Search NASA⌕ Search

SEARCH · Search NASA

Results for “Real-time simulation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Lightweight Metal Stamping Optimization Enabled by Artificial Intelligence

Successfully manufacturing an automotive body structure made via the sheet metal stamping process depends upon simultaneous consideration of component design, tooling design, stamping process control, and material properties. In many cases, introducing lightweight sheet materials (e.g., aluminum alloys, magnesium alloys, advanced high strength steels) holds the potential to significantly reduce vehicle weight, but challenges the stamping process by introducing materials with inherently less ductility. Successful and repeatable applications require co-developing the stamping process controls with the varying material properties, including formability. During the stamping process, as soon as the forming limit of the sheet is exceeded, the material shows localized necking which quickly leads to splits. Controlling process variability to avoid these material splits will enable deployment of less formable, lighter, and stronger materials for stamped automotive components. A typical optimization procedure for manufacturing requires an iterative process involving parameter setting, execution of computational simulations, and modifying the parameters. The entire process demands substantial computational time, making it impractical for real-time feedback towards rapid corrective actions required for in-line control for running production processes. To overcome this challenge, artificial intelligence (AI) can be leveraged to determine optimal manufacturing parameters within a single manufacturing cycle time. This research proposes an in-line optimization framework incorporating a trained AI model to predict kidney-shaped die forming. Preliminary results indicate that the AI framework can accurately predict draw-in values based on a given parameter set, a process referred to as forward prediction. Furthermore, the AI framework can also predict the optimal parameter set that leads to the desired draw-in values, referred to as inverse optimization (or backward prediction). This research has been performed in collaborations with USCAR (US Council for Automotive Research) and AutoForm. The members of USCAR are Ford, GM, and Stellantis.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine Learning-Based Technique for Automated Sensor Characterization

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.

Zepeda, Cuevas [Chicago U., KICP]↗
DOE OSTI2026

Real-Time Inference For MI/RR Deblending

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 [Cornell U.]↗
NASA NTRS1972

Procedure for estimating stability and control parameters from flight test data by using maximum likelihood methods employing a real-time digital system

A maximum likelihood parameter estimation procedure and program were developed for the extraction of the stability and control derivatives of aircraft from flight test data. Nonlinear six-degree-of-freedom equations describing aircraft dynamics were used to derive sensitivity equations for quasilinearization. The maximum likelihood function with quasilinearization was used to derive the parameter change equations, the covariance matrices for the parameters and measurement noise, and the performance index function. The maximum likelihood estimator was mechanized into an iterative estimation procedure utilizing a real time digital computer and graphic display system. This program was developed for 8 measured state variables and 40 parameters. Test cases were conducted with simulated data for validation of the estimation procedure and program. The program was applied to a V/STOL tilt wing aircraft, a military fighter airplane, and a light single engine airplane. The particular nonlinear equations of motion, derivation of the sensitivity equations, addition of accelerations into the algorithm, operational features of the real time digital system, and test cases are described.

Grove, R. D.↗
NASA NTRS1972

Apollo experience report: Real-time auxiliary computing facility development

The Apollo real time auxiliary computing function and facility were an extension of the facility used during the Gemini Program. The facility was expanded to include support of all areas of flight control, and computer programs were developed for mission and mission-simulation support. The scope of the function was expanded to include prime mission support functions in addition to engineering evaluations, and the facility became a mandatory mission support facility. The facility functioned as a full scale mission support activity until after the first manned lunar landing mission. After the Apollo 11 mission, the function and facility gradually reverted to a nonmandatory, offline, on-call operation because the real time program flexibility was increased and verified sufficiently to eliminate the need for redundant computations. The evaluation of the facility and function and recommendations for future programs are discussed in this report.

Allday, C. E.↗
NASA NTRS1974

Effects of simulated space environment on Skylab parasol material

A material consisting of ripstop nylon bonded to the Mylar side of aluminized Mylar film was used to construct the first Skylab parasol. The mechanical properties of elongation and tensile strength and the radiative properties of solar absorptance and thermal emittance were measured before and after exposure to simulated solar radiation at intensities of 1.0 and 3.5 solar constants for exposure times as long as 947 hours or 3316 equivalent solar hours. The accelerated testing indicated more severe degradation than was experienced in the real-time test (1 solar constant). The results predicted that this material could have given satisfactory performance throughout the planned lifetime of the Skylab workshop.

Slemp, W. S.↗
NASA NTRS1975

Crack growth in Ti-8Al-1Mo-1V with real-time and accelerated flight by flight loading

Crack growth in Ti-8Al-lMo-lV was measured and calculated for real time and accelerated simulations of supersonic airplane loading and heating. Crack-growth rates calculated on the assumption that an entire flight could be represented by a single cycle predicted the experimental rates poorly. Calculated crack growth rates were slower than the experimental rates for all tests with flight-by-flight loading. For room temperature accelerated tests, the calculated rates agreed well with the experimental rates; but the calculations became progressively less accurate for progressively more complex test conditions (tests that included elevated temperature).

Imig, L. A.↗
NASA NTRS1974

Data analysis study and performance evaluation of the scanning laser Doppler system

A simulation program which provided information on theoretically expected vortex spectra, evaluations of potential algorithms, and expected location accuracies for given scan patterns is presented. Field tests using an aircraft engine flow field and aircraft vortices during flyby tests were compared to the results of the simulation. From these studies, a vortex location algorithm was developed which provided vortex location for one or two vortices as a function of time. Results of this algorithm used on data from flyby tests were used to study vortex transport, to evaluate system performance, and to provide suggestions for real-time vortex location algorithms. The results of real-time analysis were compared to those which were expected based on theoretical considerations.

Sonnenschein, C. M.↗
NASA NTRS1975

Metabolic rate control during extravehicular activity simulations and measurement techniques during actual EVAS

A description of the methods used to control and measure metabolic rate during ground simulations is given. Work levels attained at the Space Environment Simulation Laboratory are presented. The techniques and data acquired during ground simulations are described and compared with inflight procedures. Data from both the Skylab and Apollo Program were utilized and emphasis is given to the methodology, both in simulation and during flight. The basic techniques of work rate assessment are described. They include oxygen consumption, which was useful for averages over long time periods, heart rate correlations based on laboratory calibrations, and liquid cooling garment temperature changes. The relative accuracy of these methods as well as the methods of real-time monitoring at the Mission Control Center are discussed. The advantages and disadvantages of each of the metabolic measurement techniques are discussed. Particular emphasis is given to the problem of utilizing oxygen decrement for short time periods and heart rate at low work levels. A summary is given of the effectiveness of work rate control and measurements; and current plans for future EVA monitoring are discussed.

David J. Horrigan↗
NASA NTRS1981

Design and implementation of a telecommunication interface for the TAATM/TCV real-time experiment

The traffic situation display experiment of the terminal configured vehicle (TCV) research program requires a bidirectional data communications tie line between an computer complex. The tie line is used in a real time environment on the CYBER 175 computer by the terminal area air traffic model (TAATM) simulation program. Aircraft position data are processed by TAATM with the resultant output sent to the facility for the generation of air traffic situation displays which are transmitted to a research aircraft.

Nolan, J. D.↗
NASA NTRS1982

The effectiveness of incorporating a real-time oculometer system in a commercial flight training program

The effectiveness on pilot and trainee performance and scanning behavior of incorporating a real time oculometer system in a commerical flight training program was assessed. Trainees received simulator training in pairs requiring the trainees to alternate the order of training within a session. The 'third day phenomenon' of performance decrement was investigated, including the role of order of training on performance.

Jones, D. H.↗
NASA NTRS1983

Development and evaluation of a Kalman-filter algorithm for terminal area navigation using sensors of moderate accuracy

Translational state estimation in terminal area operations, using a set of commonly available position, air data, and acceleration sensors, is described. Kalman filtering is applied to obtain maximum estimation accuracy from the sensors but feasibility in real-time computations requires a variety of approximations and devices aimed at minimizing the required computation time with only negligible loss of accuracy. Accuracy behavior throughout the terminal area, its relation to sensor accuracy, its effect on trajectory tracking errors and control activity in an automatic flight control system, and its adequacy in terms of existing criteria for various terminal area operations are examined. The principal investigative tool is a simulation of the system.

Kanning, G.↗
NASA NTRS1983

Algorithms for real-time flutter identification

The work reported here addresses the important algorithm issues necessary to achieve a real-time flutter monitoring system; namely, the guidelines for choosing appropriate model forms, reduction of the parameter convergence transient, handling multiple modes, the effect of overparameterization, and estimate accuracy predictions, both online and for experiment design. An approach for efficiently computing continuous-time flutter parameter Cramer-Rao estimate error bounds has been developed. This enables a convincing comparison of theoretical and simulation results, as well as off-line studies in preparation for a flight test. Theoretical predictions, simulation and flight test results from the NASA/Dryden Drones for Aerodynamic and Structural Test (DAST) Program are compared.

Walker, R. A.↗
NASA NTRS1983

A Kalman filter algorithm for terminal-area navigation with sensors of moderate accuracy

Translational state estimation in terminal area operations, using a set of commonly available position, air data, nd acceleration sensors, is described. Kalman filtering is applied to obtain maximum estimation occuracy from the sensors but feasibility in real-time computations requires a variety of approximations and devices aimed at minimizing the required computation time with only negligible loss of accuracy. Accuracy behavior throughout the terminal area, its relation to sensor accuracy, its effect on trajectory tracking errors and control activity in an automatic flight control system, and its adequacy in terms of existing criteria for various terminal area operations are examined. The principal investigative tool is a simulation of the system. Previously announced in STAR as N83-29193

Cicolani, L. S.↗
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.]↗