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Sunday, July 3, 2022

Void (astronomy)

From Wikipedia, the free encyclopedia

Structure of the Universe
Matter distribution in a cubic section of the universe. The blue fiber structures represent the matter (primarily dark matter) and the empty regions in between represent the cosmic voids.
 

Cosmic voids are vast spaces between filaments (the largest-scale structures in the universe), which contain very few or no galaxies. The cosmological evolution of the void regions differs drastically from the evolution of the Universe as a whole: there is a long stage when the curvature term dominates, which prevents the formation of galaxy clusters and massive galaxies. Hence, although even the emptiest regions of voids contain more than ~15% of the average matter density of the Universe, the voids look almost empty for an observer.  Voids typically have a diameter of 10 to 100 megaparsecs (30 to 300 million light years); particularly large voids, defined by the absence of rich superclusters, are sometimes called supervoids. They were first discovered in 1978 in a pioneering study by Stephen Gregory and Laird A. Thompson at the Kitt Peak National Observatory.

Voids are believed to have been formed by baryon acoustic oscillations in the Big Bang, collapses of mass followed by implosions of the compressed baryonic matter. Starting from initially small anisotropies from quantum fluctuations in the early universe, the anisotropies grew larger in scale over time. Regions of higher density collapsed more rapidly under gravity, eventually resulting in the large-scale, foam-like structure or "cosmic web" of voids and galaxy filaments seen today. Voids located in high-density environments are smaller than voids situated in low-density spaces of the universe.

Voids appear to correlate with the observed temperature of the cosmic microwave background (CMB) because of the Sachs–Wolfe effect. Colder regions correlate with voids and hotter regions correlate with filaments because of gravitational redshifting. As the Sachs–Wolfe effect is only significant if the universe is dominated by radiation or dark energy, the existence of voids is significant in providing physical evidence for dark energy.

Large-scale structure

A map of galaxy voids

The structure of the Universe can be broken down into components that can help describe the characteristics of individual regions of the cosmos. These are the main structural components of the cosmic web:

  • Voids – vast, largely spherical regions with very low cosmic mean densities, up to 100 megaparsecs (Mpc) in diameter.
  • Walls – the regions that contain the typical cosmic mean density of matter abundance. Walls can be further broken down into two smaller structural features:
    • Clusters – highly concentrated zones where walls meet and intersect, adding to the effective size of the local wall.
    • Filaments – the branching arms of walls that can stretch for tens of megaparsecs.

Voids have a mean density less than a tenth of the average density of the universe. This serves as a working definition even though there is no single agreed-upon definition of what constitutes a void. The matter density value used for describing the cosmic mean density is usually based on a ratio of the number of galaxies per unit volume rather than the total mass of the matter contained in a unit volume.

Discovery

Study of cosmic voids within the discipline of astrophysics began in the mid-1970s when redshift surveys led two separate teams of astrophysicists in 1978 to identify superclusters and voids in the distribution of galaxies and Abell clusters. The new redshift surveys revolutionized the field of astronomy by adding depth to the two-dimensional maps of cosmological structure, which were often densely packed and overlapping, allowing for the first three-dimensional mapping of the universe. Through redshift surveys, their depth was calculated from the individual redshifts of the galaxies due to the expansion of the universe according to Hubble's law.

Timeline

A summarized timeline of important events in the field of cosmic voids from its beginning to recent times is as follows:

  • 1961 – Large-scale structural features such as "second-order clusters", a specific type of supercluster, were brought to the astronomical community's attention.
  • 1978 – The first two papers on the topic of voids in the large-scale structure were published referencing voids found in the foreground of the Coma/A1367 clusters.
  • 1981 – Discovery of a large void in the Boötes region of the sky that was nearly 50 h−1 Mpc in diameter (which was later recalculated to be about 34 h−1 Mpc). Here h is the dimensionless Hubble parameter, approximately 0.7.
  • 1983 – Computer simulations sophisticated enough to provide relatively reliable results of growth and evolution of the large-scale structure emerged and yielded insight on key features of the large-scale galaxy distribution.
  • 1985 – Details of the supercluster and void structure of the Perseus-Pisces region were surveyed.
  • 1989 – The Center for Astrophysics Redshift Survey revealed that large voids, sharp filaments, and the walls that surround them dominate the large-scale structure of the universe.
  • 1991 – The Las Campanas Redshift Survey confirmed the abundance of voids in the large-scale structure of the universe (Kirshner et al. 1991).
  • 1995 – Comparisons of optically selected galaxy surveys indicate that the same voids are found regardless of the sample selection.
  • 2001 – The completed two-degree Field Galaxy Redshift Survey adds a significantly large amount of voids to the database of all known cosmic voids.
  • 2009 – The Sloan Digital Sky Survey (SDSS) data combined with previous large-scale surveys now provide the most complete view of the detailed structure of cosmic voids.

Methods for finding

There exist a number of ways for finding voids with the results of large-scale surveys of the universe. Of the many different algorithms, virtually all fall into one of three general categories. The first class consists of void finders that try to find empty regions of space based on local galaxy density. The second class are those which try to find voids via the geometrical structures in the dark matter distribution as suggested by the galaxies. The third class is made up of those finders which identify structures dynamically by using gravitationally unstable points in the distribution of dark matter. The three most popular methods through the study of cosmic voids are listed below:

VoidFinder algorithm

This first-class method uses each galaxy in a catalog as its target and then uses the Nearest Neighbor Approximation to calculate the cosmic density in the region contained in a spherical radius determined by the distance to the third-closest galaxy. El Ad & Piran introduced this method in 1997 to allow a quick and effective method for standardizing the cataloging of voids. Once the spherical cells are mined from all of the structure data, each cell is expanded until the underdensity returns to average expected wall density values. One of the helpful features of void regions is that their boundaries are very distinct and defined, with a cosmic mean density that starts at 10% in the body and quickly rises to 20% at the edge and then to 100% in the walls directly outside the edges. The remaining walls and overlapping void regions are then gridded into, respectively, distinct and intertwining zones of filaments, clusters, and near-empty voids. Any overlap of more than 10% with already known voids are considered to be subregions within those known voids. All voids admitted to the catalog had a minimum radius of 10 Mpc in order to ensure all identified voids were not accidentally cataloged due to sampling errors.

Zone bordering on voidness (ZOBOV) algorithm

This particular second-class algorithm uses a Voronoi tessellation technique and mock border particles in order to categorize regions based on a high-density contrasting border with a very low amount of bias. Neyrinck introduced this algorithm in 2008 with the purpose of introducing a method that did not contain free parameters or presumed shape tessellations. Therefore, this technique can create more accurately shaped and sized void regions. Although this algorithm has some advantages in shape and size, it has been criticized often for sometimes providing loosely defined results. Since it has no free parameters, it mostly finds small and trivial voids, although the algorithm places a statistical significance on each void it finds. A physical significance parameter can be applied in order to reduce the number of trivial voids by including a minimum density to average density ratio of at least 1:5. Subvoids are also identified using this process which raises more philosophical questions on what qualifies as a void. Void finders such as VIDE are based on ZOBOV.

Dynamical void analysis (DIVA) algorithm

This third-class method is drastically different from the previous two algorithms listed. The most striking aspect is that it requires a different definition of what it means to be a void. Instead of the general notion that a void is a region of space with a low cosmic mean density; a hole in the distribution of galaxies, it defines voids to be regions in which matter is escaping; which corresponds to the dark energy equation of state, w. Void centers are then considered to be the maximal source of the displacement field denoted as Sψ. The purpose for this change in definitions was presented by Lavaux and Wandelt in 2009 as a way to yield cosmic voids such that exact analytical calculations can be made on their dynamical and geometrical properties. This allows DIVA to heavily explore the ellipticity of voids and how they evolve in the large-scale structure, subsequently leading to the classification of three distinct types of voids. These three morphological classes are True voids, Pancake voids, and Filament voids. Another notable quality is that even though DIVA also contains selection function bias just as first-class methods do, DIVA is devised such that this bias can be precisely calibrated, leading to much more reliable results. Multiple shortfalls of this Lagrangian-Eulerian hybrid approach exist. One example is that the resulting voids from this method are intrinsically different than those found by other methods, which makes an all-data points inclusive comparison between results of differing algorithms very difficult.

Significance

Voids have contributed significantly to the modern understanding of the cosmos, with applications ranging from shedding light on the current understanding of dark energy, to refining and constraining cosmological evolution models. Some popular applications are mentioned in detail below.

Dark energy

The simultaneous existence of the largest-known voids and galaxy clusters requires about 70% dark energy in the universe today, consistent with the latest data from the cosmic microwave background. Voids act as bubbles in the universe that are sensitive to background cosmological changes. This means that the evolution of a void's shape is in part the result of the expansion of the universe. Since this acceleration is believed to be caused by dark energy, studying the changes of a void's shape over a period of time can be used to constrain the standard ΛCDM model, or further refine the Quintessence + Cold Dark Matter (QCDM) model and provide a more accurate dark energy equation of state. Additionally the abundance of voids is a promising way to constrain the dark energy equation of state.

Neutrinos

Neutrinos, due to their very small mass and extremely weak interaction with other matter, will free-stream in and out of voids which are smaller than the mean-free path of neutrinos. This has an effect on the size and depth distribution of voids, and is expected to make it possible with future astronomical surveys (e.g. the Euclid satellite) to measure the sum of the masses of all neutrino species by comparing the statistical properties of void samples to theoretical predictions.

Galactic formation and evolution models

Large-scale structure formation
A 43×43×43-megaparsec cube shows the evolution of the large-scale structure over a logarithmic period starting from a redshift of 30 and ending at redshift 0. The model makes it clear to see how the matter-dense regions contract under the collective gravitational force while simultaneously aiding in the expansion of cosmic voids as the matter flees to the walls and filaments.

Cosmic voids contain a mix of galaxies and matter that is slightly different than other regions in the universe. This unique mix supports the biased galaxy formation picture predicted in Gaussian adiabatic cold dark matter models. This phenomenon provides an opportunity to modify the morphology-density correlation that holds discrepancies with these voids. Such observations like the morphology-density correlation can help uncover new facets about how galaxies form and evolve on the large scale. On a more local scale, galaxies that reside in voids have differing morphological and spectral properties than those that are located in the walls. One feature that has been found is that voids have been shown to contain a significantly higher fraction of starburst galaxies of young, hot stars when compared to samples of galaxies in walls.

Voids offer opportunities to study the strength of intergalactic magnetic fields. For example, a 2015 study concludes, based on the deflection of blazar gamma-ray emissions that travel through voids, that intergalactic space contains a magnetic field of strength at least 10-17 G. The specific large-scale magnetic structure of the universe suggests primordial "magnetogenesis", which in turn could have played a role in the formation of magnetic fields within galaxies, and could also change estimates of the timeline of recombination in the early universe.

Anomalies in anisotropies

Cold spots in the cosmic microwave background, such as the WMAP cold spot found by Wilkinson Microwave Anisotropy Probe, could possibly be explained by an extremely large cosmic void that has a radius of ~120 Mpc, as long as the late integrated Sachs–Wolfe effect was accounted for in the possible solution. Anomalies in CMB screenings are now being potentially explained through the existence of large voids located down the line-of-sight in which the cold spots lie.

Cosmic Microwave Background screening of Universe.
CMB screening of the universe.

Expansion

Although dark energy is currently the most popular explanation for the acceleration in the expansion of the universe, another theory elaborates on the possibility of our galaxy being part of a very large, not-so-underdense, cosmic void. According to this theory, such an environment could naively lead to the demand for dark energy to solve the problem with the observed acceleration. As more data has been released on this topic the chances of it being a realistic solution in place of the current ΛCDM interpretation has been largely diminished but not all together abandoned.

Gravitational theories

The abundance of voids, particularly when combined with the abundance of clusters of galaxies, is a promising method for precision tests of deviations from general relativity on large scales and in low-density regions.

The insides of voids often seem to adhere to cosmological parameters which differ from those of the known universe[citation needed]. It is because of this unique feature that cosmic voids make for great laboratories to study the effects that gravitational clustering and growth rates have on local galaxies and structure when the cosmological parameters have different values from the outside universe. Due to the observation that larger voids predominantly remain in a linear regime, with most structures within exhibiting spherical symmetry in the underdense environment; that is, the underdensity leads to near-negligible particle-particle gravitational interactions that would otherwise occur in a region of normal galactic density. Testing models for voids can be performed with very high accuracy. The cosmological parameters that differ in these voids are Ωm, ΩΛ, and H0.

Energy efficiency in transport

From Wikipedia, the free encyclopedia
 

The energy efficiency in transport is the useful travelled distance, of passengers, goods or any type of load; divided by the total energy put into the transport propulsion means. The energy input might be rendered in several different types depending on the type of propulsion, and normally such energy is presented in liquid fuels, electrical energy or food energy. The energy efficiency is also occasionally known as energy intensity. The inverse of the energy efficiency in transport, is the energy consumption in transport.

Energy efficiency in transport is often described in terms of fuel consumption, fuel consumption being the reciprocal of fuel economy. Nonetheless, fuel consumption is linked with a means of propulsion which uses liquid fuels, whilst energy efficiency is applicable to any sort of propulsion. To avoid said confusion, and to be able to compare the energy efficiency in any type of vehicle, experts tend to measure the energy in the International System of Units, i.e., joules.

Therefore, in the International System of Units, the energy efficiency in transport is measured in terms of metre per joule, or m/J, whilst the energy consumption in transport is measured in terms of joules per metre, or J/m. The more efficient the vehicle, the more metres it covers with one joule (more efficiency), or the fewer joules it uses to travel over one metre (less consumption). The energy efficiency in transport largely varies by means of transport. Different types of transport range from some hundred kilojoules per kilometre (kJ/km) for a bicycle to tens of megajoules per kilometre (MJ/km) for a helicopter.

Via type of fuel used and rate of fuel consumption, energy efficiency is also often related to operating cost ($/km) and environmental emissions (e.g. CO2/km).

Units of measurement

In the International System of Units, the energy efficiency in transport is measured in terms of metre per joule, or m/J. Nonetheless, several conversions are applicable, depending on the unit of distance and on the unit of energy. For liquid fuels, normally the quantity of energy input is measured in terms of the liquid's volume, such as litres or gallons. For propulsion which runs on electricity, normally kW·h is used, while for any type of human-propelled vehicle, the energy input is measured in terms of Calories. It is typical to convert between different types of energy and units.

For passenger transport, the energy efficiency is normally measured in terms of passengers times distance per unit of energy, in the SI, passengers metres per joule (pax.m/J); while for cargo transport the energy efficiency is normally measured in terms of mass of transported cargo times distance per unit of energy, in the SI, kilograms metres per joule (kg.m/J). Volumetric efficiency with respect to vehicle capacity may also be reported, such as passenger-mile per gallon (PMPG), obtained by multiplying the miles per gallon of fuel by either the passenger capacity or the average occupancy. The occupancy of personal vehicles is typically lower than capacity by a considerable degree and thus the values computed based on capacity and on occupancy will often be quite different.

Typical conversions into SI unit


Joules
litre of petrol 0.3x108
US gallon of petrol (gasoline) 1.3x108
Imp. gallon of petrol (gasoline) 1.6x108
kilocalorie 4.2x103
kW·h 3.6x106
BTU 1.1x103

Liquid fuels

Energy efficiency is expressed in terms of fuel economy:

Energy consumption (reciprocal efficiency) is expressed terms of fuel consumption:

  • volume of fuel (or total energy) consumed per unit distance per vehicle; e.g. l/100 km or MJ/100 km.
  • volume of fuel (or total energy) consumed per unit distance per passenger; e.g., l/(100 passenger·km).
  • volume of fuel (or total energy) consumed per unit distance per unit mass of cargo transported; e.g., l/100 kg·km or MJ/t·km.

Electricity

Electricity consumption:

  • electrical energy used per vehicle per unit distance; e.g., kW·h/100 km.

Producing electricity from fuel requires much more primary energy than the amount of electricity produced.

Food energy

Energy consumption:

  • calories burnt by the body's metabolism per kilometre; e.g., Cal/km.
  • calories burnt by the body's metabolism per mile; e.g., Cal/miles.

Land Passenger Transport

Walking

A 68 kg (150 lb) person walking at 4 km/h (2.5 mph) requires approximately 210 kilocalories (880 kJ) of food energy per hour, which is equivalent to 4.55 km/MJ. 1 US gal (3.8 L) of petrol contains about 114,000 British thermal units (120 MJ) of energy, so this is approximately equivalent to 360 miles per US gallon (0.65 L/100 km).

Velomobile

Velomobiles (enclosed recumbent bicycles) have the highest energy efficiency of any known mode of personal transport because of their small frontal area and aerodynamic shape. At a speed of 50 km/h (31 mph), the velomobile manufacturer WAW claims that only 0.5 kW·h (1.8 MJ) of energy per 100 km is needed to transport the passenger (= 18 J/m). This is around 15 (20%) of what is needed to power a standard upright bicycle without aerodynamic cladding at same speed, and 150 (2%) of that which is consumed by an average fossil fuel or electric car (the velomobile efficiency corresponds to 4700 miles per US gallon, 2000 km/L, or 0.05 L/100 km). Real energy from food used by human is 4–5 times more. Unfortunately their energy efficiency advantage over bicycles becomes smaller with decreasing speed and disappears at around 10 km/h where power needed for velomobiles and triathlon bikes are almost the same.

Bicycle

A Chinese Flying Pigeon bicycle

A standard lightweight, moderate-speed bicycle is one of the most energy-efficient forms of transport. Compared with walking, a 64 kg (140 lb) cyclist riding at 16 km/h (10 mph) requires about half the food energy per unit distance: 27 kcal/km, 3.1 kW⋅h (11 MJ) per 100 km, or 43 kcal/mi. This converts to about 732 mpg‑US (0.321 L/100 km; 879 mpg‑imp). This means that a bicycle will use between 10 and 25 times less energy per distance travelled than a personal car, depending on fuel source and size of the car. This figure does depend on the speed and mass of the rider: greater speeds give higher air drag and heavier riders consume more energy per unit distance. In addition, because bicycles are very lightweight (usually between 7–15 kg) this means they consume very low amounts of materials and energy to manufacture. In comparison to an automobile weighing 1500 kg or more, a bicycle typically requires 100–200 times less energy to produce than an automobile. In addition, bicycles require less space both to park and to operate and they damage road surfaces less, adding an infrastructural factor of efficiency.

Motorised bicycle

A motorised bicycle allows human power and the assistance of a 49 cm3 (3.0 cu in) engine, giving a range of 160 to 200 mpg‑US (1.5–1.2 L/100 km; 190–240 mpg‑imp). Electric pedal-assisted bikes run on as little as 1.0 kW⋅h (3.6 MJ) per 100 km, while maintaining speeds in excess of 30 km/h (19 mph). These best-case figures rely on a human doing 70% of the work, with around 3.6 MJ (1.0 kW⋅h) per 100 km coming from the motor. This makes an electric bicycle one of the most efficient possible motorised vehicles, behind only a motorised velomobile and an electric unicycle (EUC).

Electric kick scooter

Electric kick scooters, part of a scooter-sharing system, in San Jose, California.

Electric kick scooters, such as those used by scooter-sharing systems like Bird or Lime, typically have a maximum range of under 30 km (19 mi) and a maximum speed of roughly 15.5 mph (24.9 km/h). Intended to fit into a last mile niche and be ridden in bike lanes, they require little skill from the rider. Because of their light weight and small motors, they are extremely energy-efficient with a typical energy efficiency of 1.1 kW⋅h (4.0 MJ) per 100 km (1904 MPGe 810 km/L 0.124 L/100 km), even more efficient than bicycles and walking. However, as they must be recharged frequently, they are often collected overnight with motor vehicles, somewhat negating this efficiency. The lifecycle of electric scooters is also notably shorter than that of bicycles, often reaching only a single digit number of years.

Electric Unicycle

An electric unicycle (EUC) cross electric skateboard variant called the Onewheel Pint can carry a 50 kg person 21.5 km at an average speed of 20 km/h. The battery holds 148Wh. Without taking energy lost to heat in the charging stage into account, this equates to an efficiency of 6.88Wh/km or 0.688kWh/100 km. Additionally, with regenerative braking as a standard design feature, hilly terrain would have less impact on an EUC compared to a vehicle with friction brakes such as a push bike. This combined with the single wheel ground interaction may make the EUC the most efficient known vehicle at low speeds (below 25 km/h), with the velomobile overtaking the position as most efficient at higher speeds due to superior aerodynamics.

Automobiles

Bugatti Veyron

The automobile is an inefficient vehicle compared to other modes of transport. This is because the ratio between the mass of the vehicle and the mass of the passengers is much higher when compared to other modes of transport.

Automobile fuel efficiency is most commonly expressed in terms of the volume of fuel consumed per one hundred kilometres (l/100 km), but in some countries (including the United States, the United Kingdom and India) it is more commonly expressed in terms of the distance per volume fuel consumed (km/l or miles per gallon). This is complicated by the different energy content of fuels such as petrol and diesel. The Oak Ridge National Laboratory (ORNL) states that the energy content of unleaded petrol is 115,000 British thermal unit (BTU) per US gallon (32 MJ/l) compared to 130,500 BTU per US gallon (36.4 MJ/l) for diesel. Electric cars use 38 megajoules (38 000 kJ) per 100 km in comparison to 142 megajoules per 100 km for combustion powered cars.

Car life cycle

A second important consideration is the energy costs of producing energy. Bio-fuels, electricity and hydrogen, for instance, have significant energy inputs in their production. Hydrogen production efficiency are 50–70% when produced from natural gas, and 10–15% from electricity. The efficiency of hydrogen production, as well as the energy required to store and transport hydrogen, must to be combined with the vehicle efficiency to yield net efficiency. Because of this, hydrogen automobiles are one of the least efficient means of passenger transport, generally around 50 times as much energy must be put into the production of hydrogen compared to how much is used to move the car.

A third consideration to take into account when calculating energy efficiency of automobiles is the occupancy rate of the vehicle. Although the consumption per unit distance per vehicle increases with increasing number of passengers, this increase is slight compared to the reduction in consumption per unit distance per passenger. This means that higher occupancy yields higher energy efficiency per passenger. Automobile occupancy varies across regions. For example, the estimated average occupancy rate is about 1.3 passengers per car in the San Francisco Bay Area, while the 2006 UK estimated average is 1.58.

Fourth, the energy needed to build and maintain roads is an important consideration, as is the energy returned on energy invested (EROEI). Between these two factors, roughly 20% must be added to the energy of the fuel consumed, to accurately account for the total energy used.

Finally, vehicle energy efficiency calculations would be misleading without factoring the energy cost of producing the vehicle itself. This initial energy cost can of course be depreciated over the life of the vehicle to calculate an average energy efficiency over its effective life span. In other words, vehicles that take a lot of energy to produce and are used for relatively short periods will require a great deal more energy over their effective lifespan than those that do not, and are therefore much less energy efficient than they may otherwise seem. Hybrid and electric cars use less energy in their operation than comparable petroleum-fuelled cars but more energy is used to manufacture them, so the overall difference would be less than immediately apparent. Compare, for example, walking, which requires no special equipment at all, and an automobile, produced in and shipped from another country, and made from parts manufactured around the world from raw materials and minerals mined and processed elsewhere again, and used for a limited number of years. According to the French energy and environment agency ADEME, an average motor car has an embodied energy content of 20,800 kWh and an average electric vehicle amounts to 34,700 kWh. The electric car requires nearly twice as much energy to produce, primarily due to the large amount of mining and purification necessary for the rare earth metals and other materials used in lithium-ion batteries and in the electric drive motors. This represents a significant portion of the energy used over the life of the car (in some cases nearly as much as energy that is used through the fuel that is consumed, effectively doubling the car's per-distance energy consumption), and cannot be ignored when comparing automobiles to other transport modes. As these are average numbers for French automobiles and they are likely to be significantly larger in more auto-centric countries like the United States and Canada, where much larger and heavier cars are more common.

Driving practices and vehicles can be modified to improve their energy efficiency by about 15%.

On a percentage basis, if there is one occupant in an automobile, between 0.4 and 0.6% of the total energy used is used to move the person in the car, while 99.4–99.6% (about 165 to 250 times more) is used to move the car.

Example consumption figures

Two American solar cars in Canada
  • Solar cars are electric vehicles that use little or no externally supplied energy other than from sunlight, charging the batteries from built-in solar panels, and typically use less than 3 kW·h per 100 miles (67 kJ/km or 1.86 kW·h/100 km). Most of these cars are race cars designed for competition and not for passenger or utility use. However several companies are designing solar cars for public use. As of December 2021, none have yet been released.
  • The four passenger GEM NER uses 169 Wh/mi (203 mpg‑e; 10.5 kW⋅h/100 km), which equates to 2.6 kW·h/100 km per person when fully occupied, albeit at only 24 mph (39 km/h).
  • The General Motors EV1 was rated in a test with a charging efficiency of 373 Wh-AC/mile or 23 kWh/100 km approximately equivalent to 2.6 L/100 km (110 mpg‑imp; 90 mpg‑US) for petroleum-fuelled vehicles.
  • Chevrolet Volt in full electric mode uses 36 kilowatt-hours per 100 miles (810 kJ/km; 96 mpg‑e), meaning it may approach or exceed the energy efficiency of walking if the car is fully occupied with 4 or more passengers, although the relative emissions produced may not follow the same trends if analysing environmental impacts.
  • The Daihatsu Charade 993cc turbo diesel (1987–1993) won the most fuel efficient vehicle award for going round the United Kingdom consuming an average of 2.82 l/100 km (100 mpg‑imp). It was surpassed only recently by the VW Lupo 3 L which consumes about 2.77 l/100 km (102 mpg‑imp). Both cars are rare to find on the popular market. The Daihatsu had major problems with rust and structural safety which contributes to its rarity and the quite short production run.
  • The Volkswagen Polo 1.4 TDI Bluemotion and the SEAT Ibiza 1.4 TDI Ecomotion, both rated at 3.8 l/100 km (74 mpg‑imp; 62 mpg‑US) (combined) were the most fuel efficient petroleum-fuelled cars on sale in the UK as of 22 March 2008.
  • Honda Insight – achieves 60 mpg‑US (3.9 L/100 km; 72 mpg‑imp) under real-world conditions.
  • Honda Civic Hybrid- regularly averages around 45 mpg‑US (5.2 L/100 km; 54 mpg‑imp).
  • 2012 Cadillac CTS-V Wagon 6.2 L Supercharged, 14 mpg‑US (17 L/100 km; 17 mpg‑imp).
  • 2012 Bugatti Veyron, 10 mpg‑US (24 L/100 km; 12 mpg‑imp).
  • 2018 Honda Civic: 36 mpg‑US (6.5 L/100 km; 43 mpg‑imp).
  • 2017 Mitsubishi Mirage: 39 mpg‑US (6.0 L/100 km; 47 mpg‑imp).
  • 2017 Hyundai Ioniq hybrid: 55 mpg‑US (4.3 L/100 km; 66 mpg‑imp).
  • 2017 Toyota Prius: 56 mpg‑US (4.2 L/100 km; 67 mpg‑imp) (Eco trim).
  • 2018 Nissan Leaf: 30 kWh (110 MJ)/100 mi (671 kJ/km) or 112 MPGe.
  • 2017 Hyundai Ioniq EV: 25 kWh (90 MJ)/100 mi (560 kJ/km) or 136 MPGe.
  • 2020 Tesla model 3: 24 kWh (86.4 MJ)/100 mi (540 kJ/km) or 141 MPGe.

Trains

Passenger Capacity of different Transport Modes.png

Trains are in general one of the most efficient means of transport for freight and passengers. An inherent efficiency advantage is the low friction of steel wheels on steel rails compared especially to rubber tires on asphalt. Efficiency varies significantly with passenger loads, and losses incurred in electricity generation and supply (for electrified systems), and, importantly, end-to-end delivery, where stations are not the originating final destinations of a journey. While electric engines are more efficient than internal combustion engines, power generation in thermal power plants is limited to (at best) Carnot efficiency and there are transmission losses on the way from the power plant to the train. Switzerland, which has electrified virtually its entire railway network (heritage railways like the Dampfbahn Furka-Bergstrecke being notable exceptions), derives much of the electricity used by trains from hydropower, including pumped hydro storage. While the mechanical efficiency of the turbines involved is comparatively high, pumped hydro involves energy losses and is only cost effective as it can consume energy during times of excess production (leading to low or even negative spot prices) and release the energy again during high-demand times. with some sources claiming up to 87%.

Actual consumption depends on gradients, maximum speeds, and loading and stopping patterns. Data produced for the European MEET project (Methodologies for Estimating Air Pollutant Emissions) illustrate the different consumption patterns over several track sections. The results show the consumption for a German ICE high-speed train varied from around 19 to 33 kW⋅h/km (68–119 MJ/km; 31–53 kW⋅h/mi). The Siemens Velaro D type ICE trains seat 460 (16 of which in the restaurant car) in their 200-meter length edition of which two can be coupled together. Per Deutsche Bahn calculations, the energy used per 100 seat-km is the equivalent of 0.33 litres (12 imp fl oz) of gasoline (0.33 litres per 100 kilometres (860 mpg‑imp; 710 mpg‑US)). The data also reflects the weight of the train per passenger. For example, TGV double-deck Duplex trains use lightweight materials, which keep axle loads down and reduce damage to track and also save energy. The TGV mostly runs on French nuclear fission power plants which are again limited – as all thermal power plants – to Carnot efficiency. Due to nuclear reprocessing being standard operating procedure, a higher share of the energy contained in the original Uranium is used in France than in e.g. the United States with its once thru fuel cycle.

The specific energy consumption of the trains worldwide amounts to about 150 kJ/pkm (kilojoule per passenger kilometre) and 150 kJ/tkm (kilojoule per tonne kilometre) (ca. 4.2 kWh/100 pkm and 4.2 kWh/100 tkm) in terms of final energy. Passenger transportation by rail systems requires less energy than by car or plane (one seventh of the energy needed to move a person by car in an urban context). This is the reason why, although accounting for 9% of world passenger transportation activity (expressed in pkm) in 2015, rail passenger services represented only 1% of final energy demand in passenger transportation.

Freight

Energy consumption estimates for rail freight vary widely, and many are provided by interested parties. Some are tabulated below.

Country Year Fuel economy (weight of goods) Energy Intensity
USA 2007 185.363 km/L (1 short ton) energy/mass-distance
USA 2018 473 miles/gallon (1 ton) energy/mass-distance
UK 87 t·km/L 0.41 MJ/t·km (LHV)

Passenger

Country Year Train efficiency Per passenger-km (kJ) Note
China 2018 9.7 MJ (2.7 kWh) /car-km 137 kJ/passenger-km (at 100% load) CR400AF@350 km/h
Beijing-Shanghai PDL 1302 km average
Japan 2004 17.9 MJ (5.0 kWh)/car-km 350 kJ/passenger-km JR East average
Japan 2017 1.49 kWh/car-km ≈92 kJ/passenger-km JR East Conventional Rail
EC 1997 18 kW⋅h/km (65 MJ/km)

USA
1.125 mpg‑US (209.1 L/100 km; 1.351 mpg‑imp) 468 passenger-miles/US gallon (0.503 L/100 passenger-km)
Switzerland 2011 2300 GWhr/yr 470 kJ/passenger-km
Basel, Switzerland
1.53 kWh/vehicle-km (5.51 MJ/vehicle-km) 85 kJ/passenger-km (150 kJ/passenger-km at 80% average load)
USA 2009
2,435 BTU/mi (1.60 MJ/km)
Portugal 2011 8.5 kW⋅h/km (31 MJ/km; 13.7 kW⋅h/mi)

Braking losses

N700 Series Shinkansen uses regenerative braking

Stopping is a considerable source of inefficiency. Modern electric trains like the Shinkansen (the Bullet Train) use regenerative braking to return current into the catenary while they brake. A Siemens study indicated that regenerative braking might recover 41.6% of the total energy consumed. The Passenger Rail (Urban and Intercity) and Scheduled Intercity and All Charter Bus Industries Technological and Operational Improvements – FINAL REPORT states that "Commuter operations can dissipate more than half of their total traction energy in braking for stops." and that "We estimate head-end power to be 35 percent (but it could possibly be as high as 45 percent) of total energy consumed by commuter railways." Having to accelerate and decelerate a heavy train load of people at every stop is inefficient despite regenerative braking which can recover typically around 20% of the energy wasted in braking. Weight is a determinant of braking losses.

Buses

The bus rapid transit of Metz uses a diesel-electric hybrid driving system, developed by Belgian Van Hool manufacturer.
  • In July 2005, the average occupancy for buses in the UK was stated to be 9 passengers per vehicle.
  • The fleet of 244 40-foot (12 m) 1982 New Flyer trolley buses in local service with BC Transit in Vancouver, Canada, in 1994/95 used 35,454,170 kWh for 12,966,285 vehicle km, or 9.84 MJ/vehicle km. Exact ridership on trolleybuses is not known, but with all 34 seats filled this equates to 0.32 MJ/passenger km. It is quite common to see people standing on Vancouver trolleybuses. This is a service with many stops per kilometre; part of the reason for the efficiency is the use of regenerative braking.
  • A commuter service in Santa Barbara, California, USA, found average diesel bus efficiency of 6.0 mpg‑US (39 L/100 km; 7.2 mpg‑imp) (using MCI 102DL3 buses). With all 55 seats filled this equates to 330 passenger mpg; with 70% filled, 231 passenger mpg.
  • In 2011 the fleet of 752 buses in the city of Lisbon had an average speed of 14.4 km/h and an average occupancy of 20.1 passengers per vehicle.
  • Battery electric buses combine the electric motive power of a trolleybus, the drawbacks of battery manufacture, weight and lifespan with the routing flexibility of a bus with any onboard power. Major manufacturers include BYD and Proterra.

Other

  • NASA's Crawler-Transporter was used to move the Space Shuttle from storage to the launch pad. It uses diesel and has one of the highest fuel consumption rates on record, 150 US gallons per mile (350 l/km; 120 imp gal/mi).

Air transport means

Aircraft

A principal determinant of energy consumption in aircraft is drag, which must be in the opposite direction of motion to the craft.

  • Drag is proportional to the lift required for flight, which is equal to the weight of the aircraft. As induced drag increases with weight, mass reduction, with improvements in engine efficiency and reductions in aerodynamic drag, has been a principal source of efficiency gains in aircraft, with a rule-of-thumb being that a 1% weight reduction corresponds to around a 0.75% reduction in fuel consumption.
  • Flight altitude affects engine efficiency. Jet-engine efficiency increases at altitude up to the tropopause, the temperature minimum of the atmosphere; at lower temperatures, the Carnot efficiency is higher. Jet engine efficiency is also increased at high speeds, but above about Mach 0.85 the airframe aerodynamic losses increase faster.
  • Compressibility effects: beginning at transonic speeds of around Mach 0.85, shockwaves form increasing drag.
  • For supersonic flight, it is difficult to achieve a lift to drag ratio greater than 5, and fuel consumption is increased in proportion.
Concorde fuel efficiency comparison (assuming jets are filled to capacity)
Aircraft Concorde Boeing 747-400
Passenger-miles/imperial gallon 17 109
Passenger-miles/US gallon 14 91
Litres/100 passenger-km 16.6 3.1

Passenger airplanes averaged 4.8 L/100 km per passenger (1.4 MJ/passenger-km) (49 passenger-miles per gallon) in 1998. On average 20% of seats are left unoccupied. Jet aircraft efficiencies are improving: Between 1960 and 2000 there was a 55% overall fuel efficiency gain (if one were to exclude the inefficient and limited fleet of the DH Comet 4 and to consider the Boeing 707 as the base case). Most of the improvements in efficiency were gained in the first decade when jet craft first came into widespread commercial use. Compared to advanced piston engine airliners of the 1950s, current jet airliners are only marginally more efficient per passenger-mile. Between 1971 and 1998 the fleet-average annual improvement per available seat-kilometre was estimated at 2.4%. Concorde the supersonic transport managed about 17 passenger-miles to the Imperial gallon; similar to a business jet, but much worse than a subsonic turbofan aircraft. Airbus puts the fuel rate consumption of their A380 at less than 3 L/100 km per passenger (78 passenger-miles per US gallon).

Air France Airbus A380-800

The mass of an aircraft can be reduced by using light-weight materials such as titanium, carbon fibre and other composite plastics. Expensive materials may be used, if the reduction of mass justifies the price of materials through improved fuel efficiency. The improvements achieved in fuel efficiency by mass reduction, reduces the amount of fuel that needs to be carried. This further reduces the mass of the aircraft and therefore enables further gains in fuel efficiency. For example, the Airbus A380 design includes multiple light-weight materials.

Airbus has showcased wingtip devices (sharklets or winglets) that can achieve 3.5 percent reduction in fuel consumption. There are wingtip devices on the Airbus A380. Further developed Minix winglets have been said to offer 6 percent reduction in fuel consumption. Winglets at the tip of an aircraft wing smooth out the wing-tip vortex (reducing the aircraft's wing drag) and can be retrofitted to any airplane.

NASA and Boeing are conducting tests on a 500 lb (230 kg) "blended wing" aircraft. This design allows for greater fuel efficiency since the whole craft produces lift, not just the wings. The blended wing body (BWB) concept offers advantages in structural, aerodynamic and operating efficiencies over today's more conventional fuselage-and-wing designs. These features translate into greater range, fuel economy, reliability and life cycle savings, as well as lower manufacturing costs. NASA has created a cruise efficient STOL (CESTOL) concept.

Fraunhofer Institute for Manufacturing Engineering and Applied Materials Research (IFAM) have researched a shark skin imitating paint that would reduce drag through a riblet effect. Aircraft are a major potential application for new technologies such as aluminium metal foam and nanotechnology such as the shark skin imitating paint.

Propeller systems, such as turboprops and propfans are a more fuel efficient technology than jets. But turboprops have an optimum speed below about 450 mph (700 km/h). This speed is less than used with jets by major airlines today. With the current high price for jet fuel and the emphasis on engine/airframe efficiency to reduce emissions, there is renewed interest in the propfan concept for jetliners that might come into service beyond the Boeing 787 and Airbus A350XWB. For instance, Airbus has patented aircraft designs with twin rear-mounted counter-rotating propfans. NASA has conducted an Advanced Turboprop Project (ATP), where they researched a variable pitch propfan that produced less noise and achieved high speeds.

Related to fuel efficiency is the impact of aviation emissions on climate.

Small aircraft

Dyn'Aéro MCR4S
  • Motor-gliders can reach an extremely low fuel consumption for cross-country flights, if favourable thermal air currents and winds are present.
  • At 160 km/h, a diesel powered two-seater Dieselis burns 6 litres of fuel per hour, 1.9 litres per 100 passenger km.
  • at 220 km/h, a four-seater 100 hp MCR-4S burns 20 litres of gas per hour, 2.2 litres per 100 passenger km.
  • Under continuous motorised flight at 225 km/h, a Pipistrel Sinus burns 11 litres of fuel per flight hour. Carrying 2 people aboard, it operates at 2.4 litres per 100 passenger km.
  • Ultralight aircraft Tecnam P92 Echo Classic at cruise speed of 185 km/h burns 17 litres of fuel per flight hour, 4.6 litres per 100 passenger km (2 people). Other modern ultralight aircraft have increased efficiency; Tecnam P2002 Sierra RG at cruise speed of 237 km/h burns 17 litres of fuel per flight hour, 3.6 litres per 100 passenger km (2 people).
  • Two-seater and four-seater flying at 250 km/h with old generation engines can burn 25 to 40 litres per flight hour, 3 to 5 litres per 100 passenger km.
  • The Sikorsky S-76C++ twin turbine helicopter gets about 1.65 mpg‑US (143 L/100 km; 1.98 mpg‑imp) at 140 knots (260 km/h; 160 mph) and carries 12 for about 19.8 passenger-miles per gallon (11.9 L per 100 passenger km).

Water transport means

Ships

Queen Elizabeth

Cunard stated that Queen Elizabeth 2 travelled 49.5 feet per imperial gallon of diesel oil (3.32 m/L or 41.2 ft/US gal), and that it had a passenger capacity of 1777. Thus carrying 1777 passengers we can calculate an efficiency of 16.7 passenger miles per imperial gallon (16.9 l/100 p·km or 13.9 p·mpg–US).

Cruise ships

MS Oasis of the Seas has a capacity of 6,296 passengers and a fuel efficiency of 14.4 passenger miles per US gallon. Voyager-class cruise ships have a capacity of 3,114 passengers and a fuel efficiency of 12.8 passenger miles per US gallon.

Emma Maersk

Emma Maersk uses a Wärtsilä-Sulzer RTA96-C, which consumes 163 g/kW·h and 13,000 kg/h. If it carries 13,000 containers then 1 kg fuel transports one container for one hour over a distance of 45 km. The ship takes 18 days from Tanjung (Singapore) to Rotterdam (Netherlands), 11 from Tanjung to Suez, and 7 from Suez to Rotterdam, which is roughly 430 hours, and has 80 MW, +30 MW. 18 days at a mean speed of 25 knots (46 km/h) gives a total distance of 10,800 nautical miles (20,000 km).

Assuming the Emma Maersk consumes diesel (as opposed to fuel oil which would be the more precise fuel) then 1 kg diesel = 1.202 litres = 0.317 US gallons. This corresponds to 46,525 kJ. Assuming a standard 14 tonnes per container (per teu) this yields 74 kJ per tonne-km at a speed of 45 km/h (24 knots).

Boats

A sailboat, much like a solar car, can locomote without consuming any fuel. A sail boat such as a dinghy using just wind power requires no input energy in terms of fuel. However some manual energy is required by the crew to steer the boat and adjust the sails using lines. In addition energy will be needed for demands other than propulsion, such as cooking, heating or lighting. The fuel efficiency of a single-occupancy boat is highly dependent on the size of its engine, the speed at which it travels, and its displacement. With a single passenger, the equivalent energy efficiency will be lower than in a car, train, or plane.

International transport comparisons

EffizienzLeistungFahrzeuge.png

European Public transport

Rail and bus are generally required to serve 'off peak' and rural services, which by their nature have lower loads than city bus routes and inter city train lines. Moreover, due to their 'walk on' ticketing it is much harder to match daily demand and passenger numbers. As a consequence, the overall load factor on UK railways is 35% or 90 people per train:

Conversely, airline services generally work on point-to-point networks between large population centres and are 'pre-book' in nature. Using yield management, overall load factors can be raised to around 70–90%. Intercity train operators have begun to use similar techniques, with loads reaching typically 71% overall for TGV services in France and a similar figure for the UK's Virgin Rail Group services.

For emissions, the electricity generating source needs to be taken into account.

US Passenger transport

The US Transport Energy Data Book states the following figures for passenger transport in 2018. These are based on actual consumption of energy, at whatever occupancy rates there were. For modes using electricity, losses during generation and distribution are included. Values are not directly comparable due to differences in types of services, routes, etc.

Transport mode Average passengers
per vehicle
BTU per
passenger-mile
MJ per
passenger-kilometre
Rail (transit light & heavy) 23.5 1,813 1.189
Rail (intercity Amtrak) 23.3 1,963 1.287
Motorcycles 1.2 2,369 1.553
Air 118.7 2,341 1.535
Rail (commuter) 33.6 2,398 1.572
Cars 1.5 2,847 1.866
Personal trucks 1.8 3,276 2.148
Buses (transit) 7.7 4,578 3.001
Demand response 1.1 14,660 9.61

US Freight transport

The US Transport Energy book states the following figures for freight transport in 2010:

transport mode Fuel consumption
BTU per short ton-mile kJ per tonne-kilometre
Domestic waterborne 217 160
Class 1 railroads 289 209
Heavy trucks 3,357 2,426
Air freight (approx.) 9,600 6,900

From 1960 to 2010 the efficiency of air freight has increased 75%, mostly due to more efficient jet engines.

1 gal-US (3.785 l, 0.833 gal-imp) of fuel can move a ton of cargo 857 km or 462 nmi by barge, or 337 km (209 mi) by rail, or 98 km (61 mi) by lorry.

Compare:

  • Space Shuttle used to transport freight to the other side of the Earth (see above): 40 megajoules per tonne-kilometre.
  • Net energy for lifting: 10 megajoules per tonne-kilometre.

Canadian transport

Natural Resources Canada's Office of Energy Efficiency publishes annual statistics regarding the efficiency of the entire Canadian fleet. For researchers, these fuel consumption estimates are more realistic than the fuel consumption ratings of new vehicles, as they represent the real world driving conditions, including extreme weather and traffic. The annual report is called Energy Efficiency Trends Analysis. There are dozens of tables illustrating trends in energy consumption expressed in energy per passenger km (passengers) or energy per tonne km (freight).

French environmental calculator

The environmental calculator of the French environment and energy agency (ADEME) published in 2007 using data from 2005 enables one to compare the different means of transport as regards the CO2 emissions (in terms of carbon dioxide equivalent) as well as the consumption of primary energy. In the case of an electric vehicle, the ADEME makes the assumption that 2.58 toe as primary energy are necessary for producing one toe of electricity as end energy in France (see Embodied energy: In the energy field).

This computer tool devised by the ADEME shows the importance of public transport from an environmental point of view. It highlights the primary energy consumption as well as the CO2 emissions due to transport. Due to the relatively low environmental impact of radioactive waste, compared to that of fossil fuel combustion emissions, this is not a factor in the tool. Moreover, intermodal passenger transport is probably a key to sustainable transport, by allowing people to use less polluting means of transport.

German environmental costs

Deutsche Bahn calculates the energy consumption of their various means of transportation.

Type 2018
Regional rail passenger transport (MJ/pkm) 0.85
Long-distance rail passenger transport (MJ/pkm) 0.25
Bus service (MJ/pkm) 1.14
Rail freight transport (MJ/tkm) 0.33
Road freight transport (MJ/tkm) 1.21
Air freight (MJ/tkm) 9.77
Ocean freight (MJ/tkm) 0.09

Note - External costs not included above

To include all the energy used in transport, we would need to also include the external energy costs of producing, transporting and packaging of fuel (food or fossil fuel or electricity), the energy incurred in disposing of exhaust waste, and the energy costs of manufacturing the vehicle. For example, a human walking requires little or no special equipment while automobiles require a great deal of energy to produce and have relatively short product lifespans.

However, these external costs are independent of the energy cost per distance travelled, and can vary greatly for a particular vehicle depending on its lifetime, how often it is used and how it is energized over its lifetime. Thus this article's numbers include none of these external factors.

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