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Results for “Parameters optimization”

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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Influence of Feed Rate on Microstructure and Hardness of Conventionally Spun-formed Al 6061-O Plate

The effect of feed rate on the hardness and microstructure of a 7.35-mm-thick section from a spin-formed Al 6061 cylinder is investigated via microhardness mapping and electron backscatter diffraction (EBSD) analysis. The through-thickness hardness and microtexture profiles, corresponding to a true thickness strain of ≈ 0.3, are utilized to evaluate microstructural variations that could influence mechanical anisotropy and spin formability. The microhardness data reveal distinct gradients from the outer to the inner mold line that vary with feed rate and may be a function of the level of forming strain in the material. The microtextural mapping data expose a minor influence from feed rate, but contain trends through the material cross-section that suggest variations in the location-dependent stress states. The results demonstrate for the first time that the relationship between spin forming deformation and hardness or texture variations can be exploited to optimize processing parameters and mechanical response.

Spin forming↗
NASA NTRSDate not supplied

Joining, Disassembly, and Reconfiguration of Thermoplastic Composites for Space Applications

Thermoplastic composites are increasingly being investigated for aerospace applications because of their relatively short processing time, good chemical and radiation resistance, and potential for reforming and reuse via melting. The manufacturing, reforming, and reuse of thermoplastic composites can be leveraged to advance joining, disassembly, and reassembly of structures for space exploration activities. Potential applications include, but are not limited to, habitats and on-orbit assembly and reassembly of large-scale truss structures. This work focuses on demonstrating the feasibility of joining, disassembly, and reassembly of a thermoplastic bond using heat and pressure. Polyether ether ketone (PEEK) composite adherends were joined using low-melt polyaryl ether ketone (LM-PAEK) thermoplastic films at the bonding interface. The single lap shear specimens with LM-PAEK film were tested and had a maximum shear strength between 5 and 8 MPa and consistently failed adhesively at the bondline. Reassembly of disassembled specimens was successfully demonstrated using additional thermoplastic interlayers. Thus, the reassembly of thermoplastic composite joints was found to be feasible. However, additional work is required to reduce film flowout and optimize consolidation parameters for an in space environment.

thermoplastic composites↗
NASA NTRSDate not supplied

Influence of Feed Rate on Microstructure and Hardness of Conventionally Spun-formed Al 6061-O Plate

The effect of feed rate on the mechanical properties and microstructure of a 7.35-mm-thick section from a spin-formed Al 6061 cylinder is investigated via microhardness mapping and electron backscatter diffraction (EBSD) analysis. The through-thickness hardness and microtexture profiles, corresponding to a true thickness strain of ≈ 0.3, are utilized to evaluate microstructural variations that could influence mechanical anisotropy and spin formability. The microhardness data reveal distinct gradients from the outer to the inner mold line that vary with feed rate and may be a function of the level of forming strain in the material. The microtextural mapping data expose a minor influence from feed rate, but contain trends through the material cross-section that suggest variations in the location-dependent stress states. The results demonstrate for the first time that the relationship between spin forming deformation and hardness or texture variations can be exploited to optimize processing parameters and mechanical response.

Spin forming↗
DOE OSTI2020

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 synthesizes data integrity attacks in response to the physical data measurements and applies alternating parametrized 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.

97 MATHEMATICS AND COMPUTING↗
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 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 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.↗