SEARCH · Engineering Papers

Results for “computer model calibration”

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.

SourceResearch connectionFrom yearThrough year

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

At least 1,135 records · Page 63

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

OVERFLOW Analysis of Supersonic Retropropulsion Testing on a Blunt Mars Entry Vehicle Concept

Supersonic retropropulsion (SRP) flow over a Hypersonic Inflatable Aerodynamic Decelerator (HIAD) blunt-body vehicle was simulated using the Overflow Computational Fluid Dynamics (CFD) solver. Two nozzle configurations were tested (1E and 1F) as a subset of the seven total configurations in the Descent System Study (DSS) testing campaign. A generalized, Adaptive Mesh Refinement (AMR) shock capturing and plume refinement technique was developed and calibrated for producing automatic and unique grid systems optimized for any given testing condition. Solution independence from grid resolution was determined by successively increasing grid refinement until asymptotic convergence of the mean aerodynamic loads was observed. Overflow Reynolds-averaged Navier-Stokes (RANS) solutions produced realistic SRP flow phenomena, including Mach disk normal shocks contained in high-thrust plumes and bow shock triple-points. Solutions for the 1E nozzle configuration were steady. Conversely, a subset of the 1F test conditions were unsteady and exhibited periodic, non-sinusoidal expansions and contractions of the streamwise-oriented plume, resulting in unsteady aerodynamic loads on the vehicle. Initial investigations into the effect of turbulence modeling fidelity demonstrated significant differences between the aerodynamic loads simulated with RANS and Detached Eddy Simulation (DES). Multiple flow mechanisms were identified as root causes of these differences, including bow shock shape augmentation and reduced entrainment due to reduced turbulence in the DES simulations. Initial comparisons of pitching moment control authority and aerodynamic drag performance between the tested nozzle configurations demonstrated that the 1E configuration may be more advantageous for the Mars Entry, Descent, and Landing (EDL) task.

Supersonic Retropropulsion↗

DOE OSTI2021

mosartwmpy: A Python implementation of the MOSART-WM coupled hydrologic routing and water management model

mosartwmpy is a Python implementation of the Model for Scale Adaptive River Transport with Water Management (MOSART-WM). This new version retains the functionality of the legacy model (written in FORTRAN) while providing new features to enhance user experience and extensibility. MOSART is a large-scale river-routing model used to study riverine dynamics of water, energy, and biogeochemistry cycles across local, regional, and global scales (Li et al., 2013). The WM component introduced by Voisin et al. (2013) represents river regulation through reservoir storage and release operations, diversions from reservoir releases, and allocation to sectoral water demands. Each reservoir release is independently calibrated using long-term mean monthly inflow into the reservoir, long-term mean monthly demand associated with this reservoir, and reservoir goals (flood control, irrigation, recreation, etc.). Generic monthly pre-release rules and storage targets are set up for individual reservoirs; however, those releases are updated annually for inter-annual variability (dry or wet year) and daily for environmental constraints such as flow minimum release and minimum/maximum storage levels. The WM model allows an evaluation of the impact of water management over multiple river basins at once (global, continental scales) and with consistent representation of human operations over the full domain.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Multi-fidelity kinetic theory-based approach for the prediction of particle attrition: Application to jet cup attrition system

The timescale difference between the “fast” flow dynamics of fluidized bed reactors and the relatively “slow” rate of particle degradation makes the direct computational prediction of attrition challenging. An approach to this challenge is a multi-fidelity strategy where a high fidelity model for the flow dynamics is coupled with a lower fidelity model for the long-time resolution of the bulk attrition of the reactor inventory. We implement this approach using high-fidelity kinetic theory simulations to calculate the flow dynamics which are post-processed to calculate the frequency and intensity of the particle-particle and particle-wall collisions (e.g. collision energy spectra). This is combined with the particle breakage properties to construct the coefficients for a low-fidelity model [e.g. Monazam et al., 2018, Powder Technology 340, p. 528-536]. Simulations are performed of a jet cup attrition system containing Canadian hematite (Monazam et al. 2018). Furthermore, these are first analyzed using the collision energy spectra. Quantitative predictions of the mass loss are made using a low-fidelity model derived from the collision-spectra and a calibrated material breakage coefficient. The results are found to compare favorably with the experimental measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS2004

NASA Tech Briefs, July 2004

Topics: Optoelectronic Sensor System for Guidance in Docking; Hybrid Piezoelectric/Fiber-Optic Sensor Sheets; Multisensor Arrays for Greater Reliability and Accuracy; Integrated-Optic Oxygen Sensors; Ka-Band Autonomous Formation Flying Sensor; CMOS VLSI Active-Pixel Sensor for Tracking; Lightweight, Self-Deploying Foam Antenna Structures; Electrically Small Microstrip Quarter-Wave Monopole Antennas; A 2-to-28-MHz Phase-Locked Loop; Portable Electromyograph; Open-Source Software for Modeling of Nanoelectronic Devices; Software for Generating Strip Maps from SAR Data; Calibration Software for use with Jurassicprok; Software for Probabilistic Risk Reduction; Software Processes SAR Motion-Measurement Data; Improved Method of Purifying Carbon Nanotubes; Patterned Growth of Carbon Nanotubes or Nanofibers; Lightweight, Rack-Mountable Composite Cold Plate/Shelves; SiC-Based Miniature High-Temperature Cantilever Anemometer; Inlet Housing for a Partial-Admission Turbine; Lightweight Thermoformed Structural Components and Optics; Growing High-Quality InAs Quantum Dots for Infrared Lasers; Selected Papers on Protoplanetary Disks; Module for Oxygenating Water without Generating Bubbles; Coastal Research Imaging Spectrometer; Rapid Switching and Modulation by use of Coupled VCSELs; Laser-Induced-Fluorescence Photogrammetry and Videogrammetry; Laboratory Apparatus Generates Dual-Species Cold Atomic Beam; Laser Ablation of Materials for Propulsion of Spacecraft; Small Active Radiation Monitor; Hybrid Image-Plane/Stereo Manipulation; Partitioning a Gridded Rectangle into Smaller Rectangles; Digital Radar-Signal Processors Implemented in FPGAs; Part 1 of a Computational Study of a Drop-Laden Mixing Layer; and Some Improvements in Signal-Conditioning Circuits.

Source record↗

DOE OSTI2023

Thermal Analysis of the SIRIUS3 Nuclear Propulsion Fuel Calibration Experiment

NASA is considering Nuclear Thermal Propulsion (NTP) for long range extraterrestrial missions; these engines eject hot hydrogen gas heated by a nuclear reactor for rocket thrust. To economize the use of hydrogen to the greatest extent possible, the NTP engines will be expected to, in a very short time (i.e., on the order of a minute or less), go from warm$(\sim 300 K)$ zero power conditions to full operational power, with a coolant outlet temperature on the order of 2700–3000 K \cite{en15176181}. These conditions will introduce significant thermomechanical stresses on the NTP fuel. The objective of the SIRIUS series of experiments is to examine the performance of candidate NTP fuel materials when subjected to temperature ramp rates that are prototypical of NTP system startup and operation. The SIRIUS experiments are a series of experiments that will be irradiated in the TREAT reactor and are subjected to power ramps and cycles that are prototypical for NTP operation. The experiments will be accomplished by executing a series of shaped transients on the SIRIUS specimens while collecting in situ specimen temperature data. These tests will determine whether operational startup ramps and peak temperatures will result in detrimental fuel performance phenomena (i.e. fuel deformation, fragmentation and cracking) To this end, INL has been evaluating a number of SIRIUS experiments, and these evaluations include thermal analysis of the SIRIUS experiments. Thermal analysis were performed for the calibration irradiation of the SIRIUS-3 experiment, and the focus of this memo is to document the results from the calibration irradiation thermal analysis. The thermal analysis reveals that radiation heat emission of the outer fuel elements and conduction to different metal components such as the molybdenum element tubes remove significant quantities of heat from the fuel element specimen, and future experiment designs need to consider these heat transfer mechanics. This paper begins with a brief experiment overview with a discussion of the fuel sample and experiment configuration. The experiment and model description section is followed by a set of results with a brief discussion, and finally the memo concludes with suggestion for future work.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2019

Shear Measurement Bias Due to Spatially Varying Spectral Energy Distributions in Galaxies

Galaxy color gradients (CGs)—i.e., spectral energy distributions that vary across the galaxy profile—will impact galaxy shape measurements when the modeled point-spread function (PSF) corresponds to that for a galaxy with spatially uniform color. This paper describes the techniques and results of a study of the expected impact of galaxy CGs on weak lensing measurements with the Large Synoptic Survey Telescope (LSST) when the PSF size depends on wavelength. The bias on cosmic shear measurements from CGs is computed both for parametric bulge$+$disk galaxy simulations and for more realistic chromatic galaxy surface brightness profiles based on Hubble Space Telescope V- and I-band images in the All-Wavelength Extended Groth Strip International Survey (AEGIS). For the parametric galaxies, and for the more realistic galaxies derived from AEGIS galaxies with a sufficient signal-to-noise ratio that CG bias can be isolated, the predicted multiplicative shear biases due to CGs are found to be at least a factor of two below the LSST full-depth requirement on the total systematic uncertainty in the redshift-dependent shear calibration. The analysis code and data products are publicly available (https://github.com/sowmyakth/measure_cg_bias).

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2009

Monitoring Disasters by Use of Instrumented Robotic Aircraft

Efforts are under way to develop data-acquisition, data-processing, and data-communication systems for monitoring disasters over large geographic areas by use of uninhabited aerial systems (UAS) robotic aircraft that are typically piloted by remote control. As integral parts of advanced, comprehensive disaster- management programs, these systems would provide (1) real-time data that would be used to coordinate responses to current disasters and (2) recorded data that would be used to model disasters for the purpose of mitigating the effects of future disasters and planning responses to them. The basic idea is to equip UAS with sensors (e.g., conventional video cameras and/or multispectral imaging instruments) and to fly them over disaster areas, where they could transmit data by radio to command centers. Transmission could occur along direct line-of-sight paths and/or along over-the-horizon paths by relay via spacecraft in orbit around the Earth. The initial focus is on demonstrating systems for monitoring wildfires; other disasters to which these developments are expected to be applicable include floods, hurricanes, tornadoes, earthquakes, volcanic eruptions, leaks of toxic chemicals, and military attacks. The figure depicts a typical system for monitoring a wildfire. In this case, instruments aboard a UAS would generate calibrated thermal-infrared digital image data of terrain affected by a wildfire. The data would be sent by radio via satellite to a data-archive server and image-processing computers. In the image-processing computers, the data would be rapidly geo-rectified for processing by one or more of a large variety of geographic-information- system (GIS) and/or image-analysis software packages. After processing by this software, the data would be both stored in the archive and distributed through standard Internet connections to a disaster-mitigation center, an investigator, and/or command center at the scene of the fire. Ground assets (in this case, firefighters and/or firefighting equipment) would also be monitored in real time by use of Global Positioning System (GPS) units and radio communication links between the assets and the UAS. In this scenario, the UAS would serve as a data-relay station in the sky, sending packets of information concerning the locations of assets to the image-processing computer, wherein this information would be incorporated into the geo-rectified images and maps. Hence, the images and maps would enable command-center personnel to monitor locations of assets in real time and in relation to locations affected by the disaster. Optionally, in case of a disaster that disrupted communications, the UAS could be used as an airborne communication relay station to partly restore communications to the affected area. A prototype of a system of this type was demonstrated in a project denoted the First Response Experiment (Project FiRE). In this project, a controlled outdoor fire was observed by use of a thermal multispectral scanning imager on a UAS that delivered image data to a ground station via a satellite uplink/ downlink telemetry system. At the ground station, the image data were geo-rectified in nearly real time for distribution via the Internet to firefighting managers. Project FiRE was deemed a success in demonstrating several advances essential to the eventual success of the continuing development effort.

Wegener, Steven S.↗

NASA NTRS2019

Version 4 of the SMAP Level-4 Soil Moisture Algorithm and Data Product

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 3-hourly, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture with a mean latency of ~2.5 days. The underlying L4_SM algorithm assimilates SMAP radiometer brightness temperature (Tb) observations into the NASA Catchment land surface model using a spatially-distributed ensemble Kalman filter. Version 4 of the L4_SM modeling system includes a reduction in the upward recharge of surface soil moisture from below under non-equilibrium conditions, resulting in reduced bias and improved dynamic range of L4_SM surface soil moisture compared to earlier versions. This change and additional technical modifications to the system reduce the mean and standard deviation of the observation-minus-forecast Tb residuals and overall soil moisture analysis increments while maintaining the skill of the L4_SM soil moisture estimates versus independent in situ measurements; the average, bias-adjusted RMSE in Version 4 is 0.039 m(exp 3) m(exp -3) for surface and 0.026 m(exp 3) m(exp -3) for root-zone soil moisture. Moreover, the coverage of assimilated SMAP observations in Version 4 is near-global owing to the use of additional satellite Tb records for algorithm calibration. L4_SM soil moisture uncertainty estimates are biased low (by 0.01-0.02 m(exp 3) m(exp -3)) against actual errors (computed versus in situ measurements). L4_SM runoff estimates, an additional product of the L4_SM algorithm, are biased low (by 35 mm year (exp -1)) against streamflow measurements. Compared to Version 3, bias in Version 4 is reduced by 46% for surface soil moisture uncertainty estimates and by 33% for runoff estimates.

RMSE↗

DOE OSTI2020

Nonlinear sparse Bayesian learning for physics-based models

This paper addresses the issue of overfitting while calibrating unknown parameters of over-parameterized physics-based models with noisy and incomplete observations. Here, a semi-analytical Bayesian framework of nonlinear sparse Bayesian learning (NSBL) is proposed to identify sparsity among model parameters during Bayesian inversion. NSBL offers significant advantages over machine learning algorithm of sparse Bayesian learning (SBL) for physics-based models, such as 1) the likelihood function or the posterior parameter distribution is not required to be Gaussian, and 2) prior parameter knowledge is incorporated into sparse learning (i.e. not all parameters are treated as questionable). NSBL employs the concept of automatic relevance determination (ARD) to facilitate sparsity among questionable parameters through parameterized prior distributions. The analytical tractability of NSBL is enabled by employing Gaussian ARD priors and by building a Gaussian mixture-model approximation of the posterior parameter distribution that excludes the contribution of ARD priors. Subsequently, type-II maximum likelihood is executed using Newton's method whereby the evidence and its gradient and Hessian information are computed in a semi-analytical fashion. We show numerically and analytically that SBL is a special case of NSBL for linear regression models. Subsequently, a linear regression example involving multimodality in both parameter posterior pdf and model evidence is considered to demonstrate the performance of NSBL in cases where SBL is inapplicable. Next, NSBL is applied to identify sparsity among the damping coefficients of a mass-spring-damper model of a shear building frame. These numerical studies demonstrate the robustness and efficiency of NSBL in alleviating overfitting during Bayesian inversion of nonlinear physics-based models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Direct Observations of Solute Dispersion in Rocks With Distinct Degree of Sub‐Micron Porosity

Abstract The transport of chemical species in rocks is affected by their structural heterogeneity to yield a wide spectrum of local solute concentrations. To quantify such imperfect mixing, advanced methodologies are needed that augment the traditional breakthrough curve analysis by probing solute concentration within the fluids locally. Here, we demonstrate the application of asynchronous, multimodality imaging by X‐ray computed tomography (XCT) and positron emission tomography (PET) to the study of passive tracer experiments in laboratory rock cores. The four‐dimensional concentration maps measured by PET reveal specific signatures of the transport process, which we have quantified using fundamental measures of mixing and spreading. We observe that the extent of solute spreading correlate strongly with the strength of subcore‐scale porosity heterogeneity measured by XCT, while dilution is enhanced in rocks containing substantial sub‐micron porosity. We observe that the analysis of different metrics is necessary, as they can differ in their sensitivity to the strength and forms of heterogeneity. The multimodality imaging approach is uniquely suited to probe the fundamental difference between spreading and mixing in heterogeneous media. We propose that when multi‐dimensional data is available, mixing and spreading can be independently quantified using the same metric. We also demonstrate that one‐dimensional transport models have limited predictive ability toward the internal evolution of the solute concentration, when the model is solely calibrated against the effluent breakthrough curves. The data set generated in this study can be used to build realistic digital rock models and to benchmark transport simulations that account deterministically for rock property heterogeneity.

Kurotori, Takeshi [Department of Chemical Engineer↗

NASA NTRS2018

A Blueprint for Demonstrating Quantum Supremacy with Superconducting Qubits

Long coherence times and high fidelity control recently achieved in scalable superconducting circuits paved the way for the growing number of experimental studies of many-qubit quantum coherent phenomena in these devices. Albeit full implementation of quantum error correction and fault tolerant quantum computation remains a challenge the near term pre-error correction devices could allow new fundamental experiments despite inevitable accumulation of errors. One such open question foundational for quantum computing is achieving the so called quantum supremacy, an experimental demonstration of a computational task that takes polynomial time on the quantum computer whereas the best classical algorithm would require exponential time and/or resources. It is possible to formulate such a task for a quantum computer consisting of less than a 100 qubits. The computational task we consider is to provide approximate samples from a non-trivial quantum distribution. This is a generalization for the case of superconducting circuits of ideas behind boson sampling protocol for quantum optics introduced by Arkhipov and Aaronson. In this presentation we discuss a proof-of-principle demonstration of such a sampling task on a 9-qubit chain of superconducting gmon qubits developed by Google. We discuss theoretical analysis of the driven evolution of the device resulting in output approximating samples from a uniform distribution in the Hilbert space, a quantum chaotic state. We analyze quantum chaotic characteristics of the output of the circuit and the time required to generate a sufficiently complex quantum distribution. We demonstrate that the classical simulation of the sampling output requires exponential resources by connecting the task of calculating the output amplitudes to the sign problem of the Quantum Monte Carlo method. We also discuss the detailed theoretical modeling required to achieve high fidelity control and calibration of the multi-qubit unitary evolution in the device. We use a novel cross-entropy statistical metric as a figure of merit to verify the output and calibrate the device controls. Finally, we demonstrate the statistics of the wave function amplitudes generated on the 9-gmon chain and verify the quantum chaotic nature of the generated quantum distribution. This verifies the implementation of the quantum supremacy protocol.

Kechedzhi, Kostyantyn↗

DOE OSTI2024

Computationally efficient and error aware surrogate construction for numerical solutions of subsurface flow through porous media

Limiting the injection rate to restrict the pressure below a threshold at a critical location can be an important goal of simulations that model the subsurface pressure between injection and extraction wells. The pressure is approximated by the solution of Darcy’s partial differential equation for a given permeability field. The subsurface permeability is modeled as a random field since it is known only up to statistical properties. This induces uncertainty in the computed pressure. Solving the partial differential equation for an ensemble of random permeability simulations enables estimating a probability distribution for the pressure at the critical location. These simulations are computationally expensive, and practitioners often need rapid online guidance for real-time pressure management. An ensemble of numerical partial differential equation solutions is used to construct a Gaussian process regression model that can quickly predict the pressure at the critical location as a function of the extraction rate and permeability realization. The Gaussian process surrogate analyzes the ensemble of numerical pressure solutions at the critical location as noisy observations of the true pressure solution, enabling robust inference using the conditional Gaussian process distribution. Our first novel contribution is to identify a sampling methodology for the random environment and matching kernel technology for which fitting the Gaussian process regression model scales as O ( n log n ) instead of the typical O ( n 3 ) rate in the number of samples n used to fit the surrogate. The surrogate model allows almost instantaneous predictions for the pressure at the critical location as a function of the extraction rate and permeability realization. Our second contribution is a novel algorithm to calibrate the uncertainty in the surrogate model to the discrepancy between the true pressure solution of Darcy’s equation and the numerical solution. Finally, although our method is derived for building a surrogate for the solution of Darcy’s equation with a random permeability field, the framework broadly applies to solutions of other partial differential equations with random coefficients.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2013

NASA Tech Briefs, May 2013

Topics include: Test Waveform Applications for JPL STRS Operating Environment; Pneumatic Proboscis Heat-Flow Probe; Method to Measure Total Noise Temperature of a Wireless Receiver During Operation; Cursor Control Device Test Battery; Functional Near-Infrared Spectroscopy Signals Measure Neuronal Activity in the Cortex; ESD Test Apparatus for Soldering Irons; FPGA-Based X-Ray Detection and Measurement for an X-Ray Polarimeter; Sequential Probability Ratio Test for Spacecraft Collision Avoidance Maneuver Decisions; Silicon/Carbon Nanotube Photocathode for Splitting Water; Advanced Materials and Fabrication Techniques for the Orion Attitude Control Motor; Flight Hardware Packaging Design for Stringent EMC Radiated Emission Requirements; RF Reference Switch for Spaceflight Radiometer Calibration; An Offload NIC for NASA, NLR, and Grid Computing; Multi-Scale CNT-Based Reinforcing Polymer Matrix Composites for Lightweight Structures; Ceramic Adhesive and Methods for On-Orbit Repair of Re-Entry Vehicles; Self-Healing Nanocomposites for Reusable Composite Cryotanks; Pt-Ni and Pt-Co Catalyst Synthesis Route for Fuel Cell Applications; Aerogel-Based Multilayer Insulation with Micrometeoroid Protection; Manufacturing of Nanocomposite Carbon Fibers and Composite Cylinders; Optimized Radiator Geometries for Hot Lunar Thermal Environments; A Mission Concept: Re-Entry Hopper-Aero-Space-Craft System on-Mars (REARM-Mars); New Class of Flow Batteries for Terrestrial and Aerospace Energy Storage Applications; Reliability of CCGA 1152 and CCGA 1272 Interconnect Packages for Extreme Thermal Environments; Using a Blender to Assess the Microbial Density of Encapsulated Organisms; Mixed Integer Programming and Heuristic Scheduling for Space Communication; Video Altimeter and Obstruction Detector for an Aircraft; Control Software for Piezo Stepping Actuators; Galactic Cosmic Ray Event-Based Risk Model (GERM) Code; Sasquatch Footprint Tool; and Multi-User Space Link Extension (SLE) System.

Source record↗

DOE OSTI2024

Portable, heterogeneous ensemble workflows at scale using libEnsemble

libEnsemble is a Python-based toolkit for running dynamic ensembles, developed as part of the DOE Exascale Computing Project. The toolkit utilizes a unique generator–simulator–allocator paradigm, where generators produce input for simulators, simulators evaluate those inputs, and allocators decide whether and when a simulator or generator should be called. The generator steers the ensemble based on simulation results. Generators may, for example, apply methods for numerical optimization, machine learning, or statistical calibration. libEnsemble communicates between a manager and workers. Flexibility is provided through multiple manager–worker communication substrates each of which has different benefits. These include Python’s multiprocessing, mpi4py, and TCP. Multisite ensembles are supported using Balsam or Globus Compute. We overview the unique characteristics of libEnsemble as well as current and potential interoperability with other packages in the workflow ecosystem. We highlight libEnsemble’s dynamic resource features: libEnsemble can detect system resources, such as available nodes, cores, and GPUs, and assign these in a portable way. These features allow users to specify the number of processors and GPUs required for each simulation; and resources will be automatically assigned on a wide range of systems, including Frontier, Aurora, and Perlmutter. Such ensembles can include multiple simulation types, some using GPUs and others using only CPUs, sharing nodes for maximum efficiency. We also describe the benefits of libEnsemble’s generator–simulator coupling, which easily exposes to the user the ability to cancel, and portably kill, running simulations based on models that are updated with intermediate simulation output. We demonstrate libEnsemble’s capabilities, scalability, and scientific impact via a Gaussian process surrogate training problem for the longitudinal density profile at the exit of a plasma accelerator stage. In conclusion, the study uses gpCAM for the surrogate model and employs either Wake-T or WarpX simulations, highlighting efficient use of resources that can easily extend to exascale.

Dynamic ensembles↗

NASA NTRS2008

Estimating Software-Development Costs With Greater Accuracy

COCOMOST is a computer program for use in estimating software development costs. The goal in the development of COCOMOST was to increase estimation accuracy in three ways: (1) develop a set of sensitivity software tools that return not only estimates of costs but also the estimation error; (2) using the sensitivity software tools, precisely define the quantities of data needed to adequately tune cost estimation models; and (3) build a repository of software-cost-estimation information that NASA managers can retrieve to improve the estimates of costs of developing software for their project. COCOMOST implements a methodology, called '2cee', in which a unique combination of well-known pre-existing data-mining and software-development- effort-estimation techniques are used to increase the accuracy of estimates. COCOMOST utilizes multiple models to analyze historical data pertaining to software-development projects and performs an exhaustive data-mining search over the space of model parameters to improve the performances of effort-estimation models. Thus, it is possible to both calibrate and generate estimates at the same time. COCOMOST is written in the C language for execution in the UNIX operating system.

Baker, Dan↗

DOE OSTI2026

IAM-FIRE: a Climate Emulator–Based Framework to Project Wildfire Impacts and Risks for Integrated Assessment Models

Most Integrated Assessment Models (IAMs) underrepresent dynamic feedbacks from climate-driven disturbances such as wildfires, potentially overestimating the permanence of land-based carbon sinks. In particular, representing the impacts of forest fires is becoming increasingly important, as these are expected to intensify in the coming years. We introduce IAM-FIRE (Integrated Assessment Model – Fire Impacts & Risks Emulator), a novel framework that enables the projection of wildfire burned area (BA) and carbon emissions (CE) directly from IAM outputs. IAM-FIRE combines a spatial climate emulator, land-use downscaling, vegetation productivity modelling, and an empirical fire model to generate global annual wildfire impacts for arbitrary socioeconomic and emissions scenarios at 0.5° resolution for the period 2020–2100. Calibrated against GFEDv5 observations and using inputs from the Global Change Analysis Model (GCAM), we report projections BA and CE derived from IAM-FIRE for four scenarios: SSP1-2.6, SSP2-4.5, SSP3-6.6 and SSP5-7.6. The model reproduces historical global trends for total BA, including the observed global decline since the early 2000s, and for forest BA. Projected fire trajectories differ strongly among scenarios: total BA range from declines under SSP1-2.6 (-3.36 Mha yr-1) to increases under SSP3-6.6 (+1.6 Mha yr-1). Corresponding total CE show a similar divergence ranging from -15 to +10.6 TgC yr-1. Socioeconomic development exerts a dominant suppressing effect on wildfire impacts while climate change and CO2-driven increases in vegetation productivity amplify fire risk, particularly under high-emissions pathways. Compared with CMIP6 fire-enabled Earth System Models, IAM-FIRE exhibits greater sensitivity to radiative forcing and a stronger role for human-driven fire suppression, highlighting substantial structural uncertainties in future fire projections. By providing a computationally efficient and internally consistent approach to represent wildfire impacts within IAMs, IAM-FIRE enables systematic exploration of fire–climate–land feedbacks and supports improved assessments of mitigation permanence and climate risks in future integrated scenarios.

Rouhette, Theo↗

DOE OSTI2022

High-fidelity micromechanical modeling of the effects of defects on damage and creep behavior in single tow ceramic matrix composite

Despite the superior properties of ceramic matrix composites (CMCs), their fabrication process generates inevitable defects with high density that significantly influence material integrity and residual useful life. Recent efforts have focused on CMC property prediction and investigation of their different inelastic mechanisms, but very limited work exists on understanding the influence of defects on inelastic responses of CMCs. Here, we introduce a three-dimensional micromechanics computational framework that includes experimentally informed material microstructure and architectural variabilities to investigate CMC response in the presence of manufacturing-induced defects. A developed microstructure generation algorithm is used to generate the representative volume elements in the micromechanics models from complex material morphology information obtained from extensive characterization studies of C/SiNC and SiC/SiNC CMCs. A fracture mechanics-informed matrix damage model is reformulated, in which the model considers the growth of porosity and microcracks in the as-received material. A progressive fiber damage model as well as Orthotropic viscoplasticity creep formulation are also utilized for the study. This methodology is implemented within the micromechanics framework to investigate the complex temperature- and time-dependent load transfer and damage mechanisms of CMCs under operation loading conditions. The developed framework explores the influence of as-received defects on the damage and creep behavior in service conditions. It also provides new insights into the effect of size, shape, distribution, and location of these defects on material response. The framework is then calibrated with unidirectional CMC minicomposite results available in the literature.

36 MATERIALS SCIENCE↗

NASA NTRS2003

Sonoluminescence Bubble Measurements using Vision-Based Algorithms

Vision-based measurement methods were used to measure bubble sizes in this sonoluminescence experiment. Bubble imaging was accomplished by placing the bubble between a bright light source and a microscope-CCD camera system. A collimated light-emitting diode was operated in a pulsed model with an adjustable time delay with respect to the piezo-electric transducer drive signal. The light-emitting diode produced a bubble shadowgraph consisting of a multiple exposure made by numerous light pulses imaged onto a charge-couple device camera. Each image was transferred from the camera to a computer-controlled machine vision system via a frame grabber. The frame grabber was equipped with on-board memory to accomodate sequential image buffering while images were transferred to the host processor and analyzed. This configuration allowed the host computer to perform diameter measurements, centroid position measurements and shape estimation in "real-time" as the next image was being acquired. Bubble size measurement accuracy with an uncertainty of 3 microns was achieved using standard lenses and machine vision algorithms. Bubble centroid position accuracy was also within the 3 micron tolerance of the vision system. This uncertainty estimation accounted for the optical spatial resolution, digitization errors and the edge detection algorithm accuracy. The vision algorithms include camera calibration, thresholding, edge detection, edge position determination, distance between two edges computations and centroid position computations.

Hall, Nancy R.↗