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At least 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

2018 FEARCE Development: A Robust and Accurate Engine Modeling Software

Research and development of Fast, Easy, Accurate and Robust Continuum Engineering (FEARCE, formerly KIVA-hpFE) for turbulent reactive and multiphase flow, particularly as related to engine modeling, is relevant to the DOE Vehicle Technologies Office efforts at addressing national energy security. Less dependence on petroleum products leads to greater energy security. By Environmental Protection Agency standards, some vehicles are now reaching 42–50 mpg mark. These are conventional gasoline engines. With continued investment and research into new technical innovations, the potential exists to save more than 4 million barrels of oil per day or approximately 200 to 400 dollars million per day. This would be a significant decrease in emissions and use of petroleum and a very large stimulus to the U.S. economy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

SBIR Phase II Final Report - A Robust Wireless Communication System for Harsh Environments Including Nuclear Facilities

This is the Final Report for the DOE Phase II SBIR project “A Robust Wireless Communication System for Harsh Environments Including Nuclear Facilities.” The topics covered herein include technical progress made, progress against the planned milestones and deliverables, project outcomes, and a discussion on future expectations of deployment and impacts of the results of this work. In brief, all planned work for the project was successfully completed, on or ahead of schedule and on budget. The major accomplishment was the successful development of new sensor/communications systems with wideband (WB)/ultra-wideband (UWB) communications based on the software-defined-radio (SDR) technology to improve sensing and control in nuclear power plants and then a broad range of similar sensing applications. The impacts of these disciplines/focuses of this SBIR include: A new multi-frequency SDR-based architecture with WB/UWB communications integrated with sensors was developed, utilizing 310 MHz, 450 MHz, 915 MHz, and 5 GHz frequencies; Secure communications using Advanced Encryption Standard (AES) algorithm; Sensing possibilities not effectively addressed by previous technologies was addressed; and, The newly developed wireless sensor communications system can adapt its communications parameters to its operational environment for optimal performance. The performance of this system has been evaluated by systematic laboratory and field testing. The field testing was conducted at Massachusetts Institute of Technology (MIT)’s Nuclear Test Reactor, and the McClellan Nuclear center at the University of California Davis (UC Davis), as well as at Oak Ridge National Laboratory (ORNL). The success in this project resulted in DSI being awarded “The DOE’s 2018 SBIR/STTR Small Business of the Year”. This technology has attracted interest from The Rolls Royce company with ongoing discussions under NDA for potential strategic partnership between DSI and Rolls Royce in providing wireless sensor communications in nuclear facilities. Furthermore, the success of this project has attracted significant interest from the United Nations International Atomic Agency (IAEA) in Vienna, Austria, as well as other DOE and DOD offices. Currently, per request from IAEA, DOE’s office of International Safeguards has funded DSI to develop a WB/UWB wireless communication system for real-time transmission of images from IAEA’s safeguards cameras in various nuclear facilities. DSI also has received a Phase II and Phase II.5 SBIR awards from ARMY and NAVY for wireless communications capability for various DOD aircraft.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Fast and Robust Linear Solvers based on Hierarchical Matrices (LDRD Final Report)

This report is the final report for the LDRD project "Fast and Robust Linear Solvers using Hierarchical Matrices". The project was a success. We developed two novel algorithms for solving sparse linear systems. We demonstrated their effectiveness on ill-conditioned linear systems from ice sheet simulations. We showed that in many cases, we can obtain near-linear scaling. We believe this approach has strong potential for difficult linear systems and should be considered for other Sandia and DOE applications. We also report on some related research activities in dense solvers and randomized linear algebra.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Development of Low Cost, Robust and Durable Cathode Materials to Support Solid Oxide Fuel Cell Commercialization (Final Technical Report)

The project is aimed at addressing critical issues related to chromia-poisoning in the cathode of solid oxide fuel cells. A technical hurdle to the commercialization of solid oxide fuel cells is that chromia sources from the metallic interconnect and the balance of plant (BoP) components will cause chromia-poisoning to the cathode performance. This presents a significant challenge since it is very difficult to eliminate chromia sources in the cathode environment. One aspect of the current project is to evaluate whether SFM (Sr 2 Fe 1.5 Mo 0.5 O 6-δ ) that we have recently developed as redox-stable mixed conducting oxide possesses tolerance to chromia-poisoning. The second focus of this project is to evaluate coating on the cathode materials to mitigate chromia poisoning effect. Both thermodynamic evaluation and experimental approaches have been applied to enhance the knowledge base and fundamental understanding of chromia-tolerance of cathode for solid oxide fuel cells. low cost, robust and durable cathode material to support SOFC commercialization.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2019

Improving Limited Angle CT Reconstruction with a Robust GAN Prior

Limited angle CT reconstruction is an under-determined linear inverse problem that requires appropriate regularization techniques to be solved. In this work we study how pre-trained generative adversarial networks (GANs) can be used to clean noisy, highly artifact laden reconstructions from conventional techniques, by effectively projecting onto the inferred image manifold. In particular, we use a robust version of the popularly used GAN prior for inverse problems, based on a recent technique called corruption mimicking, that significantly improves the reconstruction quality. The proposed approach operates in the image space directly, as a result of which it does not need to be trained or require access to the measurement model, is scanner agnostic, and can work over a wide range of sensing scenarios.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Fundamental Science and Technology of Flash Processing Robustness for Advanced High Strength Steels (AHSS)

“Flash processed” low carbon steel sheets were characterized using in-situ IR imaging, ex-situ dome deformation testing and microscopy. Due to flash processing set-up, cooling water flow and steam interfered with robust infrared temperature measurements. Though preliminary, rapidly heating the steel sheet to greater than 1170°C and then water quenching a few seconds later can result in through thickness microstructural gradients, where the center is harder than the exterior surfaces. This microstructural configuration sometimes results in a dome displacement ~75% relative to that observed in the more ductile as-received material. The presence of aluminum nitride (AlN) inclusions was confirmed, a likely byproduct from a de-oxidization reaction, and may reduce dome displacement (similar to strain to failure) significantly. Based on thermodynamic calculations, a high flash processing temperature (>1170°C) is recommended to dissolve these AlN inclusions in the austenite phase.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Robustness of Asymptotic Optimal Accelerated Test Plans to Small-Sample Settings

Accelerated testing is a form of testing commonly employed in industry, especially at Sandia National Laboratories, in order to extract information about product lifetime behavior that cannot be acquired under normal operating conditions due to the extensive durations of the intended life- times. As of the writing of this report, there is no known guideline for the design of accelerated tests at the laboratories and yet there is an entire field in the statistical literature on the optimal design of accelerated tests covering a wide range of limitations and constraints available for reference. A potential roadblock to incorporating this knowledge is the common difficulty at Sandia of having severely limited data available for such testing. Nearly all of the methodology presented in the statistical literature is based on asymptotic or large-sample theory and so may not be appropriate for the testing conditions present at Sandia. This report investigates optimal accelerated test plans derived based on small-sample or exact methodology for both a basic test setting as well as a more realistic setting and compares the resulting test plans to those derived based on the large- sample methodology from the literature. It is shown that the large-sample test plans are actually quite robust to the presence of limited data and are generally more flexible than the small-sample test plans.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Robust and Spectrally Selective Aerogels for Solar Receivers

The development of next generation concentrated solar thermal (CST) plants requires solar receivers to reach high temperatures (> 600°C) while minimizing costs. Efficiently generating such high temperatures using a line-focusing collector and an air-stable receiver has the potential to change the current CSP paradigm because it would: (1) result in large improvements in system-level efficiency of line-focusing systems by enabling the use of high-efficiency supercritical CO 2 power cycles; (2) decrease the LCOE of line-focusing systems to meet SETO 2030 goals; (3) maintain performance over the lifetime of the CSP plant by avoiding issues related to breakdown of vacuum. The use of aerogels as transparent insulating materials (TIMs) in solar thermal receivers has the potential to enable such improvements. Specifically, aerogels can be placed in front of a black (or partially selective) high-temperature absorber to allow sunlight to transmit to the absorber but block heat from escaping. These materials can improve the efficiency and simplify the complexity of thermal transport systems by enabling operation at moderate or no vacuum levels. These potential benefits have been explicitly identified by SETO as ‘high impact’. The use of mesoporous silica as a TIM has already led to large improvements in solar collection efficiency. Nevertheless, these materials inherently lose structural integrity and the ability to suppress radiation losses at high temperatures. Here, we address these challenges by leveraging 1-cycle atomic layer deposition of aluminum oxide onto a silica aerogel to form a thermally stable interfacial layer that is transparent to sunlight but broadly absorbs in the mid-infrared. This interfacial absorption results in a two-fold reduction in measured heat losses from an absorber at 700°C and solar thermal efficiency of 81% at 700°C under 60 Suns, based on measurements of heat loss and solar transmittance. Extended-duration heat treatment reveals that the multicomponent aerogel is stable at high temperatures relative to a silica aerogel. Furthermore, we experimentally demonstrate the concept of using infrared plasmon resonances to selectively enhance the thermal absorption coefficient of TIMs in order to strongly suppress thermal radiative losses at high temperatures. We term this mechanism plasmon-enhanced greenhouse selectivity (PEGS). Unlike silica aerogels, where much of the IR absorption is lost at high temperatures, local surface plasmon resonances (LSPRs) in doped oxide nanoparticles overlap a significant portion of the blackbody spectrum and are maintained at high temperatures. Overall, this work paves the way for next-generation solar thermal energy using thermally robust transparent insulating materials.

14 SOLAR ENERGY↗
DOE OSTI2021

Robust Molecular Predictive Methods for Novel Polymer Discovery and Applications

Polymeric materials are ubiquitous in modern society and they play an instrumental role in almost all industries, undoubtedly including the energy and environment sectors. Increased demand of energy and awareness to sustainability both necessitates the development of novel polymers with enhanced properties. Unfortunately, their structural and behavioral complexity render such discovery challenging and impeded. To address this problem, scientists are developing various computational modeling techniques and leveraging their power to depict the relationship between structural characteristics of polymers and their properties (such as rheological behaviors), and use such prediction to guide the design and syntheses of novel polymeric materials with enhanced performances. Unfortunately, predicting the relationships between polymer structure and composition with rheological properties via atomistic modeling is still a major challenge because of the extended time and length scales involved. Studying dynamic shear viscosity and linear viscoelasticity using molecular models requires capabilities that have been elusive, including representation of large molecular weight chains with an effective internal scale capable of describing entanglement, shear-rates that are in the s-1 scale with accurate quantitative stresses, and chemically-realistic combinations of both homogeneous and heterogeneous systems. Motivated by these unmet challenges, the overall technical objective of this DOE-STTR Phase II project is to develop robust molecular predictive methods for advanced polymer discovery and applications and especially for designing and demonstrating the “smart” polymer-based waterflooding enhanced oil recovery (EOR) process. In particular, we apply state-of-the-art molecular modeling methods developed by our academic partner, Materials Stimulation Center (MSC) at California Institute of Technology (Caltech), to facilitate and accelerate the experimental discovery processes. During the Phase I of this project, we had focused on development and demonstration of the molecular modeling methods to describe rheological properties of non-Newtonian polymer fluids, and to improve our fundamental understandings of shear-thickening mechanism and kinetics. In Phase II, we further apply the theoretical models to guide our experimental programs to improve our design of smart rheology modifier (SRM) polymers and their optimization for EOR. Specifically, we have three objectives in the Phase II study: (1) to further improve out computational modeling methods, coupling with the advanced machine learning algorithms; (2) to develop cost-effective and efficient SRM-flooding process suitable for EOR applications under typical reservoir conditions; and (3) to further explore the application of our molecular predictive models for innovative material discovery in other industrial applications. The recent development of our multiscale predictive framework allows the successful prediction of rheological properties from the chemical structure for polymers of experimentally relevant molecular weights, and provides an in-silico machine learning engine for screening novel compositions and structures with optimized non-Newtonian response, required for both shear-thinning and shear-thickening applications. Our framework provides: (1) procedures and tools for systematic coarsening from atomistic models and reverse mapping of coarse-grain models to atomistic, (2) unique ab initio methods to characterize the atomistic origin of colloidal and interfacial interactions and phenomena, (3) systematic structure and composition builders based on practical descriptors that drive rheological changes in polymer melts and diluted polymer mixtures, (4) a rheological properties engine capable of predicting viscosity in the zero-shear limit and under realistic dynamic conditions (for shear-rates commensurate with experiments) for large heterogeneous systems, (5) coarse-grain force fields with improved non-bond descriptions based on accurate quantum mechanics, (6) an in-silico screening machine learning engine that feeds from the systematic model builders to cover the descriptors search space, computes the rheological properties from converged trajectories spanning sub-milliseconds and ranks them for each structure/composition using an automated viscosity-vs-shear rate fitness function that can be tuned for shear-thickening, shear-thinning and other rheological responses.

02 PETROLEUM↗
DOE OSTI2020

Robust and Spectrally Selective Aerogels for Solar Receivers (Final Technical Report)

The development of next generation concentrated solar thermal (CST) plants requires solar receivers to reach high temperatures (> 600°C) while minimizing costs. Efficiently generating such high temperatures using a line-focusing collector and an air-stable receiver has the potential to change the current CSP paradigm because it would: (1) result in large improvements in system-level efficiency of line-focusing systems by enabling the use of high-efficiency supercritical CO2 power cycles; (2) decrease the LCOE of line-focusing systems to meet SETO 2030 goals; (3) maintain performance over the lifetime of the CSP plant by avoiding issues related to breakdown of vacuum. The use of aerogels as transparent insulating materials (TIMs) in solar thermal receivers has the potential to enable such improvements. Specifically, aerogels can be placed in front of a black (or partially selective) high-temperature absorber to allow sunlight to transmit to the absorber but block heat from escaping. These materials can improve the efficiency and simplify the complexity of thermal transport systems by enabling operation at moderate or no vacuum levels. These potential benefits have been explicitly identified by SETO as ‘high impact’. The use of mesoporous silica as a TIM has already led to large improvements in solar collection efficiency. Nevertheless, these materials inherently lose structural integrity and the ability to suppress radiation losses at high temperatures. Here, we address these challenges by leveraging 1-cycle atomic layer deposition of aluminum oxide onto a silica aerogel to form a thermally stable interfacial layer that is transparent to sunlight but broadly absorbs in the mid-infrared. This interfacial absorption results in a two-fold reduction in measured heat losses from an absorber at 700°C and solar thermal efficiency of 81% at 700°C under 60 Suns, based on measurements of heat loss and solar transmittance. Extended-duration heat treatment reveals that the multicomponent aerogel is stable at high temperatures relative to a silica aerogel. Furthermore, we experimentally demonstrate the concept of using infrared plasmon resonances to selectively enhance the thermal absorption coefficient of TIMs in order to strongly suppress thermal radiative losses at high temperatures. We term this mechanism plasmon-enhanced greenhouse selectivity (PEGS). Unlike silica aerogels, where much of the IR absorption is lost at high temperatures, local surface plasmon resonances (LSPRs) in doped oxide nanoparticles overlap a significant portion of the blackbody spectrum and are maintained at high temperatures. Overall, this work paves the way for next-generation solar thermal energy using thermally robust transparent insulating materials.

14 SOLAR ENERGY↗
DOE OSTI2021

Metal-Supported Ceria Electrolyte-based SOFC Stack for Scalable, Low-Cost, High-Efficiency and Robust Stationary Power Systems (Final Report)

This is the end of project report for the DOE Project DE-FE-0027844 “Metal-Supported Ceria Electrolyte-based SOFC Stack for Scalable, Low-Cost, High-Efficiency and Robust Stationary Power Systems”. The objectives of the project were to develop several technologies required for the mass adoption of Solid Oxide Fuel Cell systems for medium to large power generation applications. The key achievements of this project were: 1) Development of fuel cells with complete internal fuel reforming capability. 2) Fuel cell power scale up with production of cells with larger active areas. 3) Assembly of an integrated 5 kW modular stack platform capable of installation into medium to large power systems. 4) Demonstration of a 10 kW Fuel Cell Power System utilizing the integrated 5 kW modular stack platform. 5) Cost modelling to show system cost of $1,500/kW are achievable at production volumes. 6) Development of cathode air poison getters for reduced degradation and extended fuel cell life. 7) Assessment of fuel poison on anode degradation, particularly poisons frequently produced from the process of gasification of coal. The Cummins led Voice of Customer (VOC) work identified several significant markets for natural gas powered fuel cell systems, where the end customer is interested in reducing energy consumption and emissions, or increasing reliability of electrical supply. Data centers and truck Auxiliary Power Units (APU) were highlighted as 2 key future markets for fuel cell systems.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

A robust and efficient generalized real cascade model

This paper describes a new separation cascade model that allows for arbitrary cascade connectivity and includes the effects of mixing, material losses and generalized separation element performance. The model also accommodates mixtures with an arbitrary number of component species. The mathematical and numerical framework of this model includes both time-dependent and steady state cascade simulation. Robust numerical approximations of the continuous mathematical model are accomplished using the Sundials suite of nonlinear algebraic and differential-algebraic equation solvers. Verification of the model is accomplished using manufactured solutions while validation is demonstrated by comparison to published experimental results and benchmarks.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Multi-Stage and Multi-Timescale Robust Co-Optimization Planning for Reliable and Sustainable Power Systems. Final Report

In this project, Clarkson University, in collaboration with Southern Methodist University and University of Pittsburgh, conducted the research to model, design, and implement a sophisticated generation and transmission co-optimization planning decision tool, called Multi-stage and Multi-timescale robust Co-Optimization Planning (MMCOP). The MMCOP decision tool intends to facilitate generation and transmission co-optimization planning of emerging power systems, while mitigating risks and uncertainties in both short-term operation dynamics and long-term policy and technology changes. Long-term power system planning aims at optimizing asset utilization by investing in a proper mix of various generation technologies and transmission lines to supply the future load growth. In particular, the Clean Power Plan (CPP), which is designed to combat climate change and reduce carbon emissions by setting a national limit on carbon pollution from power plants, may dramatically change the landscape of the power industry by further promoting clean energy and phasing out emissions-intensive generation technologies. In addition, novel non-wire alternatives (e.g., demand response (DR), distributed generation (DG), energy efficiency (EE), and smart grid technologies) and the computational complexity for large-scale systems significantly complicate the system planning procedure even further. However, existing conventional planning approaches neglect short-term variability and uncertainty of renewable energy, hourly chronological operation details, and physical nonlinear characteristics of the alternating current transmission network. As a result, existing conventional planning approaches may not work properly, and power systems reliability could be in jeopardy. In observing the limitations of existing conventional planning approaches and addressing new challenges of emerging power systems, the main scope of this project to develop co-optimization planning models within a multi-stage and multi-timescale framework. In particular, random contingencies, key uncertainty factors, and AC power flows are included to derive expansion plans while considering both long-term reliability and short-term flexibility.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Robust Learning of Dynamic Interactions for Enhancing Power System Resilience (Final Scientific Report)

The overall goal of the project is to leverage robust graphical learning and phase measurement unit (PMU) data to learn the dynamic interactions of electrical grid components in order to improve the power system resilience. Specifically, the learned dynamic interaction graphs are utilized to detect known and unknown anomalous patterns in power systems, identify high-risk operational conditions, and enable risk-based cascading mitigation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Red-Ox Robust SOFC Stacks for Affordable, Reliable Distributed Generation Power Systems

While SOFC systems are expected to operate reliably and with limited degradation in steady state or transient performance, SOFC stacks may ultimately fail due to the loss of structural integrity of one or several of the cells as a result of the weakening of the materials and interfaces due to physico-chemical changes that occur during continuous operation as a result of plastic and creep deformations, modification of the temperature profile, and/or degradation of the electrochemical performance of the cells. Degradation mechanisms originating from the cell components include coarsening of the microstructure over time; decomposition of materials; chemical reaction of electrode materials with electrolyte at the interface; delamination from each other; and for the anode, coking and sulfur poisoning. Of all the reliability issues that may occur for SOFCs, the main limitation for Ni-based cermet anodes (e.g., NiO-YSZ) is the poor stability during reduction-oxidation (red-ox) cycling. This project was aimed at the development of ceramic anode SOFCs based on SFCM (SrFe0.2Co0.4Mo0.4O3), which is a conductive perovskite that is red-ox stable. Additionally, the project involved the development of a red-ox robust stack. Scale up of SFCM-based cells to a large format (10 cm by 10 cm) cell size was achieved as well as a maximum power density of 0.9 W/cm 2 at 600 °C (>0.6 W/cm 2 at 0.6 V). Up to a 10-cell stack was successfully assembled and demonstrated, and cells showed similar performance in reformed, pipeline natural gas as in hydrogen. Finally, a 3-cell stack was red-ox cycled without degradation for 40 cycles at ~600 °C.

03 NATURAL GAS↗
DOE OSTI2023

Robust highly durable solid oxide fuel cell cathodes – Improved materials compatibility & self-regulating surface chemistry

Solid oxide fuel cells (SOFCs) are electrochemical conversion devices that directly transform hydrogen or hydrocarbon fuels to electricity, with energy efficiencies as high as 90%, coupled with reduced emissions. Several factors, however, remain to be addressed when considering scale-up of SOFC technology, including the need to overcome decreased performance due to sluggish rates of the oxygen reduction reaction (ORR) at the cathode under reduced temperatures and susceptibility to degradation in performance from surface poisoning e.g. from chromia, while limiting the use of critical raw materials (lanthanides and transition metals) present in high performing mixed ionic electronic conducting electrodes like (La,Sr)CoO 3 (LSC). In this project we explored the key descriptors for determining ORR activity in SOFC electrodes and tried to recover performance degradation by applying them to SOFC electrodes. In order to do this, we first selected a model mixed ionic electronic conducting (MIEC) oxide, Pr-doped CeO 2 (Pr 0.1 Ce 0.9 O 2-δ , PCO), which is a chemically stable fluorite and free of inherent poison sources (e.g. Sr segregation in LSC) that potentially react with external impurities such as Cr-species vaporized from the interconnect. The three approaches originally planned in this project are as follows: 1) evaluation of scavenger exsolution characteristics, 2) study of scavengers gettering efficacy following Cr and Si poisoning and 3) integration of new compositions into porous electrodes. Among them, exceptional progress has been made in 2) and 3), especially understanding the role of surface infiltrants in impacting electrode performance and degradation of PCO materials. We found that the Smith acidity scale for binary oxides serves as a powerful descriptor for tuning and predicting the oxygen exchange kinetics on MIEC PCO surfaces. As a result, with infiltration with binary oxides, ranging from strongly basic (Li 2 O) to strongly acidic (SiO 2 ) onto the surface of porous PCO, it was possible to systematically vary the chemical surface exchange coefficient (k chem ) by 6 orders of magnitude! L i2 O increased k chem by nearly 1,000 times over that of pristine PCO, while SiO 2 decreased k chem by nearly the same factor. Strikingly, although the pre-exponential of k chem scales linearly with the acidity of the infiltrated binary oxide, there is nearly no change in the activation energy. With this insight, we attributed the origin of these dramatic changes in k chem values to the systematic increase and decrease in the surface electron density induced by infiltrated binary oxides. More interestingly, although both Cr 2 O 3 and SiO 2 were determined to be acidic by Smith, suggesting that this feature could likely be the primary reason that these compounds serve to poison the ORR on SOFC cathodes, the effect of poisoning could be subsequently tuned by adding multiple infiltrants and controlling their relative surface acidities. We also systematically examined the effect of serial infiltration of basic and acidic oxides. It turned out that serial infiltration of Li not only recovers approximately 20-fold degraded k chem of PCO by acidic Cr 2 O 3 but its k chem is enhanced even beyond that of the non-infiltrated PCO by more than three orders of magnitude. This was further verified with a screen-printing PCO symmetric cell in terms of the electrode performance (area-specific resistance, ASR) related to approach 3). These observations point to acidity as a key descriptor not only in tuning and predicting the ORR activity of SOFC cathodes that largely determines the overall performance of SOFC, but in mitigating and reactivating poisoned electrode performance. This work provides novel guidelines for making the electrode performance much more active and robust in SOFCs, which can further be applied to all applications requiring oxygen exchange reaction, such as electrolyzers, permeation membranes and gas sensors.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Robust Combined Heat and Hybrid Power (CHHP) for High Electrical Efficiency Cogeneration

Georgia Tech (Prime Recipient), the University of Texas at El Paso (UTEP) and the National Energy Technology Laboratory (NETL) investigated a hybrid fuel cell/ gas turbine system concept as a combined heat and hybrid power (CHHP) system for both robust and high power-to-process heat ratio cogeneration. The novelty of the proposed system entailed the distinct, elevated electrical efficiencies it maintains while simultaneously supporting a broad span of heating needs (e.g., supply temperatures) demanded across variable heat loads. The scope included: 1) leveraging a pioneering national lab facility configured for dynamic system operability development of hybrid fuel cell/gas turbine cycles; 2) enabling technology development to adjust and modulate the quality and quantity of thermal supply to bottoming heat loads via novel extreme temperature gas bypass valves. Hybrid fuel cell/gas turbine systems have primarily been reduced-to-practice in a constrained (e.g., initial proof-of-concept) manner and have still demonstrated considerable electrical efficiencies. However, these pre-pilot systems have focused upon electrical efficiencies and electrical power generation as the exclusive energy demand. Such hybrid systems had not been extensively researched or developed for flexible and variable operation consisting of both power and heat demands; however, these variable combined power and heat demands are characteristic of many types of manufacturers such as animal/poultry processing, bakeries and milk/flour/pastry manufacturing, textile mills, and electrochemical processing. Commercially, developing the system into a working combined heat and power system benefits these types of manufacturers by allowing them to meet their power and heat demands at a lower cost, higher efficiency, and/or through onsite generation. Therefore, the technical scope of this project was largely to study and facilitate these hybrid systems as combined heat and hybrid power (CHHP) systems that include dynamic operability for variable heat and power loads and/or grid dynamics for various types of manufacturers. Simulation results were used to predict the performance of the CHHP system and conceptually develop it to achieve desired dynamic operability. Experimentally, the primary goal was to design, manufacture, and experiment upon a high-temperature bypass valve. Experimental data included air mass flow rates through the valve orifice when the valve was changed to variable extent between fully closed and fully open. The experimental data was then used to create a semi-empirical computational model of the bypass valve. Concluded simulation goals for the research included developing computational heat exchanger models for the hybrid system inclusive of the bottoming heat exchanger and the recuperative heat exchanger, and then combining the computational recuperator model with the computational valve model. Afterwards, the computational models were then integrated to predict the dynamic operation of hybrid fuel cell/gas turbine cycles throughout a design space and reporting such. The scope stated in the preceding paragraph was packaged into five specific goals: 1) enabling the simulation of dynamic combined heat and power through the creation of computational, modular heat exchanger models; 2) simulation and exploration of the CHHP system’s performance by integrating the heat exchanger models with the national lab’s pre-existing hybrid system (computational) simulation, but without the recuperator bypass valve concept in order to initially determine how the (baseline) system behaves and can be controlled in order to meet variable heat and power demands; 3) development and initial deployment of the high-temperature recuperator bypass valve technology in order to confirm and characterize the approach; 4) usage of the experimental data for the valve to create a semi-empirical computational model for the bypass valve which could then be combined with the heat exchanger computational models; 5) repeat of the second task of simulating and exploring the system’s performance, but this time including the bypass valve to resolve its efficacy. Tasks were successfully completed, and the general notion of flexibly operating, high electrical efficiency CHHP was further corroborated. Supportive details are provided in the report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗