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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 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Structural Analysis of the Redesigned Ice/Frost Ramp Bracket

This paper describes the interim structural analysis of a redesigned Ice/Frost Ramp bracket for the Space Shuttle External Tank (ET). The proposed redesigned bracket consists of mounts for attachment to the ET wall, supports for the electronic/instrument cables and propellant repressurization lines that run along the ET, an upper plate, a lower plate, and complex bolted connections. The eight nominal bolted connections are considered critical in the summarized structural analysis. Each bolted connection contains a bolt, a nut, four washers, and a non-metallic spacer and block that are designed for thermal insulation. A three-dimensional (3D) finite element model of the bracket is developed using solid 10-node tetrahedral elements. The loading provided by the ET Project is used in the analysis. Because of the complexities associated with accurately modeling the bolted connections in the bracket, the analysis is performed using a global/local analysis procedure. The finite element analysis of the bracket identifies one of the eight bolted connections as having high stress concentrations. A local area of the bracket surrounding this bolted connection is extracted from the global model and used as a local model. Within the local model, the various components of the bolted connection are refined, and contact is introduced along the appropriate interfaces determined by the analysts. The deformations from the global model are applied as boundary conditions to the local model. The results from the global/local analysis show that while the stresses in the bolts are well within yield, the spacers fail due to compression. The primary objective of the interim structural analysis is to show concept viability for static thermal testing. The proposed design concept would undergo continued design optimization to address the identified analytical assumptions and concept shortcomings, assuming successful thermal testing.

Phillips, D. R.↗
DOE OSTI2020

Exploring the Effects of Node Topology, Connectivity, and Metal Identity on the Binding of Nerve Agents and Their Hydrolysis Products in Metal–Organic Frameworks

Recent studies have shown that metal–organic frameworks (MOFs) built from hexanuclear M(IV) oxide cluster nodes are effective catalysts for nerve agent hydrolysis, where the properties of the active sites on the nodes can strongly influence the reaction energetics. The connectivity and metal identity of these M6 nodes can be easily tuned, offering extensive opportunities for computational screening to predict promising new materials. Thus, we used density functional theory (DFT) to examine the effects of node topology, connectivity, and metal identity on the binding energies of multiple nerve agents and their corresponding hydrolysis products. By computing an optimization metric based on the relative binding strengths of key hydrolysis reaction species (water, agent, and bidentate-bound products), we predicted optimal M6 nodes for hydrolyzing specific nerve agent and simulant molecules, where our results are in qualitative agreement with observed experimental trends. This analysis highlighted the notion that no single metal or node topology is optimal for all possible organophosphates, suggesting that MOFs should be selected based on the agent of interest. Using the large amount of data generated from our DFT calculations, we then derived quantitative structure–activity relationship (QSAR) models to help explain the complex trends observed in the binding energies. Through linear regression, we identified the most important descriptors for describing the binding of nerve agents and their hydrolysis products to M6 nodes. These results suggested that both molecular and node properties, including both structural and chemical features, collectively contribute to the binding energetics. By performing a thorough statistical analysis, we showed that our QSAR models are capable of making quantitatively accurate binding energy predictions for nerve agents and their hydrolysis products in a wide variety of M(IV)-MOFs. In conclusion, the insights gained herein can be used to guide future experiments for the synthesis of MOFs with enhanced catalytic activity for organophosphate hydrolysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2010

A Simplified Model for Detonation Based Pressure-Gain Combustors

A time-dependent model is presented which simulates the essential physics of a detonative or otherwise constant volume, pressure-gain combustor for gas turbine applications. The model utilizes simple, global thermodynamic relations to determine an assumed instantaneous and uniform post-combustion state in one of many envisioned tubes comprising the device. A simple, second order, non-upwinding computational fluid dynamic algorithm is then used to compute the (continuous) flowfield properties during the blowdown and refill stages of the periodic cycle which each tube undergoes. The exhausted flow is averaged to provide mixed total pressure and enthalpy which may be used as a cycle performance metric for benefits analysis. The simplicity of the model allows for nearly instantaneous results when implemented on a personal computer. The results compare favorably with higher resolution numerical codes which are more difficult to configure, and more time consuming to operate.

Paxson, Daniel E.↗
DOE OSTI2014

Optimization of adsorption processes for climate control and thermal energy storage

Adsorption based heat-pumps have received significant interest owing to their promise of higher efficiencies and energy savings when coupled with waste heat and solar energy compared to conventional heating and cooling systems. While adsorption systems have been widely studied through computational analysis and experiments, general design guidelines to enhance their overall performance have not been proposed. In this work, we identified conditions suitable for the maximum utilization of the adsorbent to enhance the performance of both intermittent as well as continuously operating adsorption systems. A detailed computational model was developed based on a general framework governing adsorption dynamics in a single adsorption layer and pellet. We then validated the computational analysis using experiments with a model system of zeolite 13X-water for different operating conditions. A dimensional analysis was subsequently carried out to optimize adsorption performance for any desired operating condition, which is determined by the choice of adsorbent-vapor pair, adsorption duration, operational pressure, intercrystalline porosity, adsorbent crystal size, and intracrystalline vapor diffusivity. The scaling analysis identifies the critical dimensionless parameters and provides a simple guideline to determine the most suitable geometry for the adsorbent particles. Based on this selection criterion, the computational model was used to demonstrate maximum utilization of the adsorbent for any given operational condition. By considering a wide range of parametric variations for performance optimization, these results offer important insights for designing adsorption beds for heating and cooling systems. (C) 2014 Elsevier Ltd. All rights reserved.

Narayanan, S↗
DOE OSTI2023

Computational budget optimization for Bayesian parameter estimation in heavy-ion collisions

Abstract Bayesian parameter estimation provides a systematic approach to compare heavy-ion collision models with measurements, leading to constraints on the properties of nuclear matter with proper accounting of experimental and theoretical uncertainties. Aside from statistical and systematic model uncertainties, interpolation uncertainties can also play a role in Bayesian inference, if the model’s predictions can only be calculated at a limited set of model parameters. This uncertainty originates from using an emulator to interpolate the model’s prediction across a continuous space of parameters. In this work, we study the trade-offs between the emulator (interpolation) and statistical uncertainties. We perform the analysis using spatial eccentricities from the T R ENTo model of initial conditions for nuclear collisions. Given a fixed computational budget, we study the optimal compromise between the number of parameter samples and the number of collisions simulated per parameter sample. For the observables and parameters used in the present study, we find that the best constraints are achieved when the number of parameter samples is slightly smaller than the number of collisions simulated per parameter sample.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1989

Design sensitivity analysis of boundary element substructures

The ability to reduce or condense a three-dimensional model exactly, and then iterate on this reduced size model representing the parts of the design that are allowed to change in an optimization loop is discussed. The discussion presents the results obtained from an ongoing research effort to exploit the concept of substructuring within the structural shape optimization context using a Boundary Element Analysis (BEA) formulation. The first part contains a formulation for the exact condensation of portions of the overall boundary element model designated as substructures. The use of reduced boundary element models in shape optimization requires that structural sensitivity analysis can be performed. A reduced sensitivity analysis formulation is then presented that allows for the calculation of structural response sensitivities of both the substructured (reduced) and unsubstructured parts of the model. It is shown that this approach produces significant computational economy in the design sensitivity analysis and reanalysis process by facilitating the block triangular factorization and forward reduction and backward substitution of smaller matrices. The implementatior of this formulation is discussed and timings and accuracies of representative test cases presented.

Kane, James H.↗
NASA NTRS2006

Analysis of Acoustic Modeling and Sound Propagation in Aircraft Noise Prediction

An analysis has been performed of measured and predicted aircraft noise levels around Denver International Airport. A detailed examination was made of 90 straight-out departures that yielded good measurements on multiple monitors. Predictions were made with INM 5, INM 6 and the simulation model NMSIM. Predictions were consistently lower than measurements, less so for the simulation model than for the integrated models. Lateral directivity ("installation effect") patterns were seen which are consistent with other recent measurements. Atmospheric absorption was determined to be a significant factor in the underprediction. Calculations of atmospheric attenuation were made over a full year of upper air data at seven locations across the United States. It was found that temperature/humidity effects could cause variations of up to +/-4 dB, depending on season, for the sites examined. It was concluded that local temperature and humidity should be accounted for in aircraft noise modeling.

Plotkin, Kenneth J.↗
DOE OSTI2025

Ice storage model-predictive control in an office building with PV: scenario, error and sensitivity analysis

Thermal energy storage (TES) can enable more building-sited renewable electricity generation and lower utility bill costs for buildings owners and occupants, especially when there are high demand and variable time-of-use (TOU) charges. A model predictive control (MPC) strategy can offer additional savings over a schedule-based control with added complexity and reliance on forecasts. Here, this study examines savings for medium office buildings with chiller plants in three locations with building-installed solar photovoltaics (PV) to understand the impact of MPC. Control setpoints are fixed by a schedule-based control or optimized by nonlinear MPC. These control setpoints are actuated within EnergyPlus building models to simulate the utility cost of the chiller plant. NLP solutions can be unstable or unrealistic, but our results show that by regularizing the NLP, the solutions can be reasonably followed by the building model. MPC models make simplifications that lead to errors once the controller is participating in and changing the operation of the building. These errors average 9 % across the cases, showing that the most important parts of the system are represented. The no-thermal load costs are computed to show that the optimization can in some cases achieve both the minimum TOU and minimum monthly demand costs by demand management while reducing TOU energy costs by energy arbitrage. The MPC saves 35–66 % in the annual chiller plant operating costs, which is an additional savings above the schedule by 1–33 %. PV and TES are complementary and mostly independent, but a load with PV often results in better performance for the schedule. Our case study and sensitivity analysis show the importance of modeling and optimization for complex rates, but also the circumstances wherein a simpler strategy achieves the same performance with less potential for error.

14 SOLAR ENERGY↗
NASA NTRS2011

Ground Processing Affordability for Space Vehicles

Launch vehicles and most of their payloads spend the majority of their time on the ground. The cost of ground operations is very high. So, why so often is so little attention given to ground processing during development? The current global space industry and economic environment are driving more need for efficiencies to save time and money. Affordability and sustainability are more important now than ever. We can not continue to treat space vehicles as mere science projects. More RLV's (Reusable Launch Vehicles) are being developed for the gains of reusability which are not available for ELV's (Expendable Launch Vehicles). More human-rated vehicles are being developed, with the retirement of the Space Shuttles, and for a new global space race, yet these cost more than the many unmanned vehicles of today. We can learn many lessons on affordability from RLV's. DFO (Design for Operations) considers ground operations during design, development, and manufacturing-before the first flight. This is often minimized for space vehicles, but is very important. Vehicles are designed for launch and mission operations. You will not be able to do it again if it is too slow or costly to get there. Many times, technology changes faster than space products such that what is launched includes outdated features, thus reducing competitiveness. Ground operations must be considered for the full product Lifecycle, from concept to retirement. Once manufactured, launch vehicles along with their payloads and launch systems require a long path of processing before launch. Initial assembly and testing always discover problems to address. A solid integration program is essential to minimize these impacts, as was seen in the Constellation Ares I-X test rocket. For RLV's, landing/recovery and post-flight turnaround activities are performed. Multi-use vehicles require reconfiguration. MRO (Maintenance, Repair, and Overhaul) must be well-planned--- even for the unplanned problems. Defect limits and standard repairs need to be in-place as well as easily added. Many routine inspections and maintenance can be like an aircraft overhaul. Modifications and technology upgrades should be expected. Another factor affecting ground operations efficiency is trending. It is essential for RLV's, and also useful for ELV's which fly the same or similar models again. Good data analysis of technical and processing performance will determine fixes and improvements needed for safety, design, and future processing. Collecting such data on new or low-frequency vehicles is a challenge. Lessons can be learned from the Space Shuttle, or even the Concorde aircraft. For all of the above topics, efficient business systems must be established for comprehensive program management and good throughput. Drawings, specifications, and manuals for an entire launch vehicle are often in different formats from multiple vendors, plus they have proprietary constraints. Nonetheless, the integration team must ensure that all data needed is compatible and visible to each appropriate team member. Ground processing systems for scheduling, tracking, problem resolution, etc. must be well laid-out. The balance between COTS (commercial off the shelf) and custom software is difficult. Multiple customers, vendors, launch sites, and landing sites add to the complexity of efficient IT (Information Technology) tools.

Ingalls, John↗
DOE OSTI2020

Capabilities of multivariate Bayesian inference toward seismic hazard assessment

Multivariate Bayesian analysis can bring significant benefits to seismic hazard analysis: Its multivariate feature enables computing scalar and vector hazard without making any approximations; Correlations between intensity measures are implicitly modeled, permitting direct simulation of ground motion selection tools such as the conditional mean spectrum and the generalized conditioning intensity measure; and Its updating feature enables a seamless integration of new ground motion data into the hazard results. Here, we first develop a multivariate Bayesian ground motion model through the NGA-West2 database. The model functional form considers fault-type, magnitude, and distance dependencies, and also the linear and the rock intensity dependent site response. We use a hybrid Markov Chain Monte Carlo sampling to perform Bayesian inference consisting of Gibbs step and a multilevel Metropolis-Hastings step. We then perform several checks on the model and note that its performance is satisfactory. Finally, we illustrate the merits of this multivariate Bayesian analysis, which include: ground motion model updating with ground motion data recorded in the last four years not part of the NGA-West2 database; computation of scalar and vector seismic hazard using the un-updated and updated ground motion models for Los Angeles, CA; and simulation of the conditional mean spectrum under scalar and vector IM conditioning while accounting for different sources of aleatoric and epistemic uncertainties.

58 GEOSCIENCES↗
DOE OSTI2019

An artificial neural network framework for reduced order modeling of transient flows

Here, this paper proposes a supervised machine learning framework for the non-intrusive model order reduction of unsteady fluid flows to provide accurate predictions of non-stationary state variables when the control parameter values vary. Our approach utilizes a training process from full-order scale direct numerical simulation data projected on proper orthogonal decomposition (POD) modes to achieve an artificial neural network (ANN) model with reduced memory requirements. This data-driven ANN framework allows for a nonlinear time evolution of the modal coefficients without performing a Galerkin projection. Our POD-ANN framework can thus be considered an equation-free approach for latent space dynamics evolution of nonlinear transient systems and can be applied to a wide range of physical and engineering applications. Within this framework we introduce two architectures, namely sequential network (SN) and residual network (RN), to train the trajectory of modal coefficients. We perform a systematic analysis of the performance of the proposed reduced order modeling approaches on prediction of a nonlinear wave-propagation problem governed by the viscous Burgers equation, a simplified prototype setting for transient flows. We find that the POD-ANN-RN yields stable and accurate results for test problems assessed both within inside and outside of the database range and performs significantly better than the standard intrusive Galerkin projection model. Finally, our results show that the proposed framework provides a non-intrusive alternative to the evolution of transient physics in a POD basis spanned space, and can be used as a robust predictive model order reduction tool for nonlinear dynamical systems.

42 ENGINEERING↗
DOE OSTI2018

Development of Anabaena 33047, a Fast-Growing N 2 -Fixing Cyanobacterium, as a Carbon Neutral Bioproduction Platform

Cyanobacteria, oxygenic photosynthetic prokaryotes, are emerging as promising platforms for cost effective conversion of sunlight and CO 2 into high value end products. Anabaena sp. ATCC 33047, a heterocystous cyanobacterium, stands out in this category of fast-growing cyanobacteria for its ability to utilize atmospheric N 2 in addition to CO 2 and generate biomass at unprecedented rates. Developing such a strain as a chassis can eliminate the need for fixed nitrogen in bioproduction systems, a significant step towards replacement of petroleum derived products. Our work aimed to develop Anabaena 33047 into such a bioproduction platform. Despite its many appealing traits, Anabaena 33047 remained largely unexplored due to its recalcitrance to any genetic manipulation. During this project, we have developed a method that has enabled targeted genome modification of this strain. We also constructed a genome scale metabolic model i AnC892, which captures the diazotrophic life cycle of Anabaena 33047 by accounting for both vegetative and heterocyst cell types. The model provided insight into the importance of light dependent electron transport in the heterocyst and revealed pathway combinations that can supply reducing equivalents and ATP in the appropriate ratio for optimal N2 fixation. To validate the existing metabolic models and to have a better understanding of the metabolism of Anabaena 33047 under different growth conditions, we performed a series of stable-isotope tracing experiments and quantified metabolic fluxes using state-of-the-art tools for 13C-metabolic flux analysis. Using our novel engineering strategy and based on our model predictions, we performed targeted deletion in the strain and generated a mutant with modified light harvesting properties in heterocysts. The mutant exhibited better light utilization and increased rates of nitrogen fixation. Overall, this project has laid the foundation for developing the non-model cyanobacterium Anabaena 33047 into a highly efficient chassis for conversion of solar energy, atmospheric CO 2 and N 2 into high value bio products.

09 BIOMASS FUELS↗
DOE OSTI2026

Image processing pipeline for AI-driven nanoparticle megalibrary characterization

Recent innovations have made it possible to produce megalibraries, millions of structurally and compositionally distinct nanoparticles on a chip. These megalibraries yield vast volumes of data that are impossible to analyze manually, necessitating the development of automated tools. In previous work, we created a binary classification machine learning model to select quality nanoparticle images for downstream analysis. In this work, we show that adding a custom image processing step before training can produce significantly higher-performing models in a fraction of the time and make them more robust to different image noise levels and microscope acquisition settings. The image processing pipeline proposed here effectively cleans raw nanoparticle images, enhances key features, and allows us to use much lower resolution images and simpler neural network model architectures. These features result in higher performance and significant cost savings. Experiments demonstrate superior performance relative to baseline, including an 18.2% improvement in recall and a 13.1% increase in accuracy. Given the high cost of downstream analysis, it is critical to minimize false positives, and our best-performing model reaches a precision of 95.9% and a weighted F-score of 95.1% on an unseen test set. Additionally, model training time is reduced from hours to less than a minute. We also show that, using this custom image processing pipeline, model performance is significantly improved at lower pixel resolutions compared to downsizing alone. We expect that adopting this pipeline for AI-driven automated nanoparticle characterization will allow researchers to rapidly and accurately analyze much greater volumes of data, thereby accelerating materials discovery.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRSDate not supplied

Rotor Performance Predictions of a Next Generation Mars Science Helicopter

Since Ingenuity took flight and proved aerial capabilities on Mars, the horizon for rotorcraft on the Red Planet has only expanded. One proposed future Martian rotorcraft is the Mars Science Helicopter (MSH). The MSH is a hexacopter capable of carrying scientific payloads. As a part of its joint development by NASA Ames Research Center (ARC) and the Jet Propulsion Laboratory (JPL), key MSH rotor components will be tested inside the in-development Reduced Atmospheric Pressure Testing Of Rotors (RAPTOR) wind tunnel in the Planetary Aeolian Laboratory (PAL) at NASA ARC. In preparation for the test, the Comprehensive Hierarchical Aeromechanics Rotorcraft Model (CHARM) analysis was utilized to predict the aerodynamic performance for one MSH hexacopter isolated rotor. This paper provides an overview of the planned experiments involving the MSH reference blades, as well as CHARM pretest predictions of rotor performance in hover and forward flight. Additionally, the interference effects of wind tunnel walls on rotor performance will be evaluated to inform the test matrix of the upcoming test in the RAPTOR tunnel.

MHS↗
DOE OSTI2023

Sensitivity Analysis, Reduced-order Modeling, and Optimization of a Gas-Cooled Pebble Bed Reactor using Equilibrium-Core and DLOFC Performance

This work presents and applies a workflow for performing design optimization on gas-cooled pebble-bed reactors. Based on previous research, a representative equilibrium core of a pebble-bed reactor and a depressurized loss-of-forced-cooling model are created. These applications are built using the Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically utilizing Griffin, Pronghorn, and Bison. After defining design-related parameters and quantities of interest regarding reactor safety and efficiency, this multiphysics model is sampled using the MOOSE stochastic tools module. The result is a comprehensive dataset of configurations, enabling sensitivity analysis and the generation of reduced-order models. Subsequently, the dataset and reduced-order models are employed in an optimization study aimed at maximizing fuel utilization while adhering to safety and operational constraints. The optimization process leads to an improvement of fuel utilization by approximately 10\%, compared to engineering-judgment-based nominal conditions.

97 - MATHEMATICS AND COMPUTING↗
NASA NTRS1985

An Integrated Analysis of the Physiological Effects of Space Flight: Executive Summary

A large array of models were applied in a unified manner to solve problems in space flight physiology. Mathematical simulation was used as an alternative way of looking at physiological systems and maximizing the yield from previous space flight experiments. A medical data analysis system was created which consist of an automated data base, a computerized biostatistical and data analysis system, and a set of simulation models of physiological systems. Five basic models were employed: (1) a pulsatile cardiovascular model; (2) a respiratory model; (3) a thermoregulatory model; (4) a circulatory, fluid, and electrolyte balance model; and (5) an erythropoiesis regulatory model. Algorithms were provided to perform routine statistical tests, multivariate analysis, nonlinear regression analysis, and autocorrelation analysis. Special purpose programs were prepared for rank correlation, factor analysis, and the integration of the metabolic balance data.

Leonard, J. I.↗
NASA NTRS1999

Development, Analysis and Testing of the High Speed Research Flexible Semispan Model

This report presents the work performed by Lockheed Martin Engineering and Sciences (LMES) in support of the High Speed Research (HSR) Flexible Semispan Model (FSM) wind-tunnel test. The test was conducted in order to assess the aerodynamic and aeroelastic character of a flexible high speed civil transport wing. Data was acquired for the purpose of code validation and trend evaluation for this type of wing. The report describes a number of activities in preparing for and conducting the wind-tunnel test. These included coordination of the design and fabrication, development of analytical models, analysis/hardware correlation, performance of laboratory tests, monitoring of model safety issues, and wind-tunnel data acquisition and reduction. Descriptions and relevant evaluations associated with the pretest data are given in sections 1 through 6, followed by pre- and post-test flutter analysis in section 7, and the results of the aerodynamics/loads test in section 8. Finally, section 9 provides some recommendations based on lessons learned throughout the FSM program.

Schuster, David M.↗
NASA NTRS2017

The Effect of Laminar Flow on Rotor Hover Performance

The topic of laminar flow effects on hover performance is introduced with respect to some historical efforts where laminar flow was either measured or attempted. An analysis method is outlined using combined blade element, momentum method coupled to an airfoil analysis method, which includes the full e(sup N) transition model. The analysis results compared well with the measured hover performance including the measured location of transition on both the upper and lower blade surfaces. The analysis method is then used to understand the upper limits of hover efficiency as a function of disk loading. The impact of laminar flow is higher at low disk loading, but significant improvement in terms of power loading appears possible even up to high disk loading approaching 20 ps f. A optimum planform design equation is derived for cases of zero profile drag and finite drag levels. These results are intended to be a guide for design studies and as a benchmark to compare higher fidelity analysis results. The details of the analysis method are given to enable other researchers to use the same approach for comparison to other approaches.

Overmeyer, Austin D.↗