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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Integrated Oxygen Flow Meter/Heat Exchanger for Portable Life Support Systems

Space suits for future exploration missions will have multi-mission goals with new and challenging requirements for the portable life support system (PLSS). In particular, the space suit ventilation loop requires cooling and flow measurement components that must meet specifications that go well beyond the capabilities of the components used for the existing Extravehicular Mobility Unit. The flow meter must have high measurement accuracy over a wide flow range, compatibility with pure oxygen, low pressure losses, and very compact size. The heat exchanger that cools the ventilation loop must be built from materials that are compatible with the liquid cooling loop, and it must provide efficient gas cooling in a small package across conditions ranging from normal suit pressure to elevated pressure. This paper describes the development of a novel device that combines the flow measurement and cooling functions in a single, compact flow meter/heat exchanger (FMHX). We have developed design methods that enable us to assess trade-offs, optimize performance, and specify the design of an FMHX that meets the requirements and constraints for operation in future PLSSs. We used empirical design correlations combined with computational fluid dynamics analysis to design the FMHX design. Data from tests of a proof-of-concept FMHX validate the design methods and show that the device meets all design requirements. We used the results from these tests to refine the design parameters and predict performance of an optimized, prototype FMHX.

Izenson, Michael G.↗
DOE OSTI2016

Dakota Uncertainty Quantification Methods Applied to the CFD code Nek5000

Predictive modeling and simulation of the performance of a nuclear reactor and its fuel is a challenging task because of the large number of coupled physical phenomena that must be addressed. In addition to this intrinsic complexity, model uncertainty must be accounted for in any analysis if the model will be used to facilitate design or operational decisions which may impact safety and performance. Rigorous, structured uncertainty analyses are performed by first characterizing the model's input uncertainties and, by propagating the uncertainty through the model in order to estimate the output's uncertainty. This paper presents the state of advancement of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) project to characterize the uncertainty of the CFD code Nek5000 using the Dakota package. Nek5000 is a high order spectral element computational fluid dynamics (CFD) code developed at Argonne National Laboratory for high resolution spectral-filtered large eddy simulations (LESs) and unsteady Reynolds averaged Navier-Stokes (RANS) simulations. The Dakota package developed at Sandia National Laboratory can be integrated with many scientific and engineering codes to facilitate efficient, effective uncertainty quantification (UQ) and sensitivity analyses. Dakota is designed to interface with external packages/codes such as Nek5000 through a script-driven interface called fork. Such interface provides maximum flexibility to the users for pre- and post-processing tasks preceding and following each function evaluation. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Perform Design Support with MCNP for New Measurements

This report incorporates our work carried out during our 5-month internship at LANL under an internship agreement with EAMEA (École des Applications Militaires de l’Énergie Atomique). After outlining the context in which we worked, we present our work as aid to modeling and predicting the neutronic behavior of nuclear systems, with a view to carrying out criticality experiments qualifying the MCNP code as part of innovative projects. Fourth generation reactors will enable to tackle a lot of issues such as environmental crisis, affordable energy access, and nuclear waste management. They seem to be one of the keys for a sustainable future. Most of the projects that emerge nowadays include the use of HALEU (high assay low enriched uranium) or MOX recycled fuels. Our projects are part of this dynamic and addresses concrete scientific research issues in the nuclear field. Studies of HALEU package are essential to anticipate the need, therefore the Optimus L (OPTImal Modular Universal Shipping cask technology) designed by NAC (Nuclear Assurance Corporation) international but filled with 20 % enriched uranium dioxide (UO 2 ) will be studied to support safe transportation. However, it seems there is no benchmark with a high correlation with the combination of this fuel and this package to validate MCNP simulations. As such, the study will focus on the development of new criticality safety benchmarks for this case. On the other hand, there is a great need for critical benchmarks in the intermediate energy range with MOX fuel. An IER (Integral Experiment Request) has then be requested to answer it through a partnership between French institution IRSN and U.S. Department of Energy's Nuclear Criticality Safety Program (NCSP). This experience planned for 2025 requires to gather calculated data through a MCNP model to be realized safely. Finally, a presentation of our one-week experience at the DAF as part of our discovery of criticality experiments will be introduced in Appendix 1: Week at the DAF (Device Assembly Facility)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

Nationwide Buildings Energy Research enabled through an integrated Data Intensive Scientific Workflow and Advanced Analysis Environment

Modern workflow systems enable scientists to run ensemble simulations at unprecedented scales and levels of complexity, allowing them to study system sizes previously impossible to achieve, due to the inherent resource requirements needed for the modeling work. However as a result of these new capabilities the science teams suddenly also face unprecedented data volumes that they are unable to analyze with their existing tools and methodologies in a timely fashion. In this paper we will describe the ongoing development work to create an integrated data intensive scientific workflow and analysis environment that offers researchers the ability to easily create and execute complex simulation studies and provides them with different scalable methods to analyze the resulting data volumes. The integration of simulation and analysis environments is hereby not only a question of ease of use, but supports fundamental functions in the correlated analysis of simulation input, execution details and derived results for multi-variant, complex studies. To this end the team extended and integrated the existing capabilities of the Velo data management and analysis infrastructure, the MeDICi data intensive workflow system and RHIPE the R for Hadoop version of the well-known statistics package, as well as developing a new visual analytics interface for the result exploitation by multi-domain users. The capabilities of the new environment are demonstrated on a use case that focusses on the Pacific Northwest National Laboratory (PNNL) building energy team, showing how they were able to take their previously local scale simulations to a nationwide level by utilizing data intensive computing techniques not only for their modeling work, but also for the subsequent analysis of their modeling results. As part of the PNNL research initiative PRIMA (Platform for Regional Integrated Modeling and Analysis) the team performed an initial 3 year study of building energy demands for the US Eastern Interconnect domain, which they are now planning to extend to predict the demand for the complete century. The initial study raised their data demands from a few GBs to 400GB for the 3year study and expected tens of TBs for the full century.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

Optimization of the Coupled Cluster Implementation in NWChem on Petascale Parallel Architectures

Coupled cluster singles and doubles (CCSD) algorithm has been optimized in NWChem software package. This modification alleviated the communication bottleneck and provided from 2- to 5-fold speedup in the CCSD iteration time depending on the problem size and available memory. Sustained 0.60 petaflop/sec performance on CCSD(T) calculation has been obtained on NCSA Blue Waters. This number included all stages of the calculation from initialization till termination, iterative computation of single and double excitations, and perturbative accounting for triple excitations. In the section of perturbative triples alone, the computation maintained 1.18 petaflop/sec performance level. CCSD computations have been performed on Guanine-Cytosine deoxydinucleotide monophosphate (GC-dDMP) to probe the conformational energy difference in DNA single strand in A- and B-conformations. The computation revealed significant discrepancy between CCSD and classical force fields in prediction of relative energy of A- and B-conformations of GC-dDMP.

calculations↗
DOE OSTI2020

Evaluation of Hybrid FPOG Applications in Regulated and Deregulated Markets Using HERON

Recent changes in the U.S. energy market, such as low natural gas prices and increased electricity production for variable renewable energy (VRE) sources, have led to an economic crisis for existing light-water reactor (LWR) nuclear power plants (NPP). Many owners and operators of LWRs have elected to decommission these plants rather than continue using them as consistent sources of clean baseload power. This has led to exploration of various possibilities to increase the economic viability of these units, including market restructuring to monetize benefits LWRs already provide to the grid through ancillary markets, load following and economic dispatch, and possible integration of secondary systems directly to the NPP for production of additional products through technologies such as hydrogen electrolysis or water desalination. Previous studies have considered the technologies associated with these Integrated Energy Systems (IES) activities, and the analysis of markets for these secondary products. To analyze the economic viability of various system configurations including IES, especially given the uncertainty surrounding load demand, electricity prices, and the availability of VRE resources, the stochastic technoeconomic analysis package HERON (Heuristic Energy Resource Optimization Network) was released earlier this year as an extension of the risk analysis framework RAVEN (Risk Analysis Virtual Environment). HERON focuses foremost on making the complex uncertainty quantification analysis tools approachable for energy systems analysts, also providing general dispatch optimization algorithms for those workflows. HERON continues to be improved and tested as a significant part of the IES viability analyses performed in this work. HERON is not a capacity expansion model. To consider market and grid energy system development in a variety of scenarios, HERON is best used in coupling with modelling tools such as US-REGEN, which sacrifice some of the uncertainty analysis and resolution of HERON's modelling for the ability to efficiently predict the change in the grid energy system's profile due to economic drivers over decades. HERON can then use this information to explore the economic viability of introducing changes to the predicted outcomes, such as the introduction of an IES. In this work, experts at EPRI using US-REGEN provide six projection scenarios for use in HERON stochastic technoeconomic analysis (STEA) in considering the options available for increasing LWR economic viability through introduction of a hydrogen-centric IES using a high-temperature steam electrolysis plant (HTSE), hydrogen storage, and a constant-rate contracted hydrogen consumer. The results obtained are differential in nature; they do not report expected profits for any configuration, but rather report on the possible increase in the NPV of a configuration with respect to a baseline no-IES configuration. Due to the uncertainty captured in the variable net load of the systems, there is likewise uncertainty in the mean values reported. We consider this viability both in terms of a regulated market, where the energy producers and IES are owned and operated by single entity, as well as a deregulated market, where the IES chooses its bid for electricity generation and is then dispatched by the grid system operator. Results indicate that for deregulated markets, the inclusion of the IES is often statistically beneficial. This is especially true in policies that are not favorable towards nuclear, as nuclear is less often dispatched and is forced to deal with frequent idle capacity. In the nominal case as well as the case of carbon tax policies, inclusion of the IES clearly benefited the economic performance of the NPP. In the regulated case, however, there was a trend towards minimizing the IES, likely due to the optimal sizing performed by US-REGEN of the NPP within the system as well as the lack of penalty for idle capacity at the NPP in the regulated market analyses.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Methods in PES-Learn: Direct-Fit Machine Learning of Born–Oppenheimer Potential Energy Surfaces

The release of PES-L EARN version 1.0 as an open-source software package for the automatic construction of machine learning models of semi-global molecular potential energy surfaces (PESs) is presented. Improvements to PES-L EARN ’s interoperability are stressed with new Python API that simplifies workflows for PES construction via interaction with QCSchema input and output infrastructure. In addition, a new machine learning method is introduced to PES-L EARN : kernel ridge regression (KRR). The capabilities of KRR are emphasized with examination of select semi-global PESs. All machine learning methods available in PES-L EARN are benchmarked with benzene and ethanol datasets from the rMD17 database to illustrate PES-L EARN ’s performance ability. Fitting performance and timings are assessed for both systems. Finally, the ability to predict gradients with neural network models is presented and benchmarked with ethanol and benzene. PES-L EARN is an active project and welcomes community suggestions and contributions.

kernel ridge regression↗
DOE OSTI2016

Predictive Engineering Tools for Injection-Molded Long-Carbon-Thermoplastic Composites: Weight and Cost Analyses

This project proposed to integrate, optimize and validate the fiber orientation and length distribution models previously developed and implemented in the Autodesk Simulation Moldflow Insight (ASMI) package for injection-molded long-carbon-fiber thermoplastic composites into a cohesive prediction capability. The current effort focused on rendering the developed models more robust and efficient for automotive industry part design to enable weight savings and cost reduction. The project goal has been achieved by optimizing the developed models, improving and integrating their implementations in ASMI, and validating them for a complex 3D LCF thermoplastic automotive part (Figure 1). Both PP and PA66 were used as resin matrices. After validating ASMI predictions for fiber orientation and fiber length for this complex part against the corresponding measured data, in collaborations with Toyota and Magna PNNL developed a method using the predictive engineering tool to assess LCF/PA66 complex part design in terms of stiffness performance. Structural three-point bending analyses of the complex part and similar parts in steel were then performed for this purpose, and the team has then demonstrated the use of stiffness-based complex part design assessment to evaluate weight savings relative to the body system target (≥ 35%) set in Table 2 of DE-FOA-0000648 (AOI #1). In addition, starting from the part-to-part analysis, the PE tools enabled an estimated weight reduction for the vehicle body system using 50 wt% LCF/PA66 parts relative to the current steel system. Also, from this analysis an estimate of the manufacturing cost including the material cost for making the equivalent part in steel has been determined and compared to the costs for making the LCF/PA66 part to determine the cost per “saved” pound.

42 ENGINEERING↗
NASA NTRS2011

The Impact of Microphysical Schemes on Hurricane Intensity and Track

During the past decade, both research and operational numerical weather prediction models [e.g. the Weather Research and Forecasting Model (WRF)] have started using more complex microphysical schemes originally developed for high-resolution cloud resolving models (CRMs) with 1-2 km or less horizontal resolutions. WRF is a next-generation meso-scale forecast model and assimilation system. It incorporates a modern software framework, advanced dynamics, numerics and data assimilation techniques, a multiple moveable nesting capability, and improved physical packages. WRF can be used for a wide range of applications, from idealized research to operational forecasting, with an emphasis on horizontal grid sizes in the range of 1-10 km. The current WRF includes several different microphysics options. At NASA Goddard, four different cloud microphysics options have been implemented into WRF. The performance of these schemes is compared to those of the other microphysics schemes available in WRF for an Atlantic hurricane case (Katrina). In addition, a brief review of previous modeling studies on the impact of microphysics schemes and processes on the intensity and track of hurricanes is presented and compared against the current Katrina study. In general, all of the studies show that microphysics schemes do not have a major impact on track forecasts but do have more of an effect on the simulated intensity. Also, nearly all of the previous studies found that simulated hurricanes had the strongest deepening or intensification when using only warm rain physics. This is because all of the simulated precipitating hydrometeors are large raindrops that quickly fall out near the eye-wall region, which would hydrostatically produce the lowest pressure. In addition, these studies suggested that intensities become unrealistically strong when evaporative cooling from cloud droplets and melting from ice particles are removed as this results in much weaker downdrafts in the simulated storms. However, there are many differences between the different modeling studies, which are identified and discussed.

Tao, Wei-Kuo↗
DOE OSTI2021

wtDigiTwin (Wind Turbine Digital Twin) [SWR-21-15]

This wind turbine digital twin software (wtDigiTwin) provides a digital twin solution for wind turbine applications. The focus of wtDigiTwin is to estimate loads, motions and environmental conditions for an operating wind turbine. The program uses supervisory control and data acquisition (SCADA) measurements as inputs, together with a wind turbine model. The wind industry is currently challenged by the high cost of operation and maintenance. These costs could be mitigated if component failures are predicted, but such predictions are difficult unless the turbines are equipped with expensive measuring devices. The alternative is to use a digital twin such as wtDigiTwin to estimate the necessary signals. wtDigiTwin can perform online prediction of signals that are otherwise not measured, using a limited set of reliable measurements and a physics-based model. The predicted signals can be used in applications that have direct cost benefits: 1) real-time estimation of the fatigue consumption of key components of the wind turbine; 2) root cause analyses and failure detections ; 3) lifetime reassessments ; 4) improvements to follow-on designs. The current version provides examples to estimate wind speed, thrust, torque, tower-top position, and tower loads on an onshore wind turbine using the following measurements tower top acceleration, generator torque, pitch, and rotational speed. The model combines a linear state-space model, a wind speed estimator, and a Kalman filter algorithm that integrates measurements with the state model to perform state estimations. The state space model is obtained either using OpenFAST linearizations, or using the yams package provided with the software.

Branlard, Emmanuel↗
DOE OSTI2022

Hybrid-RL-MPC4CLR (Hybird-Reinforcement-Learning-Model-Predictive-Control-for-Reserve-Policy-Assisted-Critical-Load-Restoration-in-Distribution-Grids)

Hybrid-RL-MPC4CLR was developed as a hybrid controller for active distribution grid critical load restoration, combining deep reinforcement learning (RL) and model predictive control (MPC) aiming at maximizing total restored load following an extreme event. The RL determines a policy for quantifying operating reserve requirements, thereby hedging against uncertainty, while the MPC models grid operations incorporating the RL policy actions (i.e., reserve requirements), renewable (wind and solar) power predictions, and load demand forecasts. The developers formulated the reserve requirement determination problem as a sequential decision-making problem based on the Markov Decision Process (MDP) and design an RL learning environment based on the OpenAI Gym framework and MPC simulation. The RL agent reward and MPC objective function aim to maximize and monotonically increase total restored load and minimize load shedding and renewable power curtailment. The software is developed using various software packages in Python. The MPC's optimal power flow (OPF) model is implemented using the Pyomo package, the RL simulation environment is implemented using the MPC simulation with various scenarios of renewable energy and load demand profiles and power outage beginning times, based on the OpenAI Gym framework. The RL agent training is performed using the RLlib Ray package. The RL algorithm is trained offline using historical forecasts of renewable generation and load demand profiles. Simulation analysis and performance tests are conducted using a modified IEEE 13-bus distribution test feeder containing wind turbine, photovoltaic, microturbine, and battery.

Eseye, Abinet Tesfaye↗
NASA NTRS2025

CFD Validation of a Methanation Reactor With Cooling Jacket Used in an ISRU Propellant Production System

A digital model of an OxEon Energy methanation reactor (with a cooling jacket) planned for use in a NASA designed In-Situ Resource Utilization (ISRU) propellant production system was developed by the UTEP Aerospace Center using STAR-CCM+ software, a Siemens Computational Fluid Dynamics (CFD) package. The reactor digital model created includes two primary components: (i) a cooling jacket that maintains the required reactor chamber surface temperature and (ii) a reactor core chamber where the exothermic methanation reactions occur. The StarCCM+ digital model has two objectives: (i) maintain the surface temperature of the reactor below a set value so that the extra heat accumulation does not melt or crack the chamber, and (ii) produce the desired amount of methane (CH4) according to test cases completed by OxEon Energy for NASA JSC. The computational digital model was validated with the test data enabling the cooling jacket scale to be predicted based on the reactor's operating temperature. The StarCCM+ simulation of the digital model shows a good agreement with the cooling jacket performance and the CH4 production. One test case for the cooling jacket maintained the reactor wall temperature around 310 °C was validated and the reactor produced 185 g/hr CH 4 .

ISRU↗
DOE OSTI2025

Thermal Optimization and Experimental Demonstration of a Silicon Carbide, Half-Bridge Power Module

This paper describes modeling and experimental work to design and demonstrate a silicon carbide (SiC), single-side-cooled power module. Two SiC metal-oxide-semiconductor field-effect transistors (MOSFET) were used for each switch position and packaged on a metalized ceramic substrate. Thermal modeling was first conducted to optimize the module packaging to minimize junction-to-fluid thermal resistance and device temperature variations. This procedure identified the optimal device placement and baseplate thickness to maximize thermal performance. A heat exchanger - incorporating impinging jets of water-ethylene glycol - was designed to cool the module's baseplate to minimize thermal resistance and pumping power. The heat exchanger used relatively low fluid velocities (<1.5 m/s) to minimize erosion-corrosion effects and relatively large fluid channel sizes (>=1 mm) to prevent clogging. The power module concept was fabricated, and experiments were conducted to measure its thermal performance. Direct current was used to power the module devices, and junction temperatures were measured via device temperature-sensitive parameters. The experimental results were found to be in good agreement with model predictions (within 2%) and yielded junction-to-fluid thermal resistance values as low as about 15 mm2 K/W at relatively low pumping power values. The results demonstrate the improved performance associated with jet impingement cooling and represent an improvement over existing channel-flow-type cold plates, which can enable improvements to power density and reliability.

36 MATERIALS SCIENCE↗
DOE OSTI2021

HyPerPy (Hydrogen Extraction and Parabolic Trough Plant Performance Models using Python) [SWR-21-53]

The HyPerPy package consists of three scripts: 1) Model for Hydrogen Tracking in Parabolic Trough Power Plants, 2) Model for Receiver Performance, and 3) Model for Hydrogen Extraction Process. The power plant model (1) tracks hydrogen generation and transport within the circulating heat transfer fluid (HTF) of the power plant. This script is a transient, initial value simulation, in which the hydrogen concentration in the circulating HTF is 0 moles per cubic meter everywhere at time 0 seconds. Hydrogen concentration is calculated at discrete locations within the circulating HTF with 4-second time resolution. During each time step, the change in hydrogen concentration due to hydrogen generation and permeation is calculated for each location according to the local HTF temperature, vessel or piping properties, hydrogen concentration and partial pressure. This model predicts hourly hydrogen concentrations for typical operating days in the spring, summer, fall, and winter seasons. Data is used to create hourly mappings of hydrogen concentrations for a typical operating year. The receiver model (2) uses a 1-hour time step to estimate getter loading and annulus pressure. For each time step, the model uses HTF and ambient temperature data to estimate absorber tube, bellows, and getter temperatures for the time step. In addition, the model uses hourly HTF hydrogen concentrations that are generated by the power plant model (1), and annulus hydrogen pressure from the previous time step. With these data, the model calculates the moles of hydrogen permeating across the absorber tube and bellows during the time step. The net change in moles hydrogen is added or subtracted from the getter loading for the previous time step, and the hydrogen pressure is re-calculated based on getter loading and temperature. The model uses this algorithm to simulate hydrogen permeation and loading 24 hours per day, 365 days per year using seasonal temperature data. The model repeats these calculations for 25 years to create a mapping of receiver getter loading and annulus hydrogen pressure for four seasons of each year. The model for hydrogen extraction (3) estimates hydrogen extraction rates for a specific separation module configuration. The rate depends primarily on membrane area, vacuum pump performance, headspace gas flowrate to the membrane, and headspace gas hydrogen partial pressure. This model has two versions. The steady-state version predicts hydrogen extraction rates when the module is operating in separation mode. The dynamic version predicts hydrogen transfer through the membrane when the module is operating in sensor mode. The steady-state version is used with the plant model (1) to predict hydrogen partial pressures in the power plant when the extraction process is operating.

Glatzmaier, Gregory↗
DOE OSTI2022

CFD-Chemical Model of One-Third Scale Demonstration of DOE Sealed Canister with ATR Fuel

Road-ready and final disposition packaging configurations for the advanced test reactor (ATR) fuel currently specifies storage within helium backfilled DOE sealed standard canisters. The aluminum cladding of the ATR fuel contains an oxyhydroxide layer of boehmite/bayerite that generates hydrogen when subjected to irradiation. Understanding the effect of this hydrogen buildup over time to important for long term storage considerations. Previous modeling efforts have built a coupled CFD-chemical model to simulate the temperature gas phase concentrations within the DOE sealed standard canisters. A demonstration case for these DOE sealed canisters will be eventually performed with a one-third scale mockup that has been instrumented with thermocouples and gas concentration probes. This study seeks to model the planned canister for validation of the previously developed model, such that confidence in its long-term prediction can be increased. The deployment of the instrumented lid for online monitoring in intended for a period of 10+ years based on previous monitoring of commercial fuel storage; however, the model is still run for the previously used 50-year storage periods. This case is modeled with a G-value using a bi-linear function such that it decreases at higher dose rates to be consistent with experiments. Validation efforts of the model would likely revolve around the results for the 1st year, so the results of this timeframe are also highlighted. For dried fuel of the nominal decay heat (18W), the predicted hydrogen concentration is 0.19% after 1 year, 0.91% after 10 years, and 2.8% after 50 years, with a maximum pressure of 1.26 atm. Consistent with previous modeling, the decay heat of the fuel is the main factor that influences the results. For undried fuel in pure helium, the hydrogen concentration ranges from 0.23 to 1.2% after 1 year, 1.25-6.2% after 10 years and 8.8 to 18.84% after 50 years. For dried fuel in pure helium the hydrogen concentration ranges from 0.06% to 0.34% after 1 year, 0.35-2.11% after 10 years and 0.95 to 6.6% after 50 years. While long-term results in the presence of residual air are mostly the same, early reactions with O 2 can delay significant production of H 2 until it is consumed to form more water vapor, lowering the range to 0.14 to 1.0% after 1 year. The maximum absolute pressure that is reached across any scenario is 2.06 atm. In the event of residual air, the presence of nitric acid is possible in the range of 18-131 ppm after 1 year, 174-1180 ppm after 10 years, and 586-3500 ppm after 50 years. As long as the fuel is sufficiently dried, or of nominal decay heat, a 4% lower flammability limit of hydrogen will not be reached within a 10-year monitoring period.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1994

Friction Factor for Flow in Rectangular Ducts with One Side Rib-Roughened

Numerical simulations of incompressible turbulent flow through rectangular ducts with one side rib-roughened were performed to determine pressure drop. The "PHOENICS " software package was used for the computations, which required provision of a wall function for transverse rib-roughened surfaces. The present study was conducted in the range of 10(exp 5) less than or equal to Reynolds number less than or equal to 10(exp 7), 0.01 less than or equal to rib height to hydraulic diameter ratio less than or equal to 0.04, 10 less than or equal to pitch to rib height ratio less than or equal to 40. Using the numerical results, friction factor charts for various aspect ratios were generated. The numerical results agreed well with experimental data that was obtained for 10(exp 5) less than Reynolds less than 2 x 10(exp 5). In addition, a scheme for predicting friction factor using existing correlations for smooth and rough walls was developed.

Youn, B.↗
DOE OSTI2020

Power Electronics Materials and Bonded Interfaces - Reliability and Lifetime

The emergence of wide-bandgap (WBG) devices in power electronics packages has brought significant attention to the reliability and thermal performance of the bonded interfaces. These interfaces, such as die-attach and substrate-attach, need to withstand the severe stress induced on them by the high-temperature (> 200°C) operation of the WBG devices. Sintered silver and transient liquid phase alloys are key bonded interfaces that can enable the high-temperature operation of WBG devices, however it is essential to evaluate their reliability and study the possible failure mechanisms before they can be used in power electronics packages. Sintered silver is already being adopted in commercial packages as a die-attach, but large-area substrate-attach offers additional reliability challenges. In this project, we investigate the reliability of these interface materials under accelerated thermal cycling and correlate these results with finite element simulations to develop lifetime prediction models. The failure mechanisms of these bonded interfaces are also studied.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Cold Climate Integrated Heat Pump with Energy Storage for Grid-Responsive Control

An air-source integrated heat pump (ASIHP) is a multifunctional unit, capable of space cooling, space heating, and water heating. We developed a packaged cold climate ASIHP, using a multi-stage compressor, capable of working down to -25°C, and providing 100% rated capacity down to -15°C with a heating COP > 2.2. An innovative system configuration and related controls were developed to solve charge balance in the integrated heat pump, smooth charge migration, and mode transition among multiple working modes. Extensive laboratory investigations for individual modes were performed to verify the performance. Its integrated heating capability provides speedy water heating to meet domestic hot water use and store heating energy in a phase change material ceiling/panel. The heating energy storage can be used to shift the load when the electricity price is high. Based on the performance data and a calibrated model from the laboratory testing, we conducted building energy simulations driven by a model predictive control, using EnergyPlus in one U.S. cold climate zone to investigate the grid-responsive control strategy and estimate the utility cost reduction potential.

Shen, Bo↗