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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Facilitating Large‐Scale Snow Shedding from In‐Field Solar Arrays using Icephobic Surfaces with Low‐Interfacial Toughness

Abstract Large‐scale accrual of snow and ice on solar arrays in northern latitudes can cause significant power generation losses during winter. Depending on environmental conditions, snow can encompass a wide range in physical characteristics from dry snow (modulus ≈100 kPa and density ≈0.1 g cm −3 ) to bulk ice (modulus ≈8 GPa and density ≈0.9 g cm −3 ). This variation in snow morphology has made the development of a passive, broad‐spectrum, snow and ice‐shedding surface challenging. Here, the authors develop one of the first surfaces that simultaneously possesses both low‐interfacial strength ( τ˄ ice < 50 kPa) and toughness (Γ ice < 0.5 J m −2 ) with ice. These surfaces, fabricated via the addition of mobile polymer chains/oils to a thin polymeric coating, require extremely low detachment forces for ice, enabling its passive shedding at virtually any accretion length scale. Preliminary evidence that the new surfaces can shed different forms of snow and ice from field‐deployed solar arrays, over a range of subzero temperatures for several weeks, leading to significant increases in power generation is provided. The optically transparent surfaces are easily scalable and can be widely deployed by the solar industry in areas that see persistent snow. Other applications include automotive windshields, LIDAR covers for autonomous vehicles, and cold climate optical sensors.

Dhyani, Abhishek↗
DOE OSTI2022

Physics-Informed Gaussian Process Regression for States Estimation and Forecasting in Power Grids

Real-time state estimation and forecasting are critical for the efficient operation of power grids. In this paper, a physics-informed Gaussian process regression (PhI-GPR) method is presented and used for forecasting and estimating the phase angle, angular speed, and wind mechanical power of a three-generator power grid system using sparse measurements. In standard data-driven Gaussian process regression (GPR), parameterized models for the prior statistics are fit by maximizing the marginal likelihood of observed data. In the PhI-GPR method, we propose to compute the prior statistics offline by solving stochastic differential equations (SDEs) governing the power grid dynamics. The short-term forecast of a power grid system dominated by wind generation is complicated by the stochastic nature of the wind and the resulting uncertainty in wind mechanical power. Here, we assume that the power grid dynamics are governed by swing equations, with the wind mechanical power fluctuating randomly in time. We solve these equations for the mean and covariances of the power grid states using the Monte Carlo simulation method. We demonstrate that the proposed PhI-GPR method can accurately forecast and estimate observed and unobserved states. For the considered problem, PhI-GPR has computational advantages over the ensemble Kalman filter (EnKF) method: In PhI-GPR, ensembles are computed offline and independently of the data acquisition process, whereas for EnFK, ensembles are computed online with data acquisition, rendering real-time forecast more challenging. We also demonstrate that the PhI-GPR forecast is more accurate than the EnKF forecast when the random mechanical wind power is non-Markovian. In contrast, the two methods produce similar forecasts for the Markovian mechanical wind power. For observed states, we show that PhI-GPR provides a forecast comparable to the standard data-driven GPR; both forecasts are significantly more accurate than the autoregressive integrated moving average (ARIMA) forecast. We also show that the ARIMA forecast is more sensitive to observation frequency and measurement errors than the PhI-GPR forecast.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

A Review of the Research and Development of Brayton Cycle Technology in Nuclear Power Applications with a Focus on Compressor Technology

This study reviews the integration of Brayton Cycle (BC) systems in nuclear power generation, emphasizing their potential to enhance thermal efficiency and operational flexibility over traditional Rankine Cycle (RC) systems. Key working fluids, such as helium (He), supercritical carbon dioxide (sCO 2 ), nitrogen (N 2 ), and air, are evaluated for their performance, efficiency, and compatibility with nuclear systems. He is recognized for its high thermal conductivity and inertness at elevated temperatures, while sCO 2 demonstrates advantages in compactness and efficiency in midrange temperatures. This article also highlights the importance of compressor designs in optimizing BC performance and reviews, available compressor technologies. Axial and centrifugal compressor designs enable efficient gas compression while managing the thermal and mechanical stresses associated with high-pressure operations in nuclear systems. Combined with variable geometry components and advanced materials, these technologies address the challenges posed by varying load conditions. Despite the promising features of BC systems, several challenges persist, including high leakage rates and material degradation under extreme conditions, which necessitate robust sealing technologies and thorough testing. The insights gained from operational experiences at facilities, such as the Oberhausen II plant and the High-Temperature He Test Facility (HHV), underscore the complexities involved in designing high-temperature gas turbines for nuclear applications. This review concludes that as the nuclear industry evolves, BC systems hold significant promise for contributing to a sustainable energy future, particularly in the context of small modular reactors (SMRs) and microreactors. Further exploration of combined cycle configurations that combine BCs with RCs may enhance overall efficiency and flexibility in power generation.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Projecting Future Energy Production from Operating Wind Farms in North America. Part II: Statistical Downscaling

Abstract Capacity factors (CFs) derived from daily expected power at 22 operating wind farms in different regions of North America are used as predictands to train statistical downscaling algorithms using output from ERA5. The statistical downscaling models are then used to make CF projections for a suite of CMIP6 Earth System Models (ESMs). Downscaling is performed using a hybrid statistical approach that employs synoptic types derived using k -means clustering applied to sea level pressure fields with variance corrections applied as a function of the pressure gradient intensity. ESMs exhibit marked variability in terms of the skill with which the frequency of synoptic types and pressure gradients are reproduced relative to ERA5, and that differential skill is used to infer differential credibility in the associated CF projections. Projections of median annual mean CF [P50(CF)] in each 20-yr period from 1980 to 2099 show evidence of declines at most wind farms except in parts of the southern Great Plains, although the magnitude of the changes is strongly dependent on the ESM. For example, P50(CF) in 2080–99 deviate from those in 1980–99 by from −3.1 to +0.2 percentage points in the Northeast. The largest-magnitude declines in P50(CF) ranging from −3.9 to −2 percentage points are projected for the southern West Coast. CF trends exhibit marked seasonality and are strongly linked to changes in the relative intensity of future synoptic patterns, with much less impact from shifts in the occurrence of synoptic types over time. Internal climate modes continue to play a significant role in inducing interannual variability in wind power production, even under high radiative forcing scenarios. Significance Statement We describe how future climate changes may affect wind resources and wind power generation. Near-term changes in projected wind power electricity generation potential at operating wind farms over North America are small, but by the end of the current century electricity production is projected to decrease in many areas but may increase in parts of the southern Great Plains. The amount of change in projected wind power production is a strong function of the Earth system model that is downscaled and also depends on the continued presence of internally forced climate variability. An additional dependence on the amount of greenhouse gas–induced global warming indicates the transition of the energy sector to low-carbon sources may assist in maintaining the abundant U.S. wind resource.

Meteorology & Atmospheric Sciences↗
DOE OSTI2022

Design of Graded Cathode Catalyst Layers with Various Ionomers for Fuel Cell Application

Proton exchange membrane fuel cells (PEMFCs) powered by green hydrogen (H2) have become a promising alternative to conventional hydrocarbon-fueled power generators. Despite technological advancements, further improvements in efficiency, durability, and low-cost production are required for the widespread adoption of PEMFCs. Though numerous approaches to improve PEMFC electrodes have been reported, most strategies utilize a single material set (e.g., one combination of catalyst and ionomer) to improve performance. Alternatively, anisotropic (graded) electrode structures with locally tunable properties may yield superior electrode performance due to improved ionic and gas phase transport. In this work, graded cathode catalyst layers (CCLs) incorporating different ionomers (Nafion D2020, Aquivion D79-25BS, and HOPI) were designed and prepared. Performance screening shows that some of these graded electrode structures have comparable performance to optimized single-ionomer electrode structures (D2020) suggesting some synergistic benefit. Additionally, we show that electrodes with lower equivalent weight (EW) D79 ionomer near the membrane and D2020 ionomer near the gas diffusion media outperformed electrodes with the inverted configuration. Finally, EIS analysis shows some graded ionomer structures (e.g. D79/D2020) have better than expected H+ conductivity, generally leading to better electrode performance. However, further optimization of ionomer content and catalyst ink formulations is needed to improve overall PEMFC performance.

DIRECT ENERGY CONVERSION↗
DOE OSTI2026

Generator Frequency Response Droop Monitoring Tool

Monitoring and analyzing the frequency response performance of power generation units is essential for maintaining reliable and secure power system operation. To address this need, an automation tool has been developed to provide a pipeline for processing historical power plant generation data, including large-scale SCADA archives. The tool performs end-to-end processing, including event detection, frequency response (FR) analysis in accordance with NERC standards, and estimation of speed governor droop characteristics. The tool is designed with a modular architecture, allowing individual components of the workflow to be extended, customized, or deployed independently. In addition, the tool provides an API that enables seamless integration with other production systems and operational analytics platforms.

Etingov, PavelV [Pacific Northwest National Labora↗
DOE OSTI2024

Modelling and analysis of nuclear reactor system coupled with a liquid metal battery

Traditionally, nuclear power plants in the U.S. provide baseload power to the power grid because they have less flexibility for ramping their output power than natural gas peaking plants. However, achieving climate goals to reduce the consumption of fossil‐based natural gas places pressure on nuclear power plants and other power generators to ramp up their power output to balance grid generation with demand. This paper presents the modelling and performance analysis of a nuclear reactor system (NRS) coupled to a liquid‐metal battery (LMB) to improve its dynamic response and enable its black start capability. The NRS and LMB thermal behaviour are modelled in Dymola, while the electrical dynamics of the LMB and power grid are modelled in RTDS‐RSCAD. Both simulation platforms are coupled and share their thermal and electrical data using a Transmission Control Protocol/Internet Protocol (TCP/IP) communication protocol. The dynamic performance of the NRS‐LMB integration is tested on the IEEE 9 bus, which demonstrates its ability to respond and provide frequency and voltage regulation. The black start capability of the NRS‐LMB is also evaluated by simulating a grid outage and using the LMB to supply the auxiliary loads required to bring the NRS back online as soon as possible. The results show that coupling an NRS to an LMB improves the system dynamic performance and enables it to black start after being disconnected from the grid for several days.

25 ENERGY STORAGE↗
DOE OSTI2024

A Highly Efficient and Affordable Hybrid System for Hydrogen and Electricity Production (Final Project)

The pursuit of clean, secure, and sustainable energy has sparked significant interest in fuel cells for power generation and electrolyzer cells for hydrogen production. Among all types of fuel and electrolyzer cells, solid oxide cells (SOCs) have emerged as promising candidates due to their high efficiency and versatility. However, conventional oxygen-ion conductive SOCs face several challenges related to their performance and durability associated with their high-temperature operation (≥ 800 ºC). This has led to a growing interest in intermediate-temperature (≤ 650 ºC) proton-conducting solid oxide cells (p-SOCs) as potential alternatives. In collaboration between Phillips 66 and Georgia Tech, this project aims to achieve a 1 kW p-SOCs system to demonstrate the commercial viability of efficient SOC systems. This report addresses four primary areas and key challenges we overcame: (1) development of efficient and durable proton-conducting electrolyte (e.g., BaHf 0.1 Ce 0.7 Yb 0.2 O 3-δ ) and electrode/catalyst materials, (2) large area cell fabrication (10 x 10 cm 2 ), (3) scalable stack design and building (250 W and 1 kW), and (4) demonstration of a 1 kW prototype system. Notably, significant challenges faced during the large area cell fabrication process were addressed by achieving cell flatness, improving fabrication yield, and ensuring electrode/electrolyte interfacial adhesion. Stack designs were also developed, focusing on reducing contact resistance and optimizing stack components (e.g., sealants). These efforts resulted in the achievement of high performance and durability with promising outputs of 250 W and 1 kW. Furthermore, the integration of these stacks into a fuel-powered system was explored, with refinements made to heat management, as well as to pressure and heating conditions. The results demonstrated the potential applicability of our p-SOC technology in commercial energy storage and power generation systems. Additionally, the report discusses techno-economic analysis and a market transformation plan, aiming to evaluate and advance the commercial feasibility of this technology.

25 ENERGY STORAGE↗
DOE OSTI2023

Assessing Low-Temperature Geothermal Play Types: Relevant Data and Play Fairway Analysis Methods

The U.S. Department of Energy (DOE) Geothermal Technologies Office (GTO) is supporting the Geothermal Heating and Cooling Geospatial Datasets and Analysis project conducted by the National Renewable Energy Laboratory (NREL) as part of a broader effort to demonstrate the multi-faceted value of integrating geothermal power and geothermal heating and cooling (GHC) technologies into national decarbonization plans and community energy plans. Currently, there is a need to establish baseline low-temperature geothermal resource datasets and evaluate methods for deploying these technologies to provide the basis for supporting private sector investment. This project is focused on collecting baseline datasets, updating conceptual models, and creating Play Fairway Analysis (PFA) workflows for low-temperature (<150 degrees Celsius) geothermal resources of different geothermal play types (i.e., sedimentary basin, orogenic belts, and radiogenic geothermal play types) that could be used for geothermal heating and cooling (GHC), combined heat and power (CHP), and other geothermal direct uses (GDU) applications. Low-temperature geothermal resources are defined as reservoirs - natural or engineered - with temperatures <150 degrees Celsius. While the focus in the NREL effort is on GHC, resources at the upper end of this temperature range can also be used for small-scale power generation. This project does not include Ground Source Heat Pumps (GSHPs) technologies because they can be effectively developed almost anywhere. Low-temperature geothermal resources have not been studied as extensively as higher- to medium-temperature geothermal resources, but there is recent interest in improving understanding of these types of resources with an uptick of interest in geothermal technologies for decarbonizing heating and cooling systems. In addition, Enhanced Geothermal Systems (EGS) and other emerging technologies for exploiting petrothermal resources have opened the possibility of utilizing deep sedimentary basin systems, where porous media provide permeability and high temperatures can be reached at great depths. This project takes the approach of classifying low- temperature geothermal resources by geothermal play type (GPT). We defined and characterized three major classes of low-temperature GPT: sedimentary basins, orogenic systems, and radiogenic systems. We develop methodologies for evaluating and analyzing the potential for these resources building off the PFA approach to de-risking geothermal exploration and characterization. The proposed PFA approach for low-temperature geothermal resources includes: 1) identifying relevant data (e.g., datasets such bottom-hole temperatures from oil and gas wells, heat flow data, Quaternary faults and stress field data, geophysical data, etc.); 2) grouping and weighting of relevant datasets into PFA criteria (e.g., geological, risk, and economic criteria); 3) uncertainty quantification; 4) developing favorability or common risk maps for low-temperature geothermal resources to identify potential locations for more focused data collection; and 5) estimating electric power generation and heating potential at those locations using the GeoRePORT Resource Size Assessment Tool (RSAT). This project should facilitate future deployment of GHC, CHP, and GDU by providing data, tools, and a workflow applicable to low-temperature geothermal resources. Increased deployment of GHC and GDU will help achieve national and local decarbonization goals.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

NEAR CRITICAL POINT TESTING AND PERFORMANCE RESULTS OF A SCO2 COMPRESSOR FOR A 10MWE BRAYTON CYCLE

Development and commercial acceptance of sCO2 Brayton cycles for power generation applications are growing rapidly as they offer performance advantages over other cycles. To maximize the cycle performance, the compressors are designed to operate near the critical point of the working fluid. At the critical point the fluid properties change rapidly with variations in inlet conditions. This makes it challenging to both accurately predict the performance and guarantee the operability of the compressor, as the behavior is affected by these slight variations in inlet conditions. A full scale 1st stage main compressor for a 10MWe-Class recompression Brayton cycle was built and tested to validate performance and operability in this unique operating range. The compressor was tested in a laboratory environment with additional instrumentation, beyond what is required by PTC-10, to minimize the uncertainty in the measured performance. Complete constant speed characteristics were collected at multiple supercritical points, operating at constant inlet conditions for each speed line covering a range of compressor inlet densities from 400 to 600 kg/m3. Variations in the compressor stage efficiency and choke margin were observed, and the overall operability and stability of the compressor in response to changes in operating condition were also monitored. The compressor was shown to have excellent performance that closely matched the original design prediction. The performance at various inlet conditions showed minimal change in isentropic head coefficient at the design flow, but did show some variation in efficiency and choke margin across the map. These changes in performance were observed to be minimal, and did not affect the stable operation of the compressor. The results demonstrate that a commercial scale sCO2 compressor can operate near the critical point and achieve the high levels of performance and stability required for power generation applications.

pelton, rob↗
DOE OSTI2021

Comparison of Probabilistic Forecasts for Predictive Voltage Control

This paper explores predictive cooperative voltage control in distribution systems with highly variable sources such as photovoltaics (PV). The goal is to maintain the voltage profile within the limits despite the fluctuations due to sudden changes in solar power generation. The predictive voltage control method relies on probabilistic solar power and load forecasts to select the optimum Voltage Regulator (VR) taps appropriately. VR taps are selected to minimize the risk of voltage violation. A modified version of the IEEE 123 system is used as the case study. A 100% penetration of solar power is assumed for the distribution system with profiles for solar generation and loads added to the system. Three different probabilistic forecast models (Quantile Regression (QR), Gaussian distribution and volatility forecasting using Generalized Autoregressive Conditional Heteroskedasticity (GARCH)) are explored in this study. The results for the VR taps and Voltage Deviation Index (VDI) are compared to find the most effective forecast model.

Panamtash, Hossein↗
DOE OSTI2020

IRDFF-II: A New Neutron Metrology Library

High quality nuclear data is the most fundamental underpinning for all neutron metrology applications. This paper describes the release of version II of the International Reactor Dosimetry and Fusion File (IRDFF-II) that contains a consistent set of nuclear data for fission and fusion neutron metrology applications up to 60 MeV neutron energy. The library is intended to support: a) applications in research reactors; b) safety and regulatory applications in the nuclear power generation in commercial fission reactors; and c) material damage studies in support of the research and development of advanced fusion concepts. The paper describes the contents of the library, documents the thorough verification process used in its preparation, and provides an extensive set of validation data gathered from a wide range of neutron benchmark fields. The new IRDFF-II library includes 119 metrology reactions, four cover material reactions to support self-shielding corrections, five metrology metrics used by the dosimetry community, and cumulative fission products yields for seven fission products in three different neutron energy regions. In support of characterizing the measurement of the residual nuclei from the dosimetry reactions and the fission product decay modes, the present document lists the recommended decay data, particle emission energies and probabilities for 68 activation products. It also includes neutron spectral characterization data for 29 neutron benchmark fields for the validation of the library contents. Additional six reference fields were assessed (four from plutonium critical assemblies, two measured fields for thermal-neutron induced fission on 233U and 239Pu targets) but not used for validation due to systematic discrepancies in C/E reaction rate values or lack of reaction-rate experimental data. Another ten analytical functions are included that can be useful for calculating average cross sections, average energy, thermal spectrum average cross sections and resonance integrals. The IRDFF-II library and comprehensive documentation is available online at www-nds.iaea.org/IRDFF/. Evaluated cross sections can be compared with experimental data and other evaluations at www-nds.iaea.org/exfor/endf.htm. The new library is expected to become the international reference in neutron metrology for multiple applications.

IAEA IRDFF↗
DOE OSTI2023

Pathways to Carbon Neutrality 2050 in Malaysia and Kuala Lumpur

Malaysia has recently set an ambitious target of achieving carbon neutrality as early as 2050. To accomplish this, the country will need to strategically reduce its emissions across all sectors. In 2020, Malaysia emitted approximately 368 MtCO2e, with the largest sources of emissions including electricity (36% of total emissions), transportation (17%), and industry (15%)1. We find that the greatest reductions in emissions can therefore come from decarbonizing power generation and electrifying end-use sectors. Digitalization, smart technologies, and improved energy efficiency will significantly reduce economy-wide energy consumption. By leveraging efficient technologies, both Malaysia and Kuala Lumpur can address the challenges posed by rapid urbanization and climate change. Digitalization is a broad category that includes a variety of measures; for example, the wide adoption of high-efficiency appliances and lighting or improved building energy codes in the buildings sector. Similarly, technological improvements can advance industrial energy efficiency, and for transportation, smart technologies cover a shift from private to public transportation and the greater use of electric vehicles. While renewable energy (RE) will play a crucial role in decarbonization, achieving carbon neutrality in certain sectors will be difficult without emerging technologies like carbon capture and storage (CCS) and innovative fuel sources such as hydrogen. In order for Malaysia to rely on CCS as a mitigation option, early investment and incentives to the private sector will be critical. This holds for the use of hydrogen as well: investing in the necessary technology, infrastructure, and human capital will allow Malaysia to position itself as an innovator in the region and leverage these advanced technologies as a key part of its climate strategy. Another possible carbon removal option other than CCS would be a land-use sink; however, given that Malaysia is still developing and may deforest in the near-term, this report does not focus on the mitigation potential of land-use change. With its innovative and bold climate plans, Kuala Lumpur is primed to be a leader in regional climate change efforts. Kuala Lumpur is also engaged in several international collaborations to ensure sustainable city development such as the C40 network and the ASEAN Smart Cities Partnership. As such, the city will play a critical role in contributing to Malaysia’s overall climate goals and as a policy trendsetter through ambitious, scalable plans. One key factor in these emissions reductions is that Kuala Lumpur has full control over its building guidelines, allowing for ambitious policies resulting in significant emissions reductions. However, in other sectors, Kuala Lumpur has less direct control over regulations; for example, power generation and integration of RE are largely in the hands of the Malaysian government. With limited and primarily light industry, Kuala Lumpur’s contributions to emissions reductions here are curbed. And, while Kuala Lumpur has control over local transportation policies like increasing access to and quality of public transportation, broad shifts in transportation will stem from national-level policies. As such, multi-level governance is an integral component of Malaysia’s climate strategy and coordination between local and national governments will be essential in reducing emissions and achieving other climate goals. This report addresses these and other key challenges and opportunities Malaysia faces on the road to carbon neutrality.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Rapid Bayesian High Entropy Alloy Designs Fabricated via Wire Arc Additive Manufacturing

Purpose: This project seeks to demonstrate a new high-throughput (rapid) alloy design technique applied to creating new high entropy alloys (HEAs) for extreme environments. High entropy alloys shift the design paradigm from being focused on a single principal element (e.g. nickel-based alloys) to target alloys that include high atomic fractions (X >10%) of multiple elements. These HEA materials can exhibit sluggish diffusion and enhanced corrosion resistance, ideal for potential applications in advanced ultra supercritical (A-USC) steam cycles for power generation. Scope: The addition of multiple elements in high atomic fractions creates an enormous design space that cannot easily be investigated by traditional material design strategies such as designed of experiments (DOE). This project utilizes a Bayesian machine learning algorithm that has been modified to work with calculation of phase diagrams (CALPHAD) software. This Bayesian algorithm reduces manual inputs and increase the likelihood of achieving an optimal solution. Compositional inputs to this algorithm will be assessed using existing material property models for high temperature strength and corrosion resistance. The target for alloy performance will be a 15% (~100 ⁰C) increase in allowable service temperature beyond heat-resistant stainless steels while maintaining or improving alloy cost and corrosion resistance. Haynes 230 was selected as a baseline, which is 57 wt% Ni with 22 wt% Cr 14 wt% W, and 2 wt% Mo as solid solution strengtheners. In addition to rapid design via Bayesian machine learning, the alloys were rapidly fabricated using a multi-wire arc additive manufacturing (mWAAM) technique which allows for precise control of alloy composition and assessing of alloy design “windows” to study composition effects. Build speeds for wire-arc additive processes are among the highest for additive technologies enabling rapid and reliable sample fabrication when compared to conventional methods such as arc button melting. The mWAAM samples will be rapidly characterized via instrumented indentation for room temperature modulus and strength and for elevated temperature strength via hot hardness tests. After being screened with hardness testing, potential alloys will be further evaluated with conventional microscopy techniques including scanning electron microscopy (SEM) and transmission electron microscopy (TEM) to assess agreement with modeling results. The most promising compositions will also be evaluated by printing full sized tensile specimens for mechanical behavior tests at elevated temperatures. Results: Bayesian machine learning of a single performance function was initially used to optimize five performance metrics: 1) single phase stability, 2) yield strength, 3) creep resistance (low diffusion coefficient), 4) freezing range (weldability), and 5) material cost. The single performance function was suboptimal as assumptions had to be made about the results while formulating the optimization. A goal-oriented Bayesian optimization strategy (Hanaoka, 2021) was implemented with CALPHAD for use with the five metrics above. This multi-objective Bayesian optimization (MOBO) enabled the design of NiCrCoFe alloys with V and W additions. A base composition of NiCoCr was selected as Ni provides a stable FCC matrix, Cr aids corrosion/oxidation resistance, and Co is a solid-solutions strengthener that also improves creep by increasing the activation energy. Fe helps reduce diffusion coefficients and cost. Finally, V and W were selected for their reasonable solubility and high atomic misfit to aid in solid solution strengthening. Cracking of the mWAAM specimens was an early issue, and the Easton solidification cracking model (Easton et al., 2014a) was selected for addition to the MOBO function. High performing alloys fabricated by mWAAM included Ni 28 Cr 25 Co 26 Fe 15 V 8 and Ni 62 Cr 18 Co 1 Fe 3 W 15 . It was observed that even after adapting the mWAAM process for W, the W did not fully dissolve. To fully evaluate the Ni 62 Cr 18 Co 1 Fe 3 W 15 composition, a cored wire (80-20 NiCr sheath/powder core) was manufactured and printed via WAAM, and HIP’ing was utilized to homogenize and densify the printed alloy. The V and W alloys produced met metrics 1 (solid solution), 4 (solidification cracking), and 5 (cost). However, an unmodeled mechanism of thermal stress cracking was identified in the WAAM produced materials, perhaps exacerbated by the lack of grain boundary strengthening elements (B, C). Conclusions & Recommendations: A high-throughput (rapid) alloy design technique was applied to designing and manufacturing new high entropy alloys (HEAs) for extreme environments utilizing MOBO and mWAAM. The developed process was rapid and effective in addressing the mechanisms included in the model. The lack of grain boundary strengthening element additions (e.g., B, C) was a simplification that likely produced thermal stress cracking that turned into a large part of the investigation. Additions on the order of 0.005 wt% B and 0.05 wt% C likely would have minimized thermal stress grain boundary cracking. Overall, the high throughput design strategy is promising for rapid design of metrics-driven alloys for advanced ultra supercritical (A-USC) steam cycles for power generation. The MOBO and mWAAM process could be commercialized to accelerate metrics-driven alloy design. In addition, the cored-wire process utilized for scale-up is a promising high-volume process for WAAM alloy development and scale-up.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Functional fiber-optic sensors embedded in stainless steel components using ultrasonic additive manufacturing for distributed temperature and strain measurements

The nuclear industry is progressing toward microreactors that can be factory assembled and deployed to remote regions for reliable, scalable power generation. However, the reduced power output requires enhanced monitoring capabilities to reduce staffing and eventually move toward autonomous control to improve the economics of microreactors. The ability to embed sensors such as fiber-optics, which can provide spatially distributed temperature and strain measurements, within microreactor components for real-time health monitoring would be particularly advantageous. Recent advances in ultrasonic additive manufacturing (UAM) have demonstrated the successful embedding of fiber-optics in soft materials such as Al and Cu by placing the sensors in machined cavities and ultrasonically welding over the top with thin foils. This article reports the first successful embedment of fiber-optic sensors and thermocouples within a common nuclear reactor material, SS304, via UAM. UAM parameters were first explored with simple plate geometries before moving to more complex geometries, such as pipes and other test articles for heat pipe–based microreactors. Select samples were sectioned for microscopy to evaluate the sensor/foil and foil/foil interfaces from samples fabricated by using 100% SS304 foils vs. foils plated with a Ni coating to improve UAM bonding. Furthermore, embedding metal-coated, low-bend loss fibers resulted in greatly reduced signal attenuation and adequate compressive residual strain in the embedded region to ensure successful strain coupling. Pipe specimen functional testing was performed by monitoring temperature and strain during transient and steady-state thermal testing.

36 MATERIALS SCIENCE↗