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At least 919 records · Page 51

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DOE OSTI2013

Cell degradation of a Na–NiCl 2 (ZEBRA) battery

In this work, the parameters influencing the degradation of a Na-NiCl 2 (ZEBRA) battery were investigated. Planar Na-NiCl 2 cells using β”-alumina solid electrolyte (BASE) were tested with different C-rates, Ni/NaCl ratios, and capacity windows, in order to identify the key parameters for the degradation of Na-NiCl 2 battery. The morphology of NaCl and Ni particles were extensively investigated after 60 cycles under various test conditions using a scanning electron microscope. A strong correlation between the particle size (NaCl and Ni) and battery degradation was observed in this work. Even though the growth of both Ni and NaCl can influence the cell degradation, our results indicate that the growth of NaCl is a dominant factor in cell degradation. The use of excess Ni seems to play a role in tolerating the negative effects of particle growth on degradation since the available active surface area of Ni particles can be still sufficient even after particle growth. For NaCl, a large cycling window was the most significant factor, of which effects were amplified with decrease in Ni/NaCl ratio.

25 ENERGY STORAGE↗

DOE OSTI2025

Characterization of Fuel-to-Coolant Heat Transfer During Reactivity-Initiated Accidents Using Tightly Coupled Thermal Hydraulics and Fuel Thermomechanics

The reactivity-initiated accident (RIA) is a complex scenario with several tightly interacting physical phenomena. Accurately predicting fuel behavior during these transients is difficult due to limitations in the modeling of fuel-to-coolant heat transfer. Common approaches to simulate RIAs involve standalone calculations using either a fuel performance code or a thermal-hydraulic code. The complex interdependencies of thermal-hydraulic and fuel mechanical behavior suggest that a tight coupling between these codes may provide more accurate predictions of fuel-to-coolant heat transfer and cladding mechanical response. Here, RELAP5-3D and BISON are coupled in this paper to simulate RIAs, and a sensitivity analysis is performed to rank key thermal properties and two-phase heat transfer parameters relevant for fuel-to-coolant heat transfer and cladding failure mechanisms in UO 2 –Zircaloy-4 systems. Gas gap conductance, film boiling heat transfer uncertainty, pulse width, fuel-specific heat capacity, and cladding-specific heat capacity were identified as important parameters. Variations in figures of merit resulting from changes to pulse width and the material thermal properties indicate that time-dependent heat transfer rates are significant for safety-relevant mechanical parameters due to the time dependence of cladding ductility and pellet-cladding mechanical interaction loading. The results suggest that the thermal-hydraulic factors have a nonnegligible influence on the thermomechanical solution and vice versa. Tight coupling of both sets of physics is recommended to improve prediction of fuel behavior during RIAs. Highlights include the following: 1. The RELAP5-3D thermal-hydraulic code and the BISON fuel performance code are tightly coupled for simulation of RIA transients with energy depositions at the Zircaloy-4 cladding failure threshold. 2. Departure from nucleate boiling occurred for all simulated cases. Due to the ductility of fresh fuel, substantial ballooning occurred in most cases. 3. Gas gap conductance, fuel-specific heat capacity, cladding-specific heat capacity, transient pulse width, and film boiling heat transfer were the dominant thermal factors impacting the safety figures of merit at energy depositions.

Critical Heat Flux (CHF)↗

DOE OSTI2018

Power accounting of plasma discharges in the linear device Proto-MPEX

Plasma material interaction (PMI) studies are crucial to the successful development of future fusion reactors. Prototype Material Plasma Exposure eXperiment (Proto-MPEX) is a prototype design for the MPEX, a steady-state linear device being developed to study PMI. The primary purpose of Proto-MPEX is developing the plasma heating source concepts for MPEX. A power accounting study of Proto-MPEX works to identify machine operating parameters that could improve its performance, thereby increasing its PMI research capabilities, potentially impacting the MPEX design concept. To build a comprehensive power balance, an analysis of the helicon region has been performed implementing a diagnostic suite and software modeling to identify mechanisms and locations of heat loss from the main plasma. Of the 106.3 kW of input power, up to 90.5% of the power has been accounted for in the helicon region. When the analysis was extended to encompass the device to its end plates, 49.2% of the input power was accounted for and verified diagnostically. Areas requiring further diagnostic analysis are identified. The required improvements will be implemented in future work. The data acquisition and analysis processes will be streamlined to form a working model for future power balance studies of Proto-MPEX.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS2006

Flight Operations Analysis Tool

Flight Operations Analysis Tool (FLOAT) is a computer program that partly automates the process of assessing the benefits of planning spacecraft missions to incorporate various combinations of launch vehicles and payloads. Designed primarily for use by an experienced systems engineer, FLOAT makes it possible to perform a preliminary analysis of trade-offs and costs of a proposed mission in days, whereas previously, such an analysis typically lasted months. FLOAT surveys a variety of prior missions by querying data from authoritative NASA sources pertaining to 20 to 30 mission and interface parameters that define space missions. FLOAT provides automated, flexible means for comparing the parameters to determine compatibility or the lack thereof among payloads, spacecraft, and launch vehicles, and for displaying the results of such comparisons. Sparseness, typical of the data available for analysis, does not confound this software. FLOAT effects an iterative process that identifies modifications of parameters that could render compatible an otherwise incompatible mission set.

Easter, Robert↗

NASA NTRS1977

Aerodynamic coefficient estimation for dynamic wind tunnel models

Maximum likelihood parameter identification is used to estimate the aerodynamic coefficients of a 'flying' aircraft model which is mounted in a transonic wind tunnel by a system of cables and pulleys. The model's motion is governed by both cable and aerodynamic forces, where the parameters to be identified are functions of the aerodynamic forces but cable forces greatly predominate. The test data were successfully processed assuming a linear equation model in the identification procedure. In one instance, the procedure was able to detect a failing measurement instrument. The final parameter estimates are compared to estimates obtained in independent tests of a similar aircraft model.

Mohr, R. L.↗

NASA NTRS2005

CAPS Simulation Environment Development

The final design for an effective Comet/Asteroid Protection System (CAPS) will likely come after a number of competing designs have been simulated and evaluated. Because of the large number of design parameters involved in a system capable of detecting an object, accurately determining its orbit, and diverting the impact threat, a comprehensive simulation environment will be an extremely valuable tool for the CAPS designers. A successful simulation/design tool will aid the user in identifying the critical parameters in the system and eventually allow for automatic optimization of the design once the relationships of the key parameters are understood. A CAPS configuration will consist of space-based detectors whose purpose is to scan the celestial sphere in search of objects likely to make a close approach to Earth and to determine with the greatest possible accuracy the orbits of those objects. Other components of a CAPS configuration may include systems for modifying the orbits of approaching objects, either for the purpose of preventing a collision or for positioning the object into an orbit where it can be studied or used as a mineral resource. The Synergistic Engineering Environment (SEE) is a space-systems design, evaluation, and visualization software tool being leveraged to simulate these aspects of the CAPS study. The long-term goal of the SEE is to provide capabilities to allow the user to build and compare various CAPS designs by running end-to-end simulations that encompass the scanning phase, the orbit determination phase, and the orbit modification phase of a given scenario. Herein, a brief description of the expected simulation phases is provided, the current status and available features of the SEE software system is reported, and examples are shown of how the system is used to build and evaluate a CAPS detection design. Conclusions and the roadmap for future development of the SEE are also presented.

Murphy, Douglas G.↗

DOE OSTI2017

Algorithm sensitivity analysis and parameter tuning for tissue image segmentation pipelines

Motivation: Sensitivity analysis and parameter tuning are important processes in large-scale image analysis. They are very costly because the image analysis workflows are required to be executed several times to systematically correlate output variations with parameter changes or to tune parameters. An integrated solution with minimum user interaction that uses effective methodologies and high performance computing is required to scale these studies to large imaging datasets and expensive analysis workflows. Results: The experiments with two segmentation workflows show that the proposed approach can (i) quickly identify and prune parameters that are non-influential; (ii) search a small fraction (about 100 points) of the parameter search space with billions to trillions of points and improve the quality of segmentation results (Dice and Jaccard metrics) by as much as 1.42 x compared to the results from the default parameters; (iii) attain good scalability on a high performance cluster with several effective optimizations. Conclusions: Our work demonstrates the feasibility of performing sensitivity analyses, parameter studies and auto-tuning with large datasets. The proposed framework can enable the quantification of error estimations and output variations in image segmentation pipelines.

Biochemistry & Molecular Biology↗

DOE OSTI2020

Multi-year incubation experiments boost confidence in model projections of long-term soil carbon dynamics

Abstract Global soil organic carbon (SOC) stocks may decline with a warmer climate. However, model projections of changes in SOC due to climate warming depend on microbially-driven processes that are usually parameterized based on laboratory incubations. To assess how lab-scale incubation datasets inform model projections over decades, we optimized five microbially-relevant parameters in the Microbial-ENzyme Decomposition (MEND) model using 16 short-term glucose (6-day), 16 short-term cellulose (30-day) and 16 long-term cellulose (729-day) incubation datasets with soils from forests and grasslands across contrasting soil types. Our analysis identified consistently higher parameter estimates given the short-term versus long-term datasets. Implementing the short-term and long-term parameters, respectively, resulted in SOC loss (–8.2 ± 5.1% or –3.9 ± 2.8%), and minor SOC gain (1.8 ± 1.0%) in response to 5 °C warming, while only the latter is consistent with a meta-analysis of 149 field warming observations (1.6 ± 4.0%). Comparing multiple subsets of cellulose incubations (i.e., 6, 30, 90, 180, 360, 480 and 729-day) revealed comparable projections to the observed long-term SOC changes under warming only on 480- and 729-day. Integrating multi-year datasets of soil incubations (e.g., > 1.5 years) with microbial models can thus achieve more reasonable parameterization of key microbial processes and subsequently boost the accuracy and confidence of long-term SOC projections.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Manipulation of surface plasmon polariton propagation on isotropic and anisotropic two-dimensional materials coupled to boron nitride heterostructures

We present a comprehensive analysis of surface plasmon polariton dispersion characteristics associated with isotropic and anisotropic two-dimensional atomically thin layered materials (2D sheets) coupled to h-BN heterostructures. A scattering matrix based approach is presented to compute the electromagnetic fields and related dispersion characteristics of stacked layered systems composed of anisotropic 2D sheets and uniaxial bulk materials. We analyze specifically the surface plasmon polariton (SPP) dispersion characteristics in case of isolated and coupled two-dimensional layers with isotropic and anisotropic conductivities. An analysis based on residue theorem is utilized to identify optimum optical parameters (surface conductivity) and geometrical parameters (separation between layers) to maximize the SPP field at a given position. The effect of type and degree of anisotropy on the shapes of iso-frequency curves and propagation characteristics is discussed in detail. The analysis presented in this paper gives an insight to identify optimum setup to enhance the SPP field at a given position and in a given direction on the surface of two-dimensional materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Detecting depinning and nonequilibrium transitions with unsupervised machine learning

Using numerical simulations of a model disk system, we demonstrate that a machine learning generated order-parameter-like measure can detect depinning transitions and different dynamic flow phases in systems driven far from equilibrium. Furthermore, we specifically consider monodisperse passive disks with short range interactions undergoing a depinning phase transition when driven over quenched disorder. The machine learning derived order-parameter-like measure identifies the depinning transition as well as different dynamical regimes, such as the transition from a flowing liquid to a phase separated liquid-solid state that is not readily distinguished with traditional measures such as velocity-force curves or Voronoi tessellation. The order-parameter-like measure also shows markedly distinct behavior in the limit of high density where jamming effects occur. Our results should be general to the broad class of particle-based systems that exhibit depinning transitions and nonequilibrium phase transitions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS1972

Invariant poles feedback control of flexible highly variable spacecraft.

Description of a technique for single-axis control of a model of a highly flexible space station. Active damping of lower frequency flexibility modes is employed. In the control technique, referred to as invariant poles feedback control, feedback gains are adjusted so that the closed-loop system characteristic equation is matched to that of a reference model. Hence closed-loop system poles will not move; they will be invariant (provided that bending frequencies and parameters can be identified accurately). This is accomplished by obtaining the system characteristic equation in closed form; equating respective coefficients between terms of like powers in s in the system and reference model characteristic equations; and solving for the feedback gains. The feedback gains are explicit functions of system plant parameters and the coefficients of the reference model characteristic equation, and are easily programmed for the digital computer.

Mendel, J. M.↗

NASA NTRS1972

Invariant poles feedback control of flexible, highly variable spacecraft.

This paper describes a technique for single-axis control of a model of a highly flexible Space Station. Active damping of lower frequency flexibility modes is employed. In the control technique, referred to as invariant poles feedback control (IPFC), feedback gains are adjusted so that the closed-loop system's characteristic equation is matched to that of a reference model; hence, closed-loop system's poles will not move - they will be invariant (provided bending frequencies and parameters can be identified accurately). This is accomplished by obtaining the system's characteristic equation in closed form; equating respective coefficients between terms of like powers in s in the system and reference model characteristic equations; and, solving for the feedback gains. The feedback gains are explicit functions of system plant parameters and the coefficients of the reference model's characteristic equation, and are easily programmed for the digital computer.

Mendel, J. M.↗

DOE OSTI2024

A multiscale model of immune surveillance in micrometastases gives insights on cancer patient digital twins

Abstract Metastasis is the leading cause of death in patients with cancer, driving considerable scientific and clinical interest in immunosurveillance of micrometastases. We investigated this process by creating a multiscale mathematical model to study the interactions between the immune system and the progression of micrometastases in general epithelial tissue. We analyzed the parameter space of the model using high-throughput computing resources to generate over 100,000 virtual patient trajectories. We demonstrated that the model could recapitulate a wide variety of virtual patient trajectories, including uncontrolled growth, partial response, and complete immune response to tumor growth. We classified the virtual patients and identified key patient parameters with the greatest effect on the simulated immunosurveillance. We highlight the lessons derived from this analysis and their impact on the nascent field of cancer patient digital twins (CPDTs). While CPDTs could enable clinicians to systematically dissect the complexity of cancer in each individual patient and inform treatment choices, our work shows that key challenges remain before we can reach this vision. In particular, we show that there remain considerable uncertainties in immune responses, unreliable patient stratification, and unpredictable personalized treatment. Nonetheless, we also show that in spite of these challenges, patient-specific models suggest strategies to increase control of clinically undetectable micrometastases even without complete parameter certainty.

Mathematical & Computational Biology↗

NASA NTRSDate not supplied

Adaptive Optimization for System Performance and Combined Bernstein Polynomial, Optimal Reciprocal Collision Avoidance, Differential Dynamic Programming for Trajectory Replanning and Collision Avoidance for UAM Vehicles

The emerging urban air mobility (UAM) sector in aerospace is driving development of unconventional multi-modal vehicle configurations and autonomous flight. The combination of multi-modal vehicle dynamics, complex environment, requirements to deal with flight contingencies in an efficient and safe manner, as well as necessity for precise trajectory following and performance, are the driving influence behind adaptive optimization for system performance. We are interested in trajectory optimization algorithm that would system parameter estimation and identifying the optimal switching time between modes of hybrid dynamical systems. This presentation discusses a parameterized optimal control trajectory optimization algorithm that is an extended and generalized version of Differential Dynamic Programming (DDP), titled Parameterized Differential Dynamic Programming (PDDP). DDP is an efficient trajectory optimization algorithm relying on second order approximations of a system’s dynamics and cost function and has recently been applied to optimize systems with time invariant parameters. Experiments are presented applying PDDP to solve model predictive control (MPC) and moving horizon estimation (MHE) tasks simultaneously. In particular, PDDP is used to determine the optimal transition point between flight regimes of a complex urban air mobility (UAM) class vehicle exhibiting multiple phases of flight and to identify and compensate for actuation faults.

optimization↗

NASA NTRS1975

Additional experiments with a four-bladed cyclic pitch stirring model rotor, part 2 of second yearly report

The four bladed pitch stirring rotor model was used in a rotor dynamic wake survey at zero advance ratio, covering 2 deg, 5 deg and 8 deg collective pitch settings. Dynamic wake data were taken in planes .12 and .20 radii below the rotor disk and are to be compared with analytical wake data with parameters to be identified from pitch stirring transients. The model was modified to perform such transients. The instrumentation developed for this purpose is described together with the method of data acquisition and with the test procedures. The hardware and software for several data handling systems are discussed. These systems extract from pitch stirring transients the parameters of analytical dynamic rotor wake models.

Hohenemser, K. H.↗

DOE OSTI2024

Optimal Geometry for Focused Ion Beam-Milled Samples for Direct-Pull Micro-Tensile Testing Performed In Situ in a Scanning Electron Microscope

A thorough procedure was developed to efficiently manufacture dogbone samples using focused ion beam (FIB) milling for micro-tensile testing. A Bruker PI 89 PicoIndenter, Billerica, MA, USA, was used as a case study, although the analysis and results are applicable to other micro-mechanical testing systems capable of mounting a standard, Ø12.7 mm × Ø3.2 mm pin, scanning electron microscopy (SEM) pin stub (Ted Pella, Redding, CA, USA). Nine dogbones were made from an Fe-45Cu alloy additively manufactured using powder-fed laser-directed energy deposition (DED-LB). Testing showed that fracture was confined to the gauge section for all dogbones and that the fracture mode, ductile vs. brittle, was entirely dependent on the grain orientation relative to the loading direction. The analysis showed that the measured plastic strain to failure can vary from >11% (optimal geometry) to <1% (non-optimal geometry) in micro-tensile testing of high-tensile-strength (>1 GPa) metallic materials. Subsequently, a finite element analysis (FEA) was conducted to identify the improved dogbone geometries. A total of ten thousand dogbone geometries were tested, and their dimensions were defined by a set of four adjustable parameters (corner radius, load surface angle, load surface length, and dogbone head length). The gauge width and gauge length were fixed to 4 µm and 10 µm, respectively. Three-dimensional surface plots of the stress concentration as a function of two parameters were used to identify the optimal ranges of parameter values. The addition of maximum width and length constraints, measuring 25 µm and 30 µm, respectively, allowed us to identify an optimal geometry at load surface angles of 30° and 45°. Their respective dimensions (corner radius, load surface length, and dogbone head length) are, in µm, 12, 6, and 7 and 10, 7, and 7. Testing these two optimal geometries with a range of gauge lengths from 4 to 20 µm showed that smaller gauge lengths only slightly reduced the detrimental stress concentration outside the gauge section. However, smaller gauge lengths will notably improve the FIB surface polishing step as tapering is reduced with smaller dogbone lengths.

Chemistry↗

DOE OSTI2013

Inverse Modeling of Hydrologic Parameters Using Surface Flux and Runoff Observations in the Community Land Model

This study demonstrates the possibility of inverting hydrologic parameters using surface flux and runoff observations in version 4 of the Community Land Model (CLM4). Previous studies showed that surface flux and runoff calculations are sensitive to major hydrologic parameters in CLM4 over different watersheds, and illustrated the necessity and possibility of parameter calibration. Two inversion strategies, the deterministic least-square fitting and stochastic Markov-Chain Monte-Carlo (MCMC) - Bayesian inversion approaches, are evaluated by applying them to CLM4 at selected sites. The unknowns to be estimated include surface and subsurface runoff generation parameters and vadose zone soil water parameters. We find that using model parameters calibrated by the least-square fitting provides little improvements in the model simulations but the sampling-based stochastic inversion approaches are consistent - as more information comes in, the predictive intervals of the calibrated parameters become narrower and the misfits between the calculated and observed responses decrease. In general, parameters that are identified to be significant through sensitivity analyses and statistical tests are better calibrated than those with weak or nonlinear impacts on flux or runoff observations. Temporal resolution of observations has larger impacts on the results of inverse modeling using heat flux data than runoff data. Soil and vegetation cover have important impacts on parameter sensitivities, leading to the different patterns of posterior distributions of parameters at different sites. Overall, the MCMC-Bayesian inversion approach effectively and reliably improves the simulation of CLM under different climates and environmental conditions. Bayesian model averaging of the posterior estimates with different reference acceptance probabilities can smooth the posterior distribution and provide more reliable parameter estimates, but at the expense of wider uncertainty bounds.

inverse, modeling, hydrologic, parameters, flux, r↗

DOE OSTI2017

Numerical Investigation of a Gasoline-Like Fuel in a Heavy-Duty Compression Ignition Engine Using Global Sensitivity Analysis

Fuels in the gasoline auto-ignition range (Research Octane Number (RON) > 60) have been demonstrated to be effective alternatives to diesel fuel in compression ignition engines. Such fuels allow more time for mixing with oxygen before combustion starts, owing to longer ignition delay. Moreover, by controlling fuel injection timing, it can be ensured that the in-cylinder mixture is “premixed enough” before combustion occurs to prevent soot formation while remaining “sufficiently inhomogeneous” in order to avoid excessive heat release rates. Gasoline compression ignition (GCI) has the potential to offer diesel-like efficiency at a lower cost and can be achieved with fuels such as low-octane straight run gasoline which require significantly less processing in the refinery compared to today’s fuels. To aid the design and optimization of a compression ignition (CI) combustion system using such fuels, a global sensitivity analysis (GSA) was conducted to understand the relative influence of various design parameters on efficiency, emissions and heat release rate. The design parameters included injection strategies, exhaust gas recirculation (EGR) fraction, temperature and pressure at intake valve closure and injector configuration. These were varied simultaneously to achieve various targets of ignition timing, combustion phasing, overall burn duration, emissions, fuel consumption, peak cylinder pressure and maximum pressure rise rate. The baseline case was a three-dimensional closed-cycle computational fluid dynamics (CFD) simulation with a sector mesh at medium load conditions. Eleven design parameters were considered and ranges of variation were prescribed to each of these. These input variables were perturbed in their respective ranges using the Monte Carlo (MC) method to generate a set of 256 CFD simulations and the targets were calculated from the simulation results. GSA was then applied as a screening tool to identify the input parameters having the most significant impact on each target. The results were further assessed by investigating the impact of individual parameter variations on the targets. Overall, it was demonstrated that GSA can be an effective tool in understanding parameters sensitive to a low temperature combustion concept with novel fuels.

Pal, Pinaki↗