Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Model based development”

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 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Predicting Patient-specific Dosimetric Benefits of Proton Therapy for Skull-base Tumors Using a Geometric Knowledge-based Method

Purpose: To predict the organ at risk (OAR) dose levels achievable with proton beam therapy (PBT), solely based on the geometric arrangement of the target volume in relation to the OARs. A comparison with an alternative therapy yields a prediction of the patient-specific benefits offered by PBT. This could enable physicians at hospitals without proton capabilities to make a better-informed referral decision or aid patient selection in model-based clinical trials. Methods and Materials: Skull-base tumors were chosen to test the method, owing to their geometric complexity and multitude of nearby OARs. By exploiting the correlations between the dose and distance-to-target in existing PBT plans, the models were independently trained for 6 types of OARs: brainstem, cochlea, optic chiasm, optic nerve, parotid gland, and spinal cord. Once trained, the models could estimate the feasible dose–volume histogram and generalized equivalent uniform dose (gEUD) for OAR structures of new patients. The models were trained using 20 patients and validated using an additional 21 patients. Validation was achieved by comparing the predicted gEUD to that of the actual PBT plan. Results: The predicted and planned gEUD were in good agreement. Considering all OARs, the prediction error was +1.4 ± 5.1 Gy (mean ± standard deviation), and Pearson's correlation coefficient was 93%. By comparing with an intensity modulated photon treatment plan, the model could classify whether an OAR structure would experience a gain, with a sensitivity of 93% (95% confidence interval: 87%-97%) and specificity of 63% (95% confidence interval: 38%-84%). Conclusions: We trained and validated models that could quickly and accurately predict the patient-specific benefits of PBT for skull-base tumors. Similar models could be developed for other tumor sites. Such models will be useful when an estimation of the feasible benefits of PBT is desired but the experience and/or resources required for treatment planning are unavailable.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Constitutive behavior and processing maps of a new wrought magnesium alloy ZE20 (Mg-2Zn-0.2Ce)

ZE20 (Mg-2Zn-0.2Ce) 2 is a new wrought magnesium alloy with improved extrudability and mechanical properties [1]. To understand the constitutive behavior and workability of this new alloy, Gleeble thermomechanical testing has been carried out in this study. The flow stress behavior of ZE20 was investigated between 250 °C–450 °C and 10 –3 s –1 –1.0 s –1 in isothermal compression. Constitutive descriptions of the flow stress are provided. A new general approach at application of the extended Ludwik equation is demonstrated and was found to be more accurate than the hyperbolic sine Arrhenius model while having a similar number of model constants. Processing maps were developed based on the experimental results and are verified with microstructural investigation. A region of safe processing with non-basal texture and high activity of dynamic recrystallization (DRX) was found between 375 °C and 450 °C, from 10 –1 s –1 to 10–2.5 s –1 . A region of potentially safe processing with annealing that is associated with shear band nucleation of non-basal grains was identified for temperatures as low as 300 °C and rates as high as 10 –1 s –1 .

36 MATERIALS SCIENCE↗
DOE OSTI2017

A box model for representing estuarine physical processes in Earth system models

Appropriately treating riverine freshwater discharge into the oceans in Earth system models is a challenging problem. Commonly, the river runoff is discharged into the ocean models with zero salinity and arbitrarily distributed either horizontally or vertically over several grid cells. Those approaches entirely neglect estuarine physical processes that modify river inputs before they reach the open ocean. Here in order to realistically represent riverine freshwater inputs in Earth system models, a physically based Estuary Box Model (EBM) is developed to parameterize the mixing processes in estuaries. The EBM represents the estuary exchange circulation with a two-layer box structure. It takes as input the river volume flux from the land surface model and the subsurface salinity at the estuary mouth from the ocean model. It delivers the estuarine outflow salinity and net volume flux into and out of the estuary to the ocean model. An offline test of the EBM forced with observed conditions for the Columbia River system shows good agreement with observations of outflow salinity and high-resolution simulations of the exchange flow volume flux. To illustrate the practicality of use of the EBM in an Earth system model, the EBM is implemented for all coastal grid cells with river runoff in the Community Earth System Model (CESM). Compared to the standard version of CESM, which treats runoff as an augmentation to precipitation, the EBM increases sea surface salinity and reduces stratification near river mouths. The EBM also leads to significant regional and remote changes in CESM ocean surface salinities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Wave climate and energy resources in American Samoa from a 42-year high-resolution hindcast

Here, this paper presents an analysis of the wave climate and a characterization of the wave energy resources in American Samoa, a U.S. territory covering seven south central Pacific islands and atolls. A numerical wave model based on WAVEWATCH III® and unstructured SWAN was developed, validated, and executed for 1979–2020 to generate a hindcast dataset suitable for resource characterization. Model-data comparisons were performed with measurements collected in situ and from satellite-based altimeters. The model was found to perform well with a bias in significant wave height of -0.14 m and -0.06 m against buoy and altimeters, respectively. The multimodal sea state of American Samoa is investigated by identifying the sources of energy reaching the islands and partitioning the wave spectrum accordingly. The wave resources characterization follows the International Electrotechnical Commission Technical Specifications to be compatible with studies performed for other U.S. regions. The average omnidirectional wave power at 2 km from shore around Tutuila, the main island of American Samoa, is 14 kW/m. Locations east and west of the islands have a more consistent resource throughout the year because the northwest and southwest swells that are dominant during different seasons complement each other.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Battery asset management with cycle life prognosis

We report Battery Asset Management problem determines the minimum cost replacement schedules for each individual asset in a group of battery assets that operate in parallel. Battery cycle life varies under different operating conditions including temperature, depth of discharge (DOD), charge rate, etc., and a battery deteriorates due to usage, which cannot be handled by current asset management models. This paper presents a new battery asset management methodology where battery cycle life prognosis is integrated with parallel asset management to reduce lifecycle cost of the Battery Energy Storage Systems (BESS). For the battery failure time prognosis, a nonlinear physics-based battery capacity fade model is developed and incorporated in parallel asset management model to update battery capacity over time. Experiment results have shown that the developed battery asset management methodology can be conveniently used to facilitate BESS asset management decision making thereby decreasing asset lifecycle costs.

25 ENERGY STORAGE↗
DOE OSTI2012

Potential hazards to embryo implantation: A human endometrial in vitro model to identify unwanted antigestagenic actions of chemicals

Embryo implantation is a crucial step in human reproduction and depends on the timely development of a receptive endometrium. The human endometrium is unique among adult tissues due to its dynamic alterations during each menstrual cycle. It hosts the implantation process which is governed by progesterone, whereas 17β-estradiol regulates the preceding proliferation of the endometrium. The receptors for both steroids are targets for drugs and endocrine disrupting chemicals. Chemicals with unwanted antigestagenic actions are potentially hazardous to embryo implantation since many pharmaceutical antiprogestins adversely affect endometrial receptivity. This risk can be addressed by human tissue-specific in vitro assays. As working basis we compiled data on chemicals interacting with the PR. In our experimental work, we developed a flexible in vitro model based on human endometrial Ishikawa cells. Effects of antiprogestin compounds on pre-selected target genes were characterized by sigmoidal concentration–response curves obtained by RT-qPCR. The estrogen sulfotransferase (SULT1E1) was identified as the most responsive target gene by microarray analysis. The agonistic effect of progesterone on SULT1E1 mRNA was concentration-dependently antagonized by RU486 (mifepristone) and ZK137316 and, with lower potency, by 4-nonylphenol, bisphenol A and apigenin. The negative control methyl acetoacetate showed no effect. The effects of progesterone and RU486 were confirmed on the protein level by Western blotting. We demonstrated proof of principle that our Ishikawa model is suitable to study quantitatively effects of antiprogestin-like chemicals on endometrial target genes in comparison to pharmaceutical reference compounds. This test is useful for hazard identification and may contribute to reduce animal studies. -- Highlights: ► We compare progesterone receptor-mediated endometrial effects of chemicals and drugs. ► 4-Nonylphenol, bisphenol A and apigenin exert weak antigestagenic activity. ► SULT1E1 is a significant marker for endometrial antiprogestin effects. ► Ishikawa cells are a tissue-specific approach for characterization of SPRMs. ► Chemicals acting as progesterone receptor antagonists may exert antifertility effects.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Reduction of hexavalent chromium by fasted and fed human gastric fluid. I. Chemical reduction and mitigation of mutagenicity

Evaluation of the reducing capacity of human gastric fluid from healthy individuals, under fasted and fed conditions, is critical for assessing the cancer hazard posed by ingested hexavalent chromium [Cr(VI)] and for developing quantitative physiologically-based pharmacokinetic models used in risk assessment. In the present study, the patterns of Cr(VI) reduction were evaluated in 16 paired pre- and post-meal gastric fluid samples collected from 8 healthy volunteers. Human gastric fluid was effective both in reducing Cr(VI), as measured by using the s-diphenylcarbazide colorimetric method, and in attenuating mutagenicity in the Ames test. The mean (± SE) Cr(VI)-reducing ability of post-meal samples (20.4 ± 2.6 μg Cr(VI)/mL gastric fluid) was significantly higher than that of pre-meal samples (10.2 ± 2.3 μg Cr(VI)/mL gastric fluid). When using the mutagenicity assay, the decrease of mutagenicity produced by pre-meal and post-meal samples corresponded to reduction of 13.3 ± 1.9 and 25.6 ± 2.8 μg Cr(VI)/mL gastric fluid, respectively. These data are comparable to parallel results conducted by using speciated isotope dilution mass spectrometry. Cr(VI) reduction was rapid, with > 70% of total reduction occurring within 1 min and 98% of reduction is achieved within 30 min with post-meal gastric fluid at pH 2.0. pH dependence was observed with decreasing Cr(VI) reducing capacity at higher pH. Attenuation of the mutagenic response is consistent with the lack of DNA damage observed in the gastrointestinal tract of rodents following administration of ≤ 180 ppm Cr(VI) for up to 90 days in drinking water. Quantifying Cr(VI) reduction kinetics in the human gastrointestinal tract is necessary for assessing the potential hazards posed by Cr(VI) in drinking water. - Highlights: • Cr(VI) reduction capacity was greater in post-meal than paired pre-meal samples. • Cr(VI) reduction was rapid, pH dependent, and due to heat stable components. • Gastric fluid attenuates the mutagenicity of Cr(VI) and reduces risk of GI cancer. • The results are useful to quantitatively evaluate the fate of ingested Cr(VI).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Self similarities in desalination dynamics and performance using capacitive deionization

Charge transfer and mass transport are two underlying mechanisms which are coupled in desalination dynamics using capacitive deionization (CDI). We developed simple reduced-order models based on a mixed reactor volume principle which capture the coupled dynamics of CDI operation using closed-form semi-analytical and analytical solutions. We use the models to identify and explore self-similarities in the dynamics among flow rate, current, and voltage for CDI cell operation including both charging and discharging cycles. The similarity approach identifies the specific combination of cell (e.g. capacitance, resistance) and operational parameters (e.g. flow rate, current) which determine a unique effluent dynamic response. We here demonstrate self-similarity using a conventional flow between CDI (fbCDI) architecture, and we hypothesize that our similarity approach has potential application to a wide range of designs. We performed an experimental study of these dynamics and used well-controlled experiments of CDI cell operation to validate and explore limits of the model. For experiments, we used a CDI cell with five electrode pairs and a standard flow between (electrodes) architecture. Guided by the model, we performed a series of experiments that demonstrate natural response of the CDI system. We also identify cell parameters and operation conditions which lead to self-similar dynamics under a constant current forcing function and perform a series of experiments by varying flowrate, currents, and voltage thresholds to demonstrate self-similarity. Based on this study, we hypothesize that the average differential electric double layer (EDL) efficiency (a measure of ion adsorption rate to EDL charging rate) is mainly dependent on user-defined voltage thresholds, whereas flow efficiency (measure of how well desalinated water is recovered from inside the cell) depends on cell volumes flowed during charging, which is determined by flowrate, current and voltage thresholds. Results of experiments strongly support this hypothesis. Results show that cycle efficiency and salt removal for a given flowrate and current are maximum when average EDL and flow efficiencies are approximately equal. We further explored a range of CC operations with varying flowrates, currents, and voltage thresholds using our similarity variables to highlight trade-offs among salt removal, energy, and throughput performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Accurate Force Field for Carbon Dioxide–Silica Interactions Based on Density Functional Theory

Fluid–silica interfaces are ubiquitous in chemistry, occurring in both natural geochemical environments and practical applications ranging from separations to catalysis. Simulations of these interfaces have been, and continue to be, a significant avenue for understanding their behavior. A constraining factor, however, is the availability of accurate force fields. Most simulations use traditional “mixing rules” to determine nonbonded dispersion interactions, an approach that has not been critically examined. Here, in this study, we present Lennard-Jones parameters for the interaction of carbon dioxide with silica interfaces that are optimized to reproduce density functional theory (DFT)-based binding energies. The modeling is based on the recently developed silica-DDEC force field, whose atomic charges are consistent with DFT calculations. Standard mixing rules are found to predict weaker CO 2 binding to silica than that obtained from DFT, an effect corrected by the optimized parameters given here. This behavior extends to other silica force fields (Clayff and Gulmen-Thompson), and the present Lennard-Jones parameters improve their performance as well. The effects of improved Lennard-Jones parameters on the structural and dynamical properties of condensed CO 2 in silica slit pores are also examined.

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

Revealing the Formation Energy–Exfoliation Energy–Structure Correlation of MAB Phases Using Machine Learning and DFT

MAB phases are popular as high-temperature materials with high damage tolerance and excellent electrical conductivity that are used to exfoliate two-dimensional (2D) transition metal borides (MBenes); a promising material for developing next- generation nanodevices. In this report, we explore the correlation between the formation energy, exfoliation energy and structural factors of MAB phases using DFT and machine learning. In order to do so, we developed three different machine learning models based on the support vector machine, deep neural network, and random forest regressor to study the stability of the MAB phases by calculating their formation energies. Furthermore, our support vector machine and deep neural network models are capable of predicting the formation energies with mean absolute errors less than 0.1 eV/atom. We demonstrated that the stability of a MAB phase for a given transition metal decreases when the A element changes from Al to Tl. The density functional theory (DFT) re- vealed that M-A and M-B bond strength strongly correlates with the stability of MAB phases. In addition, the exfoliation possibility of 2D MBenes becomes higher when the A element changes from Al to Tl due to weakening of M-A and M-B bonds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries

Machine-learning (ML) techniques have rapidly found applications in many domains of materials chemistry and physics where large data sets are available. Aiming to accelerate the discovery of materials for battery applications, in this work, we develop a tool based on ML models to predict voltages of electrode materials for metal-ion batteries. To this end, we use deep neural network, support vector machine, and kernel ridge regression as ML algorithms in combination with data taken from the Materials Project database, as well as feature vectors from properties of chemical compounds and elemental properties of their constituents. We show that our ML models have predictive capabilities for different reference test sets and, as an example, we utilize them to generate a voltage profile diagram and compare it to density functional theory calculations. In addition, using our models, we propose nearly 5000 candidate electrode materials for Na- and K-ion batteries. We also make available a web-accessible tool that, within a minute, can be used to estimate the voltage of any bulk electrode material for a number of metal ions. Here, these results show that ML is a promising alternative for computationally demanding calculations as a first screening tool of novel materials for battery applications.

25 ENERGY STORAGE↗
DOE OSTI2021

Assessing Adaptive Irrigation Impacts on Water Scarcity in Nonstationary Environments—A Multi‐Agent Reinforcement Learning Approach

Abstract One major challenge in water resource management is to balance the uncertain and nonstationary water demands and supplies caused by the changing anthropogenic and hydroclimate conditions. To address this issue, we developed a reinforcement learning agent‐based modeling (RL‐ABM) framework where agents (agriculture water users) are able to learn and adjust water demands based on their interactions with the water systems. The intelligent agents are created by a reinforcement learning algorithm adapted from the Q‐learning algorithm. We illustrated this framework in a case study where the RL‐ABM is two‐way coupled with the Colorado River Simulation System (CRSS), a long‐term planning model used for the administration of the Colorado River Basin, for assessing agriculture water uses impacts on water scarcity. Seventy‐eight intelligent agents are simulated, which can be grouped into three categories based on their parameter values: the “aggressive” (swift actions; low regrets), the “forward‐looking conservative” (mild actions; high regrets; fast learning), and the “myopic conservative” (mild actions; median regrets; slow learning). The ABM‐CRSS results showed that the major reservoirs in the Upper Colorado Basin might experience more frequent water shortages due to the increasing water uses compared to the original CRSS results. If the drought continues, the case study also demonstrates that agents can learn and adjust their demands.

Hung, Fengwei↗
DOE OSTI2026

Modeling Large Dust Aerosols in the Community Earth System Model Version 2 (CESM2)

Dust aerosols have a wide size distribution from less than 0.1 to over 100 μm and dominate Earth's atmospheric aerosol mass. However, most Earth system models (ESMs) inadequately represent dust aerosols larger than 10 μm in diameter, limiting the accuracy of the simulated dust cycle and climate impacts. Here, we introduce a new modeling framework that captures the full observed size distribution of dust aerosols, incorporating recent advances into a mineral-resolved version of the Community ESM, while addressing known issues in previous versions. Comprehensive evaluation against diverse observations of bulk dust and component minerals demonstrates that the model reproduces the observed dust cycle across particle sizes. Incorporating the previously unrepresented large-dust fractions substantially alters dust budget estimates, highlighting potential changes in simulated climate impacts and underscoring the importance of comprehensive size-resolved dust modeling. Despite these advancements, uncertainties persist. Our results indicate that a size-dependent reduction in settling velocity is required to reproduce the observed dust size distribution downwind of source regions. Specifically, in the new model, the gravitational settling velocity of dust particles larger than 10 μm in diameter must be reduced by as much as 85% to achieve agreement with observations. This empirical reduction serves as a constraint on physics-based models of dust settling. Future developments should address misrepresented physical processes that hinder accurate modeling of the large dust aerosol transport. Expanding observational data sets covering the full-size distribution is also essential to better constrain the dust cycle and improve the representation of dust optical properties and climate effects.

Li, Longlei [Cornell Univ., Ithaca, NY (United Sta↗
DOE OSTI2021

Closing the methane gap in US oil and natural gas production emissions inventories

Methane (CH 4 ) emissions from oil and natural gas (O&NG) systems are an important contributor to greenhouse gas emissions. In the United States, recent synthesis studies of field measurements of CH 4 emissions at different spatial scales are ~1.5–2× greater compared to official greenhouse gas inventory (GHGI) estimates, with the production-segment as the dominant contributor to this divergence. Based on an updated synthesis of measurements from component-level field studies, we develop a new inventory-based model for CH 4 emissions, for the production-segment only, that agrees within error with recent syntheses of site-level field studies and allows for isolation of equipment-level contributions. We find that unintentional emissions from liquid storage tanks and other equipment leaks are the largest contributors to divergence with the GHGI. If our proposed method were adopted in the United States and other jurisdictions, inventory estimates could better guide CH 4 mitigation policy priorities.

03 NATURAL GAS↗
DOE OSTI2020

Enhancement of Ni–(Y 2 O 3 ) 0.08 (ZrO 2 ) 0.92 fuel electrode performance by infiltration of Ce 0.8 Gd 0.2 O 2–δ nanoparticles

This paper addresses the use of Ce 0.8 Gd 0.2 O 2–δ (GDC) infiltration into the Ni–(Y 2 O 3 ) 0.08 (ZrO 2 ) 0.92 (YSZ) fuel electrode of solid oxide cells (SOCs) for improving their electrochemical performance in fuel cell and electrolysis operation. Although doped ceria infiltration into Ni–YSZ has recently been shown to improve the electrode performance and stability, the mechanisms defining how GDC impacts electrochemical characteristics are not fully delineated. Furthermore, the electrochemical characteristics have not yet been determined over the full range of conditions normally encountered in fuel cell and electrolysis operation. Here we present a study of both symmetric and full cells aimed at understanding the electrochemical mechanisms of GDC-modified Ni–YSZ over a wide range of fuel compositions and temperatures. Single-step GDC infiltration at an appropriate loading substantially reduced the polarization resistance of Ni–YSZ electrodes in electrolyte-supported cells, as measured using electrochemical impedance spectroscopy (EIS) at various temperatures (600–800 °C) in a range of H 2 O–H 2 mixtures (3–90 vol% H 2 O). Fuel-electrode-supported cells had significant concentration polarization due to the thick Ni–YSZ supports. Here, a distribution of relaxation times approach is used to develop a physically-based electrochemical model; the results show that GDC reduces the reaction resistance associated with three-phase boundaries, but also appears to improve oxygen transport in the electrode. Increasing the H 2 O fraction in the H 2 –H 2 O fuel mixture reduced both the three-phase boundary resistance and the gas diffusion resistance for Ni–YSZ; with GDC infiltration, the electrode resistance showed less variation with fuel composition. GDC infiltration improved the performance of fuel-electrode-supported full cells, which yielded a maximum power density of 2.28 W cm –2 in fuel cell mode and an electrolysis current density at 1.3 V of 2.22 A cm –2 , both at 800°C.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Life Cycle Cost Analysis of Prestressed Concrete Poles Subjected to Wind, Surges, and Waves

Prestressed concrete (PC) poles are becoming popular choices to support coastal power transmission systems. However, the existing literature does not offer a detailed analysis of the effectiveness of PC poles in terms of long-term vulnerabilities and the direct and indirect costs. This is due to (1) lack of fragility models for PC poles and (2) lack of probabilistic wind, storm surge, and wave models in coastal settings. In this study, we address these gaps through a series of Monte Carlo simulations to estimate fragility of PC poles as a function of age and hazard (wind, surges, and waves) intensity, and the development of a probabilistic hazard model based on 10,000 years of synthetic tropical cyclone data. The probabilistic hazard model is used in conjunction with high-resolution hydrodynamic models to generate realizations of coastal wind, storm surges, and waves for the Louisiana and Mississippi coasts. A comprehensive life cycle cost analysis for a service life of 70 years considering direct and indirect losses is conducted to compare the performance of a transmission line located in Pascagoula, Mississippi, when wood poles are replaced by PC poles. Results showed that aging has a minor effect on the reliability of PC poles, highlighting the advantages of replacing wood poles with PC poles, especially in coastal areas. In addition, PC poles are significantly more cost-effective compared with wood poles over their life cycle, leading to an estimated saving of $11.55 million (68.17% reduction). The results of this study provide key insight to inform decision-making processes to keep the coastal power grids resilient and cost-effective against future storm hazards.

natural disasters↗
DOE OSTI2013

Nonlinear dynamics of toroidal Alfvén eigenmodes driven by energetic particles

A kinetic simulation code based on a reduced model is developed to study dynamic evolutions of a single toroidicity-induced shear Alfvén eigenmode driven by energetic particles. For zero background damping, it is found that the wave amplitude in nonlinear phase can either saturate for weak energetic particle drives or slowly increase for strong drives. This slow nonlinear growth in strong drive cases is found to be associated with broadening and overlapping of resonances between the wave and trapped particles. For the near-marginal-stability case with a large background damping, the mode nonlinear evolution exhibits strong upward and downward frequency chirping in multiple branches. A hole/clump formation is observed clearly in the corresponding evolution of energetic particle distribution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗