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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 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Optimization of artificial viscosity in production codes based on Gaussian Regression surrogate models

To accurately model flows with shock waves using staggered-grid Lagrangian hydrodynamics, artificial viscosity has to be introduced to convert kinetic energy into internal energy, thereby increasing the entropy across shocks. Determining the appropriate strength of the artificial viscosity is an art and strongly depends on the particular problem and experience of the researcher. The objective of this study is to pose the problem of finding the appropriate strength of artificial viscosity as an optimization problem and solve this problem using machine learning (ML) tools, specifically using surrogate models based on Gaussian Process regression and Bayesian analysis. We describe the optimization method and discuss various practical details of its implementation. The shock-containing problems for which we apply this method all have been implemented in the LANL code FLAG. First, we apply ML to find optimal values to isolated shock problems of different strengths. Second, we apply ML to optimize viscosity for a 1D propagating detonation problem based on Zel’dovich-von Neumann-Doring (ZND) detonation theory using a reactive burn model. We compare results for default (currently used values in FLAG) and optimized values of artificial viscosity for these problems demonstrating the potential for significant improvement in the accuracy of computations.

42 ENGINEERING↗
DOE OSTI2025

Understanding the Effect of Sample Geometry on Temperature Distribution during Optical Floating Zone Crystal Growth in Vacuum Environment through Heat Transfer Modeling

Optical floating zone furnaces (OFZ) have had a transformative impact on fundamental science due to their ability to rapidly produce large single crystals of a wide variety of complex materials. However, a quantitative understanding of the OFZ growth environment is generally lacking due to the difficulty of measuring the local sample temperatures during OFZ growth, as well as to the general lack of information about the temperature-dependent physical parameters needed to model heat transfer. To overcome these challenges, we apply a physics-based heat transfer model, parametrized by measurements from synchrotron experiments and a machine-learning (ML) algorithm, to simulate the temperature distributions of samples heated in an OFZ furnace in a vacuum environment. This model is used to quantitatively understand how the sample maximum temperature and temperature gradient (key parameters that influence the success of crystal growth) are affected by the rod size, rod shape, and heat-zone position on the rod. The results of this study can be applied to make informed decisions on how crystal growth parameters can be tuned to modify temperature profiles and to optimize crystal growth outcomes even when data on internal sample temperature profiles (e.g., those obtained through in situ synchrotron experiments) are not accessible.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Targeted Adaptive Design

Modern advanced manufacturing and advanced materials design often require searches of relatively high-dimensional process control parameter spaces for settings that result in optimal structure, property, and performance parameters. The mapping from the former to the latter must be determined from noisy experiments or from expensive simulations. Here, we abstract this problem to a mathematical framework in which an unknown function from a control space to a design space must be ascertained by means of expensive noisy measurements, which locate control settings generating desired design features within specified tolerances, with quantified uncertainty. We describe targeted adaptive design (TAD), a new algorithm that performs this sampling task efficiently. TAD creates a Gaussian process surrogate model of the unknown mapping at each iterative stage, proposing a new batch of control settings to sample experimentally and optimizing the updated expected log-predictive probability density of the target design. TAD either stops upon locating a solution with uncertainties that fit inside the tolerance box or uses a measure of expected future information to determine that the search space has been exhausted with no solution. TAD thus embodies the exploration-exploitation tension in a manner that recalls, but is essentially different from, Bayesian optimization and optimal experimental design.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

Data management for interdisciplinary field experiments: OTTER project support

The ability of investigators of an interdisciplinary science project to properly manage the data that are collected during the experiment is critical to the effective conduct of science. When the project becomes large, possibly including several scenes of large-format remotely sensed imagery shared by many investigators requiring several services, the data management effort can involve extensive staff and computerized data inventories. The OTTER (Oregon Transect Ecosystem Research) project was supported by the PLDS (Pilot Land Data System) with several data management services, such as data inventory, certification, and publication. After a brief description of these services, experiences in providing them are compared with earlier data management efforts and some conclusions regarding data management in support of interdisciplinary science are discussed. In addition to providing these services, a major goal of this data management capability was to adopt characteristics of a pro-active attitude, such as flexibility and responsiveness, believed to be crucial for the effective conduct of active, interdisciplinary science. These are also itemized and compared with previous data management support activities. Identifying and improving these services and characteristics can lead to the design and implementation of optimal data management support capabilities, which can result in higher quality science and data products from future interdisciplinary field experiments.

Angelici, Gary↗
DOE OSTI2022

Fast Particle-Wave Interactions and Alfvén Eigenmodes in JET Tokamak Plasmas

This document serves as the closeout report of DOE Grant Award No. DE-FG02-99ER54563 with project period 1 April 2015 through 31 March 2020. The project comprised the international collaboration between MIT and EU scientists on the JET facility to improve our understanding of the physics of energetic particle-wave interactions by measuring the damping rates of stable Alfvén Eigenmodes (AEs) and unstable energetic particle driven modes. More specifically, this project involved the continued participation of MIT, the Culham Centre for Fusion Energy, the Swiss Plasma Center, and theorists from various European laboratories and from UC Irvine. Past contributions from the University of São Paulo are gratefully acknowledged. The Alfvén Eigenmode Active Diagnostic (AEAD) on JET discharges was successfully upgraded in this period and has extracted physically useful information, in primarily deuterium plasma discharges. It is expected that such experiments would be continued during the DT campaign in CY 2021 to assess the damping rates of similar modes in the presence of alpha particles, a product of fusing burning plasma. Only JET would carry out such experiments in the near term in the world. It is important to note that this collaboration was continued under DOE Grant Award No. DE-SC0014264 from 1 April 2020 through the present. In this grant period, the AEAD was upgraded with a set of individual amplifiers for each of a set of six antennas in two toroidally opposite locations. These new amplifiers allowed targeted selection of the antennas’ toroidal spectrum for AEs of interest with toroidal mode number | n | ≤ 20. The resonant detection and measurement of the damping rates of AEs was obtained from magnetic probes that could be compared with theory and simulations. This is a key topic of investigation for ITER and all other next-step fusion experiments, where such modes will interact with energetic particles produced by fusion reactions (alpha particles), Neutral Beam Injection (NBI) and Ion Cyclotron Resonance Heating (ICRH). The majority of the observations during this period were of Toroidal Alfvén Eigenmodes (TAEs) as these are most commonly observed on JET; a number of measurements were made during dedicated TAE experiments. However, a new set of lower frequency band filters were procured with the goal of studying Geodesic Acoustic Modes (GAMs), Beta (Acoustic) AEs (BAE/BAAEs), and Reverse Shear AEs (RSAEs), also predicted by theory. Commissioning and optimizations were successfully completed in this period, and the diagnostic has been in operation during the more recent JET campaigns. Initial results have been obtained in dedicated TAE experiments and successfully compared to drift-kinetic theory. A wide range of theoretical studies have been undertaken in support of the upcoming JET campaigns experiments. To supplement the ongoing use of ideal MHD codes, such as MISHKA during the studies of TAEs, gyrokinetic simulations of low frequency AEs were performed in collaboration with UC Irvine. The Gyrokinetic Toroidal Code (GTC) was used to determine the structure, frequencies and stability of AEs in JET plasmas. Thus, a solid scientific foundation has been laid for future DT campaigns in JET in the 2021 operation period when the damping rates of relevant modes in the presence of alpha particles could be assessed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1993

Simulations of satellite Doppler wind observations

This study will involve two objectives: (1) to develop, through computer simulations, optimal satellite-based sensor scanning techniques for direct measurement of tropospheric winds on the meso- and synoptic scales; and (2) to construct simulations of remotely measured wind fields for assessing impact of such fields on the diagnosis and prognosis of atmospheric phenomena through the use of Observing System Simulation Experiments (OSSE). Using the LAWS Simulation Model (LSM), various global coverage scenarios have been investigated as part of an effort to define the optimal orbit, configuration and sampling strategies for observations of winds for use in global circulation models. Simulated data sets have been provided to GSFC, FSU and several LAWS team members. Particular emphasis has been on providing realistic cloud cover, cirrus backscatter, aerosol distribution and wind variance on scales less than 600 km. Progress is currently being made to incorporate other remote sensors (AIRS/AMSU, STIKSCAT) into the global OSSEs.

Emmitt, George D.↗
DOE OSTI2023

Determination of Thermal Damage Threshold in THz Photomixers Using Raman Spectroscopy

The increase of device lifetime and reliability of THz photomixers will play an essential role in their possible future application. Therefore, their optimal work conditions/operation range, i.e., the maximal incident optical power should be experimentally estimated. We fabricated and tested THz photomixer devices based on nitrogen-implanted GaAs integrated with a Bragg reflector. Raman spectroscopy was applied to investigate the material properties and to disclose any reversible or irreversible material changes. The results indicate that degradation effects in the photomixer structures/material could be avoided if the total optical power density does not exceed levels of about 0.7 mW/µm 2 for 100 min of operation. Furthermore, the investigations performed during 1000 min of optical exposure on the photomixer devices’ central region comprising interdigitated metal-semiconductor-metal (MSM) structures suggest a reversible “curing” mechanism if the power density level of ~0.58 mW/µm 2 is not exceeded. Long-term operation (up to 1000 h) reveals that the photomixer structures can withstand an average optical power density of up to ~0.4 mW/µm 2 without degradation when biased at 10 V. Besides the decrease of the position of the A1g (LO) Raman mode from ~291 cm -1 down to ~288 cm -1 with increasing optical power density and operation time, broad Raman modes evolve at about 210 cm -1 , which can be attributed to degradation effects in the active photomixer/MSM area. In addition, the performed carrier lifetime and photomixer experiments demonstrated that these structures generated continuous wave sub-THz radiation efficiently as long as their optimal work conditions/operation range were within the limits established by our Raman studies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Autonomous phase mapping of gold nanoparticles synthesis with differentiable models of spectral shape

Autonomous experimentation–or self-driving labs–offers a systematic approach to accelerate materials discovery by integrating automated synthesis, characterization, and data-driven decision-making. We present a closed-loop workflow for the on-demand synthesis and structural characterization of colloidal gold nanoparticles, enabling direct mapping from composition to nanoscale structure. Our framework leverages differentiable models of spectral shape to address two central tasks in self-driving labs: (a) phase mapping, or identifying compositional regions with distinct structural behavior; and (b) material retrosynthesis, or optimizing compositions for target structure. Using functional data analysis, we develop a data-driven model with generative pre-training, active learning, and high-throughput experiments to predict spectral responses across composition space. We demonstrate the approach on seed-mediated growth of gold nanoparticles, showcasing its ability to extract design rules, reveal secondary interactions, and efficiently navigate morphology space. Gradient-based optimization of the models enables inverse design, making this a unified platform.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Optimization of 3D controlled ELM-free state with recovered global confinement for KSTAR with n = 1 resonant magnetic field perturbation

Mitigation of deleterious heat flux from edge-localized modes (ELMs) on fusion reactors is often attempted with 3D perturbations of the confining magnetic fields. However, the established technique of resonant magnetic perturbations (RMPs) also degrades plasma performance, complicating implementation on future fusion reactors. Here, we introduce an adaptive real-time control scheme on the KSTAR tokamak as a viable approach to achieve an ELM-free state and simultaneously recover high-confinement (βN ~ 1.91, β p ~ 1.53, and H 98 ~ 0.9), demonstrating successful handling of a volatile complex system through adaptive measures. We show that, by exploiting a salient hysteresis process to adaptively minimize the RMP strength, stable ELM suppression can be achieved while actively encouraging confinement recovery. This is made possible by a self-organized transport response in the plasma edge which reinforces the confinement improvement through a widening of the ion temperature pedestal and promotes control stability, in contrast to the deteriorating effect on performance observed in standard RMP experiments. These results establish the real-time approach as an up-and-coming solution toward an optimized ELM-free state, which is an important step for the operation of ITER and reactor-grade tokamak plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

The Effect of the Prior and the Experimental Design on the Inference of the Precision Matrix in Gaussian Chain Graph Models

Here, we investigate whether (and how) experimental design could aid in the estimation of the precision matrix in a Gaussian chain graph model, especially the interplay between the design, the effect of the experiment and prior knowledge about the effect. Estimation of the precision matrix is a fundamental task to infer biological graphical structures like microbial networks. We compare the marginal posterior precision of the precision matrix under four priors: flat, conjugate Normal-Wishart, Normal-MGIG and a general independent. Under the flat and conjugate priors, the Laplace-approximated posterior precision is not a function of the design matrix rendering useless any efforts to find an optimal experimental design to infer the precision matrix. In contrast, the Normal-MGIG and general independent priors do allow for the search of optimal experimental designs, yet there is a sharp upper bound on the information that can be extracted from a given experiment. We confirm our theoretical findings via a simulation study comparing (i) the KL divergence between prior and posterior and (ii) the Stein’s loss difference of MAPs between random and no experiment. Our findings provide practical advice for domain scientists conducting experiments to better infer the precision matrix as a representation of a biological network.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Understanding and improving anode performance in an alkaline membrane electrolyzer using statistical design of experiments

The behavior of the oxygen-evolving positive electrode (i.e. anode) in the anion exchange membrane water electrolyzer (AEMEL) is complex and influenced by several factors. Very few studies have been performed to understand oxygen evolution reaction (OER) behavior by optimizing the individual factors that influence performance. Here, this study highlights the effects of catalyst loading, catalyst selection, porous transport layer (PTL) type and conductive additive content. The influence of each factor is elucidated through a design of experiments (DoE) approach with a full statistical analysis. Electrochemical data, alongside Pareto charts, parametric trends and their mutual interactions are discussed. This DoE approach is also helpful in making useful predictions and discovering new combinations to be tested. The end result was a high-performance AEMEL able to operate at a current density of 1.0 A/cm 2 at 1.80 V with IrOx OER and PtNi hydrogen evolution reaction (HER) catalysts using 0.3 M KOH fed to the anode. Even lower operating voltage was observed with PbRuOx catalyst at the anode, 1.64 V @ 1.0 A/cm 2 , though the cell decay rate was higher. Lastly, a IrOx/PtNi cell was stably operated continuously for 30 days (720 h) at 1.0 A/cm 2 . This study can serve as a guide for optimal electrode design with insights into component-performance compromises, which can aid in making design choices and performing techno-economic analyses.

08 HYDROGEN↗
NASA NTRS2014

Direct Numerical Simulations of High-Speed Turbulent Boundary Layers over Riblets

Direct numerical simulations (DNS) of spatially developing turbulent boundary layers over riblets with a broad range of riblet spacings are conducted to investigate the effects of riblets on skin friction at high speeds. Zero-pressure gradient boundary layers under two flow conditions (Mach 2:5 with T(sub w)/T(sub r) = 1 and Mach 7:2 with T(sub w)/T(sub r) = 0:5) are considered. The DNS results show that the drag-reduction curve (delta C(sub f)/C(sub f) vs l(sup +)(sub g )) at both supersonic speeds follows the trend of low-speed data and consists of a `viscous' regime for small riblet size, a `breakdown' regime with optimal drag reduction, and a `drag-increasing' regime for larger riblet sizes. At l l(sup +)(sub g) approx. 10 (corresponding to s+ approx 20 for the current triangular riblets), drag reduction of approximately 7% is achieved at both Mach numbers, and con rms the observations of the few existing experiments under supersonic conditions. The Mach- number dependence of the drag-reduction curve occurs for riblet sizes that are larger than the optimal size, with smaller slopes of (delta C(sub f)/C(sub f) for larger freestream Mach numbers. The Reynolds analogy holds with 2(C(sub h)=C(sub f) approximately equal to that of at plates for both drag-reducing and drag-increasing configurations.

Duan, Lian↗
DOE OSTI2022

Perspectives on Polyolefin Catalysis in Microfluidics for High-Throughput Screening: A Minireview

Polyolefins are the largest produced plastics in the world which traditionally employ continuous stirred tank reactors and fluidized bed reactors for commercial production. The operating condition, reaction kinetics, and molecular interactions inside the reactor strongly affect the polyolefin properties, which require stringent process control in conventional procedures. Understanding the catalytic pathway, behavior of polymer particles and effect of reactor conditions are essential for designing specific polymer properties, namely the molecular weight, chain length, polydispersity, etc. Microfluidics can play a significant role in designing polymers tailored to the user needs. Smaller channel dimensions help obtain uniform reaction conditions over the length of the microfluidic reactor in a controlled environment. With real-time monitoring techniques in microfluidics, even single particle growth of polymer can be studied to understand the parameters affecting the polymer properties. High throughput microfluidics can help catalyst screening in a short duration with less consumption of reagents generating less waste. When supplemented with efficient machine learning algorithms, automated high throughput microfluidics has the potential to rapidly optimize the process and develop new knowledge even with a limited data set. When trained on data sets generated using microfluidic experiments that are designed efficiently with working knowledge of the process, machine learning algorithms can provide the relationship between the multivariable parameters space and polymer properties, which is not possible with the traditional statistical methods and interpolation techniques. Here, the rise in the utilization of microfluidics, with the advancement of machine learning algorithms, for polyolefin catalysis, highlights the importance of microfluidics for catalyst discovery, parameter optimization, and understanding reaction pathway for producing polymers with specific properties for specialized applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

AI Driven Optimization of Public Transit

This project explores the application of AI-driven methods to optimize public transit operations for the Chattanooga Area Regional Transportation Authority (CARTA). By leveraging data analytics, machine learning, and predictive modeling, the initiative seeks to enhance system efficiency, improve rider experience, and support sustainability goals. This research, supported by the National Science Foundation and the U.S. Department of Energy, integrates real-time transit data with advanced computational tools to inform decision-making, optimize routes, and balance operational demands. The work exemplifies a forward-looking model for mid-sized cities aiming to modernize mobility systems through intelligent technology integration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1984

Application of optimization techniques to vehicle design: A review

The work that has been done in the last decade or so in the application of optimization techniques to vehicle design is discussed. Much of the work reviewed deals with the design of body or suspension (chassis) components for reduced weight. Also reviewed are studies dealing with system optimization problems for improved functional performance, such as ride or handling. In reviewing the work on the use of optimization techniques, one notes the transition from the rare mention of the methods in the 70's to an increased effort in the early 80's. Efficient and convenient optimization and analysis tools still need to be developed so that they can be regularly applied in the early design stage of the vehicle development cycle to be most effective. Based on the reported applications, an attempt is made to assess the potential for automotive application of optimization techniques. The major issue involved remains the creation of quantifiable means of analysis to be used in vehicle design. The conventional process of vehicle design still contains much experience-based input because it has not yet proven possible to quantify all important constraints. This restraint on the part of the analysis will continue to be a major limiting factor in application of optimization to vehicle design.

Prasad, B.↗
DOE OSTI2025

Autonomous Flow Electrochemistry for Accelerated Catalyst Discovery

Our objective is to develop an Autonomous Chemical Experimentation (ACE) platform that accelerates discovery of new catalytic transformations and other energy-relevant chemical reactions and processes. We intentionally designed ACE to be highly modular, both with respect to its rapid deployment to different chemistries and experimental workflows as well as incorporation of a wide range of different AI algorithms. In addition to the development of the core software architecture, initial efforts were made to incorporate Large Language Models to provide human-interpretable reasoning of the optimizer’s actions, and to develop a user-friendly graphical interface for experimental researchers. ACE was demonstrated using a flow electrocatalysis platform containing an inline FTIR spectrometer for real-time analysis and quantification of the reaction outcome. Human-in-the-loop experiments were performed in which a human researcher conducted an experiment using electrode potentials suggested by ACE, then fed the spectral data back to ACE for decision making. After confirming the successful function of the optimizer, efforts were next directed to automation of the hardware and performed full autonomy tests using three reactions: catalytic oxidation of formate, catalytic oxidation of cyclohexanol, and oxidation of hydroquinone. These studies confirm that ACE can close the loop between reaction execution, analysis, and optimization. They also reveal that more improved product detection methods will be essential for ACE to make well-informed decisions for reactions with low conversions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A Review of Machine Learning Classification Using Quantum Annealing for Real-World Applications

Optimizing the training of a machine learning pipeline helps in reducing training costs and improving model performance. One such optimizing strategy is quantum annealing, which is an emerging computing paradigm that has shown potential in optimizing the training of a machine learning model. The implementation of a physical quantum annealer has been realized by D-wave systems and is available to the research community for experiments. Recent experimental results on a variety of machine learning applications using quantum annealing have shown interesting results where the performance of classical machine learning techniques is limited by limited training data and high dimensional features. This article explores the application of D-wave’s quantum annealer for optimizing machine learning pipelines for real-world classification problems. Finally, we review the application domains on which a physical quantum annealer has been used to train machine learning classifiers. We discuss and analyze the experiments performed on the D-Wave quantum annealer for applications such as image recognition, remote sensing imagery, computational biology, and particle physics. We discuss the possible advantages and the problems for which quantum annealing is likely to be advantageous over classical computation.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2010

Messaging with Cost-Optimized Interstellar Beacons

On Earth, how would we build galactic-scale beacons to attract the attention of extraterrestrials, as some have suggested we should do? From the point of view of expense to a builder on Earth, experience shows an optimum trade-off. This emerges by minimizing the cost of producing a desired power density at long range, which determines the maximum range of detectability of a transmitted signal. We derive general relations for cost-optimal aperture and power. For linear dependence of capital cost on transmitter power and antenna area, minimum capital cost occurs when the cost is equally divided between antenna gain and radiated power. For nonlinear power-law dependence, a similar simple division occurs. This is validated in cost data for many systems; industry uses this cost optimum as a rule of thumb. Costs of pulsed cost-efficient transmitters are estimated from these relations by using current cost parameters ($/W, $/sq m) as a basis. We show the scaling and give examples of such beacons. Galactic-scale beacons can be built for a few billion dollars with our present technology. Such beacons have narrow "searchlight" beams and short "dwell times" when the beacon would be seen by an alien observer in their sky. More-powerful beacons are more efficient and have economies of scale: cost scales only linearly with range R, not as R(exp 2), so number of stars radiated to increases as the square of cost. On a cost basis, they will likely transmit at higher microwave frequencies, -10 GHz. The natural corridor to broadcast is along the galactic radius or along the local spiral galactic arm we are in. A companion paper asks "If someone like us were to produce a beacon, how should we look for it?"

Benford, James↗