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

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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Expanding Robust HCCI Operation with Advanced Valve and Fuel Control Technologies

Delphi Automotive Systems and ORNL established this CRADA to advance the commercialization potential of the homogeneous charge compression ignition (HCCI) advanced combustion strategy for gasoline engine platforms. HCCI combustion has been shown by others to produce high diesel-like efficiency on a gasoline engine platform while simultaneously producing low NOX and particulate matter emissions. However, the commercialization barriers that face HCCI combustion are significant, with requirements for a more active engine control system, likely with next-cycle closed-loop feedback control, and with advanced valve train technologies to enable negative valve overlap conditions. In the partnership between Delphi and ORNL, each organization brought a unique and complementary set of skills to the project. Delphi has made a number of breakthroughs with production-intent valve train technologies and controls in recent years to make a part time production-intent HCCI engine plausible. ORNL has extensive knowledge and expertise with HCCI combustion, and also has a versatile research engine with hydraulic valve actuation (HVA) that is useful for guiding production of a cam-based HCCI system. Partnering these knowledge bases and capabilities was essential towards making progress to better understand HCCI combustion and the commercialization barriers that it faces. ORNL and Delphi maintained strong collaboration throughout the project. Meetings were held regularly, with additional reports, presentations, and meetings as necessary to maintain progress. Delphi provided guidance to ORNL regarding operational strategies to investigate on their single-cylinder research engine with HVA and data from their experimental multi-cylinder engine for modeling. ORNL provided single-cylinder engine data and modeling results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Discovery of Tarantula Venom-Derived Na V 1.7-Inhibitory JzTx-V Peptide 5-Br-Trp24 Analogue AM-6120 with Systemic Block of Histamine-Induced Pruritis

Inhibitors of the voltage-gated sodium channel Na V 1.7 are being investigated as pain therapeutics due to compelling human genetics. We previously identified Na V 1.7-inhibitory peptides GpTx-1 and JzTx-V from tarantula venom screens. Potency and selectivity were modulated through attribute-based positional scans of native residues via chemical synthesis. In this paper, we report JzTx-V lead optimization to identify a pharmacodynamically active peptide variant. Molecular docking of peptide ensembles from NMR into a homology model-derived Na V 1.7 structure supported prioritization of key residues clustered on a hydrophobic face of the disulfide-rich folded peptide for derivatization. Replacing Trp24 with 5-Br- Trp24 identified lead peptides with activity in electrophysiology assays in engineered and neuronal cells. 5-Br-Trp24 containing peptide AM-6120 was characterized in X-ray crystallography and pharmacokinetic studies and blocked histamine-induced pruritis in mice after subcutaneous administration, demonstrating systemic Na V 1.7-dependent pharmacodynamics. Our data suggests a need for high target coverage based on plasma exposure for impacting in vivo end points with selectivity-optimized peptidic Na V 1.7 inhibitors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

FN-DFE: Fuzzy-Neural Data Fusion Engine for Enhanced State-Awareness of Resilient Hybrid Energy System

Resiliency and improved state-awareness of modern critical infrastructures, such as energy production and industrial systems, is becoming increasingly important. As control systems become increasingly complex, the number of inputs and outputs increase. Therefore, in order to maintain sufficient levels of state-awareness, a robust system state monitoring must be implemented that correctly identifies system behavior even when one or more sensors are faulty. Furthermore, as intelligent cyber adversaries become more capable, incorrect values may be fed to the operators. To address these needs, this paper proposes a Fuzzy-Neural Data Fusion Engine (FN-DFE) for resilient state-awareness of control systems. The designed FN-DFE is composed of a three-layered system consisting of: 1) traditional threshold based alarms, 2) anomalous behavior detector using self-organizing fuzzy logic system, and 3) artificial neural network based system modeling and prediction. The improved control system state-awareness is achieved via fusing input data from multiple sources and combining them into robust anomaly indicators. In addition, the neural network based signal predictions are used to augment the resiliency of the system and provide coherent state-awareness despite temporary unavailability of sensory data. The proposed system was integrated and tested with a model of the Idaho National Laboratory’s (INL) hybrid energy system facility know as HYTEST. Experimental results demonstrate that the proposed FN-DFE provides timely plant performance monitoring and anomaly detection capabilities. It was shown that the system is capable of identifying intrusive behavior significantly earlier than conventional threshold based alarm systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Numerical Methodology for Optimization of Compression-Ignited Engines Considering Combustion Noise Control

It is challenging to develop highly efficient and clean engines while meeting user expectations in terms of performance, comfort, and drivability. One of the critical aspects in this regard is combustion noise control. Combustion noise accounts for about 40 percent of the overall engine noise in typical turbocharged diesel engines. The experimental investigation of noise generation is difficult due to its inherent complexity and measurement limitations. Therefore, it is important to develop efficient numerical strategies in order to gain a better understanding of the combustion noise mechanisms. In this work, a novel methodology was developed, combining computational fluid dynamics (CFD) modeling and genetic algorithm (GA) technique to optimize the combustion system hardware design of a high-speed direct injection (HSDI) diesel engine, with respect to various emissions and performance targets including combustion noise. The CFD model was specifically set up to reproduce the unsteady pressure field inside the combustion chamber, thereby allowing an accurate prediction of the acoustic response of the combustion phenomena. The model was validated by simulating several steady operating conditions and comparing the numerical results against experimental data, in both temporal and frequency domains. Thereafter, a GA optimization was performed with the goal of minimizing indicated specific fuel consumption (ISFC) and combustion noise, while restricting pollutant (soot and NOx) emissions to their respective baseline values. Eight design variables were selected pertaining to piston bowl geometry, nozzle inclusion angle, number of injector nozzle holes, and in-cylinder swirl. An objective merit function (MF) based on the emissions, ISFC, and combustion noise was constructed to quantify the strength of the engine designs and was determined using the CFD model as the function evaluator. The in-cylinder noise level was characterized by the total resonance energy of local pressure oscillations. The optimum engine configuration thus obtained showed a significant improvement in terms of efficiency and combustion noise compared to the baseline system, along with both soot and NOx emissions within their respective constraints. This optimum configuration included a deeper and tighter bowl geometry with higher swirl and larger number of nozzle holes. Subsequently, a more detailed acoustics analysis based on proper orthogonal decomposition (POD) technique was carried out to further explore the combustion noise benefits achieved by the GA optimum. In conclusion, this computational study is a first of its kind (to the best of the authors’ knowledge), which demonstrates a comprehensive framework to incorporate combustion noise into a numerical optimization strategy for engine design.

42 ENGINEERING↗
DOE OSTI2020

Band-edge Engineering to Eliminate Radiation-Induced Defect States in Perovskite Scintillators

Under radiative environments such as extended hard X- or γ-rays, degradation of scintillation performance is often due to irradiation-induced defects. To overcome the effect of deleterious defects, novel design mitigation strategies are needed to identify and design more resilient materials. The potential for band-edge engineering to eliminate the effect of radiation-induced defect states in rare-earth-doped perovskite scintillators is explored, taking Ce 3+ -doped LuAlO 3 as a model material system, using density functional theory (DFT)-based DFT + U and hybrid Heyd–Scuseria–Ernzerhof (HSE) calculations. Furthermore, from spin-polarized hybrid HSE calculations, the Ce 3+ activator ground-state 4f position is determined to be 2.81 eV above the valence band maximum in LuAlO 3 . Except for the oxygen vacancies which have a deep level inside the band gap, all other radiation-induced defects in LuAlO 3 have shallow defect states or are outside the band gap, that is, relatively far away from either the 5d 1 or the 4f Ce 3+ levels. Finally, we examine the role of Ga doping at the Al site and found that LuGaO 3 has a band gap that is more than 2 eV smaller than that of LuAlO 3 . Specifically, the lowered conduction band edge envelopes the defect gap states, eliminating their potential impact on scintillation performance and providing direct theoretical evidence for how band-edge engineering could be applied to rare-earth-doped perovskite scintillators.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Long-term performance of engineered barriers for high-level waste repositories - 15358

The evolution of the engineered barrier system (EBS) of geological repositories for high-level radioactive waste (HLW) and spent fuel (SF) has been the subject of many national and international research programs during recent years. The emphasis of the research activities was on the elaboration of a detailed understanding of the complex coupled thermo-hydro-mechanical and -chemical (THM-C) processes, which are expected to evolve in the early post-closure period in the near field. From the perspective of radiological long-term safety, an in-depth understanding of these coupled processes is of great significance, because the evolution of the EBS during the early post-closure phase may have a non-negligible impact on the safety functions at later stages of the repository's lifetime. Process interactions during the re-saturation phase (heat pulse, gas generation, non-uniform water uptake from the host rock) could impair the homogeneity of the safety-relevant properties in the EBS (e.g. swelling pressure, hydraulic conductivity, diffusivity). The 7. Framework EURATOM PEBS project (long-term Performance of Engineered Barrier Systems) was initiated in 2010 and completed in March 2014. The project aimed at evaluating the sealing and barrier performance of clay-based EBS over time. The project approach included experiments, model development and consideration of the potential impact on long-term safety functions. The specific project aims were to: -deepen the understanding of the THM and THM-C evolution of the EBS system with time, - provide an enhanced quantitative basis for relating the evolutionary behavior to the safety functions, - clarify further the significance of residual uncertainties for long-term performance assessment. It can be concluded that uncertainties in process understanding occurring in the re-saturation period have been better constrained through PEBS studies, thus the uncertainties in the long-term performance of bentonite barriers have been reduced in some areas. Improvements are in the areas of evolution of materials properties and model development and testing at various scales. On-going in-situ experiments in underground research laboratories may play an important role in further confirming bentonite performance over periods of 10 - 20 years. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

A machine learning-based fast frequency response control for a VSC-HVDC system

An HVDC system can realize a very fast frequency response to the disturbed system under a contingency because its active power control is decoupled from the frequency deviation. However, most of existing HVDC frequency control strategies are coupled with system primary frequency control and secondary frequency control. Since the traditional system frequency control is dominated by the thermal generators, the advantage of the fast response of the HVDC system is not made fully used. The development of a frequency response estimation based on a machine learning algorithm provides another approach to improve the frequency response capability of the HVDC system. Different from other frequency deviation tracking strategies, a machine learning based HVDC frequency response control can directly increase the power flow of a HVDC system by estimation of the system generator or load lost. In this paper, a fast frequency response control using a HVDC system for a large power system disturbance based on the multivariate random forest regression (MRFR) algorithm is proposed. The simulation is carried out with an integrated power system model based on the North American interconnections. The simulation results indicate that the proposed MRFR based frequency response control can significantly improve the frequency low point during an event, while stabilizing the frequency in advance.

42 ENGINEERING↗
DOE OSTI2013

MOOSE Enhancements Towards Delivery of an Integrated Framework

Started in 2008, the Mulitphysics Object Oriented Simulation Environment (MOOSE) project delivers a comprehensive framework for development of parallel, multiphysics simulation tools. MOOSE provides an object-oriented interface that allows scientists and engineers to quickly and efficiently describe all aspects of a physical system relevant to numerical modeling. The MOOSE software development strategy has been quite successful: over thirty different MOOSE-based applications are under development both internally and outside the DOE complex, and over forty different external institutions have licensed MOOSE. Several NEAMS products have been built using MOOSE, including BISON (Williamson et. al., 2012), MARMOT (Tonks et. al., 2012), RELAP-7 (Zou et. al., 2013) and RAVEN (Rabiti, 2013).

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

Significance of Dynamic and Transient Analysis in the Design and Operation of Hybrid Energy Systems

Energy systems were historically designed and operated with a specific energy conversion objective, while managing loads and resources. In the recent years, the increased utilization of non-dispatchable renewable sources such as wind and solar has played a role in power quality and the reliability of power systems. In order to mitigate the risk associated with the non-dispatchable resources an integrated approach, such as Hybrid Energy Systems (HES), has to be taken, integrating the loads and resource management between the traditional thermal power plants and the non-dispatchable resources. As our electric energy becomes more diverse in its generation resources, the HES with its operational control system, its real-time view and its dynamic decisions making will become an essential part of the integrated energy systems and improve the overall grid reliability. The operational constraints of the energy sources on both the thermal power plants and the non-dispatchable resources in HES, plays a vital role in the planning and design stage. It is an established fact that the choice of energy source depends on the available natural resources and possible infrastructure. A critical component of decision-making depends on the complementary nature and controllability of the energy sources to supply the load demands with high reliability. Controllability of complex HES to achieve desired performance and flexibility is implemented via coordinated control systems while simultaneously generating electricity and other useful products such as useful heat or hydrogen. These systems are based on instrumentation, signal processing, control theory, and engineering system design. The entire HES along with the control systems are characterized by widely varying time constants. Hence, for a well-coordinated control and operation, we propose physics based modeling of the subsystems to assist in a dynamic and transient analysis. Dynamic and transient analysis in real and non-real time domain of the potential sources is capable of providing satisfactory results at the feasibility analysis stage. A simplified HES consisting of complementary energy sources is used to demonstrate the proposed approach. Suitable real time simulations to perform dynamic and transient analysis will be performed to verify results from the non-real time analysis. The role of such analyses, especially the real time dynamic and transient analysis for manual controls is elaborated. It is also discussed that faster than real time simulation will pave the future for controller rooms and hence manual control procedures. Multiple tools such as Real Time Simulator, Matlab®, Simulink®, PSCAD®, MOOSE, etc. are expected to be used for the HES analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Neural-based time series forecasting of loss of coolant accidents in nuclear power plants

During the last few years, deep learning in neural networks has demonstrated impressive successes in the areas of computer vision, speech and image recognition, text generation, and many others. However, sensitive engineering areas such as nuclear engineering benefited less from these efficient techniques. In this work, deep learning expert systems are utilized to model and predict time series progression of a design-basis nuclear accident, featuring a loss of coolant accident. Two major findings are accomplished in this work. First, the ability to train expert systems with high accuracy, which could help nuclear power plant operators to figure out plant responses during the accident. Second, building fast, efficient, and accurate deep models to simulate nuclear phenomena, which could be valuable to nuclear computational science. In this work, large amount of time series data is obtained from simulation tools by simulating different conditions of the base-case/nominal accident scenario. Four critical outputs/responses are monitored during the accident (e.g. temperature, pressure, break flow rate, water level). Two approaches are adopted in this work. The first approach is to use feedforward deep neural networks (DNN) to fit all time steps and outputs in a single model. The second approach is to use long short-term memory (LSTM) to fit all time steps together for each reactor response separately. Both DNN and LSTM demonstrate very good performance in predicting the test and base-case scenarios, with accuracy as low as 92% and as high as 99%, where these test scenarios are unknown to the expert systems and are not included in the model training. In addition, both approaches demonstrate a significant reduction in computational costs, as the deep expert system is able to accurately predict the accident 100,000 times faster than the original simulation tool. Given sufficient data, the methodology adopted in this study demonstrates that DNN/LSTM expert systems can be used as a decision support system to model advanced time series phenomena within nuclear power plants with high accuracy and negligible computational costs.

42 ENGINEERING↗
DOE OSTI2020

Series of semi-Markov processes to model infrastructure resilience under multihazards

Civil infrastructure systems are subjected to multiple hazards, including natural and anthropogenic, that disrupt their function or the level of service offered. Estimating the function recovery of these systems (or how soon normalcy of operations will be restored) when subjected to repeated hazard events by considering the inter-event dependencies is an important problem in multihazard infrastructure resilience. However, this problem has been less addressed in the field. This paper proposes a series of semi-Markov processes model to capture the inter-event dependencies in infrastructure recovery when subjected to successive hazard events. Recovery after each new hazard event is represented by a unique semi-Markov process that models the reduced recovery rates and the increased recovery times caused by the system’s incomplete recovery from the preceding event. Two novel formulations of the inter-event dependency modeling, namely Maximal Effects Dependency (considers the worst impact of two successive hazard events) and Cumulative Effects Dependency (considers the aggregated impacts of two successive hazard events), are proposed and discussed. The model is demonstrated by considering the following applications: Three-state system subjected to deterministic and random occurrences of identical hazard events; and Multihazard resilience of a building in Charleston, SC, considering earthquake and hurricane hazards. Results indicate that considering inter-event dependencies in recovery modeling can lead to lesser-predicted resilience, thereby affecting resilience-based decision-making.

42 ENGINEERING↗
DOE OSTI2022

Application of machine learning to evaluating and remediating models for energy and environmental engineering

Machine learning (ML) algorithms have been increasingly successful in their applications to solve energy and environmental engineering problems. ML algorithms have the advantage of being able to solve highly nonlinear issues effectively. Furthermore, considering the limited sample size of data collected in energy and environmental engineering, obtaining a ML model with reasonable accuracy is simple. Unfortunately, the vast majority of the current applications of ML algorithms lack effective screening of dominant factors and comprehensive model validation, which weakens the predictive ability of the models. The present study takes the minimum miscible pressure (MMP) of CO 2 - oil systems as an example. It establishes a systematic and robust predictive model to address this issue. Based on 147 sets of slim tube tests, the predictive models of the MMPs are investigated by application of eight ML algorithms. The paper concludes that most of the published ML models in the field of energy and environmental engineering prediction are not reliable. Furthermore, it addresses the main reasons for the poor performance of some predictive models built by ML and provides guidelines on how to make such models robust. Further, to the best of our knowledge, this is the first study to point out the defects of current ML modeling methods and propose countermeasures for their application in energy and environmental engineering problems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Analysis of Lobe Power Calculator and Indication System with Physics and Cycle Based Models

The Advanced Test Reactor (ATR) at INL measures reactor power through two methods, thermal and Nitrogen-16 (N-16) activity. Water power calculator (WPC) is a thermal power system that measures flow and temperature to determine the thermal quadrant powers. The N-16 system utilizes a beta chamber detector that outputs reactivity levels to calculate lobe power through an algorithm called lobe power calculation and indication system (LPCIS). The LPCIS utilizes the N-16 system and the WPC system to determine reactor core power levels. The WPC provides accurate calculations of quadrant and total reactor thermal power. With the use of WPC measurements, thermal-to-N-16 (T2N) power ratios are produced to determine if the two indication systems agree on core power. Relative magnitude equations are used to utilize N-16 coefficients and multipliers to improve the indications of the LPCIS. This is crucial for maintaining safety limits. Currently, the LPCIS system uses linear equations and matrices to calculate lobe power through multipliers and coefficients. Advancements in technology and system upgrades have assisted system engineers at INL with the objective to reach a more dynamic system. In return the system demonstrates an increase in the accuracy of power reading while maintaining safety margins. The new proposed coefficient and multiple method implements a cycle specific, and a physics-based model intended for changing coefficients during operation. This calibration experiment focuses on power splitting, outer shim and neck shim, as well as fuel burning into the RDAS weighting factors. Results show that the Physics learning method yields a smaller error margin inside of the desired power range for the data set 166-A. This proved true for both constrained and unconstrained testing. This is most likely due to the physics data fitting approach that resulted in favorable coefficients and multipliers for 166-A. Continuing to improve the physics-based model will help improve the power accuracy of the LPCIS system.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Theoretical Prediction of Thermal Expansion Anisotropy for Y 2 Si 2 O 7 Environmental Barrier Coatings Using a Deep Neural Network Potential and Comparison to Experiment

Environmental barrier coatings (EBCs) are an enabling technology for silicon carbide (SiC)-based ceramic matrix composites (CMCs) in extreme environments such as gas turbine engines. However, the development of new coating systems is hindered by the large design space and difficulty in predicting the properties for these materials. Density Functional Theory (DFT) has successfully been used to model and predict some thermodynamic and thermo-mechanical properties of high-temperature ceramics for EBCs, although these calculations are challenging due to their high computational costs. In this work, we use machine learning to train a deep neural network potential (DNP) for Y 2 Si 2 O 7 , which is then applied to calculate the thermodynamic and thermo-mechanical properties at near-DFT accuracy much faster and using less computational resources than DFT. We use this DNP to predict the phonon-based thermodynamic properties of Y 2 Si 2 O 7 with good agreement to DFT and experiments. We also utilize the DNP to calculate the anisotropic, lattice direction-dependent coefficients of thermal expansion (CTEs) for Y 2 Si 2 O 7 . Molecular dynamics trajectories using the DNP correctly demonstrate the accurate prediction of the anisotropy of the CTE in good agreement with the diffraction experiments. In the future, this DNP could be applied to accelerate additional property calculations for Y 2 Si 2 O 7 compared to DFT or experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Development of Integrated Thermal Fluids Modeling Capability for MSRs

The DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program supports a full range of computational thermal fluids analysis capabilities and code developments for a broad class of light-water and advanced reactor concepts. The research and development approach under thermal fluids technical area synergistically combines three length and time scales in a hierarchal multi-scale approach. To demonstrate the feasibility and capabilities of a multi-scale thermal fluids capability using these codes, a key joint effort has been pursued to develop an integrated system-and engineering-scale thermal fluids analysis capability with the MOOSE-based codes, through integration of SAM and Pronghorn, both based on the MOOSE framework. This report summarizes the progress in developing an integrated system- and engineering-scale thermal fluids analysis capability based on SAM and Pronghorn for molten salt reactors (MSRs), which gained significant interest in recently years. Two coupling approaches were studied, i.e. separate domain or domain-segregated coupling approach and the domain-overlapping approach. A series of coupled multi-physics models have been developed for a common reference molten salt fast reactor (MSFR) concept, ranging from standalone SAM system model to integrated SAM-Pronghorn-Griffin models. Both the steady state and the transient simulations are performed to the state-of-the-art simulation capabilities of NEAMS software suite in MSFR system applications. This report also covers further development and testing of the gas transport model in SAM for MSR modeling support. The presence of noncondensable gas in MSR systems would have strong impacts on fission gas removal and transport of noble metals throughout the system. Fission products removed through the off-gas system can also impact reactivity and can act as an additional point of heat removal. To ensure that the gas transport model is adequately tested, the supported modeling capabilities and features of SAM were identified, and a suite of tests were developed to test the model for each identified feature. Validation and UQ testing were also performed for the model and demonstrated that the buoyancy term, which was originally developed using non-salt/helium gas experimental data, can capture the experimental gas bubble velocity and diameter to within experimental and code uncertainty. An existing MSRE model was also modified in this work to include the gas transport model and to demonstrate the gas model behavior for realistic conditions of interest.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Reinforcement learning for adaptive maintenance policy optimization under imperfect knowledge of the system degradation model and partial observability of system states

Maintenance policy optimization usually is faced with challenges that arise from an imperfect knowledge of system degradation models and from the partial observability of system degradation states. Here, this paper proposes a reinforcement learning method to address these two challenges for a class of maintenance problems with Markov degradation processes. The reinforcement learning approach consists of a learning component and a planning component. Using sequentially collected observations, at each step of decision-making the learning component improves the knowledge of system degradation in terms of the probability distributions of the transition rates based on sequential Bayesian inference. Using the updated transition rates, at each step of decision-making the maintenance policy optimization problem is then formulated as a partially observable Markov decision problem, and the planning component computes the optimal maintenance policy that maximizes the expected cumulative reward. The proposed method is illustrated using a numerical example with repair and inspection maintenance actions. The result shows that as more observations are collected, the learning component progressively learns the true system degradation process, and the planning component adjusts the optimal maintenance policy accordingly as well, which leads to increased reward.

42 ENGINEERING↗
DOE OSTI2026

Finite-element-based simulations of electrodes for CO 2 cascade reduction reactions

The multielectron reduction of CO 2 to liquid fuels could be a path to scalable energy storage, but reaching this goal requires major advances in catalysis and systems engineering. Cascade catalysis, which couples sequential reactions without isolating intermediates, has emerged as a promising route to enhance selectivity and efficiency in CO 2 reduction (CO 2 R). In this review, we examine how finite-element-based simulations of continuum model [finite element method (FEM)] approaches are being used to analyze and guide CO 2 R cascade systems. We first outline the fundamentals of cascade catalysis and recent advances in catalytic materials (metallic, molecular, and hybrid architectures). We then focus on FEM developments at the electrode and device scales, emphasizing how these models capture transport phenomena, local microenvironments, and geometry-dependent effects. To clarify design principles, we present case studies of cascade electrodes organized in systems without and with integrated semiconductors. We further emphasize the integration of FEM with multiscale frameworks (density functional theory, molecular dynamics, kinetic Monte Carlo) and its role in bridging atomic-level insights with device-level performance. Finally, we identify current limitations and future prospects, including improved boundary conditions, coupling with operando experiments, and machine learning-accelerated model development. Together, these insights provide design principles for next-generation CO 2 R cascade systems for efficient solar fuel production.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Asymptotic analysis of the fluid flow with a pressure-dependent viscosity in a system of thin pipes

We consider the incompressible fluid with a pressure-dependent viscosity flowing through a multiple pipe system. The viscosity–pressure relation is given by the Barus law commonly used in the engineering applications. Assuming that the ratio between pipes thickness and its length is small, we propose a rigorous asymptotic approach based on the concept of the transformed pressure. As a result, we obtain new macroscopic model describing the effective behavior of the fluid in the system. In particular, the generalized version of the Kirchhoff’s law is derived giving the explicit formula for the junction pressure. The error estimate for the asymptotic approximation is also provided. Mathematical analysis presented here can be applied to a general viscosity–pressure relation satisfied by other empiric laws.

97 MATHEMATICS AND COMPUTING↗