Search NASA⌕ Search

SEARCH · Search NASA

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

System-of-systems optimization of hydrogen infrastructure for heavy-duty freight corridors: The interstate 10 case study

Medium and heavy-duty freight transportation requires hydrogen energy infrastructure that is cost-effective, operationally reliable, spatially coherent, and resilient to demand variability along major corridors. This paper presents an integrated hydrogen corridor planning framework using Oak Ridge National Laboratory's OR-AGENT that couples freight-driven, route-resolved hydrogen demand modeling with optimized station siting, sizing, and station-level techno-economic analysis. The framework is demonstrated for the Interstate 10 freight corridor and the Houston-to-Los-Angeles region. Hydrogen demand is derived from high-resolution origin–destination freight data, duty-cycle characterization, and physics-based energy consumption modeling. Candidate refueling sites are selected from existing heavy-duty diesel fueling locations and optimized subject to onboard storage and station capacity constraints. Resulting station throughputs are evaluated using established techno-economic models for electrolytic hydrogen production and dispensing infrastructure. Results show that a regional, portfolio-level aggregation, average dispensed electrolytic hydrogen cost of $6.87–$7.26/kg is currently feasible, and is strongly influenced by demand density and utilization.

Sujan, Vivek [ORNL] (ORCID:0000000269882342)↗
DOE OSTI2019

Hyperaging Tuning of a Carbon Molecular-Sieve Hollow Fiber Membrane with Extraordinary Gas-Separation Performance and Stability

Here, we report 6FDA:BPDA-DAM polyimide-derived hollow fiber carbon molecular-sieve (CMS) membranes for hydrogen and ethylene separation. Since H 2 /C 2 H 4 selectivity is the lowest among H 2 /(C 1 -C 3 ) hydrocarbons, an optimized CMS fiber for this gas pair is useful for removing hydrogen from all-cracked gas mixtures. A process we term hyperaging provides highly selective CMS fiber membranes by tuning CMS ultramicropores to favor H 2 over larger molecules to give a H 2 /C 2 H 4 selectivity of over 250. Hyperaging conditions and a hyperaging mechanism are discussed in terms of an expedited physical aging process, which is largely controlled by the hyperaging temperature. For the specific CMS material considered here, a hyperaging temperature beyond 90°C but less than 250°C works best. Hyperaging also stabilizes CMS materials against physical aging and stabilizes the performance of H 2 separation over extended periods. This research opens a door in the development of CMS materials for the separation of small molecules from large molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Mass-dependent cuts in longitudinal phase space

Here, longitudinal phase space analyses as introduced by van Hove provided a simplified method of separating different reaction production mechanisms. Cuts in the longitudinal phase space can help to select specific reaction kinematics but also induce nonflat acceptance effects in angular distributions. We show that in photoproduction reactions dominated by t -channel exchanges, selection of meson or baryon production over a large mass range can be optimized through calculating mass-dependent cut limits compared to cuts on a van Hove plot sector alone. A cut is presented that improves this selection of one type of hadron production by rejecting another. In addition we demonstrate that using cuts in longitudinal phase space preserves sufficient information to reliably extract observables from the angular distribution of the final state particles.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Integrating Carbon Nanomaterials with Metals for Bio-sensing Applications

Age structure in most developed countries is changing fast as the average lifespan is increasing significantly, calling for solutions to provide improved treatments for age-related neurological diseases and disorders. In order to address these problems, a reliable way of recording information about neurotransmitters from in vitro and in vivo applications is needed to better understand neurological diseases and disorders as well as currently used treatments. Likewise, recent developments in medicine, especially with the opioid crisis, are demanding a swift move to personalized medicine to administer patient needs rather than population-wide averages. In order to enable the so-called personalized medicine, it is necessary to be able to do measurements in vivo and in real time. These actions require sensitive and selective detection of different analytes from very demanding environments. Current state-of-the-art materials are unable to provide sensitive and selective detection of neurotransmitters as well as the required time resolution needed for drug molecules at a reasonable cost. To meet these challenges, we have utilized different metals to grow carbon nanomaterials and applied them for sensing applications showing that there are clear differences in their electrochemical properties based on the selected catalyst metal. Additionally, we have combined atomistic simulations to support optimizing materials for experiments and to gain further understanding of the atomistic level reactions between different analytes and the sensor surface. With carbon nanostructures grown from Ni and Al + Co + Fe hybrid, we can detect dopamine, ascorbic acid, and uric acid simultaneously. On the other hand, nanostructures grown from platinum provide a feasible platform for detection of H 2 O 2 making them suitable candidates for enzymatic biosensors for detection of glutamate, for example. Tetrahedral amorphous carbon electrodes have an ability to detect morphine, paracetamol, tramadol, and O-desmethyltramadol. With carbon nanomaterial-based sensors, it is possible to reach metal-like properties in sensing applications using only a fraction of the metal as seed for the material growth. We have also seen that by using nanodiamonds as growth catalyst for carbon nanofibers, it is not possible to detect dopamine and ascorbic acid simultaneously, although the morphology of the resulting nanofibers is similar to the ones grown using Ni. This further indicates the importance of the metal selection for specific applications. However, Ni as a continuous layer or as separate islands does not provide adequate performance. Thus, it appears that metal nanoparticles combined with fiber-like morphology are needed for optimized sensor performance for neurotransmitter detection. This opens up a new research approach of application-specific nanomaterials, where carefully selected metals are integrated with carbon nanomaterials to match the needs of the sensing application in question.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Nickel promotes selective ethylene epoxidation on silver

Over the last 80 years, chlorine (Cl) has been the primary promoter of the ethylene epoxidation reaction valued at ~40 billion USD per year, providing a ~25% selectivity increase over unpromoted silver (Ag) (~55%). Promoters such as cesium, rhenium, and molybdenum each add a few percent of selectivity enhancements to achieve 90% overall, but their codependence on Cl makes optimizing and understanding their function complex. Here, we took a theory-guided, single-atom alloy approach to identify nickel (Ni) as a dopant in Ag that can facilitate selective oxidation by activating molecular oxygen (O 2 ) without binding oxygen (O) too strongly. Surface science experiments confirmed the facile adsorption/desorption of O 2 on NiAg, as well as demonstrating that Ni serves to stabilize unselective nucleophilic oxygen. Supported Ag catalyst studies revealed that the addition of Ni in a 1:200 Ni to Ag atomic ratio provides a ~25% selectivity increase without the need for Cl co-flow and acts cooperatively with Cl, resulting in a further 10% initial increase in selectivity.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Methanol Synthesis from CO 2 Hydrogenation over a Potassium-Promoted Cu x O/Cu(111) ( x ≤ 2) Model Surface: Rationalizing the Potential of Potassium in Catalysis

Alkalis have been reported as a promotor in the heterogeneous catalysis, being able to enhance the activity and selectivity of catalysts. The effective utilization of alkalis in catalyst optimization requires the fundamental understanding of the underlying mechanism. In this work, we take a potassium (K)-modified Cu x O/Cu(111) (x ≤ 2) model surface as a case study to rationalize the nature of K during the carbon dioxide hydrogenation using combined density functional theory (DFT) calculation and the kinetic Monte Carlo (KMC) simulation. Our result demonstrates the significant tuning of selectivity from carbon monoxide to methanol on going from Cu(111) to K-modified Cu x O/Cu(111). The deposited K + stabilizes the Cu x O thin film under the reducing condition of carbon dioxide hydrogenation. More importantly, our study reveals that K + acts as an active center for selective tuning in the binding, an accelerator for charge transfer, and a mediator for the electron tunneling. As a result, the K-modified Cu x O/Cu(111) opens a methanediol [H 2 C(OH) 2 ]-mediated formate pathway to facilitate the selective conversion of carbon dioxide to methanol. Our study develops the intrinsic rules of design to tune the catalytic performance using alkali metals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

High temperature selective emitters via critical coupling of weak absorbers

Tailoring the emission spectra of a solar thermophotovoltaic emitter away from that of a blackbody, thereby minimizing transmission and thermalization loss in the energy receiver, is a viable approach to circumventing the Shockley-Queisser limit to single junction solar energy conversion. Embodiments allow for radically tuned selective thermal emission that leverages the interplay between two resonant phenomena in a simple planar structure—absorption in weakly-absorbing thin films and reflection in multi-layer dielectric stacks. A virtual screening approach is employed based on Pareto optimality to identify a small number of promising structures for a selective thermal emitter from a search space of millions, several of which approach the ideal values of a step-function selective thermal emitter.

Martinson, Alex B.↗
DOE OSTI2026

An Adaptive Multiparameter Penalty Selection Method for Multiconstraint and Multiblock ADMM

This work presents a new method for online selection of multiple penalty parameters for the alternating direction method of multipliers (ADMM) algorithm applied to optimization problems with multiple constraints or functions with block matrix components. ADMM is widely used for solving constrained optimization problems in a variety of fields, including signal and image processing. Implementations of ADMM often utilize a single hyperparameter, referred to as the penalty parameter, which needs to be tuned to control the rate of convergence. However, in problems with multiple constraints, ADMM may demonstrate slow convergence regardless of penalty parameter selection due to scale differences between constraints. Accounting for scale differences between constraints to improve convergence in these cases requires introducing a penalty parameter for each constraint. The proposed method is able to adaptively account for differences in scale between constraints, providing robustness with respect to problem transformations and initial selection of penalty parameters. It is also simple to understand and implement. Our numerical experiments demonstrate that the proposed method performs favorably compared to a variety of existing penalty parameter selection methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Microbiome breeding: conceptual and practical issues

Microbiome breeding is a new artificial selection technique that seeks to change the genetic composition of microbiomes in order to benefit plant or animal hosts. Recent experimental and theoretical analyses have shown that microbiome breeding is possible whenever microbiome-encoded genetic factors affect host traits (e.g., health) and microbiomes are transmissible between hosts with sufficient fidelity, such as during natural microbiome transmission between individuals of social animals, or during experimental microbiome transplanting between plants. Further, to address misunderstandings that stymie microbiome-breeding programs, we (i) clarify and visualize the corresponding elements of microbiome selection and standard selection; (ii) elucidate the eco-evolutionary processes underlying microbiome selection within a quantitative genetic framework to summarize practical guidelines that optimize microbiome breeding; and (iii) characterize the kinds of host species most amenable to microbiome breeding.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Engineered enzymatic cascade converts diols to amino alcohols

Aliphatic amino alcohols such as 6-amino-1-hexanol are potential platform chemicals for a variety of advanced materials, but applications are currently limited by reagent costs. Aliphatic amino alcohols can currently be synthesized from biomass-derived diols at elevated temperatures and pressures using Ru-based catalysts that produce a mixture of amino-alcohol, diamine, and cyclic amine products. Replacing chemical amination with an enzymatic cascade would reduce resource needs and enable reactions under milder conditions. In this work, we characterized a two-enzyme cascade that selectively converts C4–C7 diols to the corresponding amino alcohols under aqueous conditions at room temperature and pressure. By engineering the rate-limiting enzyme and optimizing reaction conditions, we increased amino alcohol production nearly 30-fold, achieving a selectivity of 99%. The same enzyme cascade could also be used to convert amino alcohols into cyclic amines through reduction of the corresponding cyclic imine. This engineered cascade provides a green opportunity to sustainably synthesize asymmetric bifunctional platform chemicals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Applications of targeted proteomics in metabolic engineering: advances and opportunities

Optimization of metabolically engineered organisms requires good understanding of producing balanced level of pathway proteins. Targeted proteomics via selected-reaction monitoring (SRM) has been increasingly used in metabolic engineering studies to detect and quantify sets of proteins with high selectivity, multiplexity, and reproducibility. In combination with metabolomics and other omics tools, targeted proteomics has helped optimize the production of many bio-based chemicals in various metabolic engineering cell factories. In this review, we present recent applications of targeted proteomics in metabolic engineering studies and highlight several successful cases of targeted proteomics in boosting production of commodity and high value chemicals. Additionally, we also discuss challenges and limitations of current targeted proteomics and map opportunities for future research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Using a Chromatographic Pseudophase Model To Elucidate the Mechanism of Olefin Separation by Silver(I) Ions in Ionic Liquids

Silver(I) ions undergo selective olefin complexation and have been utilized in various olefin/paraffin separation techniques such as argentation chromatography and facilitated transport membranes. Ionic liquids (ILs) are solvents known for their low vapor pressure, high thermal stability, low melting points, and ability to promote a favorable solvation environment for silver(I) ion–olefin interactions. To develop highly selective separation systems, a fundamental understanding of analyte partitioning to the stationary phase and the thermodynamic driving forces behind solvation is required. In this study, a chromatographic model treating silver(I) ions as a pseudophase is constructed and employed for the first time to investigate the olefin separation mechanism in silver(I) salt/IL mixtures. Stationary phases containing varying amounts of noncoordinated silver(I) salt ([Ag + ][NTf 2 – ]) dissolved in the 1-decyl-3-methylimidazolium bis[(trifluoromethyl)sulfonyl]imide ([C 10 MIM + ][NTf 2 – ]) IL are utilized to determine the partition coefficients of various analytes including alkanes, alkenes, alkynes, aromatics, aldehyde, esters, and ketones. As ligand coordination to silver(I) ions is known to lower its olefin complexation capability, this study also examines two different types of coordinated silver(I) ion pseudophases, namely, monocoordinated silver(I) salt ([Ag + (1-decyl-2-methylimidazole, DMIM)][NTf 2 – ]) and dicoordinated silver(I) salt ([Ag + (1-methylimidazole, MIM)(DMIM)][NTf 2 – ]). The extent of olefin partitioning to the coordinated silver(I) ion pseudophases over the carrier gas and IL decreased by up to two orders of magnitude. Values for enthalpy, entropy, and free energy of solvation were determined for the three silver(I) ion-containing systems. Olefin retention was observed to be enthalpically dominated, while ligand coordination to the silver(I) ion pseudophase resulted in variations for both enthalpic and entropic contributions to the free energy of solvation. Furthermore, the developed model can be used to study chemical changes that occur in silver(I) ions over time as well as identify optimal silver(I) salt/IL mixtures that yield high olefin selectivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Development of near-optimal advanced control sequences for chiller plants with water-side economizers in U.S. Climates (ASHRAE RP-1661)

Various advanced control sequences for chiller plants with water-side economizers (WSE) have been proposed in literature, but the evaluation and optimization of those controls is limited. It is possible to maximize energy savings by selecting different sequences and related parameters based on the plant configuration, load, and climate. This paper addresses this gap by developing near-optimal advanced control sequences for chiller plants with WSEs. First, advanced control sequences for chiller plants with WSEs are categorized into condenser water, chilled water, and hybrid controls and representative sequences from each category are identified. Next, 504 different scenarios are optimized. These scenarios represent all possible combinations of two plant configurations, a constant or variable load profile, three advanced control sequences, and seven optimization parameter combinations in six climate zones. The results show the recommended near-optimal sequences can reduce energy consumption by up to 15% relative to the baseline depending on the configuration, load profile, and climate. Specifically, the CW-CHW sequence is recommended for the majority of systems because it is often the most energy efficient and/or reduces the runtime of chillers. The methodology in this paper provides practical guidance for achieving energy savings through near-optimal control of chiller plants with WSEs.

42 ENGINEERING↗
DOE OSTI2017

Parallel variable selection for effective performance prediction

Large data analysis problems often involve a large number of variables, and the corresponding analysis algorithms may examine all variable combinations to find the optimal solution. For example, to model the time required to complete a scientific workflow, we need to consider the impact of dozens of parameters. To reduce the model building time and reduce the likelihood of overfitting, we look to variable selection methods to identify the critical variables for the performance model. In this work, we create a combination of variable selection and performance prediction methods that is as effective as the exhaustive search approach when the exhaustive search could be completed in a reasonable amount of time. To handle the cases where the exhaustive search is too time consuming, we develop the parallelized variable selection algorithm. Additionally, we develop a parallel grouping mechanism that further reduces the variable selection time by 70%.As a case study, we exercise the variable selection technique with the performance measurement data from the Palomar Transient Factory (PTF) workflow. The application scientists have determined that about 50 variables and parameters are important to the performance of the workflows. Our tests show that the Sequential Backward Selection algorithm is able to approximate the optimal subset relatively quickly. By reducing the number of variables used to build the model from 50 to 4, we are able to maintain the prediction quality while reducing the model building time by a factor of 6. Using the parallelization and grouping techniques we developed in this work, the variable selection process was reduced from over 18 hours to 15 minutes while ending up with the same variable subset.

Wang, J↗
DOE OSTI2023

Defect Engineering of WO 3 by Rapid Flame Reduction for Efficient Photoelectrochemical Conversion of Methane into Liquid Oxygenates

Photoelectrochemical (PEC) conversion is a promising way to use methane (CH 4 ) as a chemical building block without harsh conditions. However, the PEC conversion of CH 4 to value-added chemicals remains challenging due to the thermodynamically favorable overoxidation of CH 4 . Here, we report WO 3 nanotube (NT) photoelectrocatalysts for PEC CH 4 conversion with high liquid product selectivity through defect engineering. By tuning the flame reduction treatment, we carefully controlled the oxygen vacancies of WO 3 NTs. The optimally reduced WO 3 NTs suppressed overoxidation of CH4 showing a high total C1 liquid selectivity of 69.4% and a production rate of 0.174 μmol cm –2 h –1 . Scanning electrochemical microscopy revealed that oxygen vacancies can restrain the production of hydroxyl radicals, which, in excess, could further oxidize C1 intermediates to CO 2 . Additionally, band diagram analysis and computational studies elucidated that oxygen vacancies thermodynamically suppress overoxidation. In conclusion, this work introduces a strategy for understanding and controlling the selectivity of photoelectrocatalysts for direct conversion of CH 4 to liquids.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Optimization of an aerostructural machining process using physics-guided Bayesian stability modelling

Existing algorithms for predicting milling chatter have not been widely adopted in industry since they require specialized instruments to measure the stability inputs. This study describes how the machining process for a meter-scale aluminum aerostructure was optimized using a physics-guided Bayesian stability model. The study was performed in collaboration with an industrial partner on production machines to evaluate the practicality of the proposed method under real-world conditions. For each cutting tool, the Bayesian approach automatically selected a small number of cutting tests, which were monitored using a microphone to observe the chatter frequency. The algorithm learned the system dynamics, cutting forces, and stability map from these test results. A novel algorithm for predicting tool bending stress was incorporated into the test selection algorithm to avoid tool breakage. On average, each set of optimized cutting parameters required less than six tests to identify and were 97% more productive than baseline parameters from the cutting tool manufacturer. The machining program was then further optimized using commercial feedrate scheduling software to remove cutting force spikes and reduce air cutting time. Five components were machined using the optimized process. These results demonstrate the potential for physics-guided Bayesian models to improve productivity in industrial settings.

Cornelius, Aaron [UT Knoxville]↗
DOE OSTI2016

SU-F-T-198: Dosimetric Comparison of Carbon and Proton Radiotherapy for Recurrent Nasopharynx Carcinoma

Purpose: Various radiotherapy planning methods for locally recurrent nasopharynx carcinoma (R-NPC) have been proposed. The purpose of this study was to compare carbon and proton therapy for the treatment of R-NPC in terms of dose coverage for target volume and sparing for organs at risk (OARs). Methods: Six patients who were suffering from R-NPC and treated using carbon therapy were selected for this study. Treatment plans with a total dose of 57.5Gy (RBE) in 23 fractions were made using SIEMENS Syngo V11. An intensity-modulated radiotherapy optimization method was chosen for carbon plans (IMCT) while for proton plans both intensity-modulated radiotherapy (IMPT) and single beam optimization (proton-SBO) methods were chosen. Dose distributions, dose volume parameters, and selected dosimetric indices for target volumes and OARs were compared for all treatment plans. Results: All plans provided comparable PTV coverage. The volume covered by 95% of the prescribed dose was comparable for all three plans. The average values were 96.11%, 96.24% and 96.11% for IMCT, IMPT, and proton-SBO respectively. A significant reduction of the 80% and 50% dose volumes were observed for the IMCT plans compared to the IMPT and proton-SBO plans. Critical organs lateral to the target such as brain stem and spinal cord were better spared by IMPT than by proton-SBO, while IMCT spared those organs best. For organs in the beam path, such as parotid glands, the mean dose results were similar for all three plans. Conclusion: Carbon plans yielded better dose conformity than proton plans. They provided similar or better target coverage while significantly lowering the dose for normal tissues. Dose sparing for critical organs in IMPT plans was better than proton-SBO, however, IMPT is known to be more sensitive to range uncertainties. For proton plans it is essential to find a balance between the two optimization methods.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Centralized and Decentralized Optimal Control of Variable Speed Heat Pumps

Utility service providers are often challenged with the synchronization of thermostatically controlled loads. Load synchronization, as a result of naturally occurring and demand-response events, has the potential to damage power distribution equipment. Because thermostatically controlled loads constitute most of the power consumed by the grid at any given time, the proper control of such devices can lead to significant energy savings and improved grid stability. The contribution of this paper is the development of an optimal control algorithm for commonly used variable speed heat pumps. By means of selective peer-to-peer communication, our control architecture allows for the regulation of home temperatures while simultaneously minimizing aggregate power consumption, and aggregate load volatility. An optimal centralized controller is also explored and compared against its decentralized counterpart.

30 DIRECT ENERGY CONVERSION↗