Results for “reduced cost” — Page 11

DOE OSTI2020

Using Strain in the Vadose Zone During Sinusoidal-Rate Well Tests to Characterize an Aquifer

Well tests with sinusoidal variation in pumping rate are appealing because of the ability to filter competing signals and they also can be set up with no net removal of water, which is an advantage at contaminated sites. Current methods of interpreting sinusoidal-rate well tests analyze the phase lag and amplitude of pressure signals in the aquifer. This approach is restricted by the availability of monitoring wells. An alternative approach could be to measure the stain in the vadose zone instead of pressure in the aquifer. The goal of this research is to evaluate the feasibility of using signals from in-situ sensors in the vadose zone to characterize sinusoidal-rate well tests. Well tests with a variety of periods and amplitudes were performed using a well completed in an unconfined saprolite aquifer with a water table at 9m depth at our test site near Clemson, South Carolina. Vertical strain was measured every meter between 1 to 7 m using a distributed optical fiber sensing technique called coherence length gated microwave photonics interferometry (CMPI). A novel packaging system was designed to couple the optical fiber to the saprolite. The results show that strain measured in the vadose zone during sinusoidal-rate pumping varies periodically with amplitudes from 0.1 to 3 microstrain, depending on the amplitude of the sinusoidal pumping rate. Hydraulic heads at similar radial distances were also periodic with changing amplitudes ranging up to ±0.2 meters. The strain in the vadose zone responds to pressure in the underlying aquifer—the strain is tensile when the pressure is falling, and compressive when the pressure is rising. The phase lag between the pumping rate and the strain was 3 min with a pumping period of 15 minutes, and this gives a hydraulic diffusivity of 0.06 m2/s. This is the same diffusivity obtained by analyzing a constant rate pumping test, and it is slightly larger than the diffusivity (0.044 m2/s) obtained by analyzing the phase lag of the pressure in a nearby monitoring well. Strain measurements in the vadose zone could be made with much less cost and effort than installing a deep monitoring well. These results suggest that strain measurements in the vadose zone could be used to supplement monitoring wells, thereby increasing the resolution, and reducing costs of analyzing well tests.

well test, sinusoidal, periodic pumping, strain↗

DOE OSTI2022

Effectively using multifidelity optimization for wind turbine design

Abstract. Wind turbines are complex multidisciplinary systems that are challenging to design because of the tightly coupled interactions between different subsystems. Computational modeling attempts to resolve these couplings so we can efficiently explore new wind turbine systems early in the design process. Low-fidelity models are computationally efficient but make assumptions and simplifications that limit the accuracy of design studies, whereas high-fidelity models capture more of the actual physics but with increased computational cost. This paper details the use of multifidelity methods for optimizing wind turbine designs by using information from both low- and high-fidelity models to find an optimal solution at reduced cost. Specifically, a trust-region approach is used with a novel corrective function built from a nonlinear surrogate model. We find that for a diverse set of design problems – with examples given in rotor blade geometry design, wind turbine controller design, and wind power plant layout optimization – the multifidelity method finds the optimal design using 38 %–58 % of the computational cost of the high-fidelity-only optimization. The success of the multifidelity method in disparate applications suggests that it could be more broadly applied to other wind energy or otherwise generic applications.

17 WIND ENERGY↗

DOE OSTI2020

Supercritical CO2 Recuperators, Presented at The ASME Turbo Expo 2017, June 29, 2017, by Dr. John Kelly, President, Altex Technologies Corporation

Closed Brayton super-critical CO2 power cycles are well suited to waste heat bottoming cycles, due to their increased efficiency and compactness, relative to Rankine steam bottoming cycles. Since waste heat applications would be retrofits, the power system compactness is important. To achieve high efficiency, these power cycles require high pressure recuperative type heat exchangers, of substantial heat duty. Current Printed Circuit Heat Exchangers (PCHE), originally developed for high pressure gas and oil applications, can be used as recuperators, but costs are higher than desired. Altex is developing a purpose-built high pressure and effectiveness recuperator to provide the reliability, compactness, performance and pressure capability of current recuperators, but at a reduced cost. The High Effectiveness Low Cost (HELC) recuperative heat exchanger design yields volume and weight metrics of .0024 m3/UA and 10.2 kg/UA, which are 4% and 77.3% below recuperator target metrics, respectively. A 50 kW test article was designed and fabricated. Performance tests on water and oil showed that the HELC design model could predict heat transfer, to within 10% of the measured value. This model was then used to project HELC performance, when operating on supercritical CO2. Besides performance tests, the test article was hydrostatically tested, for integrity at up to 4,000 psi pressure. At these conditions, some distortion of channels was encountered. To mitigate distortion, the inserts were redesigned and these results were used to project the cost of 500 kW and 5,083 kW HELC units. The cost metric for the 5,083 kW unit was determined to be $1,349/UA, which is 10% lower than the recuperator desired cost metric desired target of $1,500/UA. In addition, the 5,083 kW unit HELC cost of $61.09/kW is 33.6% lower than the $92/kW estimated cost for a PCHE. Hydrostatic pressure tests, at up to 4,000psi, showed that the unit did not leak. However, channels were distorted at this pressure differential. Design updates to minimize stress concentrations and distortion were prepared and analyzed, to show that distortion could be controlled, but to date tests have not been run to prove the design. Project results show the potential of the HELC approach, but more work is required to confirm this potential, at the larger scales of interest.

14 SOLAR ENERGY↗

DOE OSTI2020

High Effectiveness, Compact, High Pressure and Low-Cost Recuperator, Presented at The Fifth International Symposium – Super-Critical CO2 Power Cycles, San Antonio, Texas, March 28-31, 2016, by Dr. John Kelly, President, Altex Technologies Corporation

Closed Brayton super-critical CO2 power cycles are well suited to waste heat bottoming cycles, due to their increased efficiency and compactness, relative to Rankine steam bottoming cycles. Since waste heat applications would be retrofits, the power system compactness is important. To achieve high efficiency, these power cycles require high pressure recuperative type heat exchangers, of substantial heat duty. Current Printed Circuit Heat Exchangers (PCHE), originally developed for high pressure gas and oil applications, can be used as recuperators, but costs are higher than desired. Altex is developing a purpose-built high pressure and effectiveness recuperator to provide the reliability, compactness, performance and pressure capability of current recuperators, but at a reduced cost. The High Effectiveness Low Cost (HELC) recuperative heat exchanger design yields volume and weight metrics of .0024 m3/UA and 10.2 kg/UA, which are 4% and 77.3% below recuperator target metrics, respectively. A 50 kW test article was designed and fabricated. Performance tests on water and oil showed that the HELC design model could predict heat transfer, to within 10% of the measured value. This model was then used to project HELC performance, when operating on supercritical CO2. Besides performance tests, the test article was hydrostatically tested, for integrity at up to 4,000 psi pressure. At these conditions, some distortion of channels was encountered. To mitigate distortion, the inserts were redesigned and these results were used to project the cost of 500 kW and 5,083 kW HELC units. The cost metric for the 5,083 kW unit was determined to be $1,349/UA, which is 10% lower than the recuperator desired cost metric desired target of $1,500/UA. In addition, the 5,083 kW unit HELC cost of $61.09/kW is 33.6% lower than the $92/kW estimated cost for a PCHE. Hydrostatic pressure tests, at up to 4,000psi, showed that the unit did not leak. However, channels were distorted at this pressure differential. Design updates to minimize stress concentrations and distortion were prepared and analyzed, to show that distortion could be controlled, but to date tests have not been run to prove the design. Project results show the potential of the HELC approach, but more work is required to confirm this potential, at the larger scales of interest.

14 SOLAR ENERGY↗

DOE OSTI2020

High Effectiveness, Compact, High Pressure and Low Cost Recuperator for Super-Critical CO2 Power Cycles, Paper Presented at The Fifth International Symposium – Super-Critical CO2 Power Cycles, San Antonio, Texas, March 28-31, 2016, by Dr. John Kelly, President, Altex Technologies Corporation

Closed Brayton super-critical CO2 power cycles are well suited to waste heat bottoming cycles, due to their increased efficiency and compactness, relative to Rankine steam bottoming cycles. Since waste heat applications would be retrofits, the power system compactness is important. To achieve high efficiency, these power cycles require high pressure recuperative type heat exchangers, of substantial heat duty. Current Printed Circuit Heat Exchangers (PCHE), originally developed for high pressure gas and oil applications, can be used as recuperators, but costs are higher than desired. Altex is developing a purpose-built high pressure and effectiveness recuperator to provide the reliability, compactness, performance and pressure capability of current recuperators, but at a reduced cost. The High Effectiveness Low Cost (HELC) recuperative heat exchanger design yields volume and weight metrics of .0024 m3/UA and 10.2 kg/UA, which are 4% and 77.3% below recuperator target metrics, respectively. A 50 kW test article was designed and fabricated. Performance tests on water and oil showed that the HELC design model could predict heat transfer, to within 10% of the measured value. This model was then used to project HELC performance, when operating on supercritical CO2. Besides performance tests, the test article was hydrostatically tested, for integrity at up to 4,000 psi pressure. At these conditions, some distortion of channels was encountered. To mitigate distortion, the inserts were redesigned and these results were used to project the cost of 500 kW and 5,083 kW HELC units. The cost metric for the 5,083 kW unit was determined to be $1,349/UA, which is 10% lower than the recuperator desired cost metric desired target of $1,500/UA. In addition, the 5,083 kW unit HELC cost of $61.09/kW is 33.6% lower than the $92/kW estimated cost for a PCHE. Hydrostatic pressure tests, at up to 4,000psi, showed that the unit did not leak. However, channels were distorted at this pressure differential. Design updates to minimize stress concentrations and distortion were prepared and analyzed, to show that distortion could be controlled, but to date tests have not been run to prove the design. Project results show the potential of the HELC approach, but more work is required to confirm this potential, at the larger scales of interest.

14 SOLAR ENERGY↗

DOE OSTI2023

Evaluating Impacts of the Inflation Reduction Act and Bipartisan Infrastructure Law on the U.S. Power System

The Inflation Reduction Act of 2022 (IRA) and the Infrastructure Investment and Jobs Act of 2021, commonly referred to as the 'Bipartisan Infrastructure Law (BIL),' collectively represent the largest commitment of the U.S. Federal Government to invest in the modernization and decarbonization of the U.S. energy system. The Congressional Budget Office (CBO) estimates that total support for the broad range of climate and clean energy programs, tax credits, and other incentives authorized through the two laws will exceed $430 billion from 2022 through 2031 (CRS 2022; CBO 2021, 2022). While the climate and clean energy provisions are numerous and have the potential to impact all aspects of the U.S. energy system from fuel and electricity production to final consumption in industry, transportation, and buildings, the provisions relevant to the electricity sector - in particular the suite of tax credits for clean generation, storage, and carbon dioxide ( CO 2 ) capture and storage - are expected to be some of the most consequential in terms of emissions reduction and clean energy deployment (Larsen et al. 2022; Jenkins, Mayfield, et al. 2022; Mahajan et al. 2022; Zhao et al. 2022). In this report, we detail the methods and results of a study estimating the potential impacts of key provisions of IRA and BIL on the contiguous U.S. power sector from present day through 2030. The analysis employs an advanced power system planning model, the Regional Energy Deployment System (ReEDS), to evaluate how major provisions from both laws impact investment in and operation of utility-scale generation, storage, and transmission, and, in turn, how those changes impact power system costs, emissions, and climate and health damages. While not exhaustive in capturing every provision, the analysis estimates the possible scale of power-sector impacts that could result from the modeled provisions in IRA and BIL. The study is structured around two scenarios to evaluate the potential impacts of both laws on the power sector: 1) No New Policy: A counter-factual scenario that reflects all Federal and state policies enacted as of September 2022, with exception to IRA and BIL, and assumes load growth consistent with the Energy Information Administration's Annual Energy Outlook 2022 (AEO22) Reference case (EIA 2022a); 2) IRA-BIL: A scenario reflecting all Federal and state policies enacted as of September 2022, including key IRA and BIL provisions, most notably the investment and production tax credits for zero-carbon emitting electricity generation and storage (ITC and PTC), the tax credit for CO 2 capture and storage (45Q), and the tax credit for existing nuclear plants (described further in Section 2.3). To account for the impacts of IRA and BIL on electrification, assumes increased load growth consistent with a scaled version of the Medium Electrification scenario from the Electrification Futures Study (Mai et al. 2018). These scenarios are simulated across seven sets of assumptions with varying projected future electricity market conditions, including technology costs and performance, natural gas prices, and the degree of availability, feasibility, and cost of development of renewable resources, electricity transmission, and CO 2 pipeline, injection, and storage infrastructure. In addition, we simulate two sensitivities on the 'policy' treatment in which we vary key assumptions pertaining to the realized value of the clean electricity ITC and PTC: 1) the cost of monetization of tax credits, and 2) the level of bonus crediting realized by project developers. We demonstrate that IRA and BIL have the collective potential to drive substantial growth in clean electricity by 2030, while reducing costs for consumers, mitigating climate change, and decreasing the human health impacts of power sector emissions. However, we also demonstrate that if expected cost improvements of clean technologies are not realized and/or constraints on deployment driven by factors such as supply-chain challenges, regulatory hurdles, and the social acceptability of energy infrastructure development limit the rate of clean energy and associated infrastructure deployment (such as transmission), then the share of clean generation achieved and the associated emissions benefits realized may be substantively reduced.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Raspberry colloid-templated approach for the synthesis of palladium-based oxidation catalysts with enhanced hydrothermal stability and low-temperature activity

It is becoming increasingly urgent to develop and utilize novel, more efficient and stable materials for mobile and stationary emission control applications as the deleterious consequences of anthropogenic air pollution are becoming more evident and pressing. Tightening regulations, particularly related to automotive exhaust treatment, together with continued improvements in engine design, that result in lowering the engine operating temperatures and inadvertently lead to the release of an overwhelming proportion of pollutants during the cold start, present new challenges for materials design, specifically for oxidation catalysts. In particular, improvements in the low-temperature activity while maintaining catalyst stability at high temperatures are required from the next-generation catalyst. Typical catalysts for removal of pollutants from automotive exhaust streams incorporate platinum group metals (PGMs). They tend to be inefficient at low temperatures (below 250 °C), thus accounting for the cold start problem, yet sinter and lose their activity at high temperatures that are frequently encountered during catalyst operation. High PGM loadings are often employed to compensate for catalyst inefficiencies and fast degradation, ultimately resulting in high-cost catalytic converters. Here, we have developed a new approach for the design and formation of catalytic materials that allows for both significantly more efficient PGM incorporation and improved overall catalyst performance at reduced PGM loadings. The method provides control over the composition and geometry of the support through self-assembly of sacrificial composite template — “raspberry” polymeric colloids decorated with catalytic particles — accompanied by infiltration with metal-oxide precursor and subsequent removal of the colloids. This method simultaneously structures the porous network and organizes the catalytic particles within it. Uniquely, the resulting catalytic particles are partially embedded in the support matrix and partially exposed to the pore interior, producing catalytic sites that are both stable and accessible. Herein, the feasibility of this novel and versatile approach for automotive catalytic conversion is demonstrated: the studies include testing alumina-based raspberry-colloid-templated (RCT) catalysts containing Pd nanoparticles (RCT Pd/Al2O3) for oxidation of propane and carbon monoxide under simulated diesel exhaust conditions and hydrothermal aging at 800 °C for 50 h in the simulated stream. The RCT Pd/Al2O3 catalysts exhibit exceptional activity toward CO oxidation, reduced reaction onset temperature, and high stability to elevated temperatures (demonstrated through prolonged exposure to temperatures up to 950 °C) and reactive gas streams, without migration, sintering or loss of the precious metal NPs. Notably, the novel catalyst shows the same or slightly better performance than the commercial catalysts even when the PGM load is reduced by ~80 % compared to the commercial counterparts. These results provide confidence for the utilization of the RCT approach for the fabrication of robust nanostructured catalysts for next-generation, energy-efficient catalytic converters with improved performance at low and high temperatures and reduced costs. The RCT methodology is, in addition, highly generalizable, and can thus be applied for the design of a wide range of catalytic systems in the automotive sector and beyond.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Carbon fiber design improvements based on economic models

Circular fiber geometries are predominant in commercial carbon fiber material systems, but the use of this fiber shape has numerous limitations. Circular geometries have continuous symmetry, which is helpful for various processing considerations, but also have the largest possible maximum diffusion thickness for a given fiber area. This characteristic means that circular carbon fibers always have the highest material processing cost and lowest production throughput compared to any other fiber shape with the same area and tow count. To quantify material cost and other benefits for non-circular carbon fiber geometries, process models for polyacrylonitrile based carbon fiber production are developed in relationship to the carbon fiber shape, size, and tow count. For a given fiber shape, precursor production costs are shown to favor maximizing fiber size while conversion costs are minimized by the smallest fiber size. These competing cost trends result in a numerically optimal fiber size for a given shape and tow count, while both cost components are decreased by increasing tow count. Cost–performance tradeoffs for three lobe fiber geometries are studied by supplementing the cost trends with a numerical failure model to predict compressive strength for discrete shape variants. The shape selection is shown to be more sensitive to variations in cost than compressive strength while suboptimal shape designs can improve manufacturing robustness and achievable fiber volume fractions. Finally, an optimal three lobe carbon fiber is identified that balances the set of considerations while reducing costs and embodied energy and increasing production throughput compared to a commercial carbon fiber.

Carbon fiber↗

DOE OSTI2020

Kinetics Modeling for Design of Continuous Enzymatic Hydrolysis

Enzymatic hydrolysis of cellulose to monomeric sugars continues to be a limiting step in cost-effectively producing sugar and fermentation-based biofuels from biomass. In particular, the high cost of enzymes coupled with the long time-scale of reaction pose challenges to economic viability. Performing enzymatic hydrolysis in a continuous mode with enzyme recycle may provide a path towards substantially reduced costs for sugar production, but design and analysis are complicated by a lack of suitable kinetics models. Computationally attainable models, such as fractal-based models, require knowledge of the reaction-history of the biomass, and are thus only suitable for describing the batch reactions from which they are derived. Fundamental models, while potentially more generalizable, are often too computationally intensive to use in process or reactor modeling. In this work, a phenomenological rate model is proposed based on a two-phase substrate representation. Good agreement is seen between batch and continuous enzymatic hydrolysis (CEH) experiment data, which validates the model and enables us to solve for reactor design parameters, such as CEH reactor size and stream flow rates, based on process variables like yield. This model is integrated with techno-economic analysis software to explore economic sensitivities. Important design optimizations and tradeoffs are identified and quantified, including the relative cost imposed by rate slowdown from sugar inhibition versus the cost to remove and concentrate sugars at a lower concentration. It also identifies, high-leverage avenues for further exploration, such as increasing the maximum feasible solids concentration, and sustaining high membrane flux and reliability.

09 BIOMASS FUELS↗

DOE OSTI2025

SolarAPP+ Enhancements and Commercialization (Final Technical Report)

With millions of distributed photovoltaic (PV) systems expected to be installed over the next five years, the permitting and inspection processes of authorities having jurisdiction (AHJs) may become overburdened, causing delays and increased costs for installed systems (Cruce et al. 2022). The central goal of this project was to automate and streamline permitting processes for distributed PV systems and complementary technologies, such as battery storage. Deploying automated permitting has been hypothesized to reduce permit review times, resulting in reduced costs and improved customer experience. This has the potential to expand the PV and PV-plus-storage market nationwide. The National Renewable Energy Laboratory (NREL) and its project partners, including UL Solutions, the Interstate Renewable Energy Council (IREC), the International Code Council (ICC), and more, developed the Solar Automated Permit Processing (SolarAPP+™) software platform to reduce permit review times. NREL and its partners also collaborated with the solar industry, the building safety community, and local governments to develop SolarAPP+.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Assessing the impact of demand response on peak demand in a developing country: The case of Ghana

Peak demand on electricity grids is a growing problem that increases costs and risks to supply security. Residential sector loads often contribute significantly to seasonal and daily peak demand. Demand response refers to consumer actions that change the utility load profile in a way that reduces costs or improves grid security by applying price signals and automated load shedding technologies. The methodologies that are used to achieve demand response can hardly be applicable in developing countries. Peak pricing of electricity, for instance, can hardly be implemented in many developing countries as high prices would disproportionately affect the many low-income households who do not have the capacity to take action to avoid paying high peak prices. This study aims to develop demand response methodology that can be applied in developing countries to achieve residential peak demand reduction. We use a consumer preference survey to develop a methodology suitable for developing countries. The method of diversified demand is used with energy audit and monitored data to estimate the potential peak load reduction and its cost-effectiveness for Ghana. Results show that peak reduction of 15-210 MW is expected by 2040 with a positive return on investment of 2-22% for all designed scenarios.

Diawuo, FA↗

DOE OSTI2025

Applying Corrective Machine Learning in the E3SM Atmosphere Model in C++ (EAMxx)

The Simplified Cloud-Resolving E3SM Atmosphere Model (SCREAM) is the newest addition to the family of Earth System Models capable of explicitly resolving convective systems. SCREAM is a kilometer-scale configuration of the advanced E3SM Atmosphere Model (EAMxx), designed for heterogeneous systems. While the enhanced accuracy of kilometer-scale modeling offers significant benefits, it comes with a substantial computational cost, limiting feasible simulation durations to only a few years, even on the fastest supercomputers. Machine learning presents an opportunity for scientists to achieve the high accuracy of storm-resolving models at a significantly reduced cost. Building on the previous success of applying corrective machine learning (ML) to the FV3 model, this study explores the effects of implementing corrective ML in EAMxx-SCREAM. We also address the computational challenges of integrating the corrective ML, which is written in Python, with the C++/Kokkos EAMxx driver, as well as the potential pitfalls of generalizing an approach that was effective with one atmosphere model to another.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Leveraging the Advantages of Additive Manufacturing to Produce Advanced Hybrid Composite Structures for Marine Energy Systems

Many marine energy systems designers and developers are beginning to implement composite materials into the load-bearing structures of their devices, but traditional mold-making costs for composite prototyping are disproportionately high and lead times can be long. Furthermore, established molding techniques for marine energy structures generally require many manufacturing steps, such as secondary bonding and tooling. This research explores the possibilities of additively manufactured internal composite molds and how they can be used to reduce costs and lead times through novel design features and processes for marine energy composite structures. In this approach, not only can the composite mold be additively manufactured but it can also serve as part of the final load-bearing structure. We developed a conceptual design and implemented it to produce a reduced-scale additive/composite tidal turbine blade section to fully demonstrate the manufacturing possibilities. The manufacturing was successful and identified several critical features that could expedite the tidal turbine blade manufacturing process, such as single-piece construction, an integrated shear web, and embedded root fasteners. The hands-on manufacturing also helped identify key areas for continued research to allow for efficient, durable, and low-cost additive/composite-manufactured structures for future marine energy systems.

16 TIDAL AND WAVE POWER↗

DOE OSTI2023

Cost‐Responsive Optimization of Nickel Nanoparticle Synthesis

Abstract Early‐stage cost evaluation during catalyst development holds the potential to accelerate the commercialization and deployment of advanced catalytic materials for sustainable chemical processes. The modeling and assessment of manufacturing costs as early as the laboratory synthesis scale, for example, focusing on materials costs and synthesis performance metrics, can support the development of an experimental–economic feedback loop that enables rapid insight into cost drivers associated with catalyst synthesis and highlights areas that require focused research and development effort. Ultimately, this feedback loop supports the realization of an economic understanding of the overall synthetic process and highlights opportunities to reduce costs, serving as the foundation for the scale‐up of catalyst manufacturing. Herein, a case study is presented utilizing CatCost, a free and publicly available estimation tool for the evaluation of catalyst manufacturing costs, to perform a cost‐responsive optimization of the synthesis of nickel nanoparticles (Ni NPs). It is demonstrated that reagent substitutions with more cost‐effective analogs, coupled with stoichiometric optimization, afford a 58% reduction in raw materials cost without changing the product yield or properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Advancing Asset Management in Water Infrastructure Systems

Aging water system infrastructure, including drinking water, wastewater, and stormwater, poses a growing challenge for utilities and municipalities. These water systems have well documented challenges with respect to their age, condition, and level of service. ASCE annual report cards consistently rate these infrastructure systems in the United States as underfunded, overcapacity, or past service life (ASCE 2025 Report Card). For example, Chini and Stillwell (2017) estimated that the mean water loss in drinking water systems, i.e., non-revenue water, is approximately 16% across the United States. These concerns are not just relegated to the United States, with Courtenay, British Columbia, identifying 17% of their water main pipes as in a ‘poor’ condition state, defined as a category condition 5 out of 5 (City of Courtenay, 2024). These cases illustrate the challenges utilities are facing to manage extensive networks of infrastructure to deliver a consistent and high level of service. For buried infrastructure such as water systems, studies suggest that preventative interventions can lead to lower maintenance costs and fewer service disruptions (Mazumder et al, 2018; Li et al, 2014). The demonstrated need and benefit of appropriately applied asset management is juxtaposed against the relatively sparse literature that evaluates water systems within an asset management construct. Since 2020, just 37 papers specifically reference asset management in the Journal of Water Resources Planning and Management. Of those, only a few specifically look to develop strategies for improved asset management. Therefore, we highlight four key research areas that represent opportunities for advancement of asset management research for water systems. First, advances in condition assessment and forecasting are needed to better estimate asset deterioration using diverse datasets. Second, machine learning (ML) and artificial intelligence (AI) hold promise for predictive maintenance and investment prioritization, though questions of generalizability and model transparency remain. Third, applying a value of information framework can guide utilities in making cost-effective sensor deployment and data collection decisions, to direct monitoring strategies towards data-informed asset management decisions. Finally, integrated infrastructure management is critical, requiring coordinated planning with other infrastructure systems and stakeholder engagement to reduce costs and enhance service delivery.

Chini, Christopher M.↗

DOE OSTI2020

Scalable Wind Turbine Generator Bearing Fault Prediction Using Machine Learning: A Case Study

Operation and maintenance (O&M) costs for wind turbines pose a risk to competitiveness and asset owners. With machine-learning technologies and digitalization rapidly maturing, the wind industry is actively investigating these new technologies to optimize O&M practices and reduce costs. This paper reviews recent work on machine-learning approaches to generator bearing failure prediction and presents a relevant real-world case study through a collaboration between the National Renewable Energy Laboratory and Envision Digital Corporation. In the case study, we evaluate the performance of representative machine-learning algorithms for predicting wind turbine generator bearing failures. Operational supervisory control and data acquisition data from one wind power plant was used to train and test the machine-learning models. The investigated data channels are chosen based on whether physically they reflect the failed generator bearing conditions and the component historical usage, including both environmental and operational conditions. Benefits and drawbacks of different methods are identified.

generator bearing failures↗

DOE OSTI2023

Trajectory sampling and finite-size effects in first-principles stopping power calculations

Abstract Real-time time-dependent density functional theory (TDDFT) is presently the most accurate available method for computing electronic stopping powers from first principles. However, obtaining application-relevant results often involves either costly averages over multiple calculations or ad hoc selection of a representative ion trajectory. We consider a broadly applicable, quantitative metric for evaluating and optimizing trajectories in this context. This methodology enables rigorous analysis of the failure modes of various common trajectory choices in crystalline materials. Although randomly selecting trajectories is common practice in stopping power calculations in solids, we show that nearly 30% of random trajectories in an FCC aluminum crystal will not representatively sample the material over the time and length scales feasibly simulated with TDDFT, and unrepresentative choices incur errors of up to 60%. We also show that finite-size effects depend on ion trajectory via “ouroboros” effects beyond the prevailing plasmon-based interpretation, and we propose a cost-reducing scheme to obtain converged results even when expensive core-electron contributions preclude large supercells. This work helps to mitigate poorly controlled approximations in first-principles stopping power calculations, allowing 1–2 order of magnitude cost reductions for obtaining representatively averaged and converged results.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Arrayed in vivo barcoding for multiplexed sequence verification of plasmid DNA and demultiplexing of pooled libraries

Sequence verification of plasmid DNA is critical for many cloning and molecular biology workflows. To leverage high-throughput sequencing, several methods have been developed that add a unique DNA barcode to individual samples prior to pooling and sequencing. However, these methods require an individual plasmid extraction and/or in vitro barcoding reaction for each sample processed, limiting throughput and adding cost. Here, we develop an arrayed in vivo plasmid barcoding platform that enables pooled plasmid extraction and library preparation for Oxford Nanopore sequencing. This method has a high accuracy and recovery rate, and greatly increases throughput and reduces cost relative to other plasmid barcoding methods or Sanger sequencing. We use in vivo barcoding to sequence verify >45 000 plasmids and show that the method can be used to transform error-containing dispersed plasmid pools into sequence-perfect arrays or well-balanced pools. In vivo barcoding does not require any specialized equipment beyond a low-overhead Oxford Nanopore sequencer, enabling most labs to flexibly process hundreds to thousands of plasmids in parallel.

59 BASIC BIOLOGICAL SCIENCES↗