Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Optimal experimental 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.

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

At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Fourier Analysis and Design of a Shielded 120kW Inductive Wireless System

High-power inductive wireless power transfer (WPT) systems for EVs are designed to meet specifications such as stray field, power level, efficiency, misalignment tolerance, and ground clearance. These metrics are all heavily influenced by the coil geometry. Herein this paper proposes a coil design method based on the Fourier Analysis Method (FAM) which is an analytical method for directly designing coil geometries to meet stray field and power level requirements through an optimization of Fourier basis function coefficients. In this work, two 120 kW WPT proof-of-concept demonstrators with low stray field and high efficiency are built from FAM optimization results to validate the models and show the impact of the FAM design process. Experimental validation of the Gen. 2 demonstrator at 120 kW output power resulted in a measured DC/DC efficiency of 97.2% at alignment with a 125 mm airgap. At the 120 kW test point, the stray fields 80 cm away from the center of the airgap between the coil assemblies were 3.4 µT(rms) on the X-axis and 3.5 µT(rms) on the Y-axis, much lower than the 27 µT(rms) ICNIRP limit.

42 ENGINEERING↗
NASA NTRS1999

Electrical Properties and Power Considerations of a Piezoelectric Actuator

This paper assesses the electrical characteristics of piezoelectric wafers for use in aeronautical applications such as active noise control in aircraft. Determination of capacitive behavior and power consumption is necessary to optimize the system configuration and to design efficient driving electronics. Empirical relations are developed from experimental data to predict the capacitance and loss tangent of a PZT5A ceramic as nonlinear functions of both applied peak voltage and driving frequency. Power consumed by the PZT is the rate of energy required to excite the piezoelectric system along with power dissipated due to dielectric loss and mechanical and structural damping. Overall power consumption is thus quantified as a function of peak applied voltage and driving frequency. It was demonstrated that by incorporating the variation of capacitance and power loss with voltage and frequency, satisfactory estimates of power requirements can be obtained. These relations allow general guidelines in selection and application of piezoelectric actuators and driving electronics for active control applications.

Jordan, T.↗
DOE OSTI2022

Generation of bright monomeric red fluorescent proteins via computational design of enhanced chromophore packing

Red fluorescent proteins (RFPs) have found widespread application in chemical and biological research due to their longer emission wavelengths. Here, we use computational protein design to increase the quantum yield and thereby brightness of a dim monomeric RFP (mRojoA, quantum yield = 0.02) by optimizing chromophore packing with aliphatic residues, which we hypothesized would reduce torsional motions causing non-radiative decay. Experimental characterization of the top 10 designed sequences yielded mSandy1 (λ em = 609 nm, quantum yield = 0.26), a variant with equivalent brightness to mCherry, a widely used RFP. We next used directed evolution to further increase brightness, resulting in mSandy2 (λ em = 606 nm, quantum yield = 0.35), the brightest Discosoma sp. derived monomeric RFP with an emission maximum above 600 nm reported to date. Crystallographic analysis of mSandy2 showed that the chromophore p-hydroxybenzylidene moiety is sandwiched between the side chains of Leu63 and Ile197, a structural motif that has not previously been observed in RFPs, and confirms that aliphatic packing leads to chromophore rigidification. Our results demonstrate that computational protein design can be used to generate bright monomeric RFPs, which can serve as templates for the evolution of novel far-red fluorescent proteins.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research

Protein-ligand docking is an in silico tool used to screen potential drug compounds for their ability to bind to a given protein receptor within a drug-discovery campaign. Experimental drug screening is expensive and time consuming, and it is desirable to carry out large scale docking calculations in a high-throughput manner to narrow the experimental search space. Few of the existing computational docking tools were designed with high performance computing in mind. Therefore, optimizations to maximize use of high-performance computational resources available at leadership-class computing facilities enables these facilities to be leveraged for drug discovery. Here we present the porting, optimization, and validation of the AutoDock-GPU program for the Summit supercomputer, and its application to initial compound screening efforts to target proteins of the SARS-CoV-2 virus responsible for the current COVID-19 pandemic.

LeGrand, Scott↗
DOE OSTI2022

Inverse design of plasma metamaterial devices with realistic elements

In an expansion of a previous study, we apply inverse design methods to produce two-dimensional plasma metamaterial devices with realistic plasma elements which incorporate quartz envelopes, collisionality (loss), non-uniform density profiles, and resistance to experimental error/perturbation. Finite difference frequency domain simulations are used along with forward-mode differentiation to design waveguides and demultiplexers operating under the transverse magnetic polarization. Additionally, optimal devices with realistic elements are compared to previous devices with idealized elements, and several parameter initialization schemes for the optimization algorithm are explored, yielding a robust procedure for producing such devices. Demultiplexing and waveguiding are demonstrated for microwave-regime devices composed of plasma elements with reasonable space-averaged plasma frequencies ~10GHz and a collision frequency ~1 GHz, allowing for future in-situ training and experimental realization of these designs.

42 ENGINEERING↗
DOE OSTI2024

Blade and Rim Seal Design of a First Stage High Pressure Turbine for a 300 MWe Supercritical CO2 Power Cycle

A first stage high-pressure turbine (HPT) blade is optimized for a 300 MWe supercritical CO2 (sCO2) power cycle using the surrogate-assisted genetic algorithm optimizer in Numeca FINE/Design 3D with objectives of increasing efficiency and decreasing heat load to the blade. The National Institute of Standards and Technology Reference Fluid Thermodynamic and Transport Properties Database (NIST REFPROP) [1] data for supercritical CO2 is formatted into tables of bicubic polynomial coefficients for use in condensable gas simulations in FINE/Turbo. Nearly 3000 unique shapes are evaluated via three-dimensional Reynolds Averaged Navier Stokes simulations, yielding increases in efficiency of up to 0.85 percentage points and decreases in heat load of 14%. A final blade, deemed the advanced blade, is chosen for future experimental analysis. Following this, a squealer tip optimization is performed on both the baseline and advanced blade designs. This optimization resulted in a performance gain of 1.25 points in efficiency and 15% reduction in tip heat load compared to the baseline flat tip design at the same clearance. In tandem, an optimization of the rotor-stator platform rim seal is performed using a parametrized geometry allowing for straight, meandering, and knife seal cavities. This multi-objective optimization focuses on decreasing the cooling mass flow and increasing the heat flux from the rotor and stator disk. The optimization resulted in cooling mass flow decreases of up to 26% while maintaining the average heat flux on the rim seal.

Tuite, Logan↗
DOE OSTI2024

Denoising diffusion probabilistic models for generative alloy design

Inverse material design is an extremely challenging optimization task made difficult by, in part, the highly nonlinear relationship linking performance with composition. Quantitative approaches have improved significantly owing to advances in high throughput experimentation and computational thermodynamics. However, existing physics-based tools are mostly forward models; input a chemistry and obtain a prediction. More recently the materials community has leveraged advances in the machine learning community to establish novel inverse design frameworks. Very recently denoising diffusion probabilistic models have been shown to be extremely powerful generators producing synthetic data of various modalities e.g. images, text, audio, tables, etc.. In this work a novel framework for alloy design and optimization is proposed leveraging these class of models. Five key generative tasks are demonstrated (1) unconditional generation (2) composition conditioned generation (3) property conditioned generation (4) multi-feedstock conditioned generation and (5) generative optimization. These methods were tested on three case studies: high entropy alloy design, superalloy binder jet additive manufacturing, and in-situ dual-feedstock wire-arc additive manufacturing. Results indicate that the established models are extremely flexible, expressive, and robust. The architecture’s flexibility and training procedure empower the model to learn complex intra-compositional and composition-property relationships. Furthermore, the probabilistic nature of these models makes them well suited for addressing solution non-uniqueness and tackling uncertainty quantification tasks. While the fidelity and quantity of the underlying training data is paramount, we envision that future alloy design frameworks will make extensive use of these kinds of machine learning models as “search” tools bolstering the utility of experimental and computational approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The 140 GHz notch filter development for millimeter-wave diagnostics protection on the stellarator Wendelstein 7-X

The notch filter plays a crucial role as a protective component in microwave diagnostics, primarily by addressing issues related to catastrophic interference. Designed for millimeter-wave diagnostics on the stellarator Wendelstein 7-X (W7-X), a WR-6 waveguide-based notch filter has been successfully developed to effectively isolate leakage from auxiliary heating gyrotrons operating at 140 GHz. The filter incorporates cylindrical cavities resonating at 140 GHz for the TE11p mode, with coupling structures that are designed and optimized for high-efficiency coupling. This configuration simplifies fabrication, thereby ensuring high-yield production. Experimental fabrication and in-house characterization confirm the notch filter's exceptional performance, with over 60 dB rejection in the vicinity of 140 GHz and low insertion loss (< 2 dB) above and below the notch frequency across a broad frequency bandwidth (121–138 GHz, 142–163 GHz). Furthermore, the utilization of this high-frequency structure fabrication technology can be applied to millimeter-wave diagnostics on other machines. In addition to the design elements of the notch filter, this paper also provides a detailed discussion of the fabrication process and methodology.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Spark-Assisted HCCI Residential CHP

A transformative small spark ignition (SI) internal combustion (IC) engine fueled with natural gas was developed for combined heat and power (CHP) applications using a combination of cycle simulations, computational fluid dynamics (CFD) modeling, and engine experiments. The resulting 1 kW CHP engine was demonstrated to achieve 36.1% brake thermal efficiency (BTE) while meeting aggressive exhaust emissions targets using a low-cost three-way catalyst. Starting from a “clean-sheet” design, modeling tools were used to select the optimal engine parameters and operating characteristics. The disruptive technologies developed for the CHP system have a target lifetime of at least 10 years. The engine cost at volume of 10,000 units was estimated to be $\$$1,050. A combination of experimental and simulation results were used to identify a path to the program target of 38.6% BTE. Base engine design parameters (i.e., displacement, speed, bore, stroke, valve timings, and compression ratio) were optimized to reduce friction, heat transfer, and exhaust losses. The base clean sheet prototype engine achieved 32% BTE. Optimization of the lube oil and coolant temperatures added 1.8% BTE. The addition of exhaust gas recirculation added 1.1% BTE. Novel thermal barrier coatings (TBCs) developed over the course of the project added 1.2% BTE. Further improvements in engine friction reduction (1.3% BTE) and improved combustion (1.2% BTE) are estimated to meet the program target 38.6% BTE, while also meeting the stringent criteria pollutant emissions targets.

42 ENGINEERING↗
DOE OSTI2022

Rossi-alpha Analysis of Thermal/Epithermal eXperiments Optimized for Polyethylene Thermal Neutron Scattering

Accurate nuclear data is the foundation for predictive simulations and design of new experimental in the nuclear community. The criticality safety community has particular interest in benchmarking assemblies with thermal neutron data. To address such needs the Nuclear Criticality Safety Program (NCSP) funded the Thermal/Epithermal eXperiments (TEX) campaigns that were to be completed between Lawrence Livermore National Laboratory (LLNL) and Los Alamos National Laboratory (LANL). Specifically, analysis of plutonium with a thermal neutron spectrum expands on previous work to help validate thermal scattering law data, which can have larger impacts in thermal applications. The first of these experiments were successfully conducted in 2018, but this paper will focus on the 2021 measurement focused on investigating the the thermal scattering law (TSL) for polyethylene. These experiments were successfully conducted at the National Criticality Experiments Research Center (NCERC) using the Planet vertical lift critical assembly machine. Polethylene plates were layered with trays of Zero Power Physics Reactor (ZPPR) 24 plates in a 12" by 12" square. The ZPPR plates were 2" by 3" by 0.125" bearing weapons grade plutonium. The polyethylene moderator was either 2" or 1.6875" thick. This work builds on the Rossi-alpha calculations done by McKenzie et al. for the same detector-assembly system and will only focus on the Rossi-alpha neutron noise method. This work will aim to further validate the results of the experiment through a novel neutron noise python package. Following similar methodology to the previous analysis, analysis of TEX evaluated the prompt neutron decay constant at delayed critical, $α_{DC}$ using Rossi-alpha for different polyethylene moderator thicknesses. These results will help improve understanding of TSL in critical experiments. Alpha (α), is the prompt neutron decay constant of the measured system and allows for the evaluation of a systems propensity to sustain fission chains via prompt neutrons. The single value description of the assemblies allow for comparison between experiments regardless of composition, geometry, and reflectors/moderators. Rossi-alpha measurements were performed on the polyethylene moderated TEX experiments to estimate $α_{DC}$.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2009

Data Analysis for the LISA Pathfinder Mission

The LTP (LISA Technology Package) is the core part of the Laser Interferometer Space Antenna (LISA) Pathfinder mission. The main goal of the mission is to study the sources of any disturbances that perturb the motion of the freely-falling test masses from their geodesic trajectories as well as 10 test various technologies needed for LISA. The LTP experiment is designed as a sequence of experimental runs in which the performance of the instrument is studied and characterized under different operating conditions. In order to best optimize subsequent experimental runs, each run must be promptly analysed to ensure that the following ones make best use of the available knowledge of the instrument ' In order to do this, all analyses must be designed and tested in advance of the mission and have sufficient built-in flexibility to account for unexpected results or behaviour. To support this activity, a robust and flexible data analysis software package is also required. This poster presents two of the main components that make up the data analysis effort: the data analysis software and the mock-data challenges used to validate analysis procedures and experiment designs.

Thorpe, James Ira↗
NASA NTRSDate not supplied

Experimental and Computational Analysis of a Rotating Detonation Combustor

Rotating detonation combustor (RDC) research has progressed along parallel experimental and computational paths with limited opportunities for validation. Validation is a crucial step to ensure RDC simulations provide an accurate representation of the physical phenomena, and thus, can be used with confidence for design optimization in different applications. In this study, data from a quasi-two-dimensional computational fluid dynamic simulation in an annular RDC are compared with experimental measurements including particle image velocimetry at 100 kHz. Results show good agreement between measured and simulated dynamic pressures, Chapman-Jouguet speeds, and static pressure distributions. Velocity measurements at the annulus exit show good temporal and quantitative agreement with simulations including spikes from the passing shock.

Detonation↗
DOE OSTI2022

Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing

Perovskite photovoltaics (PV) have achieved rapid development in the past decade in terms of power conversion efficiency of small-area lab-scale devices; however, successful commercialization still requires further development of low-cost, scalable, and high-throughput manufacturing techniques. One of the critical challenges of developing a new fabrication technique is the high-dimensional parameter space for optimization, but machine learning (ML) can readily be used to accelerate perovskite PV scaling. Herein, we present an ML-guided framework of sequential learning for manufacturing process optimization. We apply our methodology to the Rapid Spray Plasma Processing (RSPP) technique for perovskite thin films in ambient conditions. With a limited experimental budget of screening 100 process conditions, we demonstrated an efficiency improvement to 18.5% as the best-in-our-lab device fabricated by RSPP, and we also experimentally found 10 unique process conditions to produce the top-performing devices of more than 17% efficiency, which is 5 times higher rate of success than the control experiments with pseudo-random Latin hypercube sampling. Our model is enabled by three innovations: (a) flexible knowledge transfer between experimental processes by incorporating data from prior experimental data as a probabilistic constraint; (b) incorporation of both subjective human observations and ML insights when selecting next experiments; (c) adaptive strategy of locating the region of interest using Bayesian optimization first, and then conducting local exploration for high-efficiency devices. Furthermore, in virtual benchmarking, our framework achieves faster improvements with limited experimental budgets than traditional design-of-experiments methods (e.g., one-variable-at-a-time sampling). This framework shows the capability of incorporating researchers’ domain knowledge into the ML-guided optimization loop; therefore, it has the potential to facilitate the wider adoption of ML in scaling to perovskite PV manufacturing.

14 SOLAR ENERGY↗
NASA NTRS2014

Optimal Energy Measurement in Nonlinear Systems: An Application of Differential Geometry

Design of TES microcalorimeters requires a tradeoff between resolution and dynamic range. Often, experimenters will require linearity for the highest energy signals, which requires additional heat capacity be added to the detector. This results in a reduction of low energy resolution in the detector. We derive and demonstrate an algorithm that allows operation far into the nonlinear regime with little loss in spectral resolution. We use a least squares optimal filter that varies with photon energy to accommodate the nonlinearity of the detector and the non-stationarity of the noise. The fitting process we use can be seen as an application of differential geometry. This recognition provides a set of well-developed tools to extend our work to more complex situations. The proper calibration of a nonlinear microcalorimeter requires a source with densely spaced narrow lines. A pulsed laser multi-photon source is used here, and is seen to be a powerful tool for allowing us to develop practical systems with significant detector nonlinearity. The combination of our analysis techniques and the multi-photon laser source create a powerful tool for increasing the performance of future TES microcalorimeters.

Nonlinear response↗
DOE OSTI2022

Stellarator coil optimization supporting multiple magnetic configurations

Abstract We present a technique that can be used to design stellarators with a high degree of experimental flexibility. For our purposes, flexibility is defined by the range of values the rotational transform can take on the magnetic axis of the vacuum field while maintaining satisfactory quasisymmetry. We show that accounting for configuration flexibility during the modular coil design improves flexibility beyond that attained by previous methods. Careful placement of planar control coils and the incorporation of an integrability objective enhance the quasisymmetry and nested flux surface volume of each configuration. We show that it is possible to achieve flexibility, quasisymmetry, and nested flux surface volume to reasonable degrees with a relatively simple coil set through an NCSX-like example. This example coil design is optimized to achieve three rotational transform targets and nested flux surface volumes in each magnetic configuration larger than the NCSX design plasma volume. Our work suggests that there is a tradeoff between flexibility, quasisymmetry, and volume of nested flux surfaces.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing

Advanced manufacturing techniques have enabled the production of materials with state-of-the-art properties. In many cases however, the development of physics-based models of these techniques lags behind their development in the lab. This means that material and process development proceeds largely via trial and error. This is sub-optimal since experiments are cost-, time-, and labor-intensive. In this work we propose a machine learning framework, differential property classification (DPC), which enables an experimenter to leverage machine learning's unparalleled pattern matching capability to pursue data-driven experimental design. DPC takes two possible experiment parameter sets and outputs a prediction of which will produce a material with a more desirable property specified by the operator. We demonstrate the success of DPC on AA7075 tube manufacturing process and mechanical property data using shear assisted processing and extrusion (ShAPE), an emerging solid phase processing technology. We show that by focusing on the experimenter's need to choose between multiple candidate experimental parameters, we can reframe the challenging regression task of predicting material properties from processing parameters, into a classification task on which machine learning models can achieve good performance.

advanced manufacturing, machine learning, ShAPE↗
DOE OSTI2023

Experimental Validation of Models of a Hull-Based Tuned Mass Damper System for a Semisubmersible Floating Offshore Wind Turbine Platform

Floating offshore wind turbine designs can be further optimized if the controller and remaining systems are designed together, known as control co-design. To effectively perform control co-design, modeling tools predicting the influence of the control systems on the response of a system must be validated. This article presents an experimental validation that utilizes a scale model of a semisubmersible platform for an offshore wind turbine that is fitted with adjustable tuned mass dampers. These dampers can be tuned to attenuate either the hull-pitch resonance or the tower-bending resonance. The data from the experiment are used to validate state-of-the-art modeling tools. It is shown that the models capture the overall effects of the tuned mass dampers; however, some models overpredict the reduction in platform pitch motion when the dampers are tuned to the pitch resonance. The relative reduction in the tower-base bending moment is more consistently captured by the models when the dampers are tuned to the tower-bending resonance. However, there are significant differences in the absolute level of tower-base bending moment between the models and the experiment. Much of the differences observed are a consequence of the challenge with accurately predicting the baseline platform resonance motion and the tower-bending moment, which should be addressed in future modeling efforts.

17 WIND ENERGY↗
NASA NTRS2009

Development of a Reactor for the Extraction of Oxygen and Volatiles From Lunar Regolith

The RESOLVE (Regolith and Environment Science, Oxygen and Lunar Volatiles Extraction) Project, aims to extract and quantify useful resources from lunar soil. The reactor developed for RESOLVE is a dual purpose system, designed to evolve both water, at 150 C and up to 80 psig, and oxygen, using hydrogen reduction at 900 C. A variety of laboratory tests were performed to verify its operation and to explore the properties of the analog site soil. The results were also applied to modeling efforts which are being used to estimate the apparent thermal properties of the soil. The experimental and numerical results, along with the analog site tests, will be used to evolve and optimize future reactor designs.

Kleinhenz, Julie↗