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This presentation summarizes the challenge of decarbonizing the last 10%" of the power system and discusses six strategies for addressing that challenge. This presentation was given at the IRA, BIL, and the Future of Energy: A Summit to Support State Implementation" workshop in Washington, D.C., in April 2024.
A phase space is a mathematical representation of all possible physical states of a system. Particle beams at Fermilab exist within a six-dimensional (6D) phase space defined by three positional components, (x, y, z) and three momentum components, (px, py, pz). To reconstruct this space implies taking measurement data from detectors and mapping out particle behavior using computational methods. The beam detectors, however, are only able to detect spatial distribution among the events of the beam, therefore being limited to positional data. Also, due to the vast number of events in a particle beam, it is extremely difficult to analyze and differentiate every single one’s behavior. However, with Machine Learning (ML), which can distinguish between patterns and map out particle behavior more efficiently. We first used the particle beam software, G4beamline, to simulate a 10,000-event muon beam, adjusting parameters such as initial momentum magnitude (p¬0) and virtual detector position. Using ten virtual detectors, we analyzed p0 values such that minimum 9,990 events were analyzed by every detector. We then input the data from these beam simulations to a C++ program, that randomly selects 100 events, and creates a 2D histogram based on spatial distribution, detector position, and event intensity. This process is repeated 100 times to create 100 histograms per p0 value. These images were then input to a modified ResNet18 Convolutional Neural Network (CNN) for training, and to predict p0 from some unseen set of histograms. The model was accurate when trained on momentum increments of 5 MeV/c and provided with denser training samples around highly variable test values. These results displayed machine learning being able to accurately predict p0 from being trained on different particle behaviors.
Abstract Growing lignocellulosic crops on marginal lands is a promising solution for sustainable biofuel production. We evaluated the productivity of bioenergy cropping systems (switchgrass [ Panicum virgatum L., var. Cave‐In‐Rock], miscanthus [ Miscanthus × giganteus , ‘Illinois clone’], hybrid poplar [ Populus nigra × P. maximowiczii A. Henry ‘NM6’], native grasses [five species], early successional vegetation, and restored prairie vs. historical vegetation [as reference control]) with and without nitrogen fertilization on low‐fertility former cropland at five sites in the Great Lakes Region, United States. We reported biomass yields for the first 7 years after establishment. Switchgrass was most consistently productive across all sites but miscanthus was more productive at three of the five sites. When averaged across sites, years, and nitrogen (N) treatments, biomass yields followed the order miscanthus > switchgrass > hybrid poplar ≈ native grasses > restored prairie > early successional vegetation ≈ historical vegetation, but varied substantially by crop and site, with a significant crop by site interaction. Yields of miscanthus and switchgrass peaked after four–five growing seasons and declined thereafter, while yields of both native grasses and restored prairie increased throughout 6 years with no sign of follow‐on decline, suggesting that polycultures may outperform monocultures over the long term. Yields of early successional vegetation—similar in composition to historical vegetation at each site—did not improve with time. Nitrogen fertilization increased the yields of all cropping systems at all sites. Our results demonstrate the viability of low‐productivity former cropland for long‐term bioenergy production and suggest there is no single crop best suited for all low fertility soils.
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In extensions of two Higgs doublet models with vectorlike quarks, the decays of vectorlike quarks may easily be dominated by cascade decays through charged or neutral Higgs bosons leading to signatures with 6 top or bottom quarks. Since top quark decays also contain bottom quarks, a common signature for many possible decay chains is 6 bottom quarks in the final state. We present a search strategy focusing on this final state and find the mass ranges of vectorlike quarks and Higgs bosons that can be explored at the Large Hadron Collider. Among other results, the sensitivity to the charged and neutral Higgs bosons, extending to about 2 TeV, stands out when compared to models without vectorlike matter.
This work introduces a novel, gradient-free metamaterial design method based on Gaussian process regression to represent the density field of a unit cell. The dimension of the design space is determined by the covariance matrix dimension in the Gaussian process regression. We propose compressing this matrix using an autoencoder, enabling the decoder to generate the density field and effectively reduce the originally large design space to a lower-dimensional subspace. In this compressed space, we employ an active learning method, Bayesian Adaptive Direct Search (BADS), for efficient exploration of the design space. We demonstrate that for simple 2D designs aimed at maximizing unit cell stiffness, our method yields results comparable to those of standard topology optimization. Furthermore, we extend our approach to various mechanical problems, from linear elasticity to hyperelastic large deformation and elasto-plasticity under finite deformation, to 3D metamaterial design. This illustrates the method’s versatility and effectiveness across a range of applications.
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Beam matching is a common technique that is routinely employed in accelerator design with the aim of minimizing beam losses and preservation of beam brightness. Despite being widely used, a full theoretical understanding of beam matching in 6D remains elusive. In this work, we present an analytical treatment of 6D beam matching of a high-intensity beam onto an RF structure. We begin our analysis within the framework of a linear model, and apply the averaging method to a set of 3D beam envelope equations. Accordingly, we obtain a matched solution that is comprised of smoothed envelopes and periodic terms, describing envelope oscillations with the period of the focusing structure. We then consider the nonlinear regime, where the beam size is comparable with the separatrix size. Stating with a Hamiltonian analysis in 6D phase space, we attain a self-consistent beam profile and show that it is significantly different from the commonly used ellipsoidal shape. Subsequently, we analyze the special case of an equilibrium with equal space charge depression between all degrees of freedom. Comparison of beam dynamics for equipartitioned, equal space charge depression, and equal emittances beams is given. Finally, we present experimental results on beam matching in the LANSCE linac.
Three storage ring lattices have been designed as options for a future upgrade of the Stanford synchrotron radiation lightsource (SSRL). The three options differ in circumference and targeted future site, with one to be built in the tunnel of the present SPEAR3 ring, one as a green field ring on the SLAC campus, and the third in the tunnel of the decommissioned PEP-II ring. The lattices are based on the newly proposed hybrid 6-bend achromat (H6BA) lattice cells, which is ideal for pushing the photon beam brightness while achieving excellent nonlinear dynamics performance. The transparent matching conditions are enforced to minimize the negative impact of the loss of periodicity due to insertion of various long straight sections. Numerical optimization is performed to further improve the nonlinear dynamics. Finally, in addition to reaching very low emittances, the lattices can accommodate traditional off-axis injection and achieve beam lifetimes similar to or exceeding that of typical third generation rings.
Land-use change (LUC) is a major source of global anthropogenic carbon emissions that contribute to climate change. However, current estimates of LUC-induced emissions vary widely with differences in data and models, making it challenging to identify and manage emissions hotspots by location and LUC activity. Here, we estimated spatially explicit carbon fluxes associated with global major gross land-use transitions based on a new bookkeeping model (i.e., LUCE). Between 1961 and 2020, LUC induced 215 and 142 Pg C of global carbon emissions and removals, respectively, resulting in average net emissions (E LUC ) of 1.21 Pg C year −1 . Latin America, Southeast Asia, and Sub-Saharan Africa dominated global E LUC with 69% of 60-year cumulative emissions, or about 86% between 2001 and 2020. Forest-related LUC activities (e.g., deforestation, reforestation) contributed largely to both regional and global carbon fluxes. Our findings shed new light on identifying LUC-induced emissions hotspots and managing land for climate mitigation and conservation.