SEARCH · Engineering Papers

Results for “Model Based Systems Engineering”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,063 records · Page 59

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Mechanism of structural colors in binary mixtures of nanoparticle-based supraballs

Inspired by structural colors in avian species, various synthetic strategies have been developed to produce noniridescent, saturated colors using nanoparticle assemblies. Nanoparticle mixtures varying in particle chemistry and size have additional emergent properties that affect the color produced. For complex multicomponent systems, understanding the assembled structure and a robust optical modeling tool can empower scientists to identify structure-color relationships and fabricate designer materials with tailored color. Here, we demonstrate how we can reconstruct the assembled structure from small-angle scattering measurements using the computational reverse-engineering analysis for scattering experiments method and use the reconstructed structure in finite-difference time-domain calculations to predict color. We successfully, quantitatively predict experimentally observed color in mixtures containing strongly absorbing nanoparticles and demonstrate the influence of a single layer of segregated nanoparticles on color produced. The versatile computational approach that we present is useful for engineering synthetic materials with desired colors without laborious trial-and-error experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Surface Roughness Effects from Additive Manufacturing in High Efficiency Gas Turbine Combustion Systems

In the past, clean combustion systems were characterized by high system development costs due to growing complexity and escalating manufacturing costs from conventional manufacturing processes. As a result, high efficiency concepts are difficult to design and more difficult to be cost-effectively manufactured. In recent years, with the introduction of additive manufacturing (AM) technologies, the rapid prototyping and mass production processes of clean combustion systems are promising to be significantly simplified with significant reduction in terms of engine manufacturing cost and engine energy cost. Compared with conventional manufacturing built parts, the AM process enabled a simpler design to be adopted for the nozzle, reducing the number of required braze and weld joints from twenty-five to just five. The resulting nozzle was 25% lighter and five times more durable and contributed to a 15% reduction in fuel burn in comparison with the previous model produced. Applying these improvements to a fleet of 5,000 turbofan engines at 150 kN thrust would result in fuel cost savings of $ \$ $6B annually (at $ \$ $5/gallon Jet-A fuel price, 700 gallon/hour fuel consumption rate, and 2,300 operational hours per year), and reduce CO2 emissions by more than 25 million tons per year. These advances support core Department of Energy (DOE) missions in energy efficiency, improving productivity, and environmental sustainability. Maximizing the benefit of these new capabilities will require high prediction capability of high speed turbulent flow with wall modeled Large Eddy Simulation (LES). GE Aviation maintains that advanced simulation technology and supercomputing is required in order to provide the appropriate boundary conditions to realize the maximum potential of AM and Ceramic Matrix Composite to reduce cooling flow, a first order penalty on the Brayton cycle. The algorithm developed using the ANSYS/Fluent software would provide the foundation for entirely new avenues of research and development with potential multi-billion dollar impact to the US economy, and significant reduction in carbon based emissions across the aerospace and power generation industries. The formulation of this iWLES (integral wall model for LES) is generic and allows to capture the changes in flow dynamics that have a significant impact on the wall bounded flow characteristics, such as swirler effective area, bulk swirl number, local pressure distribution, exit velocity profile, and turbulence kinetic energy profile. A periodic channel flow with rough flat plate is simulated using LES (Wall-Adapting Local Eddy-viscosity) model to verify the implementation of iWLES in the Fluent User Defined Function (UDF). As the flow fields are highly sensitive due to surface roughness of the wall bounded flows in the combustion systems, there is significant potential to conduct further LES studies focusing on the turbulence boundary layer interaction. Accurately capturing near wall physics will elucidate the impact of rough surfaces on combustor flow and aero-thermal interactions.

42 ENGINEERING↗

DOE OSTI2022

Experimental realization of an extended Fermi-Hubbard model using a 2D lattice of dopant-based quantum dots

The Hubbard model is an essential tool for understanding many-body physics in condensed matter systems. Artificial lattices of dopants in silicon are a promising method for the analog quantum simulation of extended Fermi-Hubbard Hamiltonians in the strong interaction regime. However, complex atom-based device fabrication requirements have meant emulating a tunable two-dimensional Fermi-Hubbard Hamiltonian in silicon has not been achieved. Here, we fabricate 3 × 3 arrays of single/few-dopant quantum dots with finite disorder and demonstrate tuning of the electron ensemble using gates and probe the many-body states using quantum transport measurements. By controlling the lattice constants, we tune the hopping amplitude and long-range interactions and observe the finite-size analogue of a transition from metallic to Mott insulating behavior. We simulate thermally activated hopping and Hubbard band formation using increased temperatures. As atomically precise fabrication continues to improve, these results enable a new class of engineered artificial lattices to simulate interactive fermionic models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

A Functional All-Hazard Approach to Critical Infrastructure Dependency Analysis

The critical infrastructures protection landscape is a vast and varied pattern of independent, but interconnected infrastructure systems that are essential to the function of our modern society. The U.S. policy on critical infrastructure protection has been continually evolving since the “President’s Commission on Critical Infrastructure Protection” was published in 1997. In response to these policies, federal, state, and local governments, along with research institutions, have invested a substantial amount of time and effort into identifying and analyzing critical infrastructure, their functions, and dependencies/interdependencies to better understand their vulnerabilities. To date, the ability to assess vulnerabilities, resiliency, and priorities for protecting interdependent critical infrastructure systems from an all-hazards perspective remains a difficult problem. In this paper we introduce the All-Hazards Analysis (AHA) methodology, which provides an integrated functional basis across infrastructure systems, through the implementation of a common language and a scalable level of decomposition to effectively evaluate the resilience of interconnected infrastructure systems. AHA models infrastructure systems as directed multidimensional graphs, which enable the evaluation of cross-sector interdependencies prior to, during, and after disruptive events. Finally, and by design, AHA enables the cross linking of data taxonomies to enable more effective data sharing, such as the National Critical Functions (NCF) and Infrastructure Data Taxonomy (IDT).

02 PETROLEUM↗

DOE OSTI2023

Thermodynamic modeling of double azeotropy systems

Azeotropes are boiling liquid mixtures which have identical vapor and liquid compositions. Known as polyazeotropy, azeotropic mixtures may exhibit more than one azeotrope at a given temperature or pressure. While the thermodynamic condition for azeotrope formation in vapor-liquid equilibrium is well established, thermodynamic modeling of mixtures exhibiting polyazeotropy remains challenging and prior modeling studies often failed to satisfactorily correlate the polyazeotropic behavior of associating systems. Based on the association NRTL model, this study presents successful correlation and analysis of double azeotrope formation for three diverse systems: strongly associating methanol + diethylamine binary, weakly associating ammonia + pentafluoroethane binary, and non-associating benzene + hexafluorobenzene binary systems. Furthermore, these systems exhibit polyazeotropic behavior as the temperature increases and approaches their Bancroft points.

42 ENGINEERING↗

DOE OSTI2018

Advanced trust region optimization strategies for glass box / black box models

We present an improved trust region filter (TRF) method for optimization of combined glass box/black box systems. Glass box systems refer to models that are easily expressed in an algebraic modeling language, providing cheap and accurate derivative information. By contrast, black box systems may be computationally expensive and derivatives are unavailable. The TRF method, as first introduced in our previous work (Eason and Biegler, AIChE J . 2016; 62:3124–3136), is able to handle hybrid systems containing both glass and black box components, which can frequently arise in chemical engineering, for example, when a multiphase reactor model is included in a flow sheet optimization problem. We discuss several recent modifications in the algorithm such as the sampling region, which maintains the algorithm's global convergence properties without requiring the trust region to shrink to zero in the limit. To benchmark the development of this optimization method, a test set of problems is generated based on modified problems from the CUTEr and COPS sets. The modified algorithm demonstrates improved performance using the test problem set. Finally, the algorithm is implemented within the Pyomo environment and demonstrated on a rigorous process optimization case study for carbon capture. © 2018 American Institute of Chemical Engineers AIChE J , 64: 3934–3943, 2018

Eason, John P.↗

DOE OSTI2025

Identification of short-range ordering motifs in semiconductors

Chemical short-range ordering is expected to be a key factor for tuning the electronic structure of semiconductors. However, experimental evidence of short-range ordering is still lacking due to the challenge of characterizing atomic-scale ordering motifs. Here, we determined the presence of short-range order in a ternary GeSiSn semiconductor system using advanced energy-filtered four-dimensional scanning transmission electron microscopy and large-scale atomistic models generated by a machine learning neuroevolution potential of first-principles accuracy. This approach revealed preferred ordering of different atomic species with the dominant occurrence of Si–Ge–Sn triplets. Our findings not only confirmed the presence of short-range order but also directly revealed the actual atomic structure, demonstrating the potential for informed atomic order–based band engineering as a third degree of freedom beyond composition and strain tuning.

Vogl, Lilian M. [University of California Berkeley↗

DOE OSTI2021

EKAT v.1.0

E3SM Kokkos Application Toolkit (EKAT) is a collection of C++, Fortran, and CMake utilities for providing a single implementation of common kernels based on the Kokkos programming model. The library contains utilities for vectorization, tridiagonal linear system solvers, and linear interpolation as well as some general-purpose utilities such as testing utilities, parameter lists, representation of physical units, and additional interfaces. The goal is to provide a centralized implementation for high-performance computing structures and common utilities that reduce code duplication and streamline maintenance efforts. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. SAND2022-1327 O

Bertagna, Luca↗

DOE OSTI2013

SciDAC-Center for Plasma Edge Simulation Report

The Common Component Architecture (CCA) effort is the embodiment of a long-range program of research and development into the formulation, roles, and use of component technologies in high-performance scientific computing. CCA components can interoperate with other components in a variety of frameworks, including SCIRun2 from the University of Utah. The SCIRun2 framework is also developing the ability to connect components from a variety of different models through a mechanism called meta-components. The meta component model operates by providing a plugin architecture for component models. Abstract components are manipulated and managed by the SCIRun2 framework, while concrete component models perform the actual work and communicate with each other directly. We will leverage the SCIRun2 framework and the Kepler system to orchestrate components in the Fusion Simulation Project (FSP) and to provide a CCA-based interface with Kepler. The groundwork for this functionality is being performed with the Scientific Data Management center. The SDM center is developing CCA-compliant interfaces for expressing and executing workflows and create workflow components based on SCIRun and Ptolemy (Kepler) execution engines, including development of uniform interfaces for selecting, starting, and monitoring scientific workflows. Accomplishments include Introduction to CCA and Simulation Software Systems, Introduction into SCIRun2 and Bridging within SCIRun2, CCALoop: A scalable design for a distributed component framework, and Combining Workflow methodologies with Component Architectures.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

Stakeholder-guided holistic, Adaptive Framework for enhancing community Energy Resilience (SAFER) (Final Technical Report)

The Stakeholder-guided holistic, Adaptive Framework for enhancing community Energy Resilience (SAFER) project advances resilience science and engineering by addressing challenges in rural Kansas communities where aging infrastructure, extreme weather, and socioeconomic disparities heighten vulnerability to energy disruptions. Traditional approaches often focus on technical performance while overlooking community concerns and priorities. SAFER responds by integrating community perspectives with advanced analytical frameworks to create a holistic model for measuring and improving resilience. Project objectives included developing novel resilience metrics, advancing modeling frameworks that capture interdependencies across infrastructures, and embedding community-centric indicators directly into planning processes for distributed energy resources. The key technical innovations included the creation of self-organizing map (SOM)-based indices for objective resilience quantification, hetero-functional graph theory (HFGT) models linking power, water, transportation, and community assets, and graph neural network (GNN) tools for identifying critical nodes in complex systems. Community-centric energy planning was demonstrated through optimal siting and sizing of (photovoltaic) PV and battery storage, ensuring resilience enhancements also addressed energy burden and energy insecurity. SAFER engaged community partners in Dodge City and Ford County through surveys, focus groups, and workshops, generating more than 600 responses that established baseline measures of energy burden, financial insecurity, and willingness-to-pay to avoid outages. This data, organized in terms of a community capitals framework, informed the development of weighted reliability indices that better reflect community costs than traditional utility metrics. SAFER’s GNN-based critical node identification framework identified expert-labelled critical nodes with over 99% accuracy, while also uncovering additional functionalities essential for proactive resilience planning. The project’s models demonstrated that optimal PV and storage deployment could improve resilience indices by over 11 percent, with dispatch strategies further enhancing outcomes, confirming both the technical effectiveness and economic feasibility of these approaches. Through its combined emphasis on rigorous modeling, community-focused planning, and community engagement, SAFER advances the state of resilience research while delivering direct benefits to rural communities. The project provides tools, guidelines, and resilience heatmaps that help utilities, local governments, and residents better anticipate disruptions, prioritize investments, and strengthen the capacity to withstand and recover from energy-related hazards. Furthermore, the developed HFG and GNN frameworks are designed for transferability, allowing them to be adapted for resilience planning in other communities with minimal retraining. This inductive learning capability provides a scalable pathway to extend the SAFER project’s impact. Thus, creating a foundation for a nationally applicable model of infrastructure resilience. Additionally, the HFG can also be extended to include other FEMA community lifelines.

14 SOLAR ENERGY↗

DOE OSTI2018

Capacity Optimization of a Centralized Charging Station in Joint Operation with a Wind Farm

In the context of large-scale wind power integration and rapid development of electric vehicles (EVs), a joint operation pattern was proposed to use a centralized charging station (CCS) to address high uncertainties incurred by wind power integration. Additionally, this would directly remove the significant indeterminacy of wind power. Because the CCS is adjacent to a wind power gathering station, it could work jointly with wind farms to operate in power system as an independent enterprise. By combining the actual operational characteristics of the wind farm and the CCS, a multidimensional operating index evaluation system was created for the joint system. Based on the wind farm’s known capacity, a multi-target capacity planning model was established for CCS to maximize the probability of realizing diverse indices of the system based on dependent-chance goal programming. In this model, planning and dispatching are combined to improve the feasibility of results in the operational stage. This approach takes the effects of randomness into account for wind power and battery swapping. The model was solved through combining Monte Carlo simulation and a genetic algorithm (GA) based on segmented encoding. Finally, as shown in the simulation results, the proposed model could comprehensively include factors such as initial investment, wind power price, battery life, etc., to optimize CCS capacity and to ultimately improve the operating indices of the joint system.

17 WIND ENERGY↗

DOE OSTI2016

IDC Re-Engineering Phase 2 Iteration E2 Use Case Realizations

This architecturally significant use case describes how the System acquires meteorological data to build atmospheric models used in automatic and interactive processing of infrasound data. The System requests the latest available high-resolution global meteorological data from external data centers and puts it into the correct formats for generation of infrasound propagation models. The system moves the meteorological data from Data Acquisition Partition to the Data Processing Partition and stores the meteorological data. The System builds a new atmospheric model based on the meteorological data. This use case is architecturally significant because it describes acquiring meteorological data from various sources and creating dynamic atmospheric transmission model to support the prediction of infrasonic signal detection

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2016

IDC Re-Engineering Phase 2 Iteration E2 Use Case Realizations

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

MO-E-18C-01: Open Access Web-Based Peer-To-Peer Training and Education in Radiotherapy

Purpose: Current training and education delivery models have limitations which result in gaps in clinical proficiency with equipment, procedures, and techniques. Educational and training opportunities offered by vendors and professional societies are by their nature not available at point of need or for the life of clinical systems. The objective of this work is to leverage modern communications technology to provide peer-to-peer training and education for radiotherapy professionals, in the clinic and on demand, as they undertake their clinical duties. Methods: We have developed a free of charge web site ( https://i.treatsafely.org ) using the Google App Engine and datastore (NDB, GQL), Python with AJAX-RPC, and Javascript. The site is a radiotherapy-specific hosting service to which user-created videos illustrating clinical or physics processes and other relevant educational material can be uploaded. Efficient navigation to the material of interest is provided through several RT specific search tools and videos can be scored by users, thus providing comprehensive peer review of the site content. The site also supports multilingual narration\translation of videos, a quiz function for competence assessment and a library function allowing groups or institutions to define their standard operating procedures based on the video content. Results: The website went live in August 2013 and currently has over 680 registered users from 55 countries; 27.2% from the United States, 9.8% from India, 8.3% from the United Kingdom, 7.3% from Brazil, and 47.5% from other countries. The users include physicists (57.4%), Oncologists (12.5%), therapists (8.2%) and dosimetrists (4.8%). There are 75 videos to date including English, Portuguese, Mandarin, and Thai. Conclusion: Based on the initial acceptance of the site, we conclude that this open access web-based peer-to-peer tool is fulfilling an important need in radiotherapy training and education. Site functionality should expand in the future to include document sharing and continuing education credits.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2012

A Soft-Switching Inverter for High-Temperature Advanced Hybrid Electric Vehicle Traction Motor Drives

The state-of-the-art hybrid electric vehicles (HEVs) require the inverter cooling system to have a separate loop to avoid power semiconductor junction over temperatures because the engine coolant temperature of 105°C does not allow for much temperature rise in silicon devices. The proposed work is to develop an advanced soft-switching inverter that will eliminate the device switching loss and cut down the power loss so that the inverter can operate at high-temperature conditions while operating at high switching frequencies with small current ripple in low inductance based permanent magnet motors. The proposed tasks also include high-temperature packaging and thermal modeling and simulation to ensure the packaged module can operate at the desired temperature. The developed module will be integrated with the motor and vehicle controller for dynamometer and in-vehicle testing to prove its superiority. This report will describe the detailed technical design of the soft-switching inverters and their test results. The experiments were conducted both in module level for the module conduction and switching characteristics and in inverter level for its efficiency under inductive and dynamometer load conditions. The performance will be compared with the DOE original specification.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2015

A Framework to Support Generator Ramping Uncertainty Analysis and Visualization

Power system operation requires maintaining a continuous balance between system demand and generation within certain constraints. Traditionally, the balancing processes are based on deterministic models, which do not consider possible random deviations of system generation and load from their predicted values. With the increasing penetration of the renewable generation, unexpected balancing problems can happen due to the deviations. This can result in serious risks to system reliability and efficiency. When the available balancing reserve is not enough to cover the predicted net load range with uncertainty, deficiency of balancing needs occurs. In this case, it is necessary to commit or de-commit additional conventional generators to achieve the desired confidence level for the balancing needs. The framework is built for solving this problem. The ramping tool engine is used to predict additional balancing requirements caused by the variability and uncertainty of the renewable energy, under the constraints of the generation ramping capability and interchange schedule. The web browser- based GUI is used to visualize the data in web-environment, which provides flexibility to allow user to see the ramping outputs in any platform. The GOSS structure provides strong support to allow easy communication between ramping engine, and system inputs, as well as the GUI.

Chen, Yousu↗

DOE OSTI2017

Realization of a Hole-Doped Mott Insulator on a Triangular Silicon Lattice

The physics of doped Mott insulators is at the heart of some of the most exotic physical phenomena in materials research including insulator-metal transitions, colossal magnetoresistance, and high-temperature superconductivity in layered perovskite compounds. Advances in this field would greatly benefit from the availability of new material systems with a similar richness of physical phenomena but with fewer chemical and structural complications in comparison to oxides. In this paper, using scanning tunneling microscopy and spectroscopy, we show that such a system can be realized on a silicon platform. The adsorption of one-third monolayer of Sn atoms on a Si(111) surface produces a triangular surface lattice with half filled dangling bond orbitals. Modulation hole doping of these dangling bonds unveils clear hallmarks of Mott physics, such as spectral weight transfer and the formation of quasiparticle states at the Fermi level, well-defined Fermi contour segments, and a sharp singularity in the density of states. These observations are remarkably similar to those made in complex oxide materials, including high-temperature superconductors, but highly extraordinary within the realm of conventional $sp$-bonded semiconductor materials. Finally, it suggests that exotic quantum matter phases can be realized and engineered on silicon-based materials platforms.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Development of Community, Capabilities and Understanding Through Unmanned Aircraft-Based Atmospheric Research: The LAPSE-RATE Campaign

Members of the international community developing unmanned aircraft for atmospheric research conducted a highly-successful coordinated flight week in Colorado during summer 2018 to collect measurements, develop capabilities, and enhance community in the field. Because unmanned aircraft systems (UAS) offer new perspectives on the atmosphere, their use in atmospheric science is expanding rapidly. In support of this growth, the International Society for Atmospheric Research using Remotely-piloted Aircraft (ISARRA) has been developed and have convened annual meetings and 'flight weeks'. The 2018 flight week, dubbed the Lower Atmospheric Profiling Studies at Elevation - a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE), involved a one-week deployment to Colorado's San Luis Valley. Between 14-20 July 2018 over 100 students, scientists, engineers, pilots and outreach coordinators conducted an intensive field operation using unmanned aircraft and ground-based assets to develop datasets, community, and capabilities. In addition to a coordinated 'community day' which offered a chance for groups to share their aircraft and science with the San Luis Valley community, LAPSE-RATE participants conducted nearly 1300 research flights totaling over 250 flight hours. The measurements collected have been used to advance capabilities (instrumentation, platforms, sampling techniques and modeling tools), conduct a detailed system intercomparison study, develop new collaborations, and foster community support for the use of UAS in atmospheric science.

54 ENVIRONMENTAL SCIENCES↗