Fundamental Physical Constraints Research Articles

Global analysis of geological CO2 storage by pressure-limited injection sites

Limiting global warming to a 2 °C rise may require large-scale deployment of carbon capture and storage (CCS). Due to the key role CCS plays in integrated assessment models of climate change mitigation, it is important that fundamental physical constraints are accounted for. We produce a global estimate of CO2 storage resource that accounts for pressure-limits within basin-scale reservoir systems. We use a dynamic physics model of reservoir pressurisation that is sufficiently simple to be incorporated into energy systems models. Our estimates address regionally inconsistent methodologies and the general lack of consideration for pressure limitations in global storage resource estimates. We estimate a maximum pressure-limited resource base and explore scenarios with different injection patterns, and scenarios where the extent of CCS deployment is limited by the history of regional hydrocarbon exploration and the readiness of countries for deployment. The maximum pressure-limited global storage achievable after thirty years of injection is 3640GtCO2 (121GtCO2yr-1), increasing to 5630GtCO2 (70 GtCO2yr-1) at the end of the century. These represent an update to volumetric-based estimates that suggest in excess of 10,000Gt of storage resource available. When CCS deployment is limited to the top ten countries ranked by the GCCSI Storage Readiness Index, our maximum storage estimate decreases to 780GtCO2 (26GtCO2yr-1) at the mid-century and 1177GtCO2 (15GtCO2yr-1) at the end of the century. These latter results fall within the range of projected deployment by the IPCC and IEA and suggest that reservoir pressurisation will limit CCS deployment if development does not rapidly expand beyond the current implementation.

Alpine glacier algal bloom during a record melt year.

Glacier algal blooms dominate the surfaces of glaciers and ice sheets during summer melt seasons, with larger blooms anticipated in years that experience the greatest melt. Here, we characterize the glacier algal bloom proliferating on Morteratsch glacier, Switzerland, during the record 2022 melt season, when the Swiss Alps lost three times more ice than the decadal average. Glacier algal cellular abundance (cells ml-1), biovolume (μm3 cell-1), photophysiology (Fv/Fm, rETRmax), and stoichiometry (C:N ratios) were constrained across three elevations on Morteratsch glacier during late August 2022 and compared with measurements of aqueous geochemistry and outputs of nutrient spiking experiments. While a substantial glacier algal bloom was apparent during summer 2022, abundances ranged from 1.78 × 104 to 8.95 × 105 cells ml-1 of meltwater and did not scale linearly with the magnitude of the 2022 melt season. Instead, spatiotemporal heterogeneity in algal distribution across Morteratsch glacier leads us to propose melt-water-redistribution of (larger) glacier algal cells down-glacier and presumptive export of cells from the system as an important mechanism to set overall bloom carrying capacity on steep valley glaciers during high melt years. Despite the paradox of abundant glacier algae within seemingly oligotrophic surface ice, we found no evidence for inorganic nutrient limitation as an important bottom-up control within our study site, supporting our hypothesis above. Fundamental physical constraints may thus cap bloom carrying-capacities on valley glaciers as 21st century melting continues.

Symplectic neural surrogate models for beam dynamics

Development of robust machine-learning (ML) based surrogates for particle accelerators can significantly benefit the modeling, design, optimization, monitoring and control of such accelerators. It is desirable that the surrogate models embed fundamental physical constraints to the interaction and dynamics of the beams, for which an accelerator must be designed to operate upon. We implement and train a class of phase space structure-preserving neural networks — Henon Neural Networks (HenonNets) [1], for nonlinear beam dynamics problems. It is demonstrated that the trained HenonNet model predicts the beam transfer matrix to a reasonably good accuracy while strongly maintaining the symplecticity. To explore such model’s applicability and flexibility for high brightness or intensity beams, we further test it with beam dynamics in the presence of electrostatic and radiative collective effects. Our results indicate that HenonNet may be used as a base ML model for the surrogate of complex beam dynamics, thus opening up a wide range of applications.

Gradient enhanced gaussian process regression for constitutive modelling in finite strain hyperelasticity

This paper introduces a metamodelling technique that leverages gradient-enhanced Gaussian process regression (also known as gradient-enhanced Kriging), effectively emulating the response of diverse hyperelastic strain energy densities. The approach adopted incorporates principal invariants as inputs for the surrogate of the strain energy density. This integration enables the surrogate to inherently enforce fundamental physical constraints, such as material frame indifference and material symmetry, right from the outset. The proposed approach provides accurate interpolation for energy and the first Piola–Kirchhoff stress tensor (e.g. first order derivatives with respect to inputs). The paper presents three notable innovations. Firstly, it introduces the utilisation of Gradient-Enhanced Kriging to approximate a diverse range of phenomenological models, encompassing numerous isotropic hyperelastic strain energies and a transversely isotropic potential. Secondly, this study marks the inaugural application of this technique for approximating the effective response of composite materials. This includes rank-one laminates, for which analytical solutions are feasible. However, it also encompasses more complex composite materials characterised by a Representative Volume Element (RVE) comprising an elastomeric matrix with a centred spherical inclusion. This extension opens the door for future application of this technique to various RVE types, facilitating efficient three-dimensional computational analyses at the macro-scale of such composite materials, significantly reducing computational time compared to FEM2. The third innovation, facilitated by the integration of these surrogate models into a 3D Finite Element computational framework, lies in the assessment of these models scenarios encompassing intricate cases of extreme twisting and more importantly, buckling instabilities in thin-walled structures, thereby highlighting both the practical applicability and robustness of the proposed approach.

Thermodynamic bounds on ultrasensitivity in covalent switching

Switch-like motifs are among the basic building blocks of biochemical networks. A common motif that can serve as an ultrasensitive switch consists of two enzymes acting antagonistically on a substrate, one making and the other removing a covalent modification. To work as a switch, such covalent modification cycles must be held out of thermodynamic equilibrium by continuous expenditure of energy. Here, we exploit the linear framework for timescale separation to establish tight bounds on the performance of any covalent-modification switch in terms of the chemical potential difference driving the cycle. The bounds apply to arbitrary enzyme mechanisms, not just Michaelis-Menten, with arbitrary rate constants and thereby reflect fundamental physical constraints on covalent switching.

Multiresonant metasurfaces for arbitrarily broad bandwidth pulse chirping and dispersion compensation

We show that ultrathin metasurfaces with a specific multiresonant response can enable simultaneously arbitrarily strong and arbitrarily broadband dispersion compensation, pulse (de)chirping, and compression or broadening. This breakthrough overcomes the fundamental limitations of both conventional nonresonant approaches (bulky) and modern singly resonant metasurfaces (narrowband) for quadratic phase manipulations of electromagnetic signals. The required nonuniform trains of resonances in the electric and magnetic sheet conductivities that completely control phase delay, group delay, and chirp are rigorously derived and the limitations imposed by fundamental physical constraints are thoroughly discussed. Subsequently, a practical, truncated approximation by finite sequences of physically realizable linear resonances is constructed and the associated error is quantified. By appropriate spectral ordering of the resonances, operation can be achieved either in transmission or reflection mode, enabling full space coverage. The proposed concept is not limited to dispersion compensation, but introduces a generic and powerful ultrathin platform for the spatiotemporal control of broadband real-world signals with a myriad of applications in modern optics, microwave photonics, radar, and communication systems.

Detachable all-carbon-linked 3D covalent organic framework films for semiconductor/COF heterojunctions by continuous flow synthesis

Detachable all-carbon-linked 3D covalent organic framework films for semiconductor/COF heterojunctions by continuous flow synthesis

Revealing configurational attractors in the evolution of modern Australian and US cities

The spatial structure of modern cities exhibits highly diverse patterns and keeps evolving under numerous constraints and sustainability demands. However, it is unclear if there are fundamental physical constraints guiding the evolution of cities. Here, we offer a concise model revealing key invariants within urban forms shaped by human resettlement over the years. In doing so, we assess the heterogeneity and spreading of population density in 25 Australian and 175 US cities. We observe that larger cities tend to form a cluster with low spreading and high heterogeneity, and explain this observation using dynamic properties of the intra-urban migration in these cities. As a result, we report three distinct feasible phases of urban structures: uniform, monocentric, and polycentric, separated by abrupt regime shifts. We demonstrate that transitions between these phases, resulting from the population redistribution, are not necessarily driven by external factors (such as city growth) and can exist even in a closed system. Our analysis reveals that the set of all possible equilibrium configurations (“configurational attractors”) form a narrow region in the heterogeneityspreading space, thus explaining the emergence of clustering patterns.

On Maneuverability of Fixed-Wing Unmanned Aerial Vehicle Formations

The problem of determining feasible maneuver envelope in multi-unmanned aerial vehicle (multi-UAV) formations is considered. In the leader–follower framework, an analysis is carried out relating the three fundamental physical constraints of follower UAVs, namely, bank angle, stalling speed, and maximum speed, to the leader’s maneuver. The closed-form relationships are further analyzed in detail to determine the set of follower UAVs that characterize the envelope boundary and their dependency on the maneuver. The maneuver envelope for a general multiple UAV formation is expressed in the turn radius-speed space of the leader, and validation studies are considered for various formation sizes and geometries. As a first, the work presents a complete and analytic solution to the problem considering arbitrary formation configuration and accounting for all the fundamental fixed-wing UAV physical constraints.

Orders & Contracts

The spatial structure of modern cities exhibits highly diverse patterns and keeps evolving under numerous constraints and sustainability demands. However, it is unclear if there are fundamental physical constraints guiding the evolution of cities. Here, we offer a concise model revealing key invariants within urban forms shaped by human resettlement over the years. In doing so, we assess the heterogeneity and spreading of population density in 25 Australian and 175 US cities. We observe that larger cities tend to form a cluster with low spreading and high heterogeneity, and explain this observation using dynamic properties of the intra-urban migration in these cities. As a result, we report three distinct feasible phases of urban structures: uniform, monocentric, and polycentric, separated by abrupt regime shifts. We demonstrate that transitions between these phases, resulting from the population redistribution, are not necessarily driven by external factors (such as city growth) and can exist even in a closed system. Our analysis reveals that the set of all possible equilibrium configurations (“configurational attractors”) form a narrow region in the heterogeneityspreading space, thus explaining the emergence of clustering patterns.

The Scales That Limit: The Physical Boundaries of Evolution

Organisms are subject to the laws of physics, so the process of evolution by genetic variation and natural selection is constrained by these fundamental laws. Classic and recent studies of the biophysical limits facing organisms have shown how fundamental physical constraints can be used to predict broad-scale relationships between body size and organismal biomechanics and physiology. These relationships often take the form of power laws across a wide range of body sizes for organisms sharing a common body plan. However, such biophysical perspectives have not been fully connected with the detailed dynamics of evolution by natural selection, nor with the variation between species around the central scaling relationships. Here we first discuss what a general biophysical theory of evolution would require and provide an abstracted framework for aspects of such a theory. We discuss how such a theory should eventually be capable of not only predicting scaling relationships, but also of identifying the types of tradeoffs shown by different species occupying a particular niche. We then discuss how one key higher-order requirement of a biophysical theory of evolution is the ability to predict asymptotic behavior and the limits of a particular body plan. We use several examples to illustrate how dominant physical constraints can be used to predict the minimum and maximum body sizes for a particular body plan, and we argue that prediction of these limits is essential for identifying the dominant physical constraints for a given category of organisms. Our framework proposes that a major portion of fitness should be the overlay of how all traits of a particular body plan interact with fundamental physical constraints. In our examples we investigate multiple physical limits on particular traits, and also how the interaction of a number of traits determines the size limits on entire body plans, such as those of vascular plants. We use bacteria as an example of the shifts in which traits are most limiting at various sizes. Finally, we address the effects of environmental conditions and ecological interactions in determining which of the physical constraints faced by organisms are most likely to affect their fitness.

Seamlessly fused digital-analogue reconfigurable computing using memristors

As the world enters the age of ubiquitous computing, the need for reconfigurable hardware operating close to the fundamental limits of energy consumption becomes increasingly pressing. Simultaneously, scaling-driven performance improvements within the framework of traditional analogue and digital design become progressively more restricted by fundamental physical constraints. Emerging nanoelectronics technologies bring forth new prospects yet a significant rethink of electronics design is required for realising their full potential. Here we lay the foundations of a design approach that fuses analogue and digital thinking by combining digital electronics with analogue memristive devices for achieving charge-based computation; information processing where every dissipated charge counts. This is realised by introducing memristive devices into standard logic gates, thus rendering them reconfigurable and capable of performing analogue computation at a power cost close to digital. The versatility and benefits of our approach are experimentally showcased through a hardware data clusterer and an analogue NAND gate.

Loss and Dispersion Limitations in mm-Wave Dielectric Waveguides for High-Speed Links

We investigate the fundamental physical constraints and tradeoffs in common millimeter-wave dielectric waveguides proposed for high speed links. We consider waveguides with different geometries, dimensions and operating frequencies, and show that the capacity of optimized longer links is limited by the dispersion and not necessarily the loss. In our analysis, the required capacity and distance determine the choice of waveguide and the operating frequency, which would then determine the energy efficiency of the link. Our generic theoretical analyses are in agreement with the measurements presented in the recent literature.

ALONE IN THE UNIVERSE

AbstractWe are probably alone in the universe—a conclusion based on observations of over 4,000 exoplanets and fundamental physical constraints. This article updates earlier arguments with the latest astrophysical results. Since the discovery of exoplanets, theologians have asked with renewed urgency what the presence of extraterrestrial intelligence (ETI) says about salvation and human purpose, but this is the wrong question. The more urgent question is what their absence says. The “Misanthropic Principle” is the observation that, in a universe fine‐tuned for life (“Anthropic Principle”), the circumstances necessary for intelligence are rare.Rabbis for 2,000 years discussed the existence of ETI using scriptural passages. We examine the traditional Jewish approaches to ETI, including insights on how ETI affects our perception of God, self, free‐will, and responsibility. We explore the implications of our probable solitude, and offer a Jewish response to the ethical lessons to be drawn from the absence of ETI.

Fundamental physical cellular constraints drive self-organization of tissues.

Morphogenesis is driven by small cell shape changes that modulate tissue organization. Apical surfaces of proliferating epithelial sheets have been particularly well studied. Currently, it is accepted that a stereotyped distribution of cellular polygons is conserved in proliferating tissues among metazoans. In this work, we challenge these previous findings showing that diverse natural packed tissues have very different polygon distributions. We use Voronoi tessellations as a mathematical framework that predicts this diversity. We demonstrate that Voronoi tessellations and the very different tissues analysed share an overriding restriction: the frequency of polygon types correlates with the distribution of cell areas. By altering the balance of tensions and pressures within the packed tissues using disease, genetic or computer model perturbations, we show that as long as packed cells present a balance of forces within tissue, they will be under a physical constraint that limits its organization. Our discoveries establish a new framework to understand tissue architecture in development and disease.

Non-radial instabilities and progenitor asphericities in core-collapse supernovae

Since core-collapse supernova simulations still struggle to produce robust neutrino-driven explosions in 3D, it has been proposed that asphericities caused by convection in the progenitor might facilitate shock revival by boosting the activity of non-radial hydrodynamic instabilities in the post-shock region. We investigate this scenario in depth using 42 relativistic 2D simulations with multi-group neutrino transport to examine the effects of velocity and density perturbations in the progenitor for different perturbation geometries that obey fundamental physical constraints (like the anelastic condition). As a framework for analysing our results, we introduce semi-empirical scaling laws relating neutrino heating, average turbulent velocities in the gain region, and the shock deformation in the saturation limit of non-radial instabilities. The squared turbulent Mach number, <Ma^2>, reflects the violence of aspherical motions in the gain layer, and explosive runaway occurs for <Ma^2>~0.3, corresponding to a reduction of the critical neutrino luminosity by ~25% compared to 1D. In the light of this theory, progenitor asphericities aid shock revival mainly by creating anisotropic mass flux onto the shock: Differential infall efficiently converts velocity perturbations in the progenitor into density perturbations (Delta rho/rho) at the shock of the order of the initial convective Mach number Ma. The anisotropic mass flux and ram pressure deform the shock and thereby amplify post-shock turbulence. Large-scale (l=2,l=1) modes prove most conducive to shock revival, whereas small-scale perturbations require unrealistically high convective Mach numbers. Initial density perturbations in the progenitor are only of order Ma^2 and therefore play a subdominant role.

Methodological Approaches to Projecting the Hydrologic Impacts of Climate Change*

AbstractClimate change due to anthropogenic greenhouse gases (GHG) is expected to have important impacts on water resources, with a variety of societal impacts. Recent research has shown that applying different methodologies to assess hydrologic impacts can lead to widely diverging projections of water resources. The authors classify methods of projecting hydrologic impacts of climate change into those that estimate potential evapotranspiration (PET) based on air temperature and those that estimate PET based on components of the surface energy budget. In general, air temperature–based methods more frequently show reductions in measures of water resources (e.g., water yield or soil moisture) and greater sensitivity than those using energy budget–based methods. There are significant trade-offs between these two methods in terms of ease of use, input data required, applicability to specific locales, and adherence to fundamental physical constraints: namely, conservation of energy at the surface. Issues of uncertainty in climate projections, stemming from imperfectly known future atmospheric GHG concentrations and disagreement in projections of the resultant climate, are compounded by questions of methodology and input data availability for models that connect climate change to accompanying changes in hydrology. In the joint atmospheric–hydrologic research community investigating climate change, methods need to be developed in which the energy and moisture budgets remain consistent when considering their interaction with both the atmosphere and water resources. This approach should yield better results for both atmospheric and hydrologic processes.

Physical principles for scalable neural recording.

Simultaneously measuring the activities of all neurons in a mammalian brain at millisecond resolution is a challenge beyond the limits of existing techniques in neuroscience. Entirely new approaches may be required, motivating an analysis of the fundamental physical constraints on the problem. We outline the physical principles governing brain activity mapping using optical, electrical, magnetic resonance, and molecular modalities of neural recording. Focusing on the mouse brain, we analyze the scalability of each method, concentrating on the limitations imposed by spatiotemporal resolution, energy dissipation, and volume displacement. Based on this analysis, all existing approaches require orders of magnitude improvement in key parameters. Electrical recording is limited by the low multiplexing capacity of electrodes and their lack of intrinsic spatial resolution, optical methods are constrained by the scattering of visible light in brain tissue, magnetic resonance is hindered by the diffusion and relaxation timescales of water protons, and the implementation of molecular recording is complicated by the stochastic kinetics of enzymes. Understanding the physical limits of brain activity mapping may provide insight into opportunities for novel solutions. For example, unconventional methods for delivering electrodes may enable unprecedented numbers of recording sites, embedded optical devices could allow optical detectors to be placed within a few scattering lengths of the measured neurons, and new classes of molecularly engineered sensors might obviate cumbersome hardware architectures. We also study the physics of powering and communicating with microscale devices embedded in brain tissue and find that, while radio-frequency electromagnetic data transmission suffers from a severe power–bandwidth tradeoff, communication via infrared light or ultrasound may allow high data rates due to the possibility of spatial multiplexing. The use of embedded local recording and wireless data transmission would only be viable, however, given major improvements to the power efficiency of microelectronic devices.

Development of a new SMP model satisfying all known physical constraints in environmental application

A new Solver for Mixture Problem (SMP) model has been developed that satisfies all known fundamental natural physical constraints identified in environmental application. Previously, nonnegative matrix factorization (NMF) models were developed and applied successfully in chemometrics application and aerosol source apportionment studies. NMF models are based on nonnegativity constraints of loadings and scores that are equivalent to source compositions and source apportionments, respectively, in aerosol source apportionment study. In environmental applications of aerosol source apportionment studies, however, more physical constraints must be satisfied in addition to nonnegativity constraints of loadings and scores. A new model has been developed based on alternating primal-dual interior point nonlinear programming, subject to inequality constraints of all known fundamental physical constraints. Previous multivariate receptor models have been partial implementations; they have not been able to satisfy meaningful physical constraints when estimating both source compositions and source apportionments from ambient data. The SMP model, however, successfully estimates both source apportionments and source compositions while satisfying all physical constraints. Source compositions estimated by the SMP model can be used in other source apportionment studies using the Chemical Mass Balance (CMB) receptor model. The SMP model has been applied to an error free data set to examine the capability of estimating source compositions and source apportionments. Two sets of simulations were conducted and the results are compared and discussed. Simulation results show that SMP estimated pairs of source compositions and source apportionments satisfy all known physical constraints and are in good agreement with true values.

Spatial Patterns of Protein Secretion on Bacterial Surfaces

Proteins expressed on bacterial cell surfaces must initially cross the cytoplasmic membrane and then either traverse the thick cell wall in Gram-positive organisms or the thinner cell wall plus outer membrane in Gram-negative organisms. We have used protein tagging techniques to explore the spatial and temporal patterns of protein secretion in both types of bacteria. Overall, despite the profound structural differences between Gram-positive and Gram-negative cell surfaces, we find that both are largely immobile environments where protein behavior is dictated by fundamental physical constraints. For the Gram-negative bacterium E. coli, we used exogenous enzyme-based sequence-specific labeling of LamB to detect the location of newly synthesized exported protein. LamB initially appears in discrete puncta distributed over the cylindrical sides of the cell, rather than uniformly. These results are most consistent with a model where all outer membrane material (including transmembrane proteins and lipopolysaccharides) is added in irregular bursts along the cylindrical sides of the cell, and proteins are essentially immobile with respect to their neighbors. For the Gram-positive bacterium Listeria monocytogenes, we have also observed punctate initial secretion of the surface protein ActA along the cylindrical sides of the cell, and very slow accumulation at cell poles. ActA is a transmembrane protein with a large unstructured ectodomain exposed on the cell surface. We have developed a physical model proposing that the entropic constraint imposed by a small periplasmic space could drive the translocation of a large, unstructured protein across a barrier with a thickness and porosity similar to that of the Listeria cell wall. Consistent with this purely physical model for unstructured protein translocation and polarization in Gram-positive bacteria, we have found that eukaryotic nuclear pore proteins expressed in these bacteria will also translocate and polarize.