Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Process modelling”

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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Model-Based Energy and Cost Analysis of Direct Air Capture Using ePTFE-Based Laminate-Structured Gas–Solid Contactors

Carbon dioxide removal (CDR) technologies will play a significant role in limiting global warming if implemented on a large scale. Direct air capture (DAC) is a scalable approach for removing atmospheric carbon, yet the true scope of its scalability remains unclear due to the early stage of technology development and high first plant costs. This study provides groundwork for understanding the technoeconomic trade-offs in developing DAC systems using laminate-structured gas–solid contactors, encompassing the analysis of both contactor and process design spaces. The robust mass transfer and process models outlined in this study provide tools for evaluating DAC processes and designing DAC plants based on cost and energy analysis. First, the key contactor geometrical parameters are identified to understand the CO 2 productivity–energy demand trade-offs, where geometries yielding higher mass transfer rates can achieve higher CO 2 productivities at the expense of energy consumption by fans and steam use. Next, a detailed process parametric study is conducted for DAC systems coupled with steam-assisted temperature-vacuum swing adsorption (S-TVSA) to visualize the trade-offs in the multidimensional design space. The main cost driver dramatically changes over different process conditions, but the operating cost prevailed on the Pareto front, with potential to operate as low as 150 $/tonne-CO 2 (within the cost range of 148–504 $/tonne-CO 2 in this study where the DAC system is coupled with industrial facilities for steam production).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Active learning of a crystal plasticity flow rule from discrete dislocation dynamics simulations

Continuum-scale material deformation models, such as crystal plasticity (CP), can significantly enhance their predictive accuracy by incorporating input from lower-scale (i.e. mesoscale) models. The procedure to generate and extract the relevant information is however typically complex and ad hoc, involving decision and intervention by domain experts, leading to long development times. In this study, we develop a principled approach for calibration of continuum-scale models using lower scale information by representing a CP flow rule as a Gaussian process model. This representation allows for efficient parameter space exploration, guided by the uncertainty embedded in the model through a process known as Bayesian optimization (BO). We demonstrate a semi-autonomous BO loop which instantiates discrete dislocation dynamics simulations whose initial conditions are automatically chosen to optimize the uncertainty of a model CP flow rule. Our self-guided computational pipeline efficiently generated a dataset and corresponding model whose error, uncertainty, and physical feature sensitivities were validated with comparison to an independent dataset four times larger, demonstrating a valuable and efficient active learning implementation readily transferable to similar material systems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Linking Plant and Microbial Traits to Soil Carbon for Reliable and Resilient Bioenergy Systems

Bioenergy systems in the United States offer a dual opportunity to supply renewable feedstocks while enhancing ecosystem services such as hydrologic regulation, erosion control, and soil carbon (C) storage. National assessments highlight the potential to grow perennial energy crops to improve soil function and ecosystem resilience. Realizing this potential requires understanding the ecological mechanisms that govern how C is added, transformed, and stabilized in soils. Plant traits determine the quantity, depth, and chemistry of organic inputs, while microbial processes—including carbon use efficiency, necromass formation, and trophic interactions—mediate their transformation and partitioning among soil carbon pools. These biological pathways are shaped by soil physical and chemical properties, including aggregation, texture, and mineralogy, and by environmental drivers such as temperature, moisture, and disturbance, leading to context-dependent outcomes across landscapes. Management practices that diversify feedstocks, minimize disturbance, and maintain soil cover can promote both biomass production and C retention, while microbial amendments and rhizosphere engineering offer emerging, but often context-dependent, tools to optimize plant–microbe interactions. Trade-offs between biomass yield and soil carbon storage may arise when systems favor rapid aboveground productivity at the expense of belowground inputs and microbial processing, underscoring the importance of trait combinations that support both functions. Advances in monitoring, reporting, and verification—spanning precision agriculture, remote sensing, and biosensing—are improving predictive capacity through microbial-explicit process models and model–experiment (ModEx) frameworks. By connecting soil, plant, and microbial processes with advances in modeling and biosensing, this review outlines research priorities focused on trait-based parameterization and ModEx integration. These priorities will support the design of bioenergy systems that are both reliable and resilient, enhancing renewable energy production and ecosystem sustainability.

bioenergy systems↗
DOE OSTI2020

Economic Perspectives of Biogas Production via Anaerobic Digestion

As the demand for utilizing environment-friendly and sustainable energy sources is increasing, the adoption of waste-to-energy technologies has started gaining attention. Producing biogas via anaerobic digestion (AD) is promising and well-established; however, this process in many circumstances is unable to be cost competitive with natural gas. In this research, we provide a technical assessment of current process challenges and compare the cost of biogas production via the AD process from the literature, Aspen Plus process modeling, and CapdetWorks software. We also provide insights on critical factors affecting the AD process and recommendations on optimizing the process. We utilize four types of wet wastes, including wastewater sludge, food waste, swine manure, and fat, oil, and grease, to provide a quantitative assessment of theoretical energy yields of biogas production and its economic potential at different plant scales. Our results show that the cost of biogas production from process and economic models are in line with the literature with a potential to go even lower for small-scale plants with technological advancements. This research illuminates potential cost savings for biogas production using different wastes and guide investors to make informed decisions, while achieving waste management goals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

FY21 Report on Activities for EBS International

This report summarizes the FY21 Activities for EBS International Collaborations Work Package. The international collaborations work packages aim to leverage knowledge, expertise, and tools from the international nuclear waste community, as deemed relevant according to SFWST “roadmap” priorities. This report describes research and development (R&D) activities conducted during fiscal year 2021(FY21) specifically related to the Engineered Barrier System (EBS) R&D Work Package in the Spent Fuel and Waste Science and Technology (SFWST) Campaign supported by the United States (U.S.) Department of Energy (DOE). It fulfills the SFWST Campaign deliverable M4SF- 21SN010308062. The R&D activities described in this report focus on understanding EBS component evolution and interactions within the EBS, as well as interactions between the host media and the EBS. A primary goal is to advance the development of process models that can be implemented directly within the Generic Disposal System Analysis (GDSA) platform or that can contribute to the safety case in some manner such as building confidence, providing further insight into the processes being modeled, establishing better constraints on barrier performance, etc. Sandia National Laboratories is participating in THM modeling in the international projects EBS Task Force and DECOVALEX 2023. EBS Task Force, Task 11 is on modeling of laboratory-scale High Temperature Column Test conducted at Lawrence Berkeley National Laboratory. DECOVALEX 2023, Task C is on THM modeling of the full-scale emplacement experiment (FE experiment) at the Mont Terri Underground Rock Laboratory, Switzerland. This report summarizes Sandia’s progress in the modeling studies of DECOVALEX 2023, Task C. Modeling studies related to the High Temperature Column Test will be documented in future reports.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Evaluation of Engineered Barrier Systems FY21 Report

This report describes research and development (R&D) activities conducted during fiscal year 2021 (FY21) specifically related to the Engineered Barrier System (EBS) R&D Work Package in the Spent Fuel and Waste Science and Technology (SFWST) Campaign supported by the United States (U.S.) Department of Energy (DOE). The R&D activities focus on understanding EBS component evolution and interactions within the EBS, as well as interactions between the host media and the EBS. A primary goal is to advance the development of process models that can be implemented directly within the Generic Disposal System Analysis (GDSA) platform or that can contribute to the safety case in some manner such as building confidence, providing further insight into the processes being modeled, establishing better constraints on barrier performance, etc.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

A Hybrid Energy System Workflow for Energy Portfolio Optimization

This manuscript develops a workflow, driven by data analytics algorithms, to support the optimization of the economic performance of an Integrated Energy System. The goal is to determine the optimum mix of capacities from a set of different energy producers (e.g., nuclear, gas, wind and solar). A stochastic-based optimizer is employed, based on Gaussian Process Modeling, which requires numerous samples for its training. Each sample represents a time series describing the demand, load, or other operational and economic profiles for various types of energy producers. These samples are synthetically generated using a reduced order modeling algorithm that reads a limited set of historical data, such as demand and load data from past years. Numerous data analysis methods are employed to construct the reduced order models, including, for example, the Auto Regressive Moving Average, Fourier series decomposition, and the peak detection algorithm. All these algorithms are designed to detrend the data and extract features that can be employed to generate synthetic time histories that preserve the statistical properties of the original limited historical data. The optimization cost function is based on an economic model that assesses the effective cost of energy based on two figures of merit: the specific cash flow stream for each energy producer and the total Net Present Value. An initial guess for the optimal capacities is obtained using the screening curve method. The results of the Gaussian Process model-based optimization are assessed using an exhaustive Monte Carlo search, with the results indicating reasonable optimization results. The workflow has been implemented inside the Idaho National Laboratory’s Risk Analysis and Virtual Environment (RAVEN) framework. The main contribution of this study addresses several challenges in the current optimization methods of the energy portfolios in IES: First, the feasibility of generating the synthetic time series of the periodic peak data; Second, the computational burden of the conventional stochastic optimization of the energy portfolio, associated with the need for repeated executions of system models; Third, the inadequacies of previous studies in terms of the comparisons of the impact of the economic parameters. The proposed workflow can provide a scientifically defendable strategy to support decision-making in the electricity market and to help energy distributors develop a better understanding of the performance of integrated energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln

Abstract Cement manufacturing is energy‐intensive (5Gj/t) and comprises a significant portion of the energy footprint of concrete systems. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact, and lower costs of this widely used construction material. One of the goals of the CESMII roadmap project on the Smart Manufacturing of Cement included developing an analytical process model for clinker quality that includes the chemistry of the kiln feed and accounts for critical process variables. This predictive model will be used in nonlinear model predictive control system designed to significantly reduce process energy use while maintaining or improving product quality. In the cement manufacturing plant used in this study, the kiln feed (meal) is tested every 12 h and used to estimate the mineral composition of the cement kiln output (clinker) using the stoichiometry‐based Bogue's model and the expertise of the plant operators. During kiln operation, kiln output (clinker) is sampled and tested every 2 h to measure its chemical and mineral composition. The predicted and measured values of the clinker composition are used by the plant operators to adjust the kiln input stream and the production process characteristics to maintain stable operation and uniform product quality. However, the time delay between prediction and testing, along with inaccuracies inherent in the Bogue's model have made any process changes designed to minimize energy use problematic, especially in‐light of potential clinker quality issues that process changes often pose. A new analytical model that integrates quality information and process operation information has been developed from data collected from 2 years of production from an operating cement facility. To make the model fuel‐type‐independent, consumed heat energy was computed in the model instead of fuel type and amount. A Feedforward Network was trained and tailored from collected data. Many data‐based simulations were conducted to quantitatively evaluate the proposed model and the 5‐fold cross‐validation procedure was used to test the models. The resulting predictive model was shown to have a low root mean square error (MSE) with respect to the estimated clinker mineral composition compared to that using the industry standard “Bogue’ model”. The end goal of this work was to develop a single machine learning tool that allows the use of quality control data and process control variables to improve energy efficiency of the process in a continuous fashion. The proposed nonlinear model predictive control system (NMPC) can generate predicted kiln production characteristics based on manipulated variables in manner that accurately follows the target product quality values. Simulation results also show that the proposed model produced accurate predictions of kiln outputs that fell within the required constraints, while manipulating control variables within typical operational ranges.

Ali, Asem M.↗
DOE OSTI2020

Search for direct production of electroweakinos in final states with missing transverse momentum and a Higgs boson decaying into photons in pp collisions at $ \sqrt{s} $ = 13 TeV with the ATLAS detector

A search for a chargino-neutralino pair decaying via the 125 GeV Higgs boson into photons is presented. The study is based on the data collected between 2015 and 2018 with the ATLAS detector at the LHC, corresponding to an integrated luminosity of 139 fb –1 of pp collisions at a centre-of-mass energy of 13 TeV. No significant excess over the expected background is observed. Upper limits at 95% confidence level for a massless $χ$~ $^0_1$ are set on several electroweakino production cross-sections and the visible cross-section for beyond the Standard Model processes. In the context of simplified supersymmetric models, 95% confidence-level limits of up to 310 GeV in m($χ$~$^±_1$/ $χ$~ $^0_2$), where m($χ$~ $^0_1$) = 0.5 GeV, are set. Limits at 95% confidence level are also set on the $χ$~$^±_1$/ $χ$~ $^0_2$ cross-section in the mass plane of m($χ$~$^±_1$/ $χ$~ $^0_2$) and m($χ$~ $^0_1$), and on scenarios with gravitino as the lightest supersymmetric particle. Upper limits at the 95% confidence-level are set on the higgsino production cross-section. Higgsino masses below 380 GeV are excluded for the case of the higgsino fully decaying into a Higgs boson and a gravitino.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Search for new particles in events with a hadronically decaying W or Z boson and large missing transverse momentum at $\sqrt{s}$ = 13 TeV using the ATLAS detector

A search is presented for new particles produced in proton-proton collisions at a centre-of-mass energy of 13 TeV that result in final states comprising a massive vector ( W or Z ) boson that decays hadronically and large missing transverse momentum. The data sample was collected with the ATLAS experiment at the Large Hadron Collider from 2015 to 2018 and corresponds to an integrated luminosity of 140 fb - 1 . No significant excess over the Standard Model expectation is observed. Model-independent 95% confidence-level limits on the visible cross-section that range from 0.3 fb to 79.5 fb are obtained for non-Standard-Model processes. Exclusion limits are also presented for models with axion-like particles, for two-Higgs-doublet models with a pseudo-scalar mediator between the Standard Model and the dark sector, for the invisible decay of the Higgs boson and for pair-produced weakly interacting dark matter candidates.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Uncertainty-aware mixed-variable machine learning for materials design

Abstract Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods. Bayesian optimization (BO) employs uncertainty-aware machine learning models to select promising designs to evaluate, hence reducing the cost. However, BO with mixed numerical and categorical variables, which is of particular interest in materials design, has not been well studied. In this work, we survey frequentist and Bayesian approaches to uncertainty quantification of machine learning with mixed variables. We then conduct a systematic comparative study of their performances in BO using a popular representative model from each group, the random forest-based Lolo model (frequentist) and the latent variable Gaussian process model (Bayesian). We examine the efficacy of the two models in the optimization of mathematical functions, as well as properties of structural and functional materials, where we observe performance differences as related to problem dimensionality and complexity. By investigating the machine learning models’ predictive and uncertainty estimation capabilities, we provide interpretations of the observed performance differences. Our results provide practical guidance on choosing between frequentist and Bayesian uncertainty-aware machine learning models for mixed-variable BO in materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Accuracy versus precision in boosted top tagging with the ATLAS detector

The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as top tagging , is a crucial component in many measurements of Standard Model processes and searches for beyond the Standard Model physics at the Large Hadron Collider. Machine learning techniques have improved the performance of top tagging algorithms, but the size of the systematic uncertainties for all proposed algorithms has not been systematically studied. This paper presents the performance of several machine learning based top tagging algorithms on a dataset constructed from simulated proton-proton collision events measured with the ATLAS detector at $\sqrt{s}$ = 13 TeV. The systematic uncertainties associated with these algorithms are estimated through an approximate procedure that is not meant to be used in a physics analysis, but is appropriate for the level of precision required for this study. The most performant algorithms are found to have the largest uncertainties, motivating the development of methods to reduce these uncertainties without compromising performance. To enable such efforts in the wider scientific community, the datasets used in this paper are made publicly available.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Towards Net-Zero: Nuclear-Assisted Waste Biomass to Liquid Fuel in Eastern Idaho

A nuclear assisted carbon negative hybrid energy process that enables production of synthetic bio-crude oil and biochar from Eastern Idaho waste biomass is proposed. The process integrates nuclear powered electricity with high temperature steam electrolysis and biomass hydropyrolysis. The bio-crude oil is of sufficient composition and blended with traditional crude oil at a refinery. Hydrogen from the electrolyzer is pressurized and inserted into the pyrolyzer. Non condensable gases generated in the hydropyrolysis process are burned with oxygen from the electrolyzer to produce heat for the electrolyzer, biomass dryer, and pyrolyzer. The biochar is returned to the soil via fertilizer application and remains there for thousands of years. Since the total process uses nuclear generated electricity, the carbon in the biochar is ultimately sequestered from the atmosphere, thus making the process carbon negative. Using Eastern Idaho wheat or barley straw, this hybrid energy process has the potential to provide an alternative petroleum source. Two options exist for the system design: 1) send electricity from the nuclear plant and straw to a chemical processing plant to produce the bio-crude and biochar, 2) construct the biomass processing facility near the nuclear plant to allow use of nuclear-generated process heat to drive the chemical. Process model description and results are discussed. The process is sized to produce gasoline and diesel at the rate that the INL uses every day for fleet usage.

09 BIOMASS FUELS↗
DOE OSTI2024

Tree water uptake patterns across the globe

Plant water uptake from the soil is a crucial element of the global hydrological cycle and essential for vegetation drought resilience. Yet, knowledge of how the distribution of water uptake depth (WUD) varies across species, climates, and seasons is scarce relative to our knowledge of aboveground plant functions. With a global literature review, we found that average WUD varied more among biomes than plant functional types (i.e. deciduous/evergreen broadleaves and conifers), illustrating the importance of the hydroclimate, especially precipitation seasonality, on WUD. By combining records of rooting depth with WUD, we observed a consistently deeper maximum rooting depth than WUD with the largest differences in arid regions – indicating that deep taproots act as lifelines while not contributing to the majority of water uptake. The most ubiquitous observation across the literature was that woody plants switch water sources to soil layers with the highest water availability within short timescales. Hence, seasonal shifts to deep soil layers occur across the globe when shallow soils are drying out, allowing continued transpiration and hydraulic safety. While there are still significant gaps in our understanding of WUD, the consistency across global ecosystems allows integration of existing knowledge into the next generation of vegetation process models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Energy Arbitrage with Hydrogen: HTSE + Combustion Turbine

The Light Water Reactor Sustainability (LWRS) program seeks to keep Nuclear Power Plants (NPPs) competitive in the changing energy market. The Flexible Plant Operation and Generation (FPOG) pathway investigates Energy Storage Systems (ESS) to give operators an alternative to curtailment or paying to put power on the grid. This work investigates the concept of diverting heat and electricity to High Temperature Steam Electrolysis (HTSE) to produce hydrogen when grid demand is low, storing that hydrogen, then burning it in a combustion turbine to produce zero-emission electricity when grid demand is high. A process model of the combustion turbine was developed in Aspen HYSYS, which was used to complete a parametric analysis and techno-economic analysis.

08 HYDROGEN↗
DOE OSTI2021

Linear and Nonlinear Solvers for Simulating Multiphase Flow within Large-Scale Engineered Subsurface Systems

Simulation of multiphase flow in the subsurface is well-known to be computationally challenging. While there have been many studies that have explored approaches to overcoming these challenges, they often utilize relatively simple case studies. In this paper, we focus on the unique numerical challenges posed by modeling large-scale engineered subsurface systems, characterized by discrete features embedded in a heterogeneous natural subsurface setting. The man-made features such as shafts, tunnels, and barriers often cause multiple challenges in modeling the domain for multiphase porous media flow. This flow scenario can have a wide range of applications such as nuclear waste repositories, enhanced recovery of a petroleum reservoir, geothermal engineering, and carbon sequestration. An example of these severe numerical challenges is the case of performance assessment (PA) for Waste Isolation Pilot Plant (WIPP), the only operating deep geological repository in the US, which simulates extreme material properties of bedded salt rock formation and extreme contrast due to open excavation next to the formation. The models have extremes not only of permeability and porosity but also of the constitutive models needed for multiphase flow; additionally, they have process models like salt creep closure reducing porosity over time, fracturing in clay and anhydrite interbeds of the bedded salt, gas generation from the waste materials, and unintentional human borehole intrusions in some scenarios. Numerical simulations require the solution of coupled systems of nonlinear PDEs; in our work, we use the open-source simulator PFLOTRAN which is based on Finite Volume discretization. The solution of the nonlinear equations requires use of the Newton-Raphson iteration at each time step, which entails the solution of the linearized Jacobian system at each iteration. The effects of all the processes (i.e., large number of unknowns, highly nonlinear constitutive relations, large contrasts in material properties in short distances) lead to an ill-conditioned Jacobian matrix that severely challenges traditional linear solver, i.e., stabilized biconjugate gradient with block Jacobi incomplete LU preconditioner (BCGS-ILU) leading to non-convergence for traditional Newton-Raphson nonlinear solver causing unacceptably long computation time for each model. This paper presents linear solvers such as constrained pressure residual (CPR) two-stage preconditioner with alternate-block-factorization (ABF) and quasi- implicit pressure and explicit saturation (QIMPES) decouplers and flexible generalized residual solver (FGMRES). The new general-purpose nonlinear solver, Newton trust-region dogleg Cauchy (NTRDC), is also introduced to resolve extreme nonlinearities in the models. We demonstrate the effectiveness of each method relative to the default BCGS-Newton solver. The two best cases had nearly 50 times speed-up and achieved completion of a simulation in 14 hours that never completed due to non-convergence with the default solver. We also investigate the strong scalability of each method and discuss some of the deficiencies found for Block Jacobi preconditioner using parallel domain decomposition, and node packing effects of modern processor architecture.

Preconditioner, Nonlinear, Porous media, Multiphas↗
DOE OSTI2024

Techno-economic and life cycle analysis of synthetic natural gas production from low-carbon H 2 and point-source or atmospheric CO 2 in the United States

Synthetic natural gas (SNG) is of great interest in reducing fossil energy consumption while maintaining compatibility with existing NG infrastructure and end-use applications equipment. SNG can be produced using clean H 2 generated from renewable or nuclear energy and CO 2 captured from stationary sources or the atmosphere. In this study, we develop an engineering process model of SNG production using Aspen Plus® and production scales reported by the industry. We examine the levelized cost and life cycle greenhouse gas (GHG) emissions of SNG production under various CO 2 supply scenarios. Considering the higher cost of H 2 transportation compared with CO 2 transportation, we assume that CO 2 feedstock is transported via pipeline to the H 2 production location, which is collocated with the SNG plant. We also evaluate the cost of CO 2 captured from the atmosphere, assuming the direct air capture process can occur near the SNG facility. Depending on the CO 2 supply chain, the levelized cost of SNG is estimated to be in the range of $\$45–76$ per million British thermal units (MMBtu) on a higher heating value (HHV) basis. The SNG production cost may be reduced to $\$27–57$/MMBtu-HHV by applying a tax credit available in the United States for low-carbon H 2 production (45V). With a lower electricity price of 3¢/kWh for water electrolysis and accounting for a 45V tax credit, the SNG cost reaches parity with the cost of fossil NG. Depending on the CO 2 supply chain, SNG can reduce life cycle GHG emissions by 52–88% compared with fossil NG.

03 NATURAL GAS↗