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Results for “Performance Tuning”

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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Non-thermal plasma-assisted steam methane reforming for electrically-driven hydrogen production

Plasma-assisted steam methane reforming (SMR) has become a promising approach for low temperature and small-scale hydrogen production. To increase H 2 yields, water-gas-shift reactions are needed to drive the formed CO to CO 2 and H 2 . In this study, bulk gas temperature, plasma power and water feed rate strongly impacted the CO and CO 2 product selectivity at high methane conversions of 60–80% in the presence of a Ni-based catalyst. CO 2 -enriched hydrogen could be formed directly with H 2 O/methane ratios > 4. To further increase the CO 2 /CO product selectivity, a “one-pot” cascade design with a Cu/ZnO/Al 2 O 3 /MgO catalyst bed placed downstream of the plasma zone achieved substantially higher CO 2 /CO selectivity (>15) in the effluent gas at 60% methane conversion and 300°C. Comparably, placing the Cu-based catalyst in the plasma zone does not alter the CO 2 /CO selectivity. Further, this study highlights the use of plasma reactor systems to directly tune the catalytic SMR performance and lead to an electrified route for hydrogen production.

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

Comparison of time-frequency-analysis techniques applied in building energy data noise cancellation for building load forecasting: A real-building case study

Time-frequency analysis that disaggregates a signal in both time and frequency domain is an important supporting technique for building energy analysis such as noise cancellation in data-driven building load forecasting. There is a gap in the literature related to comparing various time–frequency-analysis techniques, especially discrete wavelet transform (DWT) and empirical mode decomposition (EMD), to guide the selection and tuning of time–frequency-analysis techniques in data-driven building load forecasting. This article provides a framework to conduct a comprehensive comparison among thirteen DWT/EMD techniques with various parameters in a load forecasting modeling task. A real campus building is used as a case study for illustration. The DWT and EMD techniques are also compared under various data-driven modeling algorithms for building load forecasting. The results in the case study show that the load forecasting models trained with noise-cancelled energy data have increased their accuracy to 9.6% on average tested under unseen data. This study also shows that the effectiveness of DWT/EMD techniques depends on the data-driven algorithms used for load forecasting modeling and the training data. Hence, DWT/EMD-based noise cancellation needs customized selection and tuning to optimize their performance for data-driven building load forecasting modeling.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Atomic-scale tuned interface of nickel-rich cathode for enhanced electrochemical performance in lithium-ion batteries

The Ni-rich layered LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC6 22 ) is one promising cathode for lithium-ion batteries (LIBs), but suffers from poor cycling stability under high cutoff potentials. The performance degradation was reflected as capacity fading and voltage drop, having their roots in instable interface of NMC622. Aimed at improving interfacial stability, in this study, we deposited nanoscale ZrO 2 coatings conformally over NMC622 cathodes using atomic layer deposition (ALD). We found that, under a high cutoff voltage (4.5 V), the ALD ZrO 2 coatings evidently improved the performance of NMC622 cathode, showing better cyclability and higher sustainable capacity. In addition, the ALD coatings dramatically boosted the rate capability of NMC622. All these compelling performance results are ascribed to the atomic-scale tunable ZrO 2 coatings via ALD, which create stable interface and thereby inhibit unfavorable evolutions. In the study, we utilize a suite of characterization tools and various analyses to clarify the effects of ALD ZrO 2 coatings. This study will be helpful for improving the performance of nickel-rich cathodes via interfacial engineering using ALD.

25 ENERGY STORAGE↗
DOE OSTI2025

Customizable Aperture Geometry in Metal–Organic Frameworks for Kinetic Hydrocarbon Separation

Precise control of aperture dimensions is crucial in adsorptive separations of hydrocarbons, as it directly affects key parameters such as selectivity, capacity, diffusion, and recyclability. The development of metal–organic frameworks (MOFs) has enabled the fine-tuning of local pore environments to address important hydrocarbon separations. However, customizing the aperture geometry to tune the kinetic separation performance remains challenging. Here, we deploy a mixed-linker synthesis strategy, combining long and short linkers on fcu net Zr-MOFs with equilateral triangular apertures to construct isoreticular multivariate MOFs, NU-415 and NU-416, with tailored isosceles triangular apertures suitable for the separation of hexane isomers. Sorption, liquid batch separation, and X-ray diffraction measurements demonstrate significantly improved selectivity, capacity, stability, and recyclability of NU-415 and NU-416 compared with Zr-muconate and MOF-801. Notably, both NU-415 and NU-416 achieve uptake capacities of 2.2 mmol g –1 in 1 min with an n-hexane to 2,2-dimethylbutane selectivity over 200 in an equimolar ternary mixture at ambient conditions, comparable to leading reported materials. Mechanistic studies confirm that separation performance is predominantly governed by significant kinetic differences rather than by thermodynamics. Furthermore, the successful customization of aperture geometry not only enables superior linear to monobranched hexane selectivity in NU-415 but also demonstrates the mixed-linker synthesis strategy as a promising solution for precise and predictable pore architecture control in MOFs.

Adsorption↗
DOE OSTI2024

Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors

Abstract Efficient and accurate algorithms are necessary to reconstruct particles in the highly granular detectors anticipated at the High-Luminosity Large Hadron Collider and the Future Circular Collider. We study scalable machine learning models for event reconstruction in electron-positron collisions based on a full detector simulation. Particle-flow reconstruction can be formulated as a supervised learning task using tracks and calorimeter clusters. We compare a graph neural network and kernel-based transformer and demonstrate that we can avoid quadratic operations while achieving realistic reconstruction. We show that hyperparameter tuning significantly improves the performance of the models. The best graph neural network model shows improvement in the jet transverse momentum resolution by up to 50% compared to the rule-based algorithm. The resulting model is portable across Nvidia, AMD and Habana hardware. Accurate and fast machine-learning based reconstruction can significantly improve future measurements at colliders.

Physics↗
DOE OSTI2026

A Machine Learning based Approach of Estimating Equivalent Circuit Model Parameters at Different SoCs of Li-ion Batteries from Voltage Relaxation

Abstract: In this study, an approach of estimating the equivalent circuit model (ECM) parameters for Li-ion batteries (LIBs) is proposed based on the voltage value at different intervals while relaxing the LIB after discharge. The typical approach for estimating ECM parameters of a LIB is to conduct electrochemical impedance spectroscopy (EIS) measurements at different frequencies and fit them to a predefined circuit model, which requires additional measuring arrangements and specialized devices. The proposed methodology utilizes four different voltages at 0s, 60s, 360s, and 1800s alongside the specific state of charge (SoC) value for a specific constant discharge current value of ~1C until the relaxation stage to train and evaluate three regression-based machine learning models— Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Gaussian Process Regression (GPR)—for estimating the ECM parameters of the selected model. Bayesian optimization is employed for hyperparameter tuning to achieve optimal performance for all the regressor models, among which, the GPR provided the best performance with the root-mean-squared error (RMSE) of less than 4x10-4 on average for the resistive components and less than 0.27 for capacitive components with excellent R2 scores. The simplicity of the approach enables it to eliminate the need for sophisticated measuring equipment and computation power.

Sagar, Md. Samiul [The University of Alabama (UA)]↗
DOE OSTI2024

Experimental Combustion and Flame Characterization of a Chemical Looping-Based Oxidative Dehydrogenation Byproduct Fuel Mixture Containing High CO2 Dilution

Abstract This study investigates the combustion performance of a CO2-rich fuel mixture containing ethane and methane as active species using a constant volume combustion chamber. This fuel is obtained as a byproduct of a chemical looping-based oxidative dehydrogenation (Cl-ODH) process ethylene production. The byproduct gas mixture has 40.79% CO2, 39.49% ethane, and 4.88% methane by weight with other minor compounds. Using this fuel for energy extraction would improve the process efficiency of the ethane to ethylene conversion. After initial combustion modeling, the gas fuel mixture was reduced to just the major species: CO2, ethane, and methane. The mixture was then tested for flammability limits and combustion performance under spark-ignition conditions. Effects of ambient conditions like temperatures between 300 and 400 K with initial pressures from 1 to 10 bar were tested. The effects of stoichiometry were tested to understand flame velocities and heat release. The fuel mixture showed an overall reduced flame velocity compared to gasoline. Instability in combustion was believed to be caused by the dissociation of ethane under elevated conditions. At higher pressures, the flame produces lower cumulative heat release. Simulations were also performed using a model tuned to replicate the operations of the combustion chamber used in the experiments. Heat release and unburnt fuel mass data were calculated to identify the discrepancies in the combustion completeness at elevated pressures. The effects of CO2 quenching the flame coupled with the increased dissociation of the fuel species can lead to up to more than 75% of the fuel mixture being unburnt. Data from this study were used to modify a small-scale spark-ignition engine to use this fuel and produce usable energy.

Energy & Fuels↗
DOE OSTI2024

Development of an Optimal Variable-Pitch Controller for Floating Axial-Flow Marine Hydrokinetic Turbines

This article discusses the development of an optimal variable-pitch controller for floating, axial-flow marine turbines. Recently, OpenFAST, an open-source wind turbine modeling tool, has been extended to model marine turbines. A controller is necessary to simulate marine turbines for different load cases using OpenFAST, which greatly impacts the performance of the energy system. Previous studies have designed controllers using a linearized model of the marine turbine, which can be time-consuming and require the expertise of a control engineer. In this study, we use an automated approach that uses generic models of the marine turbine to identify the controller gains, which can expedite the process of designing a controller. Using an optimizer to identify the control system parameters can additionally improve the controller's performance. The optimal controller tuned using such an approach results in a 20% reduction in the tower-base damage equivalent loading and better tracking of the rated generator speed and power.

axial flow↗
DOE OSTI2026

Predicting initial trans-membrane pressure across cycles in the ultrafiltration process using random forest

With growing freshwater scarcity, direct potable reuse (DPR) systems that reclaim wastewater for drinking are becoming increasingly important for sustainable water supply. Reliable operation requires minimizing downtime in ultrafiltration (UF) units, where membrane fouling leads to elevated trans-membrane pressure (TMP). This study develops data-driven regression models based on random forest (RF) and autoregressive (AR) approaches to forecast the initial TMP at the start of each UF filtration cycle in a pilot-scale DPR system. The RF model consistently outperforms baseline methods, including historical mean, last observation carried forward, and AR models, across multiple forecast horizons, achieving the lowest root mean square error. To evaluate how different classes of process variables contribute to TMP dynamics over time, we examine the feature importance of independent input variables across multiple forecast horizons. This analysis provides insight into the temporal relevance of operational and sensor-derived features, guiding control and monitoring strategies. Additionally, the impact of hyperparameter tuning on TMP prediction performance is assessed for both direct and recursive RF modelling approaches. The proposed RF framework establishes a robust foundation for predictive monitoring and real-time optimization of UF operations, supporting sustainable and reliable water reuse.

direct potable reuse↗
DOE OSTI2021

Check valves for microfluidic systems and methods thereof

The present invention relates to microfluidic check valves, as well as fluidic cartridges including such check valves. In particular examples, the check valve includes a pre-stressed spring formed from a planar substrate. Various characteristics of the valves, such as size, profile, opening pressure, etc., can be tuned to provide desired performance when employed within a fluidic cartridge.

Ball, Cameron Scott↗
DOE OSTI2024

Development of an Optimal Variable-Pitch Controller for Floating Axial-Flow Marine Hydrokinetic Turbines: Preprint

This article discusses the development of an optimal variable-pitch controller for floating, axial-flow marine turbines. Recently, OpenFAST, an open-source wind turbine modeling tool, has been extended to model marine turbines. A controller is necessary to simulate marine turbines for different load cases using OpenFAST, which greatly impacts the performance of the energy system. Previous studies have designed controllers using a linearized model of the marine turbine, which can be timeconsuming and require the expertise of a control engineer. In this study, we use an automated approach that uses generic models of the marine turbine to identify the controller gains, which can expedite the process of designing a controller. Using an optimizer to identify the control system parameters can additionally improve the controller's performance. The optimal controller tuned using such an approach results in a 20% reduction in the towerbase damage equivalent loading and better tracking of the rated generator speed and power.

closed-loop control↗
DOE OSTI2021

Recent advances in integrating platinum group metal-free catalysts in proton exchange membrane fuel cells

Platinum group metal (PGM)-free catalysts are promising low-cost materials for the oxygen reduction reaction in proton exchange membrane fuel cells (PEMFCs). A variety of chemical precursors and synthesis methods have been proposed to increase their catalytic activity. In comparison, significantly less attention has been dedicated to the integration of these PGM-free catalysts into operating electrodes by investigating the role of the membrane electrode assembly (MEA) fabrication on the PEMFC performance. Here, we discuss here some remarkable performance improvements recently achieved by tuning catalyst loading, ionomer content, and ink solvent composition, and call for further explorations of the ink processing and MEA fabrication to improve performance.

25 ENERGY STORAGE↗
DOE OSTI2023

Recent Developments in Revealing the Impact of Complex Metal Oxide Reconstruction on Catalysis

Surface reconstruction of complex metal oxides refers to the alterations of the topmost catalyst surface, relative to the bulk structure, following exposure to pretreatment or reaction conditions during heterogeneous catalysis. (1) This structural and electronic transformation may involve (1) changes of the exposed crystallographic facets, (2) changes in chemical composition, such as the enrichment of specific elements, and, (3) morphological variations, including surface roughness and defects (Scheme 1). Beyond relaxation, surface reconstruction here also encompasses disruptions to the periodicity of the sublayers and changes to its stoichiometry. This change significantly influences the chemical properties of the catalyst surface, such as redox sites, (2) acid/base pairs, hydroxyl groups, (3) and surface defects. Surface reconstruction occurs due to the thermodynamic drive to lower the surface energy of the catalyst under the surrounding environment (e.g., temperature, chemical potential of species present). In addition to the treatment or reaction conditions, the extent of reconstruction also depends on the elements in the metal oxide structure. For instance, annealing ABO 3 perovskites (SrTiO 3 , BaTiO 3 , and BaZrO 3 ) at high temperatures (500 °C) in O 2 has a very different impact on the A/B ratio at the surface (ranging from 0.9 to 2.5), depending on the identity of A and B. (4) Metal oxide surface reconstruction under an electric field and exposure to acidic and alkaline media has recently gained increased attention in electrocatalysis, such as in the oxygen evolution reaction (OER); (5,6) however, in this Viewpoint we focus on the impact of surface reconstruction on thermal catalysis, a critical enduring research theme with ongoing challenges. Here, we start showcasing approaches to characterize surface reconstruction at the topmost surface layer, such as low-energy ion scattering (LEIS), discussing advantages and limitations. Next, we connect characterization of surface reconstruction to site-specific kinetic analysis, enabled via steady-state isotopic transient kinetic analysis (SSITKA), and we express our viewpoint on building structure–performance relationships by deeply understanding reconstructed surfaces. Later, we comment on the potential of tuning surface reconstruction to enhance catalytic performance. Finally, we explore the interplay between surface reconstruction and the “intelligent behavior”.

alcohol conversion↗
DOE OSTI2022

Super-resolution demodulation for fiber sensor arrays based on the MUSIC algorithm

This paper studies the use of MUltiple SIgnal Classification (MUSIC) as a super-resolution algorithm to improve demodulation results for intrinsic Fabry–Perot interferometer (IFPI) sensor arrays. Through distinction between noise and signal subspaces in an observation matrix, this paper shows that a 38-fold improvement in the full width at half maximum (FWHM) estimation of IFPI optical path differences (OPD) can be achieved using this algorithm. Based on this improved method, this paper demonstrates that a tunable laser with a 1.3-nm tuning range can achieve the same sensor demodulation performance as a tunable laser with a 50-nm tuning range if a conventional Fourier transform-based algorithm is used. This paper presents a new approach to analyzing optical signals produced by multiple multiplexed interferometers with similar OPDs with potential applications for both single-mode and multiple-mode devices.

Li, Yuqi (ORCID:0000000291643663)↗
DOE OSTI2022

Application and evaluation of a pattern-based building energy model calibration method using public building datasets

Building performance simulation has been adopted to support decision making in the building life cycle. An essential issue is to ensure a building energy simulation model can capture the reality and complexity of buildings and their systems in both the static characteristics and dynamic operations. Building energy model calibration is a technique that takes various types of measured performance data (e.g., energy use) and tunes key model parameters to match the simulated results with the actual measurements. This study performed an application and evaluation of an automated pattern-based calibration method on commercial building models that were generated based on characteristics of real buildings. A public building dataset that includes high-level building attributes (e.g., building type, vintage, total floor area, number of stories, zip code) of 111 buildings in San Francisco, California, USA, was used to generate building models in EnergyPlus. Monthly level energy use calibrations were then conducted by comparing building model results against the actual buildings' monthly electricity and natural gas consumption. The results showed 57 out of 111 buildings were successfully calibrated against actual buildings, while the remaining buildings showed opportunities for future calibration improvements. Enhancements to the pattern-based model calibration method are identified to expand its use for: (1) central heating, ventilation and air conditioning (HVAC) systems with chillers, (2) space heating and hot water heating with electricity sources, (3) mixed-use building types, and (4) partially occupied buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Criticality analysis of nuclear binding energy neural networks

Machine learning methods, in particular deep learning methods such as artificial neural networks (ANNs) with many layers, have become widespread and useful tools in nuclear physics. However, these ANNs are typically treated as ‘black boxes’, with their architecture (width, depth, and weight/bias initialization) and the training algorithm and parameters chosen empirically by optimizing learning based on limited exploration. We test a non-empirical approach to understanding and optimizing nuclear physics ANNs by adapting a criticality analysis based on renormalization group flows in terms of the hyperparameters for weight/bias initialization, training rates, and the ratio of depth to width. This treatment utilizes the statistical properties of neural network initialization to find a generating functional for network outputs at any layer, allowing for a path integral formulation of the ANN outputs as a Euclidean statistical field theory. We use a prototypical example to test the applicability of this approach: a simple ANN for nuclear binding energies. We find that with training using a stochastic gradient descent optimizer, the predicted criticality behavior is realized, and optimal performance is found with critical tuning. However, the use of an adaptive learning algorithm leads to somewhat superior results without concern for tuning and thus obscures the analysis. Nevertheless, the criticality analysis offers a way to look within the black box of ANNs, which is a first step towards potential improvements in network performance beyond using adaptive optimizers.

artificial neural network↗
DOE OSTI2025

Advanced Modeling of Beam Physics and Performance Optimization for Nuclear Physics Colliders

High energy colliders provide a critical tool in nuclear physics study by probing the fundamental structure and dynamics of matter. To maximize the potential of scientific discovery in nuclear physics study, it is important to optimize the parameters of these colliders to attain the best performance. The performance of a collider is typically measured by its integrated luminosity of colliding beams since the probability of a new event is proportional to the integrated luminosity. However, the achievable luminosity is limited by the electromagnetic interactions (beam-beam effects) of two colliding beams at higher energy, and the interplay between the space-charge effects and the beam-beam effects at lower energy. To achieve the best performance of a collider means to attain the highest luminosity of the collider with optimized collider parameters. Optimizing the collider’s machine parameters is both computationally and experimentally expensive. A fast and robust computational framework including beam-beam and space-charge effects will be critical to attaining the best performance of the collider. In this project, we will study the beam dynamics challenges, specifically the interplay of the space-charge and the beam-beam effects, and the machine tuning models for maximizing the performance of RHIC experiments. We will develop an advanced modeling framework based on first-principles physical simulations, lattice models and the state-of-the-art machine learning methods and apply this framework to performance improvement of the RHIC in operation. We will build data manipulation packages to connect the simulation data and the experimental data with the framework, develop a self-consistent hybrid model of space-charge and beam-beam effects, study underlying physics mechanisms, build surrogate models using the labeled data, integrate the models into the advanced modeling framework, and apply the framework to RHIC luminosity (STAR and sPHENIX) optimization. The success of this project would substantially improve the performance of existing and future colliders and increase the opportunity for scientific discovery.

43 PARTICLE ACCELERATORS↗