Results for “behavioral sciences” — Page 30

NASA NTRS2003

Cognitive deficits induced by 56Fe radiation exposure

Exposing rats to particles of high energy and charge (e.g., 56Fe) disrupts neuronal systems and the behaviors mediated by them; these adverse behavioral and neuronal effects are similar to those seen in aged animals. Because cognition declines with age, and our previous study showed that radiation disrupted Morris water maze spatial learning and memory performance, the present study used an 8-arm radial maze (RAM) to further test the cognitive behavioral consequences of radiation exposure. Control rats or rats exposed to whole-body irradiation with 1.0 Gy of 1 GeV/n high-energy 56Fe particles (delivered at the alternating gradient synchrotron at Brookhaven National Laboratory) were tested nine months following exposure. Radiation adversely affected RAM performance, and the changes seen parallel those of aging. Irradiated animals entered baited arms during the first 4 choices significantly less than did controls, produced their first error sooner, and also tended to make more errors as measured by re-entries into non-baited arms. These results show that irradiation with high-energy particles produces age-like decrements in cognitive behavior that may impair the ability of astronauts to perform critical tasks during long-term space travel beyond the magnetosphere. Published by Elsevier Science Ltd on behalf of COSPAR.

Non-NASA Center↗

DOE OSTI2024

Deployment of BISON models of fuel restructuring at high burnup and related fission gas behavior in UO 2

This milestone report details the advancements made in fiscal year 2024 under the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to improve the modeling of fission gas behavior in high burnup UO 2 nuclear fuel in the BISON fuel performance code. As nuclear fuel is pushed to higher burnups, significant microstructural changes occur within the fuel, including the formation of a high burnup structure (HBS) on the pellet rim and a dark zone deeper within the pellet. These regions, characterized by subgrain formation and increased pore densities, have critical implications for fission gas behavior and release, which are not well understood. The modeling capabilities in BISON did not adequately predict these phenomena, leading to an underestimation of fuel restructuring and - potentially - of fission gas release. To address these gaps, this milestone focused on three key objectives: (1) reviewing and assessing Sifgrs's capabilities for low burnup fuel, on which high burnup capabilities rely, (2) validating and expanding HBS fission gas modeling capabilities, including investigating mechanisms for fission gas release from HBS, and (3) expanding Sifgrs to enable modeling of dark zone formation and its effects on fission gas behavior. These objectives were achieved and are described herein. The achievements of this NEAMS milestone are significant for the industry's goal of burnup extension. The improved predictive modeling capabilities for both low- and high-burnup conditions enhance our understanding of fuel performance under both normal operations and transient scenarios. Although goals were reached, future work is necessary to validate these models against experimental data and quantify their accuracy in different conditions. In parallel, mechanistic modeling efforts should continue to extend and refine these capabilities to increase accuracy while reducing reliance on empirical models. This will ensure robust performance across a broader range of conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2015

NASA's Rodent Research Project on ISS: Validation of a New Platform for Conducting Biomedical and Basic Research into the Consequences of Long Duration Habitation in Space

Rodent research has played a key role in advancing biomedical discoveries both on Earth and in space. The National Research Counsel’s Decadal survey(1) emphasized the importance of expanding NASAs life sciences research to perform long duration, rodent experiments on the International Space Station (ISS). To accomplish this objective, flight hardware, operations, and science capabilities were developed at NASA ARC to support both commercial and government-sponsored rodent research.Rodent Research-1 (RR-1) was the first mission in which animals were delivered and maintained in the ISS for a long duration mission in modified Animal Enclosure Module hardware. Both RR validation and commercial science objectives were pursued on the RR-1 mission. Adult female mice (20 total Flight, FLT) were launched Sept 21, 2014 in RR hardware within a Dragon Capsule (SpaceX4), then after 4 days in transit, were transferred for habitation on the ISS for 17 days (commercial) or 33 days (validation), when animals were euthanized and select tissues recovered on orbit. Various controls groups consisted of: 1) Basal mice from the same cohorts as FLT mice, but tissues were recovered at time of launch, 2) Vivarium (VIV) were housed in standard cages 3) Ground Controls (GC) were housed in flight hardware within an environmental chamber at Kennedy Space Center. The health and behavior of all mice on the ISS were monitored by video feed on a daily basis. Mice were euthanized by injection of Euthasol, then either fast frozen intact or dissected to preserve livers (fast frozen) and spleens (RNAlater). Samples were stored at ≤ -80˚C until their return to Earth for later analyses.Hardware performed nominally throughout the mission and the planned in-flight science operations were completed successfully. FLT mice appeared generally more physically active on orbit than respective GC groups. After 33 days on the ISS, mean body weights of FLT mice did not differ from GC, with both groups showing a 6% rise compared to time of launch, while VIV mice showed an 8% rise over the same period. Importantly, there were no significant differences in body weights between groups at the end of 33 days on the ISS, providing an indication that the RR hardware supported the health of the mice both on Earth and in space. Based on the preliminary data obtained from the livers and spleens of mice after 17 days on the ISS, purified RNA was of high quality (RIN values of spleen: FLT=9.48 +0.40, GC=9.28 +0.44, n=5/group); therefore, RNA quality from samples retrieved on orbit was acceptable for even the most demanding transcriptomic analyses. In addition, liver enzyme activity levels (units/mg protein) of FLT mice (after 17d on ISS) and all control mice were similar in magnitude to samples that were optimally prepared by freezing in liquid nitrogen in the laboratory (enzymes analyzed included catalase, glutathione reductase and glyceraldehyde-3-phosphate dehydrogenase). Validation analyses still in progress include behavior and tissue biochemistries, as well as optimization of science return by post-flight recovery of tissues for biospecimen sharing and global expression analyses.Together, these preliminary findings demonstrate new capability for supporting long duration rodent research on the ISS to achieve both basic science and biomedical objectives.

Globus, R. K.↗

DOE OSTI2026

“One Table to Rule Them All”: How a Single Table can Enable Extensive Insights, Analytics and Assessment on Human Mobility Data

While much research has been conducted in Human Mobility Science, most studies on the analytics/insights part generally focus on one of the following: processing and analytics on human stop-trip behavior, design of individual mobility metrics (often in silos), calculation and characterization of only a handful (typically 5-6) of human mobility metrics on geospatial-temporal human mobility data of interest. Although human mobility research offers a vast and diverse array of available metrics, most individual studies typically compute only a small subset of five or six metrics at a time when analyzing trajectory datasets of human mobility across different areas of interest. This paper is motivated by the critical need to repeatedly compute an extensive array of human mobility metrics across several trajectory datasets and perform individual metric-level benchmarking to establish a new, standardized Test and Evaluation (T&E) suite for the field of Human Mobility Science. We first present our findings on the minimal yet sufficient pre-processing required to reliably and efficiently compute a wide range of human mobility metrics. The key findings are specifically related to the proposed Composite Stop Locations table, which serves as a core pre-processing data layer. Subsequently, we present a case study demonstrating how the Composite Stop Locations table facilitates computation of at least 14 distinct human mobility metrics (unlike 5-6 different set of metrics used for studies in the literature) using the popular and open-source OpenPFLOW dataset. Finally, we have also presented an example of our benchmarking methodology to evaluate the quality and performance of the trajectory dataset of interest, assessed across multiple human mobility metrics.

De, Debraj [ORNL] (ORCID:0000000233630020)↗

DOE OSTI2026

Uncertainty guided online ensemble for non-stationary data streams in fusion science

Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior with distribution drifts, resulted by both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with such non-stationary data streams. Online learning techniques have been leveraged in other domains, however it has been largely unexplored for fusion applications. In this paper, we investigate online learning for continuous adaptation to drifting data streams in the prediction of Toroidal Field (TF) coils deflection at the DIII-D fusion facility. We further address the short-term performance degradation inherent to standard online learning, which arises because ground truth is unavailable at prediction time. To mitigate this issue, we propose an uncertainty-guided online ensemble framework. The method leverages the Deep Gaussian Process Approximation (DGPA) for calibrated uncertainty estimation and uses these uncertainty measures to guide a meta-algorithm that aggregates predictions from learners trained over different historical horizons. Our results show that online learning reduces prediction error by 80% compared to a static model. The online ensemble and the proposed uncertainty-guided ensemble further reduce error by approximately 6%, and 10% respectively, relative to standard single-model online learning, while also providing calibrated uncertainty estimates to support operational decision-making.

AI↗

DOE OSTI2022

Mechanical behavior and shear band of a powder-metallurgy-fabricated CoCrFeMnNi high-entropy alloy during high strain-rate deformation

High-entropy alloys (HEAs) fabricated by powder metallurgy are considered to exhibit extremely-great potential application values in the field for high-speed impact deformation due to their uniform microstructures and compositions. The mechanical behavior of a powder-metallurgy-fabricated (PM) CoCrFeMnNi HEA was investigated at room temperature with strain rates ranging from 1200 s -1 to 2800 s -1 . It was found that the serration behavior and shear localization of the alloy under dynamic conditions occurred. Results also indicated that the serrated flow took place in the PM CoCrFeMnNi HEA during mechanical deformation at high strain rates (>1000 s -1 ) and became more pronounced as the strain rate increased. Furthermore, it was determined that the yield-strength values of the PM CoCrFeMnNi HEA were sensitive to strain rates, where the values increased from 500 MPa to 700 MPa when the strain rates increased from 1200 s -1 to 2800 s -1 . Shear bands with a width of about 20 μm were generated in the PM CoCrFeMnNi HEA after the shear stress reached a maximum value of 635 MPa, which corresponded to a nominal strain of about 8.71. Nanotwins and ultrafine-equiaxed grains with a diameter of about 150 nm also developed at the core of the shear band during deformation. The results revealed that the strength of the shear band was much higher than that of the matrix grain. Nanotwins in the shear band were composed of a single face-centered-cubic (FCC) structure with the twin plane of ($\bar{1}$1$\bar1$), which could be formed via a rotational dynamic recrystallization (RDR).

36 MATERIALS SCIENCE↗

DOE OSTI2021

Characterization of Extremes and Compound Impacts: Applications of Machine Learning and Interpretable Neural Networks

Focal Area: This white paper responds to Focal area III by exploring data fusion, learning and explainable AI methods in characterizing hydrological extremes and interconnections. It also addresses Focal area II by using probabilistic AI and ensemble ML for predicting extremes and compound extremes. Science Challenge: A key question associated with the integrated water (or hydrological) cycle grand challenge in the Earth and Environmental Systems Sciences Division (EESSD) strategic plan, is how the frequency and intensity of hydrological events will change. Prediction of the tail behavior (extremes) of the hydrological cycle is especially challenging, because of their stochasticity and low probability. These extreme events and their compound impacts have significant societal and economic consequences. It is anticipated for the next-generation Earth System models (ESMs), that model predictability of the water cycle will improve with increased resolution (e.g., regionally refined E3SM), advanced software and computational architectures, and improved model physics based on the data from ARM measurements and high-fidelity models. However, the challenges for predictability of low-probability high-impact extreme events will unlikely be alleviated with conventional modeling and data-driven approaches, as ESMs are calibrated largely for capturing the high-frequency mean climate states. Recent AI and ML applications have shown great potential in quantifying well-defined climate extremes (e.g., supervised learning of tropical cyclones/atmospheric rivers by ClimateNet1) but few efforts are dedicated to compound events, extreme drivers and uncertainty estimation. We envision the opportunity to develop and apply ML and interpretable AI methods extended on the existing efforts, specifically, for: (1) identification of compound extremes, (2) diagnosing drivers of extremes, (3) bias correction in extreme predictions and (4) probabilistic modeling of extremes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Experimental Investigation of Concrete Fatigue in Axial Compression

The use of concrete in fatigue critical structures, such as wind turbine towers, has necessitated the replacement of simplistic (and therefore insufficient) fatigue design recommendations with more accurate and advanced fatigue models that can consider a greater complexity of stress states and materials. As the first part of a multistage test series, an experimental investigation was conducted to determine the fatigue life of concrete subjected to axial compressive fatigue loading, focusing on the relatively high-cycle fatigue domain. Here, a wide range of values for maximum stress levels and stress ratios were considered. The experimental results were compared against existing predictive models, and the most applicable model was identified. Concrete strain and stiffness behavior during fatigue loading were also investigated and reported. Based on the observed behavior, a hypothesis for identifying the impending occurrence of fatigue failure, based on the monotonic stress-strain curve, is examined.

17 WIND ENERGY↗

NASA NTRS1999

Measuring Astronaut Performance in Microgravity: Loads and Modeling

Quantitative analysis of human performance in microgravity is important for both scientific investigations and spacecraft engineering design. By collecting and evaluating the kinematics and kinetics data of astronauts in space, it becomes possible to characterize human motor strategies, postural behavior in weightlessness, improve the design of orbital modules, help maintain a quiescent microgravity for acceleration-sensitive material science and life science experiments (NASA JSC, 1996), and optimize the human operative capabilities during long-duration space missions. Hence, there is a need for a precise measurement of the forces and moments exerted by the astronauts on the space station and their postures and movements.

Newman, D.↗

NASA NTRS2018

Organic Ices in Titan's Stratosphere

Titan’s stratospheric ice clouds are by far the most complex of any observed in the solar system, with over a dozen organic vapors condensing out to form a suite of pure and co-condensed ices, typically observed at high winter polar latitudes. Once these stratospheric ices are formed, they will diffuse throughout Titan’s lower atmosphere and most will eventually precipitate to the surface, where they are expected to contribute to Titan’s regolith. Early and important contributions were first made by the InfraRed Interferometer Spectrometer (IRIS) on Voyager 1, followed by notable contributions from IRIS’ successor, the Cassini Composite InfraRed Spectrometer (CIRS), and to a lesser extent, from Cassini’s Visible and Infrared Mapping Spectrometer (VIMS) and the Imaging Science Subsystem (ISS) instruments. All three remote sensing instruments made new ice cloud discoveries, combined with monitoring the seasonal behaviors and time evolution throughout Cassini’s 13-year mission tenure. A significant advance by CIRS was the realization that co-condensing chemical compounds can account for many of the CIRS-observed stratospheric ice cloud spectral features, especially for some that were previously puzzling, even though some of the observed spectral features are still not well understood. Relevant laboratory transmission spectroscopy efforts began just after the Voyager encounters, and have accelerated in the last few years due to new experimental efforts aimed at simulating co-condensed ices in Titan’s stratosphere. This review details the current state of knowledge regarding the organic ice clouds in Titan’s stratosphere, with perspectives from both observational and experimental standpoints.

Astronomy↗

NASA NTRS2018

Organic Ices in Titan's Stratosphere

Titan’s stratospheric ice clouds are by far the most complex of any observed in the solar system, with over a dozen organic vapors condensing out to form a suite of pure and co-condensed ices, typically observed at high winter polar latitudes. Once these stratospheric ices are formed, they will diffuse throughout Titan’s lower atmosphere and most will eventually precipitate to the surface, where they are expected to contribute to Titan’s regolith. Early and important contributions were first made by the InfraRed Interferometer Spectrometer (IRIS) on Voyager 1, followed by notable contributions from IRIS’ successor, the Cassini Composite InfraRed Spectrometer (CIRS), and to a lesser extent, from Cassini’s Visible and Infrared Mapping Spectrometer (VIMS) and the Imaging Science Subsystem (ISS) instruments. All three remote sensing instruments made new ice cloud discoveries, combined with monitoring the seasonal behaviors and time evolution throughout Cassini’s 13-year mission tenure. A significant advance by CIRS was the realization that co-condensing chemical compounds can account for many of the CIRS-observed stratospheric ice cloud spectral features, especially for some that were previously puzzling, even though some of the observed spectral features are still not well understood. Relevant laboratory transmission spectroscopy efforts began just after the Voyager encounters, and have accelerated in the last few years due to new experimental efforts aimed at simulating co-condensed ices in Titan’s stratosphere. This review details the current state of knowledge regarding the organic ice clouds in Titan’s stratosphere, with perspectives from both observational and experimental standpoints.

C. M. Anderson↗

NASA NTRS2018

Organic Ices in Titan’s Stratosphere

Titan’s stratospheric ice clouds are by far the most complex of any observed in the solar system, with over a dozen organic vapors condensing out to form a suite of pure and co-condensed ices, typically observed at high winter polar latitudes. Once these stratospheric ices are formed, they will diffuse throughout Titan’s lower atmosphere and most will eventually precipitate to the surface, where they are expected to contribute to Titan’s regolith. Early and important contributions were first made by the InfraRed Interferometer Spectrometer (IRIS) on Voyager 1, followed by notable contributions from IRIS’ successor, the Cassini Composite InfraRed Spectrometer (CIRS), and to a lesser extent, from Cassini’s Visible and Infrared Mapping Spectrometer (VIMS) and the Imaging Science Subsystem (ISS) instruments. All three remote sensing instruments made new ice cloud discoveries, combined with monitoring the seasonal behaviors and time evolution throughout Cassini’s 13-year mission tenure. A significant advance by CIRS was the realization that co-condensing chemical compounds can account for many of the CIRS-observed stratospheric ice cloud spectral features, especially for some that were previously puzzling, even though some of the observed spectral features are still not well understood. Relevant laboratory transmission spectroscopy efforts began just after the Voyager encounters, and have accelerated in the last few years due to new experimental efforts aimed at simulating co-condensed ices in Titan’s stratosphere. This review details the current state of knowledge regarding the organic ice clouds in Titan’s stratosphere, with perspectives from both an observational and experimental standpoint.

Ices↗

NASA NTRS2021

The Current State and Future of the Former Central Nervous System Risk Managed by Space Radiation Element Part of NASA’s Human Research Program

Historically, the Space Radiation Element, as part of the NASA Human Research Program, was responsible for the scientific strategy and funding of research focused on characterizing and mitigating the effects of space radiation exposure on the central nervous system (CNS). During the past few of years, there have been two major changes that have affected the management of the science associated with the CNS risk. The first major change was the integration of several related risks: CNS (C), Behavioral medicine (B), and sensorimotor (S) to be managed as an integrated effort called CBS. CBS was formed to accelerate research on combined spaceflight stressors, specifically space radiation, microgravity, isolation, and confinement. CBS was managed by Human Factors and Behavioral Performance (HFBP) with support and inputs from Space Radiation as well as Human Health Countermeasures. Secondly, and more recently, the CNS risk was absorbed by the behavioral medicine risk currently also held by HFPB. As a result, the gaps associated with CNS were removed and aspects of them are represented in the BMED gaps (link). However, due to recent changes in element leadership, budget, and timeline, extensive restructuring of CBS is in progress. In addition, in an effort to harmonize datasets to be used in modelling efforts, an Animal Standardization TIM was held in June 2021 to standardize the type of behavioral and cognitive tasks as well as experimental set-ups. This presentation is an effort to communicate with the radiation research community concerning the strategy, management, and future of the former central nervous system risk and the role of the Space Radiation Element.

central nervous system↗

NASA NTRSDate not supplied

The Current State and Future of the Former Central Nervous System Risk Managed by Space Radiation Element Part of NASA’s Human Research Program

Historically, the Space Radiation Element, as part of the NASA Human Research Program, was responsible for the scientific strategy and funding of research focused on characterizing and mitigating the effects of space radiation exposure on the central nervous system (CNS). During the past few years, there have been two major changes that have affected the management of the science associated with the CNS risk. The first major change was the integration of several related risks: CNS (C), behavioral medicine (B), and sensorimotor (S) to be managed as an integrated effort called CBS. CBS was formed to accelerate research on combined spaceflight stressors, specifically space radiation, microgravity, isolation, and confinement. CBS was managed by Human Factors and Behavioral Performance (HFBP) with support and inputs from Space Radiation as well as Human Health Countermeasures. Secondly, and more recently, the CNS risk was absorbed by the behavioral medicine risk currently also held by HFPB. As a result, the gaps associated with CNS were removed and aspects of them are represented in the BMED gaps (link). However, due to recent changes in element leadership, budget, and timeline, extensive restructuring of CBS is in progress. In addition, in an effort to harmonize datasets to be used in modeling efforts, an Animal Standardization TIM was held in June 2021 to standardize the type of behavioral and cognitive tasks as well as experimental set-ups used in animal models of future funded research. This presentation is an effort to communicate with the radiation research community concerning the strategy, management, and future of the former central nervous system risk and the role of the Space Radiation Element.

Janice A Zawaski↗

NASA NTRS2022

Mars 2020 Entry, Descent, and Landing System Software Implementation

On February 18th, 2021, the Mars 2020 project's Perseverance Rover successfully touched down on the Martian surface after nearly eight years of development. The Mars 2020 Entry, Descent, and Landing (EDL) System largely leveraged heritage from the Mars Science Laboratory (MSL) EDL System while employing targeted technological advancements. The landing process is autonomously directed by a software behavior implemented in the rover's primary flight computer called the EDL Timeline that assumes control of the vehicle six days before atmospheric entry. This paper first walks through the basics of the EDL Timeline mechanics and how the behavior is designed to account for internal system variations and environmental unknowns. It then summarizes the interactions between the EDL timeline and other high-level system behaviors like spacecraft mode transitions and system fault protection, focusing on the complications that arise when passing spacecraft control between executive functions. Although the MSL-inherited EDL System is reliable and capable, targeted updates and a thorough verification and validation program were required for Mars 2020. This paper discusses changes made to close vulnerabilities discovered during both MSL and Mars 2020 development cycles, landing system capability enhancements that were enabling for Mars 2020's mission, and how these updates were integrated with the heritage system. It then describes how both analysis and testing campaigns were utilized to verify and validate all aspects of EDL and system behaviors that run during the six days before landing, as well as the operational workarounds that were needed to address problems found during the development and commissioning process. Finally, this paper imparts lessons learned from Mars 2020 EDL development, implementation, and operations, emphasizing how systems designed to conduct time-critical mission events with low margin of error can be improved in the future.

Stehura, Aaron↗

NASA NTRS2022

Mars 2020 Entry, Descent, and Landing Software Implementation

On February 18th, 2021, the Mars 2020 project's Perseverance Rover successfully touched down on the Martian surface after nearly eight years of development. The Mars 2020 Entry, Descent, and Landing (EDL) System largely leveraged heritage from the Mars Science Laboratory (MSL) EDL System while employing targeted technological advancements. The landing process is autonomously directed by a software behavior implemented in the rover's primary flight computer called the EDL Timeline that assumes control of the vehicle six days before atmospheric entry. In addition to performing the critical function of landing the rover on the Martian surface, the EDL timeline behavior must co-exist in a non-partitioned software and system environment with other high-level functions that accomplish the goals for the rest of the mission. Due to the criticality of EDL, the potential for loss of mission, and a need for complete system autonomy, the standard for how the EDL Timeline interacts with other functions in the system is highly constrained. This paper first walks through the basics of the EDL Timeline mechanics and how the behavior is designed to account for internal system variations and environmental unknowns. It then summarizes the interactions between the EDL timeline and other high-level system behaviors like spacecraft mode transitions and system fault protection, focusing on the complications that arise when passing spacecraft control between executive functions. Although the MSL-inherited EDL System is reliable and capable, targeted updates and a thorough verification and validation program were required for Mars 2020. This paper discusses changes made to close vulnerabilities discovered during both MSL and Mars 2020 development cycles, landing system capability enhancements that were enabling for Mars 2020's mission, and how these updates were integrated with the heritage system. It then describes how both analysis and testing campaigns were utilized to verify and validate all aspects of EDL and system behaviors that run during the six days before landing, as well as the operational workarounds that were needed to address problems found during the development and commissioning process. Finally, this paper imparts lessons learned from Mars 2020 EDL development, implementation, and operations, emphasizing how systems designed to conduct time-critical mission events with low margin of error can be improved in the future.

Stehura, Aaron↗

NASA NTRS1995

Multi-Criterion Preliminary Design of a Tetrahedral Truss Platform

An efficient method is presented for multi-criterion preliminary design and demonstrated for a tetrahedral truss platform. The present method requires minimal analysis effort and permits rapid estimation of optimized truss behavior for preliminary design. A 14-m-diameter, 3-ring truss platform represents a candidate reflector support structure for space-based science spacecraft. The truss members are divided into 9 groups by truss ring and position. Design variables are the cross-sectional area of all members in a group, and are either 1, 3 or 5 times the minimum member area. Non-structural mass represents the node and joint hardware used to assemble the truss structure. Taguchi methods are used to efficiently identify key points in the set of Pareto-optimal truss designs. Key points identified using Taguchi methods are the maximum frequency, minimum mass, and maximum frequency-to-mass ratio truss designs. Low-order polynomial curve fits through these points are used to approximate the behavior of the full set of Pareto-optimal designs. The resulting Pareto-optimal design curve is used to predict frequency and mass for optimized trusses. Performance improvements are plotted in frequency-mass (criterion) space and compared to results for uniform trusses. Application of constraints to frequency and mass and sensitivity to constraint variation are demonstrated.

Wu, K. Chauncey↗

NASA NTRSDate not supplied

Multicriterion Design Methods for Aerospace Systems

An efficient method is presented for multi-criterion preliminary design and demonstrated for a tetrahedral truss platform. The present method requires minimal analysis effort and permits rapid estimation of optimized truss behavior for preliminary design. A 14-m-diameter, 3-ring truss platform represents a candidate reflector support structure for space-based science spacecraft. The truss members are divided into 9 groups by truss ring and position. Design variables are the cross-sectional area of all members in a group, and are either 1, 3 or 5 times the minimum member area. Non-structural mass represents the node and joint hardware used to assemble the truss structure. Taguchi methods are used to efficiently identify key points in the set of Pareto-optimal truss designs. Key points identified using Taguchi methods are the maximum frequency, minimum mass, and maximum frequency-to-mass ratio truss designs. Low-order polynomial curve fits through these points are used to approximate the behavior of the full set of Pareto-optimal designs. The resulting Pareto-optimal design curve is used to predict frequency and mass for optimized trusses. Performance improvements are plotted in frequency-mass (criterion) space and compared to results for uniform trusses. Application of constraints to frequency and mass and sensitivity to constraint variation are demonstrated.

taguchi methods↗