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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Probabilistic neural networks for fluid flow surrogate modeling and data recovery

We consider the use of probabilistic neural networks for fluid flow surrogate modeling and data recovery. This framework is constructed by assuming that the target variables are sampled from a Gaussian distribution conditioned on the inputs. Consequently, the overall formulation sets up a procedure to predict the hyperparameters of this distribution which are then used to compute an objective function given training data. We demonstrate that this framework has the ability to provide for prediction confidence intervals based on the assumption of a probabilistic posterior, given an appropriate model architecture and adequate training data. The applicability of the present framework to cases with noisy measurements and limited observations is also assessed. To demonstrate the capabilities of this framework, we consider canonical regression problems of fluid dynamics from the viewpoint of reduced-order modeling and spatial data recovery for four canonical data sets. The examples considered in this study arise from (i) the shallow-water equations, (ii) a two-dimensional cylinder flow, (iii) the wake of a NACA0012 airfoil with a Gurney flap, and (iv) the NOAA sea surface temperature data set. Furthermore, the present results indicate that the probabilistic neural network not only produces a machine-learning-based fluid flow surrogate model but also systematically quantifies the uncertainty therein to assist with model interpretability.

42 ENGINEERING↗
DOE OSTI2020

Automated Threat Recognition For Aviation Security Applications

We have developed a framework for automated threat recognition (ATR) of explosive threat materials for both single-energy and dual-energy X-ray CT systems. Under this framework, two different types of ATR have been developed. The first type of ATR employs supervised machine learning with statistical characterization of target materials for threat identification training. The reliance only on statistical characterization information for threat training uniquely enables this style of ATR to adapt quickly to evolving threats. The second type of ATR employs deep learning through convolutional neural networks. Convolutional neural networks are attractive due to their human-like capacity for learning and strong ability to identify trends and patterns. Although this method is more powerful than the first type, it requires large amounts of training data and therefore is less agile. Each ATR performs threat characterization at the voxel neighborhood level. This approach avoids the use of threat shape as a detection criterion, which is prohibited by DHS and TSA guidelines.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

Truth-Valued-Flow Inference (TVFI) and its applications in approximate reasoning

The framework of the theory of Truth-valued-flow Inference (TVFI) is introduced. Even though there are dozens of papers presented on fuzzy reasoning, we think it is still needed to explore a rather unified fuzzy reasoning theory which has the following two features: (1) it is simplified enough to be executed feasibly and easily; and (2) it is well structural and well consistent enough that it can be built into a strict mathematical theory and is consistent with the theory proposed by L.A. Zadeh. TVFI is one of the fuzzy reasoning theories that satisfies the above two features. It presents inference by the form of networks, and naturally views inference as a process of truth values flowing among propositions.

Wang, Pei-Zhuang↗
NASA NTRS1994

Sensor Fault Detection and Diagnosis Simulation of a Helicopter Engine in an Intelligent Control Framework

This paper presents an application of a fault detection and diagnosis scheme for the sensor faults of a helicopter engine. The scheme utilizes a model-based approach with real time identification and hypothesis testing which can provide early detection, isolation, and diagnosis of failures. It is an integral part of a proposed intelligent control system with health monitoring capabilities. The intelligent control system will allow for accommodation of faults, reduce maintenance cost, and increase system availability. The scheme compares the measured outputs of the engine with the expected outputs of an engine whose sensor suite is functioning normally. If the differences between the real and expected outputs exceed threshold values, a fault is detected. The isolation of sensor failures is accomplished through a fault parameter isolation technique where parameters which model the faulty process are calculated on-line with a real-time multivariable parameter estimation algorithm. The fault parameters and their patterns can then be analyzed for diagnostic and accommodation purposes. The scheme is applied to the detection and diagnosis of sensor faults of a T700 turboshaft engine. Sensor failures are induced in a T700 nonlinear performance simulation and data obtained are used with the scheme to detect, isolate, and estimate the magnitude of the faults.

Litt, Jonathan↗
NASA NTRS2013

Characterizing Wheel-Soil Interaction Loads Using Meshfree Finite Element Methods: A Sensitivity Analysis for Design Trade Studies

A wheel experiencing sinkage and slippage events poses a high risk to planetary rover missions as evidenced by the mobility challenges endured by the Mars Exploration Rover (MER) project. Current wheel design practice utilizes loads derived from a series of events in the life cycle of the rover which do not include (1) failure metrics related to wheel sinkage and slippage and (2) performance trade-offs based on grouser placement/orientation. Wheel designs are rigorously tested experimentally through a variety of drive scenarios and simulated soil environments; however, a robust simulation capability is still in development due to myriad of complex interaction phenomena that contribute to wheel sinkage and slippage conditions such as soil composition, large deformation soil behavior, wheel geometry, nonlinear contact forces, terrain irregularity, etc. For the purposes of modeling wheel sinkage and slippage at an engineering scale, meshfree nite element approaches enable simulations that capture su cient detail of wheel-soil interaction while remaining computationally feasible. This study implements the JPL wheel-soil benchmark problem in the commercial code environment utilizing the large deformation modeling capability of Smooth Particle Hydrodynamics (SPH) meshfree methods. The nominal, benchmark wheel-soil interaction model that produces numerically stable and physically realistic results is presented and simulations are shown for both wheel traverse and wheel sinkage cases. A sensitivity analysis developing the capability and framework for future ight applications is conducted to illustrate the importance of perturbations to critical material properties and parameters. Implementation of the proposed soil-wheel interaction simulation capability and associated sensitivity framework has the potential to reduce experimentation cost and improve the early stage wheel design proce

terramechanics↗
DOE OSTI2015

Water Governance Process Assessment: Evaluating the Link between Decision Making Processes and Outcomes in the Columbia River Basin

Academics and practitioners agree that in water governance, the quality of a decision making process should influence the quality of the outcome and the degree to which it is accepted by interested parties. However, finding a feasible way to evaluate and then improve the quality of a decision making process has proven elusive. Systematically collecting evidence of a link between process and outcome is also challenging. In my dissertation, I developed a synthesis framework for evaluating and improving water governance decision making to address these two challenges. The synthesis framework, which I call the Water Governance Process Assessment (Water GPA), draws upon 22 existing frameworks rooted in resilience, adaptive governance, and good governance. From these frameworks, I identified and provided a way to evaluate four characteristics critical to good water governance decision making processes: 1) accountability, 2) inclusivity, and 3) information, and 4) context. I applied the Water GPA framework to the recent reviews of the Columbia River Treaty by the United States and Canada. I collected data for the case studies through semi-structured interviews and surveys of process participants from the federal agencies, Tribal and First Nations, state/provincial governments, local governments, stakeholder interests, and citizens. I coded and analyzed the interviews using the qualitative analysis software QSR NVivo and the characteristics identified in the Water GPA framework. I used the two case study applications to demonstrate how to use the framework. I identified what aspects of the four process categories served as barriers and building blocks for good water governance in each water governance process. I also gleaned lessons learned and recommendations including some for determining process leadership, ensuring meaningful engagement and inclusivity, addressing sovereignty issues, setting decision criteria, sharing decision authority, allocating resources in future processes in the US and BC portions of the Columbia River Basin and similar basins. In my case studies, I also investigated which characteristics of the water governance decision making process influenced the direct outcomes of those processes (the decisions) as well as other non-target outcomes (such as trust, co-produced science, new coalitions, etc.). All four characteristics outlined in the Water Governance Process Assessment (Water GPA), played some role in the development of the content and/or support of the two case study decision documents (US Regional Recommendation and BC Provincial Decision). Generally, when the characteristic of the process was done well, it improved the legitimacy and acceptance of the decision. At the same time performing poorly in one area did not necessarily torpedo the process or decision. In both case studies, it appears that the interplay between the process characteristics (that is where two or more characteristics converge) had a greater influence over the decision. Further work is needed to clearly identify what characteristics of a process are most influential in different situations. The Water GPA is one useful tool for this effort.

13 HYDRO ENERGY↗
DOE OSTI2018

Spring Boot based REST API to Improve Data Quality Report Generation for Big Scientific Data: ARM Data Center Example

Web application technologies are growing rapidly with continuous innovation and improvements. This paper focuses on the popular Spring Boot [1] java-based framework for building web and enterprise applications and how it provides the flexibility for service-oriented architecture (SOA). One challenge with any Spring-based applications is its level of complexity with configurations. Spring Boot makes it easy to create and deploy stand-alone, production-grade Spring applications with very little Spring configuration. Example, if we consider Spring Model-View-Controller (MVC) framework [2], we need to configure dispatcher servlet, web jars, a view resolver, and component scan among other things. To solve this, Spring Boot provides several Auto Configuration options to setup the application with any needed dependencies. Another challenge is to identify the framework dependencies and associated library versions required to develop a web application. Spring Boot offers simpler dependency management by using a comprehensive, but flexible, framework and the associated libraries in one single dependency, which provides all the Spring related technology that you need for starter projects as compared to CRUD web applications. This framework provides a range of additional features that are common across many projects such as embedded server, security, metrics, health checks, and externalized configuration. Web applications are generally packaged as war and deployed to a web server, but Spring Boot application can be packaged either as war or jar file, which allows to run the application without the need to install and/or configure on the application server. In this paper, we discuss how Atmospheric Radiation Measurement (ARM) Data Center (ADC) at Oak Ridge National Laboratory, is using Spring Boot to create a SOA based REST [4] service API, that bridges the gap between frontend user interfaces and backend database. Using this REST service API, ARM scientists are now able to submit reports via a user form or a command line interface, which captures the same data quality or other important information about ARM data.

Guntupally, Kavya↗
NASA NTRS1996

A Framework for the Optimization of Discrete-Event Simulation Models

With the growing use of computer modeling and simulation, in all aspects of engineering, the scope of traditional optimization has to be extended to include simulation models. Some unique aspects have to be addressed while optimizing via stochastic simulation models. The optimization procedure has to explicitly account for the randomness inherent in the stochastic measures predicted by the model. This paper outlines a general purpose framework for optimization of terminating discrete-event simulation models. The methodology combines a chance constraint approach for problem formulation, together with standard statistical estimation and analyses techniques. The applicability of the optimization framework is illustrated by minimizing the operation and support resources of a launch vehicle, through a simulation model.

Joshi, B. D.↗
DOE OSTI2015

Thermodynamics of metal-organic frameworks

Although there have been extensive studies over the past decade in the synthesis and application of metal-organic frameworks (MOFs), investigation of their thermodynamic stability and of the energetics of guest–host interactions has been much more limited. This review summarizes recent progress in experimental (calorimetric) determination of the thermodynamics of MOF materials. The enthalpies of MOFs relative to dense phase assemblages suggest only modest metastability, with a general increase of enthalpy with increasing molar volume, which becomes less pronounced at higher porosity. The energy landscape of nanoporous materials (inorganic and hybrid) consists of a pair of parallel patterns within a fairly narrow range of metastability of 5–30 kJ per mole of tetrahedra in zeolites and mesoporous silicas or per mole of metal in MOFs. Thus strong thermodynamic instability does not seem to limit framework formation. There are strong interactions within the chemisorption range for small molecule–MOF interactions with defined chemical binding at the metal centers or other specific locations. Coexistence of surface binding and confinement can lead to much stronger guest–host interactions. - Graphical abstract: Energy landscape of inorganic and hybrid porous materials. - Highlights: • Thermochemical data on various MOF structures were experimentally determined. • MOFs are moderately unstable relative to their dense phase assemblage. • Overall energetic landscape of porous materials was revealed. • Guest–host interactions in MOFs were evaluated directly using calorimetry. • Confinement effect and defined chemical binding lead to strong interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Fluorocarbon adsorption in hierarchical porous frameworks

Metal-organic frameworks comprise an important class of solid-state materials and have potential for many emerging applications such as energy storage, separation, catalysis and bio-medical. Here we report the adsorption behaviour of a series of fluorocarbon derivatives on a set of microporous and hierarchical mesoporous frameworks. The microporous frameworks show a saturation uptake capacity for dichlorodifluoromethane of >4 mmol g(-1) at a very low relative saturation pressure (P/P-o) of 0.02. In contrast, the mesoporous framework shows an exceptionally high uptake capacity reaching >14 mmol g(-1) at P/P-o of 0.4. Adsorption affinity in terms of mass loading and isosteric heats of adsorption is found to generally correlate with the polarizability and boiling point of the refrigerant, with dichlorodifluoromethane >chlorodifluoromethane >chlorotrifluoromethane >tetrafluoromethane >methane. These results suggest the possibility of exploiting these sorbents for separation of azeotropic mixtures of fluorocarbons and use in eco-friendly fluorocarbon-based adsorption cooling.

Motkuri, RK↗
DOE OSTI2019

An Online Plug-and-Play Algorithm for Regularized Image Reconstruction

Plug-and-play priors (PnP) is a powerful framework for regularizing imaging inverse problems by using advanced denoisers within an iterative algorithm. Recent experimental evidence suggests that PnP algorithms achieve state-of-the-art performance in a range of imaging applications. Here, we introduce a new online PnP algorithm based on the proximal gradient method (PGM). The proposed algorithm uses only a subset of measurements at every iteration, which makes it scalable to very large datasets. We present a new theoretical convergence analysis, for both batch and online variants of PnP-PGM, for denoisers that do not necessarily correspond to proximal operators. We also present simulations illustrating the applicability of the algorithm to image reconstruction in diffraction tomography. Finally, the results in this paper have the potential to expand the applicability of the PnP framework to very large datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Statistical framework to assess long-term spatio-temporal climate changes: East River mountainous watershed case study

Abstract Evaluation of long-term temporal and spatial climatic change in mountainous regions is a critical challenge because of the interactive effects of multiple land and climatic factors and processes. Here we present the application of the statistical framework to the assessment of changes of climatic conditions, using data from 17 meteorological stations across the East River watershed near Crested Butte, Colorado, USA, and spanning the period from 1966 to 2021. The framework is developed based on (1) a time-series analysis of daily, monthly, and yearly averaged meteorological parameters (temperature, relative humidity, precipitation, wind speed, etc.), (2) evaluation and time series analysis of potential evapotranspiration (ET o ), actual evapotranspiration (ET), aridity index (AI), standard precipitation index (SPI) and standard precipitation-evapotranspiration index (SPEI), and (3) a temporal-spatial climatic zonation of the studied area based on the hierarchical clustering and PCA analysis of the SPEI, because the SPEI can be considered an integrative characteristic of the changes of climatic conditions. The Budyko model, with the application of the Penman–Monteith equation for the estimation of ET o , was used to determine the ET. The time series analysis of the AI is used to identify the periods with energy limited and water limited conditions. Hierarchical clustering of site locations for the three temporal segments of the SPEI showed a significant temporal-spatial shifts, indicating that dynamic climatic processes drive zonation patterns. Therefore, the watershed climatic zonation requires periodic re-evaluation based on the structural time series analysis of meteorological and water balance data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Physics-based multiscale coupling for full core nuclear reactor simulation

Numerical simulation of nuclear reactors is a key technology in the quest for improvements in efficiency, safety, and reliability of both existing and future reactor designs. Historically, simulation of an entire reactor was accomplished by linking together multiple existing codes that each simulated a subset of the relevant multiphysics phenomena. Recent advances in the MOOSE (Multiphysics Object Oriented Simulation Environment) framework have enabled a new approach: multiple domain-specific applications, all built on the same software framework, are efficiently linked to create a cohesive application. This is accomplished with a flexible coupling capability that allows for a variety of different data exchanges to occur simultaneously on high performance parallel computational hardware. Examples based on the KAIST-3A benchmark core, as well as a simplified Westinghouse AP-1000 configuration, demonstrate the power of this new framework for tackling—in a coupled, multiscale manner—crucial reactor phenomena such as CRUD-induced power shift and fuel shuffle. 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-SA license

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Synthesis and Mechanical Properties of sub 5‐μm PolyUiO‐66 Thin Films on Gold Surfaces

Abstract Metal‐organic framework (MOF) thin films currently lack the mechanical stability needed for electronic device applications. Polymer‐based metal‐organic frameworks (polyMOFs) have been suggested to provide mechanical advantages over MOFs, however, the mechanical properties of polyMOFs have not yet been characterized. In this work, we developed a method to synthesize continuous sub‐5 μm polyUiO‐66(Zr) films on Au substrates, which allowed us to undertake initial mechanical property investigations. Comparisons between polyUiO‐66 and UiO‐66 thin films determined polyUiO‐66 thin films exhibit a lower modulus but similar hardness to UiO‐66 thin films. The initial mechanical characterization indicates that further development is needed to leverage the mechanical property advantages of polyMOFs over MOFs. Additionally, the demonstration in this work of a continuous surface‐supported polyUiO‐66 thin film enables utilization of this emerging class of polyMOF materials in sensors and devices applications.

Frederick, Esther↗
DOE OSTI2014

Dynamic Optimized Advanced Scheduling of Bandwidth Demands for Large-Scale Science Applications (Final Report)

In this project, developed scheduling frameworks for dynamic bandwidth demands for large-scale science applications. In particular, we developed scheduling algorithms for dynamic bandwidth demands in this project. Apart from theoretical approaches such as Integer Linear Programming, Tabu Search and Genetic Algorithm heuristics, we have utilized practical data from ESnet OSCARS project (from our DOE lab partners) to conduct realistic simulations of our approaches. We have disseminated our work through conference paper presentations and journal papers and a book chapter. In this project we addressed the problem of scheduling of lightpaths over optical wavelength division multiplexed (WDM) networks. We published several conference papers and journal papers on this topic. We also addressed the problems of joint allocation of computing, storage and networking resources in Grid/Cloud networks and proposed energy-efficient mechanisms for operation optical WDM networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Developing Water–Stable Pore–Partitioned Metal–Organic Frameworks with Multi–Level Symmetry for High–Performance Sorption Applications

Here a new perspective is proposed in the design of pore-space-partitioned MOFs that is focused on ligand symmetry properties sub-divided here into three hierarchical levels: 1) overall ligand, 2) ligand substructure such as backbone or core, and 3) the substituent groups. Different combinations of the above symmetry properties exist. Given the close correlation between nature of chemical moiety and its symmetry, such a unique perspective into ligand symmetry and sub-symmetry in MOF design translates into the influences on MOF properties. Five new MOFs have been prepared that exhibit excellent hydrothermal stability and high-performance adsorption properties with potential applications such as C 3 H 6 /C 2 H 4 and C 2 H 2 /CO 2 selective adsorption. The combination of high stability with high benzene/cyclohexane selectivity of ≈13.7 is also of particular interest.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗