Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “model extension”

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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

On extending parallelism to serial simulators

This paper describes an approach to discrete event simulation modeling that appears to be effective for developing portable and efficient parallel execution of models of large distributed systems and communication networks. In this approach, the modeler develops submodels using an existing sequential simulation modeling tool, using the full expressive power of the tool. A set of modeling language extensions permit automatically synchronized communication between submodels; however, the automation requires that any such communication must take a nonzero amount off simulation time. Within this modeling paradigm, a variety of conservative synchronization protocols can transparently support conservative execution of submodels on potentially different processors. A specific implementation of this approach, U.P.S. (Utilitarian Parallel Simulator), is described, along with performance results on the Intel Paragon.

Nicol, David↗
DOE OSTI2023

TDCOSMO. X. Automated modeling of nine strongly lensed quasars and comparison between lens-modeling software

When strong gravitational lenses are to be used as an astrophysical or cosmological probe, models of their mass distributions are often needed. We present a new, time-efficient automation code for the uniform modeling of strongly lensed quasars with GLEE, a lens-modeling software for multiband data. By using the observed positions of the lensed quasars and the spatially extended surface brightness distribution of the host galaxy of the lensed quasar, we obtain a model of the mass distribution of the lens galaxy. We applied this uniform modeling pipeline to a sample of nine strongly lensed quasars for which images were obtained with the Wide Field Camera 3 of the Hubble Space Telescope. The models show well-reconstructed light components and a good alignment between mass and light centroids in most cases. We find that the automated modeling code significantly reduces the input time during the modeling process for the user. The time for preparing the required input files is reduced by a factor of 3 from ~3 h to about one hour. The active input time during the modeling process for the user is reduced by a factor of 10 from ~ 10 h to about one hour per lens system. This automated uniform modeling pipeline can efficiently produce uniform models of extensive lens-system samples that can be used for further cosmological analysis. A blind test that compared our results with those of an independent automated modeling pipeline based on the modeling software Lenstronomy revealed important lessons. Quantities such as Einstein radius, astrometry, mass flattening, and position angle are generally robustly determined. Other quantities, such as the radial slope of the mass density profile and predicted time delays, depend crucially on the quality of the data and on the accuracy with which the point spread function is reconstructed. Better data and/or a more detailed analysis are necessary to elevate our automated models to cosmography grade. Nevertheless, our pipeline enables the quick selection of lenses for follow-up and further modeling, which significantly speeds up the construction of cosmography-grade models. This important step forward will help us to take advantage of the increase in the number of lenses that is expected in the coming decade, which is an increase of several orders of magnitude.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1989

Experimental Flow Models for SSME Flowfield Characterization

Full scale flow models with extensive instrumentation were designed and manufactured to provide data necessary for flow field characterization in rocket engines of the Space Shuttle Main Engine (SSME) type. These models include accurate flow path geometries from the pre-burner outlet through the throat of the main combustion chamber. The turbines are simulated with static models designed to provide the correct pressure drop and swirl for specific power levels. The correct turbopump-hot gas manifold interfaces were designed into the flow models to permit parametric/integration studies for new turbine designs. These experimental flow models provide a vehicle for understanding the fluid dynamics associated with specific engine issues and also fill the more general need for establishing a more detailed fluid dynamic base to support development and verification of advanced math models.

Abel, L. C.↗
NASA NTRS1999

Enzymes and other agents that enhance cell wall extensibility

Polysaccharides and proteins are secreted to the inner surface of the growing cell wall, where they assemble into a network that is mechanically strong, yet remains extensible until the cells cease growth. This review focuses on the agents that directly or indirectly enhance the extensibility properties of growing walls. The properties of expansins, endoglucanases, and xyloglucan transglycosylases are reviewed and their postulated roles in modulating wall extensibility are evaluated. A summary model for wall extension is presented, in which expansin is a primary agent of wall extension, whereas endoglucanases, xyloglucan endotransglycosylase, and other enzymes that alter wall structure act secondarily to modulate expansin action.

NASA Discipline Plant Biology↗
DOE OSTI2020

QCOR; A Language Extension Specification for the Heterogeneous Quantum-Classical Model of Computation

Quantum computing (QC) is an emerging computational paradigm that leverages the laws of quantum mechanics to perform elementary logic operations. Existing programming models for QC were designed with fault-tolerant hardware in mind, envisioning stand-alone applications. However, the susceptibility of near-term quantum computers to noise limits their stand-alone utility. To better leverage limited computational strengths of noisy quantum devices, hybrid algorithms have been suggested whereby quantum computers are used in tandem with their classical counterparts in a heterogeneous fashion. This modus operandi calls out for a programming model and a high-level programming language that natively and seamlessly supports heterogeneous quantum-classical hardware architectures in a single-source-code paradigm. Motivated by the lack of such a model, we introduce a language extension specification, called QCOR, which enables single-source quantum-classical programming. Programs written using the QCOR library–based language extensions can be compiled to produce functional hybrid binary executables. After defining QCOR’s programming model, memory model, and execution model, we discuss how QCOR enables variational, iterative, and feed-forward QC. Additionally, QCOR approaches quantum-classical computation in a hardware-agnostic heterogeneous fashion and strives to build on best practices of high-performance computing. The high level of abstraction in the language extension is intended to accelerate the adoption of QC by researchers familiar with classical high-performance computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A machine learning method of modern urban building energy modeling: A case study of Chicago

Urban-scale building energy modeling is vital for urban planning. However, it can be challenging to assimilate reliable non-geometry building data for urban-scale modeling without extensive investment. Here, this study introduces a novel approach to developing modern urban-scale building energy stock data using geographic information systems and machine learning algorithms without necessarily requiring pre-supplied non-geometric metadata. The proposed framework integrates building footprint and height data to estimate gross floor areas, and matches each building to a pool of candidate records from ComStock or ResStock—filtered to the same county and ranked by geometric similarity—demonstrate a proof-of-concept case study in Chicago for predicting energy use intensity (EUI) using scalable datasets. The model achieved a mean bias error (MBE) of 0.08 kWh/m² and root mean square error (RMSE) of 14.84 kWh/m² under full metadata input for EUI prediction. With only location inputs, the model captured 69.2 % of EUI within predicted ranges. These results demonstrate the model’s potential to support early-stage urban planning, identify candidates for energy-efficient retrofits. By removing the dependency on detailed pre-surveys or extensive building metadata, the approach overcomes a key barrier in traditional urban-scale building energy modeling, illustrating a pathway toward broader and more cost-effective application, though further multi-city validation and improved treatment of pre-1925 buildings are needed.

Energy Use Intensity↗
NASA NTRS2009

A Cell Kinetic Model of Granulocytopoiesis Under Radiation Exposure: Extension from Murines to Canines and Humans

Space radiation poses significant challenges to space travel, and it is essential to understand the possible adverse effects from space radiation exposure to the radiosensitive organ systems that are important for immediate survival of human, e.g., the hematopoietic system. In this presentation a biomathematical model of granulocytopoiesis is described and used to analyze the blood granulocyte changes seen in the blood of mammalians under continuous and acute radiation exposure. This is one of a set of hematopoietic models that have been successfully utilized to simulate and interpret the experimental data of acute and chronic radiation on rodents. We discuss the underlying implicit regulation mechanism and the biological relevance of the kinetic parameters estimation method. Extension of the model to predictions in dogs and humans systems indicates that the modeling results are consistent with the cumulative experimental and empirical data from various sources. This implies the potential to integrate the models into one united system for monitoring the hematopoietic response of various species under irradiation. Based on the evidence of threshold responses of dogs to extended periods of low daily dose exposures, we discuss the potential health risks of the space traveler under chronic stress of low-dose irradiation and the possibly encountered Solar Particle Events.

Hu, Shaowen↗
DOE OSTI2023

A Design of Activity-Based Mobility Intervention

Human mobility influences our society and vice versa. During the COVID-19 pandemic, non-pharmaceutical intervention that alters activity-based mobility such as work-from-home greatly impacted human mobility patterns. Many studies on developing mitigation strategies have employed or implemented their own mobility intervention within their model assumption. For fair evaluation between intervention strategies across models, it is significant to set up compatible experimental environments. However, it is difficult to apply the identical intervention to different kinds of models and compare their effectiveness because each model might have different assumptions, capabilities, and implementations. Even if one can apply intervention to heterogeneous models, it may produce undesirable artifacts due to difference of models and integration with intervention. Therefore, minimizing undesirable artifacts and facilitating intervention experiments across heterogeneous models are substantial. Taking this into account, this paper investigates a design of activity-based mobility intervention (ABMI). We define ABMI together with related concepts and develop an extensible data model and schema of ABMI based on the 5W1H method that can be used in different models. As a case study, we apply the ABMI model to a micro-simulation to demonstrate the usability of the proposed model. We expect that standardized ABMI and interfaces may help to streamline development and experiments of intervention strategies across heterogeneous models.

Kim, Joon-Seok↗
NASA NTRS2024

A Koopman-based residual modeling approach for the control of a soft robot arm

Soft robots are challenging to model and control due to their poorly defined kinematics and nonlinear dynamics. Recently, Koopman operator theory has been shown capable of constructing control-oriented soft robot models from data. However, building these models requires extensive data collection and they do not necessarily generalize well outside of the training observations. This paper presents a more data-efficient and generalizable approach to soft robot modeling that first identifies a physics-based Koopman model then supplements it with a data-driven residual Koopman model. The resulting combined model is linear and thus compatible with real-time model-based control techniques such as Model Predictive Control (MPC). The efficacy of the approach is demonstrated on several simulated systems and on a real soft robot arm, where it is shown to generate models that are more accurate than purely physics-based models and require less data to construct than purely data-driven models. Using a model-based controller, the soft arm is able to successfully track end effector trajectories, perform a pick-and-place task, and write on a dry-erase board, showcasing the applicability of this framework to increase the capabilities of soft robotic systems.

Daniel Bruder↗
DOE OSTI2025

Constraining cosmological parameters using the pairwise kinematic Sunyaev-Zel’dovich effect with CMB-S4 and future galaxy cluster surveys

We present a forecast of the pairwise kinematic Sunyaev-Zel’dovich (kSZ) measurement that will be achievable with the future CMB-S4 experiment. CMB-S4 is the next stage for ground-based cosmic microwave background experiments, with a planned wide-area survey that will observe approximately 50% of the sky. We construct a simulated sample of galaxy clusters that have been optically selected in a Legacy Survey of Space and Time–like survey and have spectroscopic redshifts. For this cluster sample, assuming the likelihood is Gaussian, we predict that CMB-S4 will reject the null hypothesis of zero pairwise kSZ signal at 36⁢𝜎. We estimate the effects of systematic uncertainties such as scatter in the mass-richness scaling relation and cluster miscentering. We find that these effects can reduce the signal-to-noise ratio of the CMB-S4 pairwise kSZ measurement by 20%. We explore the constraining power of the measured kSZ signal in combination with measurements of the galaxy clusters’ thermal SZ emission on two extensions to the standard cosmological model. The first extension allows the dark energy equation of state 𝑤 to vary. We find the CMB-S4 pairwise kSZ measurement yields a modest reduction in the uncertainty on 𝑤 by a factor of 1.36 over the Planck’s 2018 uncertainty. The second extension tests general relativity by varying the growth index 𝛾. In conclusion, we find that CMB-S4’s pairwise kSZ measurement will yield a 28⁢𝜎 constraint on 𝛾 and strongly constrain alternative theories of gravity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

RockFlow: Fast Generation of Synthetic Source Rock Images Using Generative Flow Models

Image-based evaluation methods are a valuable tool for source rock characterization. The time and resources needed to obtain images has spurred development of machine-learning generative models to create synthetic images of pore structure and rock fabric from limited image data. While generative models have shown success, existing methods for generating 3D volumes from 2D training images are restricted to binary images and grayscale volume generation requires 3D training data. Shale characterization relies on 2D imaging techniques such as scanning electron microscopy (SEM), and grayscale values carry important information about porosity, kerogen content, and mineral composition of the shale. Here, we introduce RockFlow, a method based on generative flow models that creates grayscale volumes from 2D training data. We apply RockFlow to baseline binary micro-CT image volumes and compare performance to a previously proposed model. We also show the extension of our model to 2D grayscale data by generating grayscale image volumes from 2D SEM and dual modality nanoscale shale images. The results show that our method underestimates the porosity and surface area on the binary baseline datasets but is able to generate realistic grayscale image volumes for shales. With improved binary data preprocessing, we believe that our model is capable of generating synthetic porous media volumes for a very broad class of rocks from shale to carbonates to sandstone.

58 GEOSCIENCES↗
DOE OSTI2025

Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling

AI models are criticized as being black boxes, potentially subjecting climate science to greater uncertainty. Explainable artificial intelligence (XAI) has been proposed to probe AI models and increase trust. In this review and perspective paper, we suggest that, in addition to using XAI methods, AI researchers in climate science can learn from past successes in the development of physics-based dynamical climate models. Dynamical models are complex but have gained trust because their successes and failures can sometimes be attributed to specific components or sub-models, such as when model bias is explained by pointing to a particular parameterization. We propose three types of understanding as a basis to evaluate trust in dynamical and AI models alike: (1) instrumental understanding, which is obtained when a model has passed a functional test; (2) statistical understanding, obtained when researchers can make sense of the modeling results using statistical techniques to identify input–output relationships; and (3) component-level understanding, which refers to modelers' ability to point to specific model components or parts in the model architecture as the culprit for erratic model behaviors or as the crucial reason why the model functions well. We demonstrate how component-level understanding has been sought and achieved via climate model intercomparison projects over the past several decades. Such component-level understanding routinely leads to model improvements and may also serve as a template for thinking about AI-driven climate science. Currently, XAI methods can help explain the behaviors of AI models by focusing on the mapping between input and output, thereby increasing the statistical understanding of AI models. Yet, to further increase our understanding of AI models, we will have to build AI models that have interpretable components amenable to component-level understanding. We give recent examples from the AI climate science literature to highlight some recent, albeit limited, successes in achieving component-level understanding and thereby explaining model behavior. The merit of such interpretable AI models is that they serve as a stronger basis for trust in climate modeling and, by extension, downstream uses of climate model data.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1978

Research in millimeter wave techniques

During the past six months, efforts on this project have been devoted to: (1) continuation of construction and testing of a 6 GHz subharmonic mixer model with extension of the pumping frequency of this mixer to omega sub s/4, (2) construction of a 183 GHz subharmonic mixer based on the results of tests on this 6 GHz model, (3) ground-based radiometric measurements at 183 GHz, (4) fabrication and testing of wire grid interferometers, (5) calculations of reflected and lost power in these interferometers, and (6) calculations of the antenna temperature due to water vapor to be expected in down-looking radiometry as a function of frequency. Significant events during the past six months include: (1) Receipt of a 183 GHz single-ended fundamental mixer, (2) attainment of 6 db single sideband conversion loss with the 6 GHz subharmonic mixer model by using a 1.5 GHz (omega sub s/4) pump frequency, (3) additional ground-based radiometric measurements and (4) derivation of equations for reflection and loss for wire grid interferometers.

Mcmillan, R. W.↗
NASA NTRS1980

The dependence of coronal hole size on large scale magnetic field strength

The importance of mathematical models of the coronal structure for studies of coronal energetics, to simulate global flows of the solar wind, and to obtain reliable solar terrestrial predictions is discussed. Previous coronal models, including an example of a coronal MHD flow model, are reviewed. The development of a coronal model which is a logical extension of earlier models and which allows a closer relationship to the photospheric magnetic field as it is observed daily is described. The calculations are outlined. The assumptions of the model are: axisymmetric flow with no rotation, resulting in two dimensional flow in a meridional plane; zero viscosity and infinite electrical conductivity; polytropic, single fluid flow; and no momentum addition.

Suess, S. T.↗
NASA NTRS1983

Testing a dynamical model for mid-latitude sea surface temperature anomalies

The present investigation is concerned with the development and testing of a dynamical model for sea surface temperature (SST) anomalies in the midlatitudes. The model is an extension of the stochastic forcing model, suggested by Frankignoul and Hasselmann (1977), in which SST anomalies arise naturally as a response of the slowly varying upper ocean to the rapidly varying air-sea fluxes. The conducted analysis confirms strongly the validity of this model. The results suggest that advection by mean ocean currents plays an important role in some regions. Attention is given to the governing equations, a statistical model, SST anomaly advection, heat flux anomalies, wind-driven mass transport in the mixed layer, mixed-layer depth, aspects of model testing, and the atmospheric forcing.

Frankignoul, C.↗
NASA NTRS1992

Aerospace applications of SINDA/FLUINT at the Johnson Space Center

SINDA/FLUINT has been found to be a versatile code for modeling aerospace systems involving single or two-phase fluid flow and all modes of heat transfer. Several applications of SINDA/FLUINT are described in this paper. SINDA/FLUINT is being used extensively to model the single phase water loops and the two-phase ammonia loops of the Space Station Freedom active thermal control system (ATCS). These models range from large integrated system models with multiple submodels to very detailed subsystem models. An integrated Space Station ATCS model has been created with ten submodels representing five water loops, three ammonia loops, a Freon loop and a thermal submodel representing the air loop. The model, which has approximately 800 FLUINT lumps and 300 thermal nodes, is used to determine the interaction between the multiple fluid loops which comprise the Space Station ATCS. Several detailed models of the flow-through radiator subsystem of the Space Station ATCS have been developed. One model, which has approximately 70 FLUINT lumps and 340 thermal nodes, provides a representation of the ATCS low temperature radiator array with two fluid loops connected only by conduction through the radiator face sheet. The detailed models are used to determine parameters such as radiator fluid return temperature, fin efficiency, flow distribution and total heat rejection for the baseline design as well as proposed alternate designs. SINDA/FLUINT has also been used as a design tool for several systems using pressurized gasses. One model examined the pressurization and depressurization of the Space Station airlock under a variety of operating conditions including convection with the side walls and internal cooling. Another model predicted the performance of a new generation of manned maneuvering units. This model included high pressure gas depressurization, internal heat transfer and supersonic thruster equations. The results of both models were used to size components, such as the heaters and gas bottles and also to point to areas where hardware testing was needed.

Ewert, Michael K.↗
NASA NTRS2002

Atmospheric Models for Engineering Applications

This paper will review the historical development of reference and standard atmosphere models and their applications. The evolution of the U.S. Standard Atmosphere will be addressed, along with the Range Reference Atmospheres and, in particular, the NASA Global Reference Atmospheric Model (GRAM). The extensive scope and content of the GRAM will be addressed since it represents the most extensive and complete 'Reference' atmosphere model in use today. Its origin was for engineering applications and that remains today as its principal use.

Johnson, Dale L.↗
NASA NTRS2003

A Design for Composing and Extending Vehicle Models

The Systems Development Branch (SDB) at NASA Langley Research Center (LaRC) creates simulation software products for research. Each product consists of an aircraft model with experiment extensions. SDB treats its aircraft models as reusable components, upon which experiments can be built. SDB has evolved aircraft model design with the following goals: 1. Avoid polluting the aircraft model with experiment code. 2. Discourage the copy and tailor method of reuse. The current evolution of that architecture accomplishes these goals by reducing experiment creation to extend and compose. The architecture mechanizes the operational concerns of the model's subsystems and encapsulates them in an interface inherited by all subsystems. Generic operational code exercises the subsystems through the shared interface. An experiment is thus defined by the collection of subsystems that it creates ("compose"). Teams can modify the aircraft subsystems for the experiment using inheritance and polymorphism to create variants ("extend").

Madden, Michael M.↗