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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.

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At least 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Identification of Life Models for Li-Ion Batteries Using Penalized Regression and Bilevel Optimization

Reduced-order physics-based life models are extremely useful for rapidly predicting battery state-of-health and for simulating battery lifetime in arbitrary aging conditions. However, identification of well-parameterized models is difficult. This is because, for maximum usefulness in predicting lifetime under a variety of conditions, aging test data exhibits many degradation mechanisms, which all need to be accurately modeled. However, because aging tests are time-consuming and expensive, especially for large-format batteries, a minimum of tests are conducted while probing many stress factors. Building a well-parameterized model is then very challenging: an under-parameterized model will neglect critical degradation modes, and an over-parameterized model will extrapolate poorly to new testing conditions. To complicate this matter, the functional form of the model for any individual degradation rate can be very difficult to identify. In this work, the statistical tools of penalized regression and bilevel optimization are used to help identify both the functional forms of and optimize the parameters of reduced-order life models, accelerating identification of robust models. Model robustness is demonstrated through traditional statistics methods of cross-validation and sensitivity analysis, uncertainty quantification through bootstrap resampling and Monte-Carlo simulation, and simulation of real-world use cases.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Cyber physical attack detection

A cyber-security threat detection system and method stores physical data measurements from a cyber-physical system and extracts synchronized measurement vectors synchronized to one or more timing pulses. The system and method synthesize data integrity attacks in response to the physical data measurements and applies alternating parameterized linear and non-linear operations in response to the synthesized data integrity attacks. The synthesis renders optimized model parameters used to detect multiple cyber-attacks.

Ferragut, Erik M.↗
DOE OSTI2021

Open-Loop Control of Adjustable Tuned Mass Dampers for Floating Wind Turbine Platforms

In this study, we demonstrate the ability of adjustable tuned mass dampers (TMDs) to reduce the platform motion of floating offshore wind turbines (FOWTs). The TMDs are located in the hull and provide control authority by varying the amount of water ballast and compressed air in a reservoir over time. The optimal TMD settings depend on the wind and wave conditions of the FOWT. We present an open-loop control scheme that changes the TMD natural frequency based on sea state and a simple method for estimating the peak wave period and significant wave height. The performance of this open-loop control is compared to ideal control of the TMDs (set knowing the exact sea state), a TMD with a constant natural frequency targeting the design load case with the greatest platform motion, and a baseline set of simulations of the platform without TMDs. The constant natural frequency case performs nearly as effectively as the actively controlled case, suggesting that the TMDs can be simply designed for extreme load conditions and provide consistent control over a range of environments; however, the methods used to find the optimal TMD parameters and estimate sea state statistics could be used in other aspects of FOWT control and design.

artificial intelligence↗
DOE OSTI2022

DEFECT DETECTION USING DYNAMIC ANALYSIS FOR ADDITIVE MANUFACTURED METALS

Additive manufacturing (AM) has the ability to produce parts with complex geometries and internal features, however, for demanding applications such as the automotive and aerospace industries, it is crucial that the parts can meet the demanding functional and geometric requirements. Quality control for AM parts focuses on nondestructive methods of testing, but many of the current methods are expensive and time-consuming. The research presented in this report explores various methods of nondestructive evaluation (NDE) using dynamic analysis on stainless steel parts produced with selective laser melting (SLM). Methods include, but are not limited to, frequency response functions (FRF), impedance-based measurements, and scanning laser doppler vibrometry. Additionally, mode shape analysis was performed in MATLAB and FEA simulations were used for comparison with experimental results. The results indicate that dynamic analysis has the potential to be a feasible method of defect detection and NDE in AM parts and future work should focus on refining these methods, such as optimizing test parameters to improve sensitivity to defects.

Deonarain, Gita↗
DOE OSTI2018

Soil Sorting Technology Process System - 18620

FUSRAP is currently using a ScanSortSM system at the Iowa Army Ammunition Plant (IAAAP) FUSRAP site that has demonstrated the ability to consistently detect and isolate distinct, singular DU fragments. Prior to using the ScanSortSM system, a pilot study was performed to test the variability in detection sensitivity with DU fragments at different soil depths, belt lateral locations, and belt speed; and evaluate, calibrate, and optimize system parameters for maximizing system production efficiency. The physical treatment was initially expected to allow for a 20 percent reduction in the volume of soil requiring off-site disposal. So far, the system has achieved an 82-92 percent volume reduction during the first four years of use. This volume reduction is resulting in cost savings that more than offset the initial cost of the physical treatment system. The findings of this sorting process may be useful at other FUSRAP sites. (author)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Recovery and Recrystallization Behavior of Nb-Containing Fe-Cr-Al Alloys

Nuclear-grade wrought Fe-Cr-Al alloys are currently under development as promising accident-tolerant fuel cladding materials for light water reactors (LWRs) due to their superior oxidation and corrosion resistance in high temperature steam environments when compared with current commercial Zr-based alloys. Sufficient amounts of Cr and Al additions in Fe-Cr-Al alloys provide the improved surface protection effects at both the service temperature and elevated temperatures in the accident case scenario. Minor alloying additions (such as Nb in the current study) also promote the second-phase precipitates to strengthen the alloys; e.g. sub-micron size Fe{sub 2}Nb-type Laves phase precipitates are likely to disperse at both inter- and trans-granular in BCC-Fe matrix after applying proper heat treatments. In LWRs, a seamless tube product is required for the fuel cladding which typically has ∼ 4 m length with an outside diameter of ∼ 10 mm and a wall thickness of less than 0.5 mm, and it imposes considerable challenges for tube fabrication process (e.g. tube drawing) of Fe-Cr-Al alloys. Research has been performed to achieve a balance between high-temperature performance and tube fabricability in newly developed Fe-Cr-Al alloys. BCC-Fe materials with Cr and Al additions typically suffer from poor ductility because of relatively high ductile-brittle transition temperature. In this sense, the microstructural stability of refined grain or subgrain structure, even at high temperature, is critical to maintain the deformability of the alloys during tube fabrication process, as well as sufficient mechanical properties at service condition. In this study, the recovery and recrystallization behaviors of Nb-containing Fe-Cr-Al alloys are studied. A specific focus was to evaluate the stability of the subgrain structure of the deformed Fe-Cr-Al alloys, and seek optimized processing parameters for seamless tube fabrication. The present results clearly demonstrated that the Nb addition stabilizes the refined subgrain microstructures of Fe-Cr-Al alloys below 700 deg. C due to Laves phase precipitations along sub-boundaries when compared with the Nb-free alloys, while maintaining sufficient strengths needed for fuel cladding in LWRs. At the same time, the results also showed that Nb-containing alloys can be softened through suitable annealing to improve the processability of tube drawing process. Detailed characterization efforts of subgrains and Laves phase precipitates are currently in progress. Completion of this work will deepen the understanding of the recovery and recrystallized behavior of Nb-containing Fe-Cr-Al alloys and provide guidelines to tube drawing process in the alloys. (authors)

36 MATERIALS SCIENCE↗
DOE OSTI2017

A Comparison of the FW-CADIS and MR-CADIS Variance Reduction Methods

The Consistent Adjoint Driven Importance Sampling (CADIS) method, which uses an adjoint deterministic estimate for making variance reduction parameters that optimize Monte Carlo (MC) shielding calculations, was introduced in 1998. This method was designed to optimize a single tally, typically an integrated response such as a detector count rate or a dose rate. Because both deterministic and stochastic methods are employed, CADIS is referred to as a hybrid method. In 2007, the forward-weighted CADIS (FW-CADIS) method was introduced to optimize several tallies at once, giving more uniform relative uncertainties among the tallies for both high-flux and low-flux areas of the problem. This method can be used for several small detectors, for semi-global problems (mesh tally over a large fraction of the problem), or for global problems (mesh tally over the entire problem). Optimization can be done for an integrated response such as a dose rate D(r) as a function of position, the total flux {phi}(r) as a function of position, or the space/energy flux {phi}(r,E). Both the CADIS and FW-CADIS methods have been in the MAVRIC sequence of SCALE for three versions (6.0 in 2009, 6.1 in 2011, and 6.2 in 2016). The ADVANTG package released in 2015 uses the CADIS and FW-CADIS methods to accelerate MCNP neutron/photon shielding calculations. In 2014, the multi-response CADIS (MR-CADIS) method was presented as a way to obtain even more uniform relative uncertainties for problems with multiple tallies than those obtained with FW-CADIS. The method does not use a forward deterministic calculation but instead uses one adjoint calculation for every tally to be optimized in the Monte Carlo calculation. This work compares the above methods.

42 ENGINEERING↗
DOE OSTI2024

Constrained curve fitting for semi-parametric models with radial basis function networks

Common to many analysis pipelines in lattice gauge theory and the broader scientific discipline is the need to fit a semi-parametric model to data. We propose a fit method that utilizes a radial basis function network to approximate the non-parametric component of such models. The approximate parametric model is fit to data using the basin hopping global optimization algorithm. Parameter constraints are enforced through Gaussian priors. The viability of our method is tested by examining its use in a finite-size scaling analysis of the $q$-state Potts model and $p$-state clock model with $q=2,3$ and $p=4,\infty$.

Peterson, Curtis T.↗
DOE OSTI2024

A Bayesian Learning Approach to Wireless Outdoor Heatmap Construction using Deep Gaussian Process

We present a novel Bayesian learning approach to outdoor radio heatmap construction utilizing deep Gaussian process (GP). The proposed approach employs a two-layer hierarchy which consists of two cascaded Gaussian processes that are capable of modeling more complex input-output relations than standard single-layer Gaussian processes. Since deriving the exact model likelihood is challenging, a lower bound is optimized instead so that gradient descent-based methods can be performed to find out the optimal model parameters. Typically, inducing points are used in GPs to facilitate low-rank approximation of covariance (kernel) matrices for computation speedup. However, the inaccuracy induced by inducing points can accumulate when stacking multiple layers of GP which may hinder the performance of deep GP. Moreover, since inducing points need to be learned, having them at all layers of deep GP also incurs computational burden. To overcome the above challenges, in contrast to the canonical deep GP model, we use a modified architecture where a full standard GP resides in the first layer and inducing points are only introduced for the second layer. This modified architecture strikes a balance between model accuracy and training complexity. In the proposed model, the noise parameter of the first GP layer is also eliminated to improve the training efficiency as the noise parameter at the output of the second layer suffices to model the uncertainty in the output. The proposed approach is evaluated on real-world datasets, in the form of location-Received Signal Strength (RSS) pairs, collected from the Platform for Open Wireless Data-driven Experimental Research (POWDER) located at the campus of the University of Utah. Experiment results show that the proposed approach can achieve smaller prediction errors on various training and testing data configurations than DNN-based and GP-based methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Effect of Si Content in Electrode and SiO2 Additions to the Slag during Electroslag Remelting

Evolution of Si concentration in 316 stainless steel electrodes was observed during a melting campaign consisting of recycling electroslag remelted (ESR) ingots to make new electrodes using vacuum induction melting (VIM). This campaign consisted of iterations of VIM + ESR operations to optimize melting parameters. The effect of Si content on the melt parameters and ingot quality was further evaluated and additions of SiO2 to the slag chemistry were studied using research-scale experiments, x-ray diffraction (XRF), combustion analysis, visual inspections, and computational tools. The Si concentration was found to decrease by approximately 600 ppm following ESR of 150 lb. research-scale electrodes. Eventually, this led to failure of the slag skin and direct ingot/crucible contact. Additions of SiO2 to the slag at levels matching the original calculated Si concentration in the electrode did not eliminate the slag skin failure and the current during steady state increased to maintain a constant melt rate. In this investigation, we propose a mechanism of slag skin failure consisting of local concentration of current density due to absence, or breakage, of the SiO2 layer around the molten metal drop during ESR. This theory was reinforced by additional experiments in which Nb was added to the electrode to change the structure of the oxide layer around the drops.

Jablonski, Paul↗
DOE OSTI2025

Exploration of High Depth-to-Width Ratio Micro Friction Stir Welding on Thin 6061-T6 Aluminum Butt Joints

In the autogenous joining process of thin 6061-T6 aluminum plates, micro friction stir welding (μFSW) is employed to explore the limitations of high depth to width ratio (HDWR) welding. In this explorative study, 1.25mm plate is joined in a butt joint fashion, multiple HDWR μFSW tools are developed, and the welding parameters are optimized to create a DWR greater than 0.4. A proposed method of implementing induction preheating to the joint is presented and the subsequent characterization of the welds are discussed. Upon completion of the project, the limitations on HDWR μFSW will be more accurately defined, and the best weld will be characterized to determine some of metallurgical properties. This study further expands the operating window for autogenous welding with solid state processes on thin aluminum plate as well as highlight methods for process improvement for tooling design and pre-heating methods.

Shambaugh, Colton↗
NASA NTRS1991

Least-squares/parabolized Navier-Stokes procedure for optimizing hypersonic wind tunnel nozzles

A new procedure is demonstrated for optimizing hypersonic wind-tunnel-nozzle contours. The procedure couples a CFD computer code to an optimization algorithm, and is applied to both conical and contoured hypersonic nozzles for the purpose of determining an optimal set of parameters to describe the surface geometry. A design-objective function is specified based on the deviation from the desired test-section flow-field conditions. The objective function is minimized by optimizing the parameters used to describe the nozzle contour based on the solution to a nonlinear least-squares problem. The effect of the changes in the nozzle wall parameters are evaluated by computing the nozzle flow using the parabolized Navier-Stokes equations. The advantage of the new procedure is that it directly takes into account the displacement effect of the boundary layer on the wall contour. The new procedure provides a method for optimizing hypersonic nozzles of high Mach numbers which have been designed by classical procedures, but are shown to produce poor flow quality due to the large boundary layers present in the test section. The procedure is demonstrated by finding the optimum design parameters for a Mach 10 conical nozzle and a Mach 6 and a Mach 15 contoured nozzle.

Korte, John J.↗
DOE OSTI2016

Describing the Diverse Geometries of Gold from Nanoclusters to Bulk—A First-Principles-Based Hybrid Bond-Order Potential

Molecular dynamics simulations using empirical force fields (EFFs) are crucial for gaining fundamental insights into atomic structure and long time scale dynamics of Au nanoclusters with far-reaching applications in energy and devices. This approach is thwarted by the failure of currently available EFFs in describing the size-dependent dimensionality and diverse geometries exhibited by Au clusters (e.g., planar structures, hollow cages, tubes, pyramids, space-filled structures). Here, we mitigate this issue by introducing a new hybrid bond-order potential (HyBOP), which accounts for (a) short-range interactions via Tersoff-type BOP terms that accurately treat bond directionality and (b) long-range dispersion effects by a scaled Lennard–Jones term whose contribution depends on the local atomic density. We optimized the independent parameters for our HyBOP using a global optimization scheme driven by genetic algorithms. Moreover, to ensure good transferability of these parameters across different length scales, we used an extensive training data set that encompasses structural and energetic properties of 1000 13-atom Au clusters, surface energies, as well as bulk polymorphs, obtained from density functional theory (DFT) calculations. Our newly developed HyBOP has been found to accurately describe (a) global minimum energy configurations at different cluster sizes as well as order of stability of various cluster configurations at any size, (b) critical size of transition from planar to globular clusters, (c) evolution of structural motifs with cluster size, and (c) thermodynamics, structure, elastic properties, and energetic ordering of bulk condensed phases as well as surfaces, in excellent agreement with DFT calculations and spectroscopic experiments. This makes our newly developed HyBOP a valuable, computationally robust but inexpensive tool for investigating a wide range of materials phenomena occurring in Au at the atomistic level.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Identification Uncertainty in Inverse Material Model Parameter Determination: A Sensitivity‐Based Decision Process for Load Path Selection

This research proposes a sensitivity-based framework for selecting the optimal prescribed loading path for a biaxial cruciform specimen. Optimality here is determined by the direction and magnitude of the prescribed displacement that minimizes the influence of random noise on the material model parameter identification. Using simulated experimental data based on finite element simulation, in this work, we identify the material model parameters of a Ludwik hardening model and plane stress implementation of the Hill-48 yield criterion using finite element model updating (FEMU). Our analysis reveals that the identification (or estimator) uncertainty of model parameters depends on the displacement boundary conditions (i.e., loading sequence) and the ground-truth value of the individual parameters. Optimal experimental design (OED) criteria based on the Fisher information matrix were investigated to mitigate indecision in the choice of optimal load path when the identification uncertainty of different material model parameters optimized at different load paths. The determinant of the Fisher information matrix was chosen here as the more useful metric due to its ability to capture uncertainty of the most influential material model parameters. The proposed framework demonstrates potential for real-time automated load step selection using scalar criteria derived prior to mechanical loading. The framework can be generalized to other geometries, boundary conditions and material models, allowing this procedure to be utilized for different experimental configurations and materials.

Fayad, Samuel S. [University of Illinois at Urbana↗
DOE OSTI2025

Artificial intelligence driven laser parameter search: Inverse design of photonic surfaces using greedy surrogate-based optimization

Photonic surfaces designed with specific optical characteristics are becoming increasingly crucial for novel energy harvesting and storage systems. The design of these surfaces can be achieved by texturing materials using lasers. The optimal adjustment of laser fabrication parameters to achieve target surface optical properties is an open challenge. Thus, we develop a surrogate-based optimization approach. Our framework employs the Random Forest algorithm to model the forward relationship between the laser fabrication parameters and the resulting optical characteristics. During the optimization process, we use a greedy, prediction-based exploration strategy that iteratively selects batches of laser parameters to be used in experimentation by minimizing the predicted discrepancy between the surrogate model’s outputs and the user-defined target optical characteristics. This strategy allows for efficient identification of optimal fabrication parameters without the need to model the error landscape directly. We demonstrate the efficiency and effectiveness of our approach on two synthetic benchmarks and two specific experimental applications of photonic surface inverse design targets. By calculating the average performance of our algorithm compared to other state of the art optimization methods, we show that our algorithm performs, on average, twice as well across all benchmarks. Additionally, a warm starting inverse design technique for changed target optical characteristics enhances the performance of the introduced approach.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2005

20 Meter Solar Sail Analysis and Correlation

This presentation discusses studies conducted to determine the element type and size that best represents a 20-meter solar sail under ground-test load conditions, the performance of test/Analysis correlation by using Static Shape Optimization Method for Q4 sail, and system dynamic. TRIA3 elements better represent wrinkle patterns than do QUAD3 elements Baseline, ten-inch elements are small enough to accurately represent sail shape, and baseline TRIA3 mesh requires a reasonable computation time of 8 min. 21 sec. In the test/analysis correlation by using Static shape optimization method for Q4 sail, ten parameters were chosen and varied during optimization. 300 sail models were created with random parameters. A response surfaces for each targets which were created based on the varied parameters. Parameters were optimized based on response surface. Deflection shape comparison for 0 and 22.5 degrees yielded a 4.3% and 2.1% error respectively. For the system dynamic study testing was done on the booms without the sails attached. The nominal boom properties produced a good correlation to test data the frequencies were within 10%. Boom dominated analysis frequencies and modes compared well with the test results.

Taleghani, B.↗
NASA NTRS1984

Development of a turbomachinery design optimization procedure using a multiple-parameter nonlinear perturbation method

An investigation was carried out to complete the preliminary development of a combined perturbation/optimization procedure and associated computational code for designing optimized blade-to-blade profiles of turbomachinery blades. The overall purpose of the procedures developed is to provide demonstration of a rapid nonlinear perturbation method for minimizing the computational requirements associated with parametric design studies of turbomachinery flows. The method combines the multiple parameter nonlinear perturbation method, successfully developed in previous phases of this study, with the NASA TSONIC blade-to-blade turbomachinery flow solver, and the COPES-CONMIN optimization procedure into a user's code for designing optimized blade-to-blade surface profiles of turbomachinery blades. Results of several design applications and a documented version of the code together with a user's manual are provided.

Stahara, S. S.↗