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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Temperature response surfaces for mortality risk of tree species with future drought

Widespread, high levels of tree mortality, termed forest die-off, associated with drought and rising temperatures, are disrupting forests worldwide. Drought will likely become more frequent with climate change, but even without more frequent drought, higher temperatures can exacerbate tree water stress. The temperature sensitivity of drought-induced mortality of tree species has been evaluated experimentally for only single-step changes in temperature (ambient compared to ambient + increase) rather than as a response surface (multiple levels of temperature increase), which constrains our ability to relate changes in the driver with the biological response. Here we show that time-to-mortality during drought for seedlings of two western United States tree species, Pinus edulis (Engelm.) and Pinus ponderosa (Douglas ex C. Lawson), declined in continuous proportion with increasing temperature spanning a 7.7 °C increase. Although P. edulis outlived P. ponderosa at all temperatures, both species had similar relative declines in time-to-mortality as temperature increased (5.2% per °C for P. edulis; 5.8% per °C for P. ponderosa). When combined with the non-linear frequency distribution of drought duration—many more short droughts than long droughts—these findings point to a progressive increase in mortality events with global change due to warming alone and independent of additional changes in future drought frequency distributions. As such, dire future forest recruitment patterns are projected assuming the calculated 7–9 seedling mortality events per species by 2100 under business-as-usual warming occur, congruent with additional vulnerability predicted for adult trees from stressors like pathogens and pests. Our progressive projection for increased mortality events was driven primarily by the non-linear shape of the drought duration frequency distribution, a common climate feature of drought-affected regions. These results illustrate profound benefits for reducing emissions of carbon to the atmosphere from anthropogenic sources and slowing warming as rapidly as possible to maximize forest persistence.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Optimization of the Electrochemical Extraction and Recovery of Metals from Electronic Waste Using Response Surface Methodology

The rapid growth of the electronic waste can be viewed both as an environmental threat and as an attractive source of minerals that can reduce the mining of natural resources, and stabilize the market of critical materials, such as rare earths. Here in this article surface response methodology was used to optimize a previously developed electrochemical recovery process for base metals from electronic waste using a mild oxidant (Fe 3+ ). Through this process an effective extraction of base metals can be achieved enriching the concentration of precious metals and significantly reducing environmental impacts and operational costs associated with the waste generation and chemical consumption. The optimization was performed using a bench-scale system specifically designed for this process. Operational parameters such as flow rate, applied current density and iron concentration were optimized to reduce the specific energy consumption of the electrochemical recovery process to 1.94 kWh per kg of metal recovered at a processing rate of 3.3 g of electronic waste per hour.

36 MATERIALS SCIENCE↗
DOE OSTI2021

An Iterative Response-Surface-Based Approach for Chance-Constrained AC Optimal Power Flow Considering Dependent Uncertainty

A modern power system is characterized by a stochastic variation of the loads and an increasing penetration of renewable energy generation, which results in large uncertainties in its states. These uncertainties bring formidable challenges to the power system planning and operation process. To address these challenges, we propose a cost-effective, iterative response-surface-based approach for the chance-constrained AC optimal power-flow problem that aims to ensure the secure operation of the power systems considering dependent uncertainties. Starting from a stochastic-sampling-based framework, we first utilize the copula theory to simulate the dependence among multivariate uncertain inputs. Then, to reduce the prohibitive computational time required in the traditional Monte-Carlo method, we propose, instead of using the original complicated power-system model, to rely on a polynomial-chaos-based response surface. This response surface allows us to efficiently evaluate the time-consuming power-system model at arbitrary distributed sampled values with a negligible computational cost. This further enables us to efficiently conduct an online stochastic testing for the system states that not only screens out the statistical active constraints, but also assists in a better design of the tightened bounds without using any Gaussian or symmetric assumption. Finally, an iterative procedure is executed to fine-tune the optimal solution that better satisfies a predefined probability. The simulations conducted in multiple test systems demonstrate the excellent performance of the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Biomass Grinding Process Optimization Using Response Surface Methodology and a Hybrid Genetic Algorithm

Biomass could be a key source of renewable energy. Agricultural waste products, such as corn stover, provide a convenient means to replace fossil fuels, such as coal, and a large amount of feedstock is currently available for energy consumption in the U.S. This study has two main objectives: (1) to understand the impact of corn stover moisture content and grinder speed on grind physical properties; and (2) develop response surface models and optimize these models using a hybrid genetic algorithm. The response surface models developed were used to draw surface plots to understand the interaction effects of the corn stover grind moisture content and grinder speed on the grind physical properties and specific energy consumption. The surface plots indicated that a higher corn stover grind moisture content and grinder speed had a positive effect on the bulk and tapped density. The final grind moisture content was highly influenced by the initial moisture content of the corn stover grind. Optimization of the response surface models using the hybrid genetic algorithm indicated that moisture content in the range of 17 to 19% (w.b.) and a grinder speed of 47 to 49 Hz maximized the bulk and tapped density and minimized the geomantic mean particle length. The specific energy consumption was minimized when the grinder speed was about 20 Hz and the corn stover grind moisture content was about 10% (w.b.).

09 BIOMASS FUELS↗
DOE OSTI2020

Multi-objective optimization of peel and shear strengths in ultrasonic metal welding using machine learning-based response surface methodology

Ultrasonic metal welding (UMW) is a solid-state joining technique with varied industrial applications. Despite of its numerous advantages, UMW has a relative narrow operating window and is sensitive to variations in process conditions. As such, it is imperative to quantitatively characterize the influence of welding parameters on the resulting joint quality. The quantification model can be subsequently used to optimize the parameters. Conventional response surface methodology (RSM) usually employs linear or polynomial models, which may not be able to capture the intricate, nonlinear input-output relationships in UMW. Furthermore, some UMW applications call for simultaneous optimization of multiple quality indices such as peel strength, shear strength, electrical conductivity, and thermal conductivity. To address these challenges, this paper develops a machine learning (ML)-based RSM to model the input-output relationships in UMW and jointly optimize two quality indices, namely, peel and shear strengths. The performance of various ML methods including spline regression, Gaussian process regression (GPR), support vector regression (SVR), and conventional polynomial regression models with different orders is compared. A case study using experimental data shows that GPR with radial basis function (RBF) kernel and SVR with RBF kernel achieve the best prediction accuracy. The obtained response surface models are then used to optimize a compound joint strength indicator that is defined as the average of normalized shear and peel strengths. In addition, the case study reveals different patterns in the response surfaces of shear and peel strengths, which has not been systematically studied in the literature. While developed for the UMW application, the method can be extended to other manufacturing processes.

42 ENGINEERING↗
DOE OSTI2025

An Assessment of the Error Due to Computing Waste Isolation Pilot Plant Porosity Using the Porosity Response Surface Approach

The Waste Isolation Pilot Plant Performance Assessment (WIPP PA) must predict the likelihood that radionuclides will escape into the biosphere via mechanisms that depend on geohydraulic flow. Ideally, one would predict the geohydraulic flow using coupled geohydraulic and geomechanical simulations, but such coupled simulations are not computationally tractable. Instead, Sandia has historically used a look-up table of porosities for a given fluid pressure and time, called the porosity response surface, but this approach can introduce porosity errors because it largely ignores the porosity’s dependence on the past fluid pressure history. This report discusses efforts to quantify these porosity errors for both the legacy and new porosity response surfaces. Six hundred different fluid pressure histories were fed through the legacy/new geomechanical model and the legacy/new porosity response surface to generate six hundred porosity error histories. The error associated with the legacy porosity surface was substantial, while the error associated with the new porosity surface was typically small, except when fluid pressures exceeded the lithostatic pressure at the repository. In response to the errors at high pressures, a preliminary study of the WIPP PA’s sensitivity to these porosity errors was conducted. The study found that reducing the porosity errors at high pressures negligibly affected predictions of radionuclide releases. Finally, an initial machine-learned model for porosity was developed. This ML model significantly reduced the porosity error at high pressures, but sizable errors remained, so more development is necessary before coupling an ML model to the geohydraulic model.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

An integrated approach to optimizing concentration shock wave electrodialysis using 2D multicell simulation and response surface models

Shock wave electrodialysis (SWED) is a highly promising technique for energy-efficient ion separation in the context of a circular economy. This paper presents a approach way of modeling and improving SWED using a two-dimensional multicell model combined with the COMSOL program and response surface methodology. The model integrates the Nernst-Planck equation, Darcy's law, and first-order electroosmosis to examine the local concentration, flux of ionic species, distribution of current, and velocity of flow in SWED cells under various operating conditions. We first illustrate the clear depiction of concentration, velocity, and electric potential distribution through contours which aids in identifying optimal operating conditions and designing scalable SWED systems. The results emphasize the significance of surface charge density and voltage in influencing the features of shock waves for obtaining effective ion separation while optimizing energy consumption and improving current efficiency by controlling the retention time of feed flow. Here, this study defines two crucial characteristics of shock waves, namely the length of the flat depletion zone of a fully developed shock wave (shock wave height) and the distance of shock wave propagation (shock wave length). These properties significantly impact separation performance, as determined by the simulation results. Additionally, the response surface methodology is incorporated with the COMSOL models to develop predictive models and graph responses, enabling a more comprehensive understanding of the interactions between parameters and performance indicators, such as removal ratio, energy consumption, and water recovery. Finally, this work suggests design tactics for expanding SWED processes and outlines potential areas for further research. This research provides valuable insights into the prospective applications, design optimization, and scalability of SWED in the field of electrokinetic separation technologies for green chemistry and a circular economy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Response Surface Modeling Tool Suite, Version 1.x

The Response Surface Modeling (RSM) Tool Suite is a collection of three codes used to generate an empirical interpolation function for a collection of drag coefficient calculations computed with Test Particle Monte Carlo (TPMC) simulations. The first code, "Automated RSM", automates the generation of a drag coefficient RSM for a particular object to a single command. "Automated RSM" first creates a Latin Hypercube Sample (LHS) of 1,000 ensemble members to explore the global parameter space. For each ensemble member, a TPMC simulation is performed and the object drag coefficient is computed. In the next step of the "Automated RSM" code, a Gaussian process is used to fit the TPMC simulations. In the final step, Markov Chain Monte Carlo (MCMC) is used to evaluate the non-analytic probability distribution function from the Gaussian process. The second code, "RSM Area", creates a look-up table for the projected area of the object based on input limits on the minimum and maximum allowed pitch and yaw angles and pitch and yaw angle intervals. The projected area from the look-up table is used to compute the ballistic coefficient of the object based on its pitch and yaw angle. An accurate ballistic coefficient is crucial in accurately computing the drag on an object. The third code, "RSM Cd", uses the RSM generated by the "Automated RSM" code and the projected area look-up table generated by the "RSM Area" code to accurately compute the drag coefficient and ballistic coefficient of the object. The user can modify the object velocity, object surface temperature, the translational temperature of the gas, the species concentrations of the gas, and the pitch and yaw angles of the object. Together, these codes allow for the accurate derivation of an object's drag coefficient and ballistic coefficient under any conditions with only knowledge of the object's geometry and mass.

Walker, Andrew↗
DOE OSTI2019

Robust Uncertainty Quantification using Response Surface Approximations of Discontinuous Functions

This paper considers response surface approximations for discontinuous quantities of interest. Our objective is not to adaptively characterize the manifold defining the discontinuity. Instead, we utilize an epistemic description of the uncertainty in the location of a discontinuity to produce robust bounds on sample-based estimates of probabilistic quantities of interest. We demonstrate that two common machine learning strategies for classification, one based on nearest neighbors (Voronoi cells) and one based on support vector machines, provide reasonable descriptions of the region where the discontinuity may reside. In higher dimensional spaces, we demonstrate that support vector machines are more accurate for discontinuities defined by smooth manifolds. We also show how gradient information, often available via adjoint-based approaches, can be used to define indicators to effectively detect a discontinuity and to decompose the samples into clusters using an unsupervised learning technique. Numerical results demonstrate the epistemic bounds on probabilistic quantities of interest for simplistic models and for a compressible fluid model with a shock-induced discontinuity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Response Surface Modeling Tool Suite, Version 1.x

The Response Surface Modeling (RSM) Tool Suite is a collection of three codes used to generate an empirical interpolation function for a collection of drag coefficient calculations computed with Test Particle Monte Carlo (TPMC) simulations. The first code, "Automated RSM", automates the generation of a drag coefficient RSM for a particular object to a single command. "Automated RSM" first creates a Latin Hypercube Sample (LHS) of 1,000 ensemble members to explore the global parameter space. For each ensemble member, a TPMC simulation is performed and the object drag coefficient is computed. In the next step of the "Automated RSM" code, a Gaussian process is used to fit the TPMC simulations. In the final step, Markov Chain Monte Carlo (MCMC) is used to evaluate the non-analytic probability distribution function from the Gaussian process. The second code, "RSM Area", creates a look-up table for the projected area of the object based on input limits on the minimum and maximum allowed pitch and yaw angles and pitch and yaw angle intervals. The projected area from the look-up table is used to compute the ballistic coefficient of the object based on its pitch and yaw angle. An accurate ballistic coefficient is crucial in accurately computing the drag on an object. The third code, "RSM Cd", uses the RSM generated by the "Automated RSM" code and the projected area look-up table generated by the "RSM Area" code to accurately compute the drag coefficient and ballistic coefficient of the object. The user can modify the object velocity, object surface temperature, the translational temperature of the gas, the species concentrations of the gas, and the pitch and yaw angles of the object. Together, these codes allow for the accurate derivation of an object's drag coefficient and ballistic coefficient under any conditions with only knowledge of the object's geometry and mass.

Walker, Andrew↗
DOE OSTI2018

Response surface data for sensitivity study of industrial spray injected fluidized bed reactor

An industrial fluidized bed reactor was designed to convert an aqueous solid laden stream into a consistent granular product. CFD simulations were run using the MFiX two-fluid model for this fluidizing bed operating at 650 °C. A set of simulations were run over a Latin-hypercube sample of five model parameters – bed particle size, bed particle density, coal particle size, spray feed flow rate, and fluidizing gas flow rate. Data from the simulations were collected on three quantities of interest – bed differential temperature, low solids velocity, and bed voidage. The data presented here is the full span of response surfaces generated using the process Gaussian response surface model in the Dakota code, as well as the table of data for coefficients of the fitted model. The fits to the five-dimensional Gaussian Process models were 0.7797, 0.8664, and 0.9440 for the temperature, velocity, and solids packing, respectively.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Response surface modeling of CO 2 dynamic storage efficiency factor in high permeability thick sandstones

In order to utilize as much of the pore space for CO 2 storage in high permeability thick saline aquifers, it is vital to investigate the interactions of injected CO 2 with formation brine and rock. In order to quantify the displacement process, we investigate the dynamic storage efficiency factor (DSEF) for saline aquifers where pressure increase is minimal during the injection phase. Dimensionless numbers are derived from basic governing equations, constitutive equations, initial and boundary conditions using the inspection analysis. Then using the Hammersley sequence sampling, 178 numerical experiments are designed, and a compositional reservoir simulator is used to perform these simulations. In the next step, response surface regression analysis is used to establish a relationship between DSEF obtained from the numerical simulations and the corresponding dimensionless numbers. The simulation results show that for the studied conditions the underground dynamics is mostly influenced by the gravity number, followed by effective aspect ratio and dip numbers. The results from the response surface regression analysis are used to develop a correlation, which can be used to estimate the dynamic CO 2 storage capacity of relevant zones. This study provides quantitative measures for the different competing mechanisms involved in underground displacement of fluids in CO 2 geological storage, which can serve as a useful tool during planning phase of storage projects.

02 PETROLEUM↗
DOE OSTI2022

DBD plasma-assisted ethanol steam reforming for green H 2 production: Process optimization through response surface methodology (RSM)

Herein this work investigates ethanol steam reforming (ESR) to produce hydrogen (H 2 ) in a dielectric barrier discharge (DBD) plasma reactor. A five-level, three-factor experiment design was performed using a response surface methodology (RSM) to evaluate the combined effects of the three process parameters, including discharge power, total flow rate, and ethanol-to-water (EtOH/H 2 O) molar ratio on the plasma-assisted ESR reaction. Quadratic regression models were employed in RSM to fit the experimental results and present the correlation between process parameters and targeted responses (EtOH conversion, H 2 yield, H 2 selectivity, and specific energy requirement (SER) for H 2 production). The results suggested that the EtOH/H 2 O molar ratio is considered to have the most significant effect on the EtOH conversion and H 2 , H 2 selectivity, while the total flow rate is the most significant parameter determining SER for H 2 production. Process optimization demonstrated the optimal process conditions, including a discharge power of 55.9 W, a total flow rate of 26.7 ml/min, and an EtOH/H 2 O molar ratio equal to 0.34. A validation test was performed and confirmed the feasibility of the optimization process.

08 HYDROGEN↗
DOE OSTI2019

Bayesian probability of agreement for comparing the similarity of response surfaces

For a variety of linear and generalized linear models, there may be interest in evaluating whether the expected response values in two different populations are sufficiently similar to be considered practically equivalent. The probability of agreement has been introduced as a strategy for quantifying the similarity between two groups. We propose a Bayesian version of the probability of agreement to quantify the similarity between linear and generalized linear response surfaces. The proposed methodology is based on Markov chain Monte Carlo estimation and it allows for an intuitive interpretation based directly on population parameters. As demonstrated, the methodology can be flexibly applied to a variety of different models. We illustrate its use with three examples for which the response depends on predictor variables through a linear, logistic, or Poisson regression model. The computation associated with this approach has been automated with a freely available R Shiny app.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Dynamic Probabilistic Risk Assessment Based Response Surface Approach for FLEX and Accident Tolerant Fuels for Medium Break LOCA Spectrum

After the Fukushima Daiichi Accident, the safety features such as accident tolerant fuel (ATF) and diverse and flexible coping strategies (FLEX) for existing nuclear fleets are being investigated by the US Department of Energy under the Light Water Reactor Sustainability Program. This research is being conducted to quantify the risk-benefit of these safety features. Dynamic probabilistic risk assessment (DPRA)-based response-surface approach has been presented to quantify the FLEX and ATF benefits by estimating the risk associated with each option. ATFs with multilayered silicon carbide (SiC), iron-chromium-aluminum, and chromium-coated zirconium cladding were considered in this study. While these ATF candidates perform better than the current zirconium cladding (Zr), they may introduce additional failure modes in some operating conditions. The fuel failure analysis modules (FAMs) were developed to investigate ATF performance. The dynamic risk assessments were performed using RAVEN, a DPRA tool, coupled with RELAP5 and FAMs. A cumulative distribution function-based index provided a mean of comparing the benefits of safety enhancements. For medium break loss of coolant accidents, FLEX operational timing window for each fuel type was estimated. Among these ATF candidates, SiC-type ATF was the most beneficial candidate for an increased safety margin than Zr-based fuel and was found to complement FLEX strategies in terms of risk and coping time.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Partitioning of Recombinant Pseudomonas putida POS-F84 Proline Dehydrogenase in Aqueous Two-Phase Systems: Optimization Using Response Surface Methodology

Empirical modeling the partition behavior and recovery of a recombinant Pseudomonas putida POS-F84 proline dehydrogenase (ProDH) in aqueous two-phase systems (ATPS) was carried out by response surface methodology (RSM). Polyethylene glycol 1000 (PEG-1000) concentration, sodium carbonate concentration, and pH, which were the most important factors, were chosen for modeling the partition feature of enzyme. The adequacy of the models was investigated by means of variance analysis. Also, to confirm the efficiency of the ATPS in partition and purification of recombinant ProDH, purity and enzymatic activity was studied. After numerical optimization, an optimal ATPS composed of 14.33% PEG-1000 and 11.79% sodium carbonate at pH 7.48 was achieved. Yield, purification factor, and recovery were 81.41%, 60.82, and 270.82%, respectively. Purified recombinant ProDH was found as a single protein band into the upper PEG-rich phase and the specific activity was calculated to be 46.23 ± 2.1 U/mg. Collectively, our data showed that the RSM could be an appropriate and powerful tool to define the best ATPS system for recovery and purification of P. putida ProDH.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Process Parameter Optimization in Refill Friction Spot Welding of 6061 Aluminum Alloys Using Response Surface Methodology

6061-T6 aluminum alloy joint was fabricated by refill friction stir spot welding (RFSSW) on which three process parameters of tool rotation speed, sleeve moving rate, and plunge depth make an important effect. The response surface methodology (RSM) was applied to establish a mathematical model to study the effect of process parameters on lap shear fracture load (LSFL) of the RFSSWed joints which were performed in Box–Behnken designs with three factors, three levels and 15 runs. Analysis of variance used to check the adequacy of the developed model indicates that the mathematical model is significant. LSFL increases with the increase in tool rotation speed, sleeve plunge depth, and sleeve moving rate to maximum and then decreases. Sleeve plunge depth makes the most effect on LSFL in all process parameters. The RFSSW process parameters were also optimized using RSM to predict maximize LSFL. The joint produced using a tool rotational speed of 1506 rpm, sleeve moving rate of 1.01 mm/s and sleeve plunge depth of 2.46 mm displays higher LSFL.

36 MATERIALS SCIENCE↗