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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Systems-To-Atoms (S2A): enabling hydrogen for climate security

The project addresses a critical gap in hydrogen infrastructure by integrating system-level energy models with atomic-scale material simulations in a unified Systems-to-Atoms (S2A) framework. The motivation stems from the need to develop efficient, cost-effective, and durable hydrogen transport and utilization technologies to support decarbonization of hard-to-electrify sectors such as heavy-duty transportation. Current system models lack awareness of material performance mechanisms, while material-scale models do not account for system-level usage and variability. To bridge this divide, the team developed a co-simulation capability linking techno-economic analyses, reactor/process-flow modeling, and molecular-scale catalysis simulations. Applied to hydrogen delivery in California, the framework enabled comparative evaluations of compressed, cryogenic, and liquid organic hydrogen carrier (LOHC) pathways, highlighting how catalyst operation and unit process efficiency influence overall performance. The results demonstrate that no single material or transport mode is universally optimal; instead, heterogeneous solutions tuned to specific operational contexts deliver better performance. The project delivers a new capability for cross-scale material co-design, advancing hydrogen infrastructure readiness and informing DOE and LLNL missions in climate and energy resilience.

organic↗
DOE OSTI2025

Dynamic Modeling of a Kaplan Hydroturbine Using Optimal Parametric Tuning and Real Plant Operational Data

To address grid variability caused by renewable energy integration and to maintain grid reliability and resilience, hydropower must quickly adjust its power generation over short time periods. This changing energy generation landscape requires advance technology integration and adaptive parameter optimization for hydropower systems via digital twin effort. However, this is difficult owing to the lack of characterization and modeling for the nonlinear nature of hydroturbines. To solve this issue, this paper first formulates a six-coefficient Kaplan hydroturbine model and then proposes a parametric optimization tuning framework based on the Nelder–Mead algorithm for adaptive dynamic learning of the six-coefficients so as to build models that describe the turbine. To assess the performance of the proposed optimal parametric tuning technique, operational data from a real-world Kaplan hydroturbine unit are collected and used to model the relationship between the gate opening and the generated power production. The findings show that the proposed technique can effectively and adaptively learn the unknown dynamics of the Kaplan hydroturbine while optimally tune the unknown coefficients to match the generated power output from the real hydroturbine unit with an inaccuracy of less than 5%. The method can be used to provides optimal tuning of parameters critical for controller design, operational optimization and daily maintenance for hydroturbines in general.

13 HYDRO ENERGY↗
DOE OSTI2025

Metal–Oxide Interface Sites Created Using Atomic Layer Deposition and Tested for CO Oxidation

The performance of catalysts made out of Pt supported on TiO 2 thin films grown on SBA-15 (a silica mesoporous material) by atomic layer deposition (ALD) was characterized systematically by combining in situ infrared absorption spectroscopy (IR) with other techniques including electron microscopy and adsorption−desorption isothermal measurements. The titania films in the resulting high-surface-area catalysts were evenly distributed throughout the inner surface of the SBA-15 mesopores, and their thickness could be controlled at a submonolayer level, with 3 to 4 TiO 2 ALD cycles needed for the complete coverage of the silica sites. The titania films could be deposited either before or after adding the metal (Pt), which was dispersed in the form of small nanoparticles (NPs) approximately 4−6 nm in diameter, in order to exert some control on the density and nature of the Pt/TiO 2 interface sites. One important lesson deriving from this work is that such an order of deposition leads to significantly different catalysts in spite of the fact that most of their structural properties are similar. If the Pt is deposited on the titania films, the resulting metal NPs are slightly smaller than those grown on silica and display CO adsorption sites with lower surface Pt coordination numbers. On the other hand, when TiO 2 is deposited on the Pt/SBA-15 starting material, some titania grows on the metal and partially blocks its surface while also creating new interface sites where CO binds more weakly and displays lower C−O stretching frequencies. In terms of catalytic performance, the results from in situ IR CO site titration and kinetic measurements combined suggest a mechanism where CO first adsorbs on Pt atop sites and then migrates to Pt/TiO 2 interface sites, where oxidation takes place. Both types of sites appear to be similar in all the catalysts tested, but catalytic performance could be optimized by tuning their surface densities. Maximum catalytic activity was obtained when the TiO2 films were deposited first and with TiO 2 coverages of at least half a monolayer, that is, after at least 2 ALD cycles.

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

First characterisation of the MAGO cavity, a superconducting RF detector for kHz–MHz gravitational waves

Heterodyne detection using microwave cavities is a promising method for detecting high-frequency gravitational waves (GWs) or ultralight axion dark matter. In this work, we report on studies conducted on a spherical 2-cell cavity developed by the MAGO collaboration for high-frequency GWs detection. Although fabricated around 20 years ago, the cavity had not been used since. Due to deviations from the nominal geometry, we conducted a mechanical survey and performed room-temperature plastic tuning. Measurements and simulations of the mechanical resonances and electromagnetic properties were carried out, as these are critical for estimating the cavity’s GW coupling potential. Based on these results, we plan further studies in a cryogenic environment. The cavity characterisation does not only provide valuable experience for a planned physics run but also informs the future development of improved cavity designs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Multiplicity dependent 𝐽/𝜓 and 𝜓⁡(2⁢𝑆) production at forward and backward rapidity in 𝑝 + 𝑝 collisions at $\sqrt{𝑠}$ = 200 GeV

Recent measurements of 𝐽/𝜓 production as a function of event charged-particle multiplicity at the collision energies of both the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC) show enhanced 𝐽/𝜓 production yields with increasing multiplicity. One potential explanation for this type of dependence is multiparton interactions (MPI). We present the first study of potential autocorrelations at RHIC energies and forward and backward rapidity of self-normalized 𝐽/𝜓 yields and 𝜓⁡(2⁢𝑆) to 𝐽/𝜓 ratio, as a function of self-normalized multiplicity in 𝑝 + 𝑝 collisions. In addition, detailed pythia studies tuned to RHIC energies were performed to investigate the MPI impacts. We find that the PHENIX data at RHIC are consistent with recent LHC measurements and can only be described by pythia calculations that include MPI effects. The forward and backward 𝜓⁡(2⁢𝑆) to 𝐽/𝜓 ratio is found to be less dependent on the charged-particle multiplicity.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Simulation-Based Inference for Neutrino Parameter Tuning

This code trains a simulation-based inference (SBI) model for neutrino interaction parameter tuning and subsequently evaluates its performance on MicroBooNE, T2K, and NuWro datasets.

Tame-Narvaez, Karla [Fermi National Accelerator La↗
DOE OSTI2025

femto-PIXAR: a self-supervised neural network method for reconstructing femtosecond X-ray free electron laser pulses

X-ray Free Electron Lasers (X-FELs) operate in a wide range of lasing configurations for a broad variety of scientific applications at ultrafast time-scales such as structural biology, materials science, and atomic and molecular physics. Shot-by-shot characterization of the X-FEL pulses is crucial for analysis of many experiments as well as tuning the X-FEL performance. However, for the weak pulses found in advanced configurations, e.g. those needed for coherent, two-pulse studies of quantum materials, there is no current method for reliably resolving pulse profiles. Here we show that a physics-based U-net model can reconstruct the individual pulse power profiles for sub-picosecond pulse separation without the need for simulations. Using experimental data from weak X-FEL pulse pairs, we demonstrate we can learn the pulse characteristics on a shot-by-shot basis when conventional methods fail.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Dynamic Model of a supercritical CO2 10MW Recompression Closed Brayton Cycle

This model of the 10MW recompression closed Brayton cycle (RCBC) was developed in conjunction with the DOE’s Supercritical Transformational Electric Power (STEP) project. A high-fidelity dynamic model was used extensively to study the dynamic characteristics of the cycle and develop the process control architecture and strategies for start-up and shutdown procedures. This version of the model has been simplified from the original version to be more accessible for a variety of applications and research. The controllers developed for the original model have been maintained for this version and tuned to give a similar performance to the original model. This model can be used to perform similar studies as those performed in Liese et al (2020). The overall cycle performance will not be identical but will perform similarly.

Controls,Power Cycles,Process Systems Engineering,↗
DOE OSTI2025

G2PDeep-v2: A Web-Based Deep-Learning Framework for Phenotype Prediction and Biomarker Discovery for All Organisms Using Multi-Omics Data

Multi-omics data offers rich insights into complex traits across organisms, yet integrating and analyzing these datasets for phenotype prediction and marker discovery remains challenging. Researchers need accessible tools that combine deep learning, hyperparameter optimization, visualization, and downstream analysis in a unified web platform. To address this, we developed G2PDeep-v2, a web-based platform powered by deep learning for phenotype prediction and marker discovery from multi-omics data across a wide range of organisms, including humans and plants. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases, conditions and other biological phenotypes being studied.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Recent Progress on Surface Water Quality Models Utilizing Machine Learning Techniques

Surface waterbodies are heavily exposed to pollutants caused by natural disasters and human activities. Empowering sensor technologies in water quality monitoring, sufficient measurements have become available to develop machine learning (ML) models. Numerous ML models have quickly been adopted to predict water quality indicators in various surface waterbodies. This paper reviews 78 recent articles from 2022 to October 2024, categorizing water quality models utilizing ML into three groups: Point-to-Point (P2P), which estimates the current target value based on other measurements at the same time point; Sequence-to-Point (S2P), which utilizes previous time series data to predict the target value at one time point ahead; and Sequence-to-Sequence (S2S), which uses previous time series data to forecast sequential target values in the future. The ML models used in each group are classified and compared according to water quality indicators, data availability, and model performance. Widely used strategies for improving performance, including feature engineering, hyperparameter tuning, and transfer learning, are recognized and described to enhance model effectiveness. The interpretability limitations of ML applications are discussed. This review provides a perspective on emerging ML for surface water quality models.

machine learning (ML)↗
NASA NTRS1983

Measurement of heat pump processes induced by laser radiation

A series of experiments was performed in which a suitably tuned CO2 laser, frequency doubled by a Tl3AsSe37 crystal, was brought into resonance with a P-line or two R-lines in the fundamental vibration spectrum of CO. Cooling or heating produced by absorption in CO was measured in a gas-thermometer arrangement. P-line cooling and R-line heating could be demonstrated, measured, and compared. The experiments were continued with CO mixed with N2 added in partial pressures from 9 to 200 Torr. It was found that an efficient collisional resonance energy transfer from CO to N2 existed which increased the cooling effects by one to two orders of magnitude over those in pure CO. Temperature reductions in the order of tens of degrees Kelvin were obtained by a single pulse in the core of the irradiated volume. These measurements followed predicted values rather closely, and it is expected that increase of pulse energies and durations will enhance the heat pump effects. The experiments confirm the feasibility of quasi-isentropic engines which convert laser power into work without the need for heat rejection. Of more immediate potential interest is the possibility of remotely powered heat pumps for cryogenic use, such applications are discussed to the extent possible at the present stage.

Garbuny, M.↗
NASA NTRS1987

Performance analysis of parallel branch and bound search with the hypercube architecture

With the availability of commercial parallel computers, researchers are examining new classes of problems which might benefit from parallel computing. This paper presents results of an investigation of the class of search intensive problems. The specific problem discussed is the Least-Cost Branch and Bound search method of deadline job scheduling. The object-oriented design methodology was used to map the problem into a parallel solution. While the initial design was good for a prototype, the best performance resulted from fine-tuning the algorithm for a specific computer. The experiments analyze the computation time, the speed up over a VAX 11/785, and the load balance of the problem when using loosely coupled multiprocessor system based on the hypercube architecture.

Mraz, Richard T.↗
NASA NTRS1998

Toward a More Robust Pruning Procedure for MLP Networks

Choosing a proper neural network architecture is a problem of great practical importance. Smaller models mean not only simpler designs but also lower variance for parameter estimation and network prediction. The widespread utilization of neural networks in modeling highlights an issue in human factors. The procedure of building neural models should find an appropriate level of model complexity in a more or less automatic fashion to make it less prone to human subjectivity. In this paper we present a Singular Value Decomposition based node elimination technique and enhanced implementation of the Optimal Brain Surgeon algorithm. Combining both methods creates a powerful pruning engine that can be used for tuning feedforward connectionist models. The performance of the proposed method is demonstrated by adjusting the structure of a multi-input multi-output model used to calibrate a six-component wind tunnel strain gage.

Stepniewski, Slawomir W.↗
NASA NTRS2004

Robust local search for spacecraft operations using adaptive noise

Randomization is a standard technique for improving the performance of local search algorithms for constraint satisfaction. However, it is well-known that local search algorithms are constraints satisfaction. However, it is well-known that local search algorithms are to the noise values selected. We investigate the use of an adaptive noise mechanism in an iterative repair-based planner/scheduler for spacecraft operations. Preliminary results indicate that adaptive noise makes the use of randomized repair moves safe and robust; that is, using adaptive noise makes it possible to consistently achieve, performance comparable with the best tuned noise setting without the need for manually tuning the noise parameter.

planning↗
NASA NTRS2005

Local oscillator chain for 1.55 to 1.75 THz with 100-(mu)W peak power

We report on the design and performance of a fix-tuned x2x 3x 3 frequency multiplier chain that covers 1.55-1.75 THz. The chain is nominally pumped with 100 mW at W-band. At 120 K the measured output power is larger than 4 (mu)W across the band with a peak power of 100 (mu) W at 1.665 THz. A similar chain operated at room temperature produced a peak power of 21 (mu)W. These power levels now make it possible to deploy multipixel heterodyne imaging arrays in this frequency range.

frequency multiplier↗
NASA NTRS2005

Attitude and position estimation on the Mars Exploration Rovers

NASA/JPL 's Mars Exploration Rovers acquire their attitude upon command and autonomously propagate their attitude and position. The rovers use accelerometers and images of the sun to acquire attitude, autonomously searching the sky for the sun with a pointable camera. To propagate the attitude and position the rovers use either accelerometer and gyro readings or gyro readings and wheel odometiy, depending on the nature of the movement ground operators are commanding. Where necessary, visual odometry is performed on images to fine tune the position updates, particularly in high slip environments. The capability also exists for visual odometry attitude updates. This paper describes the techniques used by the rovers to acquire and maintain attitude and position knowledge, the accuracy which is obtainable, and lessons learned after more than one year in operation.

attitude estimation↗
NASA NTRS2013

Low Profile Tunable Dipole Antenna Using BST Varactors for Biomedical Applications

In this paper a 2.4 GHz low profile (lambda/47) tunable dipole antenna is evaluated in the presence of a human core model (HCM) body phantom. The antenna uses a frequency selective surface (FSS) with interdigital barium strontium titanate (BST) varactor-tuned unit cells and its performance is compared to a similar low profile antenna that uses an FSS with semiconductor varactor diodes. The measured data of the antenna demonstrate tunability from 2.2 GHz to 2.55 GHz in free space and impedance match improvement in the presence of a HCM at different distances. This antenna has smaller size, lower cost and less weight compared to the semiconductor varactor diode counterpart.

frequency selective surfaces↗
NASA NTRS2013

Low Profile Tunable Dipole Antennas Using BST Varactors for Biomedical Applications

In this presentation a 2.4 GHz low profile (lambda45) tunable dipole antenna is evaluated in the presence of a human core model (HCM) body phantom. The antenna uses a frequency selective surface (FSS) with interdigital barium strontium titanate (BST) varactor-tuned unit cells and its performance is compared to a similar low profile antenna that uses an FSS with semiconductor varactor diodes. The measured data of the antenna demonstrate tunability from 2.2 GHz to 2.55 GHz in free space and impedance match improvement in the presence of a HCM at different distances. This antenna has smaller size, lower cost and less weight compared to the semiconductor varactor diode counterpart.

Tunable Dipole Antennas↗