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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.

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At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Energy-conserving dissipative particle dynamics with temperature-dependent properties

The dynamic properties of fluid, including diffusivity and viscosity, are temperature-dependent and can significantly influence the flow dynamics of mesoscopic non-isothermal systems. To capture the correct temperature-dependence of a fluid, an energy-conserving dissipative particle dynamics (eDPD) model is developed by expressing the weighting terms of the dissipative force and the random force as functions of temperature. The diffusivity and viscosity of liquid water at various temperatures ranging from 273 K to 373 K are used as examples for verifying the proposed model. Simulations of a Poiseuille flow and a steady case of heat conduction for reproducing the Fourier law are carried out to validate the present eDPD formulation and the thermal boundary conditions. Results show that the present eDPD model recovers the standard DPD model when isothermal fluid systems are considered. For non-isothermal fluid systems, the present model can predict the diffusivity and viscosity consistent with available experimental data of liquid water at various temperatures. Moreover, an analytical formula for determining the mesoscopic heat friction is proposed. The validity of the formula is confirmed by reproducing the experimental data for Prandtl number of liquid water at various temperatures. The proposed method is demonstrated in water but it can be readily extended to other liquids. (C) 2014 Elsevier Inc. All rights reserved.

CONSERVATION↗
DOE OSTI2021

A Consistent BGK Model with Velocity-Dependent Collision Frequency for Gas Mixtures

Abstract We derive a multi-species BGK model with velocity-dependent collision frequency for a non-reactive, multi-component gas mixture. The model is derived by minimizing a weighted entropy under the constraint that the number of particles of each species, total momentum, and total energy are conserved. We prove that this minimization problem admits a unique solution for very general collision frequencies. Moreover, we prove that the model satisfies an H-Theorem and characterize the form of equilibrium.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Solution to the sign problem in a frustrated quantum impurity model

In this work we solve the sign problem of a frustrated quantum impurity model consisting of three quantum spin-half chains interacting through an anti-ferromagnetic Heisenberg interaction at one end. We first map the model into a repulsive Hubbard model of spin-half fermions hopping on three independent one dimensional chains that interact through a triangular hopping at one end. We then convert the fermion model into an inhomogeneous one dimensional model and express the partition function as a weighted sum over fermion worldline configurations. By imposing a pairing of fermion worldlines in half the space we show that all negative weight configurations can be eliminated. This pairing naturally leads to the original frustrated quantum spin model at half filling and thus solves its sign problem.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1993

Analyzing Martian winds and tracer concentrations using Mars Observer data

During the courses of a day, the Mars Observer spacecraft will acquire globally distributed profiles of the martian atmosphere. It is highly desirable that this data be assembled into synoptic weather maps (complete specifications of the atmospheric pressure, temperature, and winds at a given time), which can in turn be used as starting points in the study of many meteorological phenomena. Unfortunately, the special nature of the Mars Observer data presents several challenges above and beyond the usual difficult problem of data initialization. Mars Observer atmospheric data will consist almost exclusively of asynoptic vertical profiles of temperatures (or radiances) and pressures, whereas winds are generally in balance with horizontal gradients of these quantities (which will not be observed). It will therefore be necessary to resort to dynamical models to analyze the wind fields. As a rule, data assimilation into atmospheric models can result in the generation of spurious gravity waves, so special steps must be taken to suppress these. In addition, the asynoptic nature of the data will require a four-dimensional (space and time) data assimilation scheme. The problem is to find a full set of meteorological fields (winds and temperatures) such that, when marched forward in time in the model, they achieve a best fit (in the weighted least-squares sense) to the data. The proposed solution is to develop a model especially for the Mars Observer data assimilation problem. Gravity waves are filtered from the model by eliminating all divergence terms from the prognostic divergence equation. This leaves a diagnostic gradient wind relation between the rotational wind and the temperature field. The divergent wind is diagnosed as the wind required to maintain the gradient wind balance in the presence of the diabatic heating. The primitive equations of atmospheric dynamics (with three principal dependent variables) are thus reduced to a simpler system with a single prognostic equation for temperature - the variable that will be best observed. (This balance system was apparently first derived by Charney as a first-order Rossby number expansion of the equations of motion). Experience with a full primitive equation model of the martian atmosphere indicates that a further simplification is possible: at least for short-term integrations, the model can be linearized about the zonally symmetric basic state.

Houben, Howard C.↗
DOE OSTI2015

Multi input single output model predictive control of non-linear bio-polymerization process

This paper focuses on Multi Input Single Output (MISO) Model Predictive Control of bio-polymerization process in which mechanistic model is developed and linked with the feedforward neural network model to obtain a hybrid model (Mechanistic-FANN) of lipase-catalyzed ring-opening polymerization of ε-caprolactone (ε-CL) for Poly (ε-caprolactone) production. In this research, state space model was used, in which the input to the model were the reactor temperatures and reactor impeller speeds and the output were the molecular weight of polymer (M{sub n}) and polymer polydispersity index. State space model for MISO created using System identification tool box of Matlab™. This state space model is used in MISO MPC. Model predictive control (MPC) has been applied to predict the molecular weight of the biopolymer and consequently control the molecular weight of biopolymer. The result shows that MPC is able to track reference trajectory and give optimum movement of manipulated variable.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

Development of a rhesus monkey lung geometry model and application to particle deposition in comparison to humans

The exposure-dose-response characterization of an inhalation hazard established in an animal species needs to be translated to an equivalent characterization in humans relative to comparable doses or exposure scenarios. Here, the first geometry model of the conducting airways for rhesus monkeys is developed based upon CT images of the conducting airways of a 6-month-old male, rhesus monkey. An algorithm was developed for adding the alveolar region airways using published rhesus morphometric data. The resultant lung geometry model can be used in mechanistic particle or gaseous dosimetry models. Such dosimetry models require estimates of the upper respiratory tract volume of the animal and the functional residual capacity, as well as of the tidal volume and breathing frequency of the animal. The relationship of these variables to rhesus monkeys of differing body weights was established by synthesizing and modeling published data as well as modeling pulmonary function measurements on 121 rhesus control animals. Deposition patterns of particles up to 10 µm in size were examined for endotracheal and and up to 5 µm for spontaneous breathing in infant and young adult monkeys and compared to those for humans. Deposition fraction of respirable size particles was found to be higher in the conducting airways of infant and young adult rhesus monkeys compared to humans. Due to the filtering effect of the conducting airways, pulmonary deposition in rhesus monkeys was lower than that in humans. Finally, future research areas are identified that would either allow replacing assumptions or improving the newly developed lung model.

Asgharian, Bahman↗
DOE OSTI2025

FM4NPP: A Scaling Foundation Model for Nuclear and Particle Physics

Large language models have revolutionized artificial intelligence by enabling large, generalizable models trained through self-supervision. This paradigm has inspired the development of scientific foundation models (FMs). However, applying this capability to experimental particle physics is challenging due to the sparse, spatially distributed nature of detector data, which differs dramatically from natural language. This work addresses if an FM for particle physics can scale and generalize across diverse tasks. We introduce a new dataset with more than 11 million particle collision events and a suite of downstream tasks and labeled data for evaluation. We propose a novel self-supervised training method for detector data and demonstrate its neural scalability with models that feature up to 188 million parameters. With frozen weights and task-specific adapters, this FM consistently outperforms baseline models across all downstream tasks. The performance also exhibits robust data-efficient adaptation. Further analysis reveals that the representations extracted by the FM are task-agnostic but can be specialized via a single linear mapping for different downstream tasks.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗
NASA NTRS2023

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be easily expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗
DOE OSTI2026

ObstacleSense: Low-Power Neuromorphic Vision for Corridor Obstacle Awareness in Low-Level ADAS

The automotive industry’s pursuit of Level 5 autonomy is constrained by substantial perception-compute power requirements, often reaching 1, 000 + watts in full autonomy stacks. Reducing this energy burden requires rethinking perception not only at the high-end autonomy level, but also at the foundational Advanced Driver Assistance Systems (ADAS) level where low-power, safety-critical sensing can have broad impact. Neuromorphic vision provides a promising starting point: HD Dynamic Vision Sensors (DVS) can operate below 100 mW at the sensor level by reporting only asynchronous brightness changes. However, low-power sensing alone is insufficient if downstream perception reintroduces dense, energy-intensive computation. In particular, many event-driven object-detection pipelines still rely on CNN backbones, while purely spiking alternatives often trade away accuracy or ignore deployment constraints. We introduce ObstacleSense, a highly compact, CNN-free hybrid ANN–SNN framework for Level 0–1 forward-corridor obstacle awareness. Instead of performing full-scene object detection with a convolutional feature backbone, ObstacleSense targets the safety-critical question of whether the ego corridor is occupied and how far the nearest obstacle is. The architecture combines polarity-conditioned event encoding, lightweight temporal spiking dynamics, axial spatial mixing, and coarse-to-fine range estimation within a regular fixed-grid compute pattern. This design avoids the dense CNN backbone commonly used in event-based detection while maintaining a small state footprint suitable for eventual small-FPGA deployment. Before hardware mapping, we evaluate the software implementation using a model-side power proxy derived from MACs, weight and activation traffic, and spiking state updates under shared FP16 assumptions. On simulated CARLA event corpora, the deployment-oriented model achieves 0.9464 objectness F1, 0.9978 grid-level mAP, and 0.8987 m distance Mean Absolute Error at an estimated 1.92 mW proxy cost, while maintaining performance on unseen generalization test sequences.

Johnson-Scott, Zac [ORNL]↗
DOE OSTI2015

THE STAR-FORMING HISTORIES OF THE NUCLEUS, BULGE, AND INNER DISK OF NGC 5102: CLUES TO THE EVOLUTION OF A NEARBY LENTICULAR GALAXY {sup ,} {sup ,}

Long slit spectra recorded with the Gemini Multi-Object Spectrograph on Gemini South are used to examine the star-forming history (SFH) of the lenticular galaxy NGC 5102. Structural and supplemental photometric information are obtained from archival Spitzer [3.6] images. Absorption features at blue and visible wavelengths are traced out along the minor axis to galactocentric radii ∼60 arcsec (∼0.9 kpc), sampling the nucleus, bulge, and disk components. Comparisons with model spectra point to luminosity-weighted metallicities that are consistent with the colors of resolved red giant branch stars in the disk. The nucleus has a luminosity-weighted age at visible wavelengths of ∼1{sub −0.1}{sup +0.2} Gyr, and the integrated light is dominated by stars that formed over a time period of only a few hundred Myr. For comparison, the luminosity-weighted ages of the bulge and disk are ∼2{sub −0.2}{sup +0.5} Gyr and 10{sub −2}{sup +2} Gyr, respectively. The g' – [3.6] colors of the nucleus and bulge are consistent with the spectroscopically based ages. In contrast to the nucleus, models that assume star-forming activity spanning many Gyr provide a better match to the spectra of the bulge and disk than simple stellar population models. Isophotes in the bulge have a disky shape, hinting that the bulge was assembled from material with significant rotational support. The SFHs of the bulge and disk are consistent with the bulge forming from the collapse of a long-lived bar, rather than from the collapse of a transient structure that formed as the result of a tidal interaction. It is thus suggested that the progenitor of NGC 5102 was a barred disk galaxy that morphed into a lenticular galaxy through the buckling of its bar.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Efficient Contingency Analysis in Power Systems via Network Trigger Nodes

Modeling failure dynamics within a power system is a complex and challenging process due to multiple inter-dependencies and convoluted inter-domain relationships. Subject matter experts (SMEs) are interested in understanding these failure dynamics for reducing the impact from future disasters (i.e., losses or failures of power system components, such as transmission lines). Contingency analysis (CA) tools enable such ’what-if’ scenario analyses to evaluate the impacts on the power system. Analyzing all possible contingencies among N system components can be computationally expensive. An important step for performing CA is identifying a set of k ‘trigger’ components, which when failed initially can significantly impact the overall system by causing multiple failures. Currently SMEs focus on identifying these trigger components by running expensive simulations on all possible subsets, which quickly becomes infeasible. Hence finding a relevant set of trigger components (contingencies) rapidly to enable efficient and useful CA is crucial.In a collaboration between computer scientists and power system experts, we propose an efficient method for performing CA by exploiting network inter-dependencies in power system components. First, we construct a network with multiple electric grid infrastructure components and dependencies as connections among them. We reformulate the problem of finding a set of trigger components as a problem of identifying critical nodes in the network, which can cascade power failures through connected nodes and cause significant damage to the network. To guide the practical CA tools, we develop a network-based model with a probabilistic edge-weights setup using intricate domain rules. Then we conduct an empirical study on real power system data in the US for both regional and national levels. Firstly, we use power system datasets for the US to create a national-scale domain-driven model. Secondly, we demonstrate that network-based model outperforms the outputs from a real CA tool and show on average 25 × improved selection of contingencies, thereby showcasing practical benefits to the power experts.

Tabassum, Anika↗
NASA NTRS1979

The solubility of olivine in basaltic liquids - An ionic model

A model is presented which enables the temperature at which olivine is in equilibrium with any alkali-depleted basaltic compound to be calculated to within + or - 30 C. It is noted that the error increases substantially when applied to terrestrial basalts which contain several weight percent alkalis. In addition the model predicts and quantifies the reduced activity of SiO4(4-) monomers due to increasing SiO2 concentrations in the melt. It is shown that the coordination of alumina in melts which precipitate olivine only appears to be dominantly octahedral, while titanium acts as a polmerizing agent by interconnecting previously isolated SiO4(4-) monomers. It is concluded that the model is sufficiently sensitive to show that there are small repulsive forces between Mg(2+) and calcium ions which are in association with normative diopside in the melt.

Herzberg, C. T.↗
NASA NTRSDate not supplied

Marin County Wildland Fires: Examining Fuel Load and Land Cover Change to Inform Fire Prevention and Suppression Decisions in Marin County, CA

Heightened occurrence of severe wildfires in the Western United States is increasing the need to better understand regions of high potential wild fire severity and develop methodologies for identifying the best locations for fuels reduction and active wildfire suppression, especially in populated regions such as Marin County, California. Marin County, located in the San Francisco Bay Area, has had significant development in the wildland-urban interface and periods of highly wildfire-prone conditions. The NASA DEVELOP team collaborated with Fire Foundry, a Marin-based fire service work force development program, to develop new models to assist with fire management. Using data from Sentinel-2A, Planet Scope, ECOSTRESS, a county-wide LiDAR mapping effort, Landsat 7 Enhanced Thematic Mapper (ETM+), and Landsat 8 Operational Land Imager (OLI), the team developed several input data layers to three models evaluating wild fire severity. One model performed a suitability analysis with weights based on scientific literature, another utilized machine learning based on past fires in Marin and neighboring Sonoma County to predict the difference normalized burn ratio, and the third inputted data layers into the Flam Map tool, which outputs risk categories. The team compared model outputs and, using the best-fit model, performed fuzzy logic analysis to identify specific locations where a fire break could be constructed to interrupt the progress of an active fire. These tools were proven useful and will assist partners in preparing for and managing an active wildfire event.

Suhani Dalal↗
NASA NTRS1994

Simplified Aerodynamic and Structural Modeling for Oblique All-Wing Aircraft. Phase 2: Structures

Any aircraft preliminary design study requires a structural model of the proposed configuration. The model must be capable of estimating the structural weight of a given configuration, and of predicting the deflections which will result from foreseen flight and ground loads. The present work develops such a model for the proposed Oblique All Wing airplane. The model is based on preliminary structural work done by Jack Williams and Peter Rudolph at Mdng, and is encoded in a FORTRAN program. As a stand-alone application, the program can calculate the weight CG location, and several types of structural deflections; used in conjunction with an aerodynamics model, the program can be used for mission analysis or sizing studies.

Kroo, Ilan↗
DOE OSTI2020

Duty-Cycle Predictive Control of Quasi-Z-Source Modular Cascaded Converter Based Photovoltaic Power System

A duty-cycle predictive control is proposed for dc grid integration of front-end isolated quasi-Z-source modular cascaded converter (qZS-MCC) photovoltaic (PV) power system. The post-stage qZS half-bridge dc-dc converter deals with PV maximum power point tracking, dc grid integration, and dc-link voltage balance; whereas, the front-end isolation converters operate at a constant duty cycle of 50%. Thus, it saves control hardware resources while overcoming challenges from PV-panel voltage variations and dc-bus voltage limit. The proposed control uses the derived circuit model to predict the global active-state duty cycle for grid-connected current control and predict the shoot-through duty cycles for dc-link voltage balance, achieving a fast and accurate tracking target. The proposed control method has advantages of: i) eliminating weighting factors that exist in conventional model predictive control (MPC), ii) no sophisticated loop parameters design that exists in proportional-integral (PI) control, iii) operating at constant switching frequency that is different from the conventional MPC with variable switching frequency. Simulation and experimental tests are carried out to verify the effectiveness of the proposed control method and compare with the PI-based control system.

42 ENGINEERING↗
DOE OSTI2014

SU-C-12A-03: The Impact of Contrast Medium On Radiation Dose in CT: A Systematic Evaluation Across 58 Patient Models

Purpose: To assess the effect of contrast medium on radiation dose as a function of time via Monte Carlo simulation from the liver CT scan across a library of 5D XCAT models Methods: A validated Monte Carlo simulation package (PENELOPE) was employed to model a CT system (LightSpeed 64 VCT, GE Healthcare). The radiation dose was estimated from a common abdomen CT examination. The dose estimation was performed on a library of adult extended cardiac-torso (5D XCAT) phantoms (35 male, 23 female, mean age 51.5 years, mean weight 80.2 kg). The 5D XCAT models were created based on patient-specific iodine concentration-time results from our computational contrast medium propagation model for different intravenous injection protocols. To enable a dynamic estimation of radiation dose, each organ in the model was assigned to its own time-concentration curve via the PENELOPE package, material.exe. Using the Monte Carlo, for each scan time point after the injection, 80 million photons were initiated and tracked through the phantoms. Finally, the dose to the liver was tallied from the deposited energy. Results: Monte Carlo simulation results of radiation dose delivered to the liver from the XCAT models indicated up to 30% increase in dose for different time after the administration of contrast medium. Conclusion: The contrast enhancement is employed in over 60% of imaging modalities, which not only remarkably affects the CT image quality, but also increases the radiation dose by as much as 70%. The postinjection multiple acquisition in several enhanced CT protocols, makes the radiation dose increment through the use of contrast medium, an inevitable factor in optimization of these protocols. The relationship between radiation dose and injected contrast medium as a function of time studied in this work allows optimization of contrast administration for vulnerable individuals.

60 APPLIED LIFE SCIENCES↗