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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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This presentation will be given at the 2024 AICHE annual meeting on October 30th. This presentation focuses on modeling a rotary packed bed contactor for CO2 capture. The RPB is an alternative contactor to fixed beds and optimization is performed to minimize the energy requirement. A design of experiments case study of the RPB is also performed.
Recycling spent lithium-ion batteries (LIBs) could alleviate supply risks for critical metals and be less harmful to the environment compared to new production of metals from mining. Developing a cost-effective LIB bioleaching process could be a promising alternative to traditional energy-intensive recycling technologies. Here, this study aimed to optimize bioleaching conditions for maximum economic competitiveness through design of experiments using iterative response surface methodology (RSM), assisted by thermodynamic modeling. The optimal condition was identified as 2.5% pulp density in 75 mM gluconic acid biolixiviant at 55°C for 30 h which could recover 57%–84% of nickel, 71%–86% of cobalt, and 100% of lithium and manganese, yielding a 17%–26% net profit margin. The recommended pulp density and acid concentrations, together with the observed metal solubilization, were supported by thermodynamic modeling predictions. Our study demonstrated that combining RSM with thermodynamic simulations could be a powerful tool for optimizing bioleaching conditions.
This viewgraph presentation reviews some of the issues that people who specialize in Non destructive evaluation (NDE) have with determining the statistics of the probability of detection. There is discussion of the use of the binominal distribution, and the probability of hit. The presentation then reviews the concepts of Directed Design of Experiments for Validating Probability of Detection of Inspection Systems (DOEPOD). Several cases are reviewed, and discussed. The concept of false calls is also reviewed.
Five specific experiments were analyzed to provide definition of experiments designed to evaluate two phase fluid behavior in low gravity. The conceptual design represents a fluid mechanics and heat transfer facility for a double rack in Spacelab. The five experiments are two phase flow patterns and pressure drop, flow boiling, liquid reorientation, and interface bubble dynamics. Hardware was sized, instrumentation and data recording requirements defined, and the five experiments were installed as an integrated experimental package. Applicable available hardware was selected in the experiment design and total experiment program costs were defined.
A study was conducted to evaluate the relative significance of input parameters on Ti- 6Al-4V deposits produced by an electron beam free form fabrication process under development at the NASA Langley Research Center. Five input parameters where chosen (beam voltage, beam current, translation speed, wire feed rate, and beam focus), and a design of experiments (DOE) approach was used to develop a set of 16 experiments to evaluate the relative importance of these parameters on the resulting deposits. Both single-bead and multi-bead stacks were fabricated using 16 combinations, and the resulting heights and widths of the stack deposits were measured. The resulting microstructures were also characterized to determine the impact of these parameters on the size of the melt pool and heat affected zone. The relative importance of each input parameter on the height and width of the multi-bead stacks will be discussed. .
This report documents the feasibility phase of the Critical Experiment Design (CED) conducted as part of integral experiment request (IER) 523. The purpose of IER-523 is to explore the effects of using 35 weight percent enriched uranium dioxide-beryllium oxide (UO 2 -BeO) material on critical configurations using the Seven Percent Critical Experiment (7uPCX) at Sandia National Laboratories (Sandia). Preliminary experiment design concepts, neutronic analysis results, and proposed paths for continuing the CED process are presented.
The behavior of the oxygen-evolving positive electrode (i.e. anode) in the anion exchange membrane water electrolyzer (AEMEL) is complex and influenced by several factors. Very few studies have been performed to understand oxygen evolution reaction (OER) behavior by optimizing the individual factors that influence performance. Here, this study highlights the effects of catalyst loading, catalyst selection, porous transport layer (PTL) type and conductive additive content. The influence of each factor is elucidated through a design of experiments (DoE) approach with a full statistical analysis. Electrochemical data, alongside Pareto charts, parametric trends and their mutual interactions are discussed. This DoE approach is also helpful in making useful predictions and discovering new combinations to be tested. The end result was a high-performance AEMEL able to operate at a current density of 1.0 A/cm 2 at 1.80 V with IrOx OER and PtNi hydrogen evolution reaction (HER) catalysts using 0.3 M KOH fed to the anode. Even lower operating voltage was observed with PbRuOx catalyst at the anode, 1.64 V @ 1.0 A/cm 2 , though the cell decay rate was higher. Lastly, a IrOx/PtNi cell was stably operated continuously for 30 days (720 h) at 1.0 A/cm 2 . This study can serve as a guide for optimal electrode design with insights into component-performance compromises, which can aid in making design choices and performing techno-economic analyses.
A comparison of design drivers for space and hazardous nuclear waste operating robots details similarities and differences in operations, performance and environmental parameters for these critical environments. The similarities are exploited to provide low risk system components based on reuse principles and design knowledge. Risk reduction techniques are used for bridging areas of significant differences. As an example, risk reduction of a new sensor design for nuclear environment operations is employed to provide upgradeable replacement units in a reusable architecture for significantly higher levels of radiation.
With the growing complexity of today's large scale problems, it has become more difficult to find optimal solutions by using exact mathematical methods. The need to find near-optimal solutions in an acceptable time frame requires heuristic approaches. In many cases, however, most heuristics have several parameters that need to be "tuned" before they can reach good results. The problem then turns into "finding best parameter setting" for the heuristics to solve the problems efficiently and timely. One-Factor-At-a-Time (OFAT) approach for parameter tuning neglects the interactions between parameters. Design of Experiments (DOE) tools can be instead employed to tune the parameters more effectively. In this paper, we seek the best parameter setting for a Genetic Algorithm (GA) to solve the single machine total weighted tardiness problem in which n jobs must be scheduled on a single machine without preemption, and the objective is to minimize the total weighted tardiness. Benchmark instances for the problem are available in the literature. To fine tune the GA parameters in the most efficient way, we compare multiple DOE models including 2-level (2k ) full factorial design, orthogonal array design, central composite design, D-optimal design and signal-to-noise (SIN) ratios. In each DOE method, a mathematical model is created using regression analysis, and solved to obtain the best parameter setting. After verification runs using the tuned parameter setting, the preliminary results for optimal solutions of multiple instances were found efficiently.
Based on guidelines from the Federal Aviation Administration, research supported by the NASA Advanced Composites Project is investigating methods to improve process control for surface preparation and pre-bond surface characterization on aerospace composite structures. The overall goal is to identify high fidelity, rapid, and reproducible surface treatments and surface characterization methods to reduce the uncertainty associated with the bonding process. The desired outcome is a more reliable bonded airframe structure, and to reduce time to achieve certification. In this work, a design of experiments (DoE) approach was conducted to determine optimum laser ablation conditions using a pulsed laser source with a nominal pulse width of 10 picoseconds. The laser power, frequency, scan speed, and number of passes (1 or 2) were varied within the laser system operating boundaries. Aerospace structural carbon fiber reinforced composites (Torayca® 3900-2/T800H) were laser treated, then characterized for contamination, and finally bonded for mechanical testing. Pre-bond characterization included water contact angle (WCA) using a handheld device, ablation depth measurement using scanning electron microscopy (SEM), and silicone contamination measurement using laser induced breakdown spectroscopy (LIBS). In order to accommodate the large number of specimens in the DoE, a rapid-screening, double cantilever beam (DCB) test specimen configuration was devised based on modifications to ASTM D5528. Specimens were tested to assess the failure modes observed under the various laser surface treatment parameters. The models obtained from this DoE indicated that results were most sensitive to variation in the average laser power. Excellent bond performance was observed with nearly 100% cohesive failure for a wide range of laser parameters. Below about 200 mW, adhesive failure was observed because contamination was left on the surface. For laser powers greater than about 600 mW, large amounts of fiber were exposed, and the failure mode was predominately fiber tear.
Diesel-fueled engines still hold a large market share in the medium and heavy-duty transportation sector. However, the increase in fossil fuel prices and the strict emission regulations are leading engine manufacturers to seek cleaner alternatives without a compromise in performance. Alcohol-based fuels, such as ethanol, offer a promising alternative to diesel fuel in meeting regulatory demands. Ethanol provides cleaner combustion and lower levels of soot due to its chemical properties, in particular its lower level of carbon content. In addition, the stoichiometric operating conditions of alcohol fueled engines enable the mitigation of NOx emissions in aftertreatment stage. With the promise of retrofitting diesel engines to run on ethanol to reduce emissions, the thermal efficiency of these engines remains the primary optimization target. In order to find the optimal ethanol-fueled engine design that maximizes the thermal efficiency, a large design space needs to be investigated using engineering tools. In this study, previous research by the authors on optimizing the design of a single-cylinder ethanol-fueled engine was extended to explore the design space for a heavy-duty multi-cylinder engine configuration. A heavy-duty engine setup with multiple operating conditions at different engine speeds and loads were considered. A design optimization analysis was performed to identify the potential designs that maximize the indicated thermal efficiency in an ethanol-fueled compression ignition engine. First, a computational fluid dynamics (CFD) model of the engine was validated using experimental data for four drive cycle points. Using a design of experiments (DoE) approach and a parameterized piston bowl geometry, the model was then exercised to explore the relationship among geometric features of the piston bowl and spray targeting angle and indicated thermal efficiency across all tested operating conditions. After evaluating 165~candidate designs, a piston bowl geometry was identified that yielded an increase between 1.3 to 2.2 percentage points in indicated thermal efficiency for all tested conditions, while satisfying the operational design constraints for peak pressure and maximum pressure rise rate. The increased performance was attributed to enhanced mixing that led to the formation of a more homogeneous distribution of in-cylinder temperature and equivalence ratio, higher combustion temperatures, and shorter combustion duration. Finally, a Bayesian optimization (BOpt) analysis was employed to find the optimal piston bowl geometry with a fixed spray injector angle for one of the operating conditions. Using BOpt, a piston candidate was identified that resulted in a 1.9~percentage point increase in thermal efficiency from the baseline design, yet only required 65\% of the design samples investigated using the DoE approach.
The CCSI2 program is supporting a six-month test campaign at the National Carbon Capture Center (NCCC) for evaluation of a novel water-lean solvent. This presentation describes CCSI2’s efforts in process modeling of the solvent system for both coal and natural gas-based flue gas sources and initial uncertainty quantification (UQ) work to estimate parametric uncertainty in key sub-models of interest (e.g., thermodynamics, mass transfer, reaction kinetics). Moreover, perspective is provided on how UQ and sequential design of experiments (SDoE) tools are used to assess the impact of model uncertainty on projected process performance, use this information to optimize data collection during the campaign, and refine process models through data collection. This framework is expected to reduce the overall model uncertainty, and thus risk associated with scale-up as the process moves towards commercialization.
The factors that determine data volume requirements in a typical wind tunnel test are identified. It is suggested that productivity in wind tunnel testing can be enhanced by managing the inference error risk associated with evaluating residuals in a response surface modeling experiment. The relationship between minimum data volume requirements and the factors upon which they depend is described and certain simplifications to this relationship are realized when specific model adequacy criteria are adopted. The question of response model residual evaluation is treated and certain practical aspects of response surface modeling are considered, including inference subspace truncation. A wind tunnel test plan developed by using the Modern Design of Experiments illustrates the advantages of an early estimate of data volume requirements. Comparisons are made with a representative One Factor At a Time (OFAT) wind tunnel test matrix developed to evaluate a surface to air missile.
This experimental study seeks to quantify the impact various composite parameters have on the structural response of a composite structure in a pyroshock environment. The prediction of an aerospace structure's response to pyroshock induced loading is largely dependent on empirical databases created from collections of development and flight test data. While there is significant structural response data due to pyroshock induced loading for metallic structures, there is much less data available for composite structures. One challenge of developing a composite pyroshock response database as well as empirical prediction methods for composite structures is the large number of parameters associated with composite materials. This experimental study uses data from a test series planned using design of experiments (DOE) methods. Statistical analysis methods are then used to identify which composite material parameters most greatly influence a flat composite panel's structural response to pyroshock induced loading. The parameters considered are panel thickness, type of ply, ply orientation, and pyroshock level induced into the panel. The results of this test will aid in future large scale testing by eliminating insignificant parameters as well as aid in the development of empirical scaling methods for composite structures' response to pyroshock induced loading.
This experimental study seeks to quantify the impact various composite parameters have on the structural response of a composite structure in a pyroshock environment. The prediction of an aerospace structure's response to pyroshock induced loading is largely dependent on empirical databases created from collections of development and flight test data. While there is significant structural response data due to pyroshock induced loading for metallic structures, there is much less data available for composite structures. One challenge of developing a composite pyroshock response database as well as empirical prediction methods for composite structures is the large number of parameters associated with composite materials. This experimental study uses data from a test series planned using design of experiments (DOE) methods. Statistical analysis methods are then used to identify which composite material parameters most greatly influence a flat composite panel's structural response to pyroshock induced loading. The parameters considered are panel thickness, type of ply, ply orientation, and pyroshock level induced into the panel. The results of this test will aid in future large scale testing by eliminating insignificant parameters as well as aid in the development of empirical scaling methods for composite structures' response to pyroshock induced loading.
This experimental study seeks to quantify the impact various composite parameters have on the structural response of a composite structure in a pyroshock environment. The prediction of an aerospace structure's response to shock induced loading is largely dependent on empirical databases created from collections of development and flight test data. While there is significant structural response data due to shock induced loading for metallic structures, there is much less data available for composite structures. One challenge of developing a composite shock response database as well as empirical prediction methods for composite structures is the large number of parameters associated with composite materials. This experimental study uses data from a test series planned using design of experiments (DOE) methods. Statistical analysis methods are then used to identify which composite material parameters most greatly influence a flat composite panel's structural response to shock induced loading. The parameters considered are panel thickness, type of ply, ply orientation, and shock level induced into the panel. The results of this test will aid in future large scale testing by eliminating insignificant parameters as well as aid in the development of empirical scaling methods for composite structures' response to shock induced loading.
This paper describes analytical methods used in verification of large DC power systems with applications to the International Space Station (ISS). Large DC power systems contain many switching power converters with negative resistor characteristics. The ISS power system presents numerous challenges with respect to system stability such as complex sources and undefined loads. The Space Station program has developed impedance specifications for sources and loads. The overall approach to system stability consists of specific hardware requirements coupled with extensive system analysis and testing. Testing of large complex distributed power systems is not practical due to size and complexity of the system. Computer modeling has been extensively used to develop hardware specifications as well as to identify system configurations for lab testing. The statistical method of Design of Experiments (DoE) is used as an analysis tool for verification of these large systems. DOE reduces the number of computer runs which are necessary to analyze the performance of a complex power system consisting of hundreds of DC/DC converters. DoE also provides valuable information about the effect of changes in system parameters on the performance of the system. DoE provides information about various operating scenarios and identification of the ones with potential for instability. In this paper we will describe how we have used computer modeling to analyze a large DC power system. A brief description of DoE is given. Examples using applications of DoE to analysis and verification of the ISS power system are provided.
Test runs in the pilot plants consume significant resources, and therefore, the learning from test runs should be maximized. Test runs conducted in the pilot plants are often steady state. It takes several hours for reaching steady-state in the pilot plants, and thus, the duration of the test runs needs to be long even for collecting few steady-state data points. On the other hand, a large number of measurements can be collected through dynamic test runs in a short span of time. This paper presents a systematic design of dynamic experiments (DoDEs) for identifiability of model parameters, which is achieved by persistently exciting the inputs signals. A pseudorandom binary sequence (PRBS) is designed as the input signal for DoDE due to its efficiency in obtaining sufficient spectral content. However, due to the long sequence size of the PRBS signal, a Schroeder-phase input signal, which is a multisine signal, is also designed. Tests for both types of signals are run in the Pilot Solvent Test Unit (PSTU) at the National Carbon Capture Center in Wilsonville, Alabama. The transient data are used to solve dynamic data reconciliation and parameter estimation problem. The estimated parameters are found to be not only superior to those estimated from using data collected from hundreds of steady-state test runs in a nonreactive (air–water) system, but the parameters could be estimated by using the dynamic data collected for about 24 h from the pilot plant for the MEA-H 2 O–CO 2 system.