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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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NASA NTRS1987

A prediction model of the depth-of-discharge effect on the cycle life of a storage cell

Cycle life requirements are very high for batteries used in aerospace applications in low Earth orbit. The data base required to establish confidence in a particular cell design is thus both extensive and expensive. Reliable accelerated cycle life testing and performance decay modeling represent attractive alternatives to real-time tests of cycle life. In light of certain long-term cycle life test results, this paper examines a very simple performance decay model developed earlier. Application of that model to available data demonstrates a rigid relationship between a battery's expected cycle life and the depth of discharge of cycling. Further, modeling analysis of the data suggests that a significantly improved cycle life can be obtained with advanced components, materials, and designs; and that cycle life can be reliably predicted from the results of accelerated testing.

Thaller, Lawrence H.↗

NASA NTRS1987

A prediction model of the depth-of-discharge effect on the cycle life of a storge cell

Cycle life requirements are very high for batteries used in aerospace applications in low earth orbit. The data base required to establish confidence in a particular cell design is thus both extensive and expensive. Reliable accelerated cycle life testing and performance decay modeling represent attractive alternatives to real-time tests of cycle life. In light of certain long-term cycle life test results, this paper examines a very simple performance decay model developed earlier. Application of that model to available data demonstrates a rigid relationship between a battery's expected cycle life and the depth of discharge of cycling. Further, modeling analysis of the data suggests that a significantly improved cycle life can be obtained with advanced components, materials, and designs; and that cycle life can be reliably predicted from the results of accelerated testing.

Thaller, Lawrence H.↗

NASA NTRS1994

A Prototype Lisp-Based Soft Real-Time Object-Oriented Graphical User Interface for Control System Development

A prototype Lisp-based soft real-time object-oriented Graphical User Interface for control system development is presented. The Graphical User Interface executes alongside a test system in laboratory conditions to permit observation of the closed loop operation through animation, graphics, and text. Since it must perform interactive graphics while updating the screen in real time, techniques are discussed which allow quick, efficient data processing and animation. Examples from an implementation are included to demonstrate some typical functionalities which allow the user to follow the control system's operation.

Litt, Jonathan↗

DOE OSTI2024

Glow Discharge Boronization and Real-Time Boronization Using an Impurity Powder Dropper in LHD

In the Large Helical Device (LHD), boronization is utilized for wall conditioning, employing two distinct methods: glow discharge using diborane gas and the injection of boron powder into the plasma via an impurity powder dropper (IPD). Glow discharge boronization is conducted after maintenance when the vacuum vessel is exposed to air and just before the start of an experimental campaign. In contrast, real-time boronization using the IPD is performed during discharges. The distribution of boron on plasma-facing components was assessed through long-term material probes in the case of glow discharge and through numerical simulations for the IPD method. Results indicated non-uniform boron distributions in both cases. The impact of these boronization techniques on impurity levels and the lifetime of their effects were also examined. Both methods effectively reduced oxygen and iron in the plasma, with the iron impurities originating from the first wall. For glow discharge boronization, the reduction in oxygen levels persisted until the end of the experimental campaign, whereas the reduction in iron levels only lasted a few days. In the case of boron injection during long pulse discharges exceeding 200 s, the suppression of oxygen and iron was observed. The effect on iron quickly diminished within several seconds after the injection ended, whereas the effect on oxygen lasted more than 100 s. The difference in the lifetimes of the boronization effects is further discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS2006

Liquid Bismuth Feed System for Electric Propulsion

Operation of Hall thrusters with bismuth propellant has been shown to be a promising path toward high-power, high-performance, long-lifetime electric propulsion for spaceflight missions. For example, the VHITAL project aims td accurately, experimentally assess the performance characteristics of 10 kW-class bismuth-fed Hall thrusters - in order to validate earlier results and resuscitate a promising technology that has been relatively dormant for about two decades. A critical element of these tests will be the precise metering of propellant to the thruster, since performance cannot be accurately assessed without an accurate accounting of mass flow rate. Earlier work used a pre/post-test propellant weighing scheme that did not provide any real-time measurement of mass flow rate while the thruster was firing, and makes subsequent performance calculations difficult. The motivation of the present work was to develop a precision liquid bismuth Propellant Management System (PMS) that provides real-time propellant mass flow rate measurement and control, enabling accurate thruster performance measurements. Additionally, our approach emphasizes the development of new liquid metal flow control components and, hence, will establish a basis for the future development of components for application in spaceflight. The design of various critical components in a bismuth PMS are described - reservoir, electromagnetic pump, hotspot flow sensor, and automated control system. Particular emphasis is given to material selection and high-temperature sealing techniques. Open loop calibration test results are reported, which validate the systems capability to deliver bismuth at mass flow rates ranging from 10 to 100 mg/sec with an uncertainty of less than +/- 5%. Results of integrated vaporizer/liquid PMS tests demonstrate all of the necessary elements of a complete bismuth feed system for electric propulsion.

Markusic, T. E.↗

DOE OSTI2024

A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics

AI-based foundation models like FourCastNet, GraphCast are revolutionizing weather and climate predictions but are not yet ready for operational use. Their limitation lies in the absence of a data assimilation system to incorporate real-time Earth system observations, crucial for accurately forecasting events like tropical cyclones. To overcome these obstacles, we introduce a generic real-time data assimilation framework and demonstrate its end-to-end performance on the Frontier supercomputer. This framework comprises two primary modules: an ensemble score filter (EnSF), which significantly outperforms the state-of-the-art data assimilation method, and a vision transformer-based surrogate capable of real-time adaptation through the integration of observational data. We demonstrate both the strong and weak scaling of our framework up to 1024 GPUs on the Exascale supercomputer, Frontier. Our results not only illustrate the framework's exceptional scalability on high-performance computing systems, but also demonstrate the importance of supercomputers in real-time data assimilation for weather and climate predictions.

Lu, Dan↗

DOE OSTI2020

Software-Defined Network for End-to-end Networked Science at the Exascale

Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work we presen here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based architecture, implementation, and deployment which enables automated end-to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an architecture definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Software-defined network for end-to-end networked science at the exascale

Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work presented here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based architecture, implementation, and deployment which enables automated end- to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an architecture definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence.

47 OTHER INSTRUMENTATION↗

DOE OSTI2018

The Fermilab Test Beam Facility Data Acquisition System Based on $otsdaq$

The Real-Time Systems Engineering Department of the Scientific Computing Division at Fermilab has deployed set of customizations to our Off-The-Shelf Data Acquisition solution (otsdaq) at the Fermilab Test Beam Facility (FTBF) to read out the beamline instrumentation in support of FTBF users. In addition to reading out several detectors which use various detection technologies and readout hardware, the FTBF Data Acquisition system (DAQ) can perform basic track reconstruction through the facility in real time and provide data to facility users. An advanced prototype smart coincidence module, known as the NIM+, performs trigger distribution and throttling, allowing the beamline instrumentation to be read out at different rates. Spill data are saved to disk for studies of the facility performance, and hit data are also made available on the FTBF network for experiments' use. A web-based run control and configuration GUI are provided, and the online monitoring snapshots created after each beam spill are viewable from any computer connected to the Fermilab network. The integrated DAQ system for the facility provides users with high-precision tracking data along the beamline and a single location for obtaining data from the facility detectors, which set the baseline for testing their own detectors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2017

Effects of adaptive automation on operator-system performance - 103

Adaptive automation (AA) is the dynamic, real-time change in the degree of automation in response to situational changes. AA systems are being developed in many industries, including for nuclear power plant applications. AA is a promising means of mitigating human performance issues that often arise in highly automated systems. The purpose of this Nuclear Regulatory Commission study was to characterize important AA design features, define the effects of AA on performance, examine the human factors engineering (HFE) guidance available to design and evaluate AA systems, and identify research and development needs. In general, we found that AA improves task performance and situation awareness (SA) while managing workload. Although the research is limited, AA also supports operator recognition of automation failure and recovery. Research on human-system interfaces has identified principles showing promise for supporting SA, including failure detection, and limiting workload. Research further suggests that AA systems are both more effective, and less disruptive and distracting, when they follow rules of interaction similar to those used by human crewmembers. Our general conclusion about the HFE guidance available to designers and reviewers is that it is limited and has not kept pace with automation technology. We discuss several research and development needs that will help support a better understanding of where and how AA should be used and support the development of more comprehensive guidance for AA safety reviews. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2007

Development and Assessment of a Novel Training Package for Basic Maneuvering Tasks on a Flight Simulator Using Self Instruction Methods and Above Real Time Training (ARTT)

One result of the relatively recent advances in computing technology has been the decreasing cost of computers and increasing computational power. This has allowed high fidelity airplane simulations to be run on personal computers (PC). Thus, simulators are now used routinely by pilots to substitute real flight hours for simulated flight hours for training for an aircraft type rating thereby reducing the cost of flight training. However, FAA regulations require that such substitution training must be supervised by Certified Flight Instructors (CFI). If the CFI presence could be reduced or eliminated for certain tasks this would mean a further cost savings to the pilot. This would require that the flight simulator have a certain level of 'intelligence' in order to provide feedback on pilot performance similar to that of a CFI. The 'intelligent' flight simulator would have at least the capability to use data gathered from the flight to create a measure for the performance of the student pilot. Also, to fully utilize the advances in computational power, the simulator would be capable of interacting with the student pilot using the best possible training interventions. This thesis reports on the two studies conducted at Tuskegee University investigating the effects of interventions on the learning of two flight maneuvers on a flight simulator and the robustness and accuracy of calculated performance indices as compared to CFI evaluations of performance. The intent of these studies is to take a step in the direction of creating an 'intelligent' flight simulator. The first study deals with the comparisons of novice pilot performance trained at different levels of above real-time to execute a level S-turn. The second study examined the effect of out-of-the-window (OTW) visual cues in the form of hoops on the performance of novice pilots learning to fly a landing approach on the flight simulator. The reliability/robustness of the computed performance metrics was assessed by comparing them with the evaluations of the landing approach maneuver by a number of CFIs.

Ali, Syed Firasat↗

DOE OSTI2021

Impact of Blockchain Delay on Grid-Tied Solar Inverter Performance: Preprint

This paper investigates the impact of the delay resulting from a blockchain, a promising security measure, for a hierarchical control system of inverters connected to the grid. The blockchain communication network is designed at the secondary control layer for resilience against cyberattacks. To represent the latency in the communication channel, a model is developed based on the complexity of the blockchain framework. Taking this model into account, this work evaluates the plant’s performance subject to communication delays, introduced by the blockchain, among the hierarchical control agents. In addition, this article considers an optimal model-based control strategy that performs the system’s internal control loop. The work shows that the blockchain’s delay size influences the convergence of the power supplied by the inverter to the reference at the point of common coupling. In the results section, real-time simulations on OPAL-RT are performed to test the resilience of two parallel inverters with increasing blockchain complexity.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2018

Chronic cold exposure results in subcutaneous adipose tissue browning and altered global metabolism in Qinghai-Tibetan plateau pika (Ochotona curzoniae)

Highlights: • Adipose tissue adaptation to winter cold stress in Tibetan plateau pika was analyzed. • Decreased white adipose tissue (AT) and increased brown AT mass were observed. • Molecular and histological evidence of white AT browning was observed. • The shift from WAT to BAT enables increased thermogenesis in winter pika. The plateau pika (Ochotona curzoniae), one of the indigenous animals of the Qinghai-Tibet Plateau, is adapted to life in a cold and hypoxic environment. We conducted a series of genomic, proteomic and morphological studies to investigate whether changes in energy metabolism contribute to adaptation of the plateau pika to cold stress by analyzing summer and winter cohorts. The winter group showed strong morphological and histological features of brown adipose tissue (BAT) in subcutaneous white adipose tissue (sWAT). To obtain molecular evidence of browning of sWAT, we performed reverse transcription and quantitative real-time PCR, which revealed that BAT-specific genes, including uncoupling protein 1 (UCP-1) and PPAR-γ coactivator 1α (PGC-1α), were highly expressed in sWAT from the winter group. Compared with the summer group, Western blot analysis also confirmed that UCP-1, PGC-1α and Cox4 protein levels were significantly increased in sWAT from the winter group. Increased BAT mass in the inter-scapular region of the winter group was also observed. These results suggest that the plateau pika adapts to cold by browning sWAT and increasing BAT in order to increase thermogenesis. These changes are distinct from the previously reported adaptation of highland deer mice. Understanding the regulatory mechanisms underlying this adaptation may lead to novel therapeutic strategies for treating obesity and metabolic disorders.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Long noncoding RNA ANCR suppresses bone formation of periodontal ligament stem cells via sponging miRNA-758

Highlights: • lncRNA-ANCR upregulation decreased bone formation of PDLSCs. • miR-758 upregulation promoted osteogenic differentiation of PDLSCs. • miR-758 is the direct target of lncRNA ANCR. • lncRNA-ANCR/miR-758/Notch2-Wnt/β-catenin signaling pathway involve in bone formation. Long noncoding RNAs (lncRNAs) were proposed to be important regulators influencing various differentiation processes. Yet, the molecular mechanisms of lncRNAs governing osteogenic differentiation of Periodontal Ligament Stem Cells (PDLSCs) remain unclear. Here, PDLSCs were isolated from normal periodontal ligament of human (PDL) whereas P-PDLSCs were isolated from periodontitis affected PDL. Quantitative real-time PCR (qRT-PCR) was performed to examine the relative expression level of lncRNA-ANCR and of Osterix (OSX), Alkaline Phosphatase (ALP) as well as Runt-related transcription factor 2 (RUNX2) in PDLSCs. Gain- and loss-of- function experiments was performed to study the role of lncRNA-ANCR. Alizarin Red staining was used to evaluate the function of lncRNA-ANCR and miRNA-758 on osteogenic differentiation. In addition, via dual luciferase reporter assay and RNA immunoprecipitation the microRNA sponge potential of lncRNA-ANCR was assessed. A luciferase reporter assay identified the correlation between miR-758 and Notch2. Our results showed that the expression of ALP, RUNX2 and OSX were increased whereas lncRNA-ANCR was decreased during the process of differentiation in PDLSCs. Overexpression of lncRNA-ANCR decreased the expression of ALP, RUNX2 and OSX as confirmed by Alizarin red staining. Overexpression of lncRNA-ANCR resulted in reduction of the miR-758 expression level. Furthermore, RNA immunoprecipitation proved that lncRNA-ANCR targets miR-758 directly. The results of dual luciferase reporter assay also demonstrated that miR-758 regulated Notch2 expression by targeting 3′-UTR of Notch2. In conclusion, the novel pathway lncRNA-ANCR/miR-758/Notch2 plays an important role in the process of regulating osteogenic differentiation of PDLSCs.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

A novel implicit hybrid machine learning model and its application for reinforcement learning

A novel methodology to develop implicit hybrid models is presented. PyTorch is used to integrate physics-based equations with machine learning models. Automatic differentiation of the hybrid model is leveraged to solve the implicit equations. Iterative solving enables gradient based updates to the machine learning model. The novel methodology is compared to an explicit hybrid approach on a continuously stirred tank reactor (CSTR). The novel method results in a lower modelling error. Both hybrid models effectively train with noisy data. To test the implicit hybrid model, it is employed as a reinforcement learning (RL) training model. The RL algorithm trained on the hybrid model outperforms real time optimization of the CSTR and performs nearly as well as RL trained directly on the CSTR and a traditional gradient based approach. Training RL directly on the CSTR requires over 60,000 system interactions compared to 6000 historical data points for hybrid model development.

42 ENGINEERING↗

DOE OSTI2025

Nuclear microreactor transient and load-following control with deep reinforcement learning

The economic feasibility of nuclear microreactors will depend on minimizing operating costs through advancements in autonomous control, especially when these microreactors are operating alongside other types of energy systems (e.g., renewable energy). This study explores the application of deep reinforcement learning (RL) for real-time drum control in microreactors, exploring performance in regard to load-following scenarios. By leveraging a point kinetics model with thermal and xenon feedback, we first establish a baseline using a single-output RL agent, then compare it against a traditional proportional–integral–derivative (PID) controller. This study demonstrates that RL controllers, including both single- and multi-agent RL (MARL) frameworks, can achieve similar or even superior load-following performance as traditional PID control across a range of load-following scenarios. In short transients, the RL agent was able to reduce the tracking error rate in comparison to PID by one half to one third. Over extended 300-minute load-following scenarios in which xenon feedback becomes a dominant factor, PID maintained better accuracy, but RL still remained within a 1% error margin despite being trained only on short-duration scenarios. This highlights RL’s strong ability to generalize and extrapolate to longer, more complex transients, affording substantial reductions in training costs and reduced overfitting. Furthermore, when control was extended to multiple drums, MARL enabled independent drum control as well as maintained reactor symmetry constraints without sacrificing performance---an objective that standard single-agent RL could not learn. We also found that, as increasing levels of Gaussian noise were added to the power measurements, the RL controllers were able to maintain lower error rates than PID, and to do so with at least 10% and upwards of 150% less control effort. These findings illustrate RL's potential for autonomous nuclear reactor control, laying the groundwork for future integration into high-fidelity simulations and experimental validation efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Analyzing inference workloads for spatiotemporal modeling

Ensuring power grid resiliency, forecasting climate conditions, and optimization of transportation infrastructure are some of the many application areas where data is collected in both space and time. Spatiotemporal modeling is about modeling those patterns for forecasting future trends and carrying out critical decision-making by leveraging machine learning/deep learning. Once trained offline, field deployment of trained models for near real-time inference could be challenging because performance can vary significantly depending on the environment, available compute resources and tolerance to ambiguity in results. Users deploying spatiotemporal models for solving complex problems can benefit from analytical studies considering a plethora of system adaptations to understand the associated performance-quality trade-offs. To facilitate the co-design of next-generation hardware architectures for field deployment of trained models, it is critical to characterize the workloads of these deep learning (DL) applications during inference and assess their computational patterns at different levels of the execution stack. In this paper, we develop several variants of deep learning applications that use spatiotemporal data from dynamical systems. We study the associated computational patterns for inference workloads at different levels, considering relevant models (Long short-term Memory, Convolutional Neural Network and Spatio-Temporal Graph Convolution Network), DL frameworks (Tensorflow and PyTorch), precision (FP16, FP32, AMP, INT16 and INT8), inference runtime (ONNX and AI Template), post-training quantization (TensorRT) and platforms (Nvidia DGX A100 and Sambanova SN10 RDU). Overall, our findings indicate that although there is potential in mixed-precision models and post-training quantization for spatiotemporal modeling, extracting efficiency from contemporary GPU systems might be challenging. Instead, co-designing custom accelerators by leveraging optimized High Level Synthesis frameworks (such as SODA High-Level Synthesizer for customized FPGA/ASIC targets) can make workload-specific adjustments to enhance the efficiency.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Real-Time TEM Study of Nanopore Evolution in Battery Materials and Their Suppression for Enhanced Cycling Performance

Battery materials, which store energy by combining mechanisms of intercalation, conversion, and alloying, give promisingly high energy density but usually suffer from fast capacity decay due to the drastic volume change upon cycling. Particularly, the significant volume shrinkage upon mass (Li + , Na + , etc.) extraction inevitably leads to the formation of pores in materials and their final pulverization after cycling. It is necessary to explore the failure mechanism of such battery materials from the microscopic level in order to understand the evolution of porous structures. In this work, prototyped Sb 2 Se 3 nanowires are targeted to understand the structural failures during repetitive (de)sodiation, which exhibits mainly alloying and conversion mechanisms. The fast growing nanosized pores embedded in the nanowire during desodiation are identified to be the key factor that weakens the mechanical strength of the material and thus cause a rapid capacity decrease. To suppress the pore development, we further limit the cutoff charge voltage in a half-cell against Na below a critical value where the conversion reaction of such a material system is yet happening, the result of which demonstrates significantly improved battery performance with well-maintained structural integrity. These results may shed some light on electrode failure investigation and rational design of advanced electrode materials with long cycling life.

25 ENERGY STORAGE↗