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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Spatiotemporal control of mitochondrial network dynamics in astroglial cells

Highlights: • Astrocytes display highly complex mitochondrial architectures in vivo. • Mitochondrial trafficking and dynamics in astrocytes are regulated. • Mitochondria critically contribute to astrocyte Ca{sup 2+} handling. • Astrocyte reacting to injury undergo marked changes in mitochondrial dynamics. Mitochondria are increasingly recognized for playing important roles in regulating the evolving metabolic state of mammalian cells. This is particularly true for nerve cells, as dysregulation of mitochondrial dynamics is invariably associated with a number of neuropathies. Accumulating evidence now reveals that changes in mitochondrial dynamics and structure may play equally important roles also in the cell biology of astroglial cells. Astroglial cells display significant heterogeneity in their morphology and specialized functions across different brain regions, however besides fundamental differences they seem to share a surprisingly complex meshwork of mitochondria, which is highly suggestive of tightly regulated mechanisms that contribute to maintain this unique architecture. Here, we summarize recent work performed in astrocytes in situ indicating that this may indeed be the case, with astrocytic mitochondrial networks shown to experience rapid dynamic changes in response to defined external cues. Although the mechanisms underlying this degree of mitochondrial re-shaping are far from being understood, recent data suggest that they may contribute to demarcate astrocyte territories undergoing key signalling and metabolic functions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Firmware Tampering Detection in Heavy-Duty Vehicles through J1939 CAN Analysis

Modern heavy-duty vehicles rely on complex networks of Electronic Control Units (ECUs) that communicate using the J1939 protocol. While this system makes it easier to update and configure vehicle components, it also opens the door to serious cybersecurity risks if not properly secured. This work investigates the potential for firmware tampering through the J1939 communication protocol, which enables ECU configuration and reprogramming over the Controller Area Network (CAN) bus. By monitoring CAN traffic during legitimate maintenance operations and reverse-engineering OEM diagnostic software, we identified common and proprietary J1939 message identifiers, authentication patterns, and vulnerabilities within Unified Diagnostic Services (UDS). These findings demonstrate that inadequate authentication mechanisms can allow malicious actors to alter ECU firmware or disable safety functions, posing severe operational and safety risks. Our analysis contributes to the development of vehicle intrusion detection systems capable of recognizing abnormal reprogramming activity and future firmware fingerprinting methods to verify software integrity across ECUs. This work highlights the importance of standardizing secure firmware authentication across manufacturers to strengthen cyber resilience in heavy-duty vehicle systems.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1986

Data communications and monitor for the Penn State University profiler network

The profiler network installed by the Department of Meteorology at Penn State University utilizes a microcomputer for network monitoring and control. The network consists of two VHF and one UHF wind profiling Doppler radars. Additional measurement systems added to the network include temperature and humidity profiling radiometers, sodar for boundary layer wind profiling and selected surface based baseline systems. Remote diagnostic capabilities were also implemented in the Penn State network. It is possible to remotely analyze many specific malfunctions of the transmitter or signal processor.

Peters, R. M.↗
NASA NTRS1988

Networks Of Executive Controllers For Automation

Paper discusses principal issues to be resolved in development of autonomous executive-controller shell for Space Station. Shell represents major increase in complexity of automated systems. More-complex control tasks require system that deals with different goals requiring sequences of tasks that change state of system world in complex ways. Requires integration of all functions. Applications include space station communications, tracking, life support, data processing support, navigation, and control of thermal and structural subsystems.

Erickson, William K.↗
NASA NTRS1989

Ground data systems resource allocation process

The Ground Data Systems Resource Allocation Process at the Jet Propulsion Laboratory provides medium- and long-range planning for the use of Deep Space Network and Mission Control and Computing Center resources in support of NASA's deep space missions and Earth-based science. Resources consist of radio antenna complexes and associated data processing and control computer networks. A semi-automated system was developed that allows operations personnel to interactively generate, edit, and revise allocation plans spanning periods of up to ten years (as opposed to only two or three weeks under the manual system) based on the relative merit of mission events. It also enhances scientific data return. A software system known as the Resource Allocation and Planning Helper (RALPH) merges the conventional methods of operations research, rule-based knowledge engineering, and advanced data base structures. RALPH employs a generic, highly modular architecture capable of solving a wide variety of scheduling and resource sequencing problems. The rule-based RALPH system has saved significant labor in resource allocation. Its successful use affirms the importance of establishing and applying event priorities based on scientific merit, and the benefit of continuity in planning provided by knowledge-based engineering. The RALPH system exhibits a strong potential for minimizing development cycles of resource and payload planning systems throughout NASA and the private sector.

Berner, Carol A.↗
NASA NTRS1989

Multi-layer neural networks for robot control

Two neural learning controller designs for manipulators are considered. The first design is based on a neural inverse-dynamics system. The second is the combination of the first one with a neural adaptive state feedback system. Both types of controllers enable the manipulator to perform any given task very well after a period of training and to do other untrained tasks satisfactorily. The second design also enables the manipulator to compensate for unpredictable perturbations.

Pourboghrat, Farzad↗
NASA NTRS1991

Neural networks modeling and control of dynamical systems

The topics are presented in viewgraph form and include: an overview of research; first year research objectives (starting 5/91); background; current work; accomplishments from 5/91-9/91; and second year research objectives.

Donald I Soloway↗
NASA NTRS1990

Navigating through large display networks in dynamic control applications

Special display navigation challenges in computer-based display systems for monitoring and controlling dynamic processes are reviewed. Particular attention is given to trends in information technology, workspace design, and paradigmatic cognitive functions related to display navigation.

Woods, David D.↗
DOE OSTI2023

Potential Cooling Energy Savings of Economizer Control and Artificial-Neural-Network-Based Air-Handling Unit Discharge Air Temperature Control for Commercial Building

Heating, ventilation, and air-conditioning (HVAC) systems play a significant role in building energy consumption, accounting for around 50% of total energy usage. As a result, it is essential to explore ways to conserve energy and improve HVAC system efficiency. One such solution is the use of economizer controls, which can reduce cooling energy consumption by using the free-cooling effect. However, there are various types of economizer controls available, and their effectiveness may vary depending on the specific climate conditions. To investigate the cooling energy-saving potential of economizer controls, this study employs a dry-bulb temperature-based economizer control approach. The dry-bulb temperature-based control strategy uses the outdoor air temperature as an indicator of whether free cooling can be used instead of mechanical cooling. This study also introduces an artificial neural network (ANN) prediction model to optimize the control of the HVAC system, which can lead to additional cooling energy savings. To develop the ANN prediction model, the EnergyPlus program is used for simulation modeling, and the Python programming language is employed for model development. The results show that implementing a temperature-based economizer control strategy can lead to a reduction of 7.6% in annual cooling energy consumption. Moreover, by employing an ANN-based optimal control of discharge air temperature in air-handling units, an additional 22.1% of cooling energy savings can be achieved. In conclusion, the findings of this study demonstrate that the implementation of economizer controls, especially the dry-bulb temperature-based approach, can be an effective strategy for reducing cooling energy consumption in HVAC systems. Additionally, using ANN prediction models to optimize HVAC system controls can further increase energy savings, resulting in improved energy efficiency and reduced operating costs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1990

Neural networks for self-learning control systems

It is shown how a neural network can learn of its own accord to control a nonlinear dynamic system. An emulator, a multilayered neural network, learns to identify the system's dynamic characteristics. The controller, another multilayered neural network, next learns to control the emulator. The self-trained controller is then used to control the actual dynamic system. The learning process continues as the emulator and controller improve and track the physical process. An example is given to illustrate these ideas. The 'truck backer-upper,' a neural network controller that steers a trailer truck while the truck is backing up to a loading dock, is demonstrated. The controller is able to guide the truck to the dock from almost any initial position. The technique explored should be applicable to a wide variety of nonlinear control problems.

Nguyen, Derrick H.↗
DOE OSTI2020

Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control

District heating and cooling (DHC) is considered one of the most sustainable technologies to meet the heating and cooling demands of buildings in urban areas. The fifth-generation district heating and cooling (5GDHC) concept, often referred to as ambient loops, is a novel solution emerging in Europe and has become a widely discussed topic in current energy system research. 5GDHC systems operate at a temperature close to the ground and include electrically driven heat pumps and associated thermal energy storage in a building-sited energy transfer station (ETS) to satisfy user comfort. This work presents new strategies for improving the operation of these energy transfer stations by means of a model predictive control (MPC) method based on recurrent artificial neural networks. The results show that, under simple time-of-use utility rates, the advanced controller outperforms a rule-based controller for smart charging of the domestic hot water (DHW) thermal energy storage under specific boundary conditions. By exploiting the available thermal energy storage capacity, the MPC controller is capable of shifting up to 14% of the electricity consumption of the ETS from on-peak to off-peak hours. Therefore, the advanced control implemented in 5GDHC networks promotes coupling between the thermal and the electric sector, producing flexibility on the electric grid.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Abiotic stress networks converging on FT2 to control growth in Populus

Regulation of growth, quiescence and dormancy by seasonally recurring and episodic environmental factors is of central importance to the productivity of woody plants. Woody biomass feedstock plants such as Populus show substantial natural variation in growth responses to various environmental conditions; however, the complex genetic nature of these traits slows breeding for optimal biomass production in specific climates and on marginal lands. These growth responses are controlled by complex gene regulatory networks that are poorly understood. Elucidating the mechanisms that determine periods of growth and quiescence will identify specific genes and modules that can be manipulated to maximize growth in different environments. The main goal of the project was to develop transcriptional networks that describe spatially and temporally explicit regulatory relationships for both daylength and nutrient-mediated quiescence. We used next generation sequencing to characterize genome-wide gene expression changes to these two abiotic signals in shoot apices, leaf, root and vascular cambium. Overall, we sequenced 270 mRNAseq libraries and 54 smallRNAseq libraries from Populus trichocarpa and 58 RNAseq libraries from Populus nigra. We found both organ-specific transcriptional responses as well as responses common to multiple organs. Similarly, we uncovered both divergent and convergent gene regulatory networks that control growth responses to changes in daylength and nutrient stress. Thus, this work provides a foundation for developing approaches to optimize tree growth and biomass yield in response to specific as well as multiple abiotic conditions. Finally, we validated the functions of candidate regulators of growth in response to photoperiod and nutrient availability by introducing mutations via gene editing and transgenic methods. This data will be available to the public through journal publications and national/international genomic data archives.

59 BASIC BIOLOGICAL SCIENCES↗