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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2007

Scope of Various Random Number Generators in ant System Approach for TSP

Experimented on heuristic, based on an ant system approach for traveling salesman problem, are several quasi- and pseudo-random number generators. This experiment is to explore if any particular generator is most desirable. Such an experiment on large samples has the potential to rank the performance of the generators for the foregoing heuristic. This is mainly to seek an answer to the controversial issue "which generator is the best in terms of quality of the result (accuracy) as well as cost of producing the result (time/computational complexity) in a probabilistic/statistical sense."

Sen, S. K.↗
NASA NTRS1973

A Comparison of the Measured Scattering Matrix Elements of Polydisperse Systems of Irregular Particles with the Matrix Elements Calculated for Spheres that Have the Same Distribution of Projected Areas

The elements of the scattering or Mueller Matrix for three polydisperse systems of irregular, randomly-oriented particles have been measured in absolute terms as a function of scattering angle for several visible wavelengths. The samples consisted of commercially available silicon dioxide particles that fit three distinct lognormal size distributions. The measured matrix elements were compared with the matrix elements calculated for spheres that had the same refractive index and fitted the same distribution of projected areas. Correlations and discrepancies between the two sets of matrix elements will be discussed.

Holland, A. C.↗
NASA NTRS2018

Real-Time Adaptive Drag Minimization Wind Tunnel Investigation of a Flexible Wing with Variable Camber Continuous Trailing Edge Flap System

This paper reports the results of a recently completed real-time adaptive drag minimization wind tunnel investigation of a highly flexible wing wind tunnel model equipped with the Variable Camber Continuous Trailing Flap (VCCTEF) technology at the University of Washington Aeronautical Laboratory (UWAL). The wind tunnel investigation is funded by NASA SBIR Phase II contract with Scientific Systems Company, Inc. (SSCI) and University of Washington (UW) as a subcontractor. The wind tunnel model is a sub-scale Common Research Model (CRM) wing constructed of foam core and fiberglass skin and is aeroelastically scaled to achieve a wing tip deflection of 10% of the wing semi-span which represents a typical wing tip deflection for a modern transport such as Boeing 787. The jig-shape twist of the CRM wing is optimized using a CART3D aero-structural model to achieve the minimum induced drag for the design cruise lift coefficient of 0.5. The wing is equipped with two chord wise cambered segments for each of the six span wise flap sections for a total of 12 individual flap segments that comprise the VCCTEF system. Each of the 12 flap segments is actively controlled by an electric servo-actuator. The real-time adaptive drag optimization strategy includes an on-board aerodynamic model identification, a model excitation, and a real-time drag optimization. The on-board aerodynamic model is constructed parametrically as a function of the angle of attack and flap positions to model the lift and drag coefficients of the wing. The lift coefficient models include a linear model and a second-order model. The drag coefficient models include a quadratic model and a higher-order up to 6th-order model to accurately model the drag coefficient at high angles of attack. The onboard aerodynamic model identification includes a recursive least-squares (RLS) algorithm and a batch least-squares (BLS) algorithm designed to estimate the model parameters. The model excitation method is designed to sample the input set that comprises the angle of attack and the flap positions. Three model excitation methods are developed: random excitation method, sweep method, and iterative angle-of-attack seeking method. The real-time drag optimization includes a generic algorithm developed by SSCI and several optimization methods developed by NASA which include a second-order gradient Newton-Raphson optimization method, an iterative gradient optimization method, a pseudo-inverse optimization method, an analytical optimization method, and an iterative refinement optimization method. The first wind tunnel test entry took place in September 2017. This test revealed major hardware issues and required further redesign of the flap servo mechanisms. The second test entry took place in April 2018. However, the test was not successful due to the issues with the onboard aerodynamic model identification RLS algorithm which incorrectly identified model parameters. This test also provides an experimental comparison study between the VCCTEF and a variable camber discrete trailing edge flap (VCDTEF) without the elastomer transition mechanisms. The experimental result confirms the benefit of the VCCTEF which produces lower drag by 5% than the VCDTEF. The third and final test entry took place in June 2018 after the issues with the RLS algorithm have been identified and corrected. Additional improvements were implemented. These include the BLS algorithm, the iterative angle-of-attack seeking method, the iterative gradient optimization method, and the pseudo-inverse optimization method. The test objectives were successfully demonstrated as the real-time drag optimization identifies several optimal solutions at off-design lift coefficients. The iterative gradient optimization method is found to achieve up to 4.7% drag reduction for the off-design lift coefficient of 0.7. The pseudo-inverse optimization method which does not require the drag coefficient model is found to be quite effective in reducing drag. Up to 9.4% drag reduction for the off-design lift coefficient of 0.7 is achieved with the pseudo-inverse optimization method. The wind tunnel investigation demonstrates the potential of real-time drag optimization technology. Several new capabilities are developed that could enable future adaptive wing technologies for flexible wings equipped with drag control devices such as the VCCTEF.

Drag Optimization↗
DOE OSTI2017

Use of Uncertainty in Modeling DFLAW Treatment at Hanford - 17115

Average or mean is understood by most; standard deviation (SD) is understood by few. Certainty is easy to model; uncertainty is not. The flowsheet simulation, described in this paper, placed uncertainties in the G2 model of the Hanford Tank Waste Treatment and Immobilization Plant's (WTP) Direct Feed Low-Activity Waste (DFLAW) process for performance and sensitivity evaluations. G2 is a dynamic material balance model that starts and proceeds with operating logic until all the feed is processed. Dynamic material balance is achieved by tracking the flows, storage, and change of all materials within the plant as time increments. Processing equipment and limitations of the flowsheet are modeled (e.g., equipment types, piping, volumes, flowrates, efficiencies, and physical and chemical environments that impact separations, phase equilibriums, and chemical reactions). Operating logic represents the rules and strategies of a running plant. The G2 model simulated both certainty and uncertainty scenarios for comparison purposes. For the uncertainty scenarios, process decisions were made using presumed values to simulate various operational measurement errors and process variabilities. Presumed means that the known G2 values are adjusted to replicate measurement errors and variabilities. These include: 1) volume measurement, 2) sample analysis, 3) melter decontamination factor (DF), 4) glass former weighing, and 5) glass former composition variations. The errors were randomly generated in accordance with SD values of measurement devices, laboratory methods, and glass former composition ranges. The G2 model runs give insight on the DFLAW process' controllability and the expected range of glass properties. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters

High-precision calorimeter simulation at current and future colliders imposes rapidly growing computational demands, motivating the development of machine-learning surrogates for traditional Monte Carlo tools such as Geant4. Flow matching and diffusion-based generative models have become leading approaches for high-dimensional fast simulation because of their sample quality, but typically require ${\cal O}(100)$ function evaluations at inference and often rely on auxiliary networks to constrain global observables, compromising streamlined end-to-end generation. We introduce a unified framework that improves the balance between speed, shower quality, and physics fidelity. The method combines: (i) an average velocity field integrator that enables sampling in one or a few evaluations; (ii) a learned generative prior in shower space, constructed from data rather than random noise; and (iii) physics-guided loss terms that impose inductive biases on key observables during training. These elements are training time regularizers, preserving end-to-end inference with no additional cost. With only one or a few evaluation steps, the model achieves shower quality competitive with state-of-the-art flow and diffusion approaches, tested on several public high granularity calorimeter datasets. The results demonstrate inter-layer shower structure consistent with the underlying physics, providing a strong candidate for future fast simulation workflows.

Jiang, Cheng [Edinburgh U.]↗
NASA NTRS1989

An opacity-sampled treatment of water vapor

Although the bands of H2O are strong in the spectra of cool stars and calculations have repeatedly demonstrated their significance as opacity sources, only approximate opacities are currently available, due both to the difficulty of accounting for the millions of lines involved and to the inadequacy of laboratory and theoretical data. To overcome these obstacles, a new treatment is presented, based upon a statistical representation of the water vapor spectrum derived from available laboratory data. This statistical spectrum of water vapor employs an exponential distribution of line strengths and random positions of lines whose overall properties are forced to reproduce the mean opacities observed in the laboratory. The resultant data set is then treated by the opacity-sampling method exactly as are all other lines, both molecular and atomic. Significant differences are found between the results of this improved treatment and the results obtained with previous treatments of water-vapor opacity.

Alexander, David R.↗
DOE OSTI2020

Accelerating Ab Initio Simulation via Nested Monte Carlo and Machine Learned Reference Potentials

As a corollary of the rapid advances in computing, ab initio simulation is playing an increasingly important role in modeling materials at the atomic scale. Two strategies are possible, ab initio Monte Carlo (AIMC) and molecular dynamics (AIMD) simulation. The former benefits from exact sampling from the correct thermodynamic distribution, while the latter is typically more efficient with its collective all-atom coordinate updates. In this study, using a relatively simple test model comprised of helium and argon, we show that AIMC can be brought up to, and even above, the performance levels of AIMD via a hybrid nested sampling/machine learning (ML) strategy. Here, ML provides an accurate classical reference potential (up to three-body explicit interactions) that can pilot long collective Monte Carlo moves that are accepted or rejected in toto à la nested Monte Carlo (NMC); this is in contrast to the single move nature of a naive implementation. Our proposed method only requires a small up front expense from evaluating the ab initio energies and forces of (100) random configurations for training. Importantly, our method does not totally rely on the trained, assuredly imperfect, interaction. We show that high performance and exact sampling at the desired level of theory can be realized even when the trained interaction has appreciable differences from the ab initio potential. Remarkably, at the highest levels of performance realized via our approach, a pair of statistically uncorrelated atomic configurations can be generated with (1) ab initio calculations.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2018

Effect of build geometry on the microstructural development of 316L parts produced by additive manufacturing

Highlights: • Stainless steel ribs were created via direct metal laser sintering to investigate microstructural effect of part design. • EBSD confirmed the presence of large elongated grains in the center of the part, with preferred orientation. • Small grains at the rib surface were found to grow towards the center of the rib with random orientation. • By changing rib thickness and build angle varying texture was observed in the final products. • Large intragranular misorientations were observed in all samples. - Abstract: Additive manufacturing (AM) technology has shown great potential in manufacturing complex products, such as lattice structures without extensive tooling. The focus of this study was to understand the effect of the part geometry on the resulting microstructure. Direct metal laser sintering was used for fabrication of ribs with thicknesses varying between 0.2 and 3.0 mm as well as inclined ribs the with build angles of 30° and 45°. The investigation showed that grains were growing in an epitaxial manner parallel to the building direction. The large elongated grains had a preferential orientation, resulting from the temperature gradient. Additionally, it was found that small grains had formed close to the part surface which were inclined towards the center of the rib. In contrast to the elongated grains, they had a random orientations. The results also indicated that at build angles below 45°, the formed microstructure consisted of the large grains elongated in the building direction. For the used process parameters, the critical part thickness to avoid large elongated grains was found to be about 0.4 mm. These findings allowed us to establish the basics for design rules when it comes to thin wall structures.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Learning with Delayed Rewards—A Case Study on Inverse Defect Design in 2D Materials

Defect dynamics in materials are of central importance to a broad range of technologies from catalysis to energy storage systems to microelectronics. Material functionality depends strongly on the nature and organization of defects-their arrangements often involve intermediate or transient states that present a high barrier for transformation. The lack of knowledge of these intermediate states and the presence of this energy barrier presents a serious challenge for inverse defect design, especially for gradient-based approaches. Here, we present a reinforcement learning (RL) [Monte Carlo Tree Search (MCTS)] based on delayed rewards that allow for efficient search of the defect configurational space and allows us to identify optimal defect arrangements in low-dimensional materials. Using a representative case of two-dimensional MoS 2 , we demonstrate that the use of delayed rewards allows us to efficiently sample the defect configurational space and overcome the energy barrier for a wide range of defect concentrations (from 1.5 to 8% S vacancies)-the system evolves from an initial randomly distributed S vacancies to one with extended S line defects consistent with previous experimental studies. Detailed analysis in the feature space allows us to identify the optimal pathways for this defect transformation and arrangement. Comparison with other global optimization schemes like genetic algorithms suggests that the MCTS with delayed rewards takes fewer evaluations and arrives at a better quality of the solution. The implications of the various sampled defect configurations on the 2H to 1T phase transitions in MoS 2 are discussed. In this study, we introduce a RL strategy employing delayed rewards that can accelerate the inverse design of defects in materials for achieving targeted functionality.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

An Evaluation of a Neutron Time Correlated Interrogation Method for Measurement of Fissile Content in Research Reactor Spent Fuel Assemblies

There are many research reactors worldwide that have been, or are being converted from the use of high enrichment uranium (HEU) to low enrichment uranium (LEU, < 20% enriched). The verification of fissile content and initial enrichment of the spent fuel is needed for the effective safeguards of the fuel. The advanced experimental fuel counter (AEFC) was developed for the measurement of spent fuel rods and assemblies from research reactors for safeguards verification. This measurement system contains components for active neutron interrogation, passive neutron totals counting, neutron coincidence counting, and gross gamma-ray counting. This report presents the first application of the time correlated interrogation technique for the measurement of the 235 U content in research reactor spent fuel assemblies. The technique, called time correlated induced fission (TCIF), uses a 252 Cf neutron source to irradiate the fuel assembly, and the subsequent induced fission events in the fissile material are measured by coincidence counting. The doubles rates are enhanced by having the neutron trigger events from both the 252 Cf source and the induced fission neutrons in the same time gate in the coincidence analysis. The average neutrons per fission of the 252 Cf source is 3.76 and the induced fission neutrons for 235 U is 2.44, so the number of neutrons that are produced is higher than for random neutron interrogation. This high effective neutron number increases the multiplicity counting rates and reduces the statistical error. The background coincidence counts from the 252 Cf are reduced by the water in the sample cavity and the polyethylene surrounding the 3 He detector tubes. This method of active neutron interrogation has been applied to the measurement of spent research reactor (IRT) fuel assemblies. The advanced experimental fuel counter (AEFC) was used to compare the TCIF method with the typically used AmLi neutron interrogation source that emits neutrons that are random in time. The statistical uncertainty for the use of the random neutron source (AmLi) and the time correlated source ( 252 Cf) for spent fuel interrogations was evaluated.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Synthesis and thermomechanical characteristics of zwitterionic poly(arylene ether sulfone) copolymers

Charge-containing polymeric materials have been widely studied for a range of applications. The fundamental relationships between the charge species, charge density, and the microscale morphology of charge-containing polymers are critical for defining the application in which they may be used. In this work, a series of thermoplastic poly(arylene ether sulfone) (PAES) copolymers with controllable sulfobetaine charge contents (0~100 mol%) and high molecular weights (Mw ~ 65 kDa) were prepared. All the zwitterionic PAESs synthesized showed thermal stability up to 250 °C. DSC and tensile tests showed a decreased T g and mechanical strength with the incorporation of increasing charge density, which suggests a plasticization effect by the charged group. Electron microscopy and X-ray scattering data confirmed the absence of microphase separation in the ion-containing random copolymer system, which corroborated with the observation of a single T g for all zwitterionic copolymers studied. Furthermore, the relative hydrophilicity of the samples was evaluated by water uptake measurements, which revealed that the water uptake of the zwitterionic PAESs can reach up to 64% for the 72 mol% zwitterion copolymer while the free-standing film in the wet state still maintains a Young's modulus of 185 MPa. Here, the thermoplastic charge-containing copolymers in this work demonstrated potential for applications such as coatings or water purification membranes, in which balancing hydrophilicity, processibility, and thermomechanical performance are important.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Artificial intelligence based analysis of nanoindentation load–displacement data using a genetic algorithm

In this work, we developed an automated tool, Nanoindentation Neo package for the analysis of nanoindentation load–displacement curves using a Genetic Algorithm (GA) applied to the Oliver-Pharr method (Oliver et al.,1992). For some materials, such as polycrystalline isotropic graphites, Least Squares Fitting (LSF) of the unload curve can produce unrealistic fit parameters. These graphites exhibit sharply peaked unloading curves not easily fit using the LSF, which tends to overestimate the indenter tip geometry parameter. To tackle this problem, we extended our general materials characterization tool Neo for EXAFS analysis (Terry et al., 2021) to fit nanoindentation data. Nanoindentation Neo automatically processes and analyzes nanoindentation data with minimal user input while producing meaningful fit parameters. GA, a robust metaheuristic method, begins with a population of temporary solutions using model parameters called chromosomes; from these we evaluate a fitness value for each solution, and select the best solutions to mix with random solutions producing the next generation. A mutation operator then modifies existing solutions by random perturbations, and the optimal solution is selected. We tested the GA method using Silica and Al reference standards. We fit samples of graphite and a high entropy alloy (HEA) consisting of BCC and FCC phases.

42 ENGINEERING↗
DOE OSTI2025

Steam generator model design parameter sensitivity study for small modular reactor system

Here, this study focuses on design parameter sensitivity studies pertaining to several Once-Through Steam Generator (OTSG) model cases both with and without a riser using python and advanced risk assessment and optimization tool, i.e. Risk Analysis Virtual Environment (RAVEN) developed at Idaho National Laboratory (INL), to support a Small Modular Reactor (SMR) system. The presented Steam Generator (SG) python-based model is a mathematical representation of a steam-generating unit for a Pressurized Water Reactor (PWR)-type SMR system, including fluid flow and heat transfer equations, models, and correlations. Design studies involve changing the model’s input design parameters (e.g., temperature, pressure, mass flow rate) to observe the resulting effects on the output of the system, such as the Heat Transfer Coefficient (HTC), Reynolds number, Nusselt number, and heat transfer performance. Sensitivity studies analyze the degree to which system output and/or desired parameters (e.g., HTC or heat transfer performance) are sensitive to changes in the input parameters. By using RAVEN, detailed design parametric sensitivity studies. Six input parameters—namely, the pressure, temperature, and mass flow rate for the inlet of the primary-side (hot fluid) and secondary-side (cold fluid) of the SG—were randomly perturbed via RAVEN’s Monte Carlo Sampler module, using uniform distributions (i.e., ±1%, ±5% and ±10 % relative changes) for 600 samples. The analysis results give valuable insights into SG system performance, and provide justification for further research and development such as optimized sensor placement, design verification, validation, and optimization.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

OzDES Reverberation Mapping Programme: Mg ii lags and R − L relation

ABSTRACT The correlation between the broad line region radius and continuum luminosity (R–L relation) of active galactic nuclei (AGNs) is critical for single-epoch mass estimates of supermassive black holes (SMBHs). At z ∼ 1–2, where AGN activity peaks, the R–L relation is constrained by the reverberation mapping (RM) lags of the Mg ii line. We present 25 Mg ii lags from the Australian Dark Energy Survey RM project based on 6 yr of monitoring. We define quantitative criteria to select good lag measurements and verify their reliability with simulations based on both the damped random walk stochastic model and the rescaled, resampled versions of the observed light curves of local, well-measured AGN. Our sample significantly increases the number of Mg ii lags and extends the R–L relation to higher redshifts and luminosities. The relative iron line strength $\mathcal {R}_{\rm Fe}$ has little impact on the R–L relation. The best-fitting Mg iiR–L relation has a slope α = 0.39 ± 0.08 with an intrinsic scatter $\sigma _{\rm rl} = 0.15^{+0.03}_{-0.02}$ . The slope is consistent with previous measurements and shallower than the H β R–L relation. The intrinsic scatter of the new R–L relation is substantially smaller than previous studies and comparable to the intrinsic scatter of the H β R–L relation. Our new R–L relation will enable more precise single-epoch mass estimates and SMBH demographic studies at cosmic noon.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Critical assessment of the recent report on the gigaparsec-scale correlation of the orientations of large quasar groups

ABSTRACT Recently, it was reported that large quasar groups (LQGs) identified from the Sloan Digital Sky Survey (SDSS) data release seven catalogue are not randomly oriented but preferentially aligned or orthogonal over scales 1–2 Gpc. To confirm this claim, I reproduced the same LQG sample and performed Sobolev tests of uniformity on the LQG orientation axes in the redshift space. Contrary to the original report based on the bimodal distribution of the LQG position angles in the sky, I found no departure from uniformity in the distribution of the LQG orientation axes. I also examined whether the LQGs are physical structures using a statistically more reliable data set constructed from the SDSS data release 16 (DR16) large-scale structure (LSS) quasar catalogue. Considering the Gaussian primordial density fluctuations and shot noise, I estimated the mass density contrasts of the LQGs from the number counts of the DR16 LSS quasars and found that most of the LQGs do not trace statistically significant high-density regions. I conclude that the LQG sample is a collection of unphysical chance associations and should not be used for any cosmological studies.

Fujii, Hirokazu (ORCID:0000000161473512)↗
DOE OSTI2024

Gradient Coding With Iterative Block Leverage Score Sampling

Gradient coding is a method for mitigating straggling servers in a centralized computing network that uses erasure-coding techniques to distributively carry out first-order optimization methods. Randomized numerical linear algebra uses randomization to develop improved algorithms for large-scale linear algebra computations. In this study, we propose a method for distributed optimization that combines gradient coding and randomized numerical linear algebra. The proposed method uses a randomized ℓ 2 -subspace embedding and a gradient coding technique to distribute blocks of data to the computational nodes of a centralized network, and at each iteration the central server only requires a small number of computations to obtain the steepest descent update. The novelty of our approach is that the data is replicated according to importance scores, called block leverage scores, in contrast to most gradient coding approaches that uniformly replicate the data blocks. Furthermore, we do not require a decoding step at each iteration, avoiding a bottleneck in previous gradient coding schemes. We show that our approach results in a valid ℓ 2 -subspace embedding, and that our resulting approximation converges to the optimal solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

2012-2013 Delaware Valley Household Travel Survey

The 2012-2013 Delaware Valley Household Travel Survey collected data for multiple planning purposes such as the calibration of a new activity-based travel demand model. It features data from households across nine counties in the region, including southern New Jersey and southeastern Pennsylvania. The Delaware Valley Regional Planning Commission (DVRPC) sponsored the survey, which was administered by Abt Srbi Inc. A sampling strategy was designed to recruit households for survey participation that would best represent overall regional travel trends. Households were selected randomly, but with special consideration given to under-represented geographies and transit propensity. On their assigned travel day, households were asked to record all trips made within a 24-hour period. Additionally, select households were chosen to participate in a wearable global positioning system (GPS) technology-based component of the study. A total of 811 participants wore the GPS system.

1Hz data↗
DOE OSTI2024

Once-Through Steam Generator Model Analysis Using Python and Advanced Optimization Tools (Summer Internship Report)

This study focuses on the parametric analysis of design parameters for a once-through steam generator (OTSG) model, using python and advanced optimization tools to facilitate applications such as the flowing autoclave steam generator (FASG) test cases. Building on previous research involving another OTSG with a different design, this project aims to enhance our understanding of how steam generators (SGs) behave and how their outputs are influenced by changes in design. The reason for this design change is to allow for more precise modeling and optimization of SG performance, to provide a comparative analysis between the two designs, and to set up the model for integration with the FASG test case. The OTSG python-model is a mathematical representation (including fluid flow and heat transfer equations/models/correlations) of a steam-generating unit in a pressurized water reactor-type small modular reactor system. Design studies involve changing the model’s input design parameters to observe the resulting effects on the output of the system. By using advanced optimization tools, such as the Risk Analysis Virtual Environment (RAVEN) developed at Idaho National Laboratory, detailed design parametric studies and model optimization were performed. Six input parameters—pressure, temperature, and mass flow rate for the inlet of the primary-side (hot fluid) and secondary-side (cold fluid), respectively, of the SG—were randomly perturbed via RAVEN’s Monte Carlo Sampler module, using uniform distributions (i.e., ±1%, ±5% and ±10% relative changes) for 600 samples. The analysis provides valuable insights into SG optimization and can be used for sensor placement optimization to effectively monitor and obtain experimental data in other tests.

20 FOSSIL-FUELED POWER PLANTS↗