Search NASA⌕ Search

SEARCH · Search NASA

Results for “Scenario Analysis”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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

At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

A Science-Focused Artificial Intelligence (AI) Responding in Real-Time to New Information: Capability Demonstration for Ocean World Missions

Introduction: Artificial intelligence (AI) has long been considered a potential mechanism to explore increasingly challenging environments, including those with extreme temperatures and pressures, limited communication capabilities, or those with demanding terrain. We posit that missions in extreme environments could deploy an onboard AI focused on science observations and goals in order to augment a traditional concept(s) of operations (ConOps). An onboard AI capability could perform functions such as data analysis in order to make high-level decisions, including prioritized data transmission for analysis by ground-based teams or autonomously-guided follow-on analyses that maximize science return. Such a capability would empower missions to respond to scientific data of interest in real-time; a mission could make observations and perform a preliminary analysis to alert ground-based scientists to an observation of interest, enabling an informed, rapid response from Earth-based teams. Enceladus Case Study for Onboard AI: We are developing an onboard AI capability for real-time telemetry response that formulates and carries-out informed decisions in service to established mission goals, enabling increased science return of a mission. We focus our AI development for use on a constellation of SmallSats orbiting Enceladus. Our Enceladus case study tests autonomous decision-making capabilities in scenarios with complex orbital dynamics, plume ejecta, extreme cold environments, power restrictions, and a requirement to maximize science return for a potential positive detection of life, while critically evaluating the potential for false positives. Telemetry includes simulated scientific data, spacecraft onboard operational data (e.g., position, velocity, and rotation), and engineering hardware performance data. Enceladus SmallSat Constellation. Our constellation includes eight SmallSat spacecraft in an 8:35 resonant orbit-based formation, leveraging Saturn’s gravitational forces to maintain stable orbits with global coverage around Enceladus. To our knowledge, we simulate the first stable configuration of multiple spacecraft in closed orbits around Enceladus, using a full ephemeris force model (Russell and Lara, 2009). Each spacecraft’s orbit will precess, causing an eastward ground track shift (from an orbiter’s perspective) of each spacecraft for each orbit. However, all spacecraft return to their original positions relative to Enceladus after eight Enceladus revolutions around Saturn. We model communication pathways between SmallSats to understand how information would need to be transmitted across the constellation to enable AI-driven decision-making and resource allocation across the fleet. Capability Demonstration. Our simulated capability demonstration inputs position, velocity, and rotation telemetry from our Enceladus-focused constellation simulations, and mass spectrometry data collected from abiotic and biotic laboratory-analog ocean world experiments (Theiling et al., 2018; Theiling, 2021; Da Poian et al., 2023). Data from these experiments are used to simulate MS measurements and different scenarios of science observations for onboard analysis performed on each of the eight spacecraft. For these demonstrations, we integrate 24 machine learning (ML) algorithms into an onboard intelligence as a ‘knowledge base’, including algorithms evaluating data quality and those predicting (with % confidence) gas composition, ocean aqueous chemistry, and whether the sample was influenced by microbial life. The onboard AI capability is designed to use the knowledge base to come to a consensus-based decision in the interpretation of the observed data in order to request additional action outside of a pre-defined ConOps. Requested actions could include e.g., prioritized downlink to Earth (for analysis by ground-based teams) or follow-on analyses performed across the constellation. The spacecraft’s intelligent onboard planner must then determine whether sufficient resources (e.g., time, power, etc.) are available and weigh the request with mission priorities. In our simulation, the constellation is able to identify potential biosignatures using onboard ML algorithms, evaluate the confidence of that prediction, and perform follow-on analyses across the fleet to confirm the detection, in order to best prepare a transmission of these data to Earth-based teams.

astrobiology↗
DOE OSTI2026

Knowledge Graph for End-to-End Traceability of an Integrated Human-Earth System Model

Integrated human-Earth system models inform energy-water-land system dynamics and policies, yet their results are difficult to trace through input-data, model structure, scenario configurations, and solved outputs. Because this information is siloed across disconnected artifacts, process-based IAMs have historically lacked a unified, queryable representation. Such lack of traceability prevents researchers from systematically isolating the multi-sector drivers of complex outcomes (such as tracing water-scarcity results back to distant energy-system dynamics) or conducting holistic uncertainty attribution across hundreds of interacting parameters. To address this concern, our work documents the software engineering process of a knowledge graph that unifies these four layers for the Global Change Analysis Model (GCAM-USA_Reference scenario, GCAM v9.1). The graph was built as a relational property graph in DuckDB from the run’s own artifacts: the input-preparation dependency map (gcamdata chunk map), the model’s XML input files, the run configuration, and the results database (BaseX), successfully mapping the model’s declared structure. The resulting graph comprises 204,321 nodes and 1,687,814 edges across 16 node types and 15 edge types, with approximately 16.3 million time-series values stored separately to maintain structural efficiency. To ensure representation fidelity, every edge carries an epistemic-status annotation recording the warrant for the relationship (structural, provenance, dependency, or model-derived), and a machine-readable provenance ledger classifying the origin of every schema element. Evaluation against a fixed five-benchmark suite with locked baselines reports zero structural orphans, zero dangling edge endpoints, and 100% of output-producing technologies traceable to raw input files. Two interactive interfaces present the graph, including a serverless browser application built on DuckDB-Wasm. By establishing the first end-to-end provenance framework for an IAM, this work enables researchers and scientists to systematically audit complex policy scenarios, debug model structures, and trace policy-relevant outputs to their data origins in real time.

Artifical Intelligence↗
DOE OSTI2020

Uncertainty Analysis of Consequence Management Data Products

An interlaboratory effort has developed a probabilistic framework to characterize uncertainty in data products that are developed by the US Department of Energy Consequence Management Program in support of the Federal Radiological Monitoring and Assessment Center. The purpose of this paper is to provide an overview of the probability distributions of input variables and the statistical methods used to propagate and quantify the overall uncertainty of the derived response levels that are used as contours on data products due to the uncertainty in input parameters. Uncertainty analysis results are also presented for several study scenarios. This paper includes an example data product to illustrate the potential real-world implications of incorporating uncertainty analysis results into data products that inform protective action decisions. Data product contours that indicate areas where public protection actions may be warranted can be customized to an acceptable level of uncertainty. Finally, the investigators seek feedback from decision makers and the radiological emergency response community to determine how uncertainty information can be used to support the protective action decision-making process and how it can be presented on data products.

61 RADIATION PROTECTION AND DOSIMETRY↗
NASA NTRS2008

Development and Application of an Integrated Approach toward NASA Airspace Systems Research

The National Aeronautics and Space Administration's (NASA) Airspace Systems Program is contributing air traffic management research in support of the 2025 Next Generation Air Transportation System (NextGen). Contributions support research and development needs provided by the interagency Joint Planning and Development Office (JPDO). These needs generally call for integrated technical solutions that improve system-level performance and work effectively across multiple domains and planning time horizons. In response, the Airspace Systems Program is pursuing an integrated research approach and has adapted systems engineering best practices for application in a research environment. Systems engineering methods aim to enable researchers to methodically compare different technical approaches, consider system-level performance, and develop compatible solutions. Systems engineering activities are performed iteratively as the research matures. Products of this approach include a demand and needs analysis, system-level descriptions focusing on NASA research contributions, system assessment and design studies, and common systemlevel metrics, scenarios, and assumptions. Results from the first systems engineering iteration include a preliminary demand and needs analysis; a functional modeling tool; and initial system-level metrics, scenario characteristics, and assumptions. Demand and needs analysis results suggest that several advanced concepts can mitigate demand/capacity imbalances for NextGen, but fall short of enabling three-times current-day capacity at the nation s busiest airports and airspace. Current activities are focusing on standardizing metrics, scenarios, and assumptions, conducting system-level performance assessments of integrated research solutions, and exploring key system design interfaces.

Barhydt, Richard↗
NASA NTRS1983

Space station needs, attributes and architectural options study. Volume 2: Mission analysis

Space environment studies, astrophysics, Earth environment, life sciences, and material sciences are discussed. Commercial communication, materials processing, and Earth observation missions are addressed. Technology development, space operations, scenarios of operational capability, mission requirements, and benefits analysis results for space-produced gallium arsenide crystals, direct broadcasting satellite systems, and a high inclination space station are covered.

Source record↗
DOE OSTI2021

Simulated Land Allocation, Nitrogen Use, and Nitrogen Loss in the Mississippi Atchafalaya River Basin for Various RFS2 (Renewable Fuel Standard) Policy Scenarios

Total nitrogen leaching rates were calculated over the Mississippi Atchafalaya River Basin (MARB) using an integrated economic-biophysical modeling approach. Land allocation for corn production and total nitrogen application rates were calculated for crop reporting districts using the Biofuel and Environmental Policy Analysis Model (BEPAM) for 5 RFS2 policy scenarios. These were used as input in the Integrated BIosphere Simulator-Agricultural Version (Agro-IBIS) and the Terrestrial Hydrologic Model with Biogeochemistry (THMB) to calculate the nitrogen loss. Land allocation and total nitrogen application simulations were simulated for the period 2016-2030 for 303 crop reporting districts (https://www.nass.usda.gov/Data_and_Statistics/County_Data_Files/Frequently_Asked_Questions/county_list.txt). The final 2030 values are reported here. Both are stored in csv files. Units for land allocation are million ha and nitrogen application are million kg. The nitrogen leaching rates were modeled with a spatial resolution of 5' x 5' using the North American Datum of 1983 projection and stored in NetCDF files. The 30-year average is calculated over the last 30 years of the 45 years being simulated. Leaching rates are calculated in kg-N/ha.

bioenergy crops↗
DOE OSTI2021

Signature Analysis Utilizing a Dynamic Molten Salt Reactor Model for MC&A

Moving from traditional fixed fuel nuclear reactor systems to a mobile, dynamic fuel system that has dissolved special nuclear material in a molten salt is a paradigm shift in several respects. One major consideration is how to develop effective nuclear material controls and accounting for these novel reactor systems. The Molten Salt Reactor Experiment is one example of a critical molten salt system and provided a significant reference library based on the documented effort. But it was low thermal power that was not intended to reflect a commercial scale electricity production design. Therefore, to facilitate and assist vendors with domestic licensing considerations, research is underway to identify methods that could be used for domestic safeguards approaches for these novel reactor systems. This research presents the results of a signature analysis of data generated from three simulated scenarios using the molten salt demonstration reactor model defined in the Transient Simulation Framework of Reconfigurable Modules. Each scenario provides 1 hour isotope inventories over a 180 day period. The scenarios investigated provide test cases to examine if direct gamma-ray spectroscopy of the fuel can be used to identify changes comparing a base case (no reactivity control and fixed fission contribution) to an insertion of reactivity (10 pcm no change in fission composition) and a change in fission composition. The analysis demonstrates that monitoring the total count rate in a highly collimated high-resolution photon energy spectrum is sensitive to perturbations imposed into the reactor model. The total photon count rate changes ≈2% for the fission composition change and ≈4.5% for the reactivity insertion compared to the base case. However, both scenarios show an increase in the total photon count rate. The total photon count rate can be used to identify changes due to power (number of fissions) but not due to a change in the material undergoing fission. To distinguish between the two cases of increased power, the photon spectrum would require an intensive analysis technique. A photon peak count rate ratio analysis could be used to identify changes in the fissile material fission generation in the core through identification of a static peak that shows little variation to the source of fission and a highly varying peak. The photon peak strength will ultimately be determined by the isotope’s fission yield. A preliminary analysis investigating the coupling of the isotope’s fission yield and its concentration in the fuel salt derived from the modeling has been performed. A ratio analysis of the photon peak count rates of 140 La to 99 Mo demonstrated that the reactivity insertion creates a distinct difference in the ratio compared to the fission composition change scenario.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS2016

Deep Space Optical Link ARQ Performance Analysis

Substantial advancements have been made toward the use of optical communications for deep space exploration missions, promising a much higher volume of data to be communicated in comparison with present-day Radio Frequency (RF) based systems. One or more ground-based optical terminals are assumed to communicate with the spacecraft. Both short-term and long-term link outages will arise due to weather at the ground station(s), space platform pointing stability, and other effects. To mitigate these outages, an Automatic Repeat Query (ARQ) retransmission method is assumed, together with a reliable back channel for acknowledgement traffic. Specifically, the Licklider Transmission Protocol (LTP) is used, which is a component of the Disruption-Tolerant Networking (DTN) protocol suite that is well suited for high bandwidth-delay product links subject to disruptions. We provide an analysis of envisioned deep space mission scenarios and quantify buffering, latency and throughput performance, using a simulation in which longterm weather effects are modeled with a Gilbert-Elliot Markov chain, short-term outages occur as a Bernoulli process, and scheduled outages arising from geometric visibility or operational constraints are represented. We find that both short- and long-term effects impact throughput, but long-term weather effects dominate buffer sizing and overflow losses as well as latency performance.

buffer analysis↗
DOE OSTI2021

Characterizing Signatures of Geothermal Exploration Data with Machine Learning Techniques: An Application to the Nevada Play Fairway Analysis

We are introducing machine learning methods to the play fairway analysis to generate geothermal potential maps to support the evaluation of geothermal resource potential and the exploration for undiscovered blind geothermal systems in the Nevada Great Basin region. Our project aims to identify new ways to combine the play fairway data and empirically organize relationships between feature weights and labels in an improved workflow. As a means of doing this, we introduce machine learning methods to evaluate the influence of certain geological and geophysical features/feature sets in predicting geothermal favorability. This report highlights promising approaches based on supervised and unsupervised learning methods. First, we demonstrate a filter method applied to supervised classification modeling. The supervised filter method is based on permutation analysis to evaluate every possible feature combination/drop out scenario and rank feature influence based on the performance variance of supervised classification models. Additionally, we present an unsupervised factor analysis based on principal component analysis coupled with a semi-supervised kmeans clustering algorithm. This analysis allows us to identify the optimal number of groups/clusters for training sites and structural settings to identify feature patterns including correlation, variance, and latent and dominant feature relationships. The results from these methods offer a promising avenue for identifying favorable sources of predictive information to identify the locations of blind geothermal systems and furthering our understanding of complex geothermal feature and label relationships in the Great Basin region and beyond.

15 GEOTHERMAL ENERGY↗
NASA NTRS2016

Deep Space Optical Link ARQ Performance Analysis

Substantial advancements have been made toward the use of optical communications for deep space exploration missions, promising a much higher volume of data to be communicated in comparison with present -day Radio Frequency (RF) based systems. One or more ground-based optical terminals are assumed to communicate with the spacecraft. Both short-term and long-term link outages will arise due to weather at the ground station(s), space platform pointing stability, and other effects. To mitigate these outages, an Automatic Repeat Query (ARQ) retransmission method is assumed, together with a reliable back channel for acknowledgement traffic. Specifically, the Licklider Transmission Protocol (LTP) is used, which is a component of the Disruption-Tolerant Networking (DTN) protocol suite that is well suited for high bandwidth-delay product links subject to disruptions. We provide an analysis of envisioned deep space mission scenarios and quantify buffering, latency and throughput performance, using a simulation in which long-term weather effects are modeled with a Gilbert -Elliot Markov chain, short-term outages occur as a Bernoulli process, and scheduled outages arising from geometric visibility or operational constraints are represented. We find that both short- and long-term effects impact throughput, but long-term weather effects dominate buffer sizing and overflow losses as well as latency performance.

optical space communications↗
DOE OSTI2025

Predicting Dynamic-to-Static Correction Factor from Petrophysical Data and Chemostratigraphy using Unsupervised Machine Learning

Estimating static mechanical properties of stratigraphic layers is critical for optimizing subsurface engineering applications. To estimate dynamic-to-static correction factor F ds (static-to-dynamic Young’s modulus ratio) across the Caney shale interval in Oklahoma, USA, we integrated triaxial test measurements and petrophysical data, including well logs and X-ray fluorescence (XRF) using unsupervised machine learning (ML). We used a novel workflow that includes principal component analysis (PCA) to reduce data set dimensionality of well logs and XRF data sets—both separately and combined—creating three scenarios, and later applied inverse distance weighting (IDW) to derive F ds profiles for these scenarios. Furthermore, we applied K-means clustering on each scenario to predict depositional facies, and built a stiffness zonation profile through chemostratigraphic analysis of the terrigenous elements to validate the predicted F ds . The predicted F ds profile from each scenario using the PCA-IDW method was compared with the constant F ds approach from our previous study by calculating the root mean square error (RMSE). The combined data sets scenario yielded the lowest RMSE value of 0.113, while the RMSE values for the well logs and XRF scenarios were 0.131 and 0.129, respectively. In addition, the predicted F ds from the XRF scenario well-matched the stiffness zonation from the chemostratigraphic analysis that was built using the optimized K-means clustering of nine clusters for that scenario. These methods and findings offer a valuable tool for refining lithological classification and improving the F ds profile, potentially enhancing drilling and stimulation strategies for subsurface energy engineering applications.

clastic rock↗
DOE OSTI2022

Direct Air Capture of CO 2 and Delivery to Photobioreactors for Algal Biofuel Production (Final Report)

A mobile DAC system was designed and constructed to pair with photobioreactors growing algae for biofuel production. The DAC system was designed as a versatile research system, rather than a compact production unit. The system was constructed and mounted on a mobile skid to facilitate transportation to the algae production site. Within the DAC system, CO 2 was captured using amine-loaded monoliths that allow for high CO 2 uptake with low pressure drop. The CO 2 is collected using a Global Thermostat patented temperature/vacuum swing adsorption (TVSA) process. Amine sorbents and process conditions were optimized to produce 10 to >20 g CO 2 .h-1. The stability of the amine sorbents was also studied, with sorbent modifications made to improve stability to degradation by oxidation. An Algenol-developed Spirulina strain (Arthrospira platensis AB2293) was selected as the production cyanobacterial strain. AB2293 cultured was inoculum for outdoor production following PBR installation by Algenol. The PBR system was composed of three independent PBRs, with each PBR composed of four hanging bags internally recirculated by a liquid turnover pump. The PBRs were operated outdoors in Atlanta, GA, and integrated with the DAC system. Algae were grown with similar productivity using DAC-CO 2 as algae grown using pure CO 2 obtained commercially (Airgas). Throughout the experimental duration, no discoloration was observed, and cellular morphology was consistent between the two experimental treatments. An LCA including lifecycle greenhouse gas emissions, full life cycle inventory of the Algenol system and the DAC system and integrated DAC+PBR system was developed. Lifecycle greenhouse gas emissions were calculated for capture of carbon dioxide using input from Global Thermostat and the National Renewable Energy Laboratory. Three scenarios for energy provision were evaluated: a natural gas combined heat and power system sized to meet the electricity requirement, a natural gas combined heat and power system sized to meet the process heat requirements, and a system without on-site power that procures the electricity from the grid. In all three cases, as expected, the major contributor to the emissions is the energy consumption associated with the desorption step of the DAC process. The LCA quantified the reduced potential energy and greenhouse gas emissions of heat and mass integration of DAC and Algenol compared to unintegrated DAC and Algenol systems. A life cycle assessment of the role of sorbent productivity and lifetime was also developed. The development of more robust, oxidation resistant DAC sorbents may enable small reductions in energy requirements and in lifecycle greenhouse gas emissions and other environmental impacts. NREL performed techno-economic analysis (TEA) to identify the integration scenario most likely to achieve a 15% cost reduction target versus the baseline. Heat and mass integration of DAC and the PBR is critical to minimizing the MFSP. The baseline case utilizes no heat and mass integration, and the DAC system provides 100% of the CO 2 required by the photobioreactors (20 tonnes/hr), operating for 12 hours/day capturing 40 tonnes CO 2 /operating hour. The minimum fuel selling price (MFSP) of ethanol calculated from the baseline case was $10.68/gal ethanol. This corresponds with a targeted MFSP of $9.07/gal ethanol (or 15% reduction). This target was achieved by integration Option 2a with the greatest cost reduction of 17.8% (or $8.78/gal) and integration Option 2b with a cost reduction of 16.4% (or $8.93/gal). Reductions in MFSP are attributed to two primary process considerations: (a) CO 2 storage at night reduces the capital expenses associated with DAC (i.e., increasing on-stream time); and (b) distributed DAC scenarios (DAC-PBR integration Options 2a and 2b) make use of boiler and DAC CHP flue gas CO 2 (free). Direct air capture on-stream time was one of the largest contributors to MFSP reduction.

09 BIOMASS FUELS↗
NASA NTRS2020

What Could Be the Observational Signature of Dark Matter in Globular Clusters?

Here we investigate the possibility that some globular clusters (GCs) harbor intermediate mass black holes (BH) in their centers and are also embedded in a low-mass dark matter (DM) halo. Up to date, there is no evidence on whether or not GCs have DM in their constitution. For standard cold DM cosmology, it is expected that GCs form with their own DM halos. Other studies investigate the possibility that GCs were initially embedded in massive DM halos that evolved during the cluster lifetime. An additional intriguing question is related to the existence of intermediate mass black holes (IMBH) in the GCs. The determination of whether GCs hold IMBHs would be able to answer important questions about GCs formation and the circumstances that gave rise to the IMBHs. DM & IMBH in the context of GCs are interesting subjects to be studied and we will perform such studies here, assuming the coexistence of both of them in some GCs. Having such information, we perform the study possible DM signals from GCs. One important subject to be studied is the DM density profile. In the inner regions of GCs, the DM density profile is still an open question of fundamental importance to DM studies, specially for the study of radio and γ-ray emission from DM annihilation in such regions. Here we consider the case of IMBHs in the inner regions of three GCs: 47 Tuc, NGC 6266 and ω Cen. The existence of a black hole (BH) directly affects the matter distribution in its vicinity. These effects are significant for IMBHs as well as supermassive black holes (SMBHs) and can lead to large DM overdensities called spikes. In this paper, we direct our studies to the radio synchrotron emission from DM annihilation with spike profiles around possible IMBHs in GCs. We present our results for synchrotron emission produced by DM annihilation via bbar b channel. We use the best-fit DM mass of 34 GeV and annihilation channel in bbar b used to explain the Galactic center "excess" and perform our analysis for the estimated radio flux. We direct our attention specially to the γ-ray emission from 47 Tuc. When considering a scenario wich includes a BH and the combined analysis of multi-wavelength studies, we conclude that some parameter choice as the annihilation cross section ⟨σ v⟩ used to fit the γ-ray excess differs by many orders of magnitude from the one necessary to fit the radio observations for this globular cluster if we consider the BH scenario.

Elaine C F S Fortes↗
DOE OSTI2025

ARCADE Technical Pathway and Industry Impact

The Advanced Reactor Cyber Analysis and Development Environment (ARCADE) simplifies the evaluation and assessment of robustness factor and cyber resilience that support secure-by-design for advanced reactor nuclear power plants. In this manner, ARCADE supports risk-informed performance based (RIPB) evaluations of cybersecurity through its integration of plant physics with high-fidelity emulations of control systems. This cross domain approach enables comprehensive analysis of control system sensitivities, cyber-attack scenarios, and their consequences. ARCADE has been custom developed to meet the demands identified in Tier 1 of the Tiered Cyber Analysis (TCA) as outlined in NRC Draft Regulation Guide (RG) 5.96, which provides a RIPB cybersecurity approach for new reactors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

ProvSec: Open Cybersecurity System Provenance Analysis Benchmark Dataset with Labels

System provenance forensic analysis has been studied by a large body of research work. This area needs fine granularity data such as system calls along with event fields to track the dependencies of events. While prior work on security datasets has been proposed, we found a useful dataset of realistic attacks and details that are needed for high-quality provenance tracking is lacking. We created a new dataset of eleven vulnerable cases for system forensic analysis. It includes the full details of system calls including syscall parameters. Realistic attack scenarios with real software vulnerabilities and exploits are used. For each case, we created two sets of benign and adversary scenarios which are manually labeled for supervised machine-learning analysis. In addition, we present an algorithm to improve the data quality in the system provenance forensic analysis. We demonstrate the details of the dataset events and dependency analysis of our dataset cases.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1979

Advanced development of atmospheric models

A set of atmospheric analysis and prediction models was developed in support of the SEASAT Program existing objective analysis models which utilize a 125x125 polar stereographic grid of the Northern Hemisphere, which were modified in order to incorporate and assess the impact of (real or simulated) satellite data in the analysis of a two-day meteorological scenario in January 1979. Program/procedural changes included: (1) a provision to utilize winds in the sea level pressure and multi-level height analyses (1000-100 MBS); (2) The capability to perform a pre-analysis at two control levels (1000 MBS and 250 MBS); (3) a greater degree of wind- and mass-field coupling, especially at these controls levels; (4) an improved facility to bogus the analyses based on results of the preanalysis; and (5) a provision to utilize (SIRS) satellite thickness values and cloud motion vectors in the multi-level height analysis.

Kesel, P. G.↗
NASA NTRS2003

Robotic Mars Sample Return: Risk Assessment and Analysis Report

A comparison of the risk associated with two alternative scenarios for a robotic Mars sample return mission was conducted. Two alternative mission scenarios were identified, the Jet Propulsion Lab (JPL) reference Mission and a mission proposed by Johnson Space Center (JSC). The JPL mission was characterized by two landers and an orbiter, and a Mars orbit rendezvous to retrieve the samples. The JSC mission (Direct/SEP) involves a solar electric propulsion (SEP) return to earth followed by a rendezvous with the space shuttle in earth orbit. A qualitative risk assessment to identify and characterize the risks, and a risk analysis to quantify the risks were conducted on these missions. Technical descriptions of the competing scenarios were developed in conjunction with NASA engineers and the sequence of events for each candidate mission was developed. Risk distributions associated with individual and combinations of events were consolidated using event tree analysis in conjunction with Monte Carlo techniques to develop probabilities of mission success for each of the various alternatives. The results were the probability of success of various end states for each candidate scenario. These end states ranged from complete success through various levels of partial success to complete failure. Overall probability of success for the Direct/SEP mission was determined to be 66% for the return of at least one sample and 58% for the JPL mission for the return of at least one sample cache. Values were also determined for intermediate events and end states as well as for the probability of violation of planetary protection. Overall mission planetary protection event probabilities of occurrence were determined to be 0.002% and 1.3% for the Direct/SEP and JPL Reference missions respectively.

Lalk, Thomas R.↗
NASA NTRSDate not supplied

The Effect of Fiber-Angle Fidelity on the Linear Response of Tow-steered Composite Plates

Composite laminate tailoring is traditionally performed by uniformly changing the in-plane ply orientation to obtain the desired mechanical performance. The emergence of tow-steered plies, where the fibers follow a prescribed curvilinear path, have increased the tailorability of composite laminates. However, characterizing the behavior of tow-steered laminates using finite element analysis is challenging because additional, and often numerous, orientation definitions may be required. The additional orientation definitions detrimentally increase the computational cost and hinder the use of advanced analysis techniques, such as Monte Carlo or uncertainty quantification, in the design process. To reduce computational cost without adversely affecting mechanics-based performance, the results of a parametric study that was used to investigate the effects of fiber-angle fidelity, element size, and tow-steered radius-of-curvature on various loading scenarios for tow-steered composite plates are presented. The three loading scenarios that were analyzed using finite element analysis included an axial tension load, an applied constant through-thickness-direction pressure load, and an axial compression load. Results for mechanics-based metrics of interest are presented and discussed for each loading scenario. Little sensitivity (less than one percent difference) to the effect of fiber-angle fidelity is observed in the mechanics-based metrics until the coarse-end of the considered range. Sensitivity to element size generally dominates the observed results for the mechanics-based metrics. Notable reductions in the preprocessing time are observed for increasingly coarse element size and fiber-rounding parameters. The preprocessing times decreased up to three orders of magnitude from a few thousand seconds to a few seconds without loss of accuracy in the mechanics-based metrics. Such increased computational performance is of particular interest to the structural design and analysis communities that may be conducting large counts of finite-element analyses, such as in other parametric studies, Monte-Carlo analyses, uncertainty quantification, or tow-steered optimization.

tow-steered composites↗