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DOE OSTI2025

The ETROC2 as the Final Version for CMS Endcap Timing Layer (ETL) Upgrade

The ETROC (Endcap Timing Readout Chip) is being developed for the LGAD-based CMS Endcap Timing Layer (ETL) at HL-LHC. The ETL on each side of the interaction region will be instrumented with a two-disk system of MIP-sensitive LGAD (Low Gain Avalanche Diodes) silicon devices, read out by ETROCs for precision timing measurement with down to ~30 ps timing resolution per track. The ETROC is designed to handle a 16 x 16 pixel cell matrix, with each pixel being 1.3 mm x 1.3 mm to match the LGAD sensor pixel size. The front-end design for preamplifier and discriminator has been specifically optimized for the reduced LGAD signals, with enough flexibilities to meet the ETL specific needs for time resolution, power budget and radiation profile. The ETROC chip is implemented in a commercial 65nm CMOS process. Each channel consists of a preamplifier, a discriminator, a TDC used for TOA (Time Of Arrival) and TOT (Time Over Threshold) measurements, and a memory for data storage and readout. An in-pixel auto threshold calibration is included, along with a self-testing pattern generator. The TOT is used for time-walk correction of the TOA measurement. The detailed hit information (TOA and TOT) from each cell will be read out from a local circular buffer after each Level-1 Accept (about 1 MHz). In addition, a charge injection circuit is implemented to allow for testing and calibration. For more detailed monitoring of the signal pulses, waveform sampling circuits are included for one pixel. The clock distribution is based on a 16x16 H-tree design with a shielding structure to alleviate potential interference. The global peripheral circuits include a PLL, a phase shifter, an I2C slave controller, a fast control block, a global readout, and a data driver along with an efuse and temperature sensor. The ETROC builds event data frames for each L1A selected event and is also capable of providing L1 trigger information for user-defined delayed hits. The main design challenge is how to extract precision timing information from the small LGAD signals in the presence of high irradiation fluence, while keeping the power consumption and digital activity low. The ETL design goal for the time resolution of 50 ps per hit is required to achieve a 35 ps arrival time measurement for a MIP particle, which has its track registered in two ETL disk layers. The LGAD contribution is known to be about 30 ps, this means that the jitter from the ETROC has to be kept below 40 ps. The ETROC2 is the first full size full functionality prototype design fully compatible with the final chip specifications for CMS ETL and now becomes the final version. The ETROC2 chips have been extensively tested. We will present here new testing results including the bump bonding yield improvement study, the time walk correction (TWC) generality study with one pixel TWC applying to all pixels, the final SEU testing using both heavy ion and proton beam, more beam test studies including different sensors, and readiness for the ETROC2 production for CMS ETL upgrade.

Liu, Tiehui [Fermilab] (ORCID:0009000765225605)↗

DOE OSTI2020

DEEP CELLULAR RECURRENT NEURAL ARCHITECTURE FOR EFFICIENT MULTIDIMENSIONAL TIME-SERIES DATA PROCESSING

Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in complexity and size to accommodate the additional dimensionality of time. Specifically, the biologically inspired learning based models known as artificial neural networks that have shown extraordinary success in pattern recognition, tend to grow prohibitively large and cumbersome in the presence of large scale multi-dimensional time series biomedical data such as EEG. Consequently, this work aims to develop representative ML and DL models for robust and efficient large scale time series processing. First, we design a novel ML pipeline with efficient feature engineering to process a large scale multi-channel scalp EEG dataset for automated detection of epileptic seizures. With the use of a sophisticated yet computationally efficient time-frequency analysis technique known as harmonic wavelet packet transform and an efficient self-similarity computation based on fractal dimension, we achieve state-of-the-art performance for automated seizure detection in EEG data. Subsequently, we investigate the development of a novel efficient deep recurrent learning model for large scale time series processing. For this, we first study the functionality and training of a biologically inspired neural network architecture known as cellular simultaneous recurrent neural network (CSRN). We obtain a generalization of this network for multiple topological image processing tasks and investigate the learning efficacy of the complex cellular architecture using several state-of-the?art training methods. Finally, we develop a novel deep cellular recurrent neural network (CDRNN) architecture based on the biologically inspired distributed processing used in CSRN for processing time series data. The proposed DCRNN leverages the cellular recurrent architecture to promote extensive weight sharing and efficient, individualized, synchronous processing of multi-source time series data. Experiments on a large scale multi-channel scalp EEG, and a machine fault detection dataset show that the proposed DCRNN offers state-of-the-art recognition performance while using substantially fewer trainable recurrent units.

Vidyaratne, Lasitha S.↗

DOE OSTI2022

Capturing the interactions between ice sheets, sea level and the solid Earth on a range of timescales: a new “time window” algorithm

Retreat and advance of ice sheets perturb the gravitational field, solid surface and rotation of the Earth, leading to spatially variable sea-level changes over a range of timescales $\textit{O}$ (10 0–6 years), which in turn feed back onto ice-sheet dynamics. Coupled ice-sheet–sea-level models have been developed to capture the interactive processes between ice sheets, sea level and the solid Earth, but it is computationally challenging to capture short-term interactions $\textit{O}$ (10 0–2 years) precisely within longer $\textit{O}$ (10 3–6 years) simulations. The standard forward sea-level modelling algorithm assigns a uniform temporal resolution in the sea-level model, causing a quadratic increase in total CPU time with the total number of input ice history steps, which increases with either the length or temporal resolution of the simulation. In this study, we introduce a new “time window” algorithm for 1D pseudo-spectral sea-level models based on the normal mode method that enables users to define the temporal resolution at which the ice loading history is captured during different time intervals before the current simulation time. Utilizing the time window, we assign a fine temporal resolution $\textit{O}$ (10 0–2 years) for the period of ongoing and recent history of surface ice and ocean loading changes and a coarser temporal resolution $\textit{O}$ (10 3–6 years) for earlier periods in the simulation. This reduces the total CPU time and memory required per model time step while maintaining the precision of the model results. We explore the sensitivity of sea-level model results to the model temporal resolution and show how this sensitivity feeds back onto ice-sheet dynamics in coupled modelling. We apply the new algorithm to simulate sea-level changes in response to global ice-sheet evolution over two glacial cycles and the rapid collapse of marine sectors of the West Antarctic Ice Sheet in the coming centuries and provide appropriate time window profiles for each application. The time window algorithm reduces the total CPU time by ~ 50 % in each of these examples and changes the trend of the total CPU time increase from quadratic to linear. This improvement would increase with longer simulations than those considered here. Our algorithm also allows for coupling time intervals of annual temporal scale for coupled ice-sheet–sea-level modelling of regions such as West Antarctica that are characterized by rapid solid Earth response to ice changes due to the thin lithosphere and low mantle viscosities.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Discrete time analog front end circuit implemented in a receiver device

A device can comprise a peaked integrator circuit that generates an output signal from a continuous time signal based on a sub rate clock timing cycle. The device can further comprise a track and hold circuit coupled to the output of the peaked integrator that generates a held discrete time signal from the output of the peaked integrator based on a second sub rate clock timing cycle that is offset in time from the sub rate clock timing cycle by a single time unit interval. The device can further comprise an integrator circuit coupled to an output of the track and hold circuit that integrates the held discrete time signal, based on the second sub rate clock timing cycle that is offset in time from the sub rate clock timing cycle by a single time unit interval.

42 ENGINEERING↗

DOE OSTI2023

Quantifying and Understanding the Access Time to Dockless Micromobility: A Case Study in Washington, D.C.

Micromobility has been widely deployed in many cities. Similar as how access time/distance affects the travel demand to use public transit and informs transit system design, access time/distance to micromobility service measures its service efficiency and also serves as an equity indicator to inform city agencies from a regulation perspective. Though there is an increasing need to understand it, access to micromobility has not been sufficiently studied. This paper developed a framework to quantity the access time to dockless micromobility service (i.e., the minimum time needed to walk to reach the closest dockless micromobility vehicles). Based on the real-time vehicle location data collected from Washington, DC, this research quantified the access time to dockless micromobility, analyzed its spatial and temporal variation patterns and investigated its relationship with socio-demographic variables (i.e., population density, employment density and low-income population). The results revealed that the access time to dockless micromobility ranges between 0 to 4 minutes with the most frequently observed range of 0.5 to 1 minutes, and the city center area tends to have shorter access time than the outskirts areas. Results also indicate a quite stable access time level in DC with access time standard deviation of 0.2 to 0.5 minutes. After correlating the access time at census-block-group level with socio-demographic data, it was discovered that shorter access time usually aligns with larger population and employment density, and the proportion of low-income population was found not helpful with explaining the access time variation, which indicating a relatively equitable micromobility program.

access time↗

DOE OSTI2020

Comparing light-front quantization with instant-time quantization

Here in this paper we compare light-front quantization and instant-time quantization both at the level of operators and at the level of their Feynman diagram matrix elements. At the level of operators light-front quantization and instant-time quantization lead to equal light-front time commutation (or anticommutation) relations that appear to be quite different from equal instant-time commutation (or anticommutation) relations. Despite this we show that at unequal times instant-time and light-front commutation (or anticommutation) relations actually can be transformed into each other, with it only being the restriction to equal times that makes the commutation (or anticommutation) relations appear to be so different. While our results are valid for both bosons and fermions, for fermions there are subtleties associated with tip of the light cone contributions that need to be taken care of. At the level of Feynman diagrams we show for non-vacuum Feynman diagrams that the pole terms in four-dimensional light-front Feynman diagrams reproduce the widely used three-dimensional light-front on-shell Hamiltonian Fock space formulation in which the light-front energy and light-front momentum are on shell. Moreover, we show that the contributions of pole terms in non-vacuum instant-time and non-vacuum light-front Feynman diagrams are equal. However, because of circle at infinity contributions we show that this equivalence of pole terms fails for four-dimensional light-front vacuum tadpole diagrams. Then, and precisely because of these circle at infinity contributions, we show that light-front vacuum tadpole diagrams are not only nonzero, they quite remarkably are actually equal to the pure pole term instant-time vacuum tadpole diagrams. Light-front vacuum diagrams are not correctly describable by the on-shell Hamiltonian formalism, and thus not by the closely related infinite momentum frame prescription either. Thus for the light-front vacuum sector we must use the off-shell Feynman formalism as it contains information that is not accessible in the on-shell Hamiltonian Fock space approach. We show that light-front quantization is intrinsically nonlocal, and that for fermions this nonlocality is present in Ward identities. One can project fermion spinors into so-called good and bad components, and both of these components contribute in Ward identities. Central to our analysis is that the transformation from instant-time coordinates and fields to light-front coordinates and fields is a unitary, spacetime-dependent translation. Consequently, not only are instant-time quantization and light-front quantization equivalent, because of general coordinate invariance they are unitarily equivalent.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Scale-Dependent Value of QPF for Real-Time Streamflow Forecasting

Incorporating rainfall forecasts into a real-time streamflow forecasting system extends the forecast lead time. Since quantitative precipitation forecasts (QPFs) are subject to substantial uncertainties, questions arise on the trade-off between the time horizon of the QPF and the accuracy of the streamflow forecasts. This study explores the problem systematically, exploring the uncertainties associated with QPFs and their hydrologic predictability. The focus is on scale dependence of the trade-off between the QPF time horizon, basin-scale, space-time scale of the QPF, and streamflow forecasting accuracy. To address this question, the study first performs a comprehensive independent evaluation of the QPFs at 140 U.S. Geological Survey (USGS) monitored basins with a wide range of spatial scales (~10 – 40,000 km 2 ) over the state of Iowa in the Midwestern United States. The study uses High-Resolution Rapid Refresh (HRRR) and Global Forecasting System (GFS) QPFs for short and medium-range forecasts, respectively. Using Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimate (QPE) as a reference, the results show that the rainfall-to-rainfall QPF errors are scale-dependent. The results from the hydrologic forecasting experiment show that both QPFs illustrate clear value for real-time streamflow forecasting at longer lead times in the short- to medium-range relative to the no-rain streamflow forecast. The value of QPFs for streamflow forecasting is particularly apparent for basin sizes below 1,000 km 2 . The space-time scale, or reference time t r ) (ratio of forecast lead time to basin travel time) ~ 1 depicts the largest streamflow forecasting skill with a systematic decrease in forecasting accuracy for t r > 1.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Technical Gaps in Hanford High-Level Waste Solids Settling Behavior and Settling Time Evaluation for Direct Feed High-Level Waste (DFHLW) Operations

Settling of high-level waste (HLW) solids in process vessels is a key conceptual process step in providing HLW feed directly to the Hanford Waste Treatment and Immobilization Plant (WTP) HLW Vitrification Facility. Direct Feed High-Level Waste (DFHLW) is a potential approach to initiating HLW vitrification prior to completing of the WTP Pretreatment Facility. Settling would be used with subsequent supernatant decant to concentrate HLW feed. To support planning for DFHLW, Washington River Protection Solutions (WRPS) requested support from the Pacific Northwest National Laboratory to evaluate the current data set available to predict the time needed for HLW solids to settle, to identify gaps in the understanding and predictive capability of HLW solids waste settling times, and to provide scoping estimates of the potential settling time. Eight technical gaps were identified for predicting settling times and characteristics of the formed sediment layers including: Gap 1: In-Tank Settling Rates Faster than Settling of Laboratory Samples, Gap 2: Effect of Sludge Leaching/Washing on Predicted Settling Times, Gap 3: Predicting Waste Settling from Waste Chemistry (Waste Type), Gap 4: Predicting Waste Settling from Particle Size and Density Distributions (PSDDs), Gap 5: Insufficient Laboratory and In-Tank Settling Data to Represent Hanford Waste, Gap 6: Methods for Real-Time, In-Tank Tracking of Settling, Gap 7: Prediction of Sediment Erosion Resistance as a Function of Settling Time, and Gap 8: Prediction of Sediment Solids Content as a Function of Settling Time. In addition to the data gaps, an overarching observation of the settling rate and settled layer data is the significant variation in behavior. At similar solids concentrations, settling rates can vary by as much as 3 orders of magnitude depending on the source waste tank, and significantly different settling rates are noted between laboratory and in situ tests for the same waste tank. The range of average solids concentration in existing HLW sediment, which may have been quiescent for decades, can vary from less than 7 wt% to greater than 74 wt% solids. The shear strengths (or yield stresses) measured on laboratory samples range from less than 27 Pa to greater than 6400 Pa. These variations can challenge process planning for the application of a settle/decant process for DFHLW. This report describes the significance of the gaps to the settle/decant process and presents uncertainties by way of examples. Potential technical approaches for resolving these gaps are described and the estimated difficulty in resolving these gaps is evaluated. Based on the significance of the gap and the difficulty of resolution, recommendations are made to address specific gaps. Scoping estimates of the potential settling times for DFHLW solids have been made based on the existing data set with its associated gaps. Depending on the process vessel depth and final sediment concentration, substantial fractions of the scoping estimate results for settling times for characterized HLW exceed the 2-week period that has been previously assumed for process planning. There is also significant disparity, potentially greater than a factor of 5000 difference, in the estimated settling times depending on process vessel depth and final sediment solid concentration. This variation in results underscores the significance of the identified gaps and uncertainties with respect to process planning for utilizing settle/decant operations for DFHLW.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

An Orthogonal Recursive Bisection (ORB) Based Time Advancement Algorithm for CFD-DEM Solvers

The time integration of the granular phase in coupled computational fluid dynamics (CFD) – discrete element method (DEM) simulations presents a unique computational challenge brought about by the large variations in particle collisional time scales. Particles in the dilute regions of the computational domain can be advanced with large time steps while dense regions require much smaller time increments. However, the time step size in most solvers is globally set as the limit for accuracy and stability imposed by the collisions and is typically orders of magnitude less than that required away from collisions. This work addresses this precise issue and provides a strategy to avoid the use of a global conservative small time step size for the entire set of particles.A novel time stepping algorithm for CFD-DEM solvers using a partitioning approach using orthogonal recursive bisection (ORB) that allows for variable time steps among particles is described and its computational performance is compared against baseline explicit methods, typically used in several CFD-DEM solvers. ORB has advantages of being relatively quick and easy to update incrementally and has the required heuristic behavior (i.e., it will split the region in half with a cluster on each side) when groups of particles are well separated (clustered). The algorithm presented in this work uses a local time stepping approach to resolve collisional time scales for subsets of particles that are present at the leaves of the ORB, thereby resulting in substantial reduction of computational cost. The parallel implementation of this method where a ``knapsack” algorithm is used in tandem with ORB for effective load-balancing is also presented, where a best possible partitioning is obtained based on number of particles and local time-stepping costs. The algorithm is tested against benchmark problems with varying particle distributions that include fluidized bed and riser flow scenarios. Preliminary results indicate that the approach is 2-3X faster than traditional explicit methods for problems that involve both dense and dilute regions, while maintaining the same level of accuracy.

adaptive timestepping↗

DOE OSTI2022

THE DESIGN OF LAYER TIME OPTIMIZATION IN LARGE SCALE ADDITIVE MANUFACTURING WITH FIBER REINFORCED POLYMER COMPOSITES

In this study, we have developed a method to optimize a layer deposition time (a.k.a. layer time) for large-scale additive manufacturing (AM) via physics-based simulations. A long layer time leads to an over-cooled surface on which a new layer is deposited, and therefore, it may result in a weak bonding or debonding between layers, cracking, or warping. A short layer time leads to a high temperature of the structure due to insufficient cooling, and therefore, the structure may not be stiff enough and may collapse during manufacturing. Therefore, it is important to estimate the optimal layer time in additive manufacturing for a high-quality product. The temperature of a top layer right before deposition is recommended to be slightly higher than the glass temperature of the material. A temperature cooling was approximated to an exponential function of time, and the optimized layer time was obtained based on a target temperature while maintaining a minimal printing time. The material used is carbon fiber-reinforced polycarbonate (CF/PC), and the large-scale deposition system used is LSAM TM from Thermwood Corporation. Three different layer time cases were used for experiments, and a series of thermal images were obtained via an infra-red (IR) camera during the entire AM processes. AM process simulations were performed using a finite element method and the temperature profiles from the simulation were in good agreement with those from experiments. The layer time optimization was performed based on the temperature profiles from the simulations. A layer temperature with the optimal layer time was confirmed as the target temperature through simulation.

Jo, Eonyeon↗

DOE OSTI2020

PPINN: Parareal physics-informed neural network for time-dependent PDEs

Physics-informed neural networks (PINNs) encode physical conservation laws and prior physical knowledge into the neural networks, ensuring the correct physics is represented accurately while alleviating the need for supervised learning to a great degree. While effective for relatively short-term time integration, when long time integration of the time-dependent PDEs is sought, the time–space domain may become arbitrarily large and hence training of the neural network may become prohibitively expensive. To this end, we develop a parareal physics-informed neural network (PPINN), hence decomposing a long-time problem into many independent short-time problems supervised by an inexpensive/fast coarse-grained (CG) solver. In particular, the serial CG solver is designed to provide approximate predictions of the solution at discrete times, while initiate many fine PINNs simultaneously to correct the solution iteratively. There is a two-fold benefit from training PINNs with small-data sets rather than working on a large-data set directly, i.e., training of individual PINNs with small-data is much faster, while training the fine PINNs can be readily parallelized. Consequently, compared to the original PINN approach, the proposed PPINN approach may achieve a significant speed-up for long-time integration of PDEs, assuming that the CG solver is fast and can provide reasonable predictions of the solution, hence aiding the PPINN solution to converge in just a few iterations. To investigate the PPINN performance on solving time-dependent PDEs, we first apply the PPINN to solve the Burgers equation, and subsequently we apply the PPINN to solve a two-dimensional nonlinear diffusion–reaction equation. Furthermore, our results demonstrate that PPINNs converge in a few iterations with significant speed-ups proportional to the number of time-subdomains employed.

42 ENGINEERING↗

DOE OSTI2022

Using dynamic mode decomposition to predict the dynamics of a two-time non-equilibrium Green’s function

Computing the numerical solution of the Kadanoff–Baym equations, a set of nonlinear integral differential equations satisfied by the two-time Green's functions derived from many-body perturbation theory for a quantum many-body system away from equilibrium, is a challenging task. Recently, we have successfully applied dynamic mode decomposition (DMD) to construct a data driven reduced order model that can be used to extrapolate the time-diagonal of a two-time Green's function from numerical solutions of the KBE within a small time window. In this paper, we extend the previous work and use DMD to predict off-diagonal elements of the two-time Green's function. We partition the two-time Green's function into a number of one-time functions along the diagonal and subdiagonals of the two-time window as well as in horizontal and vertical directions. We use DMD to construct separate reduced order models to predict the dynamics of these one-time functions in a two-step procedure. We extrapolate along diagonal and several subdiagonals within a subdiagonal band of a two-time window in the first step. In the second step, we use DMD to extrapolate the Green's function outside of the sub-diagonal band. In conclusion, we demonstrate the efficiency and accuracy of this approach by applying it to a two-band Hubbard model problem.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Investigation of a digitizer for the plastic scintillation detectors of time-of-flight mass measurements

A CAEN DT5742 digitizer has been investigated to process the fast signals from the photomultiplier tubes of the time-of-flight detectors for fast ion beams. A test setup consisting of two plastic scintillation detectors and a pulsed laser source provided signals which were recorded by the digitizer and systematically analyzed with different algorithms to derive the amplitude, rise time and arrival time of the detection signal. To obtain the best amplitude and time resolutions, various optimization techniques including peak fitting and signal smoothing, interpolation improvement, and time-walk correction by amplitude and rise time have been performed and compared in detail. Here we compared the time resolutions obtained by three digital algorithms of leading-edge, zero-crossing constant-fraction (ZC-CFD) and direct constant-fraction discriminations. Finally, we found that the best time-of-flight resolution between two PMTs can be achieved as 12 ps by the method using sample minimum for peak location and using the time-walk-resistant ZC-CFD with 4-point interpolation for timing.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Assessing methods in fusion and fitting for time series construction in remote sensing-based earth observations

This study evaluates the comparative performance of spatiotemporal fusion and time-series fitting methods for constructing high-spatiotemporal-resolution remote sensing time-series data. Due to in-class similarity of fusion methods and fitting methods, we employ the Fit-FC (Fitting, spatial Filtering, and residual Compensation) model as a representative fusion method and the linear harmonic fitting model as a representative fitting method. Both Fit-FC and the linear harmonic fitting are widely used for high-spatiotemporal-resolution time-series data construction, and we modify the original Fit-FC model to enable automatic time-series fusion. To ensure data representativeness, we use 3 years (2019–2021) of Harmonized Landsat and Sentinel-2 surface reflectance datasets and Terra MCD43A4 products. Eight experimental regions are selected worldwide to guarantee generalization of the comparative performance between fusion and fitting methods, covering diverse land-use types (cropland, developed land, forest, and grassland) and varying climatological conditions. Time-series of NDVI and surface reflectance are analyzed under both actual observations and simulated data-missing scenarios. The constructed time-series data reveals that (1) the modified Fit-FC and linear harmonic fitting model achieve excellent performance in constructing high-resolution time-series images; (2) the fusion method outperforms the fitting method in constructing time-series of NDVI and surface reflectance images in cropland-, forest-, and grassland-dominated regions; (3) both methods achieve comparable performance in developed-dominated regions; (4) the fusion method is more robust to missing data, and better captures abrupt phenological transitions under conditions of continuous missing data; (5) the fitting method is computationally more efficient, making it suitable for large-scale time-series image reconstruction. This study provides valuable insights for selecting optimal strategies to generate high-resolution time-series images across diverse application scenarios and lays a foundation for extensions to other vegetation indices or land surface variables.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Electric Vehicles Charging Time Constrained Deliverable Provision of Secondary Frequency Regulation

Aggregation of electric vehicles (EVs) is a promising technique for providing secondary frequency regulation (SFR) in highly renewable energy-penetrated power systems. Equipped with energy storage devices, EV aggregation can provide reliable SFR. However, the main challenge is to guarantee reliable intra-interval SFR capacities and inter-interval delivery following the automatic generation control (AGC) signal. Furthermore, aggregated EV SFR provision will be further complicated by the EV charging time anxiety because SFR provision might extend EV's charging time. This paper proposes a deliverable EV SFR provision with a charging-time-constrained control strategy. First, a charging-time-constrained EV aggregation is proposed to address the uncertainty of EV capacity based on the state-space model considering the charging-time restriction of EV owners. Second, a real-time economic dispatch and time domain simulation (RTED-TDS) cosimulation framework is proposed to verify financial results and the dynamic performance of the EV SFR provision. Last, the proposed charging time-constrained EV aggregation is validated on the IEEE 39-bus system. In conclusion, the results demonstrate that with charging time-constrained EV aggregation, the dynamic performance of the system can be improved with a marginal increase in total cost. More importantly, the charging time constraint can be respected in the proposed SFR provision of the EV aggregation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Jet timing

The measurement of the arrival time of a particle, such as a lepton, a photon, or a pion, reaching the detector provides valuable information. A similar measurement for a hadronic final state, however, is much more challenging as one has to extract the relevant information from a collection of particles. In this paper, we explore various possibilities in defining the time of a jet through the measurable arrival times of the jet constituents. We find that a definition of jet time based on a transverse momentum weighted sum of the times of the constituents has the best performance. For prompt jets, the performance depends on the jet trajectory. For delayed jets, the performance depends on the trajectory of the jet, the trajectory of the mother particle, and the location of the displaced vertex. Compared to the next-best-performing jet time definition, the transverse momentum weighted sum has roughly a factor of ten times better jet time resolution. We give a detailed discussion of the relevant effects and characterize the full geometrical dependence of the performance. These results highlight the critical importance of using a proper definition of jet time with its corresponding detector-dependent calibration and the exciting possibility of deepening our understanding of jets in the time domain.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Connecting Femtosecond Transient Absorption Microscopy with Spatially Coregistered Time Averaged Optical Imaging Modalities

Multimodal all-optical imaging involving coregistered femtosecond transient absorption microscopy (TAM), time-integrated photoluminescence (PL), and steady-state modalities such as confocal reflectance and transmission offers an appealing approach to gain a comprehensive understanding of complex electronic excited-state phenomena in spatially heterogeneous systems. A unique combination of these modalities allows us to unravel not only the competing electronic excited-state dynamical processes but also the underlying morphological information with simultaneous high temporal and spatial resolution. However, correlating the various images obtained from time-resolved and time-independent modalities is generally nontrivial and particularly challenging when the electronic dynamics under study evolve in both time and space. Here, we demonstrate a new approach for rationally correlating time-resolved microscopy with coregistered time-integrated or steady-state modalities. Specifically, our approach involves an extended global lifetime analysis of the time-resolved microscopic data set to separate distinct dynamical processes taking place on commensurate time scales, and the resulting decay-associated amplitude maps (DAAMs) were applied to explore correlations with the images acquired using time-independent modalities. The feasibility of our approach was validated through analyzing a multimodal data set acquired from a thin film of chloride-containing mixed lead halide perovskites (CH 3 NH 3 PbI 3–x Cl x ) using femtosecond transient absorption, time-integrated PL, and confocal reflectance microscopies. Analysis of the results obtained enable us to gain new insight into the complex ultrafast relaxation dynamics in this highly heterogeneous system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Early-time measure in eternal inflation

In a situation like eternal inflation, where our data is replicated at infinitely-many other space-time events, it is necessary to make a prior assumption about our location to extract predictions. The principle of mediocrity entails that we live at asymptotic late times, when the occupational probabilities of vacua has settled to a near-equilibrium distribution. In this paper we further develop the idea that we instead exist during the approach to equilibrium, much earlier than the exponentially-long mixing time. In this case we are most likely to reside in vacua that are easily accessed dynamically. Using first-passage statistics, we prove that vacua that maximize their space-time volume at early times have: 1. maximal ever-hitting probability; 2. minimal mean first-passage time; and 3. minimal decay rate. These requirements are succinctly captured by an early-time measure. The idea that we live at early times is a predictive guiding principle, with many phenomenological implications. First, our vacuum should lie deep in a funneled region, akin to folding energy landscapes of proteins. Second, optimal landscape regions are characterized by relatively short-lived vacua, with lifetime of order the de Sitter Page time. For our vacuum, this lifetime is ~ 10 130 years, which is consistent with the Standard Model estimate due to Higgs metastability. Third, the measure favors vacua with small, positive vacuum energy. This can address the cosmological constant problem, provided there are sufficiently many vacua in the entire ensemble of funnels. As a concrete example, we study the Bousso-Polchinski lattice of flux vacua, and find that the early-time measure favors lattices with the fewest number of flux dimensions. This favors compactifications with a large hierarchy between the lightest modulus and all other Kähler and complex structure moduli.

79 ASTRONOMY AND ASTROPHYSICS↗