Herrington, Lee P.

Reifsnyder, William E.

Splat, Karl W.

English

Yale University

Yale UniversityEliScholar – A Digital Platform for Scholarly Publishing at YaleYale School of Forestry & Environmental StudiesBulletin Series School of Forestry and Environmental Studies1967Thermophysical Properties of Bark of Shortleaf,Longleaf, and Red PineWilliam E. ReifsnyderYale UniversityLee P. HerringtonState University of New YorkKarl W. SplatMasonite CorporationFollow this and additional works at: https://elischolar.library.yale.edu/yale_fes_bulletinPart of the Environmental Sciences Commons, and the Forest Sciences CommonsThis Newsletter is brought to you for free and open access by the School of Forestry and Environmental Studies at EliScholar – A Digital Platform forScholarly Publishing at Yale. It has been accepted for inclusion in Yale School of Forestry & Environmental Studies Bulletin Series by an authorizedadministrator of EliScholar – A Digital Platform for Scholarly Publishing at Yale. For more information, please contact elischolar@yale.edu.Recommended CitationReifsnyder, William E.; Herrington, Lee P.; and Splat, Karl W., "Thermophysical Properties of Bark of Shortleaf, Longleaf, and RedPine" (1967). Yale School of Forestry & Environmental Studies Bulletin Series. 80.https://elischolar.library.yale.edu/yale_fes_bulletin/80YALE UNIVERSITY: SCHOOL OF FORESTRYBulletin No. 70THERMOPHYSICAL PROPERTIES OFBARK OF SHORTLEAF, LONGLEAF,AND RED PINEBYWILLIAM E. REIFSNYDERProfessor of Forest MeteorologyYale University School of ForestryLEE P. HERRINGTONAssistant Professor q/ Forest MeteorologyState University of New York,College of ForestryKARL W. SPALTWood TechnologistMasonite CorporationNew Haven: Yale University1967A Note to Readers2012This volume is part of a Bulletin Series inaugurated by the Yale School ofForestry & Environmental Studies in 1912. The Series contains importantoriginal scholarly and applied work by the School’s faculty, graduatestudents, alumni, and distinguished collaborators, and covers a broad rangeof topics.Bulletins 1-97 were published as bound print-only documents between 1912and 1994. Starting with Bulletin 98 in 1995, the School began publishingvolumes digitally and expanded them into a Publication Series that includesworking papers, books, and reports as well as Bulletins.To celebrate the centennial of publishing at the school, the long out-of-printBulletins 1-97 were scanned to make them available as pdfs to a broaderaudience. A caution: the scanning process is not perfect, especially for printdocuments as old as some of these, so the readers’ indulgence is requested forsome of the anomalies that remain despite our best efforts to clean them up.Everything published from 1912-present is available on the School’s website(http://environment.yale.edu/publications) for free download. Nothingin the Series requires copyright permission for reproduction when intendedfor personal or classroom use.Bound copies of everything published in the Series from 1912 to the presentare also available in the Yale University libraries and archives and can bestbe accessed by contacting the School of Forestry & Environmental Studieslibrarian.ACKNOWLEDGEMENTST HE RESEARCH reported in this bulletin was undertaken as a cooperativeproject between the Southern Forest Experiment Station, Forest Service,U.S. Department of Agriculture, and the Yale University School of Forestry. It\-vas supported in part by a grant from the National Science Foundation (G-14081), and by funds from the Hartford Foundation. The study was carried outat the School of Forestry in New Haven.A number of graduate students assisted with the laboratory work and subse-quent analyses. Chief among these were Leonard C. Rolph, Henry F. Barbour,James A. Rydelius, Thomas C. Eidson, and Roy E. Trent. David Bruce andGeorge R. Fahnestock provided technical and editorial assistance during theirperiods of tenure at the Southern Forest Experiment Station.We wish to acknowledge the assistance of George M. Furnival in establishingthe statistical procedures used to evaluate our experimental data.THERMOPHYSICAL PROPERTIES OF BARKFig. 3. Data from run 207-2, plotted on semi-log paper, with regressionlin~ 10Fig. 4. Moisture content as function of time for a shortleaf pine barkspecimen (curve, with scales at bottom and left); and moisturecontent change as portion of total change, plotted against squareroot of time (straight line, with scales at top and right) . 18Fig. s. Adsorption and desorption equilibrium moisture content as a func-tion of ambient relative humidity. Vertical lines show range of data,and circles are averages for desorption (D) and adsorption (A).Average curve for spruce wood also shown for comparison. 20Fig. 6. Seasonal distribution of moisture content of red pine inner bark(upper curve) and outer bark (lower curve). 25Fig. 7. Elevation of specific heat (adapted from Byram and others, 1952 ). 31Fig. 8. Thermal diffusivity of bark as a function of moisture content anddry density. a) longleaf/shortleaf; b) red pine. 36Fig. 9. Seasonal distribution of the thermal diffusivity of red pine bark,based on measured moisture contents of Figure 4· 37ABSTRACTTHE ROLE of bark in modulating the transfer of heat to the cambium is depend-ent on its thermal and physical properties. This study evaluates a number of theseproperties for the bark of three species of pine.Thermal conductivity was determined experimentally as a function of bark densityand moisture content. Results indicate that although thermal conductivity of bark isless than that of wood for particular values of density and moisture content, the dif-ferences are not great. However, the thermal conductivity of red pine bark was foundto be significantly less than that of shortleaf and longleaf pine bark.Density and specific heat of the three barks was also measured. Red pine bark wasless dense than that of the two southern pines which differed but little from eachother. Specific heat was found to be very similar to that of wood.Thermal diffusivity, the parameter that expresses the rate at which a temperaturewave \vill penetrate bark, was calculated on the basis of measured values of conduc-tivity, density and specific heat. Thermal diffusivity of the bark of the two southernpines was about eleven percent less than wood of the same density and moisturecontent. Red pine bark showed lower values than the southern pines.Moisture diffusion through bark was measured to be about one-quarter to one-eighth that of wood of the same density. Equilibrium moisture content of the threebarks was higher at low and medium moistures, but appeared to be somewhat lowerat the fiber saturation point.Seasonal distribution of the thermal diffusivity of red pine bark based on measuredmoisture content showed a variation of only about ten percent from the year's aver-age, \vith lowest values in the winter.THERMOPHYSICAL PROPERTIES OF BARKveloped by flame, and the bark surface is heated quickly to the temperature of theflame.For a particular cylinder of radius a the temperature-time distribution at anypoint, r (say, the cambium), is a function only of the thermal diffusivity, and,therefore, of its constituent properties: thermal conductivity, heat capacity, anddensity. The research reported herein had as its objective the determination ofthese three properties for conifer bark of three species. Because of the influenceof moisture on thermal conductivity and heat capacity, several studies of moisturecontent were also made.4STUDY MA TERIALST HE BARKS of longleaf pine (Pinus palustris Mill.) and shortleaf pine(P. echinata Mill.) were studied in order to compare thermal properties ofthe superficially similar barks of two species. The bark of red pine (P. resinosaAit.) grown in plantation near New Haven was also studied because of its higherdensity and different general characteristics from the southern species. Red pineis less fire resistant than either of the southern pines studied.Samples of shortleaf and longleaf bark approximately 6 inches high by 4inches wide were cut. The shortleaf pines were 16 inches d.b.h. and 65-75 yearsold. They grew near each other on a portion of the Alexander State Forest, Wood-worth, Louisiana, in an area that had not burned for a long time, perhaps 25years. Consequently, bark thickness was probably near the maximum attained bymature shortleaf. The samples were taken near the ground on live, standing trees.Similar samples were cut from longleaf pines that were 80- to Ioo-year-oldresiduals growing near Gardner, Louisiana. Diameters were 16 to 20 inches. Al-though heavily burned· in the past, the area had had only light fires in recentyears and had not burned at all for 5 years. The bark samples were from recentlycut butt logs.Red pine bark samples were obtained from a 40-year-old plantation near NewHaven that had been completely protected from fire. The trees ranged from 8-12inches d.b.h.5FIG1JRE 1. Pieces of bark with thern1al conductivity probe inserted in top piece.Dummy probe also shown.THERMAL CONDUCTIVITYThe sample and probe assembly were placed in a heat sink, a' box made of~ -inch aluminum, and the entire unit placed in an insulated stainless steel boxwhich, with the recording equipment, was kept in a controlled-environmentroom where humidity could be held at a level appropriate to the equilibriummoisture content of the bark sample being studied. The reference junction of theprobe thermocouple was kept in a quart vacuum flask of water in the same in-sulated box. Thus, after sufficient time, the reference and measuring junctionswere at the same temperature. Such extreme precautions were necessary becauseof the extremely small temperature differences being measured. Normally, thetotal rise in temperature of the probe during a ten-minute observation was lessthan one Celsius degree.Temperature fluctuations in the controlled-temperature room caused occa·sional difficulties in maintaining constant probe and reference junction tempera-ture immediately prior to the test. Some of the difficulty was due to the differentthermal response of the probe in the bark and the reference junction in thevacuum flask. These two thermal systems had different lags and so respondeddifferently to a fluctuating temperature on their surfaces.Output of the probe thermocouple was fed to a D.C. amplifier and thence to arecording potentiometer. Heater current was monitored by a recording milliam-meter. Variation in heater current was held to one percent or less during a test.Preliminary tests indicated that heater currents of about five milliampereswould produce temperature rises on the order of one Celsius degree in tenminutes. AU tests were, therefore, run for ten minutes. Figure 2 is a reproductionof the two chart records for one of the tests.The thermocouple output was read for each minute from one to ten, plotted onsemi-log paper, and checked visually for marked deviations from a straight line.A straight line was then fitted by regression analysis. Figure 3 shows a plot of thedata from Figure 2 together with the line of best fit.Thermal conductivity determinations were made on three bark samples fromfour trees each of red pine, longleaf·pine, and shortleaf pine. Each sample wasconditioned to equilibrium moisture contents corresponding to 80%, 40% and0% relative humidity. The lowest moisture content was produced by drying thesamples over silica gel at 70°C until no further weight loss was measured.(Oven-drying at 105°C caused distillation of volatiles and a continuous weightloss.) Three determinations were planned for each combination of species, treesample, and moisture content-a total of 324 separate determinations. Inasmuchas there was no change in experimental conditions between any of the three re-7THERMOPHYSICAL PROPERTIES OF BARKpeated determinations, the values were averaged. Because some specimens brokeduring the testing, only 294 determinations were made, yielding 98 averagevalues. Of these, 36 were for red pine, 32 for shortleaf and 30 for longleaf (TableI).Most tests, but not all, were run at a sample temperature of 720P (22°C). Wecorrected all thermal conductivity determinations to 20°C by means of Kollman's(1951) curve for wood. Although we do not know how accurate this procedureis for bark, the maximum correction was 5%. Certainly the correction removedsome of the error due to the temperature differences.Bark, like wood, swells as it absorbs moisture. Therefore, moist bark has alower density than oven-dry bark. We corrected for this density variation byassuming that bark had a moisture coefficient of expansion equal to that of woodof the same species. Corrections, based on data in the Wood' Handbook (U.S.Forest Products Laboratory, 1955), were (0.383 m) for red pine and (0.4°7 m)for shortleaf and longleaf pine, where m is the moisture content of the barkexpressed as a fraction of the dry weight.Moisture contents based on density of oven-dry bark were also corrected formoisture-induced density changes in order that thermal conductivity could beexpressed as a function of the actual bark density and water vapor density exist-ing at the time of each test.Thermal conductivity of a complex mixture of bark substance, water vapor,and air is not a simple average of the conductivities of the constituents weightedby their relative proportions in a particular sample. Although the proper weight-ing is a function of the arrangement and shape of the constituents (see, for ex-ample, van Wijk, 1963), a weighting on the basis of relative proportions can beused as a first approximation. This suggests that linear multiple regression ofthermal conductivity on density of swollen (wet) bark and water vapor densitywould be a suitable model for statistical analysis. When bark density and watervapor density are zero, the regression plane should pass through the value of thethermal conductivity of dry air. As a check of this hypothesis, linear multipleregressions with no intercept restriction can be compared with the model.Regressions were calculated for the three species with and without the interceptrestriction by the regression routine of Frampton and others (1963) at the YaleComputer Center. Since the calculated regression coefficients for shortlea£ andlongleaf pine bark were not significantly different from each other, the data werepooled and a combined regression calculated. Red pine was significantly differentfrom the two southern pines. The equations for the two groups with the intercept8DENSITY OF BARKAS SHOWN in the section on thermal conductivity, bark density signifi-£1.. cantly influences conductivity. In addition, density enters into the calcula-tion of volumetric· heat capacity, one of the parameters needed to calculatethermal diffusivity.Although we could find no reference to bark density of the species in thisstudy, values for a number of other species range from 0.30 gm cm-3 for tama-rack and pond pine to 0.54 gm cm-3 for jack pine (Spalt and Reifsnyder, 1962).As might be expected, the amount of cork in a bark influences density strongly.Douglas-fir bark may depart as much as -l-40 percent from an average densityof about 0.44 gm cm-3, depending on the size of cork Iunes. Some researchershave found relationships of density with age, size of stem, and position on stem,but none of these relationships is very strong.The density of the specimens used in the thermal conductivity determinationswas measured. They were coated with paraffin after a final drying, and im-mersed in water to determine volume and mass. Three specimens from each offour trees from the three species were used in the measurements (Table 6). Ananalysis of variance showed that although there was highly significant varia-bility between trees of a particular species, there were also highly significant dif-ferences between species means. Thus bark from red pine, with a mean of 0.365gm em-3, was highly significantly different from that of shortleaf (0.497 gmcm-3 ) and of longleaf (0.520 gm cm-3). However, the differences between thetwo southern pines were not significant. These measurements were of outer barkonly, and were based on oven dry weight and volume.A second series of density determinations was run on samples of shortleaf andlongleaf pine bark to provide a more sensitive test of species differences.· Alsotested were position on stem (I foot versus 4~ foot) and bark type (outer vs.inner). Approximately five samples were used to determine a mean for each ofthe eight categories for each of three trees (Table 7). Samples were smaller thanthe thermal conductivity specimens used in the first series, averaging about 6grams for outer bark and one-half gram for inner bark. The same method ofdetermining density was used.An analysis of variance sho\ved that the inner bark was more dense than outerbark in both species; the difference was more pronounced with shortleaf pine.Although the grand averages for the two species were not significantly different(0.602 and 0.622 for shortleaf and longleaf, respectively), this is not a meaning-26End of Document

Thermophysical Properties of Bark of Shortleaf, Longleaf, and Red Pine

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Thermophysical Properties of Bark of Shortleaf, Longleaf, and Red Pine

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