Astronomy

The relation between the energy of star and heating effect

The relation between the energy of star and heating effect


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Because a star's temperature is an indication of the energy passing through each unit of its surface, it follows that for cool red and hot blue stars of identical luminosities, the cooler red star must be considerably smaller so that the energy escapes through a reduced surface area and has a far greater heating effect. Hence, knowledge of a star's colour and luminosity can reveal its size.

In particular, I can't understand the relation between energy and the heating effect. Of course, I searched for the word "the heating effect" in my own language. Also, I Googled in English.

In this context, what is the heating effect? Could you explain to me? If you give me any help, it will be very useful to me.


I'm not sure what you mean by "heating effect". The amount another body is heated by a star with luminosity $L$ is quantified by its effective temperature: $$T_{eff}=left(frac{L(1-a)}{16pisigma D^2} ight)^{1/4}$$ where $a$ is the albedo, $D$ is the distance to the other body, and $sigma$ is the Stefan-Boltzmann constant. If the luminosity is given, then there is no direct dependence on the size of the star.

The only thing that matters is the luminosity. You stated yourself that the luminosities are the same, which means that the red star in question is likely a red supergiant - a member of a group containing many of the largest stars in the universe.

Why, mathematically, is this the case? Well, stellar models can tell us that this is the case, but we can also figure this out via the Stefan-Boltzmann law by assuming that the stars are black bodies. For an object of radius $R$ and temperature $T$, the luminosity is approximately $$L=4pisigma R^2T^4$$ If star 1 is a blue supergiant and star 2 is a red supergiant, then, setting the luminosities equal, we have $$L_1=L_2 o R_1^2T_1^4=R_2^2T_2^4$$ We know that $T_2$ is much less than $T_1$ - possibly by an order of magnitude - so $R_2$ must be much larger for the luminosities to be the same. This matches what we observe and what models predict.


Simply put, energy efficiency is about using less energy to get the same job done - and in the process, avoiding high energy bills and unnecessary pollution. Many products, homes, commercial buildings, and industrial facilities consume far more energy than needed. For example, energy efficient light bulbs certified by ENERGY STAR use 70 to 90% less energy than incandescent bulbs to do the same job: light up a room.

So how can you make a difference through energy efficiency? EPA's ENERGY STAR program is the best place to start for guidance on how to save energy, save money, and protect the environment. Behind each blue ENERGY STAR label is a product, building, or home that is independently certified to use less energy, achieving emissions reductions to reduce air pollution and help protect the climate.

  • Look for the ENERGY STAR when shopping for new products. The ENERGY STAR Product Finder provides all the information you need to start shopping for ENERGY STAR certified products, including product details, rebates, and retailers near you
  • Learn about the many ways to save in your home and track your progress with "My ENERGY STAR" - your new dashboard to savings
  • Take the pledge to save energy with ENERGY STAR
  • Use the ENERGY STAR Rebate Finder to find utility rebates and special offers near you on ENERGY STAR certified products to improve comfort and reduce your heating and cooling costs
  • Look for ENERGY STAR certified homes when purchasing a new home
  • Improve the energy performance of commercial buildings and industrial plants while simultaneously improving financial performance and reducing emissions by adopting the ENERGY STAR strategic energy management approach

For details on how these national energy use intensities are calculated, see Portfolio Manager Technical Reference: U.S. National Energy Use Intensity.

Market Sector Property type Source EUI (kBtu/ft2) Site EUI (kBtu/ft2)
Banking/Financial Services Bank Branch 209.9 88.3
Banking/Financial Services Financial Office 116.4 52.9
Education College/University 180.6 84.3
Education K-12 School 104.4 48.5
Education Pre-school/Daycare 131.5 64.8
Education Vocational School/Adult Education 110.4 52.4
Public Assembly Convention Center/Meeting Hall 109.6 56.1
Public Assembly Recreation/Athletic Centers 112.0 50.8
Public Assembly Entertainment 112.0 56.2
Public Assembly Worship Facility 58.4 30.5
Food Sales & Service Convenience Store 592.6 231.4
Food Sales & Service Bar/Nightclub 297 130.7
Food Sales & Service Fast Food Restaurant 886.4 402.7
Food Sales & Service Restaurant 573.7 325.6
Food Sales & Service Supermarket/Grocery Store 444 196
Food Sales & Service Wholesale Club/Supercenter 120 51.4
Healthcare Ambulatory Surgical Center 138.3 62.0
Healthcare Hospital (General Medical & Surgical) 426.9 234.3
Healthcare Other/Specialty Hospital 433.9 206.7
Healthcare Medical Office 121.7 51.2
Healthcare Outpatient Rehabilitation/Physical Therapy 138.3 62.0
Healthcare Urgent Care/Clinic/Other Outpatient 145.8 64.5
Lodging/Residential Barracks 107.5 57.9
Lodging/Residential Hotel 146.7 63.0
Lodging/Residential Multifamily Housing 118.1 59.6
Lodging/Residential Prison/Incarceration 156.4 69.9
Lodging/Residential Residence Hall/Dormitory 107.5 57.9
Lodging/Residential Residential Care Facility 213.2 99.0
Mixed Use Mixed Use Property 89.3 40.1
Office Medical Office 121.7 51.2
Office Office 116.4 52.9
Office Veterinary Office 145.8 64.5
Public Services Courthouse 211.4 101.2
Public Services Fire/Police Station 124.9 63.5
Public Services Library 143.6 71.6
Public Services Mailing Center/Post Office 96.9 47.9
Public Services Transportation Terminal/Station 112.0 56.2
Retail Automobile Dealership 124.1 55.0
Retail Enclosed Mall 170.7 65.7
Retail Strip Mall 228.8 103.5
Retail Retail Store 120.0 103.5
Technology/Science Laboratory 318.2 115.3
Services Dry cleaning, Shoe Repair, Locksmith, Salon, etc. 96.9 47.9
Utility Drinking Water Treatment & Distribution 5.9 2.3
Utility Energy/Power Station 89.3 40.1
Warehouse/Storage Self-Storage Facility 47.8 20.2
Warehouse/Storage Distribution Center 52.9 22.7
Warehouse/Storage Non-Refrigerated Warehouse 52.9 22.7
Warehouse/Storage Refrigerated Warehouse 235.6 84.1

For more information about national energy use intensities in Canada, see:


Astronomy Without A Telescope – Star Formation Laws

Take a cloud of molecular hydrogen add some turbulence and you get star formation – that’s the law. The efficiency of star formation (how big and how populous they get) is largely a function of the density of the initial cloud.

At a galactic or star cluster level, a low gas density will deliver a sparse population of generally small, dim stars – while a high gas density should result in a dense population of big, bright stars. However, overlying all this is the key issue of metallicity – which acts to reduce star formation efficiency.

So firstly, the strong relationship between the density of molecular hydrogen (H2) and star formation efficiency is known as the Kennicutt-Schmidt Law. Atomic hydrogen is not considered to be able to support star formation, because it is too hot. Only when it cools to form molecular hydrogen can it start to clump together – after which we can expect star formation to become possible. Of course, this creates some mystery about how the first stars might have formed within a denser and hotter primeval universe. Perhaps dark matter played a key role there.

Nonetheless, in the modern universe, unbound gas can more readily cool down to molecular hydrogen due the presence of metals, which have been added to the interstellar medium by previous populations of stars. Metals, which are any elements heavier than hydrogen and helium, are able to absorb a wider range of radiation energy levels, leaving hydrogen less exposed to heating. Hence, a metal-rich gas cloud is more likely to form molecular hydrogen, which is then more likely to support star formation.

But this does not mean that star formation is more efficient in the modern universe – and again this is because of metals. A recent paper about the dependence of star formation on metallicity proposes that a cluster of stars develops from H2 clumping within a gas cloud, first forming prestellar cores which draw in more matter via gravity, until they become stars and then begin producing stellar wind.

Relationship between the power of stellar winds and stellar mass (i.e. big star has big wind) - with the effect of metallicity overlaid. The solid line is the metallicity of the Sun (Z=Zsol). High metallicity produces more powerful winds for the same stellar mass. Credit: Dib et al.

Before long, the stellar wind begins to generate ‘feedback’, countering the infall of further material. Once the outward push of stellar wind achieves unity with the inward gravitational pull, further star growth ceases – and bigger O and B class stars clear out any remaining gas from the cluster region, so that all star formation is quenched.

The dependence of star formation efficiency on metallicity arises from the effect of metallicity on stellar wind. High metal stars always have more powerful winds than any equivalent mass, but lower metal, stars. Thus, a star cluster – or even a galaxy – formed from a gas cloud with high metallicity, will have lower efficiency star formation. This is because all stars’ growth is inhibited by their own stellar wind feedback in late stages of growth and any large O or B class stars will clear out any remaining unbound gas more quickly than their low metal equivalents.


Meteorites, Comets, and Planets

A.M. Davis , F.M. Richter , in Treatise on Geochemistry , 2007

1.15.4.2.4 Reheating mechanisms for the type B CAIs

The two most common suggestions for melting and evaporating silicon and magnesium from the type B CAIs involve processing by the x-wind (see Shu et al., 1996, 2001 ) or shock heating (see Desch and Connolly, 2002 ). Shu and coworkers have suggested that CAIs might have been heated to high temperatures as they were brought very close to the young star and then cooled as they were flung back out onto the protoplanetary disk by what they refer to as an x-wind. The reheating and subsequent cooling of CAIs in the x-wind model have not yet been quantified to the point that it can be tested in terms of the degree of elemental and isotopic fractionations that would be produced. In contrast, the nebular shock model of Desch and Connolly (2002) provides sufficiently detailed temperature and pressure histories to input into a CAI evaporation model. Figure 18 shows the calculated trajectory in temperature-composition space for a type B CAI subjected to the temperature and pressure history given by Desch and Connolly (2002) for their “canonical” shock ( Richter et al., 2006b ). The precursor in this example started with the composition on the condensation curve shown in Figure 15 . The temperature-composition trajectory is calculated using Equation (20) together with the postshock pressure of 5×10 −4 bar given by Desch and Connolly (2002) and results in melilite crystallizing at 1,380 °C with Åk25 and magnesium isotopes fractionated by 2.6‰. The calculated bulk composition, δ 25 Mg (2.6‰), and the åkermanite content of the first melilite to crystallize are those of a typical type B CAI.

Figure 18 . The blue curve gives the experimentally determined equilibrium crystallization temperature (or equivalently, dissolution temperature) of melilite in a CaO–MgO–SiO2–Al2O3 melt plotted as a function of the molar ratio of the sum of the more volatile oxides MgO and SiO2 to the sum of the more refractory oxides CaO and Al2O3. The mole fraction of åkermanite in the first melilite to crystallize varies systematically with this parameter ( Mendybaev et al., 2006 ). The trajectory in red is that of the precursor on the condensation curve shown in Figure 15 , subjected to the thermal history (shown as an inset) and pressure, (P=5×10 −4 bar) calculated by Desch and Connolly (2002) for the reheating of condensed solids by the passage of a “canonical” nebular shock. This results in a very typical type B CAI in terms of bulk composition, magnesium isotopic fractionation, and åkermanite content of the first melilite to crystallize. Reproduced by permission of Meteoritical Society from Richter et al. (2006b) .


3 Results and Discussion

3.1 Encounter Desorption of H2

First of all, we have benchmarked our model with Hincelin et al. (2015). In Figure 1, we have compared our results with those obtained in Hincelin et al. (2015). For this comparison, following Hincelin et al. (2015), we have used T = 10 K, E D ( H 2 , H 2 O ) = 440   K , E D ( H , H 2 O ) = 450   K , E D ( H 2 , H 2 ) = 23   K , and R = 0.5. Solid curves in Figure 1 represent the cases obtained here, and the rest are extracted from Hincelin et al. (2015) by using the online tool of Rohatgi (2020). Our results with and without encounter desorption show an excellent match with Hincelin et al. (2015). Presently in the KIDA database (kida.astrophy.u-bordeaux.fr), more updated BE values were listed. It suggests that E D ( H , H 2 O ) = 650   K . The results obtained from our quantum chemical calculations shown in Table 1 represent the estimated BE values with the H2 substrate. In the following section, we have used these updated energy values, and the effects of their changes are discussed.

FIGURE 1. The comparison between Figure 2 of Hincelin et al. (2015) and the cases obtained here. We have extracted Figure 2 of Hincelin et al. (2015) by using the online tool of Rohatgi (2020). Three cases are shown: (A) no encounter desorption is considered with E D ( H 2 , H 2 O ) = 440   K , (B) no encounter desorption is considered with E D ( H 2 , H 2 ) = 23   K , (C) encounter desorption of H2 was considered with E D ( H 2 , H 2 O ) = 440   K, and E D ( H 2 , H 2 ) = 23   K . We have noticed an excellent match between our calculated (solid curves) steady-state abundance of H2 on grain surface and that obtained in Hincelin et al. (2015) (dashed curves).

3.1.1 gH2

Figure 2 shows the time evolution of gH2 by considering n H = 10 7   cm − 3 , T = 10   K, and R = 0.35 − 0.80 . Interestingly, the abundance of gH2 seems to be invariant with R’s changes, whereas it strongly depends on R in encounter desorption. R’s lower value means a quicker hopping rate, whereas a higher value represents a delayed hopping rate. With the increase in R, gH2 abundance raises for the encounter desorption case. It means that as we rise R’s value, the encounter desorption effect depreciates. The left panel of Figure 5 exposes that with the increase in R’s value, a steady decrease in the ratio between the gH2 abundance with no encounter desorption case (NE) and with encounter desorption case (EN) is obtained. The probability of the encounter desorption is inversely proportional to the rate of diffusion (Eq. 5) or hopping (Eq. 7). Since the increase in the value of R induces faster diffusion and hopping, it is lowering the encounter desorption probability of H2 as expected. Figure 3 shows the time evolution of gH2 with NE and EN when we have used R = 0.35 , T = 10   K, and n H = 10 4 – 10 7   cm − 3 . In both cases, abundances of gH2 increase with the density. The middle panel of Figure 5 shows the gH2 abundance ratio between NE and EN with density. It depicts that the effect of encounter desorption is more pronounced for higher density. Figure 4 shows the gH2 abundances when we have used n H = 10 7 , R = 0.35 , and T = 5� K. In the right panel of Figure 5, we have shown the gH2 abundance ratio obtained between NE and EN with the temperature changes. From the figures, it is seen that the effect of encounter desorption is maximum toward the lower temperature (� K), and it ceases around 20 K. The curve is similar to the H2 formation efficiency discussed in Chakrabarti et al. (2006a), Chakrabarti et al. (2006b) for olivine grain. With the decrease in temperature, H atoms’ mobility decreases. Thus, the formation rate decreases. With the increase in temperature, the hopping rate increases, which can increase the formation efficiency, but at the same time, the residence time of H atoms decreases which affects the H2 formation efficiency. As a result, the H2 formation efficiency is maximum at around � K, and the encounter desorption effect is pronounced at the peak hydrogen formation efficiency.

FIGURE 2. Time evolution of the abundances of gH2 with n H = 10 7   cm − 3 and T = 10   K are shown for R = 0.35 , 0.5, and 0.8. The dash-dotted purple curve represents the time evolution of gH2 abundance with the no encounter desorption [with E D ( H , H 2 O ) = 450   K ]. It depicts that the gH2 abundance remains roughly invariant with the changes in R. However, when encounter desorption is introduced, gH2 abundance increases with the R. The time evolution of the gH2 abundance with E D ( H 2 , H 2 ) = 23   K and E D ( H , H 2 0 ) = 450   K is shown with the green dashed line when the method of Hincelin et al. (2015) is used and blue dotted line when the method of Chang et al. (2012) is used. gH2 abundances obtained with our estimated BE value [i.e., E D ( H 2 , H 2 ) = 67   K ] are shown with a solid yellow line. For this case, we have used E D ( H , H 2 O ) = 450   K and the method used in Chang et al. (2021). With the black dash-dotted line, the time evolution of gH2 abundance is shown with E D ( H , H 2 O ) = 650   K and method of Chang et al. (2021). We have seen significant differences when we have used different energy barriers and different methods (Hincelin et al., 2015 Chang et al., 2021). Obtained values of gH2 are further noted in Table 3 for better understanding.

FIGURE 3. Time evolution of gH2 with R = 0.35 and various n H ( 10 4 ,   10 5 , 10 6 , and 10 7   cm − 3 ) are shown. It depicts that the effect of encounter desorption increases with the increase in density.

FIGURE 4. Time evolution of gH2 with R = 0.35 , n H = 10 7   cm − 3 , and various temperatures (5, 10, 15, and 20 K) are shown. It depicts that the effect of encounter desorption decreases with the increase in temperature.

FIGURE 5. The ratio between the final abundances of gH2 obtained with the no encounter (NE) desorption and encounter desorption (EN) is shown. From left to right, it shows the variation of this ratio with R, n H , and temperature, respectively.

For a better illustration, the obtained abundances with R = 0.35 , T = 10 K, and n H = 10 7   cm − 3 are noted in Table 3 at the end of the total simulation time (� 6  years). Chang et al. (2021) considered the competition between hopping rate and desorption rate of H2 (Eq. 7), whereas (Hincelin et al., 2015) considered the battle between the diffusion and desorption rate of H2 (Eq. 5). This difference in consideration resulting ∼ two times higher abundance of gH2 with the consideration of Chang et al. (2021) compared to Hincelin et al. (2015) (see case 2 and 3 of Table 3 and Figure 2). Our quantum chemical calculation yields E D ( H 2 , H 2 ) = 67   K , which is higher than it was used in the earlier literature value of 23 K (Cuppen and Herbst, 2007 Hincelin et al., 2015 Chang et al., 2021). The computed adsorption energy is further increased to 79 K when we have considered the IEFPCM model. Table 3 shows that increase in the BE [ E D ( H 2 , H 2 ) = 67   K , and 79 K, case 4 and 5 of Table 3] results in sequentially higher surface coverage of gH2 than it was with E D ( H 2 , H 2 ) = 23   K (case 3 of Table 3). In case 5 of Table 3, we have noted the abundance of gH2 when no encounter desorption effect is considered, but a higher adsorption energy of H atom is used [ E D ( H , H 2 O ) = 650   K ]. Case 6 of Table 3 also considered this adsorption energy of H atom along with E D ( H 2 , H 2 ) = 67   K , and the method of Chang et al. (2021) is used. A comparison between the abundance of gH2 of case 4 and case 6 (the difference between these two cases are in consideration of the adsorption energy of gH) yields a marginal decrease in the abundance of gH2 when higher adsorption energy of gH is used.

3.1.2 gH

The obtained abundance of gH is noted in Table 3. The gH abundance is marginally decreased in Chang et al. (2021) compared to Hincelin et al. (2015). The use of higher E D ( H 2 , H 2 ) (� K and 79 K) decreases the value of gH compared to case 2. However, the use of the H atom’s higher adsorption energy (650 K) can increase the gH abundance by a couple of orders of magnitude (see case 7 of Table 3).

3.1.3 gH2O and gCH3OH

The effect of the encounter desorption on the other major surface species (gH2O and gCH3OH) is also shown in Table 3. In the bracketed term, we have noted the percentage increase in their abundances from the case where no encounter desorption was considered [for E D ( H , H 2 O ) = 450   K and 650 K, respectively]. Table 3 depicts that the consideration of encounter desorption of H2 can significantly change (decrease by ��%) the methanol abundance (case 3 and case 7) from that was obtained with the no encounter desorption (case 1 and case 6). However, the changes in the surface abundance of water are minimal (∼଑%) for the addition of the encounter desorption of H2. These changes (increase or decrease) are highly dependent on the adsorption energy of H, temperature, density, and the value of R (𢏀.35 noted in Table 3). The changes in E D ( H 2 , H 2 ) from 23 to 67 K can influence the surface abundance of methanol and water. For example, in between case 3 and case 4 of Table 3, we can see that there is a significant increase (�%) in the abundance of gH2O when higher adsorption energy [ E D ( H 2 , H 2 ) = 67   K ] is used. However, this higher adsorption energy can marginally under-produce the methanol on the grain. In brief, from Table 3, it is clear that the encounter desorption can significantly change the abundances of surface species. Still, these changes are highly dependent on the adopted adsorption energy with the water and H2 ice and adopted physical parameters ( n H , R, T).

3.2 Encounter Desorption of Other Species

The idea of encounter desorption Hincelin et al. (2015) primarily arose to eliminate the enhanced surface coverage of H2 in the relatively denser and colder medium. Since H2 has lower adsorption energy with the water surface (� K), it could move on the surface very fast and occupy a position on the top of another H2 molecule. Comparatively, in the denser and colder region, the chances of this occurrence enhance. Since the H2 molecule on H2 has negligible BE [23 K used in Cuppen and Herbst (2007), Hincelin et al. (2015)], it could easily desorb back to the gas phase. Other surface species can, of course, meet with H2, but the idea of this encounter desorption arises when the species can occupy a position on the top of the H2 molecule. For example, a carbon atom is having a BE of 10,000 K (Wakelam et al., 2017). H2 could quickly meet one C atom on the grain surface, but due to the lower mobility of atomic carbon at a low temperature, every time H2 will be on the top of the carbon atom. Since the whole C-H2 system is attached to the water substrate, this will not satisfy the encounter desorption probability. Among the various key elements considered in this study, gH, gN, and gF have the BE of 650 K (Wakelam et al., 2017), 720 K (Wakelam et al., 2017), and 800 K (listed in the original OSU gas-grain code from Eric Herbst group in 2006), respectively, with the water ice. It yields a reasonable time scale for hopping even at a low grain temperature (� K). Since the initial elemental abundance of F is negligible, we can neglect its contribution. The hopping time scale is heavily dependent on the assumed value of R. For example, by considering R = 0.35 , at 10 K, the hopping time scale for gH and gN is 1.12 × 10 4 years [with E D ( H , H 2 O ) = 650   K ] and 4.61 × 10 − 3 years [with E D ( N , H 2 O ) = 720   K ], respectively. It changes to 1.9 and 226 years for H and N atoms, respectively, for R = 0.5 . Since the typical lifetime of a dark cloud is � 6  years, the criterion related to the encounter desorption is often satisfied. Among the di-atomic species, H2 is only having a faster swapping rate (having BE 440 K, which corresponds to a hopping time scale of ∼ 1.24 × 10 − 7 years and 9 × 10 − 5 years, respectively, with R = 0.35 and R = 0.5). Looking at the faster hopping rate and their abundances on the grain surface, we have extended the consideration of the encounter desorption of these species. We have considered gX + gH 2 → X + gH 2 , where X refers to H2, H, and N.

In Figure 6, we have shown the time evolution of the abundances of gH, gH2, gN, gD, and gHD with n H = 10 7   cm − 3 , T = 10   K , and R = 0.35 . The encounter desorption of H2 and without the encounter desorption effect are shown to show the differences. Figure 6 depicts that the abundances of gN, gH, and gH2 have a reasonably high surface coverage. Since these species have a reasonable hopping rate at the low temperature, encounter desorption of these species need to be considered in the chemical model. Here, we have included the encounter desorption of these species sequentially to check their effect on the final abundances of some of the key surface species (gH2O, gCH3OH, and gNH3). To check the effect of encounter desorption of the other species, we have sequentially included the encounter desorption of H2, H, and N. Figure 7 shows the time evolution of the encounter desorption of gH2O, gCH3OH, and gNH3. We have already discussed the encounter desorption of gH2 in Section 3.1. Figure 7 shows that when we have included the encounter desorption of the H atom and N atom, the time evolution of the abundances shows significant changes in abundance. It depicts that considering the effect of encounter desorption of N atom can substantially increase the abundances of gH2O, gCH3OH, and gNH3 for the physical condition considered here ( n H = 10 7   cm − 3 , T = 10   K , and R = 0.35 ). We further have included the encounter desorption of D and HD by considering the same BE as it was obtained for H and H2 with the H2 substrate. The cumulative effect (by considering the encounter desorption of H, H2, N, D, and HD together) on the abundances is shown with the dotted curve. We have noticed that the abundance profile considering the cumulative effect shows a notable difference from that obtained with the no encounter desorption case. But the cumulative effect marginally differs from the encounter desorption effect of H2. In Figure 8, we have shown the temperature variation of the final abundances of water, methanol, and ammonia with respect to total hydrogen nuclei in all forms. It shows that the ice phase abundances of methanol, water, and ammonia can strongly deviate from the no encounter desorption case. As in Figure 7, we have also seen that the cumulative effect of the encounter desorption marginally deviates from the encounter desorption of H2. Around 20 K, we have noticed a great match between the cumulative encounter desorption case (dash-dotted cyan line), H2 encounter desorption case (solid red line), and no encounter desorption case (solid black line). The right panel of Figure 5 shows that as we have increased the temperature beyond 10 K, the effect of the encounter desorption of H2 starts to decrease. Around 20 K, it roughly diminishes. Since the cumulative effect follows the nature of H2 encounter desorption, it also matches with the no encounter desorption case at � K.

FIGURE 6. Time evolution of the abundances of H, H2, D, HD, and N obtained from our simulation is shown. Solid curves represent the cases by considering the encounter desorption [with E D ( H 2 , H 2 ) = 67   K ] of H2 and no encounter desorption (dashed curves) with E D ( H , H 2 O ) = 650   K , n H = 10 7   cm − 3 , T = 10   K , and R = 0.35 .

FIGURE 7. The time evolution of the abundances of ice phase water (first panel), methanol (second panel), and ammonia (third panel) is shown for n H = 10 7   cm − 3 , T = 10   K , and R = 0.35 . It shows a significant difference between the consideration of encounter desorption (solid green line for H2, solid red line for H, and solid blue line for N) and without encounter desorption (black line). The encounter desorption of H, N, H2, D, and HD are collectively considered (brown dotted line) and show that it marginally deviates from the encounter desorption of H2.

FIGURE 8. Temperature variation of the abundances of ice phase water (first panel), methanol (second panel), and ammonia (third panel) is shown for n H = 10 7   cm − 3 and R = 0.35 . It shows a significant difference between the consideration of encounter desorption and without encounter desorption (black line). The encounter desorption of H, N, H2, D, and HD are collectively considered, and as like Figure 7, it marginally varies from the encounter desorption of H2.


Observational Evidence of Active Galactic Nuclei Feedback

Radiation, winds, and jets from the active nucleus of a massive galaxy can interact with its interstellar medium, and this can lead to ejection or heating of the gas. This terminates star formation in the galaxy and stifles accretion onto the black hole. Such active galactic nuclei (AGN) feedback can account for the observed proportionality between the central black hole and the host galaxy mass. Direct observational evidence for the radiative or quasar mode of feedback, which occurs when AGN are very luminous, has been difficult to obtain but is accumulating from a few exceptional objects. Feedback from the kinetic or radio mode, which uses the mechanical energy of radio-emitting jets often seen when AGN are operating at a lower level, is common in massive elliptical galaxies. This mode is well observed directly through X-ray observations of the central galaxies of cool core clusters in the form of bubbles in the hot surrounding medium. The energy flow, which is roughly continuous, heats the hot intracluster gas and reduces radiative cooling and subsequent star formation by an order of magnitude. Feedback appears to maintain a long-lived heating/cooling balance. Powerful, jetted radio outbursts may represent a further mode of energy feedback that affects the cores of groups and subclusters. New telescopes and instruments from the radio to X-ray bands will come into operation over the next several years and lead to a rapid expansion in observational data on all modes of AGN feedback.


Greenhouse gases and climate change

Greenhouse gases include several naturally occurring molecules — like water vapor, carbon dioxide, methane, nitrous oxide and ozone — as well as several manufactured ones, like chlorofluorocarbons, according to the Australian Department of the Environment and Energy. Over the past century or so, human activities — such as the burning of fossil fuels, intensive agriculture, livestock raising and land clearing — have dramatically increased the concentrations of greenhouse gases in Earth's atmosphere, to the point where it's changing our planet's climate.

Since the middle of the 20th century, greenhouse gases produced by humans have become the most significant driver of climate change, according to the U.S. Environmental Protection Agency. Carbon dioxide levels in the atmosphere have increased by more than 40% since the start of the Industrial Revolution, from roughly 280 parts per million (ppm) to more than 400 ppm today.

The last time Earth's atmosphere had similar carbon dioxide concentrations was during the Pliocene epoch, between 3 million and 5 million years ago, according to the Scripps Institution of Oceanography in San Diego. That's at least 2.8 million years before modern humans roamed the planet. Fossils show that forests grew in the Canadian Arctic during the Pliocene, and savannas and woodlands spread over what's now the Sahara desert.

While some people still doubt the reality of human-induced climate change, the evidence for it is overwhelming. Since the 1850s, the average global surface-air temperature has risen by around 1.4 F (0.8 C), and ocean temperatures are now at the highest levels ever recorded.

Increases in greenhouse gases in the coming decades are expected to harm human health, increase droughts, contribute to sea level rise, and decrease national security and economic well-being throughout the world.


Types of Doors

One common type of exterior door has a steel skin with a polyurethane foam insulation core. It usually includes a magnetic strip (similar to a refrigerator door magnetic seal) as weatherstripping. If installed correctly and not bent, this type of door needs no further weatherstripping.

The R-values of most steel and fiberglass-clad entry doors range from R-5 to R-6, excluding a window. For example, a 1-1/2 inch (3.81 cm) thick door without a window offers more than five times the insulating value of a solid wood door of the same size.

Glass or "patio" doors, especially sliding glass doors, lose much more heat than other types of doors because glass is a very poor insulator. Most modern glass doors with metal frames have a thermal break, which is a plastic insulator between inner and outer parts of the frame. Models with several layers of glass, low-emissivity coatings, and/or low-conductivity gases between the glass panes are a good investment, especially in extreme climates. When buying or replacing patio doors, swinging doors generally offer a tighter seal than sliding types. Look at NFRC labels to find air leakage ratings. A door with one fixed panel will have less air leakage than a door with two operating panels.

It's impossible to stop all the air leakage around the weatherstripping on a sliding glass door and still be able to use the door. In addition, after years of use the weatherstripping wears down, so air leakage increases as the door ages. If the manufacturer has made it possible to do so, you can replace worn weatherstripping on sliding glass doors.


ENERGY STAR and equity

Beyond the emissions reductions benefits noted above, ENERGY STAR relies on several pathways to help disadvantaged consumers access the program and save money. For example, ENERGY STAR prioritizes outreach to low-income populations on products that have the greatest opportunity to save energy and dollars. And for products that may be cost-prohibitive, such as replacement windows, the ENERGY STAR program looks for alternatives. In the case of windows, EPA recently added storm windows as a new ENERGY STAR product category, giving consumers a lower-cost option that is easier to install. Paired with carefully researched bilingual messaging, utility-sponsored rebates, and geo-targeted advertising to encourage purchases, ENERGY STAR certified products can deliver significant cost savings for low-income families.

ENERGY STAR is also focused on increasing the energy efficiency of affordable homes across all sectors. Roughly 20% of ENERGY STAR builder partners work in the affordable housing space, including 550 Habitat for Humanity affiliates who have constructed more than 18,000 ENERGY STAR certified homes. ENERGY STAR also partners with 80 manufactured housing plants that have built more than 66,500 ENERGY STAR certified manufactured homes. Within the multifamily sector, more than 75 percent of ENERGY STAR multifamily high-rise projects are identified as affordable housing. In addition, ENERGY STAR home certification is used as criteria by more than 30 state government housing finance programs that provide low-income housing tax credits.

For additional details about ENERGY STAR achievements see ENERGY STAR Impacts. For ENERGY STAR facts and figures broken down geographically by state, see ENERGY STAR State Fact Sheets. For achievements by ENERGY STAR Award Winners, see the ENERGY STAR Award Winners Page.


Watch the video: PS3C - Relationship Between Energy and Forces (May 2022).


Comments:

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