design of the approach architecture.

Grebow, Dan↗
NASA NTRS2009

Alert Notification System Router

The Alert Notification System Router (ANSR) software provides satellite operators with notifications of key events through pagers, cell phones, and e-mail. Written in Java, this application is specifically designed to meet the mission-critical standards for mission operations while operating on a variety of hardware environments. ANSR is a software component that runs inside the Mission Operations Center (MOC). It connects to the mission's message bus using the GMSEC [Goddard Space Flight Center (GSFC) Mission Services Evolution Center (GMSEC)] standard. Other components, such as automation and monitoring components, can use ANSR to send directives to notify users or groups. The ANSR system, in addition to notifying users, can check for message acknowledgements from a user and escalate the notification to another user if there is no acknowledgement. When a firewall prevents ANSR from accessing the Internet directly, proxies can be run on the other side of the wall. These proxies can be configured to access the Internet, notify users, and poll for their responses. Multiple ANSRs can be run in parallel, providing a seamless failover capability in the event that one ANSR system becomes incapacitated.

Gurganus, Joseph↗
DOE OSTI2026

Flood Estimation under Snowmelt and Rain-on-Snow Processes in Alaska: A Military Installation Perspective

Accurate flood estimation in snow-dominated and high-latitude regions remains challenging due to complex interactions among rainfall, snowmelt, and rain-on-snow (ROS) processes, which are not captured in conventional precipitation-based intensity-duration-frequency (PREC-IDF) curves. This study evaluates the Next-Generation IDF (NG-IDF) framework, an extension of PREC-IDF that incorporates total water available for runoff (precipitation minus changes in snow water equivalent), in two contrasting Alaskan watersheds of Department of Defense (DoD) significance: the Little Chena River Basin (LCRB) in interior Alaska, a tributary of the Chena River that flows through Fort Wainwright and near Eielson Air Force Base, and the Upper Ship Creek Basin (USCB), which drains Joint Base Elmendorf-Richardson near Anchorage. Using long-term SNOTEL observations and event-based rainfall-runoff modeling, NG-IDF and PREC-IDF flood estimates were compared against observation-based flood frequency analyses. Results show that NG-IDF consistently reduces flood-estimation bias by 15–20% relative to PREC-IDF, particularly for snowmelt- and ROS-dominated events. In the interior LCRB, permafrost conditions can substantially amplify flood responses during snowmelt events, an effect not explicitly represented in standard design tools. These findings demonstrate that NG-IDF provides a more physically consistent and transferable framework for flood estimation in cold regions, with potential relevance to mission-critical DoD installation resilience. Projected increases in ROS frequency and permafrost degradation across Alaska further emphasize the need to integrate physics-based hydrologic models that explicitly represent snow and permafrost processes to enhance design resilience and operational readiness for military and other critical infrastructure.

Yan, Hongxiang (ORCID:000000022387403X)↗
DOE OSTI2026

REFSafE: A RAG-Enabled Framework for Predictive Risk Analysis and Automated Safety Report Generation in Mission-Critical Environments

Operational safety in mission-critical environments requires AI systems that are accurate, interpretable, and resistant to hallucination. We present an agentic Retrieval-Augmented Generation (RAG) framework, REFSafe, for grounded hazard analysis and automated safety report generation. The system integrates Large Language Models (LLMs) with structured operational data, historical incident repositories, policy documents, and external authoritative sources. Through iterative agentic reasoning, the framework retrieves, verifies, and synthesizes evidence prior to generation, enforcing citation-backed outputs with explicit source attribution (documents, links, and prior events) to ensure traceability and trust. To mitigate hallucinations and unsupported claims, all risk assessments and forecasts are constrained to retrieved evidence, with confidence signals derived from retrieval relevance and source consistency. A transparent pipeline enables subject matter experts (SMEs) to validate predictions, and provide structured feedback, forming a continuous performance calibration loop. Preliminary deployment demonstrates improved reliability in hazard detection and safety/vulnerability report generation. This work advances trustworthy, evidence-grounded AI for predictive safety intelligence in mission-critical operations.

Das, Sanjay [ORNL] (ORCID:0009000542591915)↗
DOE OSTI2025

Even Higher-Level Synthesis: An Exploration of AI Hardware Accelerators using HLS4ML

With the rise of artificial intelligence, the popularization of deep learning, and a constantly evolving industry, the demand for flexible and efficient tools has never been greater. As algorithms grow more complex, their runtime and energy consumption increase exponentially. Customized hardware accelerators, long used for specific mathematical operations, remain essential for managing modern applications' computational and power demands. Hardware accelerators can speed up complex computations by orders of magnitude, but their manual design and verification processes are often challenging and time-consuming. High-Level Synthesis (HLS) provides a solution by transforming high-level algorithm descriptions, typically written in C++ or SystemC, into synthesizable RTL suitable for hardware implementation. This approach reduces development time for RTL engineers while offering flexibility beyond what traditional handwritten RTL can provide. We extended this capability to the machine-learning domain with the open-source framework hls4ml, which allows neural networks trained in Python frameworks like Tensorflow or PyTorch to be synthesized into efficient hardware representations for the traditional FPGA and ASIC flows. This breakthrough addresses the growing need for reduced design turnaround and easy verification of ML hardware accelerators with low latency and power efficiency constraints. During this tutorial, we will demonstrate how Python complements HLS by simplifying the ML design process, bridging the gap between software and hardware development. Attendees will explore how we translate neural networks modeled in Python into fixed-point C++ models suitable for HLS workflows. We will dive into strategies like Value-Range Analysis and Quantization-Aware Training, which optimize these designs for deployment and evaluate their accuracy, power consumption, and energy efficiency. To exemplify these concepts, experts from Fermilab will share their experiences applying this technology to high-energy physics experiments, where real-time, low-latency processing is critical. Over the years, Fermilab engineers have demonstrated how deep neural networks, optimized for hardware using hls4ml, can meet the stringent requirements of trigger systems at the CERN Large Hadron Collider. These systems rely on rapid decision-making to process immense data volumes while retaining only the most relevant events for further analysis. The application of hls4ml has also been extended to innovative technologies like smart pixel arrays. These smart pixels integrate ML inference capabilities directly into sensor devices, enabling localized data processing at the pixel level. This approach drastically reduces the need to transmit raw data to external processing units, significantly decreasing power consumption and latency. By embedding neural networks within the pixel architecture, the smart pixels can identify and prioritize relevant data in real time, providing a highly efficient solution for edge computing in scenarios such as particle detectors and imaging systems. Fermilab's work highlights the potential of hardware-accelerated ML in scenarios where both speed and power efficiency are mission-critical. Through this tutorial, attendees will gain valuable insights into the challenges and solutions of deploying ML in hardware. Understanding how HLS and hls4ml streamline the development of neural network-based hardware accelerators is fundamental for the industry's future. Participants will learn how these technologies are shaping the future of AI and scientific computing.

Di Guglielmo, Giuseppe [Fermilab]↗
DOE OSTI2026

Anomaly Detection for Online Monitoring of Thermocouple Sensors in the Advanced Test Reactor

This study explores data-driven anomaly detection methods to analyze sensor fail- ures in the Advanced Gas Reactor (AGR) nuclear fuel irradiation experiments. Specifically, we examine failures of thermocouples (TCs), which are critical for mon- itoring and controlling in-reactor temperatures during operation. Failures were pri- marily observed during abrupt power transitions and manifested as sensor drop-outs, drifts, or unexplained behavior. We applied three time-series analysis techniques— rolling mean smoothing, matrix profile, and vector auto-regression (VAR)—to de- tect anomalies in TC data prior to failure events. The rolling mean method effec- tively highlighted deviations aligned with reported failures, while the matrix profile provided partial early warning but sometimes flagged normal fluctuations during power-down periods. VAR shows potential in capturing multivariate dependencies but requires further calibration. A rare case of TC drift was also documented, which did not result in failure, underscoring the challenge of building predictive models with sparse positive examples. Our findings demonstrate that traditional statistical tools can aid anomaly detection but have limited predictive power without richer training data. We propose future directions including synthetic data generation, real- time surrogate modeling, and multi-modal feature integration. This work provides a foundation for applying robust anomaly detection frameworks to mission-critical sensor systems in experimental settings.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRSDate not supplied

Communication Demand in the National Airspace - A Federated Learning Approach

Within the national airspace system (NAS), efficient use of spectrum remains a challenge; as UAS and UAM missions evolve, the amount of mission-critical aircraft communications are expected to significantly grow. To accommodate the increased demand, NASA Glenn Research Center is investigating artificial intelligence approaches that could dynamically allocate spectrum; however, these solutions are driven by communication and aviation data items, many of which are not directly available. One such cornerstone data item is communication demand, parameterizing the needs within a sector in terms that may directly inform spectrum allocation, such as channel access duration, bandwidth, and modulation type. This paper considers the complexity of predicting communication demand as a function of NAS behaviors. Unlike prior prediction work in communications, this research must consider how the NAS may be impacted by external factors - such as convective weather and closures - rather than estimating demand from time-series forecasting alone. Much of this research considers a federated learning design to predict communication events in terms of the type of event occurring (sector coordination, conflict resolution, etc.). To do so, an investigation of products from Sherlock Data Warehouse is conducted, identifying the trends, sufficiency, and correlations of each product to identified events. Additionally, a preliminary discussion for inferring associations between these event types and their communication parameters (duration, bandwidth, modulation) is presented. By utilizing federated learning, imbalances in the types of events and data present throughout the NAS can inform local models without impairing global training. Furthermore, the two-stage approach proposed allows for robust and speculative communication modelling, as communication techniques continue to evolve. As a result, this model enables a generalized approach to understanding NAS communications which is able to inform long-term changes to aviation spectrum management.

Nathan Schimpf↗
NASA NTRSDate not supplied

A Federated Learning Approach to Predicting Communication Demand in the National Airspace

Within the national airspace system (NAS), efficient use of spectrum remains a challenge; as UAS and UAM missions evolve, the amount of mission-critical aircraft communications are expected to significantly grow. To accommodate the increased demand, NASA Glenn Research Center is investigating artificial intelligence approaches that could dynamically allocate spectrum; however, these solutions are driven by communication and aviation data items, many of which are not directly available. One such cornerstone data item is communication demand, parameterizing the needs within a sector in terms that may directly inform spectrum allocation, such as channel access duration, bandwidth, and modulation type. This paper considers the complexity of predicting communication demand as a function of NAS behaviors. Unlike prior prediction work in communications, this research must consider how the NAS may be impacted by external factors - such as convective weather and closures - rather than estimating demand from time-series forecasting alone. Much of this research considers a federated learning design to predict communication events in terms of the type of event occurring (sector coordination, conflict resolution, etc). To do so, an investigation of products from Sherlock Data Warehouse is conducted, identifying the trends, sufficiency, and correlations of each product to identified events. Additionally, a preliminary discussion for inferring associations between these event types and their communication parameters (duration, bandwidth, modulation) is presented. By utilizing federated learning, imbalances in the types of events and data present throughout the NAS can inform local models without impairing global training. Furthermore, the two-stage approach proposed allows for robust and speculative communication modelling, as communication techniques continue to evolve. As a result, this model enables a generalized approach to understanding NAS communications which is able to inform long-term changes to aviation spectrum management.

Nathan Schimpf↗
NASA NTRS2010

Risk-Informed Safety Assurance and Probabilistic Assessment of Mission-Critical Software-Intensive Systems

This report validates and documents the detailed features and practical application of the framework for software intensive digital systems risk assessment and risk-informed safety assurance presented in the NASA PRA Procedures Guide for Managers and Practitioner. This framework, called herein the "Context-based Software Risk Model" (CSRM), enables the assessment of the contribution of software and software-intensive digital systems to overall system risk, in a manner which is entirely compatible and integrated with the format of a "standard" Probabilistic Risk Assessment (PRA), as currently documented and applied for NASA missions and applications. The CSRM also provides a risk-informed path and criteria for conducting organized and systematic digital system and software testing so that, within this risk-informed paradigm, the achievement of a quantitatively defined level of safety and mission success assurance may be targeted and demonstrated. The framework is based on the concept of context-dependent software risk scenarios and on the modeling of such scenarios via the use of traditional PRA techniques - i.e., event trees and fault trees - in combination with more advanced modeling devices such as the Dynamic Flowgraph Methodology (DFM) or other dynamic logic-modeling representations. The scenarios can be synthesized and quantified in a conditional logic and probabilistic formulation. The application of the CSRM method documented in this report refers to the MiniAERCam system designed and developed by the NASA Johnson Space Center.

Guarro, Sergio B.↗
NASA NTRS2012

Radiation Tests of Highly Scaled, High-Density, Commercial, Nonvolatile NAND Flash Memories - Update 2012

The space radiation environment poses a certain risk to all electronic components on Earth-orbiting and planetary mission spacecraft. In recent years, there has been increased interest in the use of high-density, commercial, nonvolatile flash memories in space because of ever-increasing data volumes and strict power requirements. They are used in a wide variety of spacecraft subsystems. At one end of the spectrum, flash memories are used to store small amounts of mission-critical data such as boot code or configuration files and, at the other end, they are used to construct multi-gigabyte data recorders that record mission science data. This report examines single-event effect (SEE) and total ionizing dose (TID) response in single-level cell (SLC) 32-Gb, multi-level cell (MLC) 64-Gb, and Triple-level (TLC) 64-Gb NAND flash memories manufactured by Micron Technology with feature size of 25 nm.

Irom, Farokh↗