Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “PERFORMANCE PREDICTION”

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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows

This report details recent progress for the ASCR funded project “Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows”. We refer to the project as IPPD/2, reflecting the 2017 renewal under expanded scope and partners In IPPD/2, we increased our research scope to include data motion. We are focusing on three major aspects: a) observe how data is generated, distributed, and used; b) analyze how data is (repeatedly) consumed with a focus both on repeated patterns and anomalies; and c) explore how to optimize data motion. This new work on data motion will augment and complement IPPD/2’s research that focused on the computational aspects of tasks. We leverage and extend our existing tools and demonstrate our work on the Belle II workflow suite as well as on workflows from NSLS-II. The highlights of our work are as follows: Provenance for Workflows: Provenance is used to provide information enabling quality control, re-run computational workflows, and reproduce results. IPPD/2 has been building a scalable provenance management system that enables the capture of provenance from the high-level workflow through all relevant system levels in one integrated environment. Leveraging this work, our recent efforts have included using provenance as an enabling technique. Workload characterization: Leveraging provenance and analysis, we characterize data movement within network, storage, and memory over a variety of workloads. This characterization enables an understanding by performance analysts and application developers of the range of behaviors that could be expected. Performance Prediction for Workflows: The goal of modeling distributed workflows is to understand performance bottlenecks and enable more intelligent task scheduling to optimize selected metrics of interest (e.g., task throughput or output data rate). IPPD/2 has utilized both analytical and AI/ML modeling methodologies for performance modeling. Advanced Scheduling and Fault Modeling for Workflows: Scheduling of large-scale scientific workflows on geographically distributed resources is a challenging problem. To improve workflow throughput, we combined novel scheduling algorithms with task predictions from performance modeling and fault modeling. Dynamically Alleviating Bottlenecks in Workflows: Exploiting our provenance, analysis, and modeling efforts, we have explored and developed several techniques for dynamically detecting and alleviating bottlenecks in data movement. In particular, we have spent considerable effort demonstrating our techniques on production-like workflow configurations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

A Machine Learning Based System Performance Prediction Model for Transportable FHR

A machine learning based system performance prediction model is currently created to support the development of autonomous control for small reactors, such as the Transportable Fluoride-salt-cooled High-temperature Reactor (TFHR), which has a 20 MWth compact core proposed by MIT for remote sites. The prediction model is constructed using support vector regression (SVR) with training data generated by RELAP5. A particle filtering framework is utilized to estimate and update model parameters with instrument measurements. Verifications of the prediction and filtering models have been carried out using TFHR reactivity insertion cases. Satisfactory performance in predicting the core behavior and in recognizing the inserted reactivity rate is concluded. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Python Performance Prediction Package for the PGET instrument: User Guide

The Performance Prediction Package (PPP) is a software suite for generating radiation transport results, or leveraging existing transport results, to produce synthetic emission tomography measurements of spent nuclear fuel assemblies. Such synthetic data can be used to mimic specific measurements, and to systematically investigate detection capabilities. Possible uses for the tool include: • Instrument design • Predictive evaluation of observables prior to an inspection visit • Post-measurement assessment of measured data • Evaluation of unusual cases – closed containers with anomalous objects • Sensitivity studies for planning inspections • Sensitivity studies for diversion scenarios • Evaluation of analysis methods A scenario is defined with an assembly type, burnup, and cooling time; as well as assembly rotation angle and position relative to the PGET instrument. Pin-to-pin activity variations or a burnup gradient may be present, but in an inspection scenario these are unlikely to be specified. The measurement will specify a measurement time and a particular detector for detector response, and an optional empirical correction can further adjust the model to better mimic the measurement. The goal is to detect missing pins, or burnup anomalies consistent with a pin substituted with a fresh pin after partial burnup.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Machine Learning-enabled Scalable Performance Prediction of Scientific Codes

Hardware architectures become increasingly complex as the compute capabilities grow to exascale. Here, we present the Analytical Memory Model with Pipelines (AMMP) of the Performance Prediction Toolkit (PPT). PPT-AMMP takes high-level source code and hardware architecture parameters as input and predicts runtime of that code on the target hardware platform, which is defined in the input parameters. PPT-AMMP transforms the code to an (architecture-independent) intermediate representation, then (i) analyzes the basic block structure of the code, (ii) processes architecture-independent virtual memory access patterns that it uses to build memory reuse distance distribution models for each basic block, and (iii) runs detailed basic-block level simulations to determine hardware pipeline usage. PPT-AMMP uses machine learning and regression techniques to build the prediction models based on small instances of the input code, then integrates into a higher-order discrete-event simulation model of PPT running on Simian PDES engine. We validate PPT-AMMP on four standard computational physics benchmarks and present a use case of hardware parameter sensitivity analysis to identify bottleneck hardware resources on different code inputs. We further extend PPT-AMMP to predict the performance of a scientific application code, namely, the radiation transport mini-app SNAP. To this end, we analyze multi-variate regression models that accurately predict the reuse profiles and the basic block counts. We validate predicted SNAP runtimes against actual measured times.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Performance Prediction of Irradiated TREAT Fuel

Poster Title: Performance Prediction of Irradiated TREAT Fuel The poster presents findings about the irradiation damage layer effects on the microscopic fuel temperature profile in TREAT. Microscopic fuel particles in TREAT are small enough that fission fragments travel out of the particles causing thermal property reduction in a shell around the fuel particle. The use of Magpie and MAMMOTH code is illustrated. The thermal temperature solution is simulated using EOS pulse power data. The reduction of microscopic fuel particle size reduced the irradiation damage due to fission fragments. The reduction of fission events is shown to be greater than the increase in fission fragments leaving the particle as size decreases. The temperature difference between the microscopic fuel particle and bulk graphite is shown to increase with irradiation as the damage layer grows.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1992

Overview on Basis and Use of Performance Prediction Methods

The lecture outlines the basic methods of component matching which are central to the prediction of gas turbine performance. Steady state prediction of off-design performance must be done at the beginning of an engine development program, to ensure that the engine can satisfy all the mission requirements. The matching techniques can be extended to predict transient performance, which is essential for controls development and to ensure good engine handling. The large amount of computation required demands the use of computer modelling and the role of modelling in the development program and the basic requirements for performance modelling are described.

H I H Saravanamuttoo↗
DOE OSTI2019

Bayesian inference and predictive performance of soil respiration models in the presence of model discrepancy

Bayesian inference of microbial soil respiration models is often based on the assumptions that the residuals are independent (i.e., no temporal or spatial correlation), identically distributed (i.e., Gaussian noise), and have constant variance (i.e., homoscedastic). In the presence of model discrepancy, as no model is perfect, this study shows that these assumptions are generally invalid in soil respiration modeling such that residuals have high temporal correlation, an increasing variance with increasing magnitude of CO 2 efflux, and non-Gaussian distribution. Relaxing these three assumptions stepwise results in eight data models. Data models are the basis of formulating likelihood functions of Bayesian inference. This study presents a systematic and comprehensive investigation of the impacts of data model selection on Bayesian inference and predictive performance. We use three mechanistic soil respiration models with different levels of model fidelity (i.e., model discrepancy) with respect to the number of carbon pools and the explicit representations of soil moisture controls on carbon degradation; therefore, we have different levels of model complexity with respect to the number of model parameters. The study shows that data models have substantial impacts on Bayesian inference and predictive performance of the soil respiration models such that the following points are true: (i) the level of complexity of the best model is generally justified by the cross-validation results for different data models; (ii) not accounting for heteroscedasticity and autocorrelation might not necessarily result in biased parameter estimates or predictions, but will definitely underestimate uncertainty; (iii) using a non-Gaussian data model improves the parameter estimates and the predictive performance; and (iv) accounting for autocorrelation only or joint inversion of correlation and heteroscedasticity can be problematic and requires special treatment. Although the conclusions of this study are empirical, the analysis may provide insights for selecting appropriate data models for soil respiration modeling.

58 GEOSCIENCES↗
DOE OSTI2020

Performance Prediction of Big Data Transfer Through Experimental Analysis and Machine Learning

Big data transfer in next-generation scientific ap- plications is now commonly carried out over connections with guaranteed bandwidth provisioned in High-performance Net- works (HPNs) through advance bandwidth reservation. To use HPN resources efficiently, provisioning agents need to carefully schedule data transfer requests and allocate appropriate band- widths. Such reserved bandwidths, if not fully utilized by the requesting user, could be simply wasted or cause extra overhead and complexity in management due to exclusive access. This calls for the capability of performance prediction to reserve bandwidth resources that match actual needs. Towards this goal, we employ machine learning algorithms to predict big data transfer perfor- mance based on extensive performance measurements, which are collected over a span of several years from a large number of data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated HPN testbeds. We first identify a comprehensive list of attributes involved in a typical big data transfer process, including end host system configurations, network connection properties, and control parameters of data transfer methods. We then conduct an in-depth exploratory analysis of their impacts on application- level throughput, which provides insights into big data transfer performance and motivates the use of machine learning. We also investigate the applicability of machine learning algorithms and derive their general performance bounds for performance prediction of big data transfer in HPNs. Experimental results show that, with appropriate data preprocessing, the proposed machine learning-based approach achieves 95% or higher pre- diction accuracy in up to 90% of the cases with very noisy real-life performance measurements.

Yun, Daqing↗
NASA NTRSDate not supplied

Rotor Performance Predictions of a Next Generation Mars Science Helicopter

Since Ingenuity took flight and proved aerial capabilities on Mars, the horizon for rotorcraft on the Red Planet has only expanded. One proposed future Martian rotorcraft is the Mars Science Helicopter (MSH). The MSH is a hexacopter capable of carrying scientific payloads. As a part of its joint development by NASA Ames Research Center (ARC) and the Jet Propulsion Laboratory (JPL), key MSH rotor components will be tested inside the in-development Reduced Atmospheric Pressure Testing Of Rotors (RAPTOR) wind tunnel in the Planetary Aeolian Laboratory (PAL) at NASA ARC. In preparation for the test, the Comprehensive Hierarchical Aeromechanics Rotorcraft Model (CHARM) analysis was utilized to predict the aerodynamic performance for one MSH hexacopter isolated rotor. This paper provides an overview of the planned experiments involving the MSH reference blades, as well as CHARM pretest predictions of rotor performance in hover and forward flight. Additionally, the interference effects of wind tunnel walls on rotor performance will be evaluated to inform the test matrix of the upcoming test in the RAPTOR tunnel.

MHS↗
DOE OSTI2020

Modular performance prediction for scientific workflows using Machine Learning

Scientific workflows provide an opportunity for declarative computational experiment design in an intuitive and efficient way. A distributed workflow is typically executed on a variety of resources, and it uses a variety of computational algorithms or tools to achieve the desired outcomes. Such a variety imposes additional complexity in scheduling these workflows on large scale computers. As computation becomes more distributed, insights into expected workload that a workflow presents become critical for effective resource allocation. In this paper, we present a modular framework that leverages Machine Learning for creating precise performance predictions of a workflow. The central idea is to partition a workflow in such a way that makes the task of forecasting each atomic unit manageable and gives us a way to combine the individual predictions efficiently. We recognize a combination of an executable and a specific physical resource as a single module. This gives us a handle to characterize workload and machine power as a single unit of prediction. Overall, our modular technique of creating atomic modules and deployment of longest-path approach to estimate workflow performance, allows the framework to adapt to highly complex nested directed acyclic workflows and scale to new scenarios, since it does not make assumptions of underlying workflow structure. We present performance estimation results of independent workflow modules executed on the XSEDE SDSC Comet cluster using various Machine Learning algorithms. The results provide insights into the behavior and effectiveness of different algorithms in the context of scientific workflow performance prediction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Impact of soil model complexity on the long-term thermal performance prediction of a new shallow bore ground heat exchanger

Shallow bore ground heat exchangers have been investigated in recent years because they have the potential to reduce the initial cost of the ground source heat pump system. Correctly modeling the transient heat transfer between the ground heat exchanger and the surrounding soil is essential for predicting a ground heat exchanger's performance. Simplification of the soil model could increase computation speed but sacrifice accuracy. This study investigates the impact of the soil model complexity on the annual performance prediction of a new shallow bore ground heat exchanger, the underground thermal battery (UTB). A simple 1D soil model and a more detailed 2D soil model were implemented, and they were validated against a 3D soil model. Overall, the resulting predictions of the UTB’s response from the 1D and 2D models to a given thermal load in different climates were compared. The results show that the root mean square differences between the hourly temperatures of the UTB during a year predicted using the two soil models range from 1.17°C to 3.39°C. As a result, the difference in the annual power consumption of the heat pump was between 0.7% and 3.4%. Furthermore, the dimensions of the UTB affected its performance, and a longer UTB was less sensitive to the soil models.

42 ENGINEERING↗
DOE OSTI2019

Comparing wall performance predicted from hygrothermal simulations to wall performance measured from environmental chamber experiments

To effectively mitigate prevailing perceptions of potential moisture risks in high-R wall assemblies, a comprehensive moisture durability assessment that incorporates BA team field evaluations, lab tests and simulations has been completed over the last couple years. In FY18, three stick-built walls were succumbed to typical Chicago weather in ORNL’s Heat, Air and Moisture chamber. The measured temperature, relative humidity and moisture content within these walls were compared to WUFI hygrothermal simulation results. In FY19, similar experiments and comparison to WUFI results were completed with two walls, a Structural Insulated Panel (SIP) based wall and a Concrete Masonry Unit (CMU) based wall. The following tasks were completed for these two walls: 1. Measure air permeance of each individual wall using ASTM E283 Standard Test Method. 2.Test walls in diffusion scenario as only moisture transport mechanism and compare measured transient temperature and relative humidity of select wall components to WUFI model results. Chicago summer and weather will be investigated. 3. Test walls in wetting event scenario and compare measured transient temperature and relative humidity of select wall components with Chicago summer weather to WUFI model results. 4. Test walls in diffusion/advection scenario. In addition to controlling outdoor chamber to match real outdoor weather, a pressure differential across the wall will be varied. Measured transient temperature and relative humidity of select wall components will be compared to WUFI model results. 5. Test walls in diffusion/advection with solar heating of cladding. In addition to the variables above, IR lamps will be used to heat the cladding on a diurnal cycle. Measured transient OSB MC and temperature will be compared to WUFI model results. This report documents the experimental method, results and comparison of the measured experimental hygrothermal performance of the two walls to simulations results.

42 ENGINEERING↗
NASA NTRS1988

An analysis for high speed propeller-nacelle aerodynamic performance prediction. Volume 2: User's manual

A user's manual for the computer program developed for the prediction of propeller-nacelle aerodynamic performance reported in, An Analysis for High Speed Propeller-Nacelle Aerodynamic Performance Prediction: Volume 1 -- Theory and Application, is presented. The manual describes the computer program mode of operation requirements, input structure, input data requirements and the program output. In addition, it provides the user with documentation of the internal program structure and the software used in the computer program as it relates to the theory presented in Volume 1. Sample input data setups are provided along with selected printout of the program output for one of the sample setups.

Egolf, T. Alan↗
NASA NTRS1992

Steady and Transient Performance Prediction of Gas Turbine Engines

This Lecture Series covers topics of aero-thermodynamic performance prediction methods for gas turbine engines with respect to steady and transient operation. This includes advanced cycle calculation methods, also taking into account variable cycle engine types. A very important objective is the consideration of installation effect, i.e., Reynolds number and inlet distortions, as well as advanced control concepts for increasing engine surge margins. In addition to these topics, individual papers include practical considerations in developing the engine cycle, dynamic simulation, inlet distortion effects in aircraft propulsion system integration, “smart” engines, performance and heal monitoring models. This Lecture Series, sponsored by the Propulsion and Energetics Panel of AGARD and the Consultant and Exchange Programme of AGARD presented on 27th -28th May 1992 in Cambridge, MA, United States, 9th-10th June 1992 in Neubiberg, Germany and 11th-12th June 1992 in Chàtillon/Bagneux (near Paris), France

Aircraft engines↗
NASA NTRS1988

An analysis for high speed propeller-nacelle aerodynamic performance prediction. Volume 1: Theory and application

A computer program, the Propeller Nacelle Aerodynamic Performance Prediction Analysis (PANPER), was developed for the prediction and analysis of the performance and airflow of propeller-nacelle configurations operating over a forward speed range inclusive of high speed flight typical of recent propfan designs. A propeller lifting line, wake program was combined with a compressible, viscous center body interaction program, originally developed for diffusers, to compute the propeller-nacelle flow field, blade loading distribution, propeller performance, and the nacelle forebody pressure and viscous drag distributions. The computer analysis is applicable to single and coaxial counterrotating propellers. The blade geometries can include spanwise variations in sweep, droop, taper, thickness, and airfoil section type. In the coaxial mode of operation the analysis can treat both equal and unequal blade number and rotational speeds on the propeller disks. The nacelle portion of the analysis can treat both free air and tunnel wall configurations including wall bleed. The analysis was applied to many different sets of flight conditions using selected aerodynamic modeling options. The influence of different propeller nacelle-tunnel wall configurations was studied. Comparisons with available test data for both single and coaxial propeller configurations are presented along with a discussion of the results.

Egolf, T. Alan↗
DOE OSTI2019

Wind Plant Performance Prediction (WP3) Benchmark Introduction

This presentation outlines the results from the Wind Plant Performance Prediction (WP3) Benchmarking group, which is concentrated on predicted versus actual wind plant performance, honing in on the variables that can cause over-estimation of power generation and subsequent 'under performance.'

17 WIND ENERGY↗