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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Stochastic Programming Approach for Resource Selection under Demand Uncertainty

Cost-efficient selection and scheduling of a subset of geographically distributed resources to meet the demands of a scientific workflow is a challenging problem. The problem is exacerbated by uncertainties in demand and availability of resources. In this paper, we present a stochastic optimization based framework for robust decision making in the selection of distributed resources over a planning horizon under demand uncertainty. We present a novel two-stage stochastic programming model for resource selection, and implement an L-shaped decomposition algorithm to solve this model. A Sample Average Approximation algorithm is integrated to enable stochastic optimization to solve problems with a large number of scenarios. Using the metric of stochastic solution, we demonstrate up to 30% cost reduction relative to solutions without explicit consideration of demand uncertainty for a 24-month problem. We also demonstrate up to 54% cost reduction relative to a previously developed solution for a 36-month problem. We further argue that the composition of resources selected is superior to solutions computed without explicit consideration of uncertainties. Given the importance of resource selection and scheduling of complex scientific workflows, especially in the context of commercial cloud computing, we believe that our novel stochastic programming framework will benefit many researchers as well as users of distributed computing resources.

scientific workflows, task scheduling, optimizatio↗
DOE OSTI2024

3D high-fidelity automated neutronics guided optimization of fusion blanket designs

The compact Fusion Pilot Plant (FPP) is defined in the recent National Academies of Sciences, Engineering, and Medicine report as the next step of fusion energy demonstration with a $50$ MWe peak net electricity production, $Q_e$ greater than $1$, and at least $3$ hours of continuous operation. This fusion pilot plant will be a test bed enabling materials, designs, and fuel management assessment, and it will represent an engineering challenge because of its high-fusion power and compact design targets. Previous reactor data is limited to experiments operating in different design space ranges. Therefore, design iterations and assessments should rely on high-fidelity first-principle theoretical and computational models. The high-fidelity integrated modeling of the plasma is a fundamental part of fusion energy research. However, the whole device modeling is often neglected, utilizing low-fidelity, system-level analysis. Recently, the need for high-fidelity multi-physics modeling was recognized, resulting in a selection of integrated tools. Further, autonomous design optimization requires a streamlined framework that perturbs the design point, reruns the analysis, and examines the outputs. However, high-fidelity analysis requires complex geometry specification that is difficult to perturb. This work presents the parametric CAD generation tool TRACER and a new neutronic workflow. TRACER allows the perturbation of the geometry representation, creating geometry files ready for further analysis. The streamlined neutronic workflow allows efficient and accurate calculations. The two new tools coupled together were used to perform a 3D high-fidelity multi-objective, multi-input optimization of an "ARC Class" compact tokamak design. The workflow was driven by an optimization driver for full automation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Creation of a Robust and Generalizable Machine Learning Classifier for Patient Ventilator Asynchrony

Background: As healthcare increasingly digitizes, streaming waveform data is being made available from an variety of sources, but there still remains a paucity of performant clinical decision support systems. For example, in the intensive care unit (ICU) existing automated alarm systems typically rely on simple thresholding that result in frequent false positives. Recurrent false positive alerts create distrust of alarm mechanisms that can be directly detrimental to patient health. To improve patient care in the ICU, we need alert systems that are both pervasive, and accurate so as to be informative and trusted by providers. Objective: We aimed to develop a machine learning-based classifier to detect abnormal waveform events using the use case of mechanical ventilation waveform analysis, and the detection of harmful forms of ventilation delivery to patients. We specifically focused on detecting injurious subtypes of patient-ventilator asynchrony (PVA).Methods: Using a dataset of breaths recorded from 35 different patients, we used machine learning to create computational models to automatically detect, and classify two types of injurious PVA, double trigger asynchrony (DTA), breath stacking asynchrony (BSA). We examined the use of synthetic minority over-sampling technique (SMOTE) to overcome class imbalance problems, varied methods for feature selection, and use of ensemble methods to optimize the performance of our model. Results: We created an ensemble classifier that is able to accurately detect DTA at a sensitivity/specificity of 0.960/0.975, BSA at sensitivity/specificity of 0.944/0.987, and non-PVA events at sensitivity/specificity of .967/.980. Conclusions: Our results suggest that it is possible to create a high-performing machine learning-based model for detecting PVA in mechanical ventilator waveform data in spite of both intra-patient, and inter-patient variability in waveform patterns, and the presence of clinical artifacts like cough and suction procedures. Our work highlights the importance of addressing class imbalance in clinical data sets, and the combined use of statistical methods and expert knowledge in feature selection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Understanding Selectivity for the Electrochemical Reduction of Carbon Dioxide to Formic Acid and Carbon Monoxide on Metal Electrodes

Increases in energy demand and in chemical production, together with the rise in CO 2 levels in the atmosphere, motivate the development of renewable energy sources. Electrochemical CO 2 reduction to fuels and chemicals is an appealing alternative to traditional pathways to fuels and chemicals due to its intrinsic ability to couple to solar and wind energy sources. Formate (HCOO – ) is a key chemical for many industries; however, greater understanding is needed regarding the mechanism and key intermediates for HCOO – production. This work reports a joint experimental and theoretical investigation of the electrochemical reduction of CO 2 to HCOO – on polycrystalline Sn surfaces, which have been identified as promising catalysts for selectively producing HCOO – . Our results show that Sn electrodes produce HCOO – , carbon monoxide (CO), and hydrogen (H 2 ) across a range of potentials and that HCOO – production becomes favored at potentials more negative than –0.8 V vs RHE, reaching a maximum Faradaic efficiency of 70% at –0.9 V vs RHE. Scaling relations for Sn and other transition metals are examined using experimental current densities and density functional theory (DFT) binding energies. While *COOH was determined to be the key intermediate for CO production on metal surfaces, we suggest that it is unlikely to be the primary intermediate for HCOO – production. Instead, *OCHO is suggested to be the key intermediate for the CO 2 RR to HCOO – transformation, and Sn’s optimal *OCHO binding energy supports its high selectivity for HCOO – . Lastly, these results suggest that oxygen-bound intermediates are critical to understand the mechanism of CO 2 reduction to HCOO – on metal surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1972

Load relief and gust alleviation control study

The results of a study are presented in which a computerized optimal control techique was applied to the synthesis of a load relief control system for an advanced Saturn 5 derivative launch vehicle. This technique included selection of a load relief control law and computerized optimization of the controller gain schedules for a set of preselected adverse synthetic wind speed profiles. The gain optimization was effected using a hybrid optimizer program to minimize (optimize) a direct performance functional which specified the design goal (load relief) in engineering terms (explicit representation of maximum bending moments).

Vieweg, R. R.↗
NASA NTRS2011

Optimal Asteroid Mass Determination from Planetary Range Observations: A Study of a Simplified Test Model

Mars ranging observations are available over the past 10 years with an accuracy of a few meters. Such precise measurements of the Earth-Mars distance provide valuable constraints on the masses of the asteroids perturbing both planets. Today more than 30 asteroid masses have thus been estimated from planetary ranging data (see [1] and [2]). Obtaining unbiased mass estimations is nevertheless difficult. Various systematic errors can be introduced by imperfect reduction of spacecraft tracking observations to planetary ranging data. The large number of asteroids and the limited a priori knowledge of their masses is also an obstacle for parameter selection. Fitting in a model a mass of a negligible perturber, or on the contrary omitting a significant perturber, will induce important bias in determined asteroid masses. In this communication, we investigate a simplified version of the mass determination problem. Instead of planetary ranging observations from spacecraft or radar data, we consider synthetic ranging observations generated with the INPOP [2] ephemeris for a test model containing ~25000 asteroids. We then suggest a method for optimal parameter selection and estimation in this simplified framework.

asteroids↗
DOE OSTI2017

Machine Learning-Based Temperature Prediction for Runtime Thermal Management Across System Components

Elevated temperatures limit the peak performance of systems because of frequent interventions by thermal throttling. Non-uniform thermal states across system nodes also cause performance variation within seemingly equivalent nodes leading to significant degradation of overall performance. In this paper we present a framework for creating a lightweight thermal prediction system suitable for run-time management decisions. We pursue two avenues to explore optimized lightweight thermal predictors. First, we use feature selection algorithms to improve the performance of previously designed machine learning methods. Second, we develop alternative methods using neural network and linear regression-based methods to perform a comprehensive comparative study of prediction methods. We show that our optimized models achieve improved performance with better prediction accuracy and lower overhead as compared with the Gaussian process model proposed previously. Specifically we present a reduced version of the Gaussian process model, a neural network-based model, and a linear regression-based model. Using the optimization methods, we are able to reduce the average prediction errors in the Gaussian process from 4: 2 degrees C to 2: 9 degrees C. We also show that the newly developed models using neural network and Lasso linear regression have average prediction errors of 2: 9 degrees C and 3: 8 degrees C respectively. The prediction overheads are 0.22, 0.097, and 0.026 ms per prediction for reduced Gaussian process, neural network, and Lasso linear regression models, respectively, compared with 0.57 ms per prediction for the previous Gaussian process model. We have implemented our proposed thermal prediction models on a two-node system configuration to help identify the optimal task placement. The task placement identified by the models reduces the average system temperature by up to 11: 9 degrees C without any performance degradation. Furthermore, these models respectively achieve 75, 82.5, and 74.17 percent success rates in correctly pointing to those task placements with better thermal response, compared with 72.5 percent success for the original model in achieving the same objective. Lastly, we extended our analysis to a 16-node system and we were able to train models and execute them in real time to guide task migration and achieve on average 17 percent reduction in the overall system cooling power.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

An experimental and numerical investigation of HD diesel engine DOC efficiency in oxidizing NO to NO 2

Reducing pollutant emissions from heavy-duty (HD) diesel engines is critical due to their significant environmental impact, particularly concerning NOx emissions. Understanding and optimizing modern diesel oxidation catalyst (DOC) and selective catalytic reduction (SCR) performance is essential for improving exhaust aftertreatment (EAT) system efficiency to meet stringent emissions regulations. The oxidation of nitric oxide (NO) to nitrogen dioxide (NO 2 ) in DOC plays a key role in improving SCR efficiency in reducing NO x . This study investigates the DOC performance in oxidizing NO to NO2 and its impact on the SCR efficiency of a 2021 MY Navistar E39 HD diesel engine. The influence of engine speed, load, exhaust gas temperature, and composition on DOC efficiency is experimentally investigated. The relationships between DOC inlet temperature, oxygen availability and NO 2 /NO x ratio at the DOC inlet are examined to better understand their effects on the overall DOC efficiency. The results indicate that DOC NO oxidation efficiency is highly dependent on exhaust temperature, with optimal oxidation occurring within a specific temperature range (275-350°C). Below this threshold, the chemical reactions are kinetically limited, while at higher temperatures, thermodynamic constraints reduce the efficiency of DOC in oxidizing NO to NO 2 . The experimental data further reveal that the NO 2 /NO x ratio peaks at medium loads before declining at higher loads due to reduced residence time and mass transfer effects. Additionally, the SCR NO x conversion efficiency is significantly influenced by the NO 2 /NO x ratio, achieving peak performance when the NO 2 /NO x ratio approaches 0.5. A DOC chemistry model was developed and validated against the experimental data to predict DOC oxidation behavior under various operating conditions. The findings of this study provide insights into the interdependencies between DOC and SCR performance, contributing to the optimization of SCR systems for optimized NOx reduction.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1974

Selection of sampling rate for digital control of aircrafts

The considerations in selecting the sample rates for digital control of aircrafts are identified and evaluated using the optimal discrete method. A high performance aircraft model which includes a bending mode and wind gusts was studied. The following factors which influence the selection of the sampling rates were identified: (1) the time and roughness response to control inputs; (2) the response to external disturbances; and (3) the sensitivity to variations of parameters. It was found that the time response to a control input and the response to external disturbances limit the selection of the sampling rate. The optimal discrete regulator, the steady state Kalman filter, and the mean response to external disturbances are calculated.

Katz, P.↗
DOE OSTI2015

CASSIS: The Cornell atlas of Spitzer/infrared spectrograph sources. II. HIGH-Resolution observations

The Infrared Spectrograph (IRS) on board the Spitzer Space Telescope observed about 15,000 objects during the cryogenic mission lifetime. Observations provided low-resolution (R=λ/Δλ≈60−127) spectra over ≈5−38 μm and high-resolution (R≈600) spectra over 10–37 μm. The Cornell Atlas of Spitzer/IRS Sources (CASSIS) was created to provide publishable quality spectra to the community. Low-resolution spectra have been available in CASSIS since 2011, and here we present the addition of the high-resolution spectra. The high-resolution observations represent approximately one-third of all staring observations performed with the IRS instrument. While low-resolution observations are adapted to faint objects and/or broad spectral features (e.g., dust continuum, molecular bands), high-resolution observations allow more accurate measurements of narrow features (e.g., ionic emission lines) as well as a better sampling of the spectral profile of various features. Given the narrow aperture of the two high-resolution modules, cosmic ray hits and spurious features usually plague the spectra. Our pipeline is designed to minimize these effects through various improvements. A super-sampled point-spread function was created in order to enable the optimal extraction in addition to the full aperture extraction. The pipeline selects the best extraction method based on the spatial extent of the object. For unresolved sources, the optimal extraction provides a significant improvement in signal-to-noise ratio over a full aperture extraction. We have developed several techniques for optimal extraction, including a differential method that eliminates low-level rogue pixels (even when no dedicated background observation was performed). The updated CASSIS repository now includes all the spectra ever taken by the IRS, with the exception of mapping observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Skipper CCD Parameter Optimization with ML

The development of novel detectors faces a bottleneck in the 'parameter selection' phase. A significant amount of a scientist's time must be spent characterizing and testing various parameters in order to optimize them for different science goals. This process can be streamlined with closed-loop Bayesian Optimization (BO), using Gaussian Processes through live measurements on the device. In this project, we demonstrate the effectiveness of this method in parameter optimization on Skipper CCDs and its potential to be fully automated.

Hope, Andrew [Michigan Tech. U.]↗
NASA NTRS2020

Jig Twist Optimization of Mach 0.745 Transonic Truss-Braced Wing Aircraft and High-Fidelity CFD Validation

This paper presents a jig twist optimization study of Mach 0.745 Transonic Truss-Braced Wing (TTBW) aircraft using an in-house developed aero-structural analysis solver VSPAERO coupled to BEAM3D. A vortex-lattice model of the TTBW model is developed, and a transonic and viscous flow correction method is implemented in the VSPAERO model to account for transonic and viscous flow effects. A correction method for the wing-strut interference aerodynamics is developed and applied to the VSPAERO solver. Also, a structural dynamic finite-element model of the TTBW aircraft is developed. This finite-element model includes the geometric nonlinear effect due to the tension in the struts which causes a deflection-dependent nonlinear stiffness. The VSPAERO model is coupled to the corresponding finite-element model to provide a rapid aero-structural analysis. A flight condition corresponding to Mach 0.745 at 42000 ft is selected for the TTBW aircraft jig twist optimization to reduce the drag coefficient. After the design is implemented, the drag coefficient of the twist optimized TTBW aircraft is reduced about 8 counts. At the end, a high-fidelity CFD solver FUN3D is used to validate the design.

Xiong, Juntao↗
NASA NTRS2020

Jig Twist Optimization of Mach 0.745 Transonic Truss-Braced Wing Aircraft and High-Fidelity CFD Validation

This paper presents a jig twist optimization study of Mach 0.745 Transonic Truss-Braced Wing (TTBW) aircraft using an in-house developed aero-structural analysis solver VSPAERO coupled to BEAM3D. A vortex-lattice model of the TTBW model is developed, and a transonic and viscous flow correction method is implemented in the VSPAERO model to account for transonic and viscous flow effects. A correction method for the wing-strut interference aerodynamics is developed and applied to the VSPAERO solver. Also, a structural dynamic finite-element model of the TTBW aircraft is developed. This finite-element model includes the geometric nonlinear effect due to the tension in the struts which causes a deflection-dependent nonlinear stiffness. The VSPAERO model is coupled to the corresponding finite-element model to provide a rapid aero-structural analysis. A design flight condition corresponding to Mach 0.745 at 42000 ft is selected for the TTBW aircraft jig twist optimization to reduce the drag coefficient. After the design is implemented, the drag coefficient of the twist optimized TTBW aircraft is reduced about 8 counts. At the end, a high-fidelity CFD solver FUN3D is used to validate the design.

BEAM3D↗
NASA NTRS2006

Provision Of Carbon Nanotube Bucky Paper Cages For Immune Shielding Of Cells, Tissues, and Medical Devices

System and method for enclosing cells and/or tissue, for purposes of growth, cell differentiation, suppression of cell differentiation, biological processing and/or transplantation of cells and tissues (biological inserts), and for secretion, sensing and monitoring of selected chemical substances and activation of gene expression of biological inserts implanted into a human body. Selected cells and/or tissue are enveloped in a "cage" that is primarily carbon nanotube Bucky paper, with a selected thickness and porosity. Optionally, selected functional groups, proteins and/or peptides are attached to the carbon nanotube cage, or included within the cage, to enhance the growth and/or differentiation of the cells and/or tissue, to select for certain cellular sub-populations, to optimize certain functions of the cells and/or tissue and/or to optimize the passage of chemicals across the cage surface(s). A cage system is also used as an immuns shield and to control operation of a nano-device or macroscopic device, located within the cage, to provide or transform a selected chemical and/or a selected signal.

Loftus, David J.↗
DOE OSTI2023

Writing the Programs of Programmable Catalysis

It has long been known that non-steady state and periodic catalytic reactor operation in terms of temperature, pressure, and composition can lead to higher overall productivity and/or product selectivity than the best steady operation. Recently, the emergence of catalysts whose intrinsic properties can be made to oscillate with time, introduces advanced forcing capabilities that can be “programmed” into the catalysts to broaden the scope and applicability of periodic operation to surface chemistry. In this work, an algorithmic approach is implemented to significantly accelerate the discovery and optimization of periodic steady states of catalytic reactors. Decomposition of complex dynamics into fundamental mechanistic fast–slow steps is seen to improve conceptual understanding of the relationship between binding energy oscillation protocols and overall catalytic rates. Finding structured forcing protocols, optimally tailored to the multiple time scales of a given individual mechanism, requires an efficient search of high-dimensional parameter spaces. Here, this is enabled here through active learning (Bayesian optimization, enhanced by our proposed Bayesian continuation). Implementation of these methods is shown to accelerate the evaluation of catalyst programs by up to several orders of magnitude. Faster screening of programmable catalysts to discover periodic steady states enables the optimization of catalytic operating protocols and thus opens the possibility for catalyst engineering based on optimal forcing programs to control rate and product selectivity, even for complex multistep catalytic mechanisms.

catalysis↗
NASA NTRSDate not supplied

Multi-point Jig Twist Optimization of Mach 0.745 Transonic Truss-Braced Wing Aircraft and High-Fidelity CFD Validation

This paper presents a multi-point jig twist optimization study of the Mach 0.745 Transonic Truss-Braced Wing (TTBW) aircraft using an in-house developed aero-structural analysis solver VSPAERO coupled to BEAM3D. A vortex-lattice model of the TTBW model is developed, and a transonic small disturbance /integral boundary layer correction method is implemented in the VSPAERO model to account for transonic and viscous flow effects. A correction method for the wing-strut interference aerodynamics is developed and applied to the VSPAERO solver. Also, a structural dynamic finite-element model of the TTBW aircraft is developed. This finite-element model includes the geometric nonlinear effect due to the tension in the struts which causes a deflection-dependent nonlinear stiffness. The VSPAERO model is coupled to the corresponding finite-element model to provide a rapid aero-structural analysis. The VSPAERO model has been validated against wind tunnel test data and FUN3D CFD simulation data. Three different flight conditions corresponding to Mach 0.745 at 40,000 ft are selected for the TTBW aircraft jig twist optimization to reduce the drag coefficients at the design and off-design cruise lift coefficients. The preliminary optimization results show that the TTBW aircraft with the optimized jig twist achieves a drag reduction of about 9 counts, 7 counts, and8 counts corresponding to the lift coefficients 0.68, 0.73, and 0.78, respectively. A high-fidelity CFD solver FUN3D is used to validate the jig twist optimization.

Multi-point↗
DOE OSTI2017

High temperature efficient, stable Si wafer-based selective solar absorbers

Creating selective solar absorber systems using simple, stable structures capable of surviving high temperatures is essential for widespread adoption of efficient, high-temperature solar thermal technologies. In this study, semiconductor-metal tandem selective solar absorbers based on commercially available Si wafers are fabricated and measured at different high temperatures. High selectivity of the devices is obtained at temperature as high as 490 °C, and the structure is demonstrated to be mechanically and thermally stable even at slightly higher temperatures (up to 535 °C). Increased free carrier absorption and lattice absorption of Si are observed at elevated temperatures, which raise thermal re-radiation dramatically. In order to mitigate this effect, a thin Si film-based selective absorber has also been computationally designed and optimized, which is predicted to exhibit even higher thermal transfer efficiency (60–70%) at a wide range of solar concentrations (20–100 suns). In conclusion, the simple structure combined with the mechanical and thermal stability enables the low-cost Si substrate-based selective solar absorber to find wide applications in solar thermal energy conversion systems.

14 SOLAR ENERGY↗