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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

ORNL Design Engineering Library: Validated Thermoelectric Digital Twins

Radioisotope thermoelectric generators (RTGs) serve a crucial role in supplying thermal and electrical energy for reliable and long-duration power in remote and extreme environments. The thermal energy is provided by the decay of radioisotopes and is converted into electrical energy using the steady-state thermoelectric process by exploiting the Seebeck effect. Maintaining a thermal gradient through the material is necessary to drive current production. Modern predictive multi-physics tools are able to effectively describe the complex and strongly coupled phenomena needed for accurate model-based system engineering efforts needed to develop higher performing RTGs. Experimental validation of model simulations is essential to confirm the accuracy, reliability, and applicability of predictive multiphysics tools. Validation fosters confidence among users, helps meet regulatory requirements, and contributes to the ongoing improvement of simulation techniques, ultimately leading to better, safer, and more efficient designs and processes. Validated models can then be used as digital twins and can be interrogated to understand and quantify the performance gaps between theoretical and physical systems. These insights can be used to build higher performing systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Conversion Kinetics and Ionic Conductivity in Na-β”-Alumina + YSZ (Naβ”AY) Sodium Solid Electrolyte via Vapor Phase Conversion Process

Sodium ion batteries have been receiving increasing attention and may see potential revival in the near future, particularly in large-scale grid energy storage coupling with wind and solar power generation, due to the abundant sodium resources, low cost, and sufficiently high energy density. Among the known sodium ion conductors, the Na-β”-alumina electrolyte remains highly attractive because of its high ionic conductivity. This study focuses on the vapor phase synthesis of a Na-β”-Alumina + YSZ (Naβ”AY) composite sodium electrolyte, which has higher mechanical strength and stability than conventional single phase β”-Alumina. The objectives are the measurement of conversion kinetics through a newly developed weight-gain based model and the determination of sodium ionic conductivity in the composite electrolyte. Starting samples contained ~70 vol% α-Alumina and ~30 vol% YSZ (3 mol% Y 2 O 3 stabilized Zirconia) with and without a thin alumina surface layer made by sintering in air at 1600 °C. The sintered samples were placed in a powder of Na-β”-alumina and heat-treated at 1250 °C for various periods. Sample dimensions and weight were measured as a function of heat treatment time. The conversion of α-Alumina in the α-Alumina + YSZ composite into Naβ”AY occurred by coupled diffusion of sodium ions through Na-β”-alumina and of oxygen ions through YSZ, effectively diffusing Na2O. From the analysis of the time dependence of sample mass and dimensions, the effective diffusion coefficient of Na 2 O through the sample, D eff , was estimated to be 1.74 x 10 -7 cm2 s -1 , and the effective interface transfer parameter, k eff , was estimated as 2.33 x 10 -6 cm s -1 . By depositing a thin alumina coating layer on top of the bulk composite, the chemical diffusion coefficient of oxygen through single phase Na-β”-alumina was estimated as 4.35 x 10 -10 cm 2 s -1 . An AC impedance measurement was performed on a fully converted Naβ”AY composite, and the conductivity of the composite electrolyte was 1.3 x 10 -1 S cm -1 at 300 °C and 1.6 x 10 -3 S cm -1 at 25 °C, indicating promising applications in solid state or molten salt batteries at low to intermediate temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2016

A DYNAMIC DENSITY FUNCTIONAL THEORY APPROACH TO DIFFUSION IN WHITE DWARFS AND NEUTRON STAR ENVELOPES

We develop a multicomponent hydrodynamic model based on moments of the Born–Bogolyubov–Green–Kirkwood–Yvon hierarchy equations for physical conditions relevant to astrophysical plasmas. These equations incorporate strong correlations through a density functional theory closure, while transport enters through a relaxation approximation. This approach enables the introduction of Coulomb coupling correction terms into the standard Burgers equations. The diffusive currents for these strongly coupled plasmas is self-consistently derived. The settling of impurities and its impact on cooling can be greatly affected by strong Coulomb coupling, which we show can be quantified using the direct correlation function.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Electricity Demand Evolution Driven by Storm Motivated Population Movement

Managing the risks posed by climate change to energy production and delivery is a challenge for communities worldwide. Sea Level rise and increased frequency and intensity of natural disasters due to sea surface temperature rise force populations to move locations, resulting in changing patterns of demand for infrastructure services. Thus, Infrastructures will evolve to accommodate new load centers while some parts of the network are underused, and these changes will create emerging vulnerabilities. Combining climate predictions and agent based population movement models shows promise for exploring the universe of these future population distributions and changes in coastal infrastructure configurations. In this work, we created a prototype agent based population distribution model and developed a methodology to establish utility functions that provide insight about new infrastructure vulnerabilities that might result from these patterns. Combining climate and weather data, engineering algorithms and social theory, we use the new Department of Energy (DOE) Connected Infrastructure Dynamics Models (CIDM) to examine electricity demand response to increased temperatures, population relocation in response to extreme cyclonic events, consequent net population changes and new regional patterns in electricity demand. This work suggests that the importance of established evacuation routes that move large populations repeatedly through convergence points as an indicator may be under recognized.

Allen, Melissa R↗
DOE OSTI2026

Kinetics Modeling and Reactor Design Study of Glucose-to-Terpenes Cell-Free Conversion

Cell-free systems offer many advantages over traditional biological conversion by eliminating biological growth constraints. It also offers easy manipulation and finetuning of the reaction conditions for each individual enzyme. The conversion of cellulosic glucose to Limonene, a terpene, is a promising pathway for producing fuels and chemicals. Recent advances in developing cell-free systems focuses on bench scale optimization of terpene yield and to demonstrate its feasibility towards commercialization [1,2]. There is significant knowledge gap regarding reaction kinetics of these cell-free systems to further study how it will perform at larger scale. We present here, our studies on reaction kinetics and reactor design implications of cell-free glucose to Limonene conversion to facilitate the further development and commercialization of this process. We developed a novel kinetic model based on the metabolic-network structure of the cell-free system with multi-substrate reversible Michaelis-Menten rate law. To estimate kinetic parameters for this system of rate equations, we employed Bayesian optimization to perform global search with the assistance of gaussian processes to balance exploration and exploitation. The model parameters estimated showed good results compared with experimental data. The estimated parameters were used to perform sensitivity analysis. We found that Hexokinase is one of the most critical enzymes that affect the conversion of the glucose. We also observed that abundance of co-factors is also critical to the conversion of glucose to limonene. We investigated packed bed reactors with enzymes immobilized on the surface of particles to convert glucose stream into Limonene for larger scale production. The reactor design such as particle size, enzyme loading, and flow rate are found to be critical for improving yields. [1] Dudley, Q.M., Nash, C.J. and Jewett, M.C., 2019. Synthetic Biology, 4(1), p.ysz003. [2] Korman, T.P., Opgenorth, P.H. and Bowie, J.U., 2017. Nature communications, 8(1), p.15526.

09 BIOMASS FUELS↗
DOE OSTI2024

Role of Hydrogen as Fuel in Decarbonizing US Clinker Manufacturing for Cement Production: Costs and CO2 Emissions Reduction Potentials

As a low-carbon fuel, feedstock, and energy source, hydrogen is expected to play a vital role in the decarbonization of high-temperature process heat during the pyroprocessing steps of clinker production in cement manufacturing. However, to accurately assess its potential for reducing CO2 emissions and the associated costs in clinker production applications, a techno-economic analysis and a study of facility-level CO2 emissions are necessary. Assuming that up to 20% hydrogen can be blended in clinker fuel mix without significant changes in equipment configuration, this study evaluates the potential reduction in CO2 emissions (scopes 1 and 2) and cost implications when replacing current carbon-intensive fuels with hydrogen. Using the direct energy substitution method, we developed an Excel-based model of clinker production, considering different hydrogen–blend scenarios. Hydrogen from steam methane reformer (gray) and renewable-based electrolysis (green) are considered as sources of hydrogen fuel for blend scenarios of 5%–20%. Metrics such as the cost of cement production, facility-level CO2 emissions, and cost of CO2 avoided were computed. Results show that for hydrogen blends (gray or green) between 5% and 20%, the cost of cement increases by 0.6% to 16%, with only a 0.4% to 6% reduction in CO2 emissions. When the cost of CO2 avoided was computed, the extra cost required to reduce CO2 emissions is $229 to $358/ metric ton CO2. In summary, although green hydrogen shows promise as a low-carbon fuel, its adoption for decarbonizing clinker production is currently impeded by costs.

Okeke, Ikenna J.↗
DOE OSTI2015

Optimal Voltage Regulation for Unbalanced Distribution Networks Considering Distributed Energy Resources

With increasing penetration of distributed generation in the distribution networks (DN), the secure and optimal operation of DN has become an important concern. In this paper, an iterative quadratic constrained quadratic programming model to minimize voltage deviations and maximize distributed energy resource (DER) active power output in a three phase unbalanced distribution system is developed. The optimization model is based on the linearized sensitivity coefficients between controlled variables (e.g., node voltages) and control variables (e.g., real and reactive power injections of DERs). To avoid the oscillation of solution when it is close to the optimum, a golden search method is introduced to control the step size. Numerical simulations on modified IEEE 13 nodes test feeders show the efficiency of the proposed model. Compared to the results solved by heuristic search (harmony algorithm), the proposed model converges quickly to the global optimum.

distributed energy resources↗
DOE OSTI2016

Hybrid Encapsulated Ionic Liquids for Post-Combustion Carbon Dioxide (CO 2 ) Capture

Ionic liquids (ILs) and Phase Change Ionic Liquids (PCILs) are excellent materials for selective removal of carbon dioxide from dilute post-combustion streams. However, they are typically characterized as having high viscosities, which impairs their effectiveness due to mass transfer limitations, caused by the high viscosities. In this project, we are examining the benefits of encapsulating ILs and PCILs in thin polymeric shells to produce particles of approximately 100 to 600 μm in diameter that can be used in a fluidized bed absorber. The particles are produced by microencapsulation of the ILs and PCILs in CO 2 -permeable polymer shells. Here we report on the synthesis of the IL and PCIL materials, measurements of thermophysical properties including CO 2 capacity and reprotonation equilibrium and kinetics, encapsulation of the ILs and PCILs, mechanical and thermodynamic testing of the encapsulated materials, development of a rate based model of the absorber, and the design of a laboratory scale unit to test the encapsulated particles for CO 2 capture ability and efficiency. We show that the IL/PCIL materials can be successfully encapsulated, that they retain CO 2 uptake capacity, and that the uptake rates are increased relative to a stagnant sample of IL liquid or PCIL powder.

36 MATERIALS SCIENCE↗
DOE OSTI2017

The National Solar Radiation Database (NSRDB): A Brief Overview

This poster presents a high-level overview of the National Solar Radiation Database (NSRDB). The NSRDB uses the physics-based model (PSM), which was developed using: adapted PATMOS-X model for cloud identification and properties, REST-2 model for clear-sky conditions, and NREL's Fast All-sky Radiation Model for Solar Applications (FARMS) for cloudy-sky Global Horizontal Irradiance (GHI) solar irradiance calculations.

14 SOLAR ENERGY↗
DOE OSTI2019

Systems and methods for estimation and prediction of battery health and performance

Systems and computer-implemented methods are used for analyzing battery information. The battery information may be acquired from both passive data acquisition and active data acquisition. Active data may be used for feature extraction and parameter identification responsive to the input data relative to an electrical equivalent circuit model to develop geometric-based parameters and optimization-based parameters. These parameters can be combined with a decision fusion algorithm to develop internal battery parameters. Analysis processes including particle filter analysis, neural network analysis, and auto regressive moving average analysis can be used to analyze the internal battery parameters and develop battery health metrics. Additional decision fusion algorithms can be used to combine the internal battery parameters and the battery health metrics to develop state-of-health estimations, state-of-charge estimations, remaining-useful-life predictions, and end-of-life predictions for the battery.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Solving Critical Problems of the Muon Collider Higgs Factory: Optics, Magnets and their Protection, Detector Backgrounds

A low-energy medium-luminosity Muon Collider (MC) is being studied as a possible Higgs Factory (HF). Electrons from muon decays will deposit more than 300 kW in superconducting magnets of the HF collider ring. This imposes significant challenges to superconducting (SC) magnets used in the MC storage ring (SR) and interaction regions (IR). Magnet designs are proposed which provide high operating gradient and magnetic field in a large aperture to accommodate the large size of muon beams (due to low beta*), as well as a cooling system to intercept the large heat deposition from the showers induced by decay electrons. The distribution of heat deposition in the MC SR lattice elements requires large-aperture magnets in order to accommodate thick high-Z absorbers to protect the SC coils. Based on the developed MARS15 model and intensive simulations, a sophisticated radiation protection system was designed for the collider SR and IR to bring the peak power density in the superconducting coils below the quench limit and reduce the dynamic heat deposition in the cold mass by a factor of 100. The system consists of tight tungsten masks in the magnet interconnect regions and elliptical tungsten liners in the magnet aperture optimized individually for each magnet. These also reduce the background particle fluxes in the collider detector.

43 PARTICLE ACCELERATORS↗
DOE OSTI2015

Inductance studies for the Quench Protection system of the Mu2e TS solenoids

This is a preliminary study on the solenoids for the Mu2e experiment. An overview of the Mu2e solenoids is first provided. Then the two solenoid prototypes were tested in the Fermilab Central Helium Liquefying facility. My analysis is focused on measuring and modeling the inductance (self and mutual) of the magnets. This aspect is crucial for Quench Detection Systems and for the control parameters of the power supply driving the magnets. During the first part of my experience at Fermilab, I worked on mathematical mod- els for the magnets to understand their expected behavior. A geometric model based on the Neumann formula was developed and implemented for inductance calculations. This model estimates the inductance (self and mu- tual) of all the Mu2e solenoids. A finite element magnetic model for the two prototype magnets was developed to compare to the results of the geometric model. In the second part of my study I collected measurements on the real Transport Solenoid prototypes. T he solenoids were powered in different configurations in order to identify the circuit model and its parameters. These experiments were first carried on the warm magnets and then on the magnets at a cryo- genic temperature. This allows for a comparison between room temperature and superconducting state.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Multi-Level Impacts of Extreme Climate-Related Events on Food Supply Chain: Idaho Potato's Case Study

Background: The world is experiencing frequent extreme weather events like droughts, floods, snowstorms, and shifting of seasons due to climate change. Increasing frequencies and severity of these extreme weather events threaten food security because agriculture is extremely vulnerable to climate change. Higher temperature reduces overall yields of the desirable crops but proliferates weeds and harmful pests. Current data shows that every year U.S. farmers spend more than 11 billion USD to fight weeds and this number is expected to rise in the coming years due to climate change. Due to droughts, the water required for irrigation is becoming scarcer. Since many areas are becoming climatically unusable for crop production, the amount of farmland area is shrinking rapidly. In addition, changes in precipitation patterns are causing crop failures and negatively impacting expected yields. In this study, we investigated how different extreme climate events impact different stages of a food supply chain. Approach: As a case study, we chose Idaho’s potato supply chain (SC). Idaho is the top potato producing state in the U.S. and contributes around 30% of the nation’s potato production. Using the agent-based modeling (ABM) approach, we developed a multi-echelon potato supply chain computer simulation model with six types of agents – seed producers, farmers, shippers, processors, retailers, and logistics companies. In addition to the baseline scenario, we developed two additional climate-related disruption events – drought and snowstorm. We investigated how each of these disruption events impacts different stakeholders across the supply chain in terms of revenue and some other key performance metrics such as lead time, food waste, number of delayed orders, and order fulfillment rate. Results: Extreme climate events not only impact the growers but there is also cascading ripple effects downstream of the supply chain. The methodology developed in this study could be applied to other food and agricultural SC to understand the vulnerabilities of the different links. The findings would potentially help to adopt new policies for the well-being of the overall SC.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

A paradigm shift - the key to optimal, defensible and transparent waste management, disposal and remediation decisions - 15236

Current methods for structuring nuclear waste management, disposal, and remediation decisions are subject to three major limitations. First, they are based on a series of extraordinarily conservative assumptions. The dominant way of thinking is that, for example, conservative models yield better, safer decisions. Second, they lack methods to meaningfully address and incorporate the 'human dimension'. The dominant way of thinking is that the current approach is sufficient to bring together the values and interests of both internal and external stakeholders in making a decision. And third, they lack a formal method through which both internal and external stakeholders are encouraged collectively to 'think through' complex problems that contain multiple competing objectives so that wise, socially acceptable, and scientifically-defensible decisions are made. To overcome these limitations, a paradigm shift is needed towards a holistic, integrated approach to structure nuclear waste management, disposal and remediation decisions. The first requirement of this paradigm shift is the realization and recognition that the problems faced are decision problems, and that there is a formal theory of decision analysis that can, and should, be used to support finding the best solutions. Decision analysis, very simply, is formalized common sense. The reason it is needed for complex problems is that there are too many moving parts (variables) for humans to manage with out the formalism. Decision analysis leads to addressing problem solving using a combination of a science-based model and a costs/values model. Expectation ('realism') and uncertainty are addressed in the science-based models, whereas stakeholder values, preferences, and desires are addressed when modeling costs and value judgments. Science-based modeling tools have been developed, improved, and used since the current regulatory setting was introduced about 30 years ago. However, modeling of costs and value judgments has not often been addressed using any formal approach, despite the openings that exist in regulations (e.g., CERCLA's modifying criteria and the concept of 'as low as reasonably achievable' referenced in DOE Orders). A discussion of the need and rationale for this paradigm shift is presented, followed by a description of the technical basics of this approach to decision modeling, and ending with an explanation of the steps that are taken in this approach to structured decision-making. A software framework program called Guided Interactive Statistical Decision Tools (GiSdT - pronounced 'gist') is introduced that supports this paradigm shift. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Development of a prediction model for radiotherapy response among patients with head and neck squamous cell carcinoma based on the tumor immune microenvironment and hypoxia signature

Abstract Introduction The immune system and hypoxia are major factors influencing radiosensitivity in patients with different cancer types. This study aimed at developing a model to predict radiotherapy response in patients with head and neck squamous cell carcinoma (HNSCC) based on the tumor immune microenvironment and hypoxia signature. Materials and Methods We first evaluated the hypoxia status and tumor immune microenvironment in the Cancer Genome Atlas (TCGA) cohort by using transcriptomic data. Differentially expressed genes (DEGs) were identified between the “high immunity and low hypoxia” and “low immunity and high hypoxia” groups and those DEGs significantly associated with disease‐specific survival in the univariate Cox regression analysis were selected as the prognostic DEGs. We selected the immune hypoxia–related genes (IHRGs) by intersecting prognostic DEGs with immune and hypoxia gene sets. We used the IHRGs to train a multivariate Cox regression model in the TCGA cohort, based on which we calculated the IHRG prognostic index (IHRGPI) for each patient and validated its efficacy in predicting radiotherapy response in the Gene Expression Omnibus cohorts. Furthermore, we explored potential mechanisms and effective combinational treatment strategies for different IHRGPI groups. Results Five IHRGs were used to construct the IHRGPI, which was used to dichotomize the cohorts. The patients with lower IHRGPI showed a better radiotherapy response across different cohorts and endpoints, including overall survival, progression‐free survival, and recurrence‐free survival ( p < 0.05). Patients with higher IHRGPI showed greater hypoxia and lesser immune cell infiltration. A lower IHRGPI indicated a better immunotherapy response, while a higher IHRGPI indicated a better chemotherapy response. Conclusions IHRGPI is promising for predicting radiotherapy response and guiding combinational treatment strategies in patients with HNSCC.

Zhu, Guang‐Li↗
DOE OSTI2023

A Design of Activity-Based Mobility Intervention

Human mobility influences our society and vice versa. During the COVID-19 pandemic, non-pharmaceutical intervention that alters activity-based mobility such as work-from-home greatly impacted human mobility patterns. Many studies on developing mitigation strategies have employed or implemented their own mobility intervention within their model assumption. For fair evaluation between intervention strategies across models, it is significant to set up compatible experimental environments. However, it is difficult to apply the identical intervention to different kinds of models and compare their effectiveness because each model might have different assumptions, capabilities, and implementations. Even if one can apply intervention to heterogeneous models, it may produce undesirable artifacts due to difference of models and integration with intervention. Therefore, minimizing undesirable artifacts and facilitating intervention experiments across heterogeneous models are substantial. Taking this into account, this paper investigates a design of activity-based mobility intervention (ABMI). We define ABMI together with related concepts and develop an extensible data model and schema of ABMI based on the 5W1H method that can be used in different models. As a case study, we apply the ABMI model to a micro-simulation to demonstrate the usability of the proposed model. We expect that standardized ABMI and interfaces may help to streamline development and experiments of intervention strategies across heterogeneous models.

Kim, Joon-Seok↗
DOE OSTI2012

Dynamic modeling and control of a solid-sorbent CO{sub 2} capture process with two-stage bubbling fluidized bed adsorber reactor

Solid-sorbent-based CO{sub 2} capture processes have strong potential for reducing the overall energy penalty for post-combustion capture from the flue gas of a conventional pulverized coal power plant. However, the commercial success of this technology is contingent upon it operating over a wide range of capture rates, transient events, malfunctions, and disturbances, as well as under uncertainties. To study these operational aspects, a dynamic model of a solid-sorbent-based CO{sub 2} capture process has been developed. In this work, a one-dimensional (1D), non-isothermal, dynamic model of a two-stage bubbling fluidized bed (BFB) adsorber-reactor system with overflow-type weir configuration has been developed in Aspen Custom Modeler (ACM). The physical and chemical properties of the sorbent used in this study are based on a sorbent (32D) developed at National Energy Technology Laboratory (NETL). Each BFB is divided into bubble, emulsion, and cloud-wake regions with the assumptions that the bubble region is free of solids while both gas and solid phases coexist in the emulsion and cloud-wake regions. The BFB dynamic model includes 1D partial differential equations (PDEs) for mass and energy balances, along with comprehensive reaction kinetics. In addition to the two BFB models, the adsorber-reactor system includes 1D PDE-based dynamic models of the downcomer and outlet hopper, as well as models of distributors, control valves, and other pressure-drop devices. Consistent boundary and initial conditions are considered for simulating the dynamic model. Equipment items are sized and appropriate heat transfer options, wherever needed, are provided. Finally, a valid pressure-flow network is developed and a lower-level control system is designed. Using ACM, the transient responses of various process variables such as flue gas and sorbent temperatures, overall CO{sub 2} capture, level of solids in the downcomer and hopper have been studied by simulating typical disturbances such as change in the temperature, flowrate, and composition of the flue gas. To maintain the overall CO{sub 2} capture at a desired level in face of the typical disturbances, two control strategies were considered–a proportional-integral-derivative (PID)-based feedback control strategy and a feedforward-augmented feedback control strategy. Dynamic simulation results show that both the strategies result in unacceptable overshoot/undershoot and a long settling time. To improve the control system performance, a linear model predictive controller (LMPC) is designed. In summary, the overall results illustrate how optimizing the operation and control of carbon capture systems can have a significant impact on the extent and the rate at which commercial-scale capture processes will be scaled-up, deployed, and used in the years to come.

20 FOSSIL-FUELED POWER PLANTS↗