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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Dynamic machine learning-based optimization algorithm to improve boiler efficiency

With decreasing computational costs, improvement in algorithms, and the aggregation of large industrial and commercial datasets, machine learning is becoming a ubiquitous tool for process and business innovations. Machine learning is still lacking applications in the field of dynamic optimization for real-time control. This work presents a novel framework for performing constrained dynamic optimization using a recurrent neural network model combined with a metaheuristic optimizer. The framework is designed to augment an existing control system and is purely data-driven, like most industrial Model Predictive Control applications. Several recurrent neural network models are compared as well as several metaheuristic optimizers. Hyperparameters and optimizer parameters are tuned with parameter sweeps, and the resulting values are reported. Further, the best parameters for each optimizer and model combination are demonstrated in closed-loop control of a dynamic simulation, and several recommendations are made for generalizing this framework to other systems. Up to 0.953% improvement is realized over the non-optimized case for a simulated coal-fired boiler. While this is not a large improvement in percentage, the total economic impact is $991,000 per year, and this study builds a foundation for future machine learning with dynamic optimization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Laser powder bed fusion of ODS Fe–Cr–Al (0.3Zr, 0.3Y 2 O 3 ): Unveiling processing-microstructure- mechanical property relationships

Here, this study investigates the fabrication of oxide dispersion strengthened (ODS) Fe-Cr-Al alloys via laser powder bed fusion (LPBF) with strategic additions of 0.3 wt% Zr and 0.3 wt% Y 2 O 3 for enhanced mechanical performance in nuclear applications. Systematic processing parameter optimization yielded three distinct conditions: one low-density product with significant defects and two near-full-density materials with improved consolidation. Comprehensive characterization confirmed single-phase α-ferrite matrix formation with successful incorporation of Y-, Zr-, O-, and C-rich precipitates characteristic of ODS alloys. However, precipitate density remained low (∼10 7 cm −3 ), resulting in sink strength values substantially below optimal levels for radiation resistance. Microhardness values (mid-200s HV) correlated inversely with grain size following the Hall-Petch relationship, indicating grain boundary strengthening as the dominant mechanism rather than precipitation strengthening. The optimized processing conditions achieved excellent mechanical properties with room temperature yield strength of approximately 500 MPa and 30 % elongation, demonstrating superior strength-ductility synergy compared to other additively manufactured ODS materials and performance consistent with literature values for LPBF-processed ODS-FeCrAl alloys. This investigation reveals both the potential and limitations of LPBF processing for ODS Fe-Cr-Al alloys. While successful defect-free fabrication was achieved, results highlight the critical need for systematic optimization of processing parameters and post-processing heat treatments to enhance precipitate density for effective dispersion strengthening and radiation resistance while maintaining additive manufacturing advantages.

Additive manufacturing↗
NASA NTRS1974

Program manual for ASTOP, an Arbitrary space trajectory optimization program

The ASTOP program (an Arbitrary Space Trajectory Optimization Program) designed to generate optimum low-thrust trajectories in an N-body field while satisfying selected hardware and operational constraints is presented. The trajectory is divided into a number of segments or arcs over which the control is held constant. This constant control over each arc is optimized using a parameter optimization scheme based on gradient techniques. A modified Encke formulation of the equations of motion is employed. The program provides a wide range of constraint, end conditions, and performance index options. The basic approach is conducive to future expansion of features such as the incorporation of new constraints and the addition of new end conditions.

Horsewood, J. L.↗
NASA NTRS2013

A Kinematic Calibration Process for Flight Robotic Arms

The Mars Science Laboratory (MSL) robotic arm is ten times more massive than any Mars robotic arm before it, yet with similar accuracy and repeatability positioning requirements. In order to assess and validate these requirements, a higher-fidelity model and calibration processes were needed. Kinematic calibration of robotic arms is a common and necessary process to ensure good positioning performance. Most methodologies assume a rigid arm, high-accuracy data collection, and some kind of optimization of kinematic parameters. A new detailed kinematic and deflection model of the MSL robotic arm was formulated in the design phase and used to update the initial positioning and orientation accuracy and repeatability requirements. This model included a higher-fidelity link stiffness matrix representation, as well as a link level thermal expansion model. In addition, it included an actuator backlash model. Analytical results highlighted the sensitivity of the arm accuracy to its joint initialization methodology. Because of this, a new technique for initializing the arm joint encoders through hardstop calibration was developed. This involved selecting arm configurations to use in Earth-based hardstop calibration that had corresponding configurations on Mars with the same joint torque to ensure repeatability in the different gravity environment. The process used to collect calibration data for the arm included the use of multiple weight stand-in turrets with enough metrology targets to reconstruct the full six-degree-of-freedom location of the rover and tool frames. The follow-on data processing of the metrology data utilized a standard differential formulation and linear parameter optimization technique.

Collins, Curtis L.↗
DOE OSTI2025

Simulating large one-dimensional neutral-atom quantum systems

While abstract models of quantum computation assume a closed system of two-level states, practical quantum devices inevitably couple to the environment in some way, creating sources of noise. Understanding the tolerance to noise of specific quantum algorithms run on specific devices is important for determining the feasibility of quantum computing in the current noisy intermediate-scale quantum era. Of particular interest is understanding the noise sensitivity of these devices as more qubits are added to the system. Classical simulations are a useful tool to understand the effects of this noise, but direct classical simulations of open quantum systems are burdened by an exponentially growing cost in the number of qubits and a large local Hilbert space dimension. For onedimensional, shallow circuits, using tensor networks can replace this exponential cost with a linear one and simulate far wider systems than what would normally be available. In this paper, we describe a tensor network simulation of a neutral atom quantum system under the presence of noise, while introducing a purity-preserving truncation technique that compromises between the simplicity of the matrix product state and the positivity of the matrix product density operator. We apply this simulation to a near-optimized iteration of the quantum approximate optimization algorithm on a transverse field Ising model in order to investigate the influence of large system sizes on the performance of the algorithm. We find that while circuits with a large number of qubits fail more often under noise that depletes the qubit population, their outputs on a successful measurement are just as robust under Rydberg atom dissipation or qubit dephasing as smaller systems. However, such circuits might not perform as well under coherent multiqubit errors such as Rydberg atom crosstalk. We also find that the optimized parameters are especially robust to noise, suggesting that a noisier quantum system can be used to find the optimal parameters before switching to a cleaner system for measurements of observables.

Allen, James↗
DOE OSTI2023

Transcriptome architecture of the three main lineages of agrobacteria

ABSTRACT Agrobacteria are a diverse, polyphyletic group of prokaryotes with multipartite genomes capable of transferring DNA into the genomes of host plants, making them an essential tool in plant biotechnology. Despite their utility in plant transformation, genome-wide transcriptional regulation is not well understood across the three main lineages of agrobacteria. Transcription start sites (TSSs) are a necessary component of gene expression and regulation. In this study, we used differential RNA-seq and a TSS identification algorithm optimized on manually annotated TSS, then validated with existing TSS to identify thousands of TSS with nucleotide resolution for representatives of each lineage. We extend upon the 356 TSSs previously reported in Agrobacterium fabrum C58 by identifying 1,916 TSSs. In addition, we completed genomes and phenotyping of Rhizobium rhizogenes C16/80 and Allorhizobium vitis T60/94, identifying 2,650 and 2,432 TSSs, respectively. Parameter optimization was crucial for an accurate, high-resolution view of genome and transcriptional dynamics, highlighting the importance of algorithm optimization in genome-wide TSS identification and genomics at large. The optimized algorithm reduced the number of TSSs identified internal and antisense to the coding sequence on average by 90.5% and 91.9%, respectively. Comparison of TSS conservation between orthologs of the three lineages revealed differences in cell cycle regulation of ctrA as well as divergence of transcriptional regulation of chemotaxis-related genes when grown in conditions that simulate the plant environment. These results provide a framework to elucidate the mechanistic basis and evolution of pathology across the three main lineages of agrobacteria. IMPORTANCE Transcription start sites (TSSs) are fundamental for understanding gene expression and regulation. Agrobacteria, a group of prokaryotes with the ability to transfer DNA into the genomes of host plants, are widely used in plant biotechnology. However, the genome-wide transcriptional regulation of agrobacteria is not well understood, especially in less-studied lineages. Differential RNA-seq and an optimized algorithm enabled identification of thousands of TSSs with nucleotide resolution for representatives of each lineage. The results of this study provide a framework for elucidating the mechanistic basis and evolution of pathology across the three main lineages of agrobacteria. The optimized algorithm also highlights the importance of parameter optimization in genome-wide TSS identification and genomics at large.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A process optimization framework for laser direct energy deposition: Densification, microstructure, and mechanical properties of an Fe-Cr alloy

Laser Direct Energy Deposition (DED) is a metal additive manufacturing technique with the ability to fabricate large and complex parts through deposition of metal powders. However, achieving high-density parts and targeted build heights using DED can be challenging due to the large number of highly sensitive process variables. This work proposes a robust fabrication parameter optimization framework to generate process maps for primary parameters in DED, including laser power, scan speed, mass flow rate, hatch spacing, and layer height. Simple single-track experiments were utilized to map out the parameter space, and a combination of geometric criteria for hatch spacing and layer height were proposed to determine parameter sets that achieve both targeted build heights and mitigate porosity formation. Using this framework, specimens with >99 % density and consistent mechanical properties were successfully fabricated over a wide range of process parameters for an Fe-9wt.%Cr (Fe9Cr) alloy, a surrogate for radiation damage-resistant reduced activation ferritic/martensitic (RAFM) steels. Processing these materials using DED is of particular interest in the development of plasma facing components for nuclear fusion applications. The microstructure and mechanical properties of as-printed Fe9Cr were characterized using optical and electron microscopy, X-ray diffraction, and uniaxial tensile tests. As-printed Fe9Cr displayed ~25 % elongation and ultimate tensile strengths of up to 475 MPa which is comparable to similar wrought alloys. Finally, the proposed framework will allow for accelerated DED parameter optimization for novel alloy systems, as well as open the possibility for local microstructure control while simultaneously mitigating defect formation.

42 ENGINEERING↗
DOE OSTI2025

Differentiable lagrangian shock hydrodynamics with application to stable shock acceleration of density interfaces

We develop a gradient based optimization approach for the equations of compressible, Lagrangian hydrodynamics and demonstrate how it can be employed to automatically uncover strategies to control hydrodynamic instabilities arising from shock acceleration of density interfaces. Strategies for controlling the Richtmyer-Meshkov instability (RMI) are of great benefit for inertial confinement fusion (ICF) where shock interactions with many small imperfections in the density interface lead to instabilities which rapidly grow over time. These instabilities lead to mixing which, in the case of laser driven ICF, quenches the runaway fusion process ruining the potential for positive energy return. Here, we demonstrate that control of these instabilities can be achieved by optimization of initial conditions with ( > 100) parameters. Optimizing over a large parameter space like this is not possible with gradient-free optimization strategies. This requires computation of the gradient of the outputs of a numerical solution to the equations of Lagrangian hydrodynamics with respect to the inputs. We show that the efficient computation of these gradients is made possible via a judicious application of (i) adjoint methods, the exact formal representation of sensitivities involving partial differential equations, and (ii) automatic differentiation (AD), the algorithmic calculation of derivatives of functions. Careful regularization of multiple operators including artificial viscosity and timestep control is required. We perform design optimization of > 100 parameter energy field driving the Richtmyer Meshkov instability showing significant suppression while simultaneously enhancing the acceleration of the interface relative to a nominal baseline case.

Hydrophysics↗
DOE OSTI2022

Machine Learning assisted optimization and parameter space exploration dataset of spin ice Hamiltonian

This repository contains both simulated and experimental structure factor data for the data challenge involving the inverse scattering problem. The simulated data were generated during a machine-learning-assisted optimization routine described in ref[1]. The experimental structure factor was measured on a rare-earth oxide, Dy2Ti2O7 using diffuse neutron scattering from time-of-flight techniques on the CORELLI instrument at the Spallation Neutron Source, Oak Ridge National Laboratory. A Metropolis Monte Carlo code implemented to run in a High-performance computing setting was used to calculated simulated structure factors for the spin-ice Hamiltonian at 680 mK, which is the same temperature as for the experimental data. The total size of all the files in this repository is 5.12 GB. A detailed description of the files is given below. ExperimentalData_630mK.dat – A linearized version of 3-dimensional experimental data of size 61×81×21. This data was processed to remove an estimation of non-magnetic background, including nuclear scattering signal and instrumentation background. Parameters.dat – 6700 samples were evaluated over the 4-dimensional parameter space (J_1, J_2, J_3 and J_(3^' )). There is an additional parameter, D in the spin Hamiltonian to mimic the dipolar interaction between magnetic ions. However, this parameter, D was fixed to a value determined by prior work. This file contains five columns for the parameters J_1, J_2, J_3, J_(3^' ) and D respectively. 3D_Simulation_Data.dat – The simulated structure factor, S(Q) data are included in this file. Each raw contains a linearized array of 3D volumes of S(Q) calculated for the parameter set given in the corresponding row of the file Parameters.dat. The size of the volume data was matched to the experimental data. Qx(h,-h,0).dat, Qy(k,k,-2k).dat, Qz(l,l,l).dat – These files contain the h, k, and l values along with the reciprocal vectors [h,-h,0], [k,k,-2k] and [l,l,l] respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Practical guide to understanding goodness-of-fit metrics used in chemical state modeling of x-ray photoelectron spectroscopy data by synthetic line shapes using nylon as an example

Chemical state analysis of a sample surface through fitting bell-shaped curves to x-ray photoelectron spectroscopic polymer data is reviewed using nylon to introduce and discuss aspects of data analysis. Different strategies for modeling chemistry in nylon spectra are presented and in so doing, a case is made to include in published science the design logic and implementation in terms of line shapes and optimization parameter constraints between components in a peak model. Imperfections in line shape relative to the true shape for photoemission lines, when compensated for using constraints to optimization parameters, are shown to provide chemical state information about a sample that justify, for peak models constructed with these limitations, metrics for goodness-of-fit different from those expected for pulse-counted data.

Materials Science↗
NASA NTRS2022

Multi-Frequency Radiometer-Based Soil Moisture Retrieval and Algorithm Parameterization Using In Situ Sites

L-band brightness temperature (TB) has been shown to provide the best sensitivity to soil moisture (SM) although C- and X-band based products offer a longer time-series from satellite-based measurements. Currently, global coverage SM is routinely produced from spaceborne measurements using all three frequency bands, but despite continued validation efforts of the products, the relative characteristics and performance of these observations have not been fully established. Therefore, this study focused on the parametrization of SM retrieval algorithms at L-, C- and X-bands using TB observations from the L-band radiometer on NASA's SM Active Passive (SMAP) mission and the C- and X-band channels of JAXA's Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W satellite. These can be applied in global SM retrieval algorithms using either one of the frequencies or a combination of them. The reference in situ SM data was obtained from 12 core validation sites across various land cover types around the world. The investigation highlighted the known challenges of retrieving SM from C- and X-band data compared to the higher sensitivity of the L-band data. Even with a site-specific retrieval algorithm parameterization, the mean correlation of the C- and X-band retrievals for the core validation site SM measurement were much lower than that for L-band, being 0.52 (0.54) and 0.45 (0.47) for vertical (horizontal) polarization, respectively, while for the L-band retrieval the corresponding values were 0.81 (0.77). The parameterization exercise showed that matching the C- and X-band TB measurements with an emission model was not difficult; the problem was relating the observations to SM under the influence of large roughness and vegetation effects. As a result, parameter optimization produced values for some sites that were not realistic or did not allow any practical sensitivity to SM at C- and X-band. Considering the L-band observations, the parameter optimization resulted in superior bias performance as compared to the operational SMAP product parameterization, but the sensitivity to SM changes (R and unbiased root mean square difference) did not improve markedly, or in some cases degraded at the expense of a smaller bias.

passive microwave↗
DOE OSTI2024

Modulated Thermomechanical Analysis of Compression-Molded High-Density Polyethylene

Thermomechanical analysis (TMA) experiments conducted on high-density polyethylene (HDPE) show both reversible and irreversible dimensional changes. To further explore these reversible and irreversible processes, modulated thermomechanical analysis (MTMA) was used. Before reliable data on compression-molded HDPE was collected, a parameter optimization was performed to obtain a suitable MTMA method. Once a suitable method was obtained, several MTMA experiments were conducted on compression-molded HDPE. This work highlights the steps taken during the MTMA parameter optimization and the results obtained from MTMA experiments conducted on pristine compression-molded HDPE samples.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Gradient-informed Hamiltonian Monte Carlo for multicomponent CALPHAD model optimization and uncertainty quantification

CALPHAD model parameter optimization is inherently challenging due to non-smooth objective functions, high-dimensional parameter spaces, and the need for uncertainty quantification (UQ). Traditional weighted nonlinear least squares approaches are computationally efficient but local, whereas black-box global optimizers and ensemble Markov Chain Monte Carlo (MCMC) methods provide broader exploration at substantial computational cost. The objective of this work is to combine the global exploration capability of gradient-informed Hamiltonian Monte Carlo – specifically the No-U-Turn Sampler (NUTS) – with local deterministic refinement using BFGS to efficiently optimize multicomponent CALPHAD models with minimal manual intervention. Analytic gradients are computed via the Jansson derivative framework. The methodology is demonstrated on the Cr—Fe binary system and extended to the Cr—Fe—Ni ternary system with 32 degrees of freedom. For Cr—Fe, NUTS achieves comparable or superior optimality relative to ensemble MCMC while requiring over an order-of-magnitude fewer likelihood evaluations. Parameter uncertainties are quantified through NUTS sampling and propagated to thermodynamic observables using local expansion, demonstrating a novel modular approach that combines binary and ternary parameter subsets without requiring global relaxation. These results establish gradient-informed exploration as a scalable strategy for multicomponent CALPHAD optimization and provide a practical route towards efficient higher-order database development with quantified uncertainty.

36 MATERIALS SCIENCE↗
DOE OSTI2025

HPC for Optimizing Process Parameters to Control Material Evolution in Seamless Induction Hardening of Wind Turbine Main Shaft Bearings

Work proposed in this project focused on understanding the effect of martensitic transformation in the steel on the potential for cracking during seamless induction hardening (SIH) as a function of process conditions to allow the process to optimally scale up. Large-scale, three-dimensional phase-field simulations of martensitic transformation were performed using MEUMAPPS-SS (Microstructure Evolution Using Massively Parallel Phase-field Simulations – Solid State) code developed at Oak Ridge National Laboratory. The simulations were guided by location-specific thermal history generated by experimental measurements of time-temperature history generated at The Timken Company. The simulations were able to capture the morphological evolution of the martensite variants in an Fe-1.0C-1.5Cr steel based on the Nishiyama-Wasserman (NW) orientation relationship. The simulations were also able to quantify the stress-state at the interface between impinging martensite variants. The simulations indicated that the magnitude of the various stress and strain components were dependent on the sizes of the impinging plates with a reduction in these quantities with reduced plate size in agreement with experimental findings. The results obtained from the simulations will be used to guide the optimization of the alloy thermal conditions to eliminate quench cracking during SIH of bearing steels.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

HPC for optimizing process parameters to control material evolution in seamless induction hardening of wind turbine main shaft bearings

Work proposed in this project focused on understanding the effect of martensitic transformation in the steel on the potential for cracking during seamless induction hardening (SIH) as a function of process conditions to allow the process to optimally scale up. Large-scale, three-dimensional phase-field simulations of martensitic transformation were performed using MEUMAPPS-SS (Microstructure Evolution Using Massively Parallel Phase-field Simulations – Solid State) code developed at Oak Ridge National Laboratory. The simulations were guided by location-specific thermal history generated by experimental measurements of time-temperature history generated at The Timken Company. The simulations were able to capture the morphological evolution of the martensite variants in an Fe-1.0C-1.5Cr steel based on the Nishiyama-Wasserman (NW) orientation relationship. The simulations were also able to quantify the stress-state at the interface between impinging martensite variants. The simulations indicated that the magnitude of the various stress and strain components were dependent on the sizes of the impinging plates with a reduction in these quantities with reduced plate size in agreement with experimental findings. The results obtained from the simulations will be used to guide the optimization of the alloy thermal conditions to eliminate quench cracking during SIH of bearing steels.

17 WIND ENERGY↗
NASA NTRS1981

Solar Power Satellite antenna phase control system hardware simulation, phase 4. Volume 2: Analytical simulation of SPS system performance

The pilot signal parameter optimization and power transponder analyses are presented. The SPS antenna phase control system is modeled and the hardware simulation study described. Ionospheric and system phase error effects and the effects of high power amplifier phase and amplitude jitters are considered. Parameter optimization of the spread spectrum receiver, consisting of the carrier tracking loop and the code tracking loop, is described.

Lindsey, W. C.↗
NASA NTRS1994

Heat transfer measurements for Stirling machine cylinders

The primary purpose of this study was to measure the effects of inflow-produced heat turbulence on heat transfer in Stirling machine cylinders. A secondary purpose was to provide new experimental information on heat transfer in gas springs without inflow. The apparatus for the experiment consisted of a varying-volume piston-cylinder space connected to a fixed volume space by an orifice. The orifice size could be varied to adjust the level of inflow-produced turbulence, or the orifice plate could be removed completely so as to merge the two spaces into a single gas spring space. Speed, cycle mean pressure, overall volume ratio, and varying volume space clearance ratio could also be adjusted. Volume, pressure in both spaces, and local heat flux at two locations were measured. The pressure and volume measurements were used to calculate area averaged heat flux, heat transfer hysteresis loss, and other heat transfer-related effects. Experiments in the one space arrangement extended the range of previous gas spring tests to lower volume ratio and higher nondimensional speed. The tests corroborated previous results and showed that analytic models for heat transfer and loss based on volume ratio approaching 1 were valid for volume ratios ranging from 1 to 2, a range covering most gas springs in Stirling machines. Data from experiments in the two space arrangement were first analyzed based on lumping the two spaces together and examining total loss and averaged heat transfer as a function of overall nondimensional parameter. Heat transfer and loss were found to be significantly increased by inflow-produced turbulence. These increases could be modeled by appropriate adjustment of empirical coefficients in an existing semi-analytic model. An attempt was made to use an inverse, parameter optimization procedure to find the heat transfer in each of the two spaces. This procedure was successful in retrieving this information from simulated pressure-volume data with artificially generated noise, but it failed with the actual experimental data. This is evidence that the models used in the parameter optimization procedure (and to generate the simulated data) were not correct. Data from the surface heat flux sensors indicated that the primary shortcoming of these models was that they assumed turbulence levels to be constant over the cycle. Sensor data in the varying volume space showed a large increase in heat flux, probably due to turbulence, during the expansion stroke.

Kornhauser, Alan A.↗