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DOE OSTI2023

Study of Storage Requirements and Costs for Shaping Renewables and Nuclear Energy (FY23 Summary Report)

To decarbonize electricity generation primarily by using wind and solar resources, it is likely that additional low (or zero) carbon dioxide (CO 2 ) alternatives will be required for ensuring a reliable electricity supply. This study extends the work and modeling framework that we consolidated in FY 2022, by assessing the zero-CO 2 pathways of a Texas power system in which the availability of variable renewable energy (VRE), nuclear energy, and energy storage types (i.e., battery and thermal energy storage [TES]) are considered the sole resources available for expansion. Using the Risk Analysis Virtual Environment (RAVEN) and Holistic Energy Resource Optimization Network (HERON) frameworks, this study investigates the market of two nuclear energy technologies (i.e., large light-water reactors [LWRs] and LWR-type small modular reactors [SMRs]) under two plausible storage coupling scenarios (i.e., electric coupling and direct thermal coupling). We also explore optimal environments for nuclear energy deployment, and identify key performance characteristics that make nuclear energy economically viable in areas with significant intermittent energy sources. Our analysis encompasses 24 cases. We model the least-cost grid systems, highlight the potential for a coupled LWR-TES approach, and utilize SMRs to achieve deep decarbonization in an affordable, technically feasible manner. We also examine seasonal variations in balancing electricity supply and demand, and account for varying performance, costs, and grid constraints. Overall, the findings of our modeling afford valuable insights for supporting future technology investment decisions in the energy sector.

25 ENERGY STORAGE↗

DOE OSTI2023

VERA Integration in the NEAMS Workbench

The Virtual Environment for Reactor Applications (VERA) is a cutting edge simulation code suite used to solve complex multiphysics problems for nuclear reactors. VERA makes use of high performance computing (HPC) systems to solve large problems that were not historically tractable. While extensive work has been put into the usability of VERA, it still takes significant effort to build reactor models, ensure their correctness, properly execute the calculations, and conduct the necessary analysis of the results. This difficulty is common for many complex codes such as VERA. As part of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the NEAMS Workbench has been developed and maintained. The goal of the NEAMS Workbench is to provide a single interface with which to execute a wide variety of physics codes, from building the input models to analyzing the results. This document serves as a manual detailing the current integration of VERA with the NEAMS Workbench and a guide for configuring Workbench to allow remote execution of VERA.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Differences In High Burnup Fuel Management Strategies to Minimize FFRD and Increase Economic Viability

The nuclear industry is pursuing approval of an increase in the length of the pressurized water reactor (PWR) cycle from 18 months to 24 months to reduce reactor downtime and enhance the economic competitiveness of nuclear energy. Such an increase in reactor cycle length will require that the maximum rod average burnup exceeds the current regulatory limit of 62 GWd/MTU, and it could peak at approximately 75 GWd/MTU, posing potential reactor safety and performance concerns. One such concern is that fuel fragmentation, relocation, and dispersal (FFRD) could occur during a severe loss-of coolant accident (LOCA) in which a fuel rod balloons and bursts, and pulverized fuel fragments are dispersed throughout the reactor’s primary coolant system. Previous analyses have identified which reactor operating conditions leave the core more susceptible to FFRD and have shown that FFRD susceptibility is strongly linked to fuel rod burnup and linear heat rate (LHR) history. The work described in this report uses an optimization strategy known as parallel simulated annealing (PSA) and a coarse mesh Purdue Advanced Reactor Core Simulator (PARCS) reactor physics model to develop two core fuel loading patterns, each with a different optimization objective. One core optimization maximized the core’s cycle length while still respecting regulatory limits on the radial peaking factor and soluble boron concentration with a peak rod average burnup of 75 GWd/MTU. The second optimization was aimed at minimizing FFRD susceptibility while still targeting a 24-month cycle length and respecting regulatory limits. PARCS model predictions were verified using the high-fidelity Virtual Environment for Reactor Applications (VERA). The two core designs were compared to highlight core design strategies to minimize FFRD susceptibility and to maximize economic viability.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Enhanced LWR High Burnup Transient Simulation Capabilities to Support AOO Margin Identification

As part of ongoing efforts to support the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program’s development of a fuel fragmentation, relocation, and dispersal (FFRD) screening methodology, a number of improvements are required for the NEAMS core simulation capabilities, namely the Virtual Environment for Reactor Applications (VERA). Three areas of improvement were identified in VERA which are important for continued development and application of the FFRD screening methodology. First, the FFRD screening methodology will soon be extended to boiling water reactors (BWRs), requiring development and validation of the VERA BWR capabilities. Second, the screening methodology occasionally requires that VERA be used to simulate a transient in addition to nominal operations. Thus, improvements to both accuracy and performance of the VERA transient capabilities are necessary. Third, the VERAOneWay component of VERA is used to develop BISON fuel performance inputs using the rod-by-rod histories calculated by VERA. Prior use of VERAOneWay exposed significant accuracy, robustness, and performance issues with VERAOneWay; these must be addressed for it to be an effective tool in the NEAMS FFRD methodology. This report documents the efforts in FY23 in each of these three areas to enable successful use of VERA and VERAOneWay for FFRD calculations in FY24 and following years.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Parametric and Sensitivity Analysis of a Steam Generator Model Using Python and Machine-Learning Tools

For this study, we used Python and machine-learning tools to perform a comprehensive parametric and sensitivity analysis on a steam generator (SG) model. (The Python model was based on a previously completed MATLAB framework for the Holtec SMR-160 SG.) We investigated the influence of various input parameters (e.g., heat transfer coefficient [HTC], Nusselt number, and heat exchanger effectiveness) on the system’s output. With machine-learning tools such as the Risk Analysis Virtual Environment (RAVEN), which was developed at Idaho National Laboratory, we were then able to perform an automated analysis of the SG inputs’ effect on the HTC. The analysis results give valuable insights into the performance and optimization of SG systems. We found the inlet mass flow rate (MFR) to have the greatest impact on the HTC, followed closely by the inlet temperature, and then pressure. Shifting of the input parameters causes the location of the maximum HTC along the SG length to change incrementally. The cold leg (CL) MFR was also found to impact the HTC magnitude as well as the location of the maximum HTC. At between 0.4–0.9 of the total SG length, the input parameters experience maximum impact on the HTC, leading us to suggest that sensors be efficiently placed on the SG so as to closely and effectively monitor thermal-hydraulic properties during reactor operation. We also found that the sensitivity data calculated manually agrees with the RAVEN – based data, confirming the same range of maximum sensitivity. However, the RAVEN-based analysis showed that cold leg pressure and hot leg temperature have a greater impact on the heat transfer coefficient than the mass flow rate, implying that a manual sensitivity study taking only two samples is not accurate.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2024

Once-Through Steam Generator Model Analysis Using Python and Advanced Optimization Tools (Summer Internship Report)

This study focuses on the parametric analysis of design parameters for a once-through steam generator (OTSG) model, using python and advanced optimization tools to facilitate applications such as the flowing autoclave steam generator (FASG) test cases. Building on previous research involving another OTSG with a different design, this project aims to enhance our understanding of how steam generators (SGs) behave and how their outputs are influenced by changes in design. The reason for this design change is to allow for more precise modeling and optimization of SG performance, to provide a comparative analysis between the two designs, and to set up the model for integration with the FASG test case. The OTSG python-model is a mathematical representation (including fluid flow and heat transfer equations/models/correlations) of a steam-generating unit in a pressurized water reactor-type small modular reactor system. Design studies involve changing the model’s input design parameters to observe the resulting effects on the output of the system. By using advanced optimization tools, such as the Risk Analysis Virtual Environment (RAVEN) developed at Idaho National Laboratory, detailed design parametric studies and model optimization were performed. Six input parameters—pressure, temperature, and mass flow rate for the inlet of the primary-side (hot fluid) and secondary-side (cold fluid), respectively, of the SG—were randomly perturbed via RAVEN’s Monte Carlo Sampler module, using uniform distributions (i.e., ±1%, ±5% and ±10% relative changes) for 600 samples. The analysis provides valuable insights into SG optimization and can be used for sensor placement optimization to effectively monitor and obtain experimental data in other tests.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2024

FORCE Update 2024

The Framework for Optimization of Resources and Economics (FORCE) tool suite is the U.S. Department of Energy’s Nuclear Integrated Energy Systems (IES) Program flagship tool suite for technoeconomic IES analysis of IES. This tool suite is useful for analysis designed to evaluate and improve the technoeconomics of energy production systems, particularly for systems including nuclear technology. In this report, we document the development activity for the FORCE tool suite to extend its capabilities as performed during fiscal year 2024. In addition to reliability and accessibility, capability is one of the three standards guiding the development of the FORCE tool suite and the software codes that are its constituent parts. Extending the capabilities of the FORCE tool suite allows analysis both within the IES program as well as industry, university, and laboratory partners to perform analysis with more accuracy, insight, and impactful narrative. Four areas of capability development were the focus of activity this year: economic parameter uncertainty quantification, multiresolution analysis, components-to-optimization workflow automation, and statespace construction workflows for real-time optimal control. In economic parameter uncertainty quantification, the ability of HERON to capture risk due to scenarios (weather and energy demand uncertainty) was expanded to also include uncertainties in financial parameters such as capital cost or operation and maintenance costs. By including these sources of uncertainty, which are sometimes very large compared with scenario uncertainty, HERON is better able to capture the risk posed by investment in various IES technology. Because of this, analysts can also consider the reduction in risks that can be realized by choice of some technologies. In multiresolution analysis, development activity extended on work completed previously. In fiscal year 2023, methods for decomposing time series signals, such as demand, solar and wind availability, and price profiles, were analyzed and down-selected to those most effective at splitting signals into different resolutions. These resolutions allow considering the influence of different energy demand and supply behaviors across different time scales. For example, energy demand might be divided into seasonal, weekly, and hourly profiles. In fiscal year 2024, this preliminary work was extended and implemented within the Risk Analysis Virtual Environment (RAVEN) risk and uncertainty analysis platform, which is used throughout the FORCE framework. This development of the “multi-resolution time series analysis” (MR-TSA) module in RAVEN allows training synthetic history generators on complex time series. These synthetic history generators can then be used in HERON for generating scenarios that represent possible market and weather scenarios that can be analyzed on different time scales. We envision completing this work in the future, implementing multiresolution dispatch optimization strategies that can make the most beneficial use of these stratified time histories. In components-to-optimization workflow development, workflows for translating user inputs of components into algorithms for algebraic optimization were selected and implemented. Similar algorithms within the Holistic Energy Resource Optimization Network (HERON) were separated from the main code base of HERON and gathered with the components-to-optimization workflows in the new Dispatch Optimization Variable Engine (DOVE) software library. This modularization allows FORCE users to analyze dispatch optimization and energy system duty cycles independently of HERON, which previously was a burdensome task. Additionally, these dispatch optimization algorithms, set up in an independent library, can now be used across all software applications within FORCE, especially including the real-time optimal control software Optimization of Real-time Capacity Allocation (ORCA). Allowing FORCE software to share dispatch optimization algorithms within a single library allows for improved software maintenance and reliability. In statespace characterization workflow development, alternative workflows for optimizing dispatch with additional technical accuracy was the focus, particularly to improve the real-time optimization decision making in ORCA. Using algorithms and workflows initially developed for the Feasible Actuator Range Modifier (FARM), workflows for determining the statespace representation of IES were identified and demonstrated. The resulting dispatch optimization required a more robust optimization algorithm than that originally used in HERON (and moved to DOVE), which required adding an alternate workflow to DOVE that can more accurately match the behavior of physical systems using a partial differential equation representation. In conclusion, capability developments in the FORCE tool suite in fiscal year 2024 have improved the ability of the FORCE tool suite to perform

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Reassessment of Boiling Water Reactor Rod Cusping Capability of VERA-MPACT

This technical report assesses the current modeling capabilities of the Virtual Environment for Reactor Applications (VERA) for minimizing control rod cusping in boiling water reactors. Assessment is performed on a 10 × 10 General Electric (GE)-14 assembly as well as a simplified version of the same assembly. Runs were performed using VERA for three available cusping treatments in the Michigan PArallel Characteristics Transport (MPACT) code: no treatment, subplane treatment, and fitted polynomial correction treatment. Subplane treatment is discussed, but specific results are not provided because of failures in the method of implementation for boiling water reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Reassessment of Boiling Water Reactor Rod Cusping Capability of VERA-MPACT

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

High-Burnup LOCA Burst Susceptibility BISON Analysis in PWRs and BWRs

Accurately assessing high-burnup fuel behavior during loss-of-coolant accidents (LOCAs) is essential for understanding fuel fragmentation, relocation, and dispersal (FFRD) risks across the US light-water reactor fleet. This work updates previous Nuclear Energy Advanced Modeling and Simulation (NEAMS) Program multiphysics LOCA analyses for a pressurized water reactor (PWR) and a boiling water reactor (BWR) by incorporating recent model and material property advancements in the BISON fuel performance code, including a high-burnup structure (HBS) model, revised cladding burst criteria, and updated thermal–mechanical correlations. This update was needed to support ongoing industry initiatives and upcoming regulatory changes. Full-core, rod-resolved operating histories generated using Virtual Environment for Reactor Analysis (VERA) and system-level LOCA conditions obtained from TRACE were applied to statistically representative rod samples in BISON to evaluate burst behavior and FFRD susceptibility. These calculations used two cladding burst correlations and three fuel pulverization models so that the predictions of these models could be compared. The updated PWR simulations show markedly improved numerical stability as the number of crashed simulations decreased by 95% compared to the previous study, and hence higher confidence in results. The updated PWR simulations predicted cladding bursts exclusively among once-burned, high-power rods, with two different cladding burst models identifying the same burst-susceptible population. Resulting FFRD susceptibility estimates are significantly reduced compared with earlier studies, driven by cooler predicted fuel and plenum temperatures, lower hoop strains, and reduced fission gas release in the updated models. In contrast, none of the BWR rods were predicted to burst under either burst criterion, reaffirming minimal BWR FFRD susceptibility even with updated HBS and material models. Comparisons between the PWR and BWR end-of-cycle predictions are made. Comparison with prior work highlights significant shifts in PWR fuel performance metrics and confirmation of earlier BWR conclusions. Overall, the updated results underscore the importance of having high-resolution detailed modeling capability and continuously integrating evolving material models and physics into high-resolution multiphysics simulations. The unified assessment presented here strengthens confidence in predicting high-burnup LOCA behavior by improving agreement between different cladding burst correlations. These results also provide an improved foundation for future BISON model development, FFRD susceptibility calculations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

NW-BRaVE T3 Hydroplane Project Close: Project Close-out for T3 Hydroplane Analysis

Thrust 3 of the Northwest Biopreparedness Research in a Virtual Environment was an expansive project including method development, sample collection and sequence analysis. The sampling occurred over a multi-year period to generate metagenomic datasets that inform cyanophage-picocyanobacterial interactions in the Salish Sea across a moderate timeframe and geographical range. Part of the thrust’s aim was to validate how much experimental structural and multiomics work in a model organism (Prochlorococcus Marinus, str. MED4) from thrusts 1 and 2 would carry over into a broader range of related organisms in the natural world, to address a fundamental question in scientific preparation for epidemics: whether and how much experimental information from known and experimentally tractable species can translate to actionable biological information in unknown species. In other words, thrust 3 aimed to find out whether the model organism experiments matter in terms of how organisms interact. This report updates work described in Johnson and Pollock 2025 (1).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Update on Radiochemical Assessment of High Burnup Commercially Irradiated Fuel

This work documents an effort to collect burnup measurements on a high burnup rod, designated 6XV, and first cycle accident tolerant fuel (ATF) rod, designated 47I, to enable benchmarking of fuel performance codes and neutronics codes. In addition to measurements, Virtual Environment for Reactor Applications (VERA) full-core-depletion analysis was also performed for the rods that were experimentally analyzed to provide an opportunity for code validation. This effort focuses on collecting data from rods irradiated at Byron Generating Station and shipped to the Oak Ridge National Laboratory (ORNL) hot-cells. This data will also anchor non-destructive examination evaluations of burnup of the various fuel rods undergoing postirradiation examination (PIE) at ORNL. Previous PIE of these fuel rods provides some guidance on the burnup trend across the fuel. Axial gamma spectroscopy scans provide a measure of relative changes in burnup across a fuel pin. Mass spectrometry based burnup measurements performed for this work at specific axial locations in the fuel are fully quantitative. By combining the mass spectrometry data with the gamma scans it is possible to more quantitatively evaluate axial variations in burnup across the entire fuel pin [1]. The combined set of burnup evaluations will be made available to other organizations that have an interest in high burnup radiochemistry data for validation of neutronic simulations and source term evaluation such as the Nuclear Regulatory Commission (NRC).

Harp, Jason [Oak Ridge National Laboratory (ORNL),↗

DOE OSTI2023

Insights into Methodologies and Stochastic Optimization of Thermal Energy Storage-Coupled Advanced Reactor Systems: A Comparison of Methods for Accessing Long-Term Sub-System Sizing Adequacy

This paper investigates the potential of coupling Thermal Energy Storage (TES) with Advanced Reactors (ARs) to address uncertainties posed by climate change in deep decarbonized power systems. The TES Use-case Team at Idaho National Laboratory (INL) has examined the potential of storing thermal energy from ARs during low demand periods and optimizing discharge during peak-priced hours, in both steady-state and transient conditions. Building on this groundwork, this study bridges the gaps in optimal sizing of the sub-system of TES-coupled AR systems using Risk Analysis Virtual Environment (RAVEN) and Holistic Energy Resource Optimization Network (HERON), INL?s framework for grid optimization. By applying this framework, we present statistically-robust optimal charge, discharge including balance of plant (BOP), and storage sizing for the High-Temperature Gas-Cooled Reactor (HTGR) with 203 MWth output. To this end, we generated synthetic price samples for 30 years using 2018 ? 2021 real-time market data from ERCOT, PJM and MISO. Our results reveals that the TES-coupled HTGR system is highly effective in maximizing revenue from electricity sales. We observed a substantial increase of 40 % in ERCOT and a noteworthy 15 % increase in PJM and MISO when compared to the conventional BOP without TES. This improvement is achieved through regionally-tailored sub-system sizing, which ranges from 398 to 416 MWth for the discharge system and 610 to 1029 MWth for the TES. We find that the average electricity price directly impacts the overall economics, while price volatility influences storage size. Additional sensitivity analyses were performed to access the impact of key assumptions on system economics and sizing, differentiating the optimization window (i.e., 24 ? 219 hours of chronological observations) and by imposing storage continuity condition in tracking TES cycles. We observed that at the 120-hour of the optimization window, a reasonable balance between computation time and accuracy was achieved. Our analysis also highlights the significance of conducting multi-day cycle analysis (> 120-hour) for TES to capture interaction between electricity prices and storage dynamics, providing a comprehensive understanding of TES behavior that AR developers should integrate into their plant designs.

25 ENERGY STORAGE↗

DOE OSTI2021

A Hybrid Energy System Workflow for Energy Portfolio Optimization

This manuscript develops a workflow, driven by data analytics algorithms, to support the optimization of the economic performance of an Integrated Energy System. The goal is to determine the optimum mix of capacities from a set of different energy producers (e.g., nuclear, gas, wind and solar). A stochastic-based optimizer is employed, based on Gaussian Process Modeling, which requires numerous samples for its training. Each sample represents a time series describing the demand, load, or other operational and economic profiles for various types of energy producers. These samples are synthetically generated using a reduced order modeling algorithm that reads a limited set of historical data, such as demand and load data from past years. Numerous data analysis methods are employed to construct the reduced order models, including, for example, the Auto Regressive Moving Average, Fourier series decomposition, and the peak detection algorithm. All these algorithms are designed to detrend the data and extract features that can be employed to generate synthetic time histories that preserve the statistical properties of the original limited historical data. The optimization cost function is based on an economic model that assesses the effective cost of energy based on two figures of merit: the specific cash flow stream for each energy producer and the total Net Present Value. An initial guess for the optimal capacities is obtained using the screening curve method. The results of the Gaussian Process model-based optimization are assessed using an exhaustive Monte Carlo search, with the results indicating reasonable optimization results. The workflow has been implemented inside the Idaho National Laboratory’s Risk Analysis and Virtual Environment (RAVEN) framework. The main contribution of this study addresses several challenges in the current optimization methods of the energy portfolios in IES: First, the feasibility of generating the synthetic time series of the periodic peak data; Second, the computational burden of the conventional stochastic optimization of the energy portfolio, associated with the need for repeated executions of system models; Third, the inadequacies of previous studies in terms of the comparisons of the impact of the economic parameters. The proposed workflow can provide a scientifically defendable strategy to support decision-making in the electricity market and to help energy distributors develop a better understanding of the performance of integrated energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

A High-Fidelity Model of the Peach Bottom 2 Turbine-Trip Benchmark Using VERA

This work presents a high-fidelity simulation of the Peach Bottom turbine trip (PBTT) benchmark using the Virtual Environment for Reactor Applications (VERA), a multiphysics reactor modeling tool developed by the U.S. Department of Energy’s Consortium for Advanced Simulation of Light Water Reactors energy innovation hub. The PBTT benchmark, based on a 1977 transient event at the end of cycle 2 in a General Electric Type-4 boiling water reactor (BWR), is a critical test case for validating core physics models with thermal feedback during rapid reactivity events. VERA was employed to perform end-to-end, pin-resolved simulations from conditions at the beginning of cycle 1 through the turbine-trip transient, incorporating detailed neutron transport, fuel depletion, and subchannel thermal hydraulics. The simulation reproduced key benchmark observables with high accuracy: the peak power excursion occurred at 0.75 s, matching the scram time and closely aligning with the benchmark average of 0.742 s; the simulated maximum power spike was approximately 7600 MW, which is within 3% of the benchmark average of 7400 MW; and void-collapse dynamics were consistent with benchmark expectations. Reactivity predictions during cycles 1 and 2 remained within 1500 pcm and 400 pcm of criticality, respectively. These results confirm VERA’s ability to model complex coupled neutronic and thermal hydraulic behavior in a BWR turbine-trip transient, which will support its use in future studies of modeling dryout, fuel performance, and uncertainty quantification for transients of this type.

BWR↗

NASA NTRS1989

Sensory conflict in motion sickness: An observer theory approach

Motion sickness is the general term describing a group of common nausea syndromes originally attributed to motion-induced cerebral ischemia, stimulation of abdominal organ afferent, or overstimulation of the vestibular organs of the inner ear. Sea-, car-, and airsicknesses are the most commonly experienced examples. However, the discovery of other variants such as Cinerama-, flight simulator-, spectacle-, and space sickness in which the physical motion of the head and body is normal or absent has led to a succession of sensory conflict theories which offer a more comprehensive etiologic perspective. Implicit in the conflict theory is the hypothesis that neutral and/or humoral signals originate in regions of the brain subversing spatial orientation, and that these signals somehow traverse to other centers mediating sickness symptoms. Unfortunately, the present understanding of the neurophysiological basis of motion sickness is far from complete. No sensory conflict neuron or process has yet been physiologically identified. To what extent can the existing theory be reconciled with current knowledge of the physiology and pharmacology of nausea and vomiting. The stimuli which causes sickness, synthesizes a contemporary Observer Theory view of the Sensory Conflict hypothesis are reviewed, and a revised model for the dynamic coupling between the putative conflict signals and nausea magnitude estimates is presented. The use of quantitative models for sensory conflict offers a possible new approach to improving the design of visual and motion systems for flight simulators and other virtual environment display systems.

Oman, Charles M.↗

NASA NTRS1991

Virtual acoustics displays

The real time acoustic display capabilities are described which were developed for the Virtual Environment Workstation (VIEW) Project at NASA-Ames. The acoustic display is capable of generating localized acoustic cues in real time over headphones. An auditory symbology, a related collection of representational auditory 'objects' or 'icons', can be designed using ACE (Auditory Cue Editor), which links both discrete and continuously varying acoustic parameters with information or events in the display. During a given display scenario, the symbology can be dynamically coordinated in real time with 3-D visual objects, speech, and gestural displays. The types of displays feasible with the system range from simple warnings and alarms to the acoustic representation of multidimensional data or events.

Wenzel, Elizabeth M.↗

NASA NTRS1990

Head-coupled remote stereoscopic camera system for telepresence applications

The Virtual Environment Workstation Project (VIEW) at NASA's Ames Research Center has developed a remotely controlled stereoscopic camera system that can be used for telepresence research and as a tool to develop and evaluate configurations for head-coupled visual systems associated with space station telerobots and remore manipulation robotic arms. The prototype camera system consists of two lightweight CCD video cameras mounted on a computer controlled platform that provides real-time pan, tilt, and roll control of the camera system in coordination with head position transmitted from the user. This paper provides an overall system description focused on the design and implementation of the camera and platform hardware configuration and the development of control software. Results of preliminary performance evaluations are reported with emphasis on engineering and mechanical design issues and discussion of related psychophysiological effects and objectives.

Bolas, M. T.↗