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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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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Photodynamics of oxybenzone sunscreen: Nonadiabatic dynamics simulations

Herein we have used combined static electronic structure calculations and “on-the-fly” global-switching trajectory surface-hopping dynamics simulations to explore the photochemical mechanism of oxybenzone sunscreen. We have first employed the multi-configurational CASSCF method to optimize minima, conical intersections, and minimum-energy reaction paths related to excited-state intramolecular proton transfer (ESIPT) and excited-state decays in the {sup 1}ππ{sup ∗}, {sup 1}nπ{sup ∗}, and S{sub 0} states (energies are refined at the higher MS-CASPT2 level). According to the mapped potential energy profiles, we have identified two ultrafast excited-state deactivation pathways for the initially populated {sup 1}ππ{sup ∗} system. The first is the diabatic ESIPT process along the {sup 1}ππ{sup ∗} potential energy profile. The generated {sup 1}ππ{sup ∗} keto species then decays to the S{sub 0} state via the keto {sup 1}ππ{sup ∗}/gs conical intersection. The second is internal conversion to the dark {sup 1}nπ{sup ∗} state near the {sup 1}ππ{sup ∗} /{sup 1}nπ{sup ∗} crossing point in the course of the diabatic {sup 1}ππ{sup ∗} ESIPT process. Our following dynamics simulations have shown that the ESIPT and {sup 1}ππ{sup ∗} → S{sub 0} internal conversion times are 104 and 286 fs, respectively. Finally, our present work demonstrates that in addition to the ESIPT process and the {sup 1}ππ{sup ∗} → S{sub 0} internal conversion in the keto region, the {sup 1}ππ{sup ∗} → {sup 1}nπ{sup ∗} internal conversion in the enol region plays as well an important role for the excited-state relaxation dynamics of oxybenzone.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

Studies of Conventional and ERL-Based Recirculator Electron Cooling for an Electron Ion Collider (Final Report)

We developed novel tools necessary for accurate and efficient modeling, simulation and design optimization of electron cooling options for a future Electron Ion Collider, or any other nuclear physics facility where high luminosity, low transverse and longitudinal emittance are required. The goals are being achieved by the combination of the development of a fast multipole method (FMM)-based algorithm for the N-body simulation required by the Coulomb interaction of ions and electrons, and the well-established transfer map-based methods available in several nonlinear beam dynamics codes. The new method will not only allow long-term tracking of the trajectories of the large number of charged particles necessary to achieve a high-fidelity, low-noise representation of the subtle physics involved in the mechanisms that lead to cooling, but will also make parameter optimization feasible in a context that includes all machine nonlinearities, misalignments, etc. The coding infrastructure is being implemented in COSY Infinity, which is already widely used in the nuclear physics community, and will serve as a resource for many current and future facilities that employ some form of electron cooling.

43 PARTICLE ACCELERATORS↗
DOE OSTI2014

MO-G-18C-05: Real-Time Prediction in Free-Breathing Perfusion MRI

Purpose: The aim is to minimize frame-wise difference errors caused by respiratory motion and eliminate the need for breath-holds in magnetic resonance imaging (MRI) sequences with long acquisitions and repeat times (TRs). The technique is being applied to perfusion MRI using arterial spin labeling (ASL). Methods: Respiratory motion prediction (RMP) using navigator echoes was implemented in ASL. A least-square method was used to extract the respiratory motion information from the 1D navigator. A generalized artificial neutral network (ANN) with three layers was developed to simultaneously predict 10 time points forward in time and correct for respiratory motion during MRI acquisition. During the training phase, the parameters of the ANN were optimized to minimize the aggregated prediction error based on acquired navigator data. During realtime prediction, the trained ANN was applied to the most recent estimated displacement trajectory to determine in real-time the amount of spatial Results: The respiratory motion information extracted from the least-square method can accurately represent the navigator profiles, with a normalized chi-square value of 0.037±0.015 across the training phase. During the 60-second training phase, the ANN successfully learned the respiratory motion pattern from the navigator training data. During real-time prediction, the ANN received displacement estimates and predicted the motion in the continuum of a 1.0 s prediction window. The ANN prediction was able to provide corrections for different respiratory states (i.e., inhalation/exhalation) during real-time scanning with a mean absolute error of < 1.8 mm. Conclusion: A new technique enabling free-breathing acquisition during MRI is being developed. A generalized ANN development has demonstrated its efficacy in predicting a continuum of motion profile for volumetric imaging based on navigator inputs. Future work will enhance the robustness of ANN and verify its effectiveness with human subjects. Research supported by National Institutes of Health National Cancer Institute Grant R01 CA159471-01.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Constant-complexity stochastic simulation algorithm with optimal binning

At the molecular level, biochemical processes are governed by random interactions between reactant molecules, and the dynamics of such systems are inherently stochastic. When the copy numbers of reactants are large, a deterministic description is adequate, but when they are small, such systems are often modeled as continuous-time Markov jump processes that can be described by the chemical master equation. Gillespie’s Stochastic Simulation Algorithm (SSA) generates exact trajectories of these systems, but the amount of computational work required for each step of the original SSA is proportional to the number of reaction channels, leading to computational complexity that scales linearly with the problem size. The original SSA is therefore inefficient for large problems, which has prompted the development of several alternative formulations with improved scaling properties. We describe an exact SSA that uses a table data structure with event time binning to achieve constant computational complexity with respect to the number of reaction channels for weakly coupled reaction networks. We present a novel adaptive binning strategy and discuss optimal algorithm parameters. We compare the computational efficiency of the algorithm to existing methods and demonstrate excellent scaling for large problems. This method is well suited for generating exact trajectories of large weakly coupled models, including those that can be described by the reaction-diffusion master equation that arises from spatially discretized reaction-diffusion processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Optimal design of fiber reinforced composite structures and their direct ink write fabrication

The majority of the current structural optimization software does not accommodate manufacturing constraints. Therefore, substantial modifications are imposed upon optimized designs to make them manufacturable and hence nonoptimal. We propose to optimize the design of composite structures that are amenable to Additive Manufacturing (AM). The printing process chosen in our study is based on Direct Ink Writing (DIW) in which short carbon fibers in a thermoset resin are extruded through a moving nozzle to build up a structure. Since the fibers are primarily aligned in the flow direction of the extrudate, the DIW printing trajectory influences the material properties of the composite structure. To accommodate this, the extrudate trajectory follows the contours of parameterized level-set functions. The parametrization allows us to prescribe the material properties and impose many DIW manufacturing constraints such as no-overlap, no-sag, minimum radius of curvature and continuity of the toolpaths. Ultimately, we obtain optimal manufacturable toolpaths that start and finish at a boundary. To minimize the fabrication time, we formulate the linking sequence of the toolpaths as a traveling salesman problem which we solve to obtain the shortest continuous toolpath per layer. Several examples illustrate the optimization procedure. Validation is also performed.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Control architecture for predictive and optimal vehicle operations in a single vehicle environment

There is disclosed a cloud computing control system for vehicle speed control and also for control of a vehicle in a platoon. The cloud computing control system determines a speed trajectory and neutral coasting command for a first vehicle of the platoon and a vehicle controller determines a reference speed for the first vehicle in response to the speed trajectory and the neutral coasting command, and is response to one or more vehicle specific factors associated with the first vehicle.

Borhan, Hoseinali↗
DOE OSTI2026

From atomistic models to machine learning: Predictive design of nanocarbons under extreme conditions

The formation of technologically valuable nanocarbon structures under extreme conditions, such as those produced during high-explosive detonations, remains poorly understood but holds significant potential for the development of controlled synthesis pathways. While detonation shockwaves provide the high-pressure, high-temperature environment required for nanodiamond formation, subsequent cooling and decompression dictate whether the diamond phase is preserved or transformed into other nanocarbon structures. Here, in this study, we employ GPU-accelerated reactive molecular dynamics (ReaxFF) simulations to investigate the graphitization and structural remodeling of detonation nanodiamond under nonlinear quench and pressure-release trajectories. We further investigate how the initial nanodiamond morphology; cuboctahedral, octahedral, or hexagonal prism influences the resulting transformation products. Evolution of nanostructure, allotrope (via simulated x-ray diffraction), carbon hybridization, and ring statistics are tracked during a two-stage quench from 5000 K to 60 GPa. Rapid cooling combined with slow decompression optimizes cubic diamond retention, whereas slow cooling with rapid pressure release promotes surface-to-core graphitization, producing concentric sp 2 -hybridized layers and hollowed inner shells. Octahedral nanodiamonds evolve into carbon nano-onions, initially forming bucky diamonds that progressively transform into fully sp 2 -hybridized structures, while hexagonal prisms preferentially form parallel-stacked graphite layers resembling carbon dots. Transient hexagonal diamond (lonsdaleite) emerges as an interfacial phase, suggesting potential reversibility in the shock-induced graphite-to-diamond transformation pathway transformation route. To extend predictive capabilities, we trained machine learning (ML) regressors on over 10 5 node-hours of molecular dynamics (MD) trajectories. A multilayer perceptron (MLP) model reliably predicts the number of graphitized layers from temperature–pressure trajectories with a coefficient of determination (R 2 ) exceeding 0.90. This high predictive fidelity enables efficient, high-throughput mapping of the synthesis parameter space for optimized graphitization outcomes. Collectively, morphological control combined with optimized quench–decompression conditions promote the selective synthesis of nanocarbon allotropes. This work establishes a data-driven framework for the rational, a priori design of carbon nanomaterials for applications in energy storage, sensing, and biomedicine.

Detonation nanodiamond remodeling↗
DOE OSTI2016

Efficient Adjoint Computation of Hybrid Systems of Differential Algebraic Equations with Applications in Power Systems

Sensitivity analysis is an important tool to describe power system dynamic behavior in response to parameter variations. It is a central component in preventive and corrective control applications. The existing approaches for sensitivity calculations, namely, finite-difference and forward sensitivity analysis, require a computational effort that increases linearly with the number of sensitivity parameters. In this work, we investigate, implement, and test a discrete adjoint sensitivity approach whose computational effort is effectively independent of the number of sensitivity parameters. The proposed approach is highly efficient for calculating trajectory sensitivities of larger systems and is consistent, within machine precision, with the function whose sensitivity we are seeking. This is an essential feature for use in optimization applications. Moreover, our approach includes a consistent treatment of systems with switching, such as DC exciters, by deriving and implementing the adjoint jump conditions that arise from state and time-dependent discontinuities. The accuracy and the computational efficiency of the proposed approach are demonstrated in comparison with the forward sensitivity analysis approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Isolating and quantifying cross-beam energy transfer in direct-drive implosions on OMEGA and the National Ignition Facility

The angularly resolved mass ablation rates and ablation-front trajectories for Si-coated CH targets were measured in direct-drive inertial confinement fusion experiments to quantify cross-beam energy transfer (CBET) while constraining the hydrodynamic coupling. A polar-direct-drive laser configuration, where the equatorial laser beams were dropped and the polar beams were repointed from a symmetric direct-drive configuration, was used to limit CBET at the pole while allowing it to persist at the equator. The combination of low- and high-CBET conditions observed in the same implosion allowed for the effects of CBET on the ablation rate and ablation pressure to be determined. Hydrodynamic simulations performed without CBET agreed with the measured ablation rate and ablation-front trajectory at the pole of the target, confirming that the CBET effects on the pole are small. The simulated mass ablation rates and ablation-front trajectories were in excellent agreement with the measurements at all angles when a CBET model based on Randall's equations [C. J. Randall et al., Phys. Fluids 24, 1474 (1981)] was included into the simulations with a multiplier on the CBET gain factor. These measurements were performed on OMEGA and at the National Ignition Facility to access a wide range of plasma conditions, laser intensities, and laser beam geometries. The presence of the CBET gain multiplier required to match the data in all of the configurations tested suggests that additional physics effects, such as intensity variations caused by diffraction, polarization effects, or shortcomings of extending the 1-D Randall model to 3-D, should be explored to explain the differences in observed and predicted drive.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Pulse-level noisy quantum circuits with QuTiP

The study of the impact of noise on quantum circuits is especially relevant to guide the progress of Noisy Intermediate-Scale Quantum (NISQ) computing. In this paper, we address the pulse-level simulation of noisy quantum circuits with the Quantum Toolbox in Python (QuTiP). We introduce new tools in qutip-qip, QuTiP's quantum information processing package. These tools simulate quantum circuits at the pulse level, leveraging QuTiP's quantum dynamics solvers and control optimization features. We show how quantum circuits can be compiled on simulated processors, with control pulses acting on a target Hamiltonian that describes the unitary evolution of the physical qubits. Various types of noise can be introduced based on the physical model, e.g., by simulating the Lindblad density-matrix dynamics or Monte Carlo quantum trajectories. In particular, the user can define environment-induced decoherence at the processor level and include noise simulation at the level of control pulses. We illustrate how the Deutsch-Jozsa algorithm is compiled and executed on a superconducting-qubit-based processor, on a spin-chain-based processor and using control optimization algorithms. We also show how to easily reproduce experimental results on cross-talk noise in an ion-based processor, and how a Ramsey experiment can be modeled with Lindblad dynamics. Finally, we illustrate how to integrate these features with other software frameworks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Accelerated quantum control using superadiabatic dynamics in a solid-state lambda system

Adiabatic evolutions find widespread utility in applications to quantum state engineering1 , geometric quantum computation2 , and quantum simulation3 . Although offering desirable robustness to experimental imperfections, adiabatic techniques are susceptible to decoherence during their long operation time. A recent strategy termed ‘shortcuts to adiabaticity’ 4–10 (STA) aims to circumvent this trade-off by designing fast dynamics to reproduce the results of infinitely slow, adiabatic processes. Here, as a realization of this strategy, we implement ‘superadiabatic’ transitionless driving11 (SATD) to speed up stimulated Raman adiabatic passage1,12–15 (STIRAP) in a solid-state lambda (Λ) system. Utilizing optical transitions to a dissipative excited state in the nitrogen vacancy (NV) center in diamond, we demonstrate the accelerated performance of different shortcut trajectories for population transfer and for the transfer and initialization of coherent superpositions. We reveal that SATD protocols exhibit robustness to dissipation and experimental uncertainty, and can be optimized when these effects are present. These results motivate STA as a promising tool for controlling open quantum systems comprising individual or hybrid nanomechanical, superconducting, and photonic elements in the solid state12–17.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Construction of quasisymmetric stellarators using a direct coordinate approach

In this work, optimized stellarator configurations and their analytical properties are obtained using a near-axis expansion approach. Such configurations are associated with good confinement as the guiding center particle trajectories and neoclassical transport are isomorphic to those in a tokamak. This makes them appealing as fusion reactor candidates. Using a direct coordinate approach, where the magnetic field and flux surface functions are found explicitly in terms of the position vector at successive orders in the distance to the axis, the set of ordinary differential equations for first and second order quasisymmetry is derived. Examples of quasi-axisymmetric shapes are constructed using a pseudospectral numerical method. Finally, the direct coordinate approach is used to independently verify two hypotheses commonly associated with quasisymmetric magnetic fields, namely that the number of equations exceeds the number of parameters at third order in the expansion and that the near-axis expansion does not prohibit exact quasisymmetry from being achieved on a single flux surface.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Implications of stop-and-go traffic on training learning-based car-following control

Learning-based car-following control (LCC) of connected and autonomous vehicles (CAVs) is gaining significant attention with the advancement of computing power and data accessibility. While the flexibility and large model capacity of model-free architecture enable LCC to potentially outperform the model-based car-following (CF) model in improving traffic efficiency and mitigating congestion, the generalizability of LCC for traffic conditions different from the training environment/dataset is not well-understood. Herein, this study seeks to explore the impact of stop-and-go traffic in the training dataset on the generalizability of LCC. It uses the characteristics of lead vehicle trajectories to describe stop-and-go traffic, and links the theory of identifiability (i.e., obtaining a unique parameter estimation result using sensor measurements) to the generalizability of behavior cloning (BC) and policy-based deep reinforcement learning (DRL). Correspondingly, the study shows theoretically that: (i) stop-and-go traffic can enable the property of identifiability and enhance the control performance of BC-based LCC in different traffic conditions; (ii) stop-and-go traffic is not necessary for DRL-based LCC to generalize to different traffic conditions; (iii) DRL-based LCC trained with only constant-speed lead vehicle trajectories (not sufficient to ensure identifiability) can be generalized to different traffic conditions; and (iv) stop-and-go traffic increases variance in the training dataset, which improves the convergence of parameter estimation while negatively impacting the convergence of DRL to the optimal control policy. Numerical experiments validate the above findings, illustrating that BC-based LCC entails comprehensive training datasets for generalizing to different traffic conditions, while DRL-based LCC can achieve generalization with simple free-flow traffic training environments. This further suggests DRL as a more promising and cost-effective LCC approach to reduce operational costs, mitigate traffic congestion, and enhance safety and mobility, which can accelerate the deployment and acceptance of CAVs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2012

Shell trajectory measurements from direct-drive implosion experiments

A technique to measure the shell trajectory in direct-drive inertial confinement fusion implosions is presented. The x-ray self emission of the target is measured with an x-ray framing camera. Optimized filtering limits the x-ray emission from the corona plasma, isolating a sharp intensity gradient very near the ablation surface. This enables one to measure the radius of the imploding shell with an accuracy better than 1 {mu}m and to determine a 200-ps average velocity to better than 2%.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Gearbox bearing crack growth prognostics and uncertainty quantification with physics-informed machine learning

This paper introduces the extreme theory of functional connections (X-TFC), a physics-informed machine learning algorithm, and tailors it to estimate the remaining useful life (RUL) of wind turbine gearbox bearings experiencing fatigue crack growth. Unlike purely data-driven methods, X-TFC embeds a physics model, based on Head's theory in this work, into its training objective. The core of X-TFC is a random-projection single-layer neural network trained via an extreme learning machine, which requires only limited damage progression data and solves for output weights with a least-squares optimization algorithm. A composite loss function balances the network's fit to observed degradation data against the residuals of the governing crack growth differential equation, ensuring the learned damage trajectory remains physically plausible. When applied to a vibration-based health-index (HI) dataset measured during the growth of a crack on the inner ring of a high-speed bearing in a wind turbine gearbox (Bechhoefer and Dubé, 2020), X-TFC achieves near-zero prediction bias. Even when trained on only the first 10 %–20 % of the damage progression data, with sufficient physics weighting its predictions remain monotonic and smooth, delivering high prognosability and trendability. To quantify the epistemic uncertainty, we employ a Monte Carlo ensemble of independently initialized X-TFC models trained on noise-perturbed data, which yields confidence intervals around each RUL estimate and captures both model-parameter and epistemic uncertainty. In addition to a vibration-based HI, we demonstrate that the proposed framework can be directly applied to a supervisory control and data acquisition (SCADA) data-based HI (Eftekhari Milani et al., 2026) measured during similar wind turbine gearbox bearing crack faults, preserving its accuracy and interpretability. This extension shows the versatility of our approach, which is applicable to bearings of multiple gearbox manufacturers, models, and ratings using only SCADA data. By integrating domain knowledge with machine learning, X-TFC offers a rapid, reliable tool for crack prognostics. Its adaptability to other bearing failure modes, such as pitch bearing ring cracks, positions X-TFC as a powerful enabler of data-driven, physics-informed asset management in the wind energy sector and beyond.

17 WIND ENERGY↗
DOE OSTI2020

Sea-Ice Effects on the Alaskan Wave Climate

This study investigates the uncertainty of wave resource assessments due to sea ice in the nearshore region of Alaska using an innovative approach of combining remotely sensed data and a third-generation phase-averaged wave model. Modeling analysis focused on winter months, when high wave energy is present along with sea ice. This study includes recommendations to improve the accuracy of wave resource assessments and optimize marine and hydrokinetic device siting with consideration of sea-ice effects. Additional recommendations, based on preliminary sea-ice information in Cook Inlet, Alaska, are provided for a trajectory analysis of drifting sea ice and a collision risk assessment of sea ice and tidal turbines. The outcome of this project will provide valuable information about the level of sea-ice impact on the accuracy of wave resource assessments in the Alaska nearshore region, and of sea-ice collision risk with MHK devices and ocean observation systems during winter months when sea ice is present.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Hydrogen and Electric Charging Infrastructure for Heavy-Duty Trucks: A Nationally Scalable Megaregion Assessment

Decarbonizing regional and long-haul freight is challenging due to the limitations of battery-electric commercial vehicles and infrastructure constraints. Hydrogen fuel cell medium- and heavy-duty vehicles (MHDVs) offer a viable alternative, aligning with the decarbonization goals of the Department of Energy and commercial entities. Historically, alternative fuels like compressed natural gas and liquefied propane gas have faced slow adoption due to barriers like infrastructure availability. To avoid similar issues, effective planning and deploying zero-emission hydrogen fueling infrastructure is crucial. This research develops deployment plans for affordable, accessible, and sustainable hydrogen refueling stations, supporting stakeholders in the decarbonized commercial vehicle freight system. It aims to benefit underserved and rural energy-stressed communities by improving air quality, reducing noise pollution, and enhancing energy resiliency. This research also provides a blueprint for replacing diesel in over-the-road Class 8 freight truck applications with hydrogen fueling solutions. The study focuses on the Texas Triangle Megaregion (I-45, I-35, and I-10), the I-10 corridor between San Antonio, TX, and Los Angeles, CA, and the I-5/CA-99 corridors between Los Angeles, CA, and San Francisco, CA. This area represents a significant portion of U.S. heavy-duty freight movement, carrying ~8.5% of the national freight volume. Using the OR-AGENT (Optimal Regional Architecture Generation for Efficient National Transport) modeling framework, the study conducts an advanced assessment of commercial vehicles, road and freight networks, and energy systems. The framework integrates data on freight mobility, traffic, weather, and energy pathways to deliver a region-specific, optimized vehicles powertrain architectures, infrastructure deployment solutions, operational logistics, and energy pathways. By considering all vehicle origin-destination pairs utilizing these corridors and all feasible fueling station location options, the framework's genetic algorithm identifies the minimum number and optimal locations of hydrogen refueling stations, ensuring no vehicle is stranded. It also determines fuel schedules and quantities at each station. A roadmap for station deployment based on multiple adoption trajectories ensures a strategic rollout of hydrogen refueling infrastructure.

Sujan, Vivek [ORNL] (ORCID:0000000269882342)↗
DOE OSTI2019

Predicting Reaction Products and Automating Reactive Trajectory Characterization in Molecular Simulations with Support Vector Machines

A machine learning-based methodology for the prediction of chemical reaction products, along with automated elucidation of mechanistic details via phase space analysis of reactive trajectories, is introduced using low-dimensional heuristic models and then applied to ab initio computer simulations of the photodissociation of acetaldehyde, an important chemical system in atmospheric chemistry. Our method is centered around training Support Vector Machines (SVMs) to identify optimal separatrices that delineate the regions of phase space that lead to different chemical reaction products. In contrast to the more common “black box” type machine learning methodologies for analyzing chemical simulation data, this SVM-based methodology allows for mechanistic insight to be gleaned from further analysis of the positioning of the phase space points used to train the SVM with respect to the separatrices. For example, a pair of phase space points that are in close proximity to each other but on opposite sides of a separatrix may be situated on opposite sides of a transition state, while phase space points occurring early in a simulation that are distant from a separatrix are likely to belong to trajectories that are highly biased toward the product state associated with the basin of attraction to which they belong. In addition to inferring mechanistic details about multiple-pathway chemical reactions, our method can also be used to increase reactive trajectory sampling efficiency in molecular simulations via rejection sampling, with trajectories leading to undesired product states being identified and terminated early in the simulation rather than being carried to completion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗