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At least 217 records · Page 12

Comfort and Safety Metrics to Evaluate Customer Centric Resilience Enhancement Strategies

As the coincident events of extreme weather and major power outages continue to rise there is an increased need to implement resilience solutions. Many of these solutions are customer-centric, requiring the flexibility of demand, and many of the standard economic evaluations of these solutions do not include the benefits of resilience that are not easily quantified and monetized. Indoor air temperature has a large impact on the comfort and safety of building occupants during an extreme weather event. This work proposes metrics that can be used to compare the performance of resilience solutions in the context of comfort and safety.

Cost benefit analysis, Demand Side Management, Pow↗

Cambium 2022 Scenario Descriptions and Documentation

The National Renewable Energy Laboratory’s (NREL’s) Cambium data sets are annually released sets of simulated hourly emission, cost, and operational data for a range of modeled futures of the U.S. electric sector with metrics designed to be useful for long-term decision making. The 2022 Cambium data set is the third annual release. The data sets are a companion product to NREL’s Standard Scenarios, which are likewise released annually and are a set of projections of how the U.S. electric sector could evolve across a suite of different potential futures, but covering more scenarios with less temporal granularity. In this documentation, we describe Cambium 2022’s scenarios (Section 3), define the metrics (Section 5), and document the Cambium-specific methods for calculating those metrics (Section 6).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Acidic Oxygen Evolution Reaction Activity–Stability Relationships in Ru-Based Pyrochlores

Ru-based oxygen evolution reaction (OER) catalysts show significant promise for efficient water electrolysis, but rapid degradation poses a major challenge for commercial applications. In this work, we explore several Ru-based pyrochlores (A 2 Ru 2 O 7 , A = Y, Nd, Gd, Bi) as OER catalysts and demonstrate improved activity and stability of catalytic Ru sites relative to RuO 2 . Furthermore, we combine complementary experimental and theoretical analysis to understand how the A-site element impacts activity and stability under acidic OER conditions. Among the A 2 Ru 2 O 7 studied herein, we find that a longer Ru–O bond and a weaker interaction of the Ru 4d and O 2p orbitals compared with RuO 2 results in enhanced initial activity. We observe that the OER activity of the catalysts changes over time and is accompanied by both A-site and Ru dissolution at different relative rates depending on the identity of the A-site. Pourbaix diagrams constructed using density functional theory (DFT) calculations reveal a driving force for this experimentally observed dissolution, indicating that all compositions studied herein are thermodynamically unstable in acidic OER conditions. Theoretical activity predictions show consistent trends between A-site cation leaching and OER activity. These trends coupled with Bader charge analysis suggest that dissolution exposes highly oxidized Ru sites that exhibit enhanced activity. Overall, using the stability number (molO2 evolved/molRu dissolved) as a comparative metric, the A 2 Ru 2 O 7 materials studied in this work show substantially greater stability than a standard RuO2 and commensurate stability to some Ir mixed metal oxides. Finally, the insights described herein provide a pathway to enhanced Ru catalyst activity and durability, ultimately improving the efficiency of water electrolyzers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Evaluating Tools and Technologies for Monitoring Baleen Whales During Offshore Wind Foundation Installation

Impacts to baleen whales are of particular concern in the context of offshore wind development because of their conservation status, the overlap of their habitat with current and planned offshore wind energy facilities, and their expected sensitivity to low-frequency sounds, such as those produced during pile driving. Therefore, this report focuses on monitoring technologies capable of detecting this species group. The scope of this report is to assess the current state of technologies and methods for monitoring baleen whales in low- and no-light conditions in conjunction with offshore wind foundation installation. This work considers the performance of various sensing technologies that can be used to observe the presence of baleen whales near offshore construction activities. The scope of work includes: 1. Technology Performance Metrics – Identify metrics that can be used to assess technology performance. 2. Technology Evaluation – Synthesize the past performance of technologies with respect to the performance metrics based on available literature. 3. Technology Characterization Framework – Describe a potential framework to characterize technology performance with a standardized approach. 4. Technology Research and Development Needs – Identify future needs and opportunities for research, development, and deployment to improve technologies.

17 WIND ENERGY↗

AI-Driven Crack Detection for Remanufacturing Cylinder Heads Using Deep Learning and Engineering-Informed Data Augmentation

Detecting cracks in cylinder heads traditionally relies on manual inspection, which is time-consuming and susceptible to human error. As an alternative, automated object detection utilizing computer vision and machine learning models has been explored. However, these methods often face challenges due to a lack of sufficiently annotated training data, limited image diversity, and the inherently small size of cracks. Addressing these constraints, this paper introduces a novel automated crack-detection method that enhances data availability through a synthetic data generation technique. Unlike general data augmentation practices, our method involves copying cracks from one location to another, guided by both random and informed engineering decisions about likely crack formations due to cyclic thermomechanical loads. The innovative aspect of our approach lies in the integration of domain-specific engineering knowledge into the synthetic generation process, which substantially improves detection accuracy. We evaluate our method’s effectiveness using two metrics: the F2 score, which emphasizes recall to prioritize detecting all potential cracks, and mean average precision (MAP), a standard measure in object detection. Experimental results demonstrate that, without engineering insights, our method increases the F2 score from 0.40 to 0.65, while maintaining a stable MAP. Incorporating detailed engineering knowledge further enhances the F2 score to 0.70 and improves MAP to 0.57, representing increases of 63% and 43%, respectively. These results confirm that our approach not only mitigates the limitations of traditional data augmentation but also significantly advances the reliability and precision of crack detection in industrial settings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Performance Metrics to Evaluate Utility Resilience Investments

In 2019, Sandia National Laboratories (Sandia) contracted Synapse Energy Economics (Synapse) to research the integration of community and electric grid resilience investment planning as part of the Designing Resilient Communities (DRC): A Consequence-Based Approach for Grid Investment project. Synapse produced a series of reports to explore the challenges and opportunities in several key areas, including benefit-cost analysis (BCA), performance metrics, microgrids, and regulatory mechanisms. This report focuses on BCA. BCA is an approach that electric utilities, electric utility regulators, and communities can use to evaluate the costs and benefits of a wide range of grid resilience investments in a comprehensive and consistent way. While BCA is regularly applied to some types of grid investments, application of BCA to grid resilience investments is in the early stages of development. Though resilience is increasingly cited in connection with grid investment proposals and plans, the resilience- related costs and benefits of grid resilience investments are typically not fully identified, infrequently quantified, and almost never monetized. Without complete assessments of costs and benefits, regulators can be hesitant to approve some types of grid resilience investments. This report provides the first application of the framework developed in the 2020 National Standard Practice Manual for Benefit-Cost Analysis of Distributed Energy Resources (NSPM for DERs) to grid resilience investments. We provide guidance on next steps for implementation to enable grid resilience investments to receive due consideration. We suggest developing BCA principles and standards for jurisdiction-specific BCA tests. We also recommend identifying the resilience impacts of the investments and quantification of these impacts by establishing utility performance metrics for resilience. Proactive integration of grid resilience investments into existing regulatory processes and practices can increase the capacity of jurisdictions to respond to and recover from the consequences of extreme events. 1 National Energy Screening Project. 2020. National Standard Practice Manual for Benefit-Cost Analysis of Distributed Energy Resources.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Improving air leakage prediction of buildings using the fan pressurization method with the Weighted Line of Organic Correlation

In many countries, the fan pressurization method is the most frequently chosen approach for measuring the air leakage of houses. The measurements are usually performed at pressures that far exceed pressures to which buildings are exposed to under normal conditions. A fit of these tests to the power-law formulation allows an extrapolation to data points outside the measured pressure range. With the Ordinary Least Square (OLS) fitting method, the pressure exponent and flow coefficient can be determined. However, the measurement results are highly sensitive to uncertainties induced by external factors like changing wind conditions during the tests, which is neglected by OLS. This may lead to errors in the prediction of flows at lower pressures. The Weighted Line of Organic Correlation (WLOC) is an alternative approach and takes measurement uncertainty into account. In this paper, a statistical analysis of an extensive data set of pressurization measurements has been performed. Both regression techniques have been compared for almost 7500 fan pressurization measurements of six houses in 109 different house leak configurations. The variability in predicting pressure exponent and flow coefficient for both WLOC and OLS regression was analyzed using probability density functions. It was found that the Weighted Line of Organic Correlation significantly decreases the uncertainty in predicting pressure exponent, flow coefficient, and other low-pressure air leakage metrics compared to the Ordinary Least Square fitting. Here, the authors highly recommend the implementation of WLOC in current measurement standards and test equipment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Australian Solar Energy Forecasting System (ASEFS) (CRADA CRD-14-541 Final Report)

This project forms a part of the broader project with the same name which is funded by ASI under the US-Australia Solar Energy Collaboration (Broader Project). The Broader Project involves a number of other participants, being the University of New South Wales, the University of South Australia, the Australian Energy Market Operator Limited and the Commonwealth of Australia acting through the Bureau of Meteorology (Other Participants). NREL and CSIRO will each work with the Other Participants in the performance of this CRADA. However, for the avoidance of doubt, this CRADA does not cover the whole of the Broader Project and only relates to the work described in this Joint Work Statement, which is to be carried out by NREL. In the course of this project, NREL may be provided with materials belonging to CSIRO or the Other Participants, which it may use solely for the purpose of performing the project described in this Joint Work Statement. Similarly, CSIRO may provide NREL project materials to Other Participants pursuant to Article IX or XV of the CRADA, for the purpose of performing research forming part of the Broader Project in collaboration with CSIRO.

14 SOLAR ENERGY↗

Large-scale Hydrogen Storage Risk Assessment

This project investigated risks involved in deploying a large-scale hydrogen storage system at the Port of Seattle (hereafter, the Port) for its on-terminal and maritime applications in an urban industrial setting. Alongside, the project attempted to address some of the barriers to risk assessment such as need for an exact system design for a systematic investigation, direct access to surrounding communities to gauge their perceptions, and an integrated software required to undertake a full-fledged risk assessment. These barriers were overcome using illustrative reference station designs, engaging with community-facing agencies through Port support, and pooling national laboratory capabilities available for risk assessments. The project identified relevant public safety risk metrics, compared various hydrogen carriers, engaged with community-facing agencies, and explored potential gaps in existing safety codes and standards. The primary impacts of this project include the development of risk assessment guidance for ports and utilities, informing them of the trade-offs in the choice of hydrogen carriers, and the ability to increase public capacity for dialog and engagement. This paves the way toward decarbonization of the Port activities, bringing about awareness around jobs in the market for risk assessments, and the need to ramp up community engagement long before any hydrogen system deployment is undertaken.

08 HYDROGEN↗

The Current State of Light-Duty Electric Vehicle Supply Equipment Costs: An Assessment of Contemporary Understanding

This study uses a hybrid meta-analysis and literature-review approach to understand the current state of knowledge regarding the costs of electric vehicle supply equipment (EVSE). We present a novel way to consider, categorize, and label measures of cost and show cost measure estimates from a sample of 13 recent studies. We find that in general, there is too much variation and too few commonly represented EVSE cost measures to reasonably provide aggregate figures for these measures. We propose a convention for presenting EVSE cost measures that includes the application (commercial or residential), the power level (Level 1, Level 2, DCFC [with further distinction based on rated power capacity]), and the type of cost measure (hardware, installation, operation, and total cost). We contend that providing researchers with standard cost measures will help to advance our knowledge of EVSE costs by ensuring that future work will use common metrics. Establishing common metrics will enable conventional meta-analyses that will make assessments of EVSE costs even more accessible. Additionally, common metrics will make tracking costs more reliable as the technology continues to evolve and become more ubiquitous.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Uncertainty-Aware Machine Learning for Small-Angle X-ray Scattering Analysis in Autonomous Experimentation

Small-angle X-ray scattering (SAXS) is a powerful high-throughput characterization tool for probing nanoscale structure in native sample environments, providing real-time morphological information such as nanoparticle size and shape during synthesis. However, automated SAXS data analysis for extracting meaningful structural parameters is non-trivial and remains a bottleneck in closed-loop experimentation towards autonomous materials discovery, which demands fast, reliable, and uncertainty-aware data analysis. Here, we develop a machine-learning approach for automated SAXS analysis tailored to closed-loop nanoparticle synthesis. A Random Forest (RF) regression model is trained on 100,000 synthetic SAXS curves generated from polydisperse spherical nanoparticles with realistic background contributions. Using normalized one-dimensional SAXS intensity profiles as input, the RF model directly predicts nanoparticle radius, size polydispersity, and background parameters, while the ensemble standard deviation across trees provides built-in uncertainty quantification (UQ). On synthetic data, we show that combining fit-quality metrics (R 2 , MAE) with thresholds on prediction uncertainty reliably identifies accurate parameter estimates without access to ground truth. We then apply the trained model to 365 experimental SAXS profiles of citrate-reduced gold nanoparticles synthesized using an automated droplet-flow microreactor with in situ SAXS at a synchrotron beamline, classifying the results into high- and low-confidence subsets based on UQ metrics. Finally, we integrate RF-based SAXS analysis into a simulated closed-loop optimization campaign using Gaussian process Bayesian optimization to minimize nanoparticle polydispersity, benchmarking against conventional automated Levenberg–Marquardt fitting. The RF-guided campaign exhibits substantially faster convergence and lower relative opportunity cost (∼0.07 vs ∼0.3), demonstrating that uncertainty-aware machine-learning SAXS analysis significantly enhances the efficiency and robustness of autonomous nanomaterials synthesis workflows.

Bayesian optimization↗

Quantification of Changes in Fill Tube Curvature During Target Assembly

National Ignition Facility (NIF) targets used for inertial confinement fusion (ICF) experiments are precision-engineered assemblies composed of more than 100 components carefully and precisely assembled with accuracies no larger than a few micrometers in most cases. When individual components deviate from these strict specifications during the assembly process, they can induce target failure. An integral component essential to ICF targets is the capsule-fill-tube assembly. This assembly involves bonding the capsule to a fused silica tube less than 5 μm in thickness, through which the tritium and deuterium (T 2 + D 2 ) fuel mixture is injected into the capsule prior to the NIF shot. The filling tube is bonded to a larger fused silica capillary, about 130 μm in outer diameter, which is coated with a polymeric layer on the order of a 10-μm thickness or less. Only tubes exhibiting a deflection equivalent to less than 1% of their total length from a perfectly straight line are suitable for assembly. Slight curvatures on the order of 1 mm over 10 cm can induce unwanted stresses, potentially causing capsule misalignment and resulting in clogging or leaks during the filling process. Despite manually qualitatively sorting each tube for straightness prior assembly, it has been found that the treatments the tubes undergo once attached to the capsule can alter their curvature. Filling tubes that initially satisfy straightness tolerances can undergo geometric deformation, resulting in curvature deviations exceeding 1% of their total length and thus failing to meet target assembly requirements. Here, in this study, we propose a metric to assess the degree of curvature of the filling tubes and to gauge their changes in curvature caused by the standard thermal processing methods employed in target assembly. In addition, we suggest alternatives to mitigate tube curvature, ensuring they conform to specifications following assembly in the final targets.

Materials science↗

Milestone 1.2.10: Steady-state H 2 “roll over” point data for aluminum alloys 1100 and 6061

Extended (> 50 years) dry storage is being evaluated by the U.S. Department of Energy (DOE) for the disposition of ~ 18 metric tons of aluminum-clad spent nuclear fuel (ASNF). Transition of the current ASNF inventory into dry storage—using the standard DOE canister—necessitates a rigorous, predictive understanding of the long-term physical and chemical factors that may influence the integrity of the proposed storage canister, including radiolytic molecular hydrogen (H 2 ) generation. Current model predictions employ initial radiolytic yields of H 2 , the values of which change as the cladding’s H 2 -precursor inventory is depleted and H 2 itself becomes progressively more involved in radiolytic and surface dissociation processes. Consequently, the absorbed radiation dose that this steady-state H 2 yield corresponds to is essential for the evaluation and improvement of model predictions. Here, we report our findings on the long-term generation of H 2 from the gamma irradiation (≤ 36 MGy) of corroded AA1100 and AA6061 coupons in helium environments at ambient temperature and ~ 50% RH. Our findings show that AA1100 systems reached steady-state by ~ 36 MGy, while higher doses were necessary for AA6061 systems. This discrepancy was attributed to the AA6061 coupons developing a thicker corrosion layer that led to the trapping of H 2 and its precursors, and potentially additional chemistries, ultimately delaying the depletion of H 2 precursors and the system’s “roll over” point. Further, current model predictions—based on previous AA1100 data—do not show steady-state attainment until above 120 MGy, which is not the case for the AA1100 data collected here. Consequently, the new alloy dependent data presented here are important for the continued improvement of predictive computer models for evaluating the feasibility of extended storage of ASNF in helium backfilled canisters.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

Sub-pilot-scale Production of High-Value Products from U.S. Coals

Investigators from the University of Utah, University of Wyoming and Marshall University pursued a program to study the conversion of raw coal to high-value products of carbon fiber and silicon carbide. Team members also developed an initial framework for a data portal that can incorporate laboratory data on coal processing and product quality, and also work with tools for machine learning for data analysis, data visualization and economic assessment. Experimental R&D efforts focused on the conversion of raw coal to coal tar and other byproducts, and the resulting tar intermediates were upgraded to form anisotropic and isotropic pitch materials. These pitch materials were produced from coal using both thermal (pyrolysis) and chemical (mild solvolysis liquefaction) decomposition of raw coal. Four different coals were studied: Utah bituminous coal (Sufco), Wyoming PRB coal (Black Thunder), Illinois bituminous coal (Illinois #6), and West Virginia bituminous coal (Flying Eagle). Both metallurgical-grade coking coals and lower-grade steam coals were investigated, and controlled secondary gas-phase reactions were used during a two-stage pyrolysis process to induce cracking and condensation reactions among the pyrolytic tar species. This approach successfully improved the performance of the lower grade coals for yielding pitch materials, with properties more consistent with a commercial-grade pitch that had previously demonstrated success for quality carbon fiber production. The use of waste plastic materials was also studied, to help improve physical and chemical characteristics of the intermediate tars and final pitch product; in particular, for lowering the pitch softening point to an acceptable level for melt spinning carbon fiber. Mild solvolysis liquefaction was also used as a method for producing pitch for carbon fiber production. As expected, significantly higher pitch yields were obtained using this approach, and waste plastic materials were also successfully used to reduce pitch softening point to an acceptable level. The plastic materials were also utilized to create a solvent for the mild solvolysis process, and this plastic-derived solvent was shown to provide results consistent with more expensive commercial chemical solvents, and could thus avoid the need for costly recovery and recycle of a liquefaction solvent. Additional experimental R&D focused on the production of silicon carbide (β-SiC) from the residual char byproduct from pitch production, and also on the production of carbon fiber from the anisotropic pitch. SiC was successfully synthesized using a mixture of residual char and sandstone at a ratio of 1:1. Reaction temperature and residence time were optimized and yielded a product purity of 81%. For carbon fiber production, the most successful pitch samples were obtained from the mild solvolysis liquefaction approach, combined with the use of a plastic (HDPE)-derived solvent. Fiber properties improved over time as laboratory fiber production methodologies improved, and final yields of carbon fiber were obtained with a diameter of 12.14 ± 1.10 um, Modulus of 173.73 ± 15.25 GPa, and Tensile Strength of 1.04 ± 0.10 GPa. A proof-of-concept Modern Community Research Data Portal (MCRDP) was developed and deployed for coal and coal-derived pitch characterization, with the full support of (i) remote web-based access, (ii) distributed analysis, (iii) interactive visualization and exploration, (iv) shared and long-term data access, (v) advanced query capabilities and (vi) real-time collaboration. The Coal to Products Data Portal “coaltoproducts.org” provides researchers with space to store and share data within a project, tools for analyzing and understanding data for scientific investigation, and the ability to publish data to the broader community for reproducibility. The portal leverages the Material Commons 2.0 (MC) platform developed by the Center for PRedictive Integrated Structural Materials Science (PRISMS) of the University of Michigan, to achieve long-term longevity of data collections and, more importantly, collaborative science. A number of data visualization tools were also assessed and implemented for interrogating the experimental and modeling data. The machine learning portion of this project analyzed datasets from two different coal conversion processes performed on a diverse set of coal samples from both the coal pyrolysis experiments and the solvent liquefaction experiments. The work was initiated by exploring standard regression models on the pyrolysis data, aiming to understand the impact of sample characteristics and processing conditions on key product metrics. Over the course of the project, the focus expanded to include a variety of machine learning tools, delving into both supervised and unsupervised learning methods. Models tested on the pyrolysis data included linear, ridge, lasso, elastic-net, Gaussian process, random forest regression, and AutoSklearn, and the approach was continually refined to enhance predictive accuracy and model interpretability. Similar techniques were applied to the liquefaction data with an additional focus on feature engineering. Along with mesophase content, additional outputs of interest were the pitch yield, softening point, and QI content. Insights derived from these analyses are crucial in determining the factors influencing the quality and yield of coal-derived products. As the work progressed, the research evolved from foundational model comparisons to analyses of random forests, decision paths, and feature importance scores. A thorough market analysis was performed to examine the prospects of coal-based carbon fibers. The best opportunities for coal come from its lower and more stable price relative to petroleum, particularly for subbituminous coals, which is the primary advantage that a coal refinery may have over a petroleum refinery. Before a commercial CTP production facility can be modeled, however, several things need to be understood regarding the nature of the would-be coal refinery. These include the technology to be deployed, the size of facility, the volume(s) of co-product(s), and the waste and emissions profile of the plant. The volume of co-products and waste may be substantial and will require separate market analysis to ensure viability. In the near-term, the importance of coal tar pitch, in the form of carbon pitch, to the aluminum and steel industries is likely to overshadow the alternative use of this material as an input for carbon fiber. The importance of steel and aluminum in building materials, and the need for carbon materials in their manufacturing, will ensure that demand for these products remains for the long run. In addition, carbon fiber may also be the best substitute for steel and aluminum well into the future. While society will eventually be able to shift production of much of its electricity needs to renewables, it will not be able to shift away from fossil fuels for production of high-strength construction and vehicular materials. Demand for carbon fiber is expected to increase quickly, but the volume of carbon fiber and the amount of coal that would be needed to produce even a sizeable share of this market may still be relatively small compared to current coal production. Thus, other coal-based products like graphene, graphite, carbon foams, resins, and carbon-based building products will play important roles in sustaining coal production as coal-fired power generation continues to decline.

01 COAL, LIGNITE, AND PEAT↗

Adaptive X-ray imaging with reinforcement learning

X-ray imaging is a powerful technique to scan samples in a variety of contexts including biological, environmental and materials science, but commonly requires a synchrotron light source to produce X-rays at sufficient intensity. As these facilities are expensive to operate, the available beam time is limited and always in high demand. Particularly if the illuminated samples are sparse, standard raster scanning methods can be time-consuming, with a majority of that time being spent on areas of the image that carry little information. To increase the efficiency and maximize the information gain for a given time budget, we split the scanning process into a series of steps where previous measurements are used to inform the decision making and adapt the exposure distribution at later stages of the sequence. We formulate this task as a reinforcement learning problem where the goal is to produce a sequence of exposure maps that maximize a predefined scalar metric. We demonstrate the potential of this approach in simulations where the adaptive illumination can accelerate the measurement process by up to an order of magnitude compared with standard raster scanning. Finally, we present the first results from deploying the trained agents on an X-ray fluorescence beamline at the Stanford Synchrotron Radiation Lightsource.

Reinforcement Learning↗

Comparing the predictive capabilities of subjective crackle metrics using leave-one-out cross validation

Leave-one-out cross validation was used to show the capability to predict crackling sound quality based on the use of the standard deviation of the time-varying sharpness and the skewness of the derivative of the pressure waveform. The predictive output of this method implies that the more sophisticated fits used to describe this data were not overfit, but instead exhibited substantial predictive value.

Swift, Stephen Hales [Sandia National Laboratories↗

Life-Cycle Cost Analysis Framework for Water Efficiency Measures

Life-Cycle Cost Analysis Framework for Water Efficiency Measures: Guidance Designed for Federal Agencies (hereafter referred to as “this report”) provides a technical framework for federal agencies to conduct a life-cycle cost analysis (LCCA) for water efficiency projects in accordance with 42 U.S.C. § 8253. This report leverages insights from the 2023 report, PNNL-34006, Water and Wastewater Annual Price Escalation Rates for Selected Cities Across the United States: 2023 Edition (Unger et al. 2023). The primary objective of this report is to provide a framework to assist federal agencies with evaluating the full economic impact of water efficiency projects by assessing both initial investments and long-term operational benefits. An LCCA can provide a comprehensive view of all costs associated with a water efficiency project, including initial investment, ongoing operations and maintenance (O&M), and eventual disposal or replacement, ensuring the most cost-effective solution is selected. The LCCA methodology outlined in this report enables users to compare base case scenarios with potential alternatives using a standardized present value approach. It incorporates key cost components such as energy, water and wastewater, installation, O&M, and equipment replacement. Additionally, the framework introduces relevant evaluation metrics, such as the net savings and the savings-to-investment ratio, to ensure that water efficiency measures are economically justified over the lifespan of the project. To support practical application, this report also describes various water efficiency strategies that may be analyzed using an LCCA, including plumbing retrofits, irrigation upgrades, alternative water use, and cooling system improvements. By applying this framework, federal agencies can ensure compliance with regulatory mandates while maximizing the return on investment and contributing to resilient water management practices. The information provided in this report is aligned with the Federal Energy Management Program (FEMP) life-cycle cost (LCC) methodology, as conveyed in National Institute of Standards and Technology (NIST) Handbook 135 (Kneifel and Webb 2022). This report is intended to provide relatively high-level guidance, acting as a complement to, rather than a substitute for, that resource.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Optimizing the HSX stellarator for microinstability by coil-current adjustments

Abstract The optimization of helically symmetric experiment (HSX) for reduced microinstability has been achieved by examining a large set of configurations within a neighborhood of the standard operating configuration. This entailed generating a database of more than 10 6 magnetic-field configurations for HSX by varying the currents in external coils. Using a set of volume-averaged metrics and gyrokinetic simulations, this database has helped to identify a set of configurations that can be used to regulate trapped-electron-mode stability in HSX. This set of configurations is also found to correlate flux-surface elongation and triangularity with an increase in magnetic-well depth, an increase in rotational transform, and low neoclassical heat-flux relative to the standard quasi-helically-symmetric configuration. These results demonstrate sensitivity of plasma behavior in response to changes in a 3D magnetic field to both neoclassical and gyrokinetic models, and the experimental potential in HSX to explore turbulence optimization. This perturbative optimization approach is not unique to HSX, and can readily be deployed on existing fusion devices to identify novel magnetic-fields to be used in turbulence-optimization experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗