Results for “lumped parameter model” — Page 8

NASA NTRS1989

Electrodynamics of the Getaway Tether Experiment

An electrodynamic circuit model of the interaction of a pair of small tethered satellites and the ionosphere is developed and analyzed. The system under study, the Getaway Tether Experiment (GATE), is composed of two small satellites and 1 km of insulated conducting tether. The nonlinear model has elements representing the emission, collection, and resistive flow of charge through an electrically conductive tether, plasma contactors, and the ionosphere. The circuit model is incorporated into a dynamic orbital simulation to predict mission performance. Simulation results show the feasibility to bilaterally transfer energy between stored electrical energy and orbital momentum. A transient model is also developed using the circuit model and a string of N lumped-parameter modules, each consisting of resistance, capacitance, and induced potential for the tether. Transients are shown via simulation to occur over millisecond intervals.

Greene, Michael↗

NASA NTRSDate not supplied

Development of an analytical transient evacuation model for the fairing jettison process

A general, lumped-parameter, control volume equation is developed to describe the rapid loss of pressure associated with a launch vehicle fairing jettison event in its initial phase. Beginning with a general mass conservation statement, a sonic constraint is applied to the expanding gap between receding fairing halves to produce a statement for transient density. This is related to fairing pressure by assuming the expansion may be described by a polytropic process. A generic, single-step, lateral fairing half separation case is created and explored for illustrative purposes.

Fairing depressurization↗

NASA NTRSDate not supplied

Development of an analytical transient evacuation model for the fairing jettison process

A general, lumped-parameter, control volume equation is developed to describe the rapid loss of pressure associated with a launch vehicle fairing jettison event in its initial phase. Beginning with a general mass conservation statement, a sonic constraint is applied to the expanding gap between receding fairing halves to produce a statement for transient density. This is related to fairing pressure by assuming the expansion may be described by a polytropic process. A generic, single-step, lateral fairing half separation case is created and explored for illustrative purposes.

fairing depressurization↗

NASA NTRSDate not supplied

Development of an Analytical Transient Evacuation Model for the Fairing Jettison Process

A general, lumped-parameter, control volume equation is developed to describe the rapid loss of pressure associated with a launch vehicle fairing jettison event in its initial phase. Beginning with a general mass conservation statement, a sonic constraint is applied to the expanding gap between receding fairing halves to produce a statement for transient density. This is related to fairing pressure by assuming the expansion may be described by a polytropic process. A generic, single-step, lateral fairing half separation case is created and explored for illustrative purposes.

fairing depressurization, jettison, venting↗

NASA NTRS1993

Implementation of interconnect simulation tools in spice

Accurate computer simulation of high speed digital computer circuits and communication circuits requires a multimode approach to simulate both the devices and the interconnects between devices. Classical circuit analysis algorithms (lumped parameter) are needed for circuit devices and the network formed by the interconnected devices. The interconnects, however, have to be modeled as transmission lines which incorporate electromagnetic field analysis. An approach to writing a multimode simulator is to take an existing software package which performs either lumped parameter analysis or field analysis and add the missing type of analysis routines to the package. In this work a traditionally lumped parameter simulator, SPICE, is modified so that it will perform lossy transmission line analysis using a different model approach. Modifying SPICE3E2 or any other large software package is not a trivial task. An understanding of the programming conventions used, simulation software, and simulation algorithms is required. This thesis was written to clarify the procedure for installing a device into SPICE3E2. The installation of three devices is documented and the installations of the first two provide a foundation for installation of the lossy line which is the third device. The details of discussions are specific to SPICE, but the concepts will be helpful when performing installations into other circuit analysis packages.

Satsangi, H.↗

NASA NTRS1968

Computer modeling of the human systemic arterial tree.

Analog computer modeling of human systemic arterial tree, based on lump parameter circuit approximation

Hillestad, R. J.↗

NASA NTRS1993

Program Computes Flows Of Fluids And Heat

SINDA'85/FLUINT incorporates lumped-parameter-network and one-dimensional-flow mathematical models. System enables analysis of mutual influences of thermal and flow phenomena. Offers two finite-difference numerical solution techniques: forward-difference explicit approximation and Crank-Nicholson approximation. Enables simulation of nonuniform heating and facilitates mathematical modeling of thin-walled heat exchangers. Ability to model nonequilibrium behavior within two-phase volumes included. Recent changes in program improve modeling of real evaporator pumps and other capillary-assist evaporators. Written in FORTRAN 77.

Cullimore, Brent↗

NASA NTRS1994

Numerical comparison of convective heat transfer augmentation devices used in cooling channels of hypersonic vehicles

Hypersonic vehicles are exposed to extreme thermal conditions compared to subsonic aircraft; therefore, some level of thermal management is required to protect the materials used. Normally, hypersonic vehicles experience the highest temperatures in the nozzle throat, and aircraft and propulsion system leading edges. Convective heat transfer augmentation techniques can be used in the thermal management system to increase heat transfer of the cooling channels in those areas. The techniques studied in this report are pin-fin, offset-fin, ribbed and straight roughened channel. A smooth straight channel is used as the baseline for comparing the techniques. SINDA '85, a lumped parameter finite difference thermal analyzer, is used to model the channels. Subroutines are added to model the fluid flow assuming steady one dimensional compressible flow with heat addition and friction. Correlations for convective heat transfer and friction are used in conjunction with the fluid flow analysis mentioned. As expected, the pin-fin arrangement has the highest heat transfer coefficient and the largest pressure drop. All the other devices fall in between the pin-fin and smooth straight channel. The selection of the best heat augmentation method depends on the design requirements. A good approach may be a channel using a combination of the techniques. For instance, several rows of pin-fins may be located at the region of highest heat flux, surrounded by some of the other techniques. Thus, the heat transfer coefficient is maximized at the region of highest heat flux while the pressure drop is not excessive.

Maldonado, Jaime J.↗

NASA NTRS1995

Quantitative Diagnosis of Continuous-Valued, Stead-State Systems

Quantitative diagnosis involves numerically estimating the values of unobservable parameters that best explain the observed parameter values. We consider quantitative diagnosis for continuous, lumped- parameter, steady-state physical systems because such models are easy to construct and the diagnosis problem is considerably simpler than that for corresponding dynamic models. To further tackle the difficulties of numerically inverting a simulation model to compute a diagnosis, we propose to decompose a physical system model in terms of feedback loops. This decomposition reduces the dimension of the problem and consequently decreases the diagnosis search space. We illustrate this approach on a model of thermal control system studied in earlier research.

Quantitative Diagnosis↗

NASA NTRSDate not supplied

Hybrid Model Based Approaches for Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Hybrid Modeling↗

NASA NTRSDate not supplied

Systems Health Management and Prognostics Approaches for Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving flying time/distance for the flying vehicles. This information is important, particularly in the case of auto vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing the remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. A systematic prediction framework is implemented to identify all possible sources of uncertainty, quantify each of them individually, and mathematically estimate their combined effect on the system-level quantity of interest, in this case, the remaining flying time/distance of the unmanned aircraft. Note - This presentation contains all previously published information.

Systems Health Managent↗

NASA NTRSDate not supplied

Hybrid Approaches to Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Systems Health Management↗

NASA NTRS2022

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗

NASA NTRSDate not supplied

Health Monitoring and Prognostics for Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving flying time/distance for the flying vehicles. This information is important, particularly in the case of auto vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing the remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. A systematic prediction framework is implemented to identify all possible sources of uncertainty, quantify each of them individually, and mathematically estimate their combined effect on the system-level quantity of interest, in this case, the remaining flying time/distance of the unmanned aircraft. Note - This presentation contains all previously approved and published information.

Systems Health Managent↗

NASA NTRS1967

CINDA - Chrysler Improved Numerical Differencing Analyzer computer program

Dimensionless multioption systems compiler computer program constructs and analyzes a mathematical model of any arbitrary one, two, or three dimensional lumped parameter representation of a physical system. It automatically optimizes the utilization of computer core space and is more general and versatile than BETA.

Gaski, J. D.↗

NASA NTRS1991

Theoretical and experimental characterization of coplanar waveguide discontinuities for filter applications

A full-wave analysis of shielded coplanar waveguide two-port discontinuities based on the solution of an appropriate surface integral equation in the space domain is presented. Frequency-dependent scattering parameters for open-end and short-end coplanar waveguide (CPW) stubs are computed using this method. The numerically derived results are compared with measurements performed in the frequency range 5-25 GHz and show very good agreement. From the scattering parameters, lumped-element equivalent circuits have been derived to model the discontinuities. The inductors and capacitors of these models have been represented by closed-form equations, as functions of the stub length, to compute the circuit element values for these discontinuities.

Dib, Nihad I.↗

NASA NTRS2005

Design Optimization Tool for Synthetic Jet Actuators Using Lumped Element Modeling

The performance specifications of any actuator are quantified in terms of an exhaustive list of parameters such as bandwidth, output control authority, etc. Flow-control applications benefit from a known actuator frequency response function that relates the input voltage to the output property of interest (e.g., maximum velocity, volumetric flow rate, momentum flux, etc.). Clearly, the required performance metrics are application specific, and methods are needed to achieve the optimal design of these devices. Design and optimization studies have been conducted for piezoelectric cantilever-type flow control actuators, but the modeling issues are simpler compared to synthetic jets. Here, lumped element modeling (LEM) is combined with equivalent circuit representations to estimate the nonlinear dynamic response of a synthetic jet as a function of device dimensions, material properties, and external flow conditions. These models provide reasonable agreement between predicted and measured frequency response functions and thus are suitable for use as design tools. In this work, we have developed a Matlab-based design optimization tool for piezoelectric synthetic jet actuators based on the lumped element models mentioned above. Significant improvements were achieved by optimizing the piezoceramic diaphragm dimensions. Synthetic-jet actuators were fabricated and benchtop tested to fully document their behavior and validate a companion optimization effort. It is hoped that the tool developed from this investigation will assist in the design and deployment of these actuators.

Gallas, Quentin↗

NASA NTRS1975

Design of experiments for measuring heat-transfer coefficients with a lumped-parameter calorimeter

A theoretical investigation was conducted to determine optimum experimental conditions for using a lumped-parameter calorimeter to measure heat-transfer coefficients and heating rates. A mathematical model of the transient temperature response of the calorimeter was used with the measured temperature response to predict the heat-transfer coefficient and the rate of heating. A sensitivity analysis was used to determine the optimum transient experiment for simultaneously measuring the heat addition during heating and the convective heat-transfer coefficient during heating and cooling of a lumped-parameter calorimeter. Optimum experiments were also designed for measuring the convective heat-transfer coefficient during both heating and cooling and cooling only.

Vanfossen, G. J., Jr.↗