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Results for “Selection 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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
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↗
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↗
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.]↗
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↗
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↗
DOE OSTI2018

Optimizing Cardiac CTA Acquisition in Congenital Heart Disease

Purpose of Review: The purpose of this review of performance of cardiac computed tomographic angiography (CCTA) in patients with congenital heart disease (CHD) is to describe a strategy for optimizing CCTA protocols for various forms of CHD at diagnosis and throughout the lifetime of a patient. Recent Findings: Recent expert consensus statements provide key recommendations for patient selection and technical performance of CCTA with tips to optimize contrast injection, scan acquisition, and understanding anatomy and postoperative changes in patients with CHD. Spectral CT will become invaluable in acquiring image data which has potential for enabling improved image quality and perhaps physiologic information. Summary: CCTA is an important non-invasive imaging modality for making initial diagnosis and providing follow-up imaging in patients of all ages with CHD. Optimization of imaging protocols requires combined expertise in all forms of CHD, surgical palliation procedures, and knowledge of surgical options for CHD surgery.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Membrane Development for CO2 Capture from Steel Manufacturing

The U.S. government is targeting a net-zero carbon-emission economy by 2050, offering an exciting opportunity for membrane-based CO2 capture from various industrial point sources. Given that industrial flue gas has low CO2 partial pressures and high volumetric flow rates, high-permeance membranes are needed to make membrane technology economically viable for large-scale deployment. Thin film composite (TFC) membranes are necessary for this implementation because they can provide high permeance by forming a thin selective layer on top of a porous support. This presentation will report the rational design and fabrication of NETL’s highly permeable non-aging TFCs achieved by: synthesizing a high-performance rubbery selective material; developing a high-porosity membrane support; optimizing coating methods to assemble the two materials into scalable membranes; and scaling up membrane supports and TFCs via a roll-to-roll process. The novel rubbery selective material shows mixed-gas CO2 permeability of 930 Barrer and CO2/N2 selectivity of 44, exceeding the 2008 Robeson upper bound. The resulting TFCs yield remarkably high CO2 permeance of 4,500 GPU and CO2/N2 selectivity of 34 at 23ºC. Moreover, the TFCs exhibit not only excellent performance stability (or non-aging behavior) for 1,000 hours in the lab, but also maintain their separation properties in a 700-hour field test at the U.S. DOE’s National Carbon Capture Center using real humid flue gas.

Tran, Thien↗
DOE OSTI2019

ParaOpt: Automated Application Parameterization and Optimization for the Cloud

The variety of instance types available on cloud platforms offers enormous flexibility to match the requirements of applications with available resources. However, selecting the most suitable instance type and configuring an application to optimally execute on that instance type can be complicated and time-consuming. For example, application parallelism flags must match available cores and problem sizes must be tuned to match available memory. As the search space of application configurations can be enormous, we propose an automated approach, called ParaOpt, to automatically explore and tune application configurations on arbitrary cloud instances. ParaOpt supports arbitrary applications, enables use of custom optimization methods, and can be configured with different optimization targets such as runtime and cost. We evaluate ParaOpt by optimizing genomics, molecular dynamics, and machine learning applications with four types of optimizers. We show with as few as 15 parameterized executions of an application, representing between 1.2%-26.7% of the search space, that ParaOpt is able to identify the optimal configuration in 32.7% of experiments and a near-optimal configuration in 83.2% of cases. As a result of using near-optimal configurations, ParaOpt reduces overall execution time by up to 85.8% when compared with using the default configuration.

Wu, Chaofeng↗
DOE OSTI2019

Bayesian-based Hyperparameter Optimization for Spiking Neuromorphic Systems

Designing a neuromorphic computing system involves selection of several hyperparameters that not only affect the accuracy of the framework, but also the energy efficiency and speed of inference and training. These hyperparameters might be inherent to the training of the spiking neural network (SNN), the input/output encoding of the real-world data to spikes, or the underlying neuromorphic hardware. In this work, we present a Bayesian-based hyperparameter optimization approach for spiking neuromorphic systems, and we show how this optimization framework can lead to significant improvement in designing accurate neuromorphic computing systems. In particular, we show that this hyperparameter optimization approach can discover the same optimal hyperparameter set for input encoding as a grid search, but with far fewer evaluations and far less time. We also show the impact of hardware-specific hyperparameters on the performance of the system, and we demonstrate that by optimizing these hyperparameters, we can achieve significantly better application performance.

Parsa, Maryam↗
DOE OSTI2017

Dosimetric comparison of single-beam multi-arc and 2-beam multi-arc VMAT optimization in the Monaco treatment planning system

The purpose of this study was to evaluate the dosimetric and practical effects of the Monaco treatment planning system “max arcs-per-beam” optimization parameter in pelvic radiotherapy treatments. We selected for this study a total of 17 previously treated patients with a range of pelvic disease sites including prostate (9), bladder (1), uterus (3), rectum (3), and cervix (1). For each patient, 2 plans were generated, one using an arc-per-beam setting of “1” and another with an arc-per-beam setting of “2” using the volumes and constraints established from the initial clinical treatments. All constraints and dose coverage objects were kept the same between plans, and all plans were normalized to 99.7% to ensure 100% of the planning target volume (PTV) received 95% of the prescription dose. Plans were evaluated for PTV conformity, homogeneity, number of monitor units, number of control points, and overall plan acceptability. Treatment delivery time, patient-specific quality assurance procedures, and the impact on clinical workflow were also assessed. We found that for complex-shaped target volumes (small central volumes with extending arms to cover nodal regions), the use of 2 arc-per-beam (2APB) parameter setting achieved significantly lower average dose-volume histogram values for the rectum V{sub 20} (p = 0.0012) and bladder V{sub 30} (p = 0.0036) while meeting the high dose target constraints. For simple PTV shapes, we found reduced monitor units (13.47%, p = 0.0009) and control points (8.77%, p = 0.0004) using 2APB planning. In addition, we found a beam delivery time reduction of approximately 25%. In summary, the dosimetric benefit, although moderate, was improved over a 1APB setting for complex PTV, and equivalent in other cases. The overall reduced delivery time suggests that the use of mulitple arcs per beam could lead to reduced patient-on-table time, increased clinical throughput, and reduced medical physics quality assurance effort.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

Impact of organic acids on extraction of rare earth elements: Mechanisms and optimization

Organic acids are increasingly recognized as an environmentally friendly and efficient selective leaching agent of critical minerals. However, their impact on subsequent extraction processes remains unclear. This study investigates the extraction behavior of rare earth elements (REEs), representative of critical minerals, in the presence of various organic acids, including citric acid, maleic acid, malonic acid, DL-malic acid, L(+)-tartaric acid, and L-ascorbic acid. The results show that organic acids slightly reduce the extraction of REEs but greatly increase the extraction of aluminum (Al³⁺), making it harder to separate REEs from other elements. To avoid this, it is best to keep organic acid concentrations as low as possible in practical applications. However, by optimizing extraction conditions, these negative effects can be minimized. Specifically, adjusting the contact time between solutions allows for efficient REE extraction while limiting unwanted aluminum extraction. Among the acids tested, malonic acid and L(+)-tartaric acid were found to be the most effective for selectively extracting REEs. Mechanistically, organic acids are likely to form complexes with REEs and D2EHPA during extraction, except for Y (III), offering practical guidelines for optimizing REE recovery. Beyond extraction, this study also highlights a way to recover organic acids by precipitating REEs from stripping solutions using oxalic acid, which adds both environmental and economic benefits. Furthermore, these findings provide useful insights for optimizing REE recovery and offer a reference for extracting other critical minerals from solutions containing organic acids.

D2EHPA↗
DOE OSTI2019

6-Amino-3-methylpyrimidinones as Potent, Selective, and Orally Efficacious SHP2 Inhibitors

Protein tyrosine phosphatase SHP2 is an oncoprotein associated with cancer as well as a potential immune modulator because of its role in the programmed cell death PD-L1/PD-1 pathway. In the preceding manuscript, we described the optimization of a fused, bicyclic screening hit for potency, selectivity, and physicochemical properties in order to further expand the chemical diversity of allosteric SHP2 inhibitors. In this manuscript, we describe the further expansion of our approach, morphing the fused, bicyclic system into a novel monocyclic pyrimidinone scaffold through our understanding of SAR and use of structure-based design. These studies led to the identification of SHP394 (1), an orally efficacious inhibitor of SHP2, with high lipophilic efficiency, improved potency, and enhanced pharmacokinetic properties. We also report other pyrimidinone analogues with favorable pharmacokinetic and potency profiles. Overall, this work improves upon our previously described allosteric inhibitors and exemplifies and extends the range of permissible chemical templates that inhibit SHP2 via the allosteric mechanism.

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

Analysis of the ratio of ℓ 1 and ℓ 2 norms in compressed sensing

We study the ratio of ℓ 1 and ℓ 2 norms ( ℓ 1 / ℓ 2 ) as a sparsity-promoting objective in compressed sensing. We first propose a novel criterion that guarantees that an s-sparse signal is the local minimizer of the ℓ 1 / ℓ 2 objective; our criterion is interpretable and useful in practice. We also give the first uniform recovery condition using a geometric characterization of the null space of the measurement matrix, and show that this condition is satisfied for a class of random matrices. We also present analysis on the robustness of the procedure when noise pollutes data. Numerical experiments are provided that compare ℓ 1 / ℓ 2 with some other popular non-convex methods in compressed sensing. Finally, we propose a novel initialization approach to accelerate the numerical optimization procedure. We call this initialization approach support selection, and we demonstrate that it empirically improves the performance of existing ℓ 1 / ℓ 2 algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Krylov subspace recycling for evolving structures

Krylov subspace recycling is a powerful tool when solving a long series of large, sparse linear systems that change only slowly over time. In PDE constrained shape optimization, these series appear naturally, as typically hundreds or thousands of optimization steps are needed with only small changes in the geometry. In this setting, however, applying Krylov subspace recycling can be a difficult task. As the geometry evolves, in general, so does the finite element mesh defined on or representing this geometry, including the numbers of nodes and elements and element connectivity. This is especially the case if re-meshing techniques are used. As a result, the number of algebraic degrees of freedom in the system changes, and in general the linear system matrices resulting from the finite element discretization change size from one optimization step to the next. Changes in the mesh connectivity also lead to structural changes in the matrices. In the case of re-meshing, even if the geometry changes only a little, the corresponding mesh might differ substantially from the previous one. Obviously, this prevents any straightforward mapping of the approximate invariant subspace of the linear system matrix (the focus of recycling in this work) from one optimization step to the next; similar problems arise for other selected subspaces. In this paper, we present an algorithm to map an approximate invariant subspace of the linear system matrix for the previous optimization step to an approximate invariant subspace of the linear system matrix for the current optimization step, for general meshes. This is achieved by exploiting the map from coefficient vectors to finite element functions on the mesh, combined with interpolation or approximation of functions on the finite element mesh. We demonstrate the effectiveness of our approach numerically with several proof of concept studies for a specific meshing technique.

42 ENGINEERING↗
DOE OSTI2017

Spanning the Core Design Space with Binary Perturbations - A Mathematical Investigation

A reactor core design engineer, even one with many years of experience or one using advanced fuel pattern optimization software, will likely fine tune the pattern before finalizing the design. Fine tuning can further minimize local enthalpy peaks, optimize reactivity to extend burnup, and add additional margin to limits on reactivity coefficients or heat flux. For reactors that have reached an equilibrium fuel cycle, the number of feeds each cycle is typically the similar to previous cycles as is their placement in the core. It is then the engineer's responsibly to efficiently select the placement of burned assemblies to ensure optimal fuel utilization, satisfy safety limits, and meet cycle energy requirements. One of the engineer's most commonly used tools in this fine tuning investigation is to swap the positions of two assemblies, most likely two previously irradiated ('burned') assemblies since their reactivity is lower than fresh fuel. Such an operation is referred to in this work as a binary perturbation or a 'swap'. If quarter core symmetry is maintained, the perturbation translates to the other three quadrants. Likewise if eighth core symmetry is maintained, the perturbation translates to other seven octants. The question is then, how much of the core design space can binary perturbations possibly span? This work seeks to quantitatively define the space spanned by m number of binary perturbations as a function of variable size through combinatorial principles and computer simulation. The results of this study are useful in understanding the in-core fuel management problem faced by both professionals who employ hand manipulations and those who attempt stochastic solutions of the problem.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Breeding Realistic D‐Brane Models

Abstract Intersecting branes provide a useful mechanism to construct particle physics models from string theory with a wide variety of desirable characteristics. The landscape of such models can be enormous, and navigating towards regions which are most phenomenologically interesting is potentially challenging. Machine learning techniques can be used to efficiently construct large numbers of consistent and phenomenologically desirable models. In this work we phrase the problem of finding consistent intersecting D‐brane models in terms of genetic algorithms, which mimic natural selection to evolve a population collectively towards optimal solutions. For a four‐dimensional supersymmetric type IIA orientifold with intersecting D6‐branes, we demonstrate that unique, fully consistent models can be easily constructed, and, by a judicious choice of search environment and hyper‐parameters, of the found models contain the desired Standard Model gauge group factor. Having a sizable sample allows us to draw some preliminary landscape statistics of intersecting brane models both with and without the restriction of having the Standard Model gauge factor.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗