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All sources NASA NTRS DOE OSTI

DOE OSTI2024

Comparative Evaluation of Control-Oriented Heavy Duty Vehicle Air Drag Coefficient Models

Heavy-duty vehicles (HDVs) are a significant source of fuel consumption and greenhouse gas emissions, prompting solutions such as HDV platooning to mitigate these negative impacts through air drag reduction. The intervehicle distance in an HDV platoon needs to be carefully selected, such that the platoon-level energy efficiency and safety considerations can be well balanced. Underlying this problem lies in accurately modeling the relationship between HDV air drag coefficient and intervehicle distance. Through comprehensive evaluation and comparison, we analyze five control-oriented HDV air drag coefficient models, including the polynomial model, rational polynomial model, rational model, semi-quadratic model, and ridge model. Leveraging Scipy Curve-Fit toolbox and our previously compiled air drag coefficient datasets, we optimally identify the parameters inside each model. The calibrated models are then thoroughly evaluated via five complementary metrics. The comparison results reveal that the semi-quadratic model has the highest overall performance, while the widely adopted rational model only exhibits suboptimal performance.

Best, Micah↗

DOE OSTI2025

Agentic AI vs ML-Based Autotuning: A Comparative Study for Loop Reordering Optimization

High Performance Computing (HPC) applications rely heavily on code optimizations to achieve good performance on modern CPU and GPU architectures. Traditional Machine Learning auto-tuning approaches have demonstrated success in exploring high-dimensional spaces, but they often require expensive compile-run evaluations and lack adaptability for large HPC applications. The recent advances in Large Language Models (LLMs) and Agentic AI systems raise intriguing questions about the potential of these approaches to address specific optimization methodologies. This work aims to answer an essential question for the HPC community: “How Agentic AI Systems Compare to Traditional ML Autotuning Techniques?” To address this question, we present a comparative analysis between a traditional ML-based optimization approach and an Agentic AI system, evaluating their respective capabilities and limitations for loop-level optimization. In addition, we introduced a new Agentic AI system named LoopGen-AI using three different Large Language Models: GPT-4.1, Claude 4.0, and Gemini 2.5. A key finding is that LoopGen-AI achieves competitive per-formance with only a few program runs, the reasoning logs from the agents revealed that their decisions rely heavily on the combination of semantic understanding of the target kernel with dynamic feedback from the environment, highlighting a promising new dimension in performance tuning. In contrast, ML-based autotuners focus on statistical exploration, and require orders of magnitude more runs to reach peak performance. Additionally, our analysis shows that prompt engineering, particularly using Persona + Context Manager patterns, significantly impacts the effectiveness of Agentic AI. Our results indicate that while Agentic AI systems are not yet a complete replacement for ML-based autotuners, it can effectively complement traditional methods.

Rosas, Miguel Romero↗

DOE OSTI2026

Catalytic Autoxidation for Depolymerization of Multilayer Plastic Films

Recycling multilayer plastic films is challenged by a diversity of polymers, prompting development of new recycling methods. For the depolymerization of mixed polymers like those in multilayer films, metal-catalyzed autoxidation offers a versatile chemical recycling method to deconstruct multiple polymers to useful oxygenates. Here, we demonstrate that catalytic autoxidation is effective for depolymerizing multilayer films across diverse chemistries. We investigated conditions for a model polyethylene substrate using a Co, Mn, and Br cocatalyst system, achieving full carbon closure with oxygenated small molecules contributing up to 48 mol% carbon. Subsequently, we characterized product distributions for several common polymers used in multilayer films using high-resolution mass spectrometry (HRMS) and developed analytical methods to quantify the resulting complex product streams. Optimized conditions for polyethylene were applied to 11 multilayer plastic films containing 10 different polymers, including films with nonpolymeric potential disrupters like aluminum foil and titanium dioxide, showing that catalytic autoxidation is effective across a broad range of polymer types and is resistant to disrupters and additives. The generation of CO 2 in these reactions overall suggests that both reaction engineering and modifications to the reaction conditions will be required to achieve higher yields of soluble oxygenated products.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Robust Restoration From Cyber-Physical Attacks in Active Distribution Grids With Grid-Edge IBRs

The inverter-based resources (IBRs) have enabled the integration of renewable energy at the grid edge with enhanced control capabilities to support the reliable operation of power grids. Different control frameworks, such as hierarchical or distributed architecture, have been proposed with the expansion of cyber networks for real-time monitoring and control. This evolution of critical infrastructure into cyber-physical systems also brings more vulnerabilities for the broadened attack surfaces, and significantly increases the possibility of physical system failures or outages caused by cyberattacks. Among tremendous efforts in the defense-in-depth approach, it remains challenging to provide prompt detection and accurate location of attack entry points or paths. Therefore, the prevailing restoration framework may struggle to fully consider the cyber-physical interdependence, successfully isolate the compromised cyber and physical components, and safely recover the systems without the potential risks leading to secondary outages. This paper is motivated to develop a cyber-physical restoration framework for distribution grids to recover from cyber attacks by harnessing grid-edge IBRs. The framework is first built on the operational guidelines of IBRs considering the compromised cyber layer. Then, an ambiguity set is established to represent the uncertainty of attack scenarios and their possibility levels. Next, a distributionally robust optimization model is developed to provide the optimal load restoration strategy across all scenarios. The effectiveness of the proposed model is demonstrated through various use cases on the modified IEEE 13-node and 123-node test systems. Finally, simulation results demonstrate the effectiveness and advancement of developed post-attack restoration strategies.

Cybersecurity↗

DOE OSTI2024

3D Printed Nanoporous Separators Based on Polymerization-Induced Phase Separation for Fast-Charging, High Cycling Stability Li-Ion Batteries

Fast charging and stable lithium-ion batteries (LIBs) require prompt Li ion transport between electrodes. 3D porous separators can potentially improve ion transport at both the microscale and macroscale levels. Additive manufacturing (AM) is an ideal tool to produce and study 3D porous separators of LIBs, although little work has been reported in this area. Here, we demonstrate porous structural control in layer-by-layer printed hexanediol diacrylate (HDDA) separators using a projection micro stereolithography (PμSL) AM technology and present their corresponding battery performance in lithium nickel manganese cobalt oxide (NMC)/graphite full cells. The composition of photocurable resin determines the porous morphology (e.g., bicontinuous and droplet-like), while the settings of printing layer thickness and number of exposures can be used to adjust the porosity (e.g., from 27% to 63%). Optimal resin and print conditions are determined, and the resulting separator exhibits superior Li ion conductivity and therefore higher rate performance and longer cycling life. Compared with a Celgard 2325 separator, the optimized porous HDDA separator increases the 10C-rate capacity by 34% and decreases the capacity degradation rate at the 1C-rate by 4.9%. In conclusion, this work paves the way for future AM of 3D separators for high-energy, high-power, and long-cycling-life LIBs.

25 ENERGY STORAGE↗

NASA NTRS1989

ROCOPT: A user friendly interactive code to optimize rocket structural components

ROCOPT is a user-friendly, graphically-interfaced, microcomputer-based computer program (IBM compatible) that optimizes rocket components by minimizing the structural weight. The rocket components considered are ring stiffened truncated cones and cylinders. The applied loading is static, and can consist of any combination of internal or external pressure, axial force, bending moment, and torque. Stress margins are calculated by means of simple closed form strength of material type equations. Stability margins are determined by approximate, orthotropic-shell, closed-form equations. A modified form of Powell's method, in conjunction with a modified form of the external penalty method, is used to determine the minimum weight of the structure subject to stress and stability margin constraints, as well as user input constraints on the structural dimensions. The graphical interface guides the user through the required data prompts, explains program options and graphically displays results for easy interpretation.

Rule, William K.↗

DOE OSTI2024

Advancing Building Energy Modeling with Large Language Models: Exploration and Case Studies

The rapid progression in artificial intelligence has facilitated the emergence of large language models like ChatGPT, offering potential applications extending into specialized engineering modeling, especially physics-based building energy modeling. This paper investigates the innovative integration of large language models with building energy modeling software, focusing specifically on the fusion of ChatGPT with EnergyPlus. A literature review is first conducted to reveal a growing trend of incorporating large language models in engineering modeling, albeit limited research on their application in building energy modeling. We underscore the potential of large language models in addressing building energy modeling challenges and outline potential applications including simulation input generation, simulation output analysis and visualization, conducting error analysis, co-simulation, simulation knowledge extraction and training, and simulation optimization. Three case studies reveal the transformative potential of large language models in automating and optimizing building energy modeling tasks, underscoring the pivotal role of artificial intelligence in advancing sustainable building practices and energy efficiency. The case studies demonstrate that selecting the right large language model techniques is essential to enhance performance and reduce engineering efforts. The findings advocate a multidisciplinary approach in future artificial intelligence research, with implications extending beyond building energy modeling to other specialized engineering modeling.

building energy modeling↗

DOE OSTI2025

Countering Weapons of Mass Destruction (CWMD) Zero Trust Framework: CWMD Zero Trust Principles Model

The research focuses on the critical need for enhanced cybersecurity within the Countering Weapons of Mass Destruction (CWMD) Office, specifically targeting Chemical, Biological, Radiological, and Nuclear devices. Traditional perimeter-based security models are insufficient against modern cyber threats, prompting a shift toward Zero Trust principles (ZTP) that emphasize continuous verification and stringent security for all devices. Federal directives mandate the adoption of Zero Trust (ZT) across agencies, supported by guidelines from National Institute of Standards and Technology (NIST), U.S. Department of Homeland Security (DHS) Cybersecurity and Infrastructure Security Agency (CISA), U.S. Department of Defense (DoD) and National Security Agency (NSA). The research involved mapping ZT guidance from these agencies to develop tailored CWMD ZTP. The study identified gaps and areas for improvement, including clear transitional guidance from traditional to ZT architectures and the focus on explicit cross cutting capabilities. Design improvements are recommended to ensure increased comprehensive protection and resilience against sophisticated cyber threats for Chemical, Biological, Radiological, and Nuclear (CBRN) devices. Collaborative efforts among federal agencies are essential for the successful deployment of an optimized ZT guidance.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2024

Understanding the Origin of Negative Temperature Dependence and Activity of N-Coordinated Cobalt Sites During Ethylene Dimerization

The on-demand production of short-chain linear alpha olefins (LAOs; C4-C8) via C2H4 dimerization and oligomerization is industrially attractive, prompting extensive research on designing active, selective, and stable catalysts for industrial use. Cobalt supported on ammoniated carbon (Co(NH3)x/C) catalysts have shown remarkable activity and selectivity in this process. However, critical aspects such as the active phase, active site structure, the role of the catalyst support, cobalt loading effects, and the inverse correlation of the reaction rate with temperature remain inadequately understood. This study systematically explores these factors using a combination of steady-state differential catalytic tests, in situ molecular characterization including diffuse reflectance UV-Vis (DR-UV-Vis), Infrared, and Raman spectroscopies, and ex situ X-ray diffraction (XRD) and high annular aberration-corrected dark field transmission electron microscopy (HAADF-STEM). Various supports (SiO2, Al2O3, NH4-ZSM-5, g-C3N4, and C) and cobalt loadings (1.0-3.0 Co nm-2) were studied to determine the optimal catalyst composition and identify the active phase and sites. Carbon-supported catalysts uniquely produce C4-8 LAOs during C2H4 dimerization, with site-time-yield remaining constant (~10-3 s-1) for 1.0-4.0 Co nm-2 at prolonged reaction times (24-48?h time-on-stream). At higher loadings of 6.0 Co nm-2, the formation of crystalline CoO and Co3O4 phases reduces catalytic activity and LAO selectivity. Our findings show that active catalysts lack crystalline cobalt oxides and instead feature dispersed Co2+ sites, tetra-coordinated to a mix of N/NH3 and O/H2O ligands, which catalyze C2H4 dimerization via the Cossee-Arlman mechanism, exhibiting 1st order dependence on C2H4 concentration. The observed inverse rate-temperature correlation is attributed to compensation effects (i.e., presence of Cremer-Constable relationship) linked to changes in adsorption enthalpic and entropic factors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Powering Data Centers with Clean Energy: A Techno-Economic Case Study of Nuclear and Renewable Energy Dependability

Rising data demands from artificial intelligence (AI) and large language models (LLMs) generating images, videos, and text have prompted increased need for larger and more robust data centers in the United States. Major companies interested in these larger data centers face the choice of linking them to existing regional grids, building stand-alone power supplies onsite, or a combination of both. The request, review, and approval process for new transmission lines to grids in the United States, however, has grown in recent years to times spans rivaling those of new construction for nuclear power plants. Building an islanded power supply for each data center is therefore becoming a prominent option. In this case study, several technologies are modeled in techno-economic simulations for long-term system costs subject to fixed electricity demand from a singular data center. A 250 MWe data center is assumed with additional 50 MWe for resiliency. Techno-economic simulations are conducted using the Holistic Energy Resource Optimization Network (HERON) software, which is a part of the Framework for Optimization of Resources and Economics (FORCE) tool suite. Technologies considered include solar, wind, lithium-ion batteries, and several types of nuclear reactors: large-scale reactors, small modular reactors, and microreactors. A low- and high-cost estimate for each technology is assumed to develop a range of expected economic performance. Low-cost estimates included several clean energy production tax credits. Different combinations of renewable energy generators with nuclear reactors are considered, ranging from a fully renewable-powered data center to a fully nuclear-powered data center. Historic time series of wind and solar availability from the Texas grid are used to train a reduced order model; this model then generates unique time series with similar characteristics of the training dataset. Multiple scenarios of weather and subsequent operations are simulated for each renewable-nuclear combination to determine total costs throughout the project lifetime. Fully renewable-powered configurations required large amounts of installed capacity (GW scale) in the simulations to meet the fixed demand of the data center. This is due to some scenarios in the historical dataset which captured low-wind and low-solar days, requiring over-building of these technologies as well as batteries to compensate for the low amounts of electricity generation. Fully nuclear-powered configurations outperformed the fully renewable and mixed renewable-nuclear configurations in terms of cost, with ranges between $1B and $10B in 2023 USDs compared to $40B+ for fully renewable configurations. Of the nuclear technologies, small modular reactors performed better economically than large-scale nuclear models due to lower projected capital costs, and both performed better than the microreactor models. These results demonstrate the applicability of firm, dispatchable electricity resources from baseload generators like nuclear power plants for operating facilities that run at constant power without daily variability.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Breakthrough innovations in carbon dioxide mineralization for a sustainable future

Greenhouse gas emissions and climate change concerns have prompted worldwide initiatives to lower carbon dioxide (CO 2 ) levels and prevent them from rising in the atmosphere, thereby controlling global warming. Effective CO 2 management through carbon capture and storage is essential for safe and permanent storage, as well as synchronically meeting carbon reduction targets. Lowering CO 2 emissions through carbon utilization can develop a wide range of new businesses for energy security, material production, and sustainability. CO 2 mineralization is one of the most promising strategies for producing thermodynamically stable solid calcium or magnesium carbonates for long-term sequestration using simple chemical reactions. Current advancements in CO 2 mineralization technologies, focusing on pathways and mechanisms using different industrial solid wastes, including natural minerals as feedstocks, are briefly discussed. However, the operating costs, energy consumption, reaction rates, and material management are major barriers to the application of these technologies in CO 2 mineralization. Further, the optimization of operating parameters, tailor-made equipment, and smooth supply of waste feedstocks require more attention to make the carbon mineralization process economically and commercially viable. Here, carbonation mechanisms, technological options to expedite mineral carbonation, environmental impacts, and prospects of CO 2 mineralization technologies are critically evaluated to suggest a pathway for mitigating climate change in the future. The integration of industrial wastes and brine with the CO 2 mineralization process can unlock its potential for the development of novel chemical pathways for the synthesis of calcium or magnesium carbonates, valuable metal recovery, and contribution to sustainability goals while reducing the impact of global warming.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

I/O in Machine Learning Applications on HPC Systems: A 360-degree Survey

Growing interest in Artificial Intelligence (AI) has resulted in a surge in demand for faster methods of Machine Learning (ML) model training and inference. This demand for speed has prompted the use of high performance computing (HPC) systems that excel in managing distributed workloads. Because data is the main fuel for AI applications, the performance of the storage and I/O subsystem of HPC systems is critical. In the past, HPC applications accessed large portions of data written by simulations or experiments or ingested data for visualizations or analysis tasks. ML workloads perform small reads spread across a large number of random files. This shift of I/O access patterns poses several challenges to modern parallel storage systems. In this paper, we survey I/O in ML applications on HPC systems, and target literature within a 6-year time window from 2019 to 2024. We define the scope of the survey, provide an overview of the common phases of ML, review available profilers and benchmarks, examine the I/O patterns encountered during offline data preparation, training, and inference, and explore I/O optimizations utilized in modern ML frameworks and proposed in recent literature. Lastly, we seek to expose research gaps that could spawn further R&D.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Sustainable extraction of rare earth elements from coal fly ash leachates using a recyclable ionic liquid

The growing demand for rare earth elements (REEs) has prompted interest in their recovery from alternative sources such as coal fly ash (CFA). This study explores the ionic liquid (IL) betainium bis(trifluoromethylsulfonyl)imide, [Hbet][Tf 2 N], for selective extraction of REEs from leachates of a Class C CFA. While previous studies have demonstrated the effectiveness of using [Hbet][Tf 2 N] to extract REEs from different types of CFA in direct ash-IL systems, this study investigates four CFA leachates prepared using HCl, HNO 3 , H 2 SO 4 , and citrate. Extraction experiments were conducted across varying pH levels and with additives such as ascorbic acid and betaine. Among the systems tested, [Hbet][Tf 2 N] achieved REE recoveries of 51% and 47% from the HCl and citrate leachates, respectively, comparable to 49% REE recovery in ash-IL extraction. Co-extraction of bulk elements was significantly reduced in the leachate-IL systems. Optimal REE extraction occurred near pH 11, and addition of ascorbic acid effectively suppressed iron co-extraction without compromising REE recovery. Recycling experiments demonstrated that [Hbet][Tf₂N] retains its performance over five cycles with manageable losses. These results reveal the promise of [Hbet][Tf 2 N] for effectively recovering REEs from leachates of solid wastes, highlighting its applicability as a sustainable strategy for other aqueous REE feedstocks.

chemistry↗

DOE OSTI2024

Vacuum-assisted extrusion to reduce internal porosity in large-format additive manufacturing

Large-scale 3D printing of polymer composite structures has gained popularity and seen extensive use over the last decade. Much of the research related to improving the mechanical properties of 3D-printed parts has focused on exploring new materials and optimizing print parameters to improve geometric control and minimize voids between printed beads. However, porosity at the microstructural level (within the printed bead) has been much less studied although it is almost universally observed at levels of 4 %-10 % when using fiber reinforced materials. This study introduces a vacuum-assist approach that minimizes internal porosity by removing ambient air from the interstitial space between pellets in the hopper and acts as a negative pressure vent for gases that evolve during the initial stages of single-screw extrusion. Vacuum-assisted extrusion was able to reduce porosity below 2 % across a wide range of processing parameters, moisture content, fiber reinforcements, and printing platforms. Specifically, when printing on a large-format extruder (Strangpresse Model-30), the vacuum-assisted extrusion reduced internal porosity by 35–75 % compared to conventional non-vacuum extrusion, and only pores with length scale > 2 microns are affected. The success of this approach prompted the design of a patent-pending continuous vacuum hopper relevant for large-scale 3D printing on commercial systems.

36 MATERIALS SCIENCE↗

NASA NTRS2006

The SWIFT Gamma-Ray Burst X-Ray Telescope

The Swift Gamma-Ray Burst Explorer is designed to make prompt multi-wavelength observations of Gamma-Ray Bursts and GRB afterglows. The X-ray Telescope enables Swift to determine GRB positions with a few arcseconds accuracy within 100 seconds of the burst onset. The XRT utilizes a mirror set built for JET-X and an XMM-Newton/ EPIC MOS CCD detector to provide a sensitive broad-band (0.2-10 keV) X-ray imager with an effective area of more than 120 sq cm at 1.5 keV, a field of view of 23.6 x 23.6 arcminutes, and an angular resolution of 18 arcseconds (HPD). The detection sensitivity is 2x10(exp 14) erg/sq cm/s in 10(exp 4) seconds. The instrument provides automated source detection and position reporting within 5 seconds of target acquisition. It can also measure the redshifts of GRBs with Iron line emission or other spectral features. The XRT operates in an auto-exposure mode, adjusting the CCD readout mode automatically to optimize the science return as the source intensity fades. The XRT measures spectra and lightcurves of the GRB afterglow beginning about a minute after the burst and follows each burst for days or weeks. We provide an overview of the X-ray Telescope scientific background from which the systems engineering requirements were derived, with specific emphasis on the design and qualification aspects from conception through to launch. We describe the impact on cleanliness and vacuum requirements for the instrument low energy response and to maintain the high sensitivity to the fading signal of the Gamma-ray Bursts.

Hill, J. E.↗

NASA NTRS1994

Use of magnetic compression to support turbine engine rotors

Ever since the advent of gas turbine engines, their rotating disks have been designed with sufficient size and weight to withstand the centrifugal forces generated when the engine is operating. Unfortunately, this requirement has always been a life and performance limiting feature of gas turbine engines and, as manufacturers strive to meet operator demands for more performance without increasing weight, the need for innovative technology has become more important. This has prompted engineers to consider a fundamental and radical breakaway from the traditional design of turbine and compressor disks which have been in use since the first jet engine was flown 50 years ago. Magnetic compression aims to counteract, by direct opposition rather than restraint, the centrifugal forces generated within the engine. A magnetic coupling is created between a rotating disk and a stationary superconducting coil to create a massive inwardly-directed magnetic force. With the centrifugal forces opposed by an equal and opposite magnetic force, the large heavy disks could be dispensed with and replaced with a torque tube to hold the blades. The proof of this concept has been demonstrated and the thermal management of such a system studied in detail; this aspect, especially in the hot end of a gas turbine engine, remains a stiff but not impossible challenge. The potential payoffs in both military and commercial aviation and in the power generation industry are sufficient to warrant further serious studies for its application and optimization.

Pomfret, Chris J.↗

NASA NTRS1996

Application of Linear Array Imaging Techniques to the Real-Time Inspection of Airframe Structures and Substructures

Current concern for ensuring, the air-worthiness of the aging commercial air fleet has prompted the establishment of broad-agency programs to develop NDT technologies that address specific aging-aircraft issues. One of the crucial technological needs that has been identified is the development of rapid, quantitative systems for depot-level inspection of bonded aluminum lap joints on aircraft. Research results for characterization of disbond and corrosion based on normal-incidence pulse-echo measurement geometries are showing promise, but are limited by the single-site nature of the measurement which requires manual or mechanical scanning to inspect an area. One approach to developing efficient systems may be to transfer specific aspects of current medical imaging technology to the NDT arena. Ultrasonic medical imaging, systems offer many desirable attributes for large scale inspection. They are portable, provide real-time imaging, and have integrated video tape recorder and printer capabilities available for documentation and post-inspection review. Furthermore, these systems are available at a relatively low cost (approximately $50,000 to $200,000) and can be optimized for use with metals with straight-forward modifications.

Miller, James G.↗

NASA NTRS1992

Experimental tests of a superposition hypothesis to explain the relationship between the vestibuloocular reflex and smooth pursuit during horizontal combined eye-head tracking in humans

1. We used a modeling approach to test the hypothesis that, in humans, the smooth pursuit (SP) system provides the primary signal for cancelling the vestibuloocular reflex (VOR) during combined eye-head tracking (CEHT) of a target moving smoothly in the horizontal plane. Separate models for SP and the VOR were developed. The optimal values of parameters of the two models were calculated using measured responses of four subjects to trials of SP and the visually enhanced VOR. After optimal parameter values were specified, each model generated waveforms that accurately reflected the subjects' responses to SP and vestibular stimuli. The models were then combined into a CEHT model wherein the final eye movement command signal was generated as the linear summation of the signals from the SP and VOR pathways. 2. The SP-VOR superposition hypothesis was tested using two types of CEHT stimuli, both of which involved passive rotation of subjects in a vestibular chair. The first stimulus consisted of a "chair brake" or sudden stop of the subject's head during CEHT; the visual target continued to move. The second stimulus consisted of a sudden change from the visually enhanced VOR to CEHT ("delayed target onset" paradigm); as the vestibular chair rotated past the angular position of the stationary visual stimulus, the latter started to move in synchrony with the chair. Data collected during experiments that employed these stimuli were compared quantitatively with predictions made by the CEHT model. 3. During CEHT, when the chair was suddenly and unexpectedly stopped, the eye promptly began to move in the orbit to track the moving target. Initially, gaze velocity did not completely match target velocity, however; this finally occurred approximately 100 ms after the brake onset. The model did predict the prompt onset of eye-in-orbit motion after the brake, but it did not predict that gaze velocity would initially be only approximately 70% of target velocity. One possible explanation for this discrepancy is that VOR gain can be dynamically modulated and, during sustained CEHT, it may assume a lower value. Consequently, during CEHT, a smaller-amplitude SP signal would be needed to cancel the lower-gain VOR. This reduction of the SP signal could account for the attenuated tracking response observed immediately after the brake. We found evidence for the dynamic modulation of VOR gain by noting differences in responses to the onset and offset of head rotation in trials of the visually enhanced VOR.(ABSTRACT TRUNCATED AT 400 WORDS).

Non-NASA Center↗