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NASA NTRS2012

Simulated Lunar Testing of Metabolic Heat Regenerated Temperature Swing Adsorption

Metabolic heat regenerated Temperature Swing Adsorption (MTSA) technology is being developed for thermal and carbon dioxide (CO2) control for a Portable Life Support System (PLSS), as well as water recycling. An Engineering Development Unit (EDU) of the MTSA Subassembly (MTSAS) was designed and assembled for optimized Martian operations, but also meets system requirements for lunar operations. For lunar operations the MTSA sorption cycle is driven via a vacuum swing between suit ventilation loop pressure and lunar vacuum. The focus of this effort was testing in a simulated lunar environment. This environment was simulated in Paragon's EHF vacuum chamber. The objective of the testing was to evaluate the full cycle performance of the MTSA Subassembly EDU, and to assess CO2 loading and pressure drop of the wash coated aluminum reticulated foam sorbent bed. Lunar environment testing proved out the feasibility of pure vacuum swing operation, making MTSA a technology that can be tested and used on the Moon prior to going to Mars. Testing demonstrated better than expected CO2 Nomenclature loading on the sorbent and nearly replicates the equilibrium data from the sorbent manufacturer. This exceeded any of the previous sorbent loading tests performed by Paragon. Subsequently, the increased performance of the sorbent bed design indicates future designs will require less mass and volume than the current EDU rendering MTSA as very competitive for Martian PLSS applications.

Padilla, Sebastian A.↗

NASA NTRS2011

Simulated Lunar Testing of Metabolic Heat Regenerated Temperature Swing Adsorption Technology

Metabolic heat regenerated Temperature Swing Adsorption (MTSA) technology is being developed for thermal and carbon dioxide (CO2) control for a Portable Life Support System (PLSS), as well as water recycling. An Engineering Development Unit (EDU) of the MTSA subassembly was designed and assembled for optimized Martian operations, but also meets system requirements for lunar operations. For lunar operations the MTSA sorption cycle is driven via a vacuum swing between suit ventilation loop pressure and lunar vacuum. The focus of this effort is operations and testing in a simulated lunar environment. This environment was simulated in Paragon s EHF vacuum chamber. The objective of this testing was to evaluate the full cycle performance of the MTSA Subassembly EDU, and to assess CO2 loading and pressure drop of the wash coated aluminum reticulated foam sorbent bed. The lunar testing proved out the feasibility of pure vacuum swing operation, making MTSA a technology that can be tested and used on the Moon prior to going to Mars. Testing demonstrated better than expected CO2 loading on the sorbent and nearly replicates the equilibrium data from the sorbent manufacturer. This had not been achieved in any of the previous sorbent loading tests performed by Paragon. Subsequently, the increased performance of the sorbent bed design indicates future designs will require less mass and volume than the current EDU rendering MTSA as very competitive for Martian PLSS applications.

Padilla, Sebastian A.↗

NASA NTRS1990

Data base architecture for instrument characteristics critical to spacecraft conceptual design

Spacecraft designs are driven by the payloads and mission requirements that they support. Many of the payload characteristics, such as mass, power requirements, communication requirements, moving parts, and so forth directly affect the choices for the spacecraft structural configuration and its subsystem design and component selection. The conceptual design process, which translates mission requirements into early spacecraft concepts, must be tolerant of frequent changes in the payload complement and resource requirements. A computer data base was designed and implemented for the purposes of containing the payload characteristics pertinent for spacecraft conceptual design, tracking the evolution of these payloads over time, and enabling the integration of the payload data with engineering analysis programs for improving the efficiency in producing spacecraft designs. In-house tools were used for constructing the data base and for performing the actual integration with an existing program for optimizing payload mass locations on the spacecraft.

Rowell, Lawrence F.↗

NASA NTRSDate not supplied

Baseline Medical System Translation for the Impact Medical Database

NASA has developed a new evidence-based data-driven probabilistic risk assessment and tradespace analysis tool as a successor to the Integrated Medical Model (IMM). This updated decision support tool is known as IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces). Whereas IMM focuses on the resources and risks associated with International Space Station (ISS) and Low Earth Orbit (LEO) missions, IMPACT estimates the frequency and consequences of medical conditions that might arise during exploration missions. One of the services offered by IMM is a series of generic or baseline medical systems associated with typical mission types or DRMs (Design Reference Missions), such that requestors may prioritize questions pertaining to the mission itself over the medical supplies indicated by the model outputs for that DRM. In a mission focused request, the appropriate baseline medical system is used in place of a prepared or optimized medical kit. In preparation, an effort was undertaken to create baseline systems of medical resources within IMPACT suitable for typical DRMs. Using an existing baseline medical system within IMM’s Integrated Medical Evidence Database (iMED) as a starting point, the resources found within the medical kit were compared to and substituted for equivalent resources available within the IMPACT MD (Medical Database). In consultation with clinicians with knowledge of IMPACT’s MD, each resource was matched as closely as possible to a similarly purposed resource in IMPACT, seeking to preserve the treatment capabilities and procedures offered by the IMM medical system while reconciling the differences in modeled resources and medical conditions between the models. To demonstrate the efficacy of this work, a prototype ISS medical system in IMPACT was translated from the baseline used in the IMM. The appropriate medical system was then run through its associated medical model to compare and validate the resultant risks and risk mitigation provided by each baseline ISS medical kit.

S Schwartz↗

NASA NTRS2011

Initial Characterization of Optical Communications with Disruption-Tolerant Network Protocols

Disruption-tolerant networks (DTNs) are groups of network assets connected with a suite of communication protocol technologies designed to mitigate the effects of link delay and disruption. Application of DTN protocols to diverse groups of network resources in multiple sub-networks results in an overlay network-of-networks with autonomous data routing capability. In space environments where delay or disruption is expected, performance of this type of architecture (such as an interplanetary internet) can increase with the inclusion of new communications mediums and techniques. Space-based optical communication links are therefore an excellent building block of space DTN architectures. When compared to traditional radio frequency (RF) communications, optical systems can provide extremely power-efficient and high bandwidth links bridging sub-networks. Because optical links are more susceptible to link disruption and experience the same light-speed delays as RF, optical-enabled DTN architectures can lessen potential drawbacks and maintain the benefits of autonomous optical communications over deep space distances. These environment-driven expectations - link delay and interruption, along with asymmetric data rates - are the purpose of the proof-of-concept experiment outlined herein. In recognizing the potential of these two technologies, we report an initial experiment and characterization of the performance of a DTN-enabled space optical link. The experiment design employs a point-to-point free-space optical link configured to have asymmetric bandwidth. This link connects two networked systems running a DTN protocol implementation designed and written at JPL for use on spacecraft, and further configured for higher bandwidth performance. Comparing baseline data transmission metrics with and without periodic optical link interruptions, the experiment confirmed the DTN protocols' ability to handle real-world unexpected link outages while maintaining capability of reliably delivering data at relatively high rates. Finally, performance characterizations from this data suggest performance optimizations to configuration and protocols for future optical-specific DTN space link scenarios.

Space networking↗

NASA NTRS2019

Prediction of Pushback Times and Ramp Taxi Times for Departures at Charlotte Airport

When optimizing the takeoff sequence and schedule for departures at busy airports, it is important to accurately predict the taxi times from gate to runway because those are used to calculate the earliest possible takeoff times. Several airports like Charlotte Douglas International Airport show relatively long taxi times inside the ramp area with large variations, with respect to the travel times in the airport movement area. Also, the pushback process times have not been accurately modeled so far mainly due to the lack of accurate data. The recent deployment of the integrated arrival, departure, and surface traffic management system at Charlotte airport by NASA enables more accurate flight data in the airport surface operations to be obtained. Taking advantage of this system, actual pushback times and ramp taxi times from historical flight data at this airport are analyzed. Based on the analysis, a simple, data-driven prediction model is introduced for estimating pushback times and ramp transit times of individual departure flights. To evaluate the performance of this prediction model, several machine learning techniques are also applied to the same dataset. The prediction results show that the data-driven prediction model is as good as the machine learning algorithms when comparing various prediction performance metrics.

Lee, Hanbong↗

NASA NTRS2019

Prediction of Pushback Times and Ramp Taxi Times for Departures at Charlotte Airport

airport surface operations↗

NASA NTRS2004

Putting Priors in Mixture Density Mercer Kernels

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. We describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using predefined kernels. These data adaptive kernels can en- code prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS). The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains template for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic- algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code. The results show that the Mixture Density Mercer-Kernel described here outperforms tree-based classification in distinguishing high-redshift galaxies from low- redshift galaxies by approximately 16% on test data, bagged trees by approximately 7%, and bagged trees built on a much larger sample of data by approximately 2%.

Srivastava, Ashok N.↗

NASA NTRS2012

Estimation and Bias Correction of Aerosol Abundance using Data-driven Machine Learning and Remote Sensing

Air quality information is increasingly becoming a public health concern, since some of the aerosol particles pose harmful effects to peoples health. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. The comparison between the AOD measured from the ground-based Aerosol Robotic Network (AERONET) system and the satellite MODIS instruments at 550 nm shows that there is a bias between the two data products. We performed a comprehensive analysis exploring possible factors which may be contributing to the inter-instrumental bias between MODIS and AERONET. The analysis used several measured variables, including the MODIS AOD, as input in order to train a neural network in regression mode to predict the AERONET AOD values. This not only allowed us to obtain an estimate, but also allowed us to infer the optimal sets of variables that played an important role in the prediction. In addition, we applied machine learning to infer the global abundance of ground level PM2.5 from the AOD data and other ancillary satellite and meteorology products. This research is part of our goal to provide air quality information, which can also be useful for global epidemiology studies.

Malakar, Nabin K.↗

NASA NTRS2017

3D Material Response Analysis of PICA Pyrolysis Experiments

The PICA decomposition experiments of Bessire and Minton are investigated using 3D material response analysis. The steady thermoelectric equations have been added to the CHAR code to enable analysis of the Joule-heated experiments and the DAKOTA optimization code is used to define the voltage boundary condition that yields the experimentally observed temperature response. This analysis has identified a potential spatial non-uniformity in the PICA sample temperature driven by the cooled copper electrodes and thermal radiation from the surface of the test article (Figure 1). The non-uniformity leads to a variable heating rate throughout the sample volume that has an effect on the quantitative results of the experiment. Averaging the results of integrating a kinetic reaction mechanism with the heating rates seen across the sample volume yield a shift of peak species production to lower temperatures that is more significant for higher heating rates (Figure 2) when compared to integrating the same mechanism at the reported heating rate. The analysis supporting these conclusions will be presented along with a proposed analysis procedure that permits quantitative use of the existing data. Time permitting, a status on the in-development kinetic decomposition mechanism based on this data will be presented as well.

Oliver, A. Brandon↗

NASA NTRS2021

Recent Advancements in Modeling and Simulation of Entry Systems at NASA

This paper describes recent development of modeling and simulation technologies for entry systems in support of NASA’s exploration missions. Mission-tailored research and development in modeling of entry systems occurs across the Agency (e.g., within the Orion and Mars 2020 Programs), however the aim of this paper is to discuss the broad, cross-mission research conducted by NASA’s Entry Systems Modeling (ESM) Project, which serves as the Agency’s only concerted effort toward advancing entry systems across a range of technical disciplines. Technology development in ESM is organized and prioritized from a system-level perspective, resulting in four broad technical areas of investment: (1) Predictive material modeling, (2) Shock layer kinetics and radiation, (3) Computational and experimental aerosciences, and (4) Guidance, navigation, and control. Investments in thermal protection material modeling are geared toward high-fidelity, predictive models capable of handling complex structures, with an eye toward optimizing design performance and quantifying thermal protection system reliability. New computational tools have been developed to characterize material properties and behavior at the microstructural level, and experimental techniques (molecular beam scattering, micro-computed tomography, among others) have been developed to measure material kinetics, morphology, and other parameters needed to inform and validate detailed simulations. Advancements have also been made in macrostructural simulation capability to enable 3-D system-scale calculations of material response with complex topological features, including differential recession of tile gaps. Research and development in the area of shock layer kinetics has focused on air and CO2-based atmospheres. Capacity and capability of the NASA Ames Electric Arc Shock Tube (EAST) have been expanded in recent years and analysis of resulting data has led to several improvements in kinetic models, while simultaneously reducing uncertainties associated with radiative heat transfer predictions. First-principles calculations of fundamental kinetic, thermodynamic, and transport data, along with state-specificmodels for non-equilibrium flow regimes, have also yielded new insights and have the potential to vastly improve model fidelity. Aerosciences is a very broad area of interest in entry systems, yet a number of important challenges are being addressed: Coupled fluid-structure simulations of parachute inflation and dynamics; Experimental and computational studies of vehicle dynamics; Multi-phase flow with dust particles to simulate entry environments at Mars during dust storms; Studies of roughness-induced heating augmentation relevant to tiled and woven thermal protection systems; and Advanced numerical methods to optimize computational analyses for desired accuracy versus cost. Guidance and control in the context of entry systems has focused on development of methods for multi-axis control (i.e. pitch and yaw, rather than bank angle alone) of spacecraft during entry and descent. With precision landing requirements driven by Mars human exploration goals, recent efforts have yielded 6-DOF models of multi-axis control with propulsive descent of both inflatable and rigid ellipsled-like architectures. Results for both configurations have demonstrated the ability to land within the 50-meter precision requirement demanded by Mars human exploration missions, while also reducing propellant requirements by enabling more efficient control through entry and descent. Ongoing research in GN&C is developing mechanical specifications for the systems and establishing engineering feasibility.

aerothermodynamics↗

NASA NTRS1993

Numerical modeling for an electric-field hyperthermia applicator

Hyperthermia, in conjunction with radiation and chemotherapy for treatment of cancers, is an area of current concern. Experiments have shown that hyperthermia can increase the potency of many chemotherapy drugs and the effectiveness of radiation for treating cancer. A combination of whole body or regional hyperthermia with chemotherapy or radiation should improve treatment results. Conventional methods for inducing whole body hyperthermia, such as exposing a patient in a radiant cabinet or under a hot water blanket, conduct heat very slowly from the skin to the body core. Thus a more efficient system, such as the three-plate electric-field hyperthermia applicator (EHA), is developed. This three-plate EHA has one top plate over and two lower plates beneath the patient. It is driven at 27.12 MHz with 500 Watts through a matching circuit. Using this applicator, a 50 kg pig was successfully heated to 42 C within 45 minutes. However, phantom and animal studies have indicated non-uniform heating near the side of the body. In addition, changes in the size and distance between the electrode plates can affect the heating (or electromagnetic field) pattern. Therefore, numerical models using the method of moments (MOM) or the finite difference time domain (FDTD) technique are developed to optimize the heating pattern of this EHA before it is used for human trials. The accuracy of the numerical modeling has been achieved by the good agreement between the MOM and FDTD results for the three-plate EHA without a biological body. The versatile FDTD technique is then applied to optimize the EHA design with a human body. Both the numerical and measured data in phantom blocks will be presented. The results of this study will be used to design an optimized system for whole body or regional hyperthermia.

Wu, Te-Kao↗

NASA NTRS2019

Recent Advancements in Modeling and Simulation of Entry Systems at NASA

This paper describes recent development of modeling and simulation technologies for entry systems in support of NASA's exploration missions. Mission-tailored research and development in modeling of entry systems occurs across the Agency (e.g., within the Orion and Mars 2020 Programs), however the aim of this paper is to discuss the broad, cross-mission research conducted by NASA's Entry Systems Modeling (ESM) Project, which serves as the Agency's only concerted effort toward advancing entry systems across a range of technical disciplines. Technology development in ESM is organized and prioritized from a system-level perspective, resulting in four broad technical areas of investment: (1) Predictive material modeling, (2) Shock layer kinetics and radiation, (3) Computational and experimental aerosciences, and (4) Guidance, navigation, and control. Investments in thermal protection material modeling are geared toward high-fidelity, predictive models capable of handling complex structures, with an eye toward optimizing design performance and quantifying thermal protection system reliability. New computational tools have been developed to characterize material properties and behavior at the microstructural level, and experimental techniques (molecular beam scattering, micro-computed tomography, among others) have been developed to measure material kinetics, morphology, and other parameters needed to inform and validate detailed simulations. Advancements have also been made in macrostructural simulation capability to enable 3-D system-scale calculations of material response with complex topological features, including differential recession of tile gaps. Research and development in the area of shock layer kinetics has focused on air and CO2-based atmospheres. Capacity and capability of the NASA Ames Electric Arc Shock Tube (EAST) have been expanded in recent years and analysis of resulting data has led to several improvements in kinetic models, while simultaneously reducing uncertainties associated with radiative heat transfer predictions. First-principles calculations of fundamental kinetic, thermodynamic, and transport data, along with state-specific models for non-equilibrium flow regimes, have also yielded new insights and have the potential to vastly improve model fidelity. Aerosciences is a very broad area of interest in entry systems, yet a number of important challenges are being addressed: Coupled fluid-structure simulations of parachute inflation and dynamics; Experimental and computational studies of vehicle dynamics; Multi-phase flow with dust particles to simulate entry environments at Mars during dust storms; Studies of roughness-induced heating augmentation relevant to tiled and woven thermal protection systems; and Advanced numerical methods to optimize computational analyses for desired accuracy versus cost. Guidance and control in the context of entry systems has focused on development of methods for multi-axis control (i.e. pitch and yaw, rather than bank angle alone) of spacecraft during entry and descent. With precision landing requirements driven by Mars human exploration goals, recent efforts have yielded 6-DOF models of multi-axis control with propulsive descent of both inflatable and rigid ellipsled-like architectures.

Barnhardt, Michael D.↗

NASA NTRS2012

The Damper Spring Unit of the Sentinel 1 Solar Array

The Damper Spring Unit (DSU, see Figure 1) has been designed to provide the damping required to control the deployment speed of the spring driven solar array deployment in an ARA Mk3 or FRED based Solar Array in situations where the standard application of a damper at the root-hinge is not feasible. The unit consists of four major parts: a main bracket, an eddy current damper, a spring unit, an actuation pulley which is coupled via Kevlar cables to a synchro-pulley of a hinge. The damper slows down the deployment speed and prevents deployment shocks at deployment completion. The spring unit includes 4 springs which overcome the resistances of the damper and the specific DSU control cable loop. This means it can be added to any spring driven deployment system without major modifications of that system. Engineering models of the Sentinel 1 solar array wing have been built to identify the deployment behavior, and to help to determine the optimal pulley ratios of the solar array and to finalize the DSU design. During the functional tests, the behavior proved to be very sensitive for the alignment of the DSU. This was therefore monitored carefully during the qualification program, especially prior to the TV cold testing. During TV "Cold" testing the measured retarding torque exceeded the max. required value: 284 N-mm versus the required 247 N-mm. Although this requirement was not met, the torque balance analysis shows that the 284 N-mm can be accepted, because the spring unit can provide 1.5 times more torque than required. Some functional tests of the DSU have been performed without the eddy current damper attached. It provided input data for the ADAMS solar array wing model. Simulation of the Sentinel-1 deployment (including DSU) in ADAMS allowed the actual wing deployment tests to be limited in both complexity and number of tests. The DSU for the Sentinel-1 solar array was successfully qualified and the flight models are in production.

Doejaaren, Frans↗

NASA NTRSDate not supplied

Quantifying the Sensitivity of Condition Incidence Parameters in the Evidence Library

One approach to quantifying spaceflight risk at NASA makes use event driven probabilistic techniques. The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) is such a tool that estimates medical risk metrics via simulation and enables optimization of medical resources subject to mission constraints [1]. Previous analyses have informed medical set composition, exercise countermeasures, and water intake, where each analysis quantifies the risk associated with proposed variations in system design. As future mission profiles extend beyond Low-Earth Orbit (LEO) and lengthen in duration, understanding these risks and contributing factors is critical. MEDPRAT employs Monte Carlo sampling techniques to simulate missions and track the occurrence of medical events. These events follow fault-tree-like progressions through levels of severity and mitigation via medical treatment to many possible outcomes and these are reported throughout the mission. Making this possible, are the medical databases that contain evidence gathered by the Human Research Program (HRP). Quantifying the impact of uncertainty or variability in the input data is an important step in evaluating the credibility of modeling and simulation results. In this work, we investigate the sensitivity of medical risk metrics with respect to the condition incidence parameters within the Evidence Library (EL) [2] as the medical database input for MEDPRAT. The medical conditions, contained in the EL, are equipped with incidence rates that describe the likelihood that the condition will occur. These incidence rates reflect historical spaceflight data or when appropriate, terrestrial data. In this presentation, we will explore how uncertainty in these rates propagate to the medical risk described by MEDPRAT. These results identify the conditions and parameters with the largest contribution to medical risks.

Ian Lim↗

NASA NTRSDate not supplied

Merging Analytic Collaborative Frameworks with New Observing Strategies Toward a Digital Twin: Earth – Episodic Pulse Event Impacts on Ocean Carbon Cycle as an Example

Virtual representations of the Earth will allow us to address some of the most critical environmental issues of our time. Here, we show the first steps toward representation of riverine, estuarine, and coastal carbon processes to enable scenario driven “what-if” analyses of the carbon system and human footprint. Excess sediment and nutrient runoff from land-based human activities impact water quality and can pose serious threats to coastal and marine ecosystems. Episodic pulse events, such as extreme precipitation events, can increase the amount of nutrients entering estuaries and coastal regions, potentially leading to large phytoplankton blooms followed by anoxic conditions. Consequences of coastal runoff are predicted to increase with the higher intensity and frequency of extreme events. Beyond the threat to coastal ecosystems, recent findings suggest these episodic pulses might play a significant role for biological production influencing regional and global carbon fluxes and budgets. An improved understanding of these events through optimal, dynamic observing strategies will increase our knowledge of the land-ocean continuum and how regional events and nutrient fluxes affect the carbon cycle and ocean ecosystem. This conceptual framework enables focused science investigations by pairing data analytics and artificial intelligence tools (otherwise termed an Analytic Center Framework, ACF) with targeted measurement acquisition through distributed sensing and intelligent asset tasking (or New Observing Strategies, NOS). This NOS and ACF iterative approach acquires and integrates complementary and coincident satellite, in-situ and model data to build a more complete and in-depth picture of science phenomena. Specifically, Apache Science Data Analytic Platform (SDAP) is extended to incorporate relevant datasets for data access, harmonized analysis, and anomaly detection. When conditions are met for a likely pulse event, NASA’s D-SHIELD (Distributed Spacecraft with Heuristic Intelligence to Enable Logistical Decisions) tool is triggered to optimize asset overpass frequency and schedule observations for persistent monitoring. Targeted data is ingested by SDAP for enhanced investigation via iterative analysis until the trigger criteria is no longer met - steps toward a digital twin.

Laura Rogers↗

NASA NTRS1997

Development of a Low-Reynolds Number, Nonlinear kappa-epsilon Model for the Reduced Navier-Stokes Equations

Previous work at NASA LeRC has shown that flow distortions in aircraft engine inlet ducts can be significantly reduced by mounting vortex generators, or small wing sections, on the inside surface of the engine inlet. The placement of the vortex generators is an important factor in obtaining the optimal effect over a wide operating envelope. In this regard, the only alternative to a long and expensive test program which would search out this optimal configuration is a good prediction procedure which could narrow the field of search. Such a procedure has been developed in collaboration with NASA LeRC, and results obtained by NASA personnel indicate that it shows considerable promise for predicting the viscous turbulent flow in engine inlet ducts in the presence of vortex generators. The prediction tool is a computer code which numerically solves the reduced Navier-Stokes equations and so is commonly referred to as RNS3D. Obvious deficiencies in RNS3D have been addressed in previous work. Primarily, it is known that the predictions of the mean velocity field of a turbulent boundary layer flow approaching separation are not in good agreement with data. It was suggested that the use of an algebraic mixing-length turbulence model in RNS3D is at least partly to blame for this. Additionally, the current turbulence model includes an assumption of isotropy which will ultimately fail to capture turbulence-driven secondary flow known to exist in noncircular ducts.

Boger, David A.↗

NASA NTRS1984

On the utilization of engineering knowledge in design optimization

Some current research work conducted at the University of Michigan is described to illustrate efforts for incorporating knowledge in optimization in a nontraditional way. The incorporation of available knowledge in a logic structure is examined in two circumstances. The first examines the possibility of introducing global design information in a local active set strategy implemented during the iterations of projection-type algorithms for nonlinearly constrained problems. The technique used algorithms for nonlinearly constrained problems. The technique used combines global and local monotinicity analysis of the objective and constraint functions. The second examines a knowledge-based program which aids the user to create condigurations that are most desirable from the manufacturing assembly viewpoint. The data bank used is the classification scheme suggested by Boothroyd. The important aspect of this program is that it is an aid for synthesis intended for use in the design concept phase in a way similar to the so-called idea-triggers in creativity-enhancement techniques like brain-storming. The idea generation, however, is not random but it is driven by the goal of achieving the best acceptable configuration.

Papalambros, P.↗