SEARCH · Engineering Papers

Results for “solution design”

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 1,405 records · Page 78

All sources NASA NTRS DOE OSTI

NASA NTRS1998

Neural Net-Based Redesign of Transonic Turbines for Improved Unsteady Aerodynamic Performance

A recently developed neural net-based aerodynamic design procedure is used in the redesign of a transonic turbine stage to improve its unsteady aerodynamic performance. The redesign procedure used incorporates the advantages of both traditional response surface methodology (RSM) and neural networks by employing a strategy called parameter-based partitioning of the design space. Starting from the reference design, a sequence of response surfaces based on both neural networks and polynomial fits are constructed to traverse the design space in search of an optimal solution that exhibits improved unsteady performance. The procedure combines the power of neural networks and the economy of low-order polynomials (in terms of number of simulations required and network training requirements). A time-accurate, two-dimensional, Navier-Stokes solver is used to evaluate the various intermediate designs and provide inputs to the optimization procedure. The optimization procedure yields a modified design that improves the aerodynamic performance through small changes to the reference design geometry. The computed results demonstrate the capabilities of the neural net-based design procedure, and also show the tremendous advantages that can be gained by including high-fidelity unsteady simulations that capture the relevant flow physics in the design optimization process.

Madavan, Nateri K.↗

NASA NTRS1999

Improving the Unsteady Aerodynamic Performance of Transonic Turbines using Neural Networks

A recently developed neural net-based aerodynamic design procedure is used in the redesign of a transonic turbine stage to improve its unsteady aerodynamic performance. The redesign procedure used incorporates the advantages of both traditional response surface methodology and neural networks by employing a strategy called parameter-based partitioning of the design space. Starting from the reference design, a sequence of response surfaces based on both neural networks and polynomial fits are constructed to traverse the design space in search of an optimal solution that exhibits improved unsteady performance. The procedure combines the power of neural networks and the economy of low-order polynomials (in terms of number of simulations required and network training requirements). A time-accurate, two-dimensional, Navier-Stokes solver is used to evaluate the various intermediate designs and provide inputs to the optimization procedure. The procedure yielded a modified design that improves the aerodynamic performance through small changes to the reference design geometry. These results demonstrate the capabilities of the neural net-based design procedure, and also show the advantages of including high-fidelity unsteady simulations that capture the relevant flow physics in the design optimization process.

Rai, Man Mohan↗

NASA NTRS1991

Optimal discrete-time dynamic output-feedback design - A w-domain approach

An alternative method for optimal digital control design is described in this paper. The method is based on the usage of the w-transform and has many attractive design features. One of these is its immediate connection with frequency loop-shaping techniques that are now popular and effective for multivariable control synthesis in continuous-time domain. Furthermore, any design algorithms originally developed for continuous-time systems can now be immediately extended to the discrete-time domain. The main results presented in this paper are the exact problem formulation and solution of an optimal discrete-time dynamic output-feedback design in the w-domain involving a quadratic performance index to random disturbances. In addition, necessary conditions for optimality are obtained for the numerical solution of the optimal output-feedback compensator design. A numerical example is presented illustrating its application to the design of a low-order dynamic compensator in a stability augmentation system of a commercial transport.

Ha, Cheolkeun↗

NASA NTRS2015

Designing Interfaces for Astronaut Autonomy in Space

As we move towards human deep space missions, astronauts will no longer be able to say, Houston, we have a problem. The restricted contact with mission control because of the incredible distance from Earth will require astronauts to make autonomous decisions. How will astronauts take on the roles of mission control? This is an area of active research that has far reaching implications for the future of distant spaceflight. Come to this talk to hear how we are using design and user research to come up with innovative solutions for astronauts to effectively explore the Moon, Mars, and beyond.

design↗

NASA NTRSDate not supplied

Knowledge Discovery for Early Failure Assessment of Complex Engineered Systems Using Natural Language Processing

Emerging complex engineered systems may have unexpected safety issues due to novel operational environments, increasing autonomy, human-machine interaction, and other factors. To prevent failures in operation or testing that necessitate costly redesign, it is desirable to predict likely failure modes early in the design process. Information about past engineering failures in natural language format presents one possible solution by enabling the retrieval of information that can inform new designs. However, identifying documents containing usable information and extracting the required information can be prohibitively time-consuming when implemented at scale. In this research, an automated natural language processing (NLP) framework is proposed to discover relevant knowledge from documents containing failure-related design information. The framework is applied to NASA’s Lessons Learned Information System (LLIS),which is publicly available. Documents containing usable information are filtered using two different NLP-based models. Next, from the identified usable documents, a failure taxonomy is extracted using a partitioned hierarchical topic modeling approach. Partitions of the document describe different sections of the failure taxonomy – i.e., failure, cause of failure, and recommendations – as indicated by the structure of the original document. The extracted failure taxonomy can be leveraged in early design failure assessment methods. Moreover, the framework can be used to identify documents containing usable failure-related design information from other databases and extract relevant information from these documents.

Documentation and Information Science↗

NASA NTRSDate not supplied

Knowledge Discovery for Early Failure Assessment of Complex Engineered Systems Using Natural Language Processing

Documentation and Information Science↗

NASA NTRS1974

Composite materials. Volume 3 - Engineering applications of composites. Volume 4 - Metallic matrix composites. Volume 8 - Structural design and analysis, Part 2

This volume discusses a vaety of applications of both low- and high-cost composite materials in a number of selected engineering fields. The text stresses the use of fiber-reinforced composites, along with interesting material systems used in the electrical and nuclear industries. As to technology transfer, a similarity is noted between many of the reasons responsible for the utilization of composites and those problems requiring urgent solution, such as mechanized fabrication processes and design for production. Features topics include road transportation, rail transportation, civil aircraft, space vehicles, builing industry, chemical plants, and appliances and equipment. The laminate orientation code devised by Air Force materials laboratory is included. Individual items are announced in this issue.

Noton, B. R.↗

DOE OSTI2020

Constrained Bayesian Optimization of Criticality Experiments [Slides]

The design of criticality experiments is typically an iterative process that employs a Monte Carlo transport code. The goal is to find a design that optimizes some variable, like the sensitivity of a response to a cross section, while simultaneously ensuring criticality. The high fidelity of the Monte Carlo code is a great asset, but it makes exploring the design space computationally expensive. Herein, we present how a constrained Bayesian optimization algorithm can be used to efficiently design a criticality experiment. It uses Gaussian processes as a surrogate model to probe the design space and to reduce the number of code executions that are needed to find the optimum. We demonstrate constrained Bayesian optimization with a Pu-239/polyethylene solution system and a TEX experiment that is designed for criticality safety validation of a nuclear waste model at the Hanford Site. For both systems, a global optimum was found within 75 Monte Carlo simulations.

42 ENGINEERING↗

DOE OSTI2020

Constrained Bayesian Optimization of Criticality Experiments at LLNL [Slides]

The design of criticality experiments is typically an iterative process that employs a Monte Carlo transport code. The goal is to find a design that optimizes some variable, like the sensitivity of a response to a cross section, while simultaneously ensuring criticality. The high fidelity of the Monte Carlo code is a great asset, but it makes exploring the design space computationally expensive. Herein, we present how a constrained Bayesian optimization algorithm can be used to efficiently design a criticality experiment. It uses Gaussian processes as a surrogate model to probe the design space and to reduce the number of code executions that are needed to find the optimum. We demonstrate constrained Bayesian optimization with a Pu-239/polyethylene solution system and a TEX experiment that is designed for critical ity safety validation of a nuclear waste model at the Hanford Site. For both systems, a global optimum was found within 75 Monte Carlo simulations.

42 ENGINEERING↗

NASA NTRS1977

Compensator improvement for multivariable control systems

A theory and the associated numerical technique are developed for an iterative design improvement of the compensation for linear, time-invariant control systems with multiple inputs and multiple outputs. A strict constraint algorithm is used in obtaining a solution of the specified constraints of the control design. The result of the research effort is the multiple input, multiple output Compensator Improvement Program (CIP). The objective of the Compensator Improvement Program is to modify in an iterative manner the free parameters of the dynamic compensation matrix so that the system satisfies frequency domain specifications. In this exposition, the underlying principles of the multivariable CIP algorithm are presented and the practical utility of the program is illustrated with space vehicle related examples.

Mitchell, J. R.↗

DOE OSTI2023

Partitioning of Radionuclides in Various Streams of the Mo-99 Purification Process

The Universal Neutron Irradiator (UNI) was designed to irradiate a small volume of uranium solution and to achieve high fission power without creating a large radionuclide (RN) inventory. The UNI system is equipped with a fully enclosed off-gas system with online hydrogen gas monitoring to prevent the production of an explosive atmosphere, as well as storage cylinders for capturing fission gases. It also includes a catalytic recombiner in the target capsule (to immediately recombine radiolytic hydrogen and oxygen gas in the system) and access ports for sampling the target solution and capsule headspace. In UNI Phase I experiments, 50 mL of uranyl sulfate solution was irradiated at 0.3–0.5 W/mL fission power using a tantalum target assembly to produce photoneutrons via irradiation with an electron beam. The purposes of the Phase I experiments were to test the efficacy of adding 200 ppm Fe 2+ to the target solution to prevent uranyl peroxide precipitation under representative irradiation conditions, and to monitor iodine speciation in the irradiated uranyl sulfate solution. The focus of the UNI Phase II experiments was to use an irradiated uranyl sulfate solution containing a mixture of fission products and monitor their distribution in various streams during the primary recovery column (titania-based column) that separates Mo-99 from uranium. Irradiations were designed to create sufficient activity for tracking their distribution in various processing streams using a combination of gamma counting and inductively coupled plasma mass spectrometry (ICP-MS) analyses. Obtained data will be used to better understand the composition of waste streams, accumulation of RN on the titania column, and identify what RNs accumulate in the uranyl sulfate solution. Furthermore, this knowledge will allow us to determine waste classifications for various streams produced during the Mo-99 purification process and develop an appropriate waste clean-up strategy.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1983

Three-dimensional compressible viscous analysis of mixer nozzles

An analysis-based design procedure for compound-mixer exhaust nozzles is presented and compared to test data. The design approach is based on two numerical solutions to the 3-D viscous compressible Navier-Stokes equations: an equation splitting technique used for the analysis of the core and bypass flow, and a parabolic marching scheme used in the analysis of the mixing duct. The selection of the analytical methods through test data comparisons and their coupling into an integrated design system are discussed. NASA test data is used to demonstrate the validity of the computations from the exhaust system rating station, upstream of the mixer lobe, to the nozzle throat. An estimate is made of the savings in development time and cost utilizing the new procedure.

Goyal, R. K.↗

NASA NTRS2012

Method to Remove Particulate Matter from Dusty Gases at Low Pressures

Future human exploration of Mars will rely on local Martian resources to reduce the mass, cost, and risk of space exploration launched from Earth. NASA's In Situ Resource Utilization (ISRU) Project seeks to produce mission consumables from local Martian resources, such as atmospheric gas. The Martian atmosphere, however, contains dust particles in the 2-to-10 -micrometer range. These dust particles must be removed before the Martian atmospheric gas can be processed. The low pressure of the Martian atmosphere, at 5 to 10 mbars, prevents the development of large voltages required for a standard electrostatic precipitator. If the voltage is increased too much, the corona transitions into a glow/streamer discharge unsuitable for the operation of a precipitator. If the voltage is not large enough, the dust particles are not sufficiently charged and the field is not strong enough to drive the particles to the collector. A method using electrostatic fields has been developed to collect dust from gaseous environments at low pressures, specifically carbon dioxide at pressures around 5 to 10 mbars. This method, commonly known as electrostatic precipitation, is a mature technology in air at one atmosphere. In this case, the high voltages required for the method to work can easily be achieved. However, in carbon dioxide at low pressures, such as those found on Mars, large voltages are not possible. The innovation reported here consists of two concentric cylindrical electrodes set at specific potential difference that generate an electric field that produces a corona capable of imparting an electrostatic charge to the incoming dust particles. The strength of the field is carefully balanced so as to produce a stable charging corona at 5 to 10 mbars, and is also capable of imparting a force to the particles that drives them to the collecting electrode. There are only two possible ways that dust can be removed from Martian atmospheric gas intakes: with this electrostatic precipitator design, and with the use of filters. However, filters require upstream compression of the gas to be treated because the atmospheric pressure on Mars is too close to vacuum to use a vacuum pump downstream to the filter to draw the gas through the filter. The electrostatic precipitator is the best and more efficient solution for this environment. No other precipitator designs have been developed for the environment of Mars due to the challenges of the low atmospheric pressure. Dust particles are charged using corona generation around the high-voltage discharge electrode, which ionizes gas molecules. Since the atmospheric gas intakes for the ISRU processing chambers will likely be cylindrical, cylindrical precipitator geometry was chosen. The electrostatic precipitator design presented here removes simulated Martian dust particles in the required range in a simulated Martian atmospheric environment. The current-voltage (I-V) characteristic curves taken for the nine precipitator configurations at 9 mbars of pressure showed that a cylindrical collecting electrode 7.0 cm in diameter with a concentric positive high voltage electrode 100 m thick provides the best range of voltage and charging corona current. This precipitator design is effective for the size of the dust particles expected in the Martian atmosphere. Mass determination, as well as microscopic images and particle size distributions of dust collected on a silicon wafer placed directly below the precipitator with the field on and off, showed excellent initial results.

Calle, Carlos↗

DOE OSTI2021

Machine Learning Tools Set for Natural Gas Fuel Cell System Design

This study is focusing on leveraging the system design tools set for the next-generation solid oxide fuel cell (SOFC) based natural gas fuel cell (NGFC) system. Conventionally, system design and optimization of NGFC systems rely heavily on traditional reduced order model (ROM) techniques and designers’ experience level. For overcoming the technical barriers of system design, multiple multi-physics models and machine learning (ML) tools have been utilized to automate the conceptual design process and enhance the reliability of solutions for the NGFC system. The proposed tools set includes a physics-informed ML tool for automated ROM construction that leverages advances in deep neural networks to significantly reduce ROM prediction error for the NGFC power island compared to traditional approaches. The constructed physics-informed ML ROM can be used in system design, and optimization tools set Institute for the Design of Advanced Energy Systems (IDAES) Process Systems Engineering (PSE) framework. The tools set also provides a user-friendly graphic user interface built within Jupyter Notebooks, and the complete tools set is open-source public available.

Wang, Dewei↗

DOE OSTI2025

An alternative electrode design for accurate spectral induced polarization measurements in variably saturated porous media

SUMMARY Applications of spectral induced polarization (SIP) require electrodes that maintain hydrologic contact with surrounding soils to capture small electrical responses, often observed as phase shifts in milliradians. For unsaturated soils, electrodes must overcome increased electrical contact impedance due to reduced pore fluid. Traditional designs use a ceramic membrane electrode (CME) with a water reservoir and metal conductor, requiring periodic maintenance to retain electrolytic solution. For field applications where maintenance is impractical, alternative designs are needed. This study evaluated a new electrode design (silica flour electrode, SFE) alongside a CME. SFEs use packed silica flour to store water via capillary forces against a metal conductor. This study examined both designs in three variably saturated soils at soil suctions up to 700 mbar and soil water contents below 1 percent, with SIP measurements across 0.01 to 10 000 Hz frequencies. SFEs match CMEs at high frequencies and perform better at lower frequencies, without requiring ongoing maintenance, making them ideal for field use. In water-only experiments, CMEs produced errors and high noise below 1.5 Hz, whereas SFEs were more accurate. However, CMEs performed better above 300 Hz. In fine sand, SFEs performed better due to the relatively lower contact impedance as compared to CMEs. Both electrode types performed comparably in silty sand and silt loam soils, although CMEs required ongoing maintenance, suggesting potential for long-term reliability issues.

58 GEOSCIENCES↗

DOE OSTI2022

Resilience Design Patterns: A Structured Approach to Resilience at Extreme Scale (V.2.0)

Reliability is a serious concern for future extreme-scale high-performance computing (HPC) systems. Projections based on the current generation of HPC systems and technology roadmaps suggest the prevalence of very high fault rates in future systems. The errors resulting from these faults will propagate and generate various kinds of failures, which may result in outcomes ranging from result corruptions to catastrophic application crashes. Therefore, the resilience challenge for extreme-scale HPC systems requires coordination between various hardware and software technologies that are capable of handling a broad set of fault models at accelerated fault rates. Also, due to practical limits on power consumption in future HPC systems, they are likely to embrace innovative architectures, increasing the levels of hardware and software complexities. Therefore, the techniques that seek to improve resilience must navigate the complex trade-off space between resilience and the overheads to power consumption and performance. While the HPC community has developed various resilience solutions, application-level techniques as well as system-based solutions, the solution space of HPC resilience techniques remains fragmented. There are no formal methods to integrate the various HPC resilience techniques into composite solutions, nor are there methods to holistically evaluate the adequacy and efficacy of such solutions in terms of their protection coverage, and their performance & power efficiency characteristics. Additionally, few implementations of current resilience solutions are portable to newer architectures and software environments that will be deployed on future systems. We developed a new structured approach to the management of HPC resilience using the concept of resilience-based design patterns. In general, a design pattern is a repeatable solution to a commonly occurring problem. We identified the well-known solutions that are commonly used to deal with faults, errors and failures in HPC systems. In the initial design patterns specification (version 1.0), we described the various solutions, which address specific problems in the design of resilient HPC environments, in the form of patterns. Each pattern describes a problem caused by a fault, error or failure event in an HPC environment, and then describes the core of the solution of the problem in such a way that this solution may be adapted to different systems and implemented at different layers of the system stack. The catalog of these resilience design patterns provides designers with a collection of design elements. To construct complete resilience solutions using combinations of various patterns, we defined a framework that enhances HPC designers' understanding of the important constraints and the opportunities for the design patterns to be implemented and deployed at various layers of the system stack. The design framework is also useful for establishing interfaces and mechanisms to coordinate flexible fault management across hardware and software components, as well as to consider the trade-off between performance, resilience, and power consumption when constructing a solution. The resilience design patterns specification version 1.1 included more detailed explanations of the pattern solutions, the context in which the patterns are applicable, and the implications for hardware or software design. It also provided several additional examples and detailed case studies to demonstrate the use of patterns to build realistic solutions. In version 1.2 of the specification document, we have improved the pattern descriptions, including graphical representations of the pattern components. These improvements are largely based on critical comments, feedback and suggestions received from pattern experts and readers of the previous versions of the specification. The pattern classification has been modified to further clarify the relationships between pattern categories. This version of the specification also introduces a pattern language for resilience design patterns. The pattern language presents the patterns in the catalog as a network, revealing the relations among the resilience patterns. The language provides designers with the means to explore alternative techniques for handling a specific fault model that may have different efficiency and complexity characteristics. Using the pattern language also enables the design and implementation of comprehensive resilience solutions as a set of interconnected resilience patterns that can be instantiated across layers of the system stack. The overall goal of this work is to provide hardware and software designers, as well as the users and operators of HPC systems, a systematic methodology for the design and evaluation of resilience technologies in HPC systems that keep scientific applications running to a correct solution in a timely and cost-efficient manner despite frequent faults, errors, and failures of various types. Version 2.0 expands the resilience design pattern classification and catalog to include self-stabilization patterns and reliability, availability and performance models for each structural pattern.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Grain boundary segregation beyond the dilute limit: Separating the two contributions of site spectrality and solute interactions

Solute segregation at grain boundaries (GBs) is known to have a profound impact on material properties, and as such is becoming routinely used as an element in alloy design. Beyond the dilute limit, the extent of solute GB segregation is known to be concentration dependent. Using hybrid Monte Carlo/Molecular Statics simulations of Mg segregation in Al, in this work we decouple the two contributions to this composition dependence: (i) spectrality of atomic environments at the boundary and (ii) solute-solute interactions. Although only contribution (ii) is typically considered in the literature, we argue that both contributions are equally important to understand concentration dependence and correctly quantify GB solute segregation in a binary alloy. Finally, a thermodynamic segregation isotherm is outlined that accounts for both the spectrality of grain boundary sites and solute-solute interactions. Unlike classical isotherms like those of McLean or Fowler-Guggenheim, which can be successfully fitted to GB segregation data only over a limited range of composition and temperature, our proposed model is shown to be accurate across the composition-temperature space.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Constrained Bayesian optimization of criticality experiments

The design of criticality experiments is typically an iterative process that employs a Monte Carlo transport code. The goal is to find a design that optimizes some variable, like the sensitivity of a response to a cross section, while simultaneously ensuring criticality. The high fidelity of the Monte Carlo code is a great asset, but it makes exploring the design space computationally expensive. Herein, we present how a constrained Bayesian optimization algorithm can be used to efficiently design a criticality experiment. It uses Gaussian processes as a surrogate model to probe the design space and to reduce the number of code executions that are needed to find the optimum. Furthermore, we demonstrate constrained Bayesian optimization with a Pu-239/polyethylene solution system and a TEX experiment that is designed for criticality safety validation of a nuclear waste model at the Hanford Site. For both systems, a global optimum was found within 75 Monte Carlo simulations.

42 ENGINEERING↗