Engineering Papers⌕ Search

SEARCH · Engineering Papers

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.

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

At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

I/O Bottleneck Detection and Tuning: Connecting the Dots using Interactive Log Analysis

Using parallel file systems efficiently is a tricky problem due to inter-dependencies among multiple layers of I/O software, including high-level I/O libraries (HDF5, netCDF, etc.), MPI-IO, POSIX, and file systems (GPFS, Lustre, etc.). Profiling tools such as Darshan collect traces to help understand the I/O performance behavior. However, there are significant gaps in analyzing the collected traces and then applying tuning options offered by various layers of I/O software. Seeking to connect the dots between I/O bottleneck detection and tuning, we propose DXT Explorer, an interactive log analysis tool. In this paper, we present a case study using our interactive log analysis tool to identify and apply various I/O optimizations. We report an evaluation of performance improvement achieved for four I/O kernels extracted from science applications.

Bez, Jean Luca↗
DOE OSTI2025

Upgrade to Fixed and Translating Scintillation-Based Loss Detector System in the Fermilab Drift Tube Linac

The closed-off structure of the Fermilab Drift Tube Linac precludes a robust array of instrumentation from directly monitoring the H- beam that is accelerated from 750 keV to 116 MeV. To improve beam tuning and op-erational assessment of Drift Tube Linac performance, scintillator-based loss monitors were previously installed along the exterior of the first two accelerating cavities to assess low energy beam losses. Here we present a recent upgrade to the loss monitor system, including significant improvements in analog signal processing to address baseline-interfering noise; digitization of the signals to enable regular operational use and tuning; and a new remote operation upgrade of the translating loss monitor with precise positioning of the loss monitor along its nine-foot track. Data from the fixed and translating de-tectors collected under varying beam conditions validate the utility of the upgrade.

Chen, Erin V. [Fermilab] (ORCID:0000000215467899)↗
DOE OSTI2026

Performance-Aligned LLMs for Generating Fast HPC Code

Optimizing scientific software is a difficult task because codebases are often large and complex, and performance can depend upon several factors including the algorithm, its implementation, and hardware among others. Causes of poor performance can originate from disparate sources and be difficult to diagnose. Recent years have seen a multitude of work that use large language models (LLMs) to assist in software development tasks. However, these tools are trained to model the distribution of code as text, and are not specifically designed to understand performance aspects of code. In this work, we introduce a reinforcement learning based methodology to align the outputs of code LLMs with performance. This allows us to build upon the current code modeling capabilities of LLMs and extend them to generate better performing code. Here, we demonstrate that our fine-tuned model improves the expected speedup of generated code over base models for a set of benchmark tasks from 0.9 to 1.6 for serial code and 1.9 to 4.5 for OpenMP parallel code.

Computer science↗
DOE OSTI2021

Measurement and analysis of GPU-accelerated applications with HPCToolkit

To address the challenge of performance analysis on the US DOE’s forthcoming exascale supercomputers, Rice University has been extending its HPCToolkit performance tools to support measurement and analysis of GPU-accelerated applications. To help developers understand the performance of accelerated applications as a whole, HPCToolkit’s measurement and analysis tools attribute metrics to calling contexts that span both CPUs and GPUs. To measure GPU-accelerated applications efficiently, HPCToolkit employs a novel wait-free data structure to coordinate monitoring and attribution of GPU performance. To help developers understand the performance of complex GPU code generated from high-level programming models, HPCToolkit constructs sophisticated approximations of call path profiles for GPU computations. To support fine-grained analysis and tuning, HPCToolkit uses PC sampling and instrumentation to measure and attribute GPU performance metrics to source lines, loops, and inlined code. To supplement fine-grained measurements, HPCToolkit can measure GPU kernel executions using hardware performance counters. To provide a view of how an execution evolves over time, HPCToolkit can collect, analyze, and visualize call path traces within and across nodes. Finally, on NVIDIA GPUs, HPCToolkit can derive and attribute a collection of useful performance metrics based on measurements using GPU PC samples. Here, we illustrate HPCToolkit’s new capabilities for analyzing GPU-accelerated applications with several codes developed as part of the Exascale Computing Project.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2016

Advanced Control Considerations for Turbofan Engine Design

This paper covers the application of a model-based engine control (MBEC) methodology featuring a self tuning on-board model for an aircraft turbofan engine simulation. The nonlinear engine model is capable of modeling realistic engine performance, allowing for a verification of the advanced control methodology over a wide range of operating points and life cycle conditions. The on-board model is a piece-wise linear model derived from the nonlinear engine model and updated using an optimal tuner Kalman Filter estimation routine, which enables the on-board model to self-tune to account for engine performance variations. MBEC is used here to show how advanced control architectures can improve efficiency during the design phase of a turbofan engine by reducing conservative operability margins. The operability margins that can be reduced, such as stall margin, can expand the engine design space and offer potential for efficiency improvements. Application of MBEC architecture to a nonlinear engine simulation is shown to reduce the thrust specific fuel consumption by approximately 1% over the baseline design, while maintaining safe operation of the engine across the flight envelope.

control↗
NASA NTRS2012

An Integrated Approach for Aircraft Engine Performance Estimation and Fault Diagnostics

A Kalman filter-based approach for integrated on-line aircraft engine performance estimation and gas path fault diagnostics is presented. This technique is specifically designed for underdetermined estimation problems where there are more unknown system parameters representing deterioration and faults than available sensor measurements. A previously developed methodology is applied to optimally design a Kalman filter to estimate a vector of tuning parameters, appropriately sized to enable estimation. The estimated tuning parameters can then be transformed into a larger vector of health parameters representing system performance deterioration and fault effects. The results of this study show that basing fault isolation decisions solely on the estimated health parameter vector does not provide ideal results. Furthermore, expanding the number of the health parameters to address additional gas path faults causes a decrease in the estimation accuracy of those health parameters representative of turbomachinery performance deterioration. However, improved fault isolation performance is demonstrated through direct analysis of the estimated tuning parameters produced by the Kalman filter. This was found to provide equivalent or superior accuracy compared to the conventional fault isolation approach based on the analysis of sensed engine outputs, while simplifying online implementation requirements. Results from the application of these techniques to an aircraft engine simulation are presented and discussed.

imon, Donald L.↗
DOE OSTI2022

Unique W-Shape Y6 isomer as effective solid additive for High-Performance PM6:Y6 polymer solar cells

The current top-performing polymer solar cell (PSC) systems are mostly based on PM6:Y6 host blend. To date, numerous materials have been explored as the third component for these systems to form ternary blends or as additives. Vitrification agents are a group of additives proved to be useful in affecting the morphology of organic semiconductors. Here to design a suitable vitrification agent for non-fullerene electron acceptor Y6, an isomer strategy was explored where the thienothiophene wings of the Y6 molecule were inversed to form a W-shaped Y6 isomer of i-Y6. It was found that i-Y6 crystallized poorly with a different packing style than Y6 and could blend well in amorphous phase of Y6. These properties enabled i-Y6 to finely tune the morphology of PM6:Y6 blend at low additive dosages. The power conversion efficiency (PCE) of PM6:Y6 based PSC was raised from 16.827% to 17.433% at a dosage of Y6:i-Y6 ratio of 24:1. This work demonstrated isomerization as a viable strategy for developing solid additives for high performance PSC blends and vitrification agents as effective additives for tuning morphology and improving performances of PSCs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Transformers and Long Short-Term Memory Transfer Learning for GenIV Reactor Temperature Time Series Forecasting

Automated monitoring of the coolant temperature can enable autonomous operation of generation IV reactors (GenIV), thus reducing their operating and maintenance costs. Automation can be accomplished with machine learning (ML) models trained on historical sensor data. However, the performance of ML usually depends on the availability of large amount of training data, which is difficult to obtain for GenIV, as this technology is still under development. We propose the use of transfer learning (TL), which involves utilizing knowledge across different domains, to compensate for this lack of training data. TL can be used to create pre-trained ML models with data from small-scale research facilities, which can then be fine-tuned to monitor GenIV reactors. In this work, we develop pre-trained Transformer and long short-term memory (LSTM) networks by training them on temperature measurements from thermal hydraulic flow loops operating with water and Galinstan fluids at room temperature at Argonne National Laboratory. The pre-trained models are then fine-tuned and re-trained with minimal additional data to perform predictions of the time series of high temperature measurements obtained from the Engineering Test Unit (ETU) at Kairos Power. The performance of the LSTM and Transformer networks is investigated by varying the size of the lookback window and forecast horizon. The results of this study show that LSTM networks have lower prediction errors than Transformers, but LSTM errors increase more rapidly with increasing lookback window size and forecast horizon compared to the Transformer errors.

LSTM↗
DOE OSTI2025

Surrogate-Based Autotuning for Randomized Sketching Algorithms in Regression Problems

Algorithms from Randomized Numerical Linear Algebra (RandNLA) are known to be effective in handling high-dimensional computational problems, providing high-quality empirical performance as well as strong probabilistic guarantees. However, their practical application is complicated by the fact that the user needs to set various algorithm-specific tuning parameters which are different from those used in traditional NLA. This paper demonstrates how a surrogate-based autotuning approach can be used to address fundamental problems of parameter selection in RandNLA algorithms. In particular, we provide a detailed investigation of surrogate-based autotuning for sketch-and-precondition (SAP)-based randomized least squares methods, which have been one of the great success stories in modern RandNLA. Empirical results show that our surrogate-based autotuning approach can achieve near-optimal performance with much less tuning cost than a random search (up to about 7.6x fewer trials of different parameter configurations). Moreover, while our experiments focus on least squares, our results demonstrate a general-purpose autotuning pipeline applicable to any kind of RandNLA algorithm.

Cho, Younghyun↗
DOE OSTI2021

Impact of electrode porosity architecture on electrochemical performances of 1 mm-thick LiFePO4 binder-free Li-ion electrodes fabricated by Spark Plasma Sintering

Thick electrodes with high active material loadings have been intensively studied over the last couple of decades in pursuit of achieving high energy density systems. To optimize and enhance the electrochemical performance of such electrodes, one has to control the pore morphology by, for example, varying the pore size and shape, and the level of porosity. In the present work, the fabrication of thick binder-free LiFePO4 (LFP) electrodes with two different pore sizes (12 and 20 mu m) and porosities (21 vol% and 44 vol%) using Spark Plasma Sintering (SPS) and templating approach is reported. The well-controlled porous architecture inside the thick electrodes is realized by fine-tuning experimental parameters. The impact of porosity architecture on electrochemical performance is quantified and correlated with the 3D tortuosity values determined from both micro-computed tomography and electrochemical impedance-based experimental methods. Based on the micro-computed tomography data analysis, estimated tortuosity values along X, Y, and Z axes reveal an anisotropic effect perpendicularly to the SPS compression axis (Z-direction). This is particularly profoundly observed in the samples with larger pores (20 mu m). The correlation between morphological properties and the rate capability performance is established indicating that the capacity loss happens mainly due to the Li-ion diffusion limitations.

Cathode material↗
DOE OSTI2024

Modulating Ce 3+ Sites in Ce-Zr Oxide Nanocatalysts through Protamine Biomineralization for Organophosphate Dephosphorylation

Catalytic is a crucial reaction for environmental detoxication of pesticides and neutralization of various molecules classified as chemical warfare agents. Herein, we report on a series of tunable Ce-Zr-based metal oxides, (Zr a Ce 1–a O x ) prepared using a facile biomineralization technique, as catalysts for organophosphates dephosphorylation. Synchrotron scattering and spectroscopy methods showcase that Zr a Ce 1–a O x catalysts are highly defective and exhibit an abundance of Ce 3+ sites that promote oxygen vacancies needed for enhanced dephosphorylation reactions. The catalytic performance was assessed using a model para-nitrophenyl phosphate reaction and showcases a strong dependence on Zr dopant concentration and subsequent tuning of the Ce 3+ /Ce 4+ ratio. Analysis of synchrotron datasets allowed structure-performance correlations between the Ce 3+ concentration and associated oxygen vacancies, the dephosphorylation rate constant, and Zr concentration to be established, confirming that Ce 3+ as active sites is positively correlated with the rate constant. We envision that similar biomineralization approaches can be used to fabricate Ce 3+ -rich Ce-Zr oxide for environmental application in dephosphorylation and other hydrolysis reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2016

GOES-16 On-Orbit Dual Isolation Performance Characterization Results

The Geostationary Operational Environmental Satellite-R Series (GOES-R) is the first of the next generation geostationary weather satellites. GOES-R successfully launched on November19, 2016 and renamed GOES-16 upon entering geostationary orbit. Subsequently, GOES-16post-launch testing began. This paper presents the GOES-16 Satellite Dynamic Interaction Characterization results for the Earth Pointed Platform (EPP) stowed, referred to as the Reaction Wheel Assembly (RWA) Isolation Only configuration, and deployed, referred to as the Dual Isolation configuration. GOES-R represents a quantum increase in Earth and solar weather observation capabilities, with 4 times the resolution, 5 times the observation rate, and 3 times the number of spectral bands for Earth observations. With the improved resolution, comes the instrument suites increased sensitivity to disturbances over a broad spectrum 0-512Hz. Sources of disturbance include reaction wheels, thruster firings for station keeping and momentum management, gimbal motion, and internal instrument disturbances. To minimize the impact of these disturbances, the baseline design included an EPP, a stiff optical bench to which the two nadir pointed instruments are collocated together with the Guidance Navigation Control (GNC) star trackers and Inertial Measurement Units (IMUs). The EPP is passively isolated from the spacecraft bus with Honeywell D-Strut isolators providing attenuation for frequencies above 5 Hz in all six degrees-of-freedom. To reduce the risk of wheel disturbances impacting performance, a secondary passive isolation system manufactured by Moog CSA Engineering was incorporated under each of the six 160 Nms reaction wheels, tuned to provide attenuation at frequencies above 50 Hz. Integrated wheel and isolator testing was performed on a Kistler table at NASA Goddard Space Flight Center. Pre-launch Satellite Dynamic Interaction Characterization high-fidelity simulations and ground testing were conducted to evaluate jitter performance for two cases: 1) deployed EPP and reaction wheel (Dual Isolation) and 2) EPP hard mounted (RWA Isolation Only) to the spacecraft. A comparison of pre-launch to post-launch Satellite Dynamic Interaction Characterization results are also presented in this paper.

Carter, Delano↗
DOE OSTI2023

Microscale Imaging of Thermal Conductivity Suppression at Grain Boundaries

Abstract Grain‐boundary engineering is an effective strategy to tune the thermal conductivity of materials, leading to improved performance in thermoelectric, thermal‐barrier coatings, and thermal management applications. Despite the central importance to thermal transport, a clear understanding of how grain boundaries modulate the microscale heat flow is missing, owing to the scarcity of local investigations. Here, thermal imaging of individual grain boundaries is demonstrated in thermoelectric SnTe via spatially resolved frequency‐domain thermoreflectance. Measurements with microscale resolution reveal local suppressions in thermal conductivity at grain boundaries. Also, the grain‐boundary thermal resistance – extracted by employing a Gibbs excess approach – is found to be correlated with the grain‐boundary misorientation angle. Extracting thermal properties, including thermal boundary resistances, from microscale imaging can provide comprehensive understanding of how microstructure affects heat transport, crucially impacting the materials design of high‐performance thermal‐management and energy‐conversion devices.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Comparative Analysis of Model Predictive Control and MPC-Informed Rule-Based Control for Thermal Storage Operation in Ultra-Low Temperature 4th Generation District Heating Networks

The integration of thermal storage and heat pumps in district heating networks (DHNs) can significantly enhance operational flexibility and energy efficiency; however, the practical deployment of advanced control strategies is often hindered by forecasting requirements and computational complexity. This study presents a comparative analysis of thermal storage control strategies in an ultra-low-temperature fourth-generation DHN, focusing on the development of a simplified rule-based control (RBC) explicitly informed by Model Predictive Control (MPC) behavior. The proposed methodology systematically analyzes the charging and discharging decisions of an MPC-controlled system under ideal forecasting conditions and extracts recurrent control patterns as a function of key system variables, including outdoor temperature, thermal demand, and electricity price. These patterns are translated into a set of structured time- and condition-based rules, resulting in an MPC-informed RBC that embeds predictive insights while preserving implementation simplicity and operational transparency. The approach is validated on a realistic mixed-use urban district in Denver, Colorado, USA, equipped with a centralized air-source heat pump, distributed water-to-water heat pumps, and a central thermal storage unit. Results show that the tuned RBC attains approximately 96% of ideal MPC economic performance (-27% of costs), preserves values of technical and environmental indicators (reduction only of 2-3%), and substantially reduces complexity. Sensitivity analyses further demonstrate the robustness of the RBC under varying operational conditions (i.e., ambient temperature, electricity price). Overall, the study demonstrates that MPC-informed rule-based control represents an effective trade-off between control performance and real-world applicability, enabling the integration of additional system components while maintaining simplicity, robustness, and ease of implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Parallel computing for power system climate resiliency: Solving a large-scale stochastic capacity expansion problem with mpi-sppy

Here we propose a nodal stochastic generation and transmission expansion planning model that incorporates the output from high-resolution global climate models through load and generation availability scenarios. We implement our model in Pyomo and perform computational studies on a realistically-sized test case of the California electric grid in a high performance computing environment. We propose model reformulations and algorithm tuning to efficiently solve this large problem using a variant of the Progressive Hedging Algorithm. We utilize the parallelization capabilities and overall versatility of mpi-sppy, exploiting its hub-and-spoke architecture to concurrently obtain inner and outer bounds on an optimal expansion plan. Initial results show that instances with 360 representative days on a system with over 8,000 buses can be solved to within 5% of optimality in under 4 h of wall clock time, a first step towards solving a large-scale power system expansion planning problem across a wide range of climate-informed operational scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Ion-gated carbon molecular sieve gas separation membranes

Membrane technology lies at the heart of many industrial gas separation processes and applications. Molecular sieving membranes that break the Robeson limit are desirable for energy-efficient gas separation. In this paper, we report a facile strategy of directly integrating ionic liquids (ILs) into porous membranes. Particularly, the ILs form an ultra-thin layer on the carbon molecular sieve (CMS) membranes rather than penetrating into the pores, acting as a smart gate for gas entry to boost the selectivity. The hybrid membrane exhibits CO 2 permeability >600 barrer and enhanced CO 2 /N 2 selectivity >50, which surpasses the Robeson limit and shows potential in CO 2 /N 2 separation process. Molecular dynamics simulations confirm the gating effect of the IL layer of molecular thickness. In conclusion, this work demonstrates a universal strategy to improve CMS membrane performance by creating an IL-membrane interface and tuning the ion-pore interaction.

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

Solvent Effects on Catalytic Reactions and Related Phenomena at Liquid-Solid Interfaces

Catalytic reactions involve the direct interaction of reactants, intermediates and products with the catalyst surface. We not only need to control the atomic structure and electronic properties of the active site, but also explore the multiple molecular interactions that occur beyond the active site; they play an essential role in altering the binding and reactivity of surface species. In liquid-phase catalysis, solvents provide additional degrees of freedom in the design of the catalytic process for desirable activity and selectivity. Here, the multi-faceted effects of solvents have a profound impact on the catalyst performance by restricting the mass transfer to the site, tuning the chemical potential of the surface species, competing for active sites, stabilizing the initial and transition states, and causing mechanistic changes by participating in the kinetically relevant elementary steps. This review addresses the different aspects of solvent effects, using a few prototype solid-liquid interfaces to illustrate these fundamental features. Recent experimental and computational studies that provide new insight at the molecular level are examined. Solvent structures in the proximity of the catalyst surface are discussed along with their influence in molecular binding and reaction at the solid-liquid interfaces. Furthermore, opportunities to alter such a solid-liquid interaction by tuning the wettability of the catalyst surfaces are explored.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗