Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “validation data”

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 1,585 records · Page 88
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Normal or abnormal? Machine learning for the leakage detection in carbon sequestration projects using pressure field data

The international commitments for atmospheric carbon reduction will require a rapid increase in carbon capture and storage (CCS) projects. The key to any successful CCS project lies in the long term storage and prevention of leakage of stored carbon dioxide (CO 2 ). In addition to being a greenhouse gas, CO 2 leaks reaching the surface can accumulate in low-lying areas resulting in a serious health risk. Among several alternatives, some of the more promising CCS storage formations are depleted oil and gas reservoirs, where the reservoirs had good geological seals prior to hydrocarbon extraction. With more CCS wells coming online, it is imperative to implement permanent, automated monitoring tools. We apply machine learning models to automate the leakage detection process in carbon storage reservoirs using rates of (CO 2 ) injection and pressure data measured by simple harmonic pulse testing (HPT). To validate the feasibility of this machine learning based workflow, we use data from HPT experiments carried out in the Cranfield oil field, Mississippi, USA. The data consist of a series of pulse tests conducted with baseline parameters and with an artificially introduced leak. Here, in this study, we pose the leakage detection task as an anomaly detection problem where deviation from the predicted behavior indicates leaks in the reservoir. Results show that different machine learning architectures such as multi-layer feed forward network, Long Short-Term Memory, and convolutional neural network are able to identify leakages and can provide early warning. These warnings can then be used to take remedial measures.

58 GEOSCIENCES↗
DOE OSTI2020

A data mining approach to investigate food groups related to incidence of bladder cancer in the BLadder cancer Epidemiology and Nutritional Determinants International Study

Abstract At present, analysis of diet and bladder cancer (BC) is mostly based on the intake of individual foods. The examination of food combinations provides a scope to deal with the complexity and unpredictability of the diet and aims to overcome the limitations of the study of nutrients and foods in isolation. This article aims to demonstrate the usability of supervised data mining methods to extract the food groups related to BC. In order to derive key food groups associated with BC risk, we applied the data mining technique C5.0 with 10-fold cross-validation in the BLadder cancer Epidemiology and Nutritional Determinants study, including data from eighteen case–control and one nested case–cohort study, compromising 8320 BC cases out of 31 551 participants. Dietary data, on the eleven main food groups of the Eurocode 2 Core classification codebook, and relevant non-diet data (i.e. sex, age and smoking status) were available. Primarily, five key food groups were extracted; in order of importance, beverages (non-milk); grains and grain products; vegetables and vegetable products; fats, oils and their products; meats and meat products were associated with BC risk. Since these food groups are corresponded with previously proposed BC-related dietary factors, data mining seems to be a promising technique in the field of nutritional epidemiology and deserves further examination.

Nutrition & Dietetics↗
DOE OSTI2024

Search for a dark leptophilic scalar produced in association with τ + τ − pair in e + e − annihilation at center-of-mass energies near 10.58 GeV

A dark leptophilic scalar ( ϕ L ) is a hypothetical particle that couples only to leptons rather than quarks. We report on a search for ϕ L in the e + e − → τ + τ − ϕ L , ϕ L → ℓ + ℓ − ( ℓ = e , μ ) process using 626 fb − 1 of data collected by the Belle experiment near the ϒ ( 4 S ) resonance. We validate the backgrounds with multiple control regions in data, using a novel multiclass multivariate event classifier. In absence of a signal, we quote upper limits at the 90% confidence level on the coupling between ϕ L and leptons. Our bounds, obtained in a blinded approach, are 19% more constraining than the previous limits, averaged over the mass range 0.04 ≤ m ϕ L ≤ 6.5 GeV . We exclude the parameter space below 4 GeV favored by measurement of the anomalous magnetic moment of the muon. Published by the American Physical Society 2024

Biswas, D. (ORCID:0000000275433471)↗
DOE OSTI2019

Los Alamos National Laboratory SAVY-4000 Field Surveillance Plan Update for 2020

The Packaging Surveillance Program section of the Department of Energy (DOE) Manual 441.1-1, Nuclear Material Packaging Manual (DOE 2008), requires DOE contractors to “ensure that a surveillance program is established and implemented to ensure the nuclear material storage package continues to meet its design criteria.” The Los Alamos National Laboratory (LANL) SAVY-4000 Field Surveillance Plan was first issued in fiscal year (FY) 2013. The surveillance plan is reviewed annually and updated as necessary based on SAVY-4000 surveillance findings, as well as results of the lifetime extension studies. Five surveillance plan updates have been issued, one in 2014, one in 2016, one in 2017, one in 2018, and one in 2019. This 2020 update documents what was actually done in 2019 and what is planned for 2020. Deviations from the 2019 surveillance plan were necessary because some of the planned surveillance containers were not available for examination. The goal of the surveillance program is to provide data to: evaluate container component performance over time against the initial baseline qualification; validate the current SAVY container design life; provide data for future design life extensions of components as indicated; identify indications of container degradation early and identify similar containers and/or contents that may need remediation; maintain and continuously improve the surveillance information system that documents the complete life cycle of the containers including the initial production baseline, usage history, surveillance, and disposition; identify container issues related to wear and tear that could indicate the need for a maintenance program for in-service containers prior to the end of the design life.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Data from a multi-year targeted proteomics study of a longitudinal birth cohort of type 1 diabetes

The deployment of liquid chromatography-mass spectrometry-based plasma proteomics experiments in a large cohort is sparse, leading to a lack of data available for benchmarking, method development or validation. Comprised of 6,426 plasma analyses, The Environmental Determinants of Diabetes in the Young (TEDDY) proteomics validation study constitutes one of the largest targeted proteomics experiments in the literature to date. The proteomics data from this study were generated over the course of 2.5 years from over 900 study subjects, each providing up to 29 longitudinal samples. The data also includes 916 quality control samples. The targeted mass spectrometry assay was comprised of 694 peptides mapping to 167 proteins and the panel was measured in each subject and QC sample. The targeted proteomic dataset presented here can be used as a resource for new computational method development, such as for batch correction, as well as for benchmarking and comparing the performance of different methods/tools.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

A deep learning-enhanced framework for multiphysics joint inversion

Joint inversion has drawn considerable attention due to the availability of multiple geophysical data sets, ever-increasing computational resources, the development of advanced algorithms, and its ability to reduce inversion uncertainty. A key issue of joint inversion is to develop effective strategies to link different geophysical data in a unified mathematical framework, in which the information obtained from different models can complement each other. We have developed a deep learning-enhanced joint inversion framework to simultaneously reconstruct different physical models by fusing different types of geophysical data. Traditionally, structure similarity constraints are pursued by joint inversion algorithms using manually crafted formulations (e.g., cross gradient). The constraint is constructed by a deep neural network (DNN) during the learning process. The framework is designed to combine the DNN and a traditional independent inversion workflow and improve the joint inversion result iteratively. The network can be easily extended to incorporate multiphysics without structural changes. Numerical experiments on the joint inversion of 2D DC resistivity data and seismic traveltime are used to validate our method. In addition, this learning-based framework demonstrates excellent generalization abilities when tested on data sets using different geologic structures. It also can handle different sensing configurations and nonconforming discretization.

Geochemistry & Geophysics↗
DOE OSTI2021

Charged Particle Transport Libraries (Final Report FY2020 Level-2 Milestone #7127)

This document is the final report on the status and developments of the Charged-Particle Transport (CPT) Level-2 (“CP2020”) ASC/PEM Milestone work. This section gives an overview of the CP2020 work partitioned into two subsections: 1) a summary of the code tools used for generating CP (and other) data in Section 1.2; and 2) and library data and codes access information for end users in Section 1.3. Section 2 gives an overview of the status of previous work on charged-particle (CP) nuclear data, particularly focused on the work of the FY2011 CP Level-2 Milestone. New work for CP2020 is detailed in the following Section 3. Section 4 discusses testing and validation of the evaluated and processed data for the CP2020 library. Conclusions based on the experience of this Level-2 work and the outlook for future development of CPT-relevant data are given in the final Section 5. Appendices, detailed throughout the report, are provided for further information. This report, related documentation, codes, and the CPT nuclear data libraries are available via online repository.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Tunable Metamaterials for Impact Mitigation

Traditional methods of shielding fragile goods and human tissues from impact energy rely on isotropic foam materials. The mechanical properties of these foams are inferior to an emerging class of metamaterials called plate lattices, which have predominantly been fabricated in simple 2.5-dimensional geometries using conventional methods that constrain the feasible design space. Here in this work, additive manufacturing is used to relax these constraints and realize plate lattice metamaterials with nontrivial, locally varying geometry. The limitations of traditional computer-aided design tools are circumvented and allow the simulation of complex buckling and collapse behaviors without a manual meshing step. By validating these simulations against experimental data from tests on fabricated samples, sweeping exploration of the plate lattice design space is enabled. Numerical and experimental tests demonstrate plate lattices absorb up to six times more impact energy at equivalent densities relative to foams and shield objects from impacts ten times more energetic while transmitting equivalent peak stresses. In contrast to previous investigations of plate lattice metamaterials, designs with nonuniform geometric prebuckling in the out-of-plane direction is explored and showed that these designs exhibit 10% higher energy absorption efficiency on average and 25% higher in the highest-performing design.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Coupling particle scale model and SuperDEM–CFD for multiscale simulation of biomass pyrolysis in a packed bed pyrolyzer

An efficient biomass pyrolysis process requires a comprehensive understanding of the chemical and physical phenomena that occur at multi-length and time scales. In this study, a multiscale computational approach was developed and validated for biomass pyrolysis in a packed-bed reactor by integrating pyrolysis kinetics, a particle scale model, and SuperDEM-CFD in open-source code MFiX. A one-dimensional particle-scale model that discretizes the characteristic length of biomass particle into layers was developed to predict the intraparticle phenomena inside a single particle. The 1D model was validated by comparing it with a single biomass particle pyrolysis experiment. Here, a recently developed SuperDEM-CFD model was employed to simulate the non-spherical particle-particle contact and fluid-particle interaction. The coupled model was applied to simulate the pyrolysis of cubic biomass particles in a packed bed and validated by comparing with experimental data. Simulation with and without particle-scale model was compared, and the effect of the gas-solid heat transfer models was also investigated.

09 BIOMASS FUELS↗
DOE OSTI2022

Gamma Radiation-Induced Degradation of Acetohydroxamic Acid (AHA) in Aqueous Nitrate and Nitric Acid Solutions Evaluated by Multiscale Modelling

Acetohydroxamic acid (AHA) has been proposed for inclusion in advanced, single-cycle, used nuclear fuel reprocessing solvent systems for the reduction and complexation of plutonium and neptunium ions. For this application, a detailed description of the degradation of AHA in dilute aqueous nitric acid is required. To this end, we present a comprehensive, multiscale computer model for the coupled radiolytic and hydrolytic degradation of AHA in aqueous sodium nitrate and nitric acid solutions. Rate coefficients for the reactions of AHA and HA with the oxidizing nitrate radical were measured for the first time using pulsed electron radiolysis and used as inputs in the kinetic model. The computer model results are validated by comparison to experimental data from steady state gamma irradiations, for which the agreement is excellent. Here, the presented model accurately predicts the yields of the major degradation products of AHA: acetic acid, hydroxylamine, nitrous oxide, and molecular hydrogen.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Control of machining-induced residual stress via tool geometry and process parameter modification

Distortion generated in machined, monolithic, thin-walled aerospace components due to residual stresses leads to significant material and economic waste in the manufacturing industry. Inherent residual stress (IRS) present in stock materials combines with machining-induced residual stress (MIRS) to influence the final machined part distortion. It is hypothesized that MIRS can be controlled, based on the part geometry, through deliberate cutting tool geometry and process parameter modifications to negate the effect of IRS on distortion, consequently resulting in distortion-free parts. A finite element (FE) orthogonal cutting model is developed to study how tool geometry and process parameters influence machining-induced residual stress (MIRS). Orthogonal cutting experiments are performed on Al 7075-T651 samples to measure cutting forces and MIRS. A cutting force dynamometer is used to measure forces during cutting and a novel digital image correlation (DIC) based hole drilling technique is employed to measure the near-surface residual stress (RS) in the cut samples. These data are subsequently utilized to validate the FE prediction model. Various cases of cutting simulations involving different depths of cut, tool tip radii, and rake angles are performed to study their effects on RS. Similar to prior literature, increasing the depth of cut, tool tip radius, or rake angle is found to promote the formation of near-surface tensile stresses. The competing effects of material plowing and temperature are shown to determine the type of RS at the end of the cut. Moreover, a window of variation of RS (up to ± 400 MPa) is estimated within the given range of conditions, allowing for the control of MIRS through tool and process modification.

Mathews, Ritin [ORNL] (ORCID:0000000301440828)↗
DOE OSTI2024

Californium-252 production at the High Flux Isotope Reactor - II: Comparison between the highly enriched uranium and a proposed low-enriched uranium core

This is the second paper on a 252 Cf production study performed in support of efforts to convert the High Flux Isotope Reactor (HFIR) from highly enriched uranium (HEU) to low-enriched uranium (LEU) fuel. The first paper primarily focuses on validating computational tools and nuclear data. This companion paper evaluates another critical aspect: the 252 Cf production capability with a proposed LEU core. HFIR must maintain its world-class performance and missions following conversion and because 252 Cf is a vital, multipurpose neutron-emitting radioisotope, the ability to efficiently produce 252 Cf must be preserved. In this study, the HFIRCON transport and depletion tool, several nuclear data libraries, and Campaign 78 data were used to compute 252 Cf production, sensitivity, and safety metrics. Further, results indicate the 252 Cf production and production rates are slightly higher with a 95MW th LEU core compared with those obtained with the 85MW th HEU core. Additionally, the target peak fission rate densities, discharge cumulative fission densities, and heat deposition rates with the LEU core are within a few percent of those calculated with the HEU core. The findings suggest HFIR’s 252 Cf production capability can be effectively maintained with an LEU core without adversely affecting the safety metrics.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

Modeling of thermal and kinetic processes in non-equilibrium plasma ignition applied to a lean combustion engine

In recent years novel ignition systems have been developed to enable stable and efficient engine operations with lean mixtures. Among them, radio-frequency corona ignition systems create discharges that involve a much wider region compared to traditional spark, and produce non equilibrium plasma with high levels of active radicals and excited species. These devices considerably increase the early flame growth speed and extend stable operating limits. With the aim of expanding the knowledge on high efficiency lean-burn SI engines, this paper investigates and compares the combustion development generated by spark and corona ignitions through computational fluid dynamics, within the Reynolds-Averaged Navier-Stokes framework for turbulence modeling. In order to simultaneously take thermal and chemical effects into account, the Perfectly Stirred Reactor combustion model is used. Experimental data are also collected for validation in an optical access engine, for different mixture levels, from stoichiometric to very lean. Furthermore, the faster burn rate generated by the corona system in the initial stage of the combustion is well predicted by the simulations, in all the relative air-fuel ratio conditions. Remarkably, as the mixture becomes lean, simulations are able to capture the non-linear transition from fast to slow kernel growth, before a self-sustainable flame propagation is established. This correlates very well with the measured engine cyclic variability and the corresponding steep change in the duration of the flame kernel formation. Ultimately, this study highlights the important role of the atomic oxygen, as active radical, in promoting and enhancing the combustion initiated by a corona discharge, in addition to the volumetric ignition effect. By contrast, the validated simulations allow to explain that the high-temperature thermal plasma generated in a traditional spark discharge is insensitive to kinetic aspects.

42 ENGINEERING↗
DOE OSTI2026

An experimental and kinetic modeling study of the ignition of 2-methyl decane

Ignition delay times (IDTs) of 2-methyl decane (C 11 H 24 -2) are measured in a high-pressure shock tube and in a rapid compression machine at equivalence ratios in the range 0.5–2.0 at 90 % dilution, at temperatures in the range 600–1430 K and at pressures of 15 and 30 bar. To clarify the effect of the branched methyl group on fuel oxidation, IDTs of n-undecane (nC 11 H 24 ) are also measured at similar conditions to those measured for C 11 H 24 -2. A new chemical kinetic mechanism, using C3MechV4.0.1 as the core chemistry, is developed and validated against the new experimental data. The thermodynamic properties of the fuel (RH), alkyl (Ṙ), alkyl peroxy (RȮ 2 ), hydroperoxy-alkyl (Q̇OOH), and peroxy hydroperoxy alkyl (Ȯ 2 QOOH) radicals are updated in both the C 11 H 24 -2 and nC 11 H24 models using THERM25. A reaction path flux analysis for C 11 H 24 -2 at different temperatures was conducted. Compared to nC 11 H 24 , C 11 H 24 -2 shows slower reactivity. At low and intermediate temperatures, the chain propagation pathway Q̇OOH ↔ C 11 cyclic ether + ȮH is favored for C 11 H 24 -2, while the chain branching pathway Q̇OOH ↔ Ȯ 2 QOOH ↔ C 11 carbonyl hydroperoxide (KHP) + ȮH is suppressed, leading to lower reactivity compared to nC 11 H 24 . At high temperatures, the presence of the branched methyl group inhibits the direct decomposition of the fuel, resulting in reduced C 2 H 4 formation, which in turn suppresses the reactivity of the fuel.

2-methyl decane↗
DOE OSTI2024

A mean field homogenization model for the mechanical response of ceramic matrix composites

SiC/SiC composites offer exceptional mechanical stability at high temperatures and under irradiation. These ceramic matrix composites are therefore strong candidate materials for future nuclear energy applications. Their mechanical response, which exhibits pseudo-plasticity, is mediated by matrix cracking, fiber debonding, and fiber pull-out due to slip. Here, this study introduces a mechanistic model for the behavior of unidirectionally reinforced SiC/SiC composites. Specifically a mean field homogenization approach is proposed to account for all deformation and degradation modes during mechanical deformation. The homogenization scheme relies on a Mori Tanaka method that is extended to consider the effects of the coating’s elasto-plastic response on the development of micromechanical fields. Further, the model proposed introduces a method to effectively account for the role of localized damage (i.e., cracks) on mechanical fields within both the fiber and the matrix. Upon validating the model against experimental data, the roles of interface sliding, coating dimensions and intrinsic elastic response, as well as of microstructure (e.g. porosity, fiber volume fraction) are discussed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Modeling the corrosion of steel casing and the damage of well cement in a borehole system

This work presents the effect of chloride-induced corrosion in an underground borehole system, specifically considering the steel casing and further damage to the surrounding cement. In the past four decades, oil well cement has been successfully used for underground construction. However, high concentrations of chloride ions gradually degrade the functionality and durability of well cement. Gasses and liquids can leak from deterioration caused by chloride ions, and thus motivate this study. The chemo-mechanical coupling diffusion model and a classical fracture mechanics model are applied. The model is validated with OPC concrete corrosion data from literature, and then applied to well cement using experimentally obtained material property data. The time when crack initiation, peak pressure, and complete fracture is reached in the model of the cement sheath provides a quantification of the expected service life of the borehole system.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Modeling of multi-component precipitation and crystallization for zero-liquid-discharge desalination

This study proposes novel ZLD treatment trains that integrate multi-component chemical precipitation and multi-effect evaporative crystallization for efficient brine management. The methodology emphasizes sustainability by integrating CO₂ for chemical precipitation and reducing environmental impact, exemplified with two case studies: produced water that includes industrial waste heat utilization by adopting the emerging vacuum air-gapped membrane distillation (VAGMD) technology, and brackish groundwater that is abundant in sulphate which undergoes treatment by low-salt-rejection reverse osmosis (LSRRO) with interstage chemical precipitation. Here, this study is the first of its kind to simultaneously account for reducing risk of mineral scaling and effective recovery of valuable solids when incorporating VAGMD and LSRRO in ZLD treatment trains. Using Reaktoro and WaterTAP, both Python-based, open-source platforms, we model the recovery of high-purity magnesium, calcium, and sodium salts while optimizing energy consumption and operational efficiency in proposed ZLD pathways for case studies of produced water and brackish desalination brine management. Validation with experimental and reference data confirms the reliability of the models used. In both case studies, the optimized ZLD process achieves recovery rates of 97%, 99%, and 90% for Mg, Ca, and Na, with purities exceeding 99%, and brine volume reduced to less than 4% of the initial feed flow.

3D CT scans↗
DOE OSTI2022

Numerical modeling and parametric study of a dual purpose underground thermal battery

We report integrating thermal energy storage with building energy systems can enable flexible building electric demands at buildings to help mitigate the mismatch between electricity supply and demand. A novel building heating and cooling system that integrates a dual-source heat pump with hybrid thermal storage named dual-purpose underground thermal battery (DPUTB) has been developed for reshaping building electric demands. The proposed DPUTB integrated geothermal heat pump system is an original innovation that enables Grid-interactive Energy Efficient buildings. This paper focuses on the study of the novel DPUTB. The DPUTB works as both a thermal storage tank (an inner tank) and a ground heat exchanger (an outer tank separated from the inner tank by the insulation material). High fidelity and computationally effective models are needed to predict the performance of the novel DPUTB. This study has developed a simplified dynamic model for the DPUTB according to heat transfer and energy conservation principles and validated it by using experimental data obtained from testing a small-scale DPUTB apparatus. A parametric study was conducted to identify a design that can achieve the target thermal storage performance of load shift and energy efficiency. The parametric study results show that the inner tank shell thermal conductivity and the phase change material's melting point are the two most influencing factors on the performance of the DPUTB. One single full-size DPUTB with the identified design could provide 1-ton cooling (3.51 kW) with the supply temperature lower than 11 °C for 4 hours in summer after being fully charged in 8 hours. The inner tank filled with phase change material is for cooling thermal storage as a latent tank in the design. However, its capacity can be as high as 60 MJ as a sensible water tank for heating storage in winter. In the future study, the DPUTB model will be incorporated into the dual-source heat pump system for evaluating the overall system performance of demand side management in the long term.

25 ENERGY STORAGE↗