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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The importance of cycle-by-cycle data in performing rapid battery technology development and validation

Lithium-ion battery (LiB) technology is playing a crucial role in transforming the predominantly fossil fuel-based transportation and stationary storage sectors to achieve a low-carbon economy. Rapid innovation in the LiB materials to electrode to cell design is happening to satisfy the performance, life, and safety metrics required by those myriads of applications. Lately, advanced analytics, such as machine-learning or artificial intelligence (ML/AI) techniques, are being used more frequently to aid in expedited LiB technology development, performance validation, and life prediction. The success of these techniques often relies on a large volume of well-defined and high-quality battery test data. On the other hand, most battery developers and research and development (R&D) communities are still following a classical approach to develop batteries, which is running calendar- and/or cycle-aging tests, performing reference performance tests (RPTs), and conducting post-mortem analyses periodically without paying attention to the wealth of data often not collected during the calendar or cycle life aging tests. This sparse data collection approach is time- and resource-intensive, requiring data capture and evaluation of months to years of RPT data to diagnose accurate battery state of performance, health, and safety. Even so, the underlying aging modes and mechanisms can be missed. If collected properly, battery test data during cycling or calendaring can be efficiently combined with ML/AI techniques to create powerful tools in the rapid diagnosis of battery state of performance, health, and safety along with insights into underlying aging modes and mechanisms. In this report, we discuss the importance of effective cycle-by-cycle (CBC) data collection with example case studies. Within a reasonable timeframe, RPT data are often inadequate in capturing many of the crucial battery aging dynamics, which often predominantly show up in CBC test data. Finally, we also show examples of ML/AI techniques that use CBC data in rapid diagnosis and projection of LiB state of health (SOH) to motivate the scientific community in collecting and using CBC data to facilitate expeditious technology development and validation.

25 ENERGY STORAGE↗
DOE OSTI2024

Steady-state fuel performance analyses for the preliminary fuel concept of general atomics fast modular reactor

Here this manuscript presents the fuel performance analysis results of the General Atomics Fast Modular Reactor (FMR) based on an axi-symmetric (2D-RZ) geometry. Three fuel performance model sets that fit the FMR fuel specifications best, i.e., a BISON baseline model set, a BISON diffusion enhancement model set, and a BISON-FASTGRASS model set, were identified and evaluated against a series of relevant experimental cases featuring high burnup and low irradiation temperature conditions. The three BISON-based model sets were then utilized to conduct a comprehensive fuel performance analysis of the FMR fuel under normal operation including the shutdown/restarting periods for refueling. The evaluation of the fuel performance parameters, represented by temperature, internal pressure, stress, and strain, shows that the FMR fuel maintains its thermal and mechanical integrity during normal operation. Technology gaps and limitations are also discussed to guide future efforts for extending the performance analysis to transient scenarios as well as improving the fuel performance evaluation through experiments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Evaluating the effect of membrane-ionomer combinations and supporting electrolytes on the performance of cobalt nanoparticle anodes in anion exchange membrane electrolyzers

In low temperature polymer electrolyte membrane electrolyzers, component choices and membrane electrode assembly (MEA) fabrication have pivotal roles in determining cell performance. Here, cobalt anodes were used in combination with different ionomers, membranes, and supporting electrolytes to delineate their effect on MEA performance in anion exchange membrane (AEM) based electrolyzers. Changes to the ionomer type suppress or enhance catalyst redox and were found to alter the resulting exchange current densities and Tafel slopes. Changes in membrane conductivity account for large differences in performance and overpotential losses among the different MEAs, with higher conductivity minimizing ohmic losses. Within supporting electrolytes, lower concentration results in lower conductivity and lower MEA performance; the performance, however, is higher in potassium hydroxide than bicarbonate. These experiments address the influence of individual components and their integration on cell-level performance, to help develop a pathway to an electrolyte free system while providing a foundation for studying electrode composition and component incorporation into MEAs.

25 ENERGY STORAGE↗
DOE OSTI2026

Power performance and loads characterization of laboratory-scale cross-flow rotors fabricated using additive manufacturing

Tidal energy conversion is a relatively new application for additive manufacturing (AM), where the focus has been on fabricating axial-flow turbine blades. AM techniques add material precisely where it is needed, creating more complex shapes with less waste. Cross-flow rotor geometry presents an opportunity for AM to improve rotor performance by fabricating features that cannot be created economically via conventional manufacturing. The challenges associated with using AM in cross-flow design include water resistance and degradation over time while retaining a level of quality equivalent to conventionally machined parts. In this work, AM materials were tested by environmentally conditioning samples in a seawater tank for 5 months, followed by performance and phase-resolved load testing of laboratory-scale rotors in a hydraulic flume. We found that while metals like titanium and Inconel have excellent performance in marine environments, achieving the desired geometry and performance is difficult. Thermoplastics degraded in seawater but were easier to form into desired geometries and could exceed the performance of an aluminum control rotor. Warping and surface finish were significant detractors from AM rotor performance. These results suggest that the primary benefits of using AM for cross-flow rotors is to quickly fabricate and test unconventional rotor geometries.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Evidence of Zintl Intermediate Phase and Its Impacts on Li and Na Storage Performance of Pb-Based Alloying Anodes

Anode materials based on conversion and alloying reactions are promising to achieve high energy density of advanced sodium-ion batteries (SIBs). While the chemical similarities between sodium and lithium as alkali elements make the benchmarking strategy practical in developing new high-performance anodes, simply borrowing the anode material from one system to the other does not always guarantee success unless it is based on sound understanding of both Li- and Na-reaction mechanisms. In this work, we report the Na storage performance of a Pb-based anode and its fundamental reaction dynamics. In contrast to its excellent electrochemical performances in Li cells (reversible ~600 mAh/g), the newly developed Pb@PbO–C nanocomposite anode has limited electrochemical Na reaction properties showing moderate capacity and rate performances (~300 mAh/g at 20 mA/g). Synchrotron-based X-ray diffraction and absorption spectroscopy studies reveal the fundamental differences in the Na and Li reaction mechanism of the Pb-based anode. Further, unlike Li reaction, the unique Na reaction mechanism involves the formation of a highly ionic NaPb Zintl phase, which comprises tetrahedral Pb 4 clusters, as an intermediate phase. The strong covalent character of the Pb 4 Zintl clusters adversely affects the electronic conductivity and thus limits the electrochemical performance of the Pb-based anode in Na cells. These findings provide new insights applicable to developing high-performance alloying anode materials.

25 ENERGY STORAGE↗
DOE OSTI2023

Enhanced Electrochemical Performance of Disordered Rocksalt Cathodes Enabled by a Graphite Conductive Additive

Cobalt-free cation-disordered rocksalt (DRX) cathodes are a promising class of materials for next-generation Li-ion batteries. Although they have high theoretical specific capacities (>300 mA h/g) and moderate operating voltages (~3.5 V vs Li/Li + ), DRX cathodes typically require a high carbon content (up to 30 wt %) to fully utilize the active material which has a detrimental impact on cell-level energy density. To assess pathways to reduce the electrode’s carbon content, the present study investigates how the carbon’s microstructure and loading (10–20 wt %) influence the performance of DRX cathodes with the nominal composition Li 1.2 Mn 0.5 Ti 0.3 O 1.9 F 0.1 . While electrodes prepared with conventional disordered carbon additives (C65 and ketjenblack) exhibit rapid capacity fade due to an unstable cathode/electrolyte interface, DRX cathodes containing 10 wt % graphite show superior cycling performance (e.g., reversible capacities ~260 mA h/g with 85% capacity retention after 50 cycles) and rate capability (~135 mA h/g at 1000 mA/g). Furthermore, a suite of characterization tools was employed to evaluate the performance differences among these composite electrodes. Overall, these results indicate that the superior performance of the graphite-based cathodes is largely attributed to the: (i) formation of a uniform graphitic coating on DRX particles which protects the surface from parasitic reactions at high states of charge and (ii) homogeneous dispersion of the active material and carbon throughout the composite cathode which provides a robust electronically conductive network that can withstand repeated charge–discharge cycles. Overall, this study provides key scientific insights on how the carbon microstructure and electrode processing influence the performance of DRX cathodes. Based on these results, exploration of alternative routes to apply graphitic coatings is recommended to further optimize the material performance.

25 ENERGY STORAGE↗
DOE OSTI2023

Effect of Interlayer Time-Lapse and Workability Retention on Printed Concrete Performance

The rapid advancement of additive manufacturing technologies for concrete construction requires further understanding of material performance and its relationship with print parameters such as interlayer time-lapse and workability retention during the print. Here this study addresses the effects of the time delay between consecutive extruded layers on the mechanical performance of printed concrete using a mix designed by Quikrete. A series of specimens were prepared with printing delays of 10, 15, 30, and 60 min. This work correlates the change in fracture properties, mechanical performance, and workability retention with the interlayer time-lapse between two consecutive concrete layers. Interlayer time-lapse is defined as the time difference in printing of two consequent layers of concrete. It affects individual properties differently based on the loading direction and test types, but it is consistent in terms of interlayer performance. The understanding of the effect of interlayer time-lapse on the fracture properties between two consecutive printed layers fills the knowledge gaps in printed concrete research. Therefore, this study can help researchers perform required optimization and safely estimate the performance of printed concrete.

3D printing↗
DOE OSTI2024

Development and performance evaluation of active insulation systems using solid-state thermal switches

Traditional building envelopes have passive insulation systems that cannot respond to dynamic changes in the environment. An Active Insulation System (AIS) consists of Active Insulation Materials (AIMs) that dynamically vary the thermal conductivity of the insulation system. Several researchers have evaluated the impact of AIS on building thermal and energy performance by using simulation tools. Up to 70% savings in annual heating and cooling energy and significant reductions in peak demand have been predicted for some climates with wall systems employing AIS. However, materials and assembly development still need a cost-effective product that achieves the required performance. Here, in this study, we present the process of developing an AIS that we will install in a test hut for its performance evaluation. Minimum performance criteria of the AIS system are developed based on R-low/R-high ratio, required time and efficiency to switch states, and cost estimates. The following steps during this study are creating the concept to meet the requirements, predicting the performance via simulations, developing the experimental setup for bench-scale testing, and finally, constructing a full-scale wall assembly and monitoring the performance when exposed to environmental chamber tests. The selected approach uses off-the-shelf products to create an AIS that can switch R-value between 0.98 ft 2 ·°F·h/BTU (0.173 m 2 ·K/W) and 5.81 ft 2 ·°F·h/BTU (1.02 m 2 ·K/W) and have a switching time of less than one minute between R-high and R-low.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Balancing performance of active magnetic regenerators: a comprehensive experimental study of aspect ratio, particle size, and operating conditions

Abstract Effective and, at the same time, efficient active magnetic regenerator (AMR) performance requires balanced geometry and operating conditions. Here the influence of regenerator shape, magnetocaloric material size, operating frequency, and utilization on the performance of gadolinium packed-particle bed AMRs is demonstrated experimentally. Various metrics are applied to assess effectiveness and efficiency. Observed temperature spans and cooling powers across a wide range of operating conditions are used to evaluate system performance and estimate exergetic cooling power and exergetic power quotient. A new metric combining exergetic cooling power and pump power provides an estimate of the maximum achievable second law efficiency. Five regenerator geometries with equal volumes and the aspect ratio from 1.0 to 3.8, and four different ranges of Gd spherical particles between 182 and 354 µ m, are investigated. Improvements in system performance are demonstrated by a boost in specific cooling power of gadolinium from 0.85 to 1.16 W g −1 and maximum temperature span from 8.9 to 15.1 K. The optimum exergetic cooling power is observed for 1.37 utilization and 3 Hz operating frequency, exergetic power quotient exhibits a maximum at the same utilization but at 2 Hz frequency, while the highest efficiency is recorded at 1 Hz and utilization of 0.5, demonstrating that multiple performance metrics must be balanced to achieve regenerator design meeting all performance targets.

42 ENGINEERING↗
DOE OSTI2025

Iso-cost-performance of thermal energy storage

As thermal energy storage (TES) systems gain increasing recognition as next-generation energy storage solutions, evaluating their techno-economic performance is crucial. This perspective analyzes the cost-performance of latent heat-based TES systems and introduces the concept of iso-cost-performance—maintaining a constant cost per unit energy ($\$$/kWh) despite material degradation. Using an empirical degradation model, we show that after 1200 thermal cycles, the thermal conductivity and volumetric energy density of a paraffin-based phase change material (PCM) decreased by 36.0% and 26.1%, respectively, resulting in a 31.2% reduction in the figure of merit (FOM) and, consequently, the system cost-performance. However, by introducing thermally conductive additives to enhance effective thermal conductivity, the TES system can recover its initial FOM, achieving iso-cost-performance operation. This framework quantitatively demonstrates how degradation-mitigation strategies—such as improving thermal conductivity—can offset material degradations and maintain long-term cost-effectiveness. Beyond PCM-based TES, the proposed FOM-based approach provides a generalized pathway for cost-performance optimization across various TES technologies.

25 ENERGY STORAGE↗
DOE OSTI2021

Battle of the Defaults: Extracting Performance Characteristics of HDF5 under Production Load

Popular parallel I/O libraries, such as HDF5, provide tuning parameters to obtain superior performance. However, the selection of effective parameters on production systems is complex due to the interdependence of I/O software and file system layers. Hence, application developers typically use the default parameters and often experience poor I/O performance. This work conducts a benchmarking-based analysis on the HDF5 behaviors with a wide variety of I/O patterns to extract performance characteristics under the production workload. To make the analysis well controlled, we exercise I/O benchmarks on POSIX-IO, MPI-IO, and HDF5 using the same I/O patterns and in the same jobs. To address high performance variability in production environments, we repeat the benchmarks across I/O patterns, storage devices, and time intervals. Based on the results, we identified consistent HDF5 behaviors that appropriate configurations and operations on dataset layout and file-metadata placement can improve performance significantly. We apply our findings and evaluate the tuned I/O library on two supercomputers: Summit and Cori. The results show that our tuned parameters can achieve more than 10× I/O performance speedup than that with default parameters on both systems, suggesting the effectiveness, stability, and generality of our solution.

Xie, Bing↗
DOE OSTI2024

A Performance and Energy Study of GPU-Resident Preconditioners for Conjugate Gradient Solvers: In the Context of Existing and Novel Approaches

Optimizing a particular subprogram out of the set of Basic (sparse) Linear Algebra Subprograms (BLAS) for a given architecture is a common topic of research. In applications, however, these BLAS functions rarely appear in isolation; usually, many of them are used together, in various combinations and with varying inputs. As the need to solve a large, sparse linear system is ubiquitous throughout HPC applications, linear solvers constitute a realistic, sufficiently complex and well-defined representative use case for composite BLAS routines. To this end, based on a representative set of matrices drawn from a diverse set of fields, we present a framework to study, from the performance and energy perspective, the efficacy of GPU- resident parallel Conjugate Gradient (CG) linear solver with different preconditioner options, including Gauss-Seidel, Jacobi, and incomplete Cholesky. We also propose a novel GPU-based preconditioner, in which the triangular solves are approximated by an iterative process. The development of this preconditioner was motivated by solving large graph Laplacian linear systems, for which the existing preconditioners either perform slow on GPU-based platforms or are not applicable. We compare the performance of these preconditioners on different hardware accelerator architectures, i.e., AMD MI250X, MI100, Nvidia A100, V100, and Jetson. Our experiments reveal performance trade-offs and provide information on how to select the best strategy for the given linear system, dictated by its properties, and the platform of interest. We demonstrate the application of our novel preconditioner for solving CG and graph Laplacian systems. Overall, the framework can be utilized as a benchmark to guide informed decisions in choosing a specific preconditioner, i.e., whether it is better to rely on the performance of a triangular solver or on the performance of sparse matrix-vector product. Finally, by considering power consumption to solve the linear systems, we report the energy footprint for the solvers.

Preconditioned Conjugate Gradient, GPUs, iterative↗
DOE OSTI2025

Computational Performance Bounds Prediction in Quantum Computing With Unstable Noise

Quantum computing has significantly advanced in recent years, boasting devices with hundreds of quantum bits (qubits), hinting at its potential quantum advantage over classical computing. Yet, noise in quantum devices poses significant barriers to realizing this supremacy. Understanding noise’s impact is crucial for reproducibility and application reuse; moreover, the next-generation quantum-centric supercomputing essentially requires efficient and accurate noise characterization to support system management (e.g., job scheduling), where ensuring correct functional performance (i.e., fidelity) of jobs on available quantum devices can even be higher-priority than traditional objectives. However, noise fluctuates over time, even on the same quantum device, which makes predicting the computational bounds for on-the-fly noise is vital. Noisy quantum simulation can offer insights but faces efficiency and scalability issues. Here, in this work, we propose a data-driven workflow, namely QuBound, to predict computational performance bounds. It decomposes historical performance traces to isolate noise sources and devises a novel encoder to embed circuit and noise information processed by a Long Short-Term Memory (LSTM) network. For evaluation, we compare QuBound with a state-of-the-art learning-based predictor, which only generates a single performance value instead of a bound. Experimental results show that the result of the existing approach falls outside of performance bounds, while all predictions from our QuBound with the assistance of performance decomposition better fit the bounds. Moreover, QuBound can efficiently produce practical bounds for various circuits with over 106 speedup over simulation; in addition, the range from QuBound is over 10× narrower than the state-of-the-art analytical approach.

Li, Jinyang [George Mason Univ., Fairfax, VA (Unit↗
DOE OSTI2025

A Modified DBC Substrate Improving Thermal Performance for Confined Space Applications

High-power modules use substrates to house the semiconductor device and for electrical insulation. These substrates are constructed with thermally conductive dielectric material sandwiched between two metals to extract heat from semiconductor chips. Thus, the required cooling performance of a power module is linked to the substrate’s thermal performance and can vary based on the substrate technologies. Here, in this study, five substrate technologies were evaluated for space-restricted applications: direct-bonded copper (DBC), an insulated metal substrate (IMS), a thermally annealed pyrolytic graphite (TPG)-based IMS, DBC-based double-sided cooling, and direct-bonded aluminum (DBA) where the heat sink is directly attached without thermal interface materials (TIMs). The finite element (FE) analysis results suggest that the popular DBC substrate has thermal performance better than that of the other substrates for space-constrained applications. To further improve the thermal performance, a modified DBC substrate was proposed where a copper block was added between the semiconductor and the DBC substrate to achieve heat spreading underneath the chip. The modified DBC performance was then compared with the aforementioned substrates and the results showed significant thermal performance improvement. The results were verified with experimental results where the proposed substrate showed 20% more loss handling capability compared to an identical DBC substrate.

42 ENGINEERING↗
DOE OSTI2021

Toward Evaluating High-Level Synthesis Portability and Performance between Intel and Xilinx FPGAs

Offloading computation from a CPU to a hardware accelerator is becoming a more common solution for improving performance because traditional gains enabled by Moore’s law and Dennard scaling have slowed. GPUs are often used as hardware accelerators, but field-programmable gate arrays (FPGAs) are gaining traction. FPGAs are beneficial because they allow hardware specific to a particular application to be created. However, they are notoriously difficult to program. To this end, two of the main FPGA manufacturers, Intel and Xilinx, have created tools and frameworks that enable the use of higher level languages to design FPGA hardware. Although Xilinx kernels can be designed by using C/C++, both Intel and Xilinx support the use of OpenCL C to architect FPGA hardware. However, not much is known about the portability and performance between these two device families other than the fact that it is theoretically possible to synthesize a kernel meant for Intel to Xilinx and vice versa.In this work, we evaluate the portability and performance of Intel and Xilinx kernels. We use OpenCL C implementations of a subset of the Rodinia benchmarking suite that were designed for an Intel FPGA and make the necessary modifications to create synthesizable OpenCL C kernels for a Xilinx FPGA. We find that the difficulty of porting certain kernel optimizations varies, depending on the construct. Once the minimum amount of modifications is made to create synthesizable hardware for the Xilinx platform, more nontrivial work is needed to improve performance. However, we find that constructs that are known to be performant for an FPGA should improve performance regardless of the platform; the difficulty comes in deciding how to invoke certain kernel optimizations while also abiding by the constraints enforced by a given platform’s hardware compiler.

Cabrera, Anthony↗
DOE OSTI2023

AIIO: Using Artificial Intelligence for Job-Level and Automatic I/O Performance Bottleneck Diagnosis

Manually diagnosing the I/O performance bottleneck for a single application (hereinafter referred to as the "job level'') is a tedious and error-prone procedure requiring domain scientists to have deep knowledge of complex storage systems. However, existing automatic methods for I/O performance bottleneck diagnosis have one major issue: the granularity of the analysis is at the platform or group level and the diagnosis results cannot be applied to the individual application. To address this issue, we designed and developed a method named "Artificial Intelligence for I/O"(AIIO), which uses AI and its interpretation technology to diagnose I/O performance bottlenecks at the job level automatically. By considering the sparsity of I/O log files, employing multiple AI models for performance prediction, merging diagnosis results across multiple models, and generalizing its performance prediction and diagnosis functions, AIIO can accurately and robustly identify the bottleneck of an even unseen application. Experimental results show that real and unseen applications can use the diagnosis results from AIIO to improve their I/O performance by at most 146 times.

Dong, Bin↗
DOE OSTI2021

Heuristic methods and performance bounds for photonic design

In the photonic design problem, a scientist or engineer chooses the physical parameters of a device to best match some desired device behavior. Many instances of the photonic design problem can be naturally stated as a mathematical optimization problem that is computationally difficult to solve globally. Because of this, several heuristic methods have been developed to approximately solve such problems. These methods often produce very good designs, and, in many practical applications, easily outperform ‘traditional’ designs that rely on human intuition. Yet, because these heuristic methods do not guarantee that the approximate solution found is globally optimal, the question remains of just how much better a designer might hope to do. This question is addressed by performance bounds or impossibility results, which determine a performance level that no design can achieve. We focus on algorithmic performance bounds, which involve substantial computation to determine. We illustrate a variety of both heuristic methods and performance bounds on two examples. In these examples (and many others not reported here) the performance bounds show that the heuristic designs are nearly optimal, and can be considered globally optimal in practice. This review serves to clearly set up the photonic design problem and unify existing approaches for calculating performance bounds, while also providing some natural generalizations and properties.

Angeris, Guillermo (ORCID:0000000249503990)↗
DOE OSTI2019

Enhancement of EM Pump Performance through Modeling and Testing

Liquid sodium has seen development as a nuclear reactor coolant in the United States for over 50 years. While sodium has many desirable characteristics as a heat transfer fluid, its reactivity poses numerous engineering challenges for even the most basic plant components like pumps. Despite the challenges, mechanical pumps have seen success in sodium service albeit at the expense of simplicity and flexibility. In fact, significant failures of these pumps have been recorded which can be attributed to their complexity. Notwithstanding the complexity, mechanical pumps are still widely proposed for the main circuit pump of sodium cooled reactors. However, an alternative to mechanical pumps are Electromagnetic Induction Pumps (EMIPs) which use the principle of the Lorentz Force. EMIPs avoid mechanically moving parts in contact with sodium which simplifies the design by avoiding mechanical wear and seals. Therefore, these pumps are generally safer and more reliable than their mechanical counterparts. That being said, EMIPs suffer from relatively low efficiencies that range from 5% to 45%. Some of the inefficiencies are inherent to the EMIPs pumps; namely, inefficiencies arise in the form of resistive heating losses in the fluid itself. However, other inefficiencies arise from Edge Effects which include Finite Length and Finite Width effects. Due to the significant safety advantages EMIPs have compared to mechanical pumps, there is a strong motivation to integrate these technologies into the design of Generation IV liquid-metal-cooled nuclear power plants. However, EMIPs have low efficiencies partly attributed to Edge Effects. Much of the literature focusing on Edge Effects is dated or simply does not exist in the public domain. More recently, numerical models of EMIPs have shown promising results. However, these models are often custom solvers or use complex software suits which limit their application. This report describes the work completed to understand Edge Effects in two EMIPs designs. These designs are the Annular Linear Induction Pump (ALIP) and Permanent Magnet Induction Pump (PMIP) also known as a Moving Magnet Pump (MMP). The methodology developed during this project aim at measuring key performance parameters (pressure-flowrate and efficiency-slip performance) using high temperature liquid-sodium. Baseline experimental measurements were then used to verify the accuracy of numerical models developed in the software suit ANSYS/FLUENT. On the experimental side, instrumentation was developed to characterize the pressure-flowrate and efficiency-slip performance of both the ALIP and PMIP in high-temperature liquid-sodium. Additionally, instrumentation was developed to measure potential output pressure pulsations and the magnetic field distributions. Moreover, a specialized ALIP was constructed to study the impacts magnetic field tapering, also known as coil grading, on the Finite Length Effect. Lastly, experimental performance curves of the PMIP were collected at 300 oC and 600 oC and compared to analytical theory which accounted for Finite Width Effects. These results showed that the Finite Width Effect and non-uniform magnetic field in a PMIP impact the performance. On the numerical side, models were developed using the software suit FLUENT/ANSYS. These methods were initially compared against a simple MHD flow in a Permanent Magnet Electromagnet Flowmeter (PMEMFM). The results were significant in that they showed agreeance between the numerical model and demonstrated that PMEMFM’s could be calibrated numerically. These modeling methods were then applied to a prototypic PMIP. Results showed that this simple model provided reasonably accurate results of the PMIP p-Q performance.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗