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At least 793 records · Page 44

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NASA NTRS1989

The formation of topological defects in phase transitions

It was argued, and fought through numerical work that the results of non-dynamical Monte Carlo computer simulations cannot be applied to describe the formation of topological defects when the correlation length at the Ginzburg temperature is significantly smaller than the horizon size. To test the current hypothesis that infinite strings at formation are essentially described by Brownian walks of size the correlation length at the Ginzburg temperature, fields at the Ginzburg temperature were equilibrated. Infinite structure do not exist in equilibrium for reasonable definitions of the Ginzburg temperature, and horizons must be included in a proper treatment. A phase transition, from small-scale to large-scale string or domain wall structure, is found to occur very close to the Ginzburg temperature, in agreement with recent work. The formation process of domain walls and global strings were investigated through the breaking of initially ordered states. To mimic conditions in the early Universe, cooling times are chosen so that horizons exist in the sample volume when topological structure formation occurs. The classical fields are evolved in real-time by the numerical solution of Langevin equations of motion on a three dimensional spatial lattice. The results indicate that it is possible for most of the string energy to be in small loops, rather than in long strings, at formation.

Hodges, Hardy M.↗

NASA NTRS1989

Precision pointing compensation for DSN antennas with optical distance measuring sensors

The pointing control loops of Deep Space Network (DSN) antennas do not account for unmodeled deflections of the primary and secondary reflectors. As a result, structural distortions due to unpredictable environmental loads can result in uncompensated boresight shifts which degrade pointing accuracy. The design proposed here can provide real-time bias commands to the pointing control system to compensate for environmental effects on pointing performance. The bias commands can be computed in real time from optically measured deflections at a number of points on the primary and secondary reflectors. Computer simulations with a reduced-order finite-element model of a DSN antenna validate the concept and lead to a proposed design by which a ten-to-one reduction in pointing uncertainty can be achieved under nominal uncertainty conditions.

Scheid, R. E.↗

NASA NTRS1989

Message routing in HARTS with faulty components

It is important to design a distributed system which is capable of delivering messages even in the presence of faulty components between their source and destination nodes. A routing scheme is developed in two steps for a wrapped hexagonal mesh, called HARTS (Hexagonal Architecture for Real-Time Systems), which assures the delivery of every message as long as there is a path between its source and destination. The proposed scheme can also detect the nonexistence of path between a pair of nodes in a finite amount of time. Moreover, the scheme requires each node in HARTS to know only the state (faulty or not) of each of its own links. The performance of the simple routing scheme is simulated for 3- and 5-dimensional H-meshes while varying the physical distribution of faulty components. It is shown that a shortest path between the source and destination of each message is taken with a high probability and a path, if it exists, is usually found very quickly.

Olson, Alan↗

NASA NTRS1990

Advanced detection, isolation, and accommodation of sensor failures in turbofan engines: Real-time microcomputer implementation

The objective of the Advanced Detection, Isolation, and Accommodation Program is to improve the overall demonstrated reliability of digital electronic control systems for turbine engines. For this purpose, an algorithm was developed which detects, isolates, and accommodates sensor failures by using analytical redundancy. The performance of this algorithm was evaluated on a real time engine simulation and was demonstrated on a full scale F100 turbofan engine. The real time implementation of the algorithm is described. The implementation used state-of-the-art microprocessor hardware and software, including parallel processing and high order language programming.

Delaat, John C.↗

NASA NTRS1989

Human task animation from performance models and natural language input

Graphical manipulation of human figures is essential for certain types of human factors analyses such as reach, clearance, fit, and view. In many situations, however, the animation of simulated people performing various tasks may be based on more complicated functions involving multiple simultaneous reaches, critical timing, resource availability, and human performance capabilities. One rather effective means for creating such a simulation is through a natural language description of the tasks to be carried out. Given an anthropometrically-sized figure and a geometric workplace environment, various simple actions such as reach, turn, and view can be effectively controlled from language commands or standard NASA checklist procedures. The commands may also be generated by external simulation tools. Task timing is determined from actual performance models, if available, such as strength models or Fitts' Law. The resulting action specification are animated on a Silicon Graphics Iris workstation in real-time.

Esakov, Jeffrey↗

NASA NTRS1990

Inverse dynamics of adaptive structures used as space cranes

As a precursor to the real-time control of fast moving adaptive structures used as space cranes, a formulation is given for the flexibility induced motion relative to the nominal motion (i.e., the motion that assumes no flexibility) and for obtaining the open loop time varying driving forces. An algorithm is proposed for the computation of the relative motion and driving forces. The governing equations are given in matrix form with explicit functional dependencies. A simulator is developed to implement the algorithm on a digital computer. In the formulations, the distributed mass of the crane is lumped by two schemes, vz., 'trapezoidal' lumping and 'Simpson's rule' lumping. The effects of the mass lumping schemes are shown by simulator runs.

Das, S. K.↗

NASA NTRS1990

Terminal weather information management

Since the mid-1960's, microburst/windshear events have caused at least 30 aircraft accidents and incidents and have killed more than 600 people in the United States alone. This study evaluated alternative means of alerting an airline crew to the presence of microburst/windshear events in the terminal area. Of particular interest was the relative effectiveness of conventional and data link ground-to-air transmissions of ground-based radar and low-level windshear sensing information on microburst/windshear avoidance. The Advanced Concepts Flight Simulator located at Ames Research Center was employed in a line oriented simulation of a scheduled round-trip airline flight from Salt Lake City to Denver Stapleton Airport. Actual weather en route and in the terminal area was simulated using recorded data. The microburst/windshear incident of July 11, 1988 was re-created for the Denver area operations. Six experienced airline crews currently flying scheduled routes were employed as test subjects for each of three groups: (1) A baseline group which received alerts via conventional air traffic control (ATC) tower transmissions; (2) An experimental group which received alerts/events displayed visually and aurally in the cockpit six miles (approx. 2 min.) from the microburst event; and (3) An additional experimental group received displayed alerts/events 23 linear miles (approx. 7 min.) from the microburst event. Analyses of crew communications and decision times showed a marked improvement in both situation awareness and decision-making with visually displayed ground-based radar information. Substantial reductions in the variability of decision times among crews in the visual display groups were also found. These findings suggest that crew performance will be enhanced and individual differences among crews due to differences in training and prior experience are significantly reduced by providing real-time, graphic display of terminal weather hazards.

Lee, Alfred T.↗

NASA NTRS1990

Real-time failure control (SAFD)

The Real Time Failure Control program involves development of a failure detection algorithm, referred as System for Failure and Anomaly Detection (SAFD), for the Space Shuttle Main Engine (SSME). This failure detection approach is signal-based and it entails monitoring SSME measurement signals based on predetermined and computed mean values and standard deviations. Twenty four engine measurements are included in the algorithm and provisions are made to add more parameters if needed. Six major sections of research are presented: (1) SAFD algorithm development; (2) SAFD simulations; (3) Digital Transient Model failure simulation; (4) closed-loop simulation; (5) SAFD current limitations; and (6) enhancements planned for.

Panossian, Hagop V.↗

NASA NTRS1991

Parallel computations and control of adaptive structures

The equations of motion for structures with adaptive elements for vibration control are presented for parallel computations to be used as a software package for real-time control of flexible space structures. A brief introduction of the state-of-the-art parallel computational capability is also presented. Time marching strategies are developed for an effective use of massive parallel mapping, partitioning, and the necessary arithmetic operations. An example is offered for the simulation of control-structure interaction on a parallel computer and the impact of the approach presented for applications in other disciplines than aerospace industry is assessed.

Park, K. C.↗

NASA NTRS1990

Time scale algorithm: Definition of ensemble time and possible uses of the Kalman filter

The comparative study of two time scale algorithms, devised to satisfy different but related requirements, is presented. They are ALGOS(BIPM), producing the international reference TAI at the Bureau International des Poids et Mesures, and AT1(NIST), generating the real-time time scale AT1 at the National Institute of Standards and Technology. In each case, the time scale is a weighted average of clock readings, but the weight determination and the frequency prediction are different because they are adapted to different purposes. The possibility of using a mathematical tool, such as the Kalman filter, together with the definition of the time scale as a weighted average, is also analyzed. Results obtained by simulation are presented.

Tavella, Patrizia↗

NASA NTRS1990

Simulated fault injection - A methodology to evaluate fault tolerant microprocessor architectures

A simulation-based fault-injection method for validating fault-tolerant microprocessor architectures is described. The approach uses mixed-mode simulation (electrical/logic analysis), and injects transient errors in run-time to assess the resulting fault impact. As an example, a fault-tolerant architecture which models the digital aspects of a dual-channel real-time jet-engine controller is used. The level of effectiveness of the dual configuration with respect to single and multiple transients is measured. The results indicate 100 percent coverage of single transients. Approximately 12 percent of the multiple transients affect both channels; none result in controller failure since two additional levels of redundancy exist.

Choi, Gwan S.↗

NASA NTRS1990

Dynamic control of robotic welding - On-going research

Two major challenges exist for the development of dynamic control systems: first, the control system must be resourceful enough to provide problem-solving capabilities in unforeseen circumstances; second, it must be rapid enough to respond to dynamic environments. Most conventional control systems do not have the ability to 'step back' and problem-solve, especially in environments with incomplete and uncertain models and data. Orthogonally, commercially available AI systems usually do not respond at the rates required to support 'real-time' control. Hence, control systems are not available that respond to complex dynamic environments within appropriate time constraints. This paper describes work on an AI-based control system designed to address these challenges. A prototype system has been used, in a simulated environment, to control the robotic welding of aerospace components. This AI-based control system has demonstrated the ability to flexibly control a complex process within required time constraints by incorporating higher level reasoning and the ability to deal with uncertainty. Further work is in progress to expand the system's problem-solving capabilities.

Ruokangas, Corinne C.↗

NASA NTRS1990

Real-time optical correlator using computer-generated holographic filter on a liquid crystal light valve

Limitations associated with the binary phase-only filter often used in optical correlators are presently circumvented in the writing of complex-valued data on a gray-scale spatial light modulator through the use of a computer-generated hologram (CGH) algorithm. The CGH encodes complex-valued data into nonnegative real CGH data in such a way that it may be encoded in any of the available gray-scale spatial light modulators. A CdS liquid-crystal light valve is used for the complex-valued CGH encoding; computer simulations and experimental results are compared, and the use of such a CGH filter as the synapse hologram in a holographic optical neural net is discussed.

Chao, Tien-Hsin↗

NASA NTRS1990

On recursive least-squares filtering algorithms and implementations

In many real-time signal processing applications, fast and numerically stable algorithms for solving least-squares problems are necessary and important. In particular, under non-stationary conditions, these algorithms must be able to adapt themselves to reflect the changes in the system and take appropriate adjustments to achieve optimum performances. Among existing algorithms, the QR-decomposition (QRD)-based recursive least-squares (RLS) methods have been shown to be useful and effective for adaptive signal processing. In order to increase the speed of processing and achieve high throughput rate, many algorithms are being vectorized and/or pipelined to facilitate high degrees of parallelism. A time-recursive formulation of RLS filtering employing block QRD will be considered first. Several methods, including a new non-continuous windowing scheme based on selectively rejecting contaminated data, were investigated for adaptive processing. Based on systolic triarrays, many other forms of systolic arrays are shown to be capable of implementing different algorithms. Various updating and downdating systolic algorithms and architectures for RLS filtering are examined and compared in details, which include Householder reflector, Gram-Schmidt procedure, and Givens rotation. A unified approach encompassing existing square-root-free algorithms is also proposed. For the sinusoidal spectrum estimation problem, a judicious method of separating the noise from the signal is of great interest. Various truncated QR methods are proposed for this purpose and compared to the truncated SVD method. Computer simulations provided for detailed comparisons show the effectiveness of these methods. This thesis deals with fundamental issues of numerical stability, computational efficiency, adaptivity, and VLSI implementation for the RLS filtering problems. In all, various new and modified algorithms and architectures are proposed and analyzed; the significance of any of the new method depends crucially on specific application.

Hsieh, Shih-Fu↗

DOE OSTI2025

Automating Sensor Characterization with Bayesian Optimization

The development of novel instrumentation requires an iterative cycle with three stages: design, prototyping, and testing. Recent advancements in simulation and nanofabrication techniques have significantly accelerated the design and prototyping phases. Nonetheless, detector characterization continues to be a major bottleneck in device development. During the testing phase, a significant time investment is required to characterize the device in different operating conditions and find optimal operating parameters. The total effort spent on characterization and parameter optimization can occupy a year or more of an expert's time. In this work, we present a novel technique for automated sensor calibration that aims to accelerate the testing stage of the development cycle. This technique leverages closed-loop Bayesian optimization (BO), using real-time measurements to guide parameter selection and identify optimal operating states. We demonstrate the method with a novel low-noise CCD, showing that the machine learning-driven tool can efficiently characterize and optimize operation of the sensor in a couple of days without supervision of a device expert.

Cuevas-Zepeda, Julian [Chicago U., KICP; Chicago U↗

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

Learning to Trigger: Reinforcement Learning at the Large Hadron Collider

High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (\textit{triggering}) under tight constraints on bandwidth, latency, and storage. In practice, trigger menus are largely static and hand-tuned and can become suboptimal as detector conditions, pileup, and background composition drift over time. We cast online threshold tuning as a sequential decision-making problem: a reinforcement learning agent ingests streaming summaries of recent rates and signal-sensitive features and updates trigger thresholds to maximize signal efficiency while tracking a target background rate within a tolerance band. We adapt Group-Filtered Policy Optimization (GFPO) to streaming control and introduce two variants (GFPO-F, GFPO-FR) that enforce background rate feasibility during training. On a benchmark that emulates realistic collider operation, we study two representative triggers: a total transverse energy ($H_{T}$) trigger sensitive to pileup variation, and an anomaly-detection (AD) trigger based on reconstruction loss for rare or non-standard signatures. On Monte Carlo streams, our agent increases the fraction of in-tolerance time intervals by 48% ($H_T$) and 28% (AD), with a cumulative gain of up to 2% in signal efficiency on those in-tolerance intervals. Transferring from simulation to \emph{real} collision data (CMS Run 283408), the same agent, without fine-tuning, achieves a 56% ($H_T$) and 28% (AD) in-tolerance improvement over baselines, with further signal-efficiency gain on both triggers. To our knowledge, this is the \emph{first} demonstration of RL-based trigger control on real Large Hadron Collider collision data. Code is available at https://github.com/Zixind/GFPO_LHC (see repo for details).

Ding, Zixin [Chicago U.]↗

DOE OSTI2026

Exploration of Real Time Inference for MI-RR Deblending on GPU/TPU Systems

The Fermilab Main Injector (MI) and Recycler Ring (RR) share a common beam loss monitor (BLM) system, making loss events difficult to attribute to their source machine when beam is present in both simultaneously. The Real-time Edge AI for Distributed Systems (READS) project addresses this by deblending BLM readings in real time using machine learning (ML). The current FPGA based implementation meets the sub-3 ms latency requirement but carries a resource intensive hls4ml development cycle, motivating exploration of GPU based deployment. This paper characterizes inference latency on an NVIDIA Jetson Orin Nano and introduces a packet organization scheme for assembling synchronized event frames from seven distributed BLM DAQ streams. Using a Python based DAQ simulation with injected timing jitter in place of unavailable live beam data, the pipeline achieved an average end to end latency of 0.456 ms (σ = 0.122 ms) across 167,000 test frames, comfortably meeting the timing constraint. Early outliers were attributed to TensorRT warm-up rather than steady state limitations, suggesting GPU based inference is a viable alternative to the existing FPGA implementation.

Yu, Kellen [Fermilab; Cornell U.]↗