Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Performance Prediction Package”

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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

WHONDRS-GUI: a web application for global survey of surface water metabolites

Background The Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) is a consortium that aims to understand complex hydrologic, biogeochemical, and microbial connections within river corridors experiencing perturbations such as dam operations, floods, and droughts. For one ongoing WHONDRS sampling campaign, surface water metabolite and microbiome samples are collected through a global survey to generate knowledge across diverse river corridors. Metabolomics analysis and a suite of geochemical analyses have been performed for collected samples through the Environmental Molecular Sciences Laboratory (EMSL). The obtained knowledge and data package inform mechanistic and data-driven models to enhance predictions of outcomes of hydrologic perturbations and watershed function, one of the most critical components in model-data integration. To support efforts of the multi-domain integration and make the ever-growing data package more accessible for researchers across the world, a Shiny/R Graphical User Interface (GUI) called WHONDRS-GUI was created. Results The web application can be run on any modern web browser without any programming or operational system requirements, thus providing an open, well-structured, discoverable dataset for WHONDRS. Together with a context-aware dynamic user interface, the WHONDRS-GUI has functionality for searching, compiling, integrating, visualizing and exporting different data types that can easily be used by the community. The web application and data package are available at https://data.ess-dive.lbl.gov/view/doi:10.15485/1484811 , which enables users to simultaneously obtain access to the data and code and to subsequently run the web app locally. The WHONDRS-GUI is also available for online use at Shiny Server ( https://xmlin.shinyapps.io/whondrs/ ).

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2004

MGGPOD: a Monte Carlo Suite for Modeling Instrumental Line and Continuum Backgrounds in Gamma-Ray Astronomy

Intense and complex instrumental backgrounds, against which the much smaller signals from celestial sources have to be discerned, are a notorious problem for low and intermediate energy gamma-ray astronomy (approximately 50 keV - 10 MeV). Therefore a detailed qualitative and quantitative understanding of instrumental line and continuum backgrounds is crucial for most stages of gamma-ray astronomy missions, ranging from the design and development of new instrumentation through performance prediction to data reduction. We have developed MGGPOD, a user-friendly suite of Monte Carlo codes built around the widely used GEANT (Version 3.21) package, to simulate ab initio the physical processes relevant for the production of instrumental backgrounds. These include the build-up and delayed decay of radioactive isotopes as well as the prompt de-excitation of excited nuclei, both of which give rise to a plethora of instrumental gamma-ray background lines in addition t o continuum backgrounds. The MGGPOD package and documentation are publicly available for download. We demonstrate the capabilities of the MGGPOD suite by modeling high resolution gamma-ray spectra recorded by the Transient Gamma-Ray Spectrometer (TGRS) on board Wind during 1995. The TGRS is a Ge spectrometer operating in the 40 keV to 8 MeV range. Due to its fine energy resolution, these spectra reveal the complex instrumental background in formidable detail, particularly the many prompt and delayed gamma-ray lines. We evaluate the successes and failures of the MGGPOD package in reproducing TGRS data, and provide identifications for the numerous instrumental lines.

Weidenspointner, G.↗
DOE OSTI2016

Dose Rate Investigation for Cobalt Isotope Rods Retrieval Campaign

GE-Hitachi (GEH) partnered with Exelon for a first of a kind program to generate cobalt-60 (Co-60) sources at Clinton Power Station (CPS). The cobalt targets are placed within the fuel bundle, double encapsulated in metal rods, and irradiated in the Boiling Water Reactor for activation. After the bundle is discharged, the Co-60 sources are harvested for medical and industrial applications. In order to ensure safe shipment of the Co-60 sources, shielding evaluations are required. As required by 10 CFR 71, a licensing basis evaluation was performed and submitted to the Nuclear Regulatory Commission (NRC) and approved as a Letter Authorization. This 10 CFR 71 evaluation utilizes bounding design inputs to demonstrate compliance with the regulatory limits. The retrieval campaign of the Co-60 rods involves disassembly of the bundle/rods, loading into a material basket, and then loading the material basket into GEHitachi's Model 2000 cask that is part of the Type B Transportation Package. As part of conservative decision making, CPS wanted confidence that measurements taken at various locations around the material basket loaded with Co-60 rods are consistent or bounded by the dose rate calculations performed by GEH. This would ensure that after all loading operations are completed, and the Model 2000 package is in the shipping configuration (i.e., cask drained and secured within the Model 2000 overpack), the dose rates would meet 10 CFR 71 shipping requirements. In addition, dose rate predictions were used to develop an ALARA plan at CPS to ensure radiological safety of the workers. To support CPS, a second, more realistic shielding evaluation was performed to verify the as planned loading. (authors)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

Bond order predictions using deep neural networks

Machine learning is an extremely powerful tool for the modern theoretical chemist since it provides a method for bypassing costly algorithms for solving the Schrödinger equation. Already, it has proven able to infer molecular and atomic properties such as charges, enthalpies, dipoles, excited state energies, and others. Most of these machine learning algorithms proceed by inferring properties of individual atoms, even breaking down total molecular energy into individual atomic contributions. In this paper, we introduce a modified version of the Hierarchically Interacting Particle Neural Network (HIP-NN) capable of making predictions on the bonds between atoms rather than on the atoms themselves. We train the modified HIP-NN to infer bond orders for a large number of small organic molecules as computed via the Natural Bond Orbital package. We demonstrate that the trained model is extensible to molecules much larger than those in the training set by studying its performance on the COMP6 dataset. This method has applications in cheminformatics and force field parameterization and opens a promising future for machine learning models to predict other quantities that are defined between atoms such as density matrix elements, Hamiltonian parameters, and molecular reactivities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1971

Pressure control and analysis report: Hydrogen Thermal Test Article (HTTA)

Tasks accomplished during the HTTA Program study period included: (1) performance of a literature review to provide system guidelines; (2) development of analytical procedures needed to predict system performance; (3) design and analysis of the HTTA pressurization system considering (a) future utilization of results in the design of a spacecraft maneuvering system propellant package, (b) ease of control and operation, (c) system safety, and (d) hardware cost; and (4) making conclusions and recommendations for systems design.

Source record↗
NASA NTRS1992

Development of a Raman lidar simulation tool

Raman Lidar is a useful and powerful tool for remote probing of the atmosphere. With Raman Lidars, one can accurately determine the identity and concentration of a particular molecular specie present in the atmosphere. We present the results from a program to develop a simulation capability of Raman Lidar systems for the remote detection of atmospheric gases and/or air polluting hydrocarbons. Our model, which integrates remote Raman spectroscopy with SPARTA's BACKSCAT atmospheric lidar simulation package, permits accurate determination of the performance of a Raman Lidar system. The accuracy with which our model operates is due to the accurate calculation, at any given excitation wavelength, of the differential scattering cross section for the molecular specie under investigation. We show excellent correlation of our calculated cross section data with experimental data from the published literature. In addition, the use of our BACKSCAT package, which provides a user friendly environment to define the operating conditions, provides an accurate calculation of the atmospheric extinction at both the excitation and Raman shifted wavelengths. Our code can be used to accurately predict the performance of a Raman Lidar system, the concentration and identification of a specie in the atmosphere, or the feasibility of making Raman measurements.

Grasso, R. J.↗
DOE OSTI2023

pyGWBSE: a high throughput workflow package for GW-BSE calculations

Abstract We develop an open-source python workflow package, py GWBSE to perform automated first-principles calculations within the GW-BSE (Bethe-Salpeter) framework. GW-BSE is a many body perturbation theory based approach to explore the quasiparticle (QP) and excitonic properties of materials. GW approximation accurately predicts bandgaps of materials by overcoming the bandgap underestimation issue of the more widely used density functional theory (DFT). BSE formalism produces absorption spectra directly comparable with experimental observations. py GWBSE package achieves complete automation of the entire multi-step GW-BSE computation, including the convergence tests of several parameters that are crucial for the accuracy of these calculations. py GWBSE is integrated with Wannier90 , to generate QP bandstructures, interpolated using the maximally-localized wannier functions. py GWBSE also enables the automated creation of databases of metadata and data, including QP and excitonic properties, which can be extremely useful for future material discovery studies in the field of ultra-wide bandgap semiconductors, electronics, photovoltaics, and photocatalysis.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Fractional occupation numbers and self-interaction correction-scaling methods with the Fermi-Löwdin orbital self-interaction correction approach

In this work, we present a new assessment of the Fermi-Löwdin orbital self-interaction correction (FLO-SIC) approach with an emphasis on its performance for predicting energies as a function of fractional occupation numbers (FONs) for various multielectron systems. Our approach is implemented in the massively parallelized NWChem quantum chemistry software package and has been benchmarked on the prediction of total energies, atomization energies, and ionization potentials of small molecules and relatively large aromatic systems. Within our study, we also derive an alternate expression for the FLO-SIC energy gradient expressed in terms of gradients of the Fermi-orbital eigenvalues and revisit how the FLO-SIC methodology can be seen as a constrained unitary transformation of the canonical Kohn–Sham orbitals. Finally, we conclude with calculations of energies as a function of FONs using various SIC-scaling methods to test the limits of the FLO-SIC formalism on a variety of multielectron systems. We find that these relatively simple scaling methods do improve the prediction of total energies of atomic systems as well as enhance the accuracy of energies as a function of FONs for other multielectron chemical species.

fractional occupation number↗
NASA NTRS1979

An analytical study of electric vehicle handling dynamics

Hypothetical electric vehicle configurations were studied by applying available analytical methods. Elementary linearized models were used in addition to a highly sophisticated vehicle dynamics computer simulation technique. Physical properties of specific EV's were defined for various battery and powertrain packaging approaches applied to a range of weight distribution and inertial properties which characterize a generic class of EV's. Computer simulations of structured maneuvers were performed for predicting handling qualities in the normal driving range and during various extreme conditions related to accident avoidance. Results indicate that an EV with forward weight bias will possess handling qualities superior to a comparable EV that is rear-heavy or equally balanced. The importance of properly matching tires, suspension systems, and brake system front/rear torque proportioning to a given EV configuration during the design stage is demonstrated.

Greene, J. E.↗
DOE OSTI2026

Challenges and Opportunities for HVAC + Phase Change Material Thermal Energy Storage in Buildings

Phase-change material (PCM) thermal-energy storage (TES) integrated with HVAC and domestic hot water (DHW) can shift a large share of building thermal loads. By flattening and shifting loads, PCM TES reduces peak electricity use, eases stress on local and grid infrastructure, and lowers costs. It can also defer costly upgrades to service panels, distribution, and transmission. Higher energy density relative to chilled or hot water storage makes PCM TES practical for small, space limited, and retrofit projects, while packaged HVAC-integrated systems expand cost-effective load shifting to commercial buildings that previously lacked options. Despite this promise, deployment faces barriers. This paper presents challenges, opportunities, and lessons learned from lab and field integrations of PCM TES with packaged HVAC systems. Key challenges include misalignment between default heat pump controls tuned for direct-to-load operation and TES charge/discharge objectives, PCM properties that diverge from manufacturer claims and evolve with cycling, and high integration and deployment costs with off-the-shelf products. These studies underscore the need for factory built, integrated systems that arrive as prepackaged modules with proven controls, reducing design and installation effort and delivering predictable performance across projects. The paper outlines steps to enable viable and scalable PCM TES HVAC and DHW systems, including 1) validated methods that can be used to characterize PCM properties and TES system performance, 2) supervisory controls that optimize charge/discharge scheduling, and 3) factory integrated packaging that eliminates bespoke field engineering.

Dutton, Spencer↗
DOE OSTI2017

A Blocked Linear Method for Optimizing Large Parameter Sets in Variational Monte Carlo

We present a modification to variational Monte Carlo’s linear method optimization scheme that addresses a critical memory bottleneck while maintaining compatibility with both the traditional ground state variational principle and our recently-introduced variational principle for excited states. For wave function ansatzes with tens of thousands of variables, our modification reduces the required memory per parallel process from tens of gigabytes to hundreds of megabytes, making the methodology a much better fit for modern supercomputer architectures in which data communication and per-process memory consumption are primary concerns. We verify the efficacy of the new optimization scheme in small molecule tests involving both the Hilbert space Jastrow antisymmetric geminal power ansatz and real space multi-Slater Jastrow expansions. Satisfied with its performance, we have added the optimizer to the QMCPACK software package, with which we demonstrate on a hydrogen ring a prototype approach for making systematically convergent, non-perturbative predictions of Mott-insulators’ optical band gaps.

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

IDAES-PSE 2.4.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications.. Deprecations • Convergence Analysis tool (idaes/core/util/convergence): deprecated in favor of new Parameter Sweep tools. To be removed in v3.0.0. New Beta Capabilities • Parameter Sweep Tool (idaes.core.util.parameter_sweep) o A new API for defining and performing parameter sweep studies on IDAES models has been developed • Diagnostics Tools (idaes.core.util.model_diagnostics) o New methods for identifying duplicate variables and constraints have been added to the diagnostics toolbox o New tools for detecting ill conditioning in Jacobians have been developed and are available in the model_diagnostics module. These provide alternatives to the existing DegeneracyHunter toolbox, and will eventually be merged with this capability, but initial working versions have been provided as beta capabilities for interested users o IpoptConvergenceAnalysis (replaces deprecated Convergence Analysis tool):  A new tool for performing convergence analysis studies that leverages the new Parameter Sweep tools has been developed. This tool allows users to define the input parameters to their model and sampling methods for these (leveraging Pysmo's sampling tools) and to then solve their model across the sampled domains and return a summary of the solver performance (IPOPT only) Improved Models • Thickener model (idaes.models.unit_models.solid_liquid.thickener) o Improved model to include predictive correlations for unit sizing based on settling velocity measurements (steady-state only) • Modular Property Packages o Added general support for calculating critical properties of mixtures using defined Equation of State modules. New API defined for Equation of State modules in order to define the necessary constraints for calculating critical properties (most EoS modules DO NOT support calculation of critical properties (yet)) o Added new methods to Cubic Equation of State module to support calculation of critical properties

DiagnosticsToolbox↗
DOE OSTI2020

CyDER: A Cyber Physical Co-simulation Platform for Distributed Energy Resources in Smartgrids

The CyDER project aimed at developing an open-source, modular and scalable co-simulation platform for power grids with large shares of Distributed Energy Resources (DERs). The project partners are the Lawrence Berkeley National Lab (LBNL), Lawrence Livermore National Lab (LLNL), PG&E, SolarCity, and ChargePoint. The prime recipient is LBNL; SolarCity and ChargePoint were partners for the project’s first two years. Increased DER integration introduces a number of challenges in power grid operation including a more dynamic interaction between the transmission grid and distribution grids, and increased modeling complexity. Although specialized software exists to precisely model different components of the power system, it is far from trivial to integrate all various models and perform a holistic simulation. Instead of replicating all models in a common simulation program, a commonly accepted approach to tackle this model diversity is to couple third-party simulators and models through a co-simulation platform that coordinates information exchange among the various components. Following this line of research, this project’s objective was to develop a co-simulation platform based on a widely accepted industrial standard called Functional Mockup Interface (FMI). Within this process, the project developed models compliant with the FMI standard, called Functional Mockup Units (FMUs), and used them to perform various operational and planning power system analyses. Relying and building upon an industrial standard is the main differentiation of this project compared with previous or parallel efforts in the co-simulation area. Particular emphasis was put on delivering software utilities to facilitate setting up and running co-simulations by end-users. Furthermore, a strong aspect of this project is demonstrating that co-simulation techniques can be used to perform Hardware-in-the-Loop (HIL) simulations that couple software components (e.g., simulated models) with hardware components (e.g., real devices such PV systems and batteries). The long-term goal of CyDER project is to help establish FMI as a powerful standard for co-simulation and promote adoption by electric utilities and other interested stakeholders. The main accomplishments of the project include the development of several FMUs including distribution and transmission grid models, PV inverters with Volt/Var/Watt controllers, batteries, and predictive optimal controllers. Additionally, a unique software package was developed, called SimulatorToFMU, which is capable of exporting any Python-driven simulator or Python script as an FMU. This is an important contribution towards establishing FMI as one of the main co-simulation standards, because more and more third-party programs for sub-system modeling and simulation are delivered with Python APIs. The CyDER platform was used to perform PV hosting capacity analyses in real utility feeders with and without smart inverter controls, battery storage, and EV charging. Smart inverter controls include conventional Volt/Var/Watt controls for reactive power support and active power curtailment, but also predictive controls that optimize the charging and discharging profile of the battery connected on the DC side in order to minimize the customer’s economic benefit. Finally, an important result of this project is delivering an experimental setup that consists of residential-scale PV inverters with battery storage, a real-time grid simulator with an ideal voltage source as grid emulator, and micro Phasor Measurement Units (PMUs). All these components and additional software modules are coupled to one another using the FMI standard and can be co-simulated with the CyDER platform.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Additive Manufacturing with Cellulose-Based Composites: Materials, Modeling, and Applications

Recent advances in large-scale additive manufacturing (AM) with polymer-based composites have enabled efficient production of high-performance materials. Cellulose nanomaterials (CNMs) have emerged as bio-based feedstocks due to their exceptional strength and sustainability. However, challenges such as hornification and poor dispersion in polymer matrices still limit large-scale CNM–polymer composite manufacturing, requiring novel strategies. Here, this review outlines an approach starting with atomic-level simulations to link molecular composition to key parameters like bulk density, viscosity, and modulus. These simulations provide data for finite element analysis (FEA), which informs large-scale experiments and reduces the need for extensive trials. The strategy explores how atomic interactions impact the morphology, adhesion, and mechanical properties of CNM-based composites in AM processes. The review also discusses current developments in AM, along with predictions of mechanical and thermal properties for structural applications, packaging, flexible electronics, and hydrogel scaffolds. By integrating experimental findings with molecular dynamics (MD) simulations and finite element modeling (FEM), valuable insights for material design, process optimization, and performance enhancement in CNM-based AM are provided to address ongoing challenges.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Technical Development for S-CO 2 Advanced Energy Conversion

This report is divided into four parts. First part of the report describes the methods used to measure and model the flow of supercritical carbon dioxide (S-CO 2 ) through annuli and straight-through labyrinth seals. The effects of shaft eccentricity in small diameter annuli were observed for length-to-hydraulic diameter (L/D) ratios of 6, 12, 143, and 235. Flow rates through tooth-cavity labyrinth seals were measured for inlet pressures of 7.7, 10, and 11 MPa with corresponding inlet densities of 325, 475, and 630 kg/m 3 . Various leakage models were compared to this result to describe their applicability in supercritical carbon dioxide applications. Flow rate measurements were made varying tooth number for labyrinth seals of same total length. Second part of the report describes the computational study performed to understand the leakage through the labyrinth seals using Open source CFD package OpenFOAM. Fluid Property Interpolation Tables (FIT) program was implemented in OpenFOAM to accurately model the properties of CO2 required to solve the governing equations. To predict the flow behavior in the two phase dome Homogeneous Equilibrium Model (HEM) is assumed to be valid. Experimental results for plain orifice (L/D ~ 5) were used to show the capabilities of the FIT model implemented in OpenFOAM. Error analysis indicated that OpenFOAM is capable of predicting experimental data within ±10% error with the majority of data close to ±5% error. Following the validation of computational model, effects of geometrical parameters and operating conditions are isolated from each other and a parametric study was performed in two parts to understand their effects on leakage flow. Third part of the report provides the details of the constructed heat exchanger test facility and presents the experimental results obtained to investigate the effects of buoyancy on heat transfer characteristics of Supercritical carbon dioxide in heating mode. Turbulent flows with Reynolds numbers up to 60,000, at operating pressures of 7.5, 8.1, and 10.2 MPa were tested in a round tube. Local heat transfer coefficients were obtained from measured wall temperatures over a large set of experimental parameters that varied inlet temperature from 20 °C to 55 °C,mass flux from 150 to 350 kg/m 2 s, and a maximum heat flux of 65 KW/m 2 . Horizontal, upward and downward flows were tested to investigate the unusual heat-transfer characteristics to the effect of buoyancy and flow acceleration caused by large variation in density. Final part of this report presents the simplified analysis performed to investigate the possibility of using wet cooling tower option to reject heat from the supercritical carbon dioxide Brayton cycle power convertor for AFR-100 and ABR-1000 plants. A code was developed to estimate the tower dimensions, power and water consumption, and to perform economic analysis. The code developed was verified by comparing the calculations to a vendor quote. The effect of ambient air and water conditions on the sizing and construction of the cooling tower as well as the cooler is studied. Finally, a cost-based optimization technique is used to estimate the optimum water conditions which will improve the plant economics.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Fusion of Geothermal and InSAR Data with Machine Learning for Enhanced Deformation Forecasting at the Geysers

The Geysers geothermal field in California is experiencing land subsidence due to the seismic and geothermal activities taking place. This poses a risk not only to the underlying infrastructure but also to the groundwater level which would reduce the water availability for the local community. Because of this, it is crucial to monitor and assess the surface deformation occurring and adjust geothermal operations accordingly. In this study, we examine the correlation between the geothermal injection and production rates as well as the seismic activity in the area, and we show the high correlation between the injection rate and the number of earthquakes. This motivates the use of this data in a machine learning model that would predict future deformation maps. First, we build a model that uses interferometric synthetic aperture radar (InSAR) images that have been processed and turned into a deformation time series using LiCSBAS, an open-source InSAR time series package, and evaluate the performance against a linear baseline model. The model includes both convolutional neural network (CNN) layers as well as long short-term memory (LSTM) layers and is able to improve upon the baseline model based on a mean squared error metric. Then, after getting preprocessed, we incorporate the geothermal data by adding them as additional inputs to the model. This new model was able to outperform both the baseline and the previous version of the model that uses only InSAR data, motivating the use of machine learning models as well as geothermal data in assessing and predicting future deformation at The Geysers as part of hazard mitigation models which would then be used as fundamental tools for informed decision making when it comes to adjusting geothermal operations.

Yazbeck, Joe (ORCID:0000000302235260)↗
NASA NTRS2001

The AIRES Optical Design

AIRES (Airborne InfraRed Echelle Spectrometer) is the facility spectrometer for SOFIA (Stratospheric Observatory For Infrared Astronomy). AIRES is a long-slit (approximately 160 in) spectrometer designed to cover the 17 to 210-micron range with good sensitivity using three spectroscopic arrays. Initially, only the 30-130 micron, mid-wavelength array will be available. The instrument has a cryogenic K-mirror to perform field rotation and a slit-viewing camera (lambda < 28 microns, FOV = 160 in diameter) to image source morphology and verify telescope pointing. AIRES employs a large echelle grating to achieve a spectral resolving power (lambda/delta lambda) of approximately 1.0 x 10(exp 6)/lambda (sub mu), where lambda (sub mu) is the wavelength in microns. Hyperfine, Inc. has ruled and tested the AIRES' echelle; its wave-front error is 0.028 waves RMS (root mean square) at 10.6 microns. The instrument is housed in a liquid-helium cryostat which is constrained in diameter (approximately 1 m) and length (approximately 2 m) by the observatory. Hence, the length of the echelle (approximately 1.1 m) and the focal length of its collimator (approximately 5.2 m) severely drive the optical design and packaging. The final design uses diamond-turned aluminum optics and has up to 19 reflections inside the cryostat, depending on the optical path. This design was generated, optimized, and toleranced using Code V. The predicted performance is nearly diffraction-limited at 17 microns; the error budget is dominated by design residuals. Light loss due to slit rotation and slit curvature has been minimized. A thorough diffraction analysis with GLAD (G-Level Analysis Drawer) was used to size the mirrors and baffles; the internal light loss is shown to be a strong function of slit width.

Haas, Michael R.↗
NASA NTRS1988

Computation of the tip vortex flowfield for advanced aircraft propellers

The tip vortex flowfield plays a significant role in the performance of advanced aircraft propellers. The flowfield in the tip region is complex, three-dimensional and viscous with large secondary velocities. An analysis is presented using an approximate set of equations which contains the physics required by the tip vortex flowfield, but which does not require the resources of the full Navier-Stokes equations. A computer code was developed to predict the tip vortex flowfield of advanced aircraft propellers. A grid generation package was developed to allow specification of a variety of advanced aircraft propeller shapes. Calculations of the tip vortex generation on an SR3 type blade at high Reynolds numbers were made using this code and a parametric study was performed to show the effect of tip thickness on tip vortex intensity. In addition, calculations of the tip vortex generation on a NACA 0012 type blade were made, including the flowfield downstream of the blade trailing edge. Comparison of flowfield calculations with experimental data from an F4 blade was made. A user's manual was also prepared for the computer code (NASA CR-182178).

Tsai, Tommy M.↗