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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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DOE OSTI2022

Chapter 2: Evaluating a Concentrating Solar Power Plantas an Extended-Duration Peaking Resource

We explore the ability of a concentrating solar power (CSP) plant with thermal energy storage (TES) to provide peaking capacity. We focus on future power systems, wherein net load patterns may be significantly different than they are today (e.g., due to higher renewable-energy penetrations). We examine 28 locations in the southwestern United States over an 18-year period. The hourly operation of the CSP plants are simulated to determine their potential to provide energy during an eight-hour peak-load window for each day up to 365 days per year. Our result shows that for the large majority of locations and years, CSP plants with certain configurations (i.e., in terms of solar field and TES sizes) can provide nearly 100% peak-load capacity. We examine also the amount of supplemental energy (e.g.,by using natural gas as a supplemental thermal-energy source) that would be required to ensure that a CSP plant could serve the eight highest-load hours of every day of the year. We find that in most cases, a CSP plant supplemented with natural gas would require less than 5% of the fuel that is used by a natural-gas fired power plant providing the same level of reliable capacity. A series of sensitivity analyses show that these results are robust to the number of peak-load hours and days that are considered and the configuration of the CSP plant.

capacity value↗
DOE OSTI2024

The power reliability event simulator tool (PRESTO): A novel approach to distribution system reliability analysis and applications

The growing interest in onsite solar photovoltaic and energy storage systems is partially motivated by customer concerns regarding grid reliability. However, accurately assessing the effectiveness of PVESS in mitigating these interruptions requires a comprehensive understanding of location-specific outage patterns and the ability to simulate realistic scenarios. To address the gap, we introduce the Power Reliability Event Simulation TOol (PRESTO), the first publicly available tool that simulates location-specific power interruptions at the county level. PRESTO allows for a more realistic assessment of system reliability by considering the unpredictability and location-specific patterns of power interruptions. We applied PRESTO in a case study of a single-family home across three U.S. counties, examining the performance of a solar photovoltaic system with 10kWh of battery storage during short-duration power interruptions. Our findings show that this system reliably met 93% of energy demand for essential non-heating and cooling loads, fully serving these loads in 84% of events, despite the constraints of daily time-of-use bill management which limits the battery's state-of-charge reserve. However, when heating and cooling loads were included, system performance decreased significantly, with only 70% of demand met and full service in 43% of events. These results highlight the challenges of using solar photovoltaic and energy storage systems for short-duration outages, emphasizing the need to consider factors like battery size and grid charging strategies to improve reliability. Our study demonstrates the practical applications of PRESTO, providing valuable insights into potential mitigation strategies including grid charging and optimizing battery size.

14 SOLAR ENERGY↗
DOE OSTI2024

Design and optimization of flexible decoupled high-temperature gas-cooled reactor plants with thermal energy storage

Advanced nuclear power plants are well-positioned for future zero-carbon grids, however, the need for flexible power generation will be required over the traditional emphasis on baseload generation for meeting historical demands. To achieve such flexibility, this work examines viable configurations for coupling nuclear energy production with thermal energy storage. Previous designs on nuclear-thermal energy storage configurations for advanced reactor designs, which utilized reactor steam as the heat source for charging the thermal energy storage, are restricted by the heat diversion ratio and efficiency losses, thus their impacts can be limited. In this context, this study proposes configurations for fully decoupling the nuclear reactor from the power cycle and positioning the storage as an intermediate loop, thereby achieving an unconstrained heat diversion ratio and improved efficiency. Compared with a standard high-temperature gas-cooled reactor’s power cycle, steady-state thermodynamic modeling and dispatch optimizations quantify the benefits of a steam reheat cycle within the fully decoupled thermal energy system to separate the plant cycle from the high-pressure primary side. These benefits are further detailed, compatible with required high-temperature and high-pressure conditions, through (1) open-source dynamic transient models that examine the impact of off-design operation on the systems, (2) the investigation of components design and costing and finally (3) sizing and dispatch optimization. The fully-decoupled design achieves a cycle efficiency of 43.1%, an enhancement over the vendor’s standard efficiency of 42.2% (Xe-100 design). Here, the proposed design offers strengthened physical barriers from the nuclear island as well as superior operational flexibility and power boosting. Dispatch optimization and market analysis reveal that thermal energy storage size is highly dependent on the peak patterns of electricity prices and the minimum generation level constraint imposed on the balance of plant. Evaluation of off-design operation demonstrates that the full decoupling design with the suggested fail-safe control mechanisms ensures a minimal impact on reactor parameters, even during rapid power ramping.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Probing the atmospheric boundary layer with integrated remote-sensing platforms during the American WAKE ExperimeNt (AWAKEN) campaign

The American WAKE ExperimeNt (AWAKEN) collaboration is an observational-based field campaign in northern Oklahoma intended to analyze the potential influence of onshore wind farms and their collective wakes on wind power production, turbine structural loads, and on the atmospheric boundary layer (ABL). Focusing on the ABL effects, the University of Oklahoma and the Lawrence Livermore National Laboratory collected continuous high-resolution kinematic and thermodynamic profile measurements during 2022 and Summer 2023. The deployment strategy for these campaigns is detailed first, followed by an initial comparison of data from two sites in the AWAKEN domain: a near-farm site to examine collective wake impacts on the ABL, and a far-field site remaining outside the wind farm-waked region. Here, we summarize the datasets available and demonstrate the benefits of these observations and multiple value-added products (VAPs) for investigation of ABL features observed during AWAKEN. We also highlight examples of preliminary analyses, including ABL height detection and nocturnal low-level jet examination, which are produced using novel VAPs based on optimal estimation to retrieve deeper Doppler lidar wind profiles than previously resolved, along with their uncertainty. By including the near-farm and far-field site in these analyses, we identified a pattern of stronger lower-atmospheric mixing at the near-farm site than the far-field site, motivating deeper investigation into the relationship between wind farms and general ABL characteristics. Future analysis will delve deeper into this relationship by examining other ABL characteristics, such as atmospheric stability and convection.

17 WIND ENERGY↗
DOE OSTI2020

Self-aligned concentrating immersion-lens arrays for patterning and efficiency recovery in scaffold-reinforced perovskite solar cells

In contrast to existing silicon, CIGS, and multi-junction cells that exhibit remarkable mechanical durability, perovskite solar cells have been shown to delaminate when subjected to the mechanical loads that occur during processing and field exposures, limiting their potential as a reliable solar technology. Mechanical reinforcement is thus essential for durable and reliable perovskite technologies. One current strategy to overcome the thermomechanical fragility of perovskite solar cells is to extrinsically shield them by introducing reinforcing scaffolds which partition the cell into many distinct microcells, but improvements in mechanical stability coincide with a reduced device efficiency due to parasitic absorption by the scaffold. We address this reduced efficiency by integrating concentrating immersion-lens arrays (CILAs) into scaffold-reinforced solar cells. These scaffolds are lithographically patterned by a maskless process, in which ultraviolet light is used to pattern the scaffolds through the CILAs, ensuring self-alignment and optical contact with the microcells. Perovskite devices are deposited into the microcells, and during operation, light is concentrated into the microcells and away from the insulating scaffolds, resulting in mechanically resilient solar cells with efficiencies comparable to planar devices. The CILAs also exhibit passive tracking of incident light—verified experimentally and with ray-tracing simulations—which is critical for optimal power output as the sun moves across the sky during the day. The simplicity of the fabrication process and the efficiency of the resulting scaffolded devices show that a lens-integrated scaffold-reinforced structure is a potential pathway to high-performance, robust, commercially viable perovskite solar cells.

14 SOLAR ENERGY↗
DOE OSTI2026

From Cell to System: Accelerated hpc Simulations of BESS Aging under Frequency Regulation and Arbitrage use cases

Lithium-ion battery energy storage systems (BESS) packs have emerged as a leading solution for grid-scale energy storage, enhancing resiliency and balancing load fluctuations. Yet, experimental characterization of large-format LIB packs-particularly to assess performance and degradation over hundreds of cycles - demands substantial hardware investment and multi-year testing campaigns. In this work, we couple a hierarchical, physics-based modeling framework agnostic to electrode chemistries with high-performance computing to accelerate systems level evaluation by upto two orders of magnitude. Building on the open-source liionpack platform, we implement cell, module, and pack-scale electrochemical models enriched with mechanistic aging mechanisms and deploy them on an HPC cluster to simulate 150−200kWh systems over 500 - 1,000 cycles with in days. We subject these virtual B ESS to both constant-current cycling and realistic grid service profiles spanning frequency regulation, ramp-rate support, and energy arbitrage-and quantify the resulting degradation patterns. Our results reveal that localized cell aging can induce substantial nonuniformity at module and pack levels, with service-specific cycling protocols driving distinct aging modes. This rapid, multiscale modeling approach provides a powerful design-space exploration tool for optimizing electrical architecture, control strategies, and operational schedules to prolong pack lifetime and lower total cost of ownership.

Ayalasomayajula, Surya [ORNL] (ORCID:0009000860788↗
DOE OSTI2024

Simulation of the Multi-Wake Evolution of Two Sandia National Labs/National Rotor Testbed Turbines Operating in a Tandem Layout

The future of wind power systems deployment is in the form of wind farms comprised of scores of such large turbines, most likely at offshore locations. Individual turbines have grown in span from a few tens of meters to today’s large turbines with rotor diameters that dwarf even the largest commercial aircraft. These massive dynamical systems present unique challenges at scales unparalleled in prior applications of wind science research. Fundamental to this effort is the understanding of the wind turbine wake and its evolution. Furthermore, the optimization of the entire wind farm depends on the evolution of the wakes of different turbines and their interactions within the wind farm. In this article, we use the capabilities of the Common ODE Framework (CODEF) model for the analysis of the effects of wake–rotor and wake-to-wake interactions between two turbines situated in a tandem layout fully and partially aligned with the incoming wind. These experiments were conducted in the context of a research project supported by the National Rotor Testbed (NRT) program of Sandia National Labs (SNL). Results are presented for a layout which emulates the turbine interspace and relative turbine emplacement found at SNL’s Scaled Wind Technologies Facility (SWiFT), located in Lubbock, Texas. The evolution of the twin-wake interaction generates a very rich series of secondary transitions in the vortex structure of the combined wake. These ultimately affect the wake’s axial velocity patterns, altering the position, number, intensity, and shape of localized velocity-deficit zones in the wake’s cross-section. This complex distribution of axial velocity patterns has the capacity to substantially affect the power output, peak loads, fatigue damage, and aeroelastic stability of turbines located in subsequent rows downstream on the farm.

Baruah, Apurva (ORCID:0000000252354068)↗
DOE OSTI2024

The Toughness of Interlocking Metasurfaces

Interlocking metasurfaces (ILMs) are arrays of autogenous latching unit cells patterned across a surface. These create structural joints similar to bioinspired suture joints but patterned over a 2D surface rather than a 1D seam. This enables ILMs to be an alternative to conventional joining technologies such as bolts, welds, and adhesives. However, compared to conventional joining methods, relatively little is known of the engineering considerations for designing structural ILMs. Herein, the interfacial toughness of an archetypal ILM is examined for the first time. Under the conditions studied here, the ILM is substantially tougher than the material from which it is made, in this case, exhibiting up to a 50% increase in interfacial crack initiation energy over the solid base material, a photocured 3D‐printed polymer. Through experimental tests using in‐situ digital image correlation along with complementary computational analyses, the mechanism of toughening in the ILM structure and the origins of toughness anisotropy are revealed. The increase in toughness is associated with cross‐cell interactions, that is, load‐sharing across unit cells, which give rise to a finite process zone length with different effective material properties. In this way, ILM toughening is analogous to crack blunting in ductile materials or fiber bridging in composites; yet here, the ILM is composed of a single‐phase base material and so the architected toughening is geometric in nature and hence amenable to future topological optimization.

fracture↗
DOE OSTI2019

Flexible Geothermal Power Generation utilizing Geologic Thermal Energy Storage (Final Seedling Project Report)

This report concludes that there is a cost-effective strategy for seasonal storage of heat that will provide firm, but dispatchable, electrical generating capacity in times when other renewable energy is not available to meet demand. Deployment of the technology appears to require no new technology, but instead combines solar, geothermal, and conventional oil and gas drilling technologies in a novel way. The study basis is the use of sedimentary geologic formations as a medium for thermal energy storage (TES), specifically for heat collected in concentrating solar collectors. The study identifies methodologies that could be used to transport this heat into and out of the subsurface in order to produce dispatchable electrical power, and reports on initial optimization results. The GeoTES system (heat input, storage, heat recovery, and heat to electric conversion) described in this analysis has the potential to provide a unique pathway for increasing the grid penetration of renewable energy in large blocks of power and across many states and regions. Further, the system can be used both to meet the nation’s flexible energy needs while also improving grid stability and reliability. The present study evaluated the use of a large number of dedicated wells to store and recover the heat, essentially creating a synthetic geothermal reservoir. The use of sedimentary geology allows the wells to be drilled at low cost. Dedicated hot and cold wells are used, arranged in a five-spot well pattern with each hot or cold well surrounded at an appropriate distance by the opposite type of well. In large numbers this becomes alternating rows of hot and cold wells. Each hot and cold well is operated using a push-pull strategy. This configuration provides the ability to immediately recover stored hot fluid from a GeoTES reservoir, or to store the heat over many months for recovery at low loss when needed. This is a practical approach for managing the system’s fluid inventory and reducing parasitic load. The production and injection power requirements are reduced because the rows of wells operating in “push” mode provide help to the wells operating in “pull” mode, and vice-versa. Initial charging of a GeoTES system increases the heat recovery temperature. Increasing the duration of the charging period decreases the magnitude of the temperature fluctuations that occur following prolonged system operation. Because of direct contact of the heated water with the reservoir formation, the production of both hot water and steam from the TES, and the temperature ranges of the recovered fluid (190 – 230°C or 375 – 445°F), conventional geothermal power cycles were used to convert the stored heat to electricity. A power cycle configuration for the GeoTES system was selected following a screening study of a number of flash, and flash/binary hybrid options. This analysis concluded that, of the configurations evaluated, a dual-stage flash steam cycle provides the lowest capital costs per unit net power generation with an acceptable hot brine inlet fluid flow rate. The evaluation included the power plant cost estimate, the cost and number of wells and the associated parasitic loads. Annual power generation performance was simulated to evaluate capacity factor and LCOE. The LCOE calculated for the inherently high capacity GeoTES system was $\$$0.13/kWhe. This value was calculated for the case where the solar thermal collector was sized in such a way that the solar collectors permitted an annual power plant capacity factor of up to 97%. The power cycle was able to provide power to the grid every night of the year, and flexible base-load power during the winter, if needed. This LCOE value compares favorably with reported values for solar photovoltaic plus battery energy storage (PV+BES) systems in the open literature, i.e. $\$$0.148/kWhe for a PV+BES system with 4 hours of electrochemical battery energy storage capacity. Addition of battery energy storage with more hours of storage would further increase PV+BES system LCOE and increase the separation between GeoTES and PV+BES. A GeoTES system would therefore provide superior economics for high capacity and long duration solar energy storage.

14 SOLAR ENERGY↗
DOE OSTI2020

Evaluation of Graph Analytics Frameworks Using the GAP Benchmark Suite

The analysis of connected data is an increasingly important application in high-performance computing. Such analyses can reveal fraudulent patterns in financial transactions, optimize telecommunications networks, predict information flow in social networks, etc. However, the landscape of graph analytics is highly diverse. Graph algorithms stress processor architectures differently, and no one graph can represent all topologies. Consequently, no single approach or framework is expected to be optimal for all graph analytics problems. To help make sense of this diverse landscape, we evaluated four approaches to graph analytics: GraphBLAS, Galois, BGL17, GraphIt; and compare them against hand-tuned implementations that take advantage of hardware features on our test platform. Graph- BLAS formulates graph analytics as sparse linear algebra. Galois provides syntactic constructs for data parallelism over irregular data structures. BGL17 is a generic C++ template library for implementing graph algorithms. GraphIt provides a domain- specific language to describe and optimize graph algorithms. We use the GAP Benchmark Suite to establish baseline performance and guide the side-by-side evaluation of each framework. GAP consists of 30 tests: six graph analytics algorithms (breadth- first search, single-source shortest path, PageRank, betweenness centrality, connected components, and triangle counting) run on five graphs, each with different topological characteristics (e.g., high diameter, skewed degree distribution, high average degree). High-performance reference implementations are included for each benchmark algorithm. Because a graph can be loaded into memory a number of ways (e.g., flat file on disk, compressed sparse format, data frames, retrieved from SQL or NoSQL databases), our evaluation focused on computational performance rather than I/O. Our results show the relative strengths of each framework.

Graph algorithms, Benchmarking, shared-memory prog↗
DOE OSTI2019

A BI-LEVEL APPROACH TO FUTURE POWER SYSTEM CO-OPTIMIZATION WITH HIGH PENETRATION OF RENEWABLE ENERGY AND RESPONSIVE DEMAND

Electricity generation accounts for nearly half of the total CO2 emissions in the United States. For this reason, the development and integration of renewable resources will play an essential role in achieving the societal objective of mitigating climate change through reduction of greenhouse gas emissions. In conjunction with the environmental benefits of renewable energy, the most common renewable sources, such as wind and solar, also increase the uncertainties surrounding generation in power systems, which adds significant challenges to the system operation and planning. The uncertainties and forecasting errors surrounding renewable generation are normally addressed through the use of reserves from traditional generators. At greater levels of renewable penetration, sufficient generator reserves may not be available or economically viable. In contrast, the promise of demand-side resources in this arena lies in the spatially widespread availability, rapid response potential, and lower cost of features that already exist in the system. However, the challenge of responsive demand arises from the need to understand, manage, and incentivize a very large number of resources to participate effectively in efficient operation of the complex power system. Since demand response comes from the distribution system, microgrids as the basic building blocks of future distribution systems will be a critical environment for the study of demand response. To support integration of microgrids with flexible loads in future power systems, the operational mode of power systems will need to evolve. Therefore, it is going to be critical to have new and efficient co-optimization methods for coordination of the various power market participants and the scheduling of resources in the power systems of the future. Motivated by the rapid increase in renewable penetration, the need for effective demand response programs, and a changing system structure, this dissertation seeks to define a new strategy that supports co-optimization of various participants in power systems with emphasis on high renewable penetration and demand response. This strategy has three components; 1) an exploration of the capabilities of different types of demand response programs in a microgrid, 2) development and implementation of a bi-level framework for co-optimization of the main grid with high renewable penetration and a microgrid with demand response capabilities, and 3) expansion of the bi-level framework from a microgrid to a general distribution system to explore the advantages of the bi-level co-optimization approach over the traditional optimization approach. Conclusions of this work illustrate that the stochastic rolling horizon approach could effectively manage the operation of a microgrid with various demand response programs. In addition, the bi-level approach is a promising co-optimization framework for the transmission and distribution levels that could increase system renewable penetration and reduce operation costs. Compared to the traditional framework, the bi-level framework yields more equitable cost sharing patterns among the market participants as well as better support for the power system evolution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Efficient Parallelization of Irregular Applications on GPU Architectures

With the enlarging computation capacity of general Graphics Processing Units (GPUs), leveraging GPUs to accelerate parallel applications has become a critical topic in academia and industry. However, a wide range of irregular applications with the computation-/memory-intensive nature cannot easily achieve high GPU utilization. The challenges mainly involve the following aspects: first, data dependence leads to coarse-grained kernel and inefficient parallelism; second, heavy GPU memory usage may cause frequent memory evictions and extra overhead of I/O; third, specific computation patterns produce memory redundancies; last, workload balance and data reusability conjunctly benefit the overall performance, but there may exist a dynamic trade-off between them. Targeting these challenges, this dissertation proposes multiple optimizations to accelerate two real-world applications: many-body correlation functions to simulate nuclear physics in a large-scale scientific system; the other is the eALS-based matrix factorization recommendation system. To accelerate the calculations of many-body correlation functions, this dissertation presents three frameworks in GPU memory management and multi-GPU scheduling. Firstly, an optimized systematic GPU memory management framework, MemHC, utilizes a series of new memory reduction designs in GPU memory allocation, CPU/GPU communications, and GPU memory oversubscription. Secondly, an enhanced multi-GPU scheduling framework, MICCO, particularly by taking both data dimension (e.g., data reuse and data eviction) and computation dimension into account. MICCO designs a heuristic scheduling algorithm and a machine learning-based regression model to generate the optimal settings of a proposed new concept to manage the trade-off. Thirdly, a locality-aware multi-GPU scheduling framework. This scheduler leverages pipeline batch generation with a looking-ahead strategy by building local dependency graphs for memory transfer reduction and better data reuse, achieving up to 79.92% memory cost reduction and 1.67x speedup. To parallelize the eALS-based recommendation system, this dissertation proposes an efficient CPU/GPU heterogeneous recommendation system, HEALS. HEALS employs newly designed architecture-adaptive data formats to achieve load balance and good data locality on CPU and GPU. To mitigate the data dependence, HEALS presents a CPU/GPU collaboration model for both task parallelism and data parallelism with multiple kernel computation optimizations. In summary, this dissertation efficiently accelerates two typical irregular applications on GPUs by building four frameworks, including CPU/GPU collaboration, GPU memory management, and multi-GPU scheduling.

Wang, Qihan↗
DOE OSTI2024

Analyzing the Effects of Atmospheric Turbulent Fluctuations on the Wake Structure of Wind Turbines and Their Blade Vibrational Dynamics

In recent trends, a rising demand for renewable energy has driven wind turbines to larger proportions, where lighter blade designs are often adopted to reduce the costs associated with logistics and production. This causes modern utility-scale wind turbine blades to be inherently more flexible, and their amplified aeroelastic sensitivity results in complex multi-physics reactions to variant atmospheric conditions, including dynamic patterns of aerodynamic loading at the rotor and vortex structure evolutions within the wake. In this paper, we analyze the influence of inflow variance for wind turbines with large, flexible rotors through simulations of the National Rotor Testbed (NRT) turbine, located at Sandia National Labs’ Scaled Wind Farm Technology (SWiFT) facility in Lubbock, Texas. The Common Ordinary Differential Equation Framework (CODEF) modeling suite is used to simulate wind turbine aeroelastic oscillatory behavior and wind farm vortex wake interactions for a range of flexible NRT blade variations, operating in differing conditions of variant atmospheric flow. CODEF solutions of turbine operation in Steady-In-The-Average (SITA) wind conditions are compared to SITA wind conditions featuring a controlled gust-like pulse overimposed, to isolate the effects of typical wind fluctuations. Finally, simulations of realistic time-varying wind conditions from SWiFT meteorological tower measurements are compared to the solutions of SITA wind conditions. These increasingly complex atmospheric inflow variations are tested to show the differing effects evoked by various patterns of spatiotemporal atmospheric flow fluctuations. An analysis is presented for solutions of wind turbine aeroelastic response and vortex wake evolution, to elucidate the consequences of variant inflow, which pertain to wind turbine dynamics at an individual and farm-collective scale. The comparisons of simulated farm flow for SITA and measured fluctuating wind conditions show that certain regions of the wake contain up to a 12% difference in normalized axial velocity, due to the introduction of wind fluctuations. The findings of this study prove valuable for practical applications in wind farm control and optimization strategies, with particular significance for modern utility-scale wind power plants operating in variant atmospheric conditions.

Farrell, Alayna (ORCID:000000023555720X)↗
DOE OSTI2018

Next Generation Hydrogen Storage Vessels Enabled by Carbon Fiber Infusion with a Low Viscosity, High Toughness System

Onboard hydrogen storage for fuel cell vehicles holds considerable commercial promise which could be accelerated by lower cost storage vessels. In past years, the use of lower cost reinforcing fiber had been studied as an intuitive avenue to reduce vessel costs. Although the matrix resin does not play a large role in bearing stress loads in many areas of a filament wound pressure vessel, the matrix is relied upon for stress transfer between fibers and appears to play an important role in certain regions of the vessel construction such as the “tangent region” where the cylinder section transitions to the dome. Also, the matrix resin can play a role in the damage tolerance of vessels after impact events. Furthermore, the effect of dry spots or voids in the laminate can affect stress transfer between fibers in a manner that is challenging to quantify. Therefore, excess fiber must be used to account for this variability in performance. Unfortunately, in the case hydrogen pressure vessels the cost of the vessel is strongly dependent on the amount of carbon-fiber used in the design and manufacturing. An extensive investigation on the use of non-traditional fabrication processes enabled by an ultra-low viscosity thermoset resin (<10 cP) with high fracture toughness was conducted to determine the opportunity to reduce carbon-fiber usage and the associated high cost. The hypothesis was based on enabling a lower variation in vessel performance from a reduction in void defects (often found with traditional wet-winding) and a more defect-tolerant vessel resulting from use of a matrix resin with high fracture toughness. These two features were anticipated to justify a more aggressive design approach to the vessel winding pattern. From the work in this project, the potential use of the vacuum infusion approach to produce high quality vessels was validated. However, the team recognized the need to re-optimize the design, the dry-process, and the vacuum infusion process at each scale-up stage. During the planning phase, the critical need to re-optimize at this frequency was under-estimated. Also, the team identified the lack of industry testing standards on subscale parts for providing predictive value of vessel performance. Therefore, significant effort is needed to produce statistically significant numbers of vessels. To produce such numbers of vessels the project needed in retrospect to partner closely with a series vessel manufactures to develop a proof of concept production line to make the vessels. However, without the appropriate data, a manufacturing partner was challenging to fully engage during this timeline. New technologies relying on changing raw materials and manufacturing processes carry inherent risks. This project did not reach its ultimate objectives of demonstrated a vessel with lower carbon-fiber. But some companies expressed interest in learning from the partial successes of this project for future challenges in pressure vessel applications.

08 HYDROGEN↗
DOE OSTI2021

Improving I/O Performance for Exascale Applications through Online Data Layout Reorganization

The applications being developed within the U.S. Exascale Computing Project (ECP) to run on imminent Exascale computers will generate scientific results with unprecedented fidelity and record turn-around time. Many of these codes are based on particle-mesh methods and use advanced algorithms, especially dynamic load-balancing and mesh-refinement, to achieve high performance on Exascale machines. Yet, as such algorithms improve parallel application efficiency, they raise new challenges for I/O logic due to their irregular and dynamic data distributions. Thus, while the enormous data rates of Exascale simulations already challenge existing file system write strategies, the need for efficient read and processing of generated data introduces additional constraints on the data layout strategies that can be used when writing data to secondary storage. We review these I/O challenges and introduce two online data layout reorganization approaches for achieving good tradeoffs between read and write performance. We demonstrate the benefits of using these two approaches for the ECP particle-in-cell simulation WarpX, which serves as a motif for a large class of important Exascale applications. Here, we show that by understanding application I/O patterns and carefully designing data layouts we can increase read performance by more than 80 percent.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Coiled Tube Gas Heaters For Nuclear Gas-Brayton Power Conversion

This project developed an alternative design for heat exchangers for application to heating supercritical carbon dioxide (S-CO 2 ) or air for power conversion. We have identified an annular coiled tube bundle configuration–where hot sodium enters tubes from multiple vertical inlet manifold pipes, flows in a spiral pattern radially inward and downward, and then exits into an equal number of vertical outlet manifold pipes–as a potentially attractive option. The S-CO 2 gas or air flows radially outward through the tube bundle. Coiled tube gas heaters (CTGHs) are expected to have excellent thermal shock, long-term thermal creep, in-service inspection, and reparability characteristics, compared to alternative options. CTGHs have significant commonality with modern nuclear steam generators. Extensive experience exists with the design, manufacture, operation, in-service inspection and maintenance of nuclear steam generators. The U.S. Nuclear Regulatory Commission also has extensive experience with regulatory guidance documented in NUREG 0800. CTGHs leverage this experience and manufacturing capability. The most important difference between steam generators and gas-Brayton cycles such as the S-CO 2 cycle is that the heat exchangers must operate with counter flow with high effectiveness to minimize the pinch-point temperature difference between the hot liquid coolant and the heated gas. S-CO 2 -cycle gas heaters also operate at sufficiently elevated temperatures that time dependent creep is important and allowable stresses are relatively low. Designing heat exchangers to operate in this regime requires configurations that minimize stresses and stress concentrations. The cylindrical tubes and cylindrical manifold pipes used in CTGHs are particularly effective geometries. The first major goal of this research project was to develop and experimentally validate a detailed, 3-D multi-phase (gas-solid-liquid) heat transport model for CTGHs, using methods similar to earlier UCB multi-scale models for PCHEs, which will enable optimization of CTGH designs with respect to the number of manifold pipes, S-CO 2 and sodium circulating power, and other design parameters. CTGHs are relatively novel because the tubes loaded in compression as well as the manifold pipes which form the tube sheets. The second major goal of the experimental and modeling effort was to optimize the assembly and heat treatment of the tube-to-tubesheet joints, using a novel tapered joint configuration, and to develop diffusion-bonded joints with high shear strength and creep resistance, as has been done in other applications for tapered plugs. The project would also conduct creep testing of these tubesheet joints in pressurized furnaces under prototypical conditions of stress and temperature, however this was not feasible due to numerous hinderances. Detailed microstructural investigations was conducted on the diffusion bond interface, while the bond strength was evaluated by tensile testing. The mechanical testing quantified the overall strength of the bond and measured the pull out force as well as long term behavior, which complemented the microstructural analysis and gave a complete understanding of the diffusion bond.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

Final Report DE-FE0031785 Mohsen Ahmadian, Ph.D. Demonstration of Proof of Concept of a Multiphysics Approach for Real-Time Remote Monitoring of Dynamic Changes in Pressure and Salinity in Hydraulically Fractured Networks

Hydraulic fracturing has evolved into a multistep process with varying flow rates, carrier fluids (e.g., gel or slickwater), proppant loadings, and proppant grain sizes. As a result, primary recovery from a hydraulically fractured tight-oil reservoir is often a tiny fraction of the original oil in place, ranging between 5 and 10%. As stated in the FOA1990, “part of this problem is due to the inability of current well completion processes to effectively stimulate the entire reservoir volume in contact with the wellbore. Innovative technologies are needed that can help improve the effectiveness of reservoir completion methods, maximize stimulated reservoir volumes, and optimize recovery over the entire producing life span of a well”. We first need to enhance the current fracture diagnostic techniques to improve a well-completion design. However, detecting and delineating a subsurface hydraulic fracture is extremely difficult because the induced fracture network is only fractionally propped, and these propped fractures are generally very thin. Microseismic and tiltmeter monitoring techniques can provide information on the fracture extent but provide little or no information on the movement and final distribution of proppant or production fluids. On the other hand, electromagnetic (EM) imaging has shown the capability to monitor proppant distribution throughout the fracture area, especially in the presence of Electrically Active Proppants (EAPs). A previous EM survey of hydraulic fracturing at the Devine Fracture Pilot Site (DFPS) and subsequent EM code developments demonstrated this survey as a robust technique to remotely interrogate the extent of the EAP-filled hydraulic fracture during its propagation. The objectives of the project were threefold: (1) to capitalize on the material properties of an EAP to demonstrate remote monitoring of relative changes in pressure, pressure, and flow that are commonly encountered during production from a hydraulically fractured reservoir; (2) to evaluate EM imaging tools, to achieve Objective 1 in near real-time; and (3) to develop a multi-physics joint inversion approach to precisely predict flow patterns and physiochemical changes within an EAP-filled fracture network. This research project was built upon our previous work at the Devine Test Site managed by the Bureau of Economic Geology (BEG) at The University of Texas at Austin (UT-Austin). It also leveraged a significant investment from the Advanced Energy Consortium (AEC) to address the DOE's interest in subsurface flow, containment, and characterization by multiple signals. This three-year and three-month project succeeded in demonstrating the feasibility of a real-time dynamic fluid flow mapping technique at Technology Readiness Level 5 (TRL5) by utilizing a commercially available surface-based Controlled-Source Electromagnetic (CSEM) method (Objectives 1, 2). We demonstrated that injections into an EAP-filled fracture could be successfully coupled with real-time electric field measurements on the surface, leading to remote monitoring of dynamic changes within the EAP-filled fracture. Furthermore, the observed electric field in our study is influenced by bottomhole pressure, flow rate, and salinity, which is demonstrated by comparing these parameters with the electrical field potentials. EM simulations solely based on assumptions of fracture conductivity changes during injection did not reproduce the whole measured electric field magnitudes. Preliminary estimates showed that including Streaming Potential (SP) in our geophysical model is likely needed to reduce the simulation misfit.

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DOE OSTI2025

Sparse chronology strategy for integrating seasonal energy storage in capacity expansion models

Here, this study develops the sparse chronology method to enhance the representative period framework in capacity expansion models, enabling the effective integration of long-duration energy storage modeling. Traditional representative period methods cannot capture the state of charge of seasonal energy storage systems because they do not establish effective inter-day linkages to connect the state of charge between periods. The sparse chronology approach addresses this limitation by establishing inter-day linkages that allow state of charge to shift inter-seasonally. At the same time, it groups identical representative days into partitions, applying constraints sparsely and implicitly to reduce computational load further. Validation results demonstrate that this method successfully simulates long-duration energy storage patterns, achieving close alignment with a continuous yearly benchmark model, with seasonal trends and state of charge cycles clearly represented. The computational load analysis reveals that the sparse chronology method efficiently applies constraints on maximum and minimum state of charge limits within the representative day framework, eliminating the need for detailed constraints on each individual day. By partitioning representative days and constraining only the start and end of each partition, the method significantly decreases computational requirements. Simulation results show that sparse chronology closely approximates the continuous yearly method's accuracy, even with as few as 20 representative days, achieving correlation values with the benchmark of nearly 0.9 in state of charge plots. Furthermore, it maintains computational efficiency, requiring only 4 % of the solver time compared to the continuous yearly method with 20 representative days. This approach allows capacity expansion models to incorporate long-duration energy storage with high temporal, spatial, and technological resolution, enabling more detailed modeling for large-scale power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗