Electrochemistry of the NaI-AlBr3 Low Temperature Molten Salt System: Implications for Applied Battery Performance.
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The Department of Energy, Office of Science operates world-leading facilities for experimental, observational, and computational science. DOE supercomputing facilities will reach performance at the scale of ExaFLOPs in the coming years, enabling new vistas of scale and precision for large scale simulations and data analysis. Experimental scientific facilities are undergoing similar upgrades that will lead to higher data rates and correspondingly larger computational demands, and will increase the need for near-real-time processing and resilient support for more complex workflows. A transformation of science is underway, with workloads at supercomputing facilities increasingly driven by this explosion of data from instruments and experimental facilities, as well as the accelerating use of Artificial Intelligence (AI) as a tool for scientific discovery. A seamless integration of computing, networking, instruments, and experimental facilities is required to support these emerging workloads and open up a new frontier of U.S. leadership in scientific discovery. We propose to accomplish this by providing frictionless access to the ASCR supercomputing facilities. We describe our vision of combining the power of ASCR supercomputers and networking infrastructure into an integrated scalable fabric, available to end user scientists via interfaces that aim to automate and simplify access to high performance computing systems. This will enable unprecedented computational science capabilities for experimental and observational facilities, and will create new opportunities to combine large simulations and modeling with experimental facility data analysis. This blueprint for creating an integrated network of computational and experimental facilities will provide an enriched discovery environment and open doors for new scientific communities to access the DOE’s world-leading computing and networking capabilities.
Driven by the exceedingly high computational demands of simulating mechanical response in complex engineered systems with finely resolved finite element models, there is a critical need to optimally reduce the fidelity of such simulations. The minimum required fidelity is constrained by error tolerances on the simulation results, but error bounds are often impossible to obtain a priori. One such source of error is the variability of material properties within a body due to spatially non-uniform processing conditions and inherent stochasticity in material microstructure. This study seeks to quantify the effects of microstructural heterogeneity on component- and system-scale performance to aid in the choice of an appropriate material model and spatial resolution for finite element analysis.
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We theoretically studied the feasibility of building a long-term read-write quantum memory using the principle of parity-time (PT) symmetry, which has already been demonstrated for classical systems. The design consisted of a two-resonator system. Although both resonators would feature intrinsic loss, the goal was to apply a driving signal to one of the resonators such that it would become an amplifying subsystem, with a gain rate equal and opposite to the loss rate of the lossy resonator. Consequently, the loss and gain probabilities in the overall system would cancel out, yielding a closed quantum system. Upon performing detailed calculations on the impact of a driving signal on a lossy resonator, our results demonstrated that an amplifying resonator is physically unfeasible, thus forestalling the possibility of PT-symmetric quantum storage. Our finding serves to significantly narrow down future research into designing a viable quantum hard drive.
Cooperative Research and Development Agreement (CRADA) NFE-19-07845 between Oak Ridge National Laboratory (ORNL) and American Nanotechnologies, Inc. (ANI) focused on studying the use of dielectrophoresis as a mechanism to purify nanoparticles, particularly semiconducting carbon nanotubes (CNTs). The successful development of low-cost purification processes is a significant bottleneck in the adoption of semiconducting CNTs in commercial semiconducting devices. ANI is a startup company developing scalable systems for performing nanoparticle dielectrophoresis, which has previously been used only in microfluidic devices. The work done under this CRADA is exploring the use of ANI’s patent resonant dielectrophoresis (rDEP) technology and its ability to purify semiconducting CNTs at large scale. While continued work is on-going, r-DEP has proven to be a viable way to control nanoparticles in bulk dispersions. Optimization of the process is now underway to reach minimum viable product and begin material sales. Additionally, ANI and ORNL continue to collaborate on leveraging this technology to reach down the value chain and create new commercial devices.
TDA Research, Inc. has developed a low-cost sorbent (alkalized alumina) based technology for post-combustion CO 2 capture. The sorbent runs in an isothermal process for adsorption and regeneration. Neither temperature swing nor pressure swing is needed. TDA designed a system which operated 10 fixed beds to simulate a moving bed process. The flow pattern was optimized to minimize the steam usage for regeneration. An excellent sorbent (Batch 1) was also developed with collaboration with our partner. The sorbent degraded after long term operation. We developed a reprocessing procedure, which can be conducted in situ, to restore the sorbent capture capacity as the fresh sorbent after running for about 1250 hours. The reprocessing extended the sorbent longevity significantly. TDA designed a pilot unit (40’ x 32’ x 11.5’), processing flue gas equivalent to 0.5 MW of power generation. It had 10 beds and each bed could hold 1.75 m 3 of sorbent. We worked with Springs Fabrication, Inc. to complete the construction of the pilot unit. It was then shipped and installed at NCCC. The pilot unit was kept running in 24-hour mode when the flue gas and steam were available. It could run automatically without operator on site. Parametric and long-term (2 months) tests were carried out successfully. The results showed that TDA’s process can achieve 90% capture and 95% CO 2 purity for both coal and NG flue gases. For coal flue gas, the system reached performance target when processing up to 0.62 MW flue gas, 24% higher than the design capacity. Thus, capital cost could be saved on the reactors and sorbent. The strip air flow was designed to be 0.25 of that of the flue gas. The test data showed the strip/flue ratio can be reduced to as little as 0.18, which saves the power consumption for the strip air blower. The pressure drop was found to be lower than what the empirical equation calculated. The sorbent still had 91.7% of original capacity after 3-month test. With the data from the pilot test, we updated the TEA. For a 550-MW e supercritical coal fired power plant with CO 2 capture, the capture cost for TDA’s process is $34.9/tonne CO 2 captured, which meets DOE’s goal of $40/tonne and is 17.1% less than the DOE baseline Case 12. Therefore, TDA’s process has a good potential for commercialization.
Climate change is a grave threat to our ability to thrive and prosper on Earth. Accumulation of greenhouse gases in the atmosphere stifle the Earth’s ability to cool itself, which leads to a rise of the average temperature – Global warming. The planetary cooling is exclusively radiative – thermal radiation escapes into cold outer space via a flux of photons at the low-infrared range. The penetration of these photons through Earth’s atmosphere is dictated by atmospheric transparency, which, serendipitously possesses a window centered around the peak of the Plank spectrum for a black body at about room temperature – the average temperature of Earth. This window spanning about 8 --13 μm wavelengths allows most of the photons in this range to escape into the Cosmos carrying our planet’s heat away. The efficiency of this outer heat flux – and thus planet’s cooling ability – can be controlled via emissivity – a function of wavelength which multiplies the fundamental Plank spectrum of a black body at a given temperature. In the presence of non-trivial atmospheric transparency it can be shown that radiative cooling can be enhanced with properly “designed” emissivity function. For the last 100 years, or so, people have been looking into ways to engineer materials to take advantage of such enhancement for various cooling applications, from making ice to cool clothes, and substantial progress has been achieved, including cooling in direct sunlight. The beauty if this approach is that it is entirely passive – no energy input is required. The objective of this project was to investigate whether biological systems possess such advanced cooling capacity, developed via millennia of evolution, to enhance plant’s adaptation to a hot environment, to increase cooling capability, and possibly modulate spectral emissivities on short or long time scales. We wanted to know if BRISK (Biological Radiative Sky Cooling) occurs and to what extent. It is unknown whether biological systems can perform this "spectral redistribution." It is reasonable to expect, however, that they might: in particular, desert plants under high heat stress and arid conditions might evolve this mechanism in order to maintain a tolerable internal temperature. Our intended approach was twofold: extensive literature search and supporting experimental investigations on select plants. We obtained the answers from the former, while the latter proved to be more complicated to perform with required accuracy in the timespan/budget of a small seedling project. After one year of research, we concluded that among the plant species studied there seem to be no evidence of BRISK, but it’s not the end of the story. Our planet’s biosphere is immense, and the evolution is continuing. With our preliminary results and a better understanding of what to look for further discoveries await, considering the accelerated pace of infrared imaging and spectroscopy technology development, from microscopic to satellite-based studies of plants and canopies. In the end, Earth’s energy balance is ultimately tied to radiative cooling so that further investigation into this problem is directly tied to solving the Climate Change challenge.
Within the field of Transactive Energy Systems (TES), there is an active need for tools that can support and accelerate the development of these new grid solutions. Among the many tools available, blockchain stands out as a viable instrument that can help researchers develop decentralized, autonomous, and tamper-resistant grid applications. In this work, we explore the use of smart contracts (SCs), a subset of blockchain technology, and analyze their applicability to facilitating the implementation of TES solutions. In particular, we focus on presenting areas of opportunity and potential drawbacks, along with use cases that can benefit from this technology building upon previous research developed by Pacific Northwest National Laboratory and other research organizations. This work builds upon the fundamentals of TES and smart contract technology to develop a series of software templates that can be used by industry to build TES-oriented grid solutions. These templates are intended to be platform agnostic and take into consideration the unique properties of SCs and distributed ledger storage mechanisms to ensure actual code implementations remain aware of the limitations of the technology. The proposed templates have the potential to enable software architects to mix and match components to satisfy their application requirements, thereby reducing the number of resources required to implement blockchain-based solutions. These templates are divided into two main components—data and behavioral models. The data models are intended to help software engineers represent the underlying grid objects along with their properties in a ledger-based storage system. The behavioral models are used to describe the processes and actions that actors within a system must perform to achieve a given outcome such as registering an asset, placing a bid, and performing bid clearances. These two components are documented in a Unified Modeling Language (UML) format and are intended for use in SC-based implementations, with special behavioral considerations to account for the asynchronous properties of the underlying ledger and the typical execution model of smart contracts. Finally, future research ideas and potential extensions to this work are discussed. In particular, known limitations and potential improvements of the developed product are identified and expected to be addressed in future revisions of the template model.
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We explore the application of Physics-Informed Neural Networks (PINNs) for simulation of thermionic electron sources. This is motivated by the need for quick surrogate models used to simulate such sources within a digital twin of a complete particle accelerator. Here, a PINN was developed on a simplified model of the thermionic source: the planar diode. This model very accurately simulated the system, and performed significantly better than a traditional neural network while also using less training data. We hope to apply this proof-of-concept in motivating the development of a PINN model for a full thermionic electron source at the University of Chicago.
The objectives of this project were to test and validate the incorporation of an air barrier and 1” of continuous thermal insulation into standard re-siding jobs to create a more durable, better performing envelope as part of a retrofit project that homeowners will do anyway.
Kairos Power, LLC, is developing its version of the Fluoride-cooled High-temperature Reactor, the KP-FHR. The design uses a pebble bed core with fluoride salt as a coolant. The pebbles used in the KP-FHR have a diameter of 4 cm, with a shell fuel region where TRISO particles are embedded. A Pebble bed core design is adopted by several Gen IV reactors, They boast many benefits, such as fuel integrity, highly efficient heat transfer, and passive safety. However, it is challenging to accurately predict temperature and flow inside a pebble bed. Traditional approaches use the porous media model, which regards the pebble bed as a continuous medium, but with different temperature fields representing different levels, such as the fluid temperature, pebble surface temperature, and pebble center temperature. Empirical heat transfer correlations are adopted to calculate the heat transfer coefficient between different phases. However, empirical correlations are usually validated with experimental data, which usually lacks detail inside the pebble bed. The available experimental data is also generally at a high Reynolds number, which falls outside of the conditions of KP-FHR. Explicit computational fluid dynamics (CFD) simulations of randomly packed pebble beds have only become feasible recently. This is thanks to the rapid development of computational power and scalable algorithms. In this work, we used the Spectral Element Method (SEM) CFD code NekRS to simulate the randomly packed pebble bed in a cylindrical container. NekRS, which is the GPU variant of Nek5000, but refactored to utilize the computational power of GPUs using the OCCA library to run on hybrid architecture high performance computing systems. It was initially developed with the libParamunal library, but truncated and tuned for large-scale turbulence simulation. As a result, the SEM reaches higher precision with the same degrees of freedom by using a high-order Lagrange polynomial basis distributed on Gauss-Lobatto-Legendre quadrature inside each element, compared to lower-order methods, such the Finite Volume Method and Finite Element Method. The report is divided into five parts. We start with a general discussion of the pebble bed reactor, along with a specific investigation into the KP-FHR. The second part presents the numerical methodology. In the third part, we study a modular pebble bed with 1741 pebbles in a container of 7 pebble-diameter radius. Beyond LES simulations done by NekRS, we also leveraged the thermal radiation model in OpenFOAM to study heat transfer under no-forced-flow scenarios. Then, in the fourth part we simulated a pebble bed similar to the size of the Hermes Test Reactor. The total number of pebbles is in these simulations is 34,374. The container radius is 14 pebble-diameters. Finally, the report concludes in part five, with a discussion of future work.
RTI International, in collaboration with Pacific Northwest National Laboratory (PNNL), Carbon Capture Simulation for Industry Impact (CCSI 2 ), Electricity Power Research Institute (EPRI), and West Virginia University (WVU), successfully completed a joint research effort in developing a cost-effective, resilient, load-following advanced CO 2 capture technology for natural gas power plants. The project’s objective was to develop a CO 2 capture process that maximizes the net present value (NPV) of the electricity sale by minimizing the levelized cost of electricity (LCOE) under dynamic plant loads and high renewable penetration environments. The two key innovations developed in this project were the use of (i) advanced water-lean solvents (WLSs) and (ii) process intensification equipment, such as a rotating packed bed (RPB) absorber and dual-stage flash regeneration. The process’s low CO 2 capture cost is realized through WLSs’ low energy required for solvent regeneration, which lowers the operating cost while RPBs intensify the absorption process and reduces the power plant capital cost. A suite of advanced computational and simulation packages was implemented to guide the process design, validate the dynamic response of the capture plant, evaluate system-wide performance, and maximize the power plant’s profit. The project also engaged with power producers and other stakeholders to ensure its technical relevance and techno-economic viability. The development of this highly disruptive CO 2 capture technology could accelerate the industry adoption and thereby lower the greenhouse gas emissions of the U.S. power sector. Deployment of this technology can increase the reliability and decrease the cost of electricity generation in the U.S. by enabling the use of low-carbon fossil fuels to balance fluctuations of renewable energy availability.
Stormwater runoff from streets and highways is typically captured by drainage structures strategically placed when roadways use curb or curb and gutter systems. These structures include catch basins with grates, inlets, or combination grates/inlets that collect and discharge storm water runoff to buried pipe conveyance systems. The performance of these drainage structures is measured in terms of hydraulic efficiency, which is defined as the percentage of flow captured by the basin as compared to the total flow draining to the structure. Understanding the performance of these drainage structures allows for a safe and economical design which prevents flooding along Ohio roadways.
As part of the Scientific Discovery through Advanced Computing (SciDAC) program, the Quantum Chromodynamics Nuclear Tomography (QuantOM) project aims to analyze data from Deep Inelastic Scattering (DIS) experiments conducted at Jefferson Lab and the upcoming Electron Ion Collider. The DIS data analysis is performed on an event-level by combining the input from theoretical and experimental nuclear physics into a single, composable workflow. The optimization itself (I.e. fitting the experimental data with theoretical predictions) is carried out by a machine / deep learning algorithm. The size of the acquired DIS data as well as the complexity of the workflow itself require that the analysis is performed across multiple GPUs on high performance computing systems, such as Polaris at Argonne National Laboratory. This presentation discusses the novelties and challenges that came along with parallelizing this workflow. Recent results are compared to common distributed training techniques.
To achieve high capacity factors, the nuclear fleet has relied on labor-intensive and time-consuming operation and preventive maintenance programs for plant systems. Manually performed inspection, calibration, testing, and maintenance of plant assets at periodic frequencies, along with the time-based replacement of assets irrespective of condition, have resulted in a costly, labor-centric business model. Fortunately, there are technologies that can eliminate unnecessary preventive maintenance activities by deploying risk-informed predictive maintenance, enabling the transition to a technology-centric business model.